Medicare Program; Inpatient Rehabilitation Facility (IRF) Prospective Payment System for Federal Fiscal Year 2020 and Updates to the IRF Quality Reporting Program, 39054-39173 [2019-16603]
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Federal Register / Vol. 84, No. 153 / Thursday, August 8, 2019 / Rules and Regulations
DEPARTMENT OF HEALTH AND
HUMAN SERVICES
Centers for Medicare & Medicaid
Services
42 CFR Part 412
[CMS–1710–F]
RIN 0938–AT67
Medicare Program; Inpatient
Rehabilitation Facility (IRF)
Prospective Payment System for
Federal Fiscal Year 2020 and Updates
to the IRF Quality Reporting Program
Centers for Medicare &
Medicaid Services (CMS), HHS.
ACTION: Final rule.
AGENCY:
This final rule updates the
prospective payment rates for inpatient
rehabilitation facilities (IRFs) for federal
fiscal year (FY) 2020. As required by the
statute, this final rule includes the
classification and weighting factors for
the IRF prospective payment system’s
(PPS) case-mix groups (CMGs) and a
description of the methodologies and
data used in computing the prospective
payment rates for FY 2020. This final
rule rebases and revises the IRF market
basket to reflect a 2016 base year rather
than the current 2012 base year.
Additionally, this final rule revises the
CMGs and updates the CMG relative
weights and average length of stay (LOS)
values beginning with FY 2020, based
on analysis of 2 years of data (FYs 2017
and 2018). Although we proposed to use
a weighted motor score to assign
patients to CMGs, we are finalizing
based on public comments the use of an
unweighted motor score to assign
patients to CMGs beginning with FY
2020. Additionally, we are finalizing the
removal of one item from the motor
score. We are updating the IRF wage
index to use the concurrent fiscal year
inpatient prospective payment system
(IPPS) wage index beginning with FY
2020. We are amending the regulations
to clarify that the determination as to
whether a physician qualifies as a
rehabilitation physician (that is, a
licensed physician with specialized
training and experience in inpatient
rehabilitation) is made by the IRF. For
the IRF Quality Reporting Program
(QRP), we are adopting two new
measures, modifying an existing
measure, and adopting new
standardized patient assessment data
elements. We are also making updates to
reflect our migration to a new data
submission system.
DATES:
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SUMMARY:
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Effective date: These regulations are
effective on October 1, 2019.
Applicability dates: The updated IRF
prospective payment rates are
applicable for IRF discharges occurring
on or after October 1, 2019, and on or
before September 30, 2020 (FY 2020).
The new and updated quality measures
and reporting requirements under the
IRF QRP are applicable for IRF
discharges occurring on or after October
1, 2020.
FOR FURTHER INFORMATION CONTACT:
Gwendolyn Johnson, (410) 786–6954,
for general information.
Catie Kraemer, (410) 786–0179, for
information about the IRF payment
policies and payment rates.
Kadie Derby, (410) 786–0468, for
information about the IRF coverage
policies.
Kate Brooks, (410) 786–7877, for
information about the IRF quality
reporting program.
SUPPLEMENTARY INFORMATION:
Inspection of Public Comments: All
comments received before the close of
the comment period are available for
viewing by the public, including any
personally identifiable or confidential
business information that is included in
a comment. We post all comments
received before the close of the
comment period as soon as possible
after they have been received at https://
www.regulations.gov. Follow the search
instructions on that website to view
public comments.
The IRF PPS Addenda along with
other supporting documents and tables
referenced in this final rule are available
through the internet on the CMS website
at https://www.cms.hhs.gov/Medicare/
Medicare-Fee-for-Service-Payment/
InpatientRehabFacPPS/.
Executive Summary
A. Purpose
This final rule updates the
prospective payment rates for IRFs for
FY 2020 (that is, for discharges
occurring on or after October 1, 2019,
and on or before September 30, 2020) as
required under section 1886(j)(3)(C) of
the Social Security Act (the Act). As
required by section 1886(j)(5) of the Act,
this final rule includes the classification
and weighting factors for the IRF PPS’s
case-mix groups (CMGs) and a
description of the methodologies and
data used in computing the prospective
payment rates for FY 2020. This final
rule also rebases and revises the IRF
market basket to reflect a 2016 base
year, rather than the current 2012 base
year. Additionally, this final rule revises
the CMGs and updates the CMG relative
weights and average LOS values
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beginning with FY 2020, based on
analysis of 2 years of data (FYs 2017 and
2018). Although we proposed to use a
weighted motor score to assign patients
to CMGs, we are finalizing based on
public comments the use of an
unweighted motor score to assign
patients to CMGs beginning with FY
2020. Additionally, we are finalizing the
removal of one item from the motor
score. We are also updating the IRF
wage index to use the concurrent FY
IPPS wage index for the IRF PPS
beginning with FY 2020. We are also
amending the regulations at 42 CFR
412.622 to clarify that the determination
as to whether a physician qualifies as a
rehabilitation physician (that is, a
licensed physician with specialized
training and experience in inpatient
rehabilitation) is made by the IRF. For
the IRF QRP, we are adopting two new
measures, modifying an existing
measure, and adopting new
standardized patient assessment data
elements. We also include updates
related to the system used for the
submission of data and related
regulation text. We are not finalizing our
proposal requiring that IRFs submit data
on measures and standardized patient
assessment data for which the source of
the data is the IRF–PAI to all patients,
regardless of payer, but plan to propose
this policy in future rulemaking.
B. Summary of Major Provisions
In this final rule, we use the methods
described in the FY 2019 IRF PPS final
rule (83 FR 38514) to update the
prospective payment rates for FY 2020
using updated FY 2018 IRF claims and
the most recent available IRF cost report
data, which is FY 2017 IRF cost report
data. This final rule also rebases and
revises the IRF market basket to reflect
a 2016 base year rather than the current
2012 base year. Additionally, this final
rule revises the CMGs and updates the
CMG relative weights and average LOS
values beginning with FY 2020, based
on analysis of 2 years of data (FYs 2017
and 2018). Although we proposed to use
a weighted motor score to assign
patients to CMGs, we are finalizing
based on public comments the use of an
unweighted motor score to assign
patients to CMGs beginning with FY
2020. Additionally, we are finalizing the
removal of one item from the motor
score. We are also updating the IRF
wage index to use the concurrent FY
IPPS wage index for the IRF PPS
beginning in FY 2020. We are also
amending the regulations at § 412.622 to
clarify that the determination as to
whether a physician qualifies as a
rehabilitation physician (that is, a
licensed physician with specialized
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training and experience in inpatient
rehabilitation) is made by the IRF. We
also update requirements for the IRF
QRP.
C. Summary of Impacts
I. Background
as the budget-neutral conversion factor).
For a detailed discussion of the budgetneutral conversion factor, please refer to
our FY 2004 IRF PPS final rule (68 FR
45684 through 45685). In the FY 2006
IRF PPS final rule (70 FR 47880), we
discussed in detail the methodology for
determining the standard payment
conversion factor.
We applied the relative weighting
factors to the standard payment
conversion factor to compute the
unadjusted prospective payment rates
under the IRF PPS from FYs 2002
through 2005. Within the structure of
the payment system, we then made
adjustments to account for interrupted
stays, transfers, short stays, and deaths.
Finally, we applied the applicable
adjustments to account for geographic
variations in wages (wage index), the
percentage of low-income patients,
location in a rural area (if applicable),
and outlier payments (if applicable) to
the IRFs’ unadjusted prospective
payment rates.
For cost reporting periods that began
on or after January 1, 2002, and before
October 1, 2002, we determined the
final prospective payment amounts
using the transition methodology
prescribed in section 1886(j)(1) of the
Act. Under this provision, IRFs
transitioning into the PPS were paid a
blend of the federal IRF PPS rate and the
payment that the IRFs would have
received had the IRF PPS not been
implemented. This provision also
allowed IRFs to elect to bypass this
blended payment and immediately be
paid 100 percent of the federal IRF PPS
rate. The transition methodology
expired as of cost reporting periods
beginning on or after October 1, 2002
(FY 2003), and payments for all IRFs
now consist of 100 percent of the federal
IRF PPS rate.
Section 1886(j) of the Act confers
broad statutory authority upon the
Secretary to propose refinements to the
IRF PPS. In the FY 2006 IRF PPS final
rule (70 FR 47880) and in correcting
amendments to the FY 2006 IRF PPS
final rule (70 FR 57166), we finalized a
number of refinements to the IRF PPS
case-mix classification system (the
CMGs and the corresponding relative
weights) and the case-level and facilitylevel adjustments. These refinements
included the adoption of the Office of
Management and Budget’s (OMB) CoreBased Statistical Area (CBSA) market
definitions; modifications to the CMGs,
tier comorbidities; and CMG relative
weights, implementation of a new
teaching status adjustment for IRFs;
rebasing and revising the market basket
index used to update IRF payments, and
updates to the rural, low-income
percentage (LIP), and high-cost outlier
adjustments. Beginning with the FY
2006 IRF PPS final rule (70 FR 47908
through 47917), the market basket index
used to update IRF payments was a
market basket reflecting the operating
and capital cost structures for
freestanding IRFs, freestanding inpatient
psychiatric facilities (IPFs), and longterm care hospitals (LTCHs) (hereinafter
referred to as the rehabilitation,
psychiatric, and long-term care (RPL)
market basket). Any reference to the FY
2006 IRF PPS final rule in this final rule
also includes the provisions effective in
the correcting amendments. For a
detailed discussion of the final key
policy changes for FY 2006, please refer
to the FY 2006 IRF PPS final rule.
In the FY 2007 IRF PPS final rule (71
FR 48354), we further refined the IRF
PPS case-mix classification system (the
CMG relative weights) and the caselevel adjustments, to ensure that IRF
PPS payments would continue to reflect
as accurately as possible the costs of
care. For a detailed discussion of the FY
2007 policy revisions, please refer to the
FY 2007 IRF PPS final rule.
In the FY 2008 IRF PPS final rule (72
FR 44284), we updated the prospective
payment rates and the outlier threshold,
revised the IRF wage index policy, and
clarified how we determine high-cost
outlier payments for transfer cases. For
more information on the policy changes
A. Historical Overview of the IRF PPS
Section 1886(j) of the Act provides for
the implementation of a per-discharge
PPS for inpatient rehabilitation
hospitals and inpatient rehabilitation
units of a hospital (collectively,
hereinafter referred to as IRFs).
Payments under the IRF PPS encompass
inpatient operating and capital costs of
furnishing covered rehabilitation
services (that is, routine, ancillary, and
capital costs), but not direct graduate
medical education costs, costs of
approved nursing and allied health
education activities, bad debts, and
other services or items outside the scope
of the IRF PPS. Although a complete
discussion of the IRF PPS provisions
appears in the original FY 2002 IRF PPS
final rule (66 FR 41316) and the FY
2006 IRF PPS final rule (70 FR 47880),
we are providing a general description
of the IRF PPS for FYs 2002 through
2019.
Under the IRF PPS from FY 2002
through FY 2005, the prospective
payment rates were computed across
100 distinct CMGs, as described in the
FY 2002 IRF PPS final rule (66 FR
41316). We constructed 95 CMGs using
rehabilitation impairment categories
(RICs), functional status (both motor and
cognitive), and age (in some cases,
cognitive status and age may not be a
factor in defining a CMG). In addition,
we constructed five special CMGs to
account for very short stays and for
patients who expire in the IRF.
For each of the CMGs, we developed
relative weighting factors to account for
a patient’s clinical characteristics and
expected resource needs. Thus, the
weighting factors accounted for the
relative difference in resource use across
all CMGs. Within each CMG, we created
tiers based on the estimated effects that
certain comorbidities would have on
resource use.
We established the federal PPS rates
using a standardized payment
conversion factor (formerly referred to
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implemented for FY 2008, please refer
to the FY 2008 IRF PPS final rule.
After publication of the FY 2008 IRF
PPS final rule (72 FR 44284), section
115 of the Medicare, Medicaid, and
SCHIP Extension Act of 2007 (Pub. L.
110–173, enacted December 29, 2007)
(MMSEA) amended section 1886(j)(3)(C)
of the Act to apply a zero percent
increase factor for FYs 2008 and 2009,
effective for IRF discharges occurring on
or after April 1, 2008. Section
1886(j)(3)(C) of the Act required the
Secretary to develop an increase factor
to update the IRF prospective payment
rates for each FY. Based on the
legislative change to the increase factor,
we revised the FY 2008 prospective
payment rates for IRF discharges
occurring on or after April 1, 2008.
Thus, the final FY 2008 IRF prospective
payment rates that were published in
the FY 2008 IRF PPS final rule (72 FR
44284) were effective for discharges
occurring on or after October 1, 2007,
and on or before March 31, 2008, and
the revised FY 2008 IRF prospective
payment rates were effective for
discharges occurring on or after April 1,
2008, and on or before September 30,
2008. The revised FY 2008 prospective
payment rates are available on the CMS
website at https://www.cms.gov/
Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/DataFiles.html.
In the FY 2009 IRF PPS final rule (73
FR 46370), we updated the CMG relative
weights, the average LOS values, and
the outlier threshold; clarified IRF wage
index policies regarding the treatment of
‘‘New England deemed’’ counties and
multi-campus hospitals; and revised the
regulation text in response to section
115 of the MMSEA to set the IRF
compliance percentage at 60 percent
(the ‘‘60 percent rule’’) and continue the
practice of including comorbidities in
the calculation of compliance
percentages. We also applied a zero
percent market basket increase factor for
FY 2009 in accordance with section 115
of the MMSEA. For more information on
the policy changes implemented for FY
2009, please refer to the FY 2009 IRF
PPS final rule.
In the FY 2010 IRF PPS final rule (74
FR 39762) and in correcting
amendments to the FY 2010 IRF PPS
final rule (74 FR 50712), we updated the
prospective payment rates, the CMG
relative weights, the average LOS
values, the rural, LIP, teaching status
adjustment factors, and the outlier
threshold; implemented new IRF
coverage requirements for determining
whether an IRF claim is reasonable and
necessary; and revised the regulation
text to require IRFs to submit patient
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assessments on Medicare Advantage
(MA) (formerly called Medicare Part C)
patients for use in the 60 percent rule
calculations. Any reference to the FY
2010 IRF PPS final rule in this final rule
also includes the provisions effective in
the correcting amendments. For more
information on the policy changes
implemented for FY 2010, please refer
to the FY 2010 IRF PPS final rule.
After publication of the FY 2010 IRF
PPS final rule (74 FR 39762), section
3401(d) of the Patient Protection and
Affordable Care Act (Pub. L. 111–148,
enacted March 23, 2010), as amended by
section 10319 of the same Act and by
section 1105 of the Health Care and
Education Reconciliation Act of 2010
(Pub. L. 111–152, enacted March 30,
2010) (collectively, hereinafter referred
to as ‘‘PPACA’’), amended section
1886(j)(3)(C) of the Act and added
section 1886(j)(3)(D) of the Act. Section
1886(j)(3)(C) of the Act requires the
Secretary to estimate a multifactor
productivity (MFP) adjustment to the
market basket increase factor, and to
apply other adjustments as defined by
the Act. The productivity adjustment
applies to FYs from 2012 forward. The
other adjustments apply to FYs 2010 to
2019.
Sections 1886(j)(3)(C)(ii)(II) and
1886(j)(3)(D)(i) of the Act defined the
adjustments that were to be applied to
the market basket increase factors in
FYs 2010 and 2011. Under these
provisions, the Secretary was required
to reduce the market basket increase
factor in FY 2010 by a 0.25 percentage
point adjustment. Notwithstanding this
provision, in accordance with section
3401(p) of the PPACA, the adjusted FY
2010 rate was only to be applied to
discharges occurring on or after April 1,
2010. Based on the self-implementing
legislative changes to section 1886(j)(3)
of the Act, we adjusted the FY 2010
prospective payment rates as required,
and applied these rates to IRF
discharges occurring on or after April 1,
2010, and on or before September 30,
2010. Thus, the final FY 2010 IRF
prospective payment rates that were
published in the FY 2010 IRF PPS final
rule (74 FR 39762) were used for
discharges occurring on or after October
1, 2009, and on or before March 31,
2010, and the adjusted FY 2010 IRF
prospective payment rates applied to
discharges occurring on or after April 1,
2010, and on or before September 30,
2010. The adjusted FY 2010 prospective
payment rates are available on the CMS
website at https://www.cms.gov/
Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/IRFRules-and-Related-Files.html.
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In addition, sections 1886(j)(3)(C) and
(D) of the Act also affected the FY 2010
IRF outlier threshold amount because
they required an adjustment to the FY
2010 RPL market basket increase factor,
which changed the standard payment
conversion factor for FY 2010.
Specifically, the original FY 2010 IRF
outlier threshold amount was
determined based on the original
estimated FY 2010 RPL market basket
increase factor of 2.5 percent and the
standard payment conversion factor of
$13,661. However, as adjusted, the IRF
prospective payments were based on the
adjusted RPL market basket increase
factor of 2.25 percent and the revised
standard payment conversion factor of
$13,627. To maintain estimated outlier
payments for FY 2010 equal to the
established standard of 3 percent of total
estimated IRF PPS payments for FY
2010, we revised the IRF outlier
threshold amount for FY 2010 for
discharges occurring on or after April 1,
2010, and on or before September 30,
2010. The revised IRF outlier threshold
amount for FY 2010 was $10,721.
Sections 1886(j)(3)(C)(ii)(II) and
1886(j)(3)(D)(i) of the Act also required
the Secretary to reduce the market
basket increase factor in FY 2011 by a
0.25 percentage point adjustment. The
FY 2011 IRF PPS notice (75 FR 42836)
and the correcting amendments to the
FY 2011 IRF PPS notice (75 FR 70013)
described the required adjustments to
the FY 2010 and FY 2011 IRF PPS
prospective payment rates and outlier
threshold amount for IRF discharges
occurring on or after April 1, 2010, and
on or before September 30, 2011. It also
updated the FY 2011 prospective
payment rates, the CMG relative
weights, and the average LOS values.
Any reference to the FY 2011 IRF PPS
notice in this final rule also includes the
provisions effective in the correcting
amendments. For more information on
the FY 2010 and FY 2011 adjustments
or the updates for FY 2011, please refer
to the FY 2011 IRF PPS notice.
In the FY 2012 IRF PPS final rule (76
FR 47836), we updated the IRF
prospective payment rates, rebased and
revised the RPL market basket, and
established a new QRP for IRFs in
accordance with section 1886(j)(7) of the
Act. We also consolidated, clarified, and
revised existing policies regarding IRF
hospitals and IRF units of hospitals to
eliminate unnecessary confusion and
enhance consistency. For more
information on the policy changes
implemented for FY 2012, please refer
to the FY 2012 IRF PPS final rule.
The FY 2013 IRF PPS notice (77 FR
44618) described the required
adjustments to the FY 2013 prospective
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payment rates and outlier threshold
amount for IRF discharges occurring on
or after October 1, 2012, and on or
before September 30, 2013. It also
updated the FY 2013 prospective
payment rates, the CMG relative
weights, and the average LOS values.
For more information on the updates for
FY 2013, please refer to the FY 2013 IRF
PPS notice.
In the FY 2014 IRF PPS final rule (78
FR 47860), we updated the prospective
payment rates, the CMG relative
weights, and the outlier threshold
amount. We also updated the facilitylevel adjustment factors using an
enhanced estimation methodology,
revised the list of diagnosis codes that
count toward an IRF’s 60 percent rule
compliance calculation to determine
‘‘presumptive compliance,’’ revised
sections of the IRF patient assessment
instrument (IRF–PAI), revised
requirements for acute care hospitals
that have IRF units, clarified the IRF
regulation text regarding limitation of
review, updated references to
previously changed sections in the
regulations text, and updated
requirements for the IRF QRP. For more
information on the policy changes
implemented for FY 2014, please refer
to the FY 2014 IRF PPS final rule.
In the FY 2015 IRF PPS final rule (79
FR 45872) and the correcting
amendments to the FY 2015 IRF PPS
final rule (79 FR 59121), we updated the
prospective payment rates, the CMG
relative weights, and the outlier
threshold amount. We also revised the
list of diagnosis codes that count toward
an IRF’s 60 percent rule compliance
calculation to determine ‘‘presumptive
compliance,’’ revised sections of the
IRF–PAI, and updated requirements for
the IRF QRP. Any reference to the FY
2015 IRF PPS final rule in this final rule
also includes the provisions effective in
the correcting amendments. For more
information on the policy changes
implemented for FY 2015, please refer
to the FY 2015 IRF PPS final rule.
In the FY 2016 IRF PPS final rule (80
FR 47036), we updated the prospective
payment rates, the CMG relative
weights, and the outlier threshold
amount. We also adopted an IRFspecific market basket that reflects the
cost structures of only IRF providers, a
blended 1-year transition wage index
based on the adoption of new OMB area
delineations, a 3-year phase-out of the
rural adjustment for certain IRFs due to
the new OMB area delineations, and
updates for the IRF QRP. For more
information on the policy changes
implemented for FY 2016, please refer
to the FY 2016 IRF PPS final rule.
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In the FY 2017 IRF PPS final rule (81
FR 52056) and the correcting
amendments to the FY 2017 IRF PPS
final rule (81 FR 59901), we updated the
prospective payment rates, the CMG
relative weights, and the outlier
threshold amount. We also updated
requirements for the IRF QRP. Any
reference to the FY 2017 IRF PPS final
rule in this final rule also includes the
provisions effective in the correcting
amendments. For more information on
the policy changes implemented for FY
2017, please refer to the FY 2017 IRF
PPS final rule.
In the FY 2018 IRF PPS final rule (82
FR 36238), we updated the prospective
payment rates, the CMG relative
weights, and the outlier threshold
amount. We also revised the
International Classification of Diseases,
10th Revision, Clinical Modification
(ICD–10–CM) diagnosis codes that are
used to determine presumptive
compliance under the ‘‘60 percent rule,’’
removed the 25 percent payment
penalty for IRF–PAI late transmissions,
removed the voluntary swallowing
status item (Item 27) from the IRF–PAI,
summarized comments regarding the
criteria used to classify facilities for
payment under the IRF PPS, provided
for a subregulatory process for certain
annual updates to the presumptive
methodology diagnosis code lists,
adopted the use of height/weight items
on the IRF–PAI to determine patient
body mass index (BMI) greater than 50
for cases of single-joint replacement
under the presumptive methodology,
and updated requirements for the IRF
QRP. For more information on the
policy changes implemented for FY
2018, please refer to the FY 2018 IRF
PPS final rule.
In the FY 2019 IRF PPS final rule (83
FR 38514), we updated the prospective
payment rates, the CMG relative
weights, and the outlier threshold
amount. We also alleviated
administrative burden for IRFs by
removing the FIMTM instrument and
associated Function Modifiers from the
IRF–PAI beginning in FY 2020 and
revised certain IRF coverage
requirements to reduce the amount of
required paperwork in the IRF setting
beginning in FY 2019. Additionally, we
incorporated certain data items located
in the Quality Indicators section of the
IRF–PAI into the IRF case-mix
classification system using analysis of 2
years of data (FYs 2017 and 2018)
beginning in FY 2020. For the IRF QRP,
we adopted a new measure removal
factor, removed two measures from the
IRF QRP measure set, and codified a
number of program requirements in our
regulations. For more information on
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39057
the policy changes implemented for FY
2019, please refer to the FY 2019 IRF
PPS final rule.
B. Provisions of the PPACA Affecting
the IRF PPS in FY 2012 and Beyond
The PPACA included several
provisions that affect the IRF PPS in FYs
2012 and beyond. In addition to what
was previously discussed, section
3401(d) of the PPACA also added
section 1886(j)(3)(C)(ii)(I) of the Act
(providing for a ‘‘productivity
adjustment’’ for FY 2012 and each
subsequent fiscal year). The
productivity adjustment for FY 2020 is
discussed in section VI.D. of this final
rule. Section 1886(j)(3)(C)(ii)(II) of the
Act provides that the application of the
productivity adjustment to the market
basket update may result in an update
that is less than 0.0 for a fiscal year and
in payment rates for a fiscal year being
less than such payment rates for the
preceding fiscal year.
Sections 3004(b) of the PPACA and
section 411(b) of the Medicare Access
and CHIP Reauthorization Act of 2015
(Pub. L. 114–10, enacted April 16, 2015)
(MACRA) also addressed the IRF PPS.
Section 3004(b) of PPACA reassigned
the previously designated section
1886(j)(7) of the Act to section 1886(j)(8)
of the Act and inserted a new section
1886(j)(7) of the Act, which contains
requirements for the Secretary to
establish a QRP for IRFs. Under that
program, data must be submitted in a
form and manner and at a time specified
by the Secretary. Beginning in FY 2014,
section 1886(j)(7)(A)(i) of the Act
requires the application of a 2
percentage point reduction to the
market basket increase factor otherwise
applicable to an IRF (after application of
paragraphs (C)(iii) and (D) of section
1886(j)(3) of the Act) for a fiscal year if
the IRF does not comply with the
requirements of the IRF QRP for that
fiscal year. Application of the 2
percentage point reduction may result
in an update that is less than 0.0 for a
fiscal year and in payment rates for a
fiscal year being less than such payment
rates for the preceding fiscal year.
Reporting-based reductions to the
market basket increase factor are not
cumulative; they only apply for the FY
involved. Section 411(b) of MACRA
amended section 1886(j)(3)(C) of the Act
by adding paragraph (iii), which
required us to apply for FY 2018, after
the application of section
1886(j)(3)(C)(ii) of the Act, an increase
factor of 1.0 percent to update the IRF
prospective payment rates.
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C. Operational Overview of the Current
IRF PPS
As described in the FY 2002 IRF PPS
final rule (66 FR 41316), upon the
admission and discharge of a Medicare
Part A Fee-for-Service (FFS) patient, the
IRF is required to complete the
appropriate sections of a PAI,
designated as the IRF–PAI. In addition,
beginning with IRF discharges occurring
on or after October 1, 2009, the IRF is
also required to complete the
appropriate sections of the IRF–PAI
upon the admission and discharge of
each Medicare Advantage (MA) patient,
as described in the FY 2010 IRF PPS
final rule (74 FR 39762 and 74 FR
50712). All required data must be
electronically encoded into the IRF–PAI
software product. Generally, the
software product includes patient
classification programming called the
Grouper software. The Grouper software
uses specific IRF–PAI data elements to
classify (or group) patients into distinct
CMGs and account for the existence of
any relevant comorbidities.
The Grouper software produces a fivecharacter CMG number. The first
character is an alphabetic character that
indicates the comorbidity tier. The last
four characters are numeric characters
that represent the distinct CMG number.
Free downloads of the Inpatient
Rehabilitation Validation and Entry
(IRVEN) software product, including the
Grouper software, are available on the
CMS website at https://www.cms.gov/
Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/
Software.html.
Once a Medicare Part A FFS patient
is discharged, the IRF submits a
Medicare claim as a Health Insurance
Portability and Accountability Act of
1996 (Pub. L. 104–191, enacted August
21, 1996) (HIPAA) compliant electronic
claim or, if the Administrative
Simplification Compliance Act of 2002
(Pub. L. 107–105, enacted December 27,
2002) (ASCA) permits, a paper claim (a
UB–04 or a CMS–1450 as appropriate)
using the five-character CMG number
and sends it to the appropriate Medicare
Administrative Contractor (MAC). In
addition, once a MA patient is
discharged, in accordance with the
Medicare Claims Processing Manual,
chapter 3, section 20.3 (Pub. 100–04),
hospitals (including IRFs) must submit
an informational-only bill (Type of Bill
(TOB) 111), which includes Condition
Code 04 to their MAC. This will ensure
that the MA days are included in the
hospital’s Supplemental Security
Income (SSI) ratio (used in calculating
the IRF LIP adjustment) for fiscal year
2007 and beyond. Claims submitted to
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Medicare must comply with both ASCA
and HIPAA.
Section 3 of the ASCA amended
section 1862(a) of the Act by adding
paragraph (22), which requires the
Medicare program, subject to section
1862(h) of the Act, to deny payment
under Part A or Part B for any expenses
for items or services for which a claim
is submitted other than in an electronic
form specified by the Secretary. Section
1862(h) of the Act, in turn, provides that
the Secretary shall waive such denial in
situations in which there is no method
available for the submission of claims in
an electronic form or the entity
submitting the claim is a small provider.
In addition, the Secretary also has the
authority to waive such denial in such
unusual cases as the Secretary finds
appropriate. For more information, see
the ‘‘Medicare Program; Electronic
Submission of Medicare Claims’’ final
rule (70 FR 71008). Our instructions for
the limited number of Medicare claims
submitted on paper are available at
https://www.cms.gov/manuals/
downloads/clm104c25.pdf.
Section 3 of the ASCA operates in the
context of the administrative
simplification provisions of HIPAA,
which include, among others, the
requirements for transaction standards
and code sets codified in 45 CFR part
160 and part 162, subparts A and I
through R (generally known as the
Transactions Rule). The Transactions
Rule requires covered entities, including
covered health care providers, to
conduct covered electronic transactions
according to the applicable transaction
standards. (See the CMS program claim
memoranda at https://www.cms.gov/
ElectronicBillingEDITrans/ and listed in
the addenda to the Medicare
Intermediary Manual, Part 3, section
3600).
The MAC processes the claim through
its software system. This software
system includes pricing programming
called the ‘‘Pricer’’ software. The Pricer
software uses the CMG number, along
with other specific claim data elements
and provider-specific data, to adjust the
IRF’s prospective payment for
interrupted stays, transfers, short stays,
and deaths, and then applies the
applicable adjustments to account for
the IRF’s wage index, percentage of lowincome patients, rural location, and
outlier payments. For discharges
occurring on or after October 1, 2005,
the IRF PPS payment also reflects the
teaching status adjustment that became
effective as of FY 2006, as discussed in
the FY 2006 IRF PPS final rule (70 FR
47880).
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D. Advancing Health Information
Exchange
The Department of Health and Human
Services (HHS) has a number of
initiatives designed to encourage and
support the adoption of interoperable
health information technology and to
promote nationwide health information
exchange to improve health care. The
Office of the National Coordinator for
Health Information Technology (ONC)
and CMS work collaboratively to
advance interoperability across settings
of care, including post-acute care.
To further interoperability in postacute care, we developed a Data
Element Library (DEL) to serve as a
publicly-available centralized,
authoritative resource for standardized
data elements and their associated
mappings to health IT standards. The
DEL furthers CMS’ goal of data
standardization and interoperability.
These interoperable data elements can
reduce provider burden by allowing the
use and exchange of healthcare data,
support provider exchange of electronic
health information for care
coordination, person-centered care, and
support real-time, data driven, clinical
decision making. Standards in the Data
Element Library (https://del.cms.gov/)
can be referenced on the CMS website
and in the ONC Interoperability
Standards Advisory (ISA). The 2019 ISA
is available at https://www.healthit.gov/
isa.
The 21st Century Cures Act (Pub. L.
114–255, enacted December 13, 2016)
(Cures Act), requires HHS to take new
steps to enable the electronic sharing of
health information ensuring
interoperability for providers and
settings across the care continuum. In
another important provision, Congress
defined ‘‘information blocking’’ as
practices likely to interfere with,
prevent, or materially discourage access,
exchange, or use of electronic health
information, and established new
authority for HHS to discourage these
practices. In March 2019, ONC and CMS
published the proposed rules, ‘‘21st
Century Cures Act: Interoperability,
Information Blocking, and the ONC
Health IT Certification Program,’’ (84 FR
7424) and ‘‘Interoperability and Patient
Access’’ (84 FR 7610) to promote secure
and more immediate access to health
information for patients and healthcare
providers through the implementation
of information blocking provisions of
the Cures Act and the use of
standardized application programming
interfaces (APIs) that enable easier
access to electronic health information.
We solicited comment on the two
proposed rules. We invited providers to
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learn more about these important
developments and how they are likely
to affect IRFs.
II. Summary of Provisions of the
Proposed Rule
In the FY 2020 IRF PPS proposed
rule, we proposed to update the IRF
prospective payment rates for FY 2020
and to rebase and revise the IRF market
basket to reflect a 2016 base year rather
than the current 2012 base year. We also
proposed to replace the previously
finalized unweighted motor score with
a weighted motor score to assign
patients to CMGs and remove one item
from the score beginning with FY 2020
and to revise the CMGs and update the
CMG relative weights and average LOS
values beginning with FY 2020, based
on analysis of 2 years of data (FYs 2017
and 2018). We also proposed to use the
concurrent FY IPPS wage index for the
IRF PPS beginning with FY 2020. We
also solicited comments on stakeholder
concerns regarding the appropriateness
of the wage index used to adjust IRF
payments. We proposed to amend the
regulations at § 412.622 to clarify that
the determination as to whether a
physician qualifies as a rehabilitation
physician (that is, a licensed physician
with specialized training and
experience in inpatient rehabilitation) is
made by the IRF.
The proposed policy changes and
updates to the IRF prospective payment
rates for FY 2020 are as follows:
• Describe a proposed weighted
motor score to replace the previously
finalized unweighted motor score to
assign a patient to a CMG, the removal
of one item from the score, and
revisions to the CMGs beginning on
October 1, 2019, based on analysis of 2
years of data (FYs 2017 and 2018) using
the Quality Indicator items in the IRF–
PAI. This includes proposed revisions
to the CMG relative weights and average
LOS values for FY 2020, in a budget
neutral manner, as discussed in section
III. of the FY 2020 IRF PPS proposed
rule (84 FR 17244, 17249 through
17260).
• Describe the proposed rebased and
revised IRF market basket to reflect a
2016 base year rather than the current
2012 base year as discussed in section
V. of the FY 2020 IRF PPS proposed rule
(84 FR 17244, 17261 through 17273).
• Update the IRF PPS payment rates
for FY 2020 by the proposed market
basket increase factor, based upon the
most current data available, with a
proposed productivity adjustment
required by section 1886(j)(3)(C)(ii)(I) of
the Act, as described in section V. of the
FY 2020 IRF PPS proposed rule (84 FR
17244, 17274 through 17275).
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• Describe the proposed update to the
IRF wage index to use the concurrent
FY IPPS wage index and the FY 2020
proposed labor-related share in a
budget-neutral manner, as described in
section V. of the FY 2020 IRF PPS
proposed rule (84 FR 17244, 17276
through 17279).
• Describe the continued use of FY
2014 facility-level adjustment factors, as
discussed in section IV. of the FY 2020
IRF PPS proposed rule (84 FR 17244,
17260 through 17261).
• Describe the calculation of the IRF
standard payment conversion factor for
FY 2020, as discussed in section V. of
the FY 2020 IRF PPS proposed rule (84
FR 17244, 17280 through 17282).
• Update the outlier threshold
amount for FY 2020, as discussed in
section VI. of the FY 2020 IRF PPS
proposed rule (84 FR 17244, 17283
through 17284).
• Update the cost-to-charge ratio
(CCR) ceiling and urban/rural average
CCRs for FY 2020, as discussed in
section VI. of the FY 2020 IRF PPS
proposed rule (84 FR 17244 at 17284).
• Describe the proposed amendments
to the regulations at § 412.622 to clarify
that the determination as to whether a
physician qualifies as a rehabilitation
physician (that is, a licensed physician
with specialized training and
experience in inpatient rehabilitation) is
made by the IRF, as discussed in section
VII. of the FY 2020 IRF PPS proposed
rule (84 FR 17244, 17284 through
17285).
• Updates to the requirements for the
IRF QRP, as discussed in section VIII. of
the FY 2020 IRF PPS proposed rule (84
FR 17244, 17285 through 17330).
III. Analysis and Response to Public
Comments
We received 1,257 timely responses
from the public, many of which
contained multiple comments on the FY
2020 IRF PPS proposed rule (84 FR
17244). The majority consisted of form
letters, in which we received multiple
copies of two types of identicallyworded letters that had been signed and
submitted by different individuals. We
received comments from various trade
associations, IRFs, individual
physicians, therapists, clinicians, health
care industry organizations, and health
care consulting firms. The following
sections, arranged by subject area,
include a summary of the public
comments that we received, and our
responses.
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IV. Refinements to the Case-Mix
Classification System Beginning With
FY 2020
A. Background
Section 1886(j)(2)(A) of the Act
requires the Secretary to establish CMGs
for payment under the IRF PPS and a
method of classifying specific IRF
patients within these groups. Under
section 1886(j)(2)(B) of the Act, the
Secretary must assign each CMG an
appropriate weighting factor that
reflects the relative facility resources
used for patients classified within the
group as compared to patients classified
within other groups. Additionally,
section 1886(j)(2)(C)(i) of the Act
requires the Secretary from time to time
to adjust the established classifications
and weighting factors as appropriate to
reflect changes in treatment patterns,
technology, case-mix, number of
payment units for which payment is
made under title XVIII of the Act, and
other factors which may affect the
relative use of resources. Such
adjustments must be made in a manner
so that changes in aggregate payments
under the classification system are a
result of real changes and are not a
result of changes in coding that are
unrelated to real changes in case mix.
In the FY 2019 IRF PPS final rule (83
FR 38533 through 38549), we finalized
the removal of the Functional
Independence Measure (FIMTM)
instrument and associated Function
Modifiers from the IRF–PAI and the
incorporation of an unweighted additive
motor score derived from 19 data items
located in the Quality Indicators section
of the IRF–PAI beginning with FY 2020
(83 FR 38535 through 38536, 38549). As
discussed in section IV.B of this final
rule, based on further analysis to
examine the potential impact of
weighting the motor score, we proposed
to replace the previously finalized
unweighted motor score with a
weighted motor score and remove one
item from the score beginning with FY
2020.
Additionally, as noted in the FY 2019
IRF PPS final rule (83 FR 38534), the
incorporation of the data items from the
Quality Indicator section of the IRF–PAI
into the IRF case-mix classification
system necessitates revisions to the
CMGs to ensure that IRF payments are
calculated accurately. We finalized the
use of data items from the Quality
Indicators section of the IRF–PAI to
construct the functional status scores
used to classify IRF patients in the IRF
case-mix classification system for
purposes of establishing payment under
the IRF PPS beginning with FY 2020,
but modified our proposal based on
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public comments to incorporate 2 years
of data (FYs 2017 and 2018) into our
analyses used to revise the CMG
definitions (83 FR 38549). We stated
that any changes to the proposed CMG
definitions resulting from the
incorporation of an additional year of
data (FY 2018) into the analysis would
be addressed in future rulemaking prior
to their implementation beginning in FY
2020. As discussed in section III.C of the
FY 2020 IRF PPS proposed rule (84 FR
17244, 17250 through 17260), we
proposed to revise the CMGs based on
analysis of 2 years of data (FYs 2017 and
2018) beginning with FY 2020. We also
proposed to update the relative weights
and average LOS values associated with
the revised CMGs beginning with FY
2020.
B. Proposed Use of a Weighted Motor
Score Beginning With FY 2020
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As noted in the FY 2019 IRF PPS final
rule (83 FR 38535), the IRF case-mix
classification system currently uses a
weighted motor score based on FIMTM
data items to assign patients to CMGs
under the IRF PPS through FY 2019.
More information on the development
and implementation of this motor score
can be found in the FY 2006 IRF PPS
final rule (70 FR 47896 through 47900).
In the FY 2019 IRF PPS final rule (83
FR 38535 through 38536, 38549), we
finalized the incorporation of an
unweighted additive motor score
derived from 19 data items located in
the Quality Indicators section of the
IRF–PAI beginning with FY 2020. We
did not propose a weighted motor score
at the time, because we believed that the
unweighted motor score would facilitate
greater understanding among the
provider community, as it is less
complex. However, we also noted that
we would take comments in favor of a
weighted motor score into consideration
in future analysis. In response to
feedback we received from various
stakeholders and professional
organizations regarding the use of an
unweighted motor score and requesting
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that we consider weighting the motor
score, we extended our contract with
Research Triangle Institute,
International (RTI) to examine the
potential impact of weighting the motor
score. Based on this analysis, discussed
further below, we believed that a
weighted motor score would improve
the accuracy of payments to IRFs and
proposed to replace the previously
finalized unweighted motor score with
a weighted motor score to assign
patients to CMGs beginning with FY
2020.
The previously finalized motor score
is calculated by summing the scores of
the 19 data items, with equal weight
applied to each item. The 19 data items
are (83 FR 38535):
• GG0130A1 Eating.
• GG0130B1 Oral hygiene.
• GG0130C1 Toileting hygiene.
• GG0130E1 Shower/bathe self.
• GG0130F1 Upper-body dressing.
• GG0130G1 Lower-body dressing.
• GG0130H1 Putting on/taking off
footwear.
• GG0170A1 Roll left and right.
• GG0170B1 Sit to lying.
• GG0170C1 Lying to sitting on side
of bed.
• GG0170D1 Sit to stand.
• GG0170E1 Chair/bed-to-chair
transfer.
• GG0170F1 Toilet transfer.
• GG0170I1 Walk 10 feet.
• GG0170J1 Walk 50 feet with two
turns.
• GG0170K1 Walk 150 feet.
• GG0170M1 One step curb.
• H0350 Bladder continence.
• H0400 Bowel continence.
In response to feedback we received
from various stakeholders and
professional organizations requesting
that we consider applying weights to the
motor score, we extended our contract
with RTI to explore the potential of
applying unique weights to each of the
19 items in the motor score.
As part of their analysis, RTI
examined the degree to which the items
used to construct the motor score were
related to one another and adjusted their
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weighting methodology to account for
their findings. RTI considered a number
of different weighting methodologies to
develop a weighted index that would
increase the predictive power of the IRF
case-mix classification system while at
the same time maintaining simplicity.
RTI used regression analysis to explore
the relationship of the motor score items
to costs. This analysis was undertaken
to determine the impact of each of the
items on cost and then to weight each
item in the index according to its
relative impact on cost. Based on
findings from this analysis, we proposed
to remove the item GG0170A1 Roll left
and right from the motor score as this
item was found to have a high degree of
multicollinearity with other items in the
motor score and would have resulted in
either a negative or non-significant
coefficient. As such, we did not believe
it would be appropriate to include this
item in the motor score calculation.
Using the revised motor score composed
of the remaining 18 items identified
above, RTI designed a weighting
methodology for the motor score that
could be applied uniformly across all
RICs. For a more detailed discussion of
the analysis used to construct the
weighted motor score, we refer readers
to the March 2019 technical report
entitled ‘‘Analyses to Inform the Use of
Standardized Patient Assessment Data
Elements in the Inpatient Rehabilitation
Facility Prospective Payment System’’,
available at https://www.cms.gov/
Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/
Research.html. Findings from this
analysis suggested that the use of a
weighted motor score index slightly
improves the ability of the IRF PPS to
predict patient costs. Based on this
analysis, we proposed to use a weighted
motor score for the purpose of
determining IRF payments.
Table 1 shows the proposed weights
for each component of the motor score,
averaged to 1, obtained through the
regression analysis.
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We proposed to determine the motor
score by applying each of the weights
indicated in Table 1 to the score of each
corresponding item, as finalized in the
FY 2019 IRF PPS final rule (83 FR 38535
through 38537), and then summing the
weighted scores for each of the 18 items
that compose the motor score.
We received several comments on the
proposal to replace the previously
finalized unweighted motor score with
a weighted motor score to assign
patients to CMGs under the IRF PPS and
our proposal to remove the item
GG0170A1 Roll left and right from the
calculation of the motor score beginning
with FY 2020, that is, for all discharges
beginning on or after October 1, 2019.
As summarized in more detail below,
with the exception of one comment
from MedPAC, the commenters
overwhelmingly requested that CMS
delay implementation of a weighted
motor score and use an unweighted
motor score to assign patients to CMGs
until we can more fully analyze and
work with stakeholders on developing a
weighted motor score methodology.
In response to public comments, we
carefully considered whether to finalize
the proposed weighted motor score or
go back to using an unweighted motor
score to assign patients to CMGs.
Although the proposed weighted motor
score results in a slight improvement in
the ability of the IRF PPS to predict
patient costs and thus the accuracy of
IRF PPS payments (less than 0.18
difference in accuracy between the
weighted and the unweighted motor
scores), we acknowledge the
unweighted motor score is conceptually
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simpler and, as such, believe it will ease
providers’ transition to the use of the
data items located in the Quality
Indicators section of the IRF–PAI (also
referred to as section GG items). Thus,
we are finalizing based on public
comments the use of an unweighted
motor score to assign patients to CMGs
beginning with FY 2020. We appreciate
the commenters’ suggestions on the
weighting methodology and will take
them into consideration as we explore
possible refinements to the case-mix
classification system in the future.
Comment: Although several
commenters noted appreciation for the
fact that we analyzed a weighted motor
score in response to their comments on
the FY 2019 IRF PPS proposed rule (83
FR 38546), these same commenters
expressed concerns with the actual
weight values that CMS proposed for FY
2020, as indicated in Table 1, and stated
that we should go back to an
unweighted motor score so that we can
do further analysis and collaborate with
stakeholders to further refine the
weighting methodology. Some
commenters expressed concern that
CMS might be proposing higher weights
for the self-care items than for the
mobility items, in contrast to the current
weighted motor score, which weights
mobility items higher than self-care
items. Some commenters specifically
requested that CMS explain why the
weight for the eating item increased
from 0.6 under the current weighting
methodology to 2.7 under the proposed
methodology, and requested we explain
what we believe this change will mean
for patients with eating deficits.
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Commenters were also generally
concerned by what they suggested were
large differences in the weight value
assignments between the current and
proposed motor score.
Response: We used simple ordinary
least squares regression analysis of the
data that IRFs submitted to us in FYs
2017 and 2018 to calculate the proposed
weight values for the motor score, in
response to stakeholder feedback on the
FY 2019 IRF PPS proposed rule (83 FR
38546). Commenters are correct that the
proposed weights for the motor score
items, in comparison with the current
weights, shift some of the weight from
the mobility to the self-care items. We
also note that the proposed weights
assigned to the bowel and bladder
function items increased compared with
the current weights. These changes are
all reflective of the data the IRFs
submitted to us in FYs 2017 and 2018.
Regarding the proposed increase in
the weight for the eating item, it is
important to note key differences in the
coding guidelines between the FIMTM
eating item and the section GG eating
item that may have contributed to the
change in the relative importance of this
item for predicting IRF costs. For item
GG0130A, Eating, assistance with tube
feedings is not considered when coding
this item. If a patient does not eat or
drink by mouth but is instead tube fed,
item GG0130A must be coded as 88—
‘‘Not attempted due to medical
condition or safety concerns’’ or 09—
‘‘Not applicable’’. Both of these
responses would be recoded to a 01—
‘‘Dependent’’ for the purposes of
assigning the patient to a CMG. This
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differs from the coding instructions for
the FIMTM eating item used in the
current motor score, which takes into
consideration assistance with tube
feedings in scoring the item. For
example, according to the FIMTM
instructions, a patient who could
administer the tube feeding completely
independently could receive a score of
7-Complete independence on the eating
item.
In regards to the suggested differences
in the weight value assignments
between the current and proposed
methodologies, we note that in certain
cases the proposed weights were
divided among multiple items in the
motor score that were found to be highly
correlated to avoid overweighting any
particular measure of function. For
instance, the three items (GG0170I1,
GG0170J1, and GG0170K1) that assess
walking function were each assigned a
proposed weight of 0.8. When summed
together, the weight value for walking
under the proposed methodology is 2.4,
which is slightly higher than the weight
value of 1.6 for the single walking item
used in the current motor score.
Comment: One commenter disagreed
with the removal of item GG0170A1 roll
left and right from the motor score and
noted it is an important functional task
in the IRF setting. Some commenters
questioned the use of averaging values
across pairs of items that were
correlated and inquired why the roll left
and right item was removed from the
motor score while other correlated items
were not removed. Commenters also
inquired about the use of the item ‘‘walk
10 feet’’ to derive the weights for the
‘‘walk 50 feet’’ and ‘‘walk 150 feet’’
items.
Response: We appreciate the
commenter’s concerns regarding the
removal of item GG0170A1 from the
motor score. As described in detail in
the technical report, ‘‘Analyses to
Inform the Use of Standardized Patient
Assessment Data Elements in the
Inpatient Rehabilitation Facility
Prospective Payment System,’’ the roll
left and right item was found to have a
high degree of multicollinearity with
other standardized patient assessment
elements and to be inversely correlated
with costs after controlling for each of
the other self-care and mobility items.
This relationship persisted when this
item was paired with the other
correlated items. The continued
inclusion of this item in the motor score
would have resulted in either a negative
or non-significant coefficient. As such,
we do not believe it is appropriate to
include this item in the construction of
the motor score. The other item pairs
that were found to be correlated did not
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generate negative or non-significant
coefficients, and were therefore
maintained in the calculation of the
motor score.
Unlike the FIMTM instrument, the
items from the quality indicator section
of the IRF–PAI sometimes use more
than one item to measure functional
areas. As discussed in more detail in the
technical report, we noted that a few
items were found to be highly
correlated. Because of the correlation,
we proposed to use an average score for
some items so as to avoid introducing
bias or inappropriately overweighting
any particular functional area. We note
this methodology is consistent with the
methodology used under the Patient
Driven Payment Model (PDPM), as
described in more detail in the FY 2019
SNF final rule (83 FR 39204) and the
accompanying technical report entitled
‘‘Skilled Nursing Facilities PatientDriven Payment Model Technical
Report’’ available on the CMS website at
https://www.cms.gov/Medicare/
Medicare-Fee-for-Service-Payment/
SNFPPS/therapyresearch.html.
Regarding the ‘‘walk 10 feet’’ item,
that item was used to derive the weights
for the ‘‘walk 50 feet’’ and ‘‘walk 150
feet’’ items as these three items were
found to be highly correlated and the
‘‘walk 150 feet’’ item had a high
proportion of observations coded on
admission with ‘‘activity not attempted’’
codes.
Comment: Some commenters
requested that CMS apply the current
motor score weights associated with the
FIMTM items to the revised motor score
while other commenters requested that
CMS postpone weighting the motor
score until additional data can be
collected and analyzed. While a few
commenters were supportive of using a
weighted motor score, other
commenters suggested that CMS use a 1year payment model or phase in the use
of a weighted motor score.
Response: We do not believe it would
be appropriate to apply the weight
values associated with the FIMTM items
to the components of the revised motor
score, as these weights would not
accurately reflect how the various
components of the revised motor score
contribute to predicting patient costs.
We used simple ordinary least squares
regression analysis of the data that IRFs
submitted to us in FYs 2017 and 2018
to calculate the proposed weight values
for the revised motor score. Changes in
patient demographics, treatment
practices, technology, and other factors
that may affect the relative use of
resources in an IRF since the motor
score weights were originally calculated
have likely contributed to changes in
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the weight values applied across the
self-care and mobility items. We
proposed to apply weights to the motor
score items because RTI’s analysis
indicated that a weighted motor score
would improve the classification of
patients into CMGs, which in turn
would improve the accuracy of
payments to IRFs. However, as
discussed above, in response to public
comments, we carefully considered
whether to finalize the proposed
weighted motor score or go back to
using an unweighted motor score to
assign patients to CMGs. Although the
proposed weighted motor score results
in a slight improvement in the ability of
the IRF PPS to predict patient costs and
thus the accuracy of IRF PPS payments
(less than 0.18 difference in accuracy
between the weighted and the
unweighted motor scores), we
acknowledge the unweighted motor
score is conceptually simpler and, as
such, believe it will ease providers’
transition to the use of the data items
located in the Quality Indicators section
of the IRF–PAI (also referred to as
section GG items). Thus, we are
finalizing based on public comments the
use of an unweighted motor score, in
which each of the 18 items have a
weight of 1, to assign patients to CMGs
beginning with FY 2020.
Comment: Commenters expressed
concern that the analysis performed by
RTI did not explicitly follow the
analysis conducted by RAND when the
motor score weights were developed for
FY 2006 (70 FR 47896 through 47900)
and that RTI based their analyses on 2
years of data instead of several years of
data. Additionally, commenters
requested more information on the other
weighting methodologies that RTI
considered.
Response: We disagree with the
commenters that the RAND analysis for
FY 2006 used more years of data than
RTI’s analysis for the FY 2020 proposed
rule. As discussed in the FY 2006 IRF
PPS final rule (70 FR 47897), RAND
performed regression analysis on less
than 2 full years of data (calendar year
(CY) 2002 and FY 2003) to derive the
current motor score weights. In contrast,
RTI used 2 full years of data (FYs 2017
and 2018) to perform the analysis for the
weighted motor score proposed in the
FY 2020 IRF PPS proposed rule. As the
FYs 2017 and 2018 data portrays the
most recent and complete picture of
patients under the IRF PPS, we believe
it was sufficient and appropriate to
utilize for the analysis for the proposed
rule.
While RTI utilized a different
weighting methodology than was used
by RAND in 2006, the overall model
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prediction using the weighted motor
score developed by RAND and the
weighted motor score developed by RTI
is extremely similar. The model using
the CMGs based on the standardized
patient assessment data elements and
comorbidity tiers to predict wageadjusted costs of care has an r-squared
value is 0.3358, while the r-squared
value is 0.3169 for the CMGs in the
current IRF PPS. This is indicative of
similar model performance regardless of
model specification. The item weights
that the RAND work notes as ‘‘optimally
weighted’’ are weights that were
constructed separately for each RIC.
These were not the weights that were
used in the final weights developed by
RAND.
RTI also examined weighing
methodologies utilizing a general linear
model (GLM) and log transformed
ordinary least squares (OLS) regression
models, as well as the OLS model
described in more detail in the technical
report. All three models had comparable
model fit and generated similar item
weights. Based on the greater simplicity
achieved through the use of the OLS
regression model we believe using the
OLS regression was appropriate to
maintain simplicity and transparency in
the payment system.
Comment: Commenters disagreed
with the omission of the wheelchair
mobility items from the items used to
construct the motor score.
Response: We appreciate the
commenters’ concerns about
wheelchair-dependent patients. As most
recently discussed in the FY 2019 IRF
PPS final rule (83 FR 38546) in response
to similar stakeholder comments, we
explained our rationale for not
including the wheelchair mobility items
in the construction of the finalized
motor score. We continue to believe that
the higher resource needs of wheelchair
dependent patients in IRFs will be
better accounted for by not including a
wheelchair item in the motor score at
this time. Patients that are considered
wheelchair dependent or unable to walk
will be accounted for through the ‘‘not
attempted’’ response codes captured
through other items, especially some of
the walking items, that are included in
the motor score. In this way, we ensure
that IRFs will be appropriately
compensated for the higher costs they
incur in treating wheelchair-dependent
patients. We refer readers to the FY
2019 IRF PPS final rule (83 FR 38546)
and the technical report entitled
‘‘Analyses to Inform the Use of
Standardized Patient Assessment Data
Elements in the Inpatient Rehabilitation
Facility Prospective Payment System’’
for more information on the rationale as
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to why this item was not included in the
calculation of the motor score.
Comment: Commenters expressed
concern with the weighted motor score
and questioned the reliability and
validity of the weighted motor score.
Some commenters stated that they
believe the weighted and unweighted
motor scores have shown little to no
correlation with the weighted motor
score currently in use, and therefore,
questioned if the weighted motor score
could accurately measure patient
severity.
Response: We disagree with the
commenters’ suggestion that
unweighted and weighted motor scores
have shown little to no correlation with
the weighted motor score currently in
use as our analysis shows a strong
correlation between the scores. In
addition, each of the proposed Quality
Indicators data items that were included
in the motor score were found to have
statistically significant correlation with
IRF costs. As discussed in the technical
report ‘‘Analyses to Inform the Use of
Standardized Patient Assessment Data
Elements in the Inpatient Rehabilitation
Facility Prospective Payment System’’
the use of a weighted motor score was
found to increase the predictive ability
of the payment model.
Comment: Commenters requested that
CMS make available the data utilized in
the analyses including patient
assessment data, matching claims data,
and additional facility and cost report
data to enable stakeholders to replicate
the analyses.
Response: We appreciate the
commenters’ feedback regarding the
types of information that would be most
useful to them in replicating our
analyses. We are unable to make patient
assessment and claims data publicly
available on the CMS website because
these data contain personally
identifiable information. However, we
believe that we released sufficient
information in the proposed rule, the
accompanying data files, and the
technical report entitled ‘‘Analyses to
Inform the Use of Standardized Patient
Assessment Data Elements in the
Inpatient Rehabilitation Facility
Prospective Payment System,’’ to enable
stakeholders to submit meaningful
comments on the underlying analyses
and methodologies used to revise the
IRF case-mix classification system, to
pose alternative approaches, and to
assess the impacts of the proposed
revisions.
Comment: A few commenters noted
that they did not believe that CMS has
performed the thorough data analyses
and engagement with the provider
community that are necessary prior to
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making significant changes to the
existing IRF PPS. These commenters
requested that we solicit additional
feedback from the stakeholder
community, including convening
technical advisory panels (TEPs), to
provide additional transparency into the
underlying analyses and to delay
implementation of a weighted motor
score until we conduct additional
engagements with stakeholders.
Response: We value transparency in
our processes and will continue to
engage stakeholders in future
development of payment policies. We
appreciate the offers from stakeholders
to assist in the development of future
revisions to payment policies and we
recognize the value from these
partnerships. However, for something as
analytically simple as running a
regression analysis to determine the
weights for the motor score items that
best reflect patients’ resource needs in
the IRF, we do not believe that a TEP
is necessary.
As noted above, although the
proposed weighted motor score results
in a slight improvement in the ability of
the IRF PPS to predict patient costs and
thus the accuracy of IRF PPS payments
(less than 0.18 difference in accuracy
between the weighted and the
unweighted motor scores), we
acknowledge the unweighted motor
score is conceptually simpler and, as
such, believe it will ease providers’
transition to the use of the data items
located in the Quality Indicators section
of the IRF–PAI (also referred to as
section GG items). Thus, we are
finalizing based on public comments the
use of an unweighted motor score to
assign patients to CMGs beginning with
FY 2020. We appreciate the
stakeholders’ comments on this topic
and will take them into consideration
for future analysis.
Comment: A few commenters
requested that CMS provide additional
information regarding the provider
specific impact analysis file that
accompanied the rule, such as a data
dictionary describing the data used to
calculate the impacts.
Response: In conjunction with the
release of the FY 2020 IRF PPS
proposed rule, we posted a providerspecific impact analysis file that
compared estimated payments to
providers for FY 2020 without the
proposed revisions to the CMGs with
estimated payments to providers for FY
2020 with the proposed revisions to the
CMGs. We believe that this file gives
IRFs added information to enable them
to see how their individual payments
would be affected by the proposed
changes to the CMGs. We updated this
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provider specific impact analysis file
shortly after it was initially posted to
include additional information
regarding the underlying data used to
calculate the provider specific impacts,
and we believe that this additional
information is responsive to
commenters’ requests. The file can be
downloaded from the CMS website at
https://www.cms.gov/Medicare/
Medicare-Fee-for-Service-Payment/
InpatientRehabFacPPS/IRF-Rules-andRelated-Files.html. We appreciate the
commenters’ suggestions regarding the
additional types of information that
would be most useful to them to further
facilitate understanding of our analyses.
As previously discussed, we proposed
a weighted motor score as it was found
to slightly improve the predicative
ability of the case-mix system and thus
the accuracy of IRF PPS payments.
However, nearly all of the comments we
received requested that we revert to an
unweighted motor score for the various
reasons discussed above. While we
continue to believe that a weighted
motor score is slightly more accurate,
the difference is small, and in light of
the conceptual simplicity achieved
through the use of an unweighted motor
score, which we believe will ease
providers’ transition to the use of the
data items located in the Quality
Indicators section of the IRF–PAI, we
are finalizing the use of an unweighted
motor score, in which each of the 18
items used in the score have an equal
weight of 1, to assign patients to CMGs
beginning with FY 2020. Additionally,
we are finalizing the proposed removal
of one item (GG0170A1 Roll left to right)
from the motor score beginning with FY
2020. Effective for all discharges
beginning on or after October 1, 2019,
we will use an unweighted motor score
as indicated in Table 2 to determine a
beneficiary’s CMG placement.
C. Revisions to the CMGs and Updates
to the CMG Relative Weights and
Average Length of Stay Values
Beginning With FY 2020
In the FY 2019 IRF PPS final rule (83
FR 38549), we finalized the use of data
items from the Quality Indicators
section of the IRF–PAI to construct the
functional status scores used to classify
IRF patients in the IRF case-mix
classification system for purposes of
establishing payment under the IRF PPS
beginning with FY 2020, but modified
our proposal based on public comments
to incorporate 2 years of data (FYs 2017
and 2018) into our analyses used to
revise the CMG definitions. We stated
that any changes to the proposed CMG
definitions resulting from the
incorporation of an additional year of
data (FY 2018) into the analysis would
be addressed in future rulemaking prior
to their implementation beginning in FY
2020. Additionally, we stated that we
would also update the relative weights
and average LOS values associated with
any revised CMG definitions in future
rulemaking.
As noted in the FY 2020 IRF PPS
proposed rule (84 FR 17251), we
continued our contract with RTI to
support us in developing proposed
revisions to the CMGs used under the
IRF PPS based on analysis of 2 years of
data (FYs 2017 and 2018). The process
RTI uses for its analysis, which is based
on a Classification and Regression Tree
(CART) algorithm, is described in detail
in the FY 2019 IRF PPS final rule (83
FR 38536 through 38540). RTI used this
analysis to revise the CMGs utilizing
FYs 2017 and 2018 claim and
assessment data and to develop revised
CMGs that reflect the use of the data
items collected in the Quality Indicators
section of the IRF–PAI, incorporating
the proposed weighted motor score
described in the FY 2020 IRF PPS
proposed rule. However, as discussed in
section IV.B of this final rule, we are
finalizing based on public comments the
use of an unweighted motor score to
assign patients to a CMGs beginning in
with FY 2020.
To develop the proposed revised
CMGs, RTI used CART analysis to
divide patients into payment groups
based on similarities in their clinical
characteristics and relative costs. As
part of this analysis, RTI imposed some
typically-used constraints on the
payment group divisions (for example,
on the minimum number of cases that
could be in the resulting payment
groups and the minimum dollar
payment amount differences between
groups) to identify the optimal set of
payment groups. For a more detailed
discussion of the analysis used to revise
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the CMGs for FY 2020, we refer readers
to the March 2019 technical report
entitled, ‘‘Analyses to Inform the Use of
Standardized Patient Assessment Data
Elements in the Inpatient Rehabilitation
Facility Prospective Payment System’’
available at https://www.cms.gov/
Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/
Research.html. Additionally, we refer
readers to the FY 2020 IRF PPS
proposed rule (84 FR 17250 through
17260) for more information on the
proposed revisions to the CMGs.
As noted above, we are finalizing the
use of an unweighted motor score
beginning with FY 2020. As the motor
score is a key input in the CART
analysis used to revise the CMGs, the
use of the unweighted motor score
required that the CART analysis be
rerun utilizing the unweighted motor
score. RTI utilized the same
methodology described in the FY 2020
IRF PPS proposed rule (84 FR 17250
through 17260) to support us in
developing revisions to the CMGs,
incorporating the unweighted motor
score, as described in section IV.B of
this final rule. The revised CMGs can be
found in Table 3.
After developing the revised CMGs,
RTI then calculated the relative weights
and average LOS values for each revised
CMG using the same methodologies that
we have used to update the CMG
relative weights and average LOS values
each fiscal year since 2009 (when we
implemented an update to this
methodology). More information about
the methodology used to update the
CMG relative weights can be found in
the FY 2009 IRF PPS final rule (73 FR
46372 through 46374). For FY 2020, we
proposed to use the FYs 2017 and 2018
IRF claims and FY 2017 IRF cost report
data to update the CMG relative weights
and average LOS values. In calculating
the CMG relative weights, we use a
hospital-specific relative value method
to estimate operating (routine and
ancillary services) and capital costs of
IRFs. As noted in the FY 2019 IRF PPS
final rule (83 FR 38521), this is the same
methodology that we have used to
update the CMG relative weights and
average LOS values each fiscal year
since we implemented an update to the
methodology in the FY 2009 IRF PPS
final rule (73 FR 46372 through 46374).
More information on the methodology
used to update calculate the CMG
relative weights and average LOS values
can found in the March 2019 technical
report entitled ‘‘Analyses to Inform the
Use of Standardized Patient Assessment
Data Elements in the Inpatient
Rehabilitation Facility Prospective
Payment System’’ available at https://
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www.cms.gov/Medicare/Medicare-Feefor-Service-Payment/InpatientRehab
FacPPS/Research.html. Consistent with
the methodology that we have used to
update the IRF classification system in
each instance in the past, we proposed
to update the relative weights associated
with the revised CMGs for FY 2020 in
a budget neutral manner by applying a
budget neutrality factor to the standard
payment amount. To calculate the
appropriate budget neutrality factor for
use in updating the FY 2020 CMG
relative weights, we used the following
steps:
Step 1. Calculate the estimated total
amount of IRF PPS payments for FY
2020 (with no changes to the CMG
relative weights).
Step 2. Calculate the estimated total
amount of IRF PPS payments for FY
2020 by applying the changes to the
CMGs and the associated CMG relative
weights (as described in this final rule).
Step 3. Divide the amount calculated
in step 1 by the amount calculated in
step 2 to determine the budget
neutrality factor (1.0016) that would
maintain the same total estimated
aggregate payments in FY 2020 with and
without the changes to the CMGs and
the associated CMG relative weights.
Step 4. Apply the budget neutrality
factor (1.0016) to the FY 2019 IRF PPS
standard payment amount after the
application of the budget-neutral wage
adjustment factor.
We note that, as we typically do, we
updated our data between the FY 2020
IRF PPS proposed and final rules to
ensure that we use the most recent
available data in calculating IRF PPS
payments. Additionally, we are
finalizing the use of unweighted motor
score beginning in with FY 2020 which
generated revisions to the CMGs and
relative weights. Based on our analysis
using this updated data and an
unweighted motor score, we now
estimate a budget neutrality factor of
(1.0010) to maintain the same total
estimated aggregate payments in FY
2020 with and without the changes to
the CMGs and the associated CMG
relative weights. For FY 2020 we will
apply the budget neutrality factor
(1.0010) to the FY 2019 IRF PPS
standard payment amount after the
application of the budget-neutral wage
adjustment factor.
The relative weights and average LOS
values for those revised CMGs (found in
Table 3) were calculated using the same
methodology described in the FY 2020
IRF PPS proposed rule, which is the
same methodology that we have used to
update the CMG relative weights and
average LOS values each fiscal year
since we implemented an update to the
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methodology in FY 2009. The revised
CMGs (reflecting the unweighted motor
score) and their respective descriptions,
as well as the comorbidity tiers,
corresponding relative weights and the
average LOS values for each CMG and
tier for FY 2020 are shown in Table 3.
The average LOS for each CMG is used
to determine when an IRF discharge
meets the definition of a short-stay
transfer, which results in a per diem
case level adjustment. In section V.H. of
this final rule, we discuss the proposed
use of the existing methodology to
calculate the standard payment
conversion factor for FY 2020.
We received a number of comments
on the proposed revisions to the CMGs
based on analysis of 2 years of data (FYs
2017 and 2018) and the proposed
updates to the relative weights and
average LOS values associated with the
revised CMGs beginning with FY 2020,
that is, for all discharges beginning on
or after October 1, 2019, which are
summarized below.
Comment: A number of commenters
were appreciative of the use of 2 years
of data to revise the CMGs; however,
commenters expressed concern with the
proposed CMG revisions and suggested
that these changes could result in
payment rate compression or a
misalignment between payments and
the costs of caring for patients.
Commenters suggested payment
compression would result in reduced
payments for higher acuity patients and
increased payments for lower acuity
patients which could compromise
access to care for patients with certain
impairments. Additionally, some
commenters questioned why there
would be fewer CMGs within some RICs
and suggested having fewer CMGs
would also contribute to payment rate
compression.
Response: We disagree with the
commenters that revisions to CMGs will
lead to payment rate compression or
could compromise access to care for any
particular group of patients. As the
revised CMGs are reflective of the data
that IRFs submitted to us in FYs 2017
and 2018, we believe the revised CMGs
reflect the distinct resource needs of the
current Medicare IRF population. We
believe the revised CMGs more
accurately predict resource use in IRFs
and better align payments with the
expected costs of treating patients in the
IRF setting. As such, we believe that the
revised CMGs may in fact improve
access to and quality of care for IRF
patients by increasing the accuracy of
IRF payments to providers.
Regarding why some RICs would have
fewer CMGs, we refer the commenters to
the Technical Report entitled ‘‘Analyses
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to Inform the Use of Standardized
Patient Assessment Data Elements in the
Inpatient Rehabilitation Facility
Prospective Payment System’’ that
describes in detail the analysis used to
derive the CMGs and the criteria
required to generate additional payment
groups. As noted in the FY 2020 IRF
PPS proposed rule (84 FR 17250
through 17252), RTI imposed some
typically-used constraints in their
analysis to identify the proposed set of
payment groups. These constraints
consisted of a minimum number of stays
within a node, a 0.5 percentage point
increase of explanatory power, and
monotonicity across the CMGs within
each RIC. We do not believe it would be
appropriate to generate additional CMGs
that did not improve the predicative
ability of the model beyond what was
produced through the CART analysis
utilizing the constraints above. We note
that while the CART analysis generated
fewer CMGs within some RICs, it
generated a greater number of CMGs
within other RICs and that the overall
number of CMGs increases through
these revisions to the case-mix
classification system. We do not believe
having fewer CMGs within any RIC will
contribute to payment rate compression
as we believe these revisions better align
payments with the expected costs of
treating patients in IRFs.
Additionally, we disagree with the
commenters’ statements that the CMG
revisions will result in higher payments
for lower acuity patients and reduced
payments for higher acuity patients. Our
analysis has found that higher function
is associated with a slight reduction in
payment under the revised CMGs and
that lower function is associated with a
slight increase in payments. The
purpose of the proposed revisions to the
CMGs is to align payments more
appropriately with the costs of caring
for all types of patients in IRFs. As such,
we do not believe that the revisions will
result in higher payments for lower
acuity patients. We appreciate the
commenters’ concerns and will
continue to monitor the IRF data closely
to ensure that IRF payments are
appropriately aligned with costs of care
and that Medicare patients continue to
have appropriate access to IRF services.
Comment: Several commenters
expressed concerns that the proposed
CMG revisions could cause a significant
redistribution of payments among IRF
provides. These commenters indicated
that they believe the section GG items
make patients appear to be less severe
and requested additional information on
how patients would be redistributed
among the revised CMGs. Additionally,
commenters encouraged CMS to
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monitor the data based on these changes
and to update the model if necessary in
the future.
Response: We agree with the
commenters that the revisions to the
CMGs may result in some redistribution
of payments among providers. As noted
in the FY 2019 IRF PPS final rule (83
FR 38547), the scales and coding
instructions are slightly different
between the item sets used to derive the
existing CMGs and those used to derive
the revised CMGs. As such, these
differences may result in some patients
grouping into different CMGs that more
accurately account for the expected
resource needs of the patient. While we
cannot make individual Medicare
beneficiary data publically available, we
believe we released adequate
information for stakeholders to
determine how beneficiaries could be
distributed across the revised CMGs. We
appreciate the commenters’ suggestions
to conduct monitoring activities and
make future updates to the case-mix
classification system and will take this
into consideration in the future.
Comment: Commenters expressed
concern with the use of section GG
items to assign a patient to a CMG and
suggested that these items are not
sensitive enough and do not capture
patients’ true burden of care.
Commenters also expressed concern
with the reliability of the data collected
through these items and suggested that
the data is not accurate or valid.
Response: As discussed in detail in
the FY 2019 IRF PPS final rule (83 FR
38541), we believe that the data items
located in the Quality Indicators section
of the IRF–PAI are sensitive and
accurately capture the functional and
cognitive status of patients and can also
be used to accurately assess changes in
patients’ functional status. As noted
above, RTI found that the model
predicting costs using the CMGs derived
from the items located in the Quality
Indicators section of the IRF–PAI had a
slightly higher R-squared value than
models using the current CMGs which
are derived from items in the FIMTM
instrument, indicating that the revised
CMGs more accurately predict resource
use in IRFs than the CMGs that are
currently utilized. As the data collected
in the Quality Indicators section of the
IRF–PAI have been collected nationally
for all IRFs since October 1, 2016, we
believe the data to be accurate and valid
at this time. We also believe it is the
responsibility of the IRF to submit
accurate and valid data that adheres to
the coding guidelines detailed in the
IRF–PAI training manual.
Comment: Commenters expressed
concern with the cognition items
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collected on the IRF–PAI and their
omission from the revised CMGs. A few
commenters noted the importance of
cognitive impairment in the IRF setting
and encouraged CMS to conduct further
analysis of the relationship between
cognitive function and resource use in
the IRF setting and to improve the items
that are used to measure cognitive
function.
Response: We appreciate the
commenters’ concerns with the
cognitive items that are collected on the
IRF–PAI. As we discussed in the FY
2019 IRF PPS final rule (83 FR 38546),
the cognitive items that we used for this
analysis are the best ones that we have
for use at the present time.
Unfortunately, we found that including
these cognitive items in generating the
CMGs would have resulted in lower
payments for patients with higher
cognitive deficits. This result does not
make sense from a clinical perspective,
and could have the unintended
consequence of reducing access to IRF
care for more cognitively impaired
beneficiaries. Thus, we determined that
it would be better at this time to remove
the CMG splits that were generated by
the cognitive items. We appreciate the
commenters’ suggestion to incorporate
improved cognition measures into the
IRF–PAI and will take this into
consideration in the future.
Comment: Commenters suggested that
CMS has not provided sufficient
education, training materials, or
supporting documentation regarding the
functional items to support their use in
developing a payment model. Some
commenters suggested revisions to the
existing training materials while other
commenters requested that CMS
provide additional training, monitor the
data, and modify the case mix groupings
as needed.
Response: We disagree with the
commenters that we have provided
insufficient training or guidance on
proper coding of this data. We believe
we have provided adequate training
opportunities for IRFs on coding the
Quality Indicator data items, including
multiple in-person training
opportunities, webinars, on-line training
and on-going help desk guidance. We
are committed to providing information
and support that will allow providers to
accurately interpret and complete
quality reporting items and we will
continue to provide these types of
opportunities to the IRF community. We
thank the commenters for their
suggestions to improve the training
materials and we appreciate the
commenters’ suggestions to continue to
monitor the data and make updates to
E:\FR\FM\08AUR2.SGM
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jbell on DSK3GLQ082PROD with RULES2
the case-mix classification system when
necessary.
After careful consideration of the
comments received, we are finalizing
revisions to the CMGs based on analysis
of 2 years of data (FYs 2017 and 2018)
and the incorporation of the unweighted
motor score described in section IV.B of
this final rule. The revised CMGs that
VerDate Sep<11>2014
17:22 Aug 07, 2019
Jkt 247001
will be effective October 1, 2019 are
presented below in Table 3. We refer
readers to Table 20 in section XIII.C of
this final rule for more information on
the distributional effects of revisions to
the CMGs. For a provider specific
impact analysis for this change, we refer
readers to the CMS website at https://
www.cms.gov/Medicare/Medicare-Fee-
PO 00000
Frm 00015
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39067
for-Service-Payment/InpatientRehab
FacPPS/IRF-Rules-and-RelatedFiles.html. We are also updating the
relative weights and average LOS values
associated with the revised CMGs
(reflecting an unweighted motor score)
beginning with FY 2020.
BILLING CODE 4120–01–P
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TABLE 3: Relative Weights and Average Length of Stay Values
for the Revised Case-Mix Groups
0101
0102
0103
0104
0105
0106
0201
0202
0203
0204
0205
0301
0302
0303
0304
0305
0401
0402
0403
0404
0405
0406
0407
0501
0502
0503
0504
0505
jbell on DSK3GLQ082PROD with RULES2
0601
0602
CMG Description
(M=motor, A=age)
Stroke M >=72.50
Stroke M >=63.50 and M <72.50
Stroke M >=50.50 and M <63.50
Stroke M >=41.50 and M <50.50
Stroke M <41.50 and A >=84.50
Stroke M <41.50 and A <84.50
Traumatic brain injury M >=73.50
Traumatic brain injury M >=61.50 and
M <73.50
Traumatic brain injury M >=49.50 and
M <61.50
Traumatic brain injury M >=35.50 and
M <49.50
Traumatic brain injury M <35.50
Non-traumatic brain injury M >=65.50
Non-traumatic brain injury M >=52.50
andM <65.50
Non-traumatic brain injury M >=42.50
andM<52.50
Non-traumatic brain injury M <42.50
and A >=78.50
Non-traumatic brain injury M <42.50
and A <78.50
Traumatic spinal cord injury M
>=56.50
Traumatic spinal cord injury M
>=47.50 and M <56.50
Traumatic spinal cord injury M
>=41.50 and M <47.50
Traumatic spinal cord injury M <31.50
and A <61.50
Traumatic spinal cord injury M
>=31.50 and M <41.50
Traumatic spinal cord injury M
>=24.50 and M <31.50 and A >=61.50
Traumatic spinal cord injury M <24.50
and A >=61.50
Non-traumatic spinal cord injury M
>=60.50
Non-traumatic spinal cord injury M
>=53.50 and M <60.50
Non-traumatic spinal cord injury M
>=48.50 and M <53.50
Non-traumatic spinal cord injury M
>=39.50 and M <48.50
Non-traumatic spinal cord injury M
<39.50
Neurological M >=64.50
Neurological M >=52.50 and M <64.50
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Average Length of Stay
No
Tier Tier Tier
Comorbidity
1
2
3
Tier
11
11
10
9
13
13
12
12
15
16
15
15
19
20
19
19
22
22
21
20
27
26
24
24
11
11
10
10
Tier 1
Tier 2
Tier 3
1.0351
1.3150
1.6790
2.1958
2.4300
2.8360
1.1593
0.8965
1.1389
1.4541
1.9017
2.1046
2.4562
0.9500
0.8300
1.0545
1.3464
1.7608
1.9487
2.2742
0.8568
No
Comorbidity
Tier
0.7906
1.0045
1.2825
1.6772
1.8562
2.1663
0.7992
1.4366
1.1772
1.0618
0.9903
13
13
12
12
1.7487
1.4330
1.2924
1.2055
15
16
14
14
2.1339
2.6631
1.2280
1.7487
2.1823
0.9995
1.5772
1.9683
0.9218
1.4710
1.8358
0.8618
21
31
11
19
24
11
17
21
10
16
19
10
1.5603
1.2700
1.1712
1.0950
14
14
13
13
1.8814
1.5313
1.4123
1.3203
17
16
15
15
2.1097
1.7171
1.5836
1.4805
20
18
17
16
2.2889
1.8630
1.7182
1.6063
21
20
18
17
1.3702
1.1748
1.0753
0.9860
14
13
12
12
1.7987
1.5423
1.4117
1.2944
15
18
16
15
2.1749
1.8649
1.7070
1.5652
20
20
19
18
3.1944
2.7390
2.5070
2.2988
36
31
27
23
2.7206
2.3328
2.1352
1.9578
27
27
23
21
3.3266
2.8523
2.6108
2.3939
39
32
27
26
4.1203
3.5330
3.2337
2.9651
49
37
32
36
1.2696
1.0371
0.9614
0.8798
13
12
11
10
1.5859
1.2954
1.2009
1.0990
15
14
13
13
1.8273
1.4926
1.3837
1.2663
17
15
15
14
2.2209
1.8141
1.6817
1.5390
20
19
18
17
2.8362
1.3431
1.6641
2.3166
1.0441
1.2937
2.1477
0.9748
1.2078
1.9654
0.8864
1.0983
30
12
14
24
11
14
23
11
13
21
10
12
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ER08AU19.003
Relative Weight
CMG
39069
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0603
0604
0701
0702
0703
0704
0801
0802
0803
0804
0805
0901
0902
0903
0904
1001
1002
1003
1004
1101
1102
1103
1201
1202
1203
1204
1301
1302
1303
1304
jbell on DSK3GLQ082PROD with RULES2
1305
1401
CMG Description
(M=motor, A=age)
Neurological M >=43.50 and M <52.50
Neurological M <43.50
Fracture oflower extremity M >=61.50
Fracture of lower extremity M >=52.50
andM <61.50
Fracture oflower extremity M >=41.50
andM <52.50
Fracture oflower extremity M <41.50
Replacement of lower-extremity joint
M>=63.50
Replacement of lower-extremity joint
M >=57.50 and M <63.50
Replacement of lower-extremity joint
M >=51.50 and M <57 .50
Replacement of lower-extremity joint
M >=42.50 and M <51.50
Replacement of lower-extremity joint
M <42.50
Other orthopedic M >=63.50
Other orthopedic M >=51.50 and M
<63.50
Other orthopedic M >=44.50 and M
<51.50
Other orthopedic M <44.5
Amputation lower extremity M
>=64.50
Amputation lower extremity M
>=55.50 and M <64.50
Amputation lower extremity M
>=47.50 and M <55.50
Amputation lower extremity M <47.50
Amputation non-lower extremity M
>=58.50
Amputation non-lower extremity M
>=52.50 and M <58.50
Amputation non-lower extremity M
<52.50
Osteoarthritis M >=61.50
Osteoarthritis M >=49.50 and M
<61.50
Osteoarthritis M <49.50 and A >=74.50
Osteoarthritis M <49.50 and A <74.50
Rheumatoid other arthritis M >=62.50
Rheumatoid other arthritis M >=51.50
andM <62.50
Rheumatoid other arthritis M >=44.50
and M <51.50 and A >=64.50
Rheumatoid other arthritis M <44.50
and A >=64.50
Rheumatoid other arthritis M <51.50
and A <64.50
Cardiac M >=68.50
VerDate Sep<11>2014
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Avera~e Len~th
Tier
1
Tier
2
Tier
3
16
20
12
16
18
12
15
17
11
of Stay
No
Comorbidity
Tier
14
16
10
1.0978
14
14
13
13
1.4615
1.6529
1.3291
1.5032
17
18
17
18
16
18
15
17
0.8826
0.7992
0.7434
10
10
9
9
1.2506
1.0402
0.9419
0.8762
11
12
11
10
1.4028
1.1669
1.0566
0.9829
13
13
12
11
1.6133
1.3419
1.2151
1.1304
15
15
13
13
1.9202
1.2066
1.5973
0.9641
1.4463
0.8950
1.3454
0.8243
16
11
17
11
15
10
15
10
1.5262
1.2196
1.1321
1.0427
13
14
13
12
1.7937
2.0358
1.4333
1.6268
1.3305
1.5101
1.2254
1.3908
15
18
15
17
14
16
14
15
1.2854
1.0952
0.9915
0.9110
12
13
11
11
1.6019
1.3648
1.2357
1.1353
15
15
13
13
1.8483
2.1480
1.5748
1.8301
1.4258
1.6570
1.3100
1.5224
16
18
17
19
16
18
15
16
1.4202
1.1802
1.0683
0.8943
13
13
12
10
1.7633
1.4653
1.3264
1.1103
15
14
14
13
2.0223
1.2378
1.6806
0.9532
1.5212
0.9256
1.2734
0.8600
17
11
19
11
15
10
14
10
1.5753
1.7998
1.9148
1.1667
1.2131
1.3860
1.4746
0.9831
1.1780
1.3459
1.4318
0.9315
1.0944
1.2505
1.3303
0.8579
14
15
15
11
14
16
15
11
13
15
16
10
13
14
15
10
1.4269
1.2023
1.1392
1.0492
12
14
12
12
1.6816
1.4169
1.3425
1.2365
13
15
14
14
1.9036
1.6040
1.5198
1.3997
16
17
16
15
1.8768
1.1425
1.5814
0.9303
1.4984
0.8576
1.3800
0.7707
14
11
17
11
16
10
14
9
Tier 1
Tier 2
Tier 3
1.9606
2.2535
1.2511
1.5242
1.7519
1.0096
1.4230
1.6356
0.9644
No
Comorbidity
Tier
1.2940
1.4873
0.8771
1.5660
1.2636
1.2072
1.8960
2.1443
1.5299
1.7303
1.0611
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ER08AU19.004
Relative Wei~ht
CMG
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1402
1403
1404
1501
1502
1503
1504
1601
1602
1603
1604
1701
1702
1703
1704
1705
1801
1802
1803
1804
1805
1806
1901
1902
1903
1904
2001
2002
jbell on DSK3GLQ082PROD with RULES2
2003
CMG Description
(M=motor, A=age)
Cardiac M >=55.50 and M <68.50
Cardiac M >=45.50 and M <55.50
Cardiac M <45.50
Pulmonary M >=68.50
Pulmonary M >=56.50 and M <68.50
Pulmonary M >=45.50 and M <56.50
Pulmonary M <45.50
Pain syndrome M >=65.50
Pain syndrome M >=58.50 and M
<65.50
Pain syndrome M >=43.50 and M
<58.50
Pain syndrome M <43.50
Major multiple trauma without brain or
spinal cord injury M >=57 .50
Major multiple trauma without brain or
spinal cord injury M >=50.50 and M
<57.50
Major multiple trauma without brain or
spinal cord injury M >=41.50 and M
<50.50
Major multiple trauma without brain or
spinal cord injury M >=36.50 and M
<41.50
Major multiple trauma without brain or
spinal cord injury M <36.50
Major multiple trauma with brain or
spinal cord injury M >=67 .50
Major multiple trauma with brain or
spinal cord injury M >=55.50 and M
<67.50
Major multiple trauma with brain or
spinal cord injury M >=45.50 and M
<55.50
Major multiple trauma with brain or
spinal cord injury M >=40.50 and M
<45.50
Major multiple trauma with brain or
spinal cord injury M >=30.50 and M
<40.50
Major multiple trauma with brain or
spinal cord injury M <30.50
Guillain-Barre M >=66.50
Guillain-Barre M >=51.50 and M
<66.50
Guillain-Barre M >=38.50 and M
<51.50
Guillain-Barre M <38.50
Miscellaneous M >=66.50
Miscellaneous M >=55.50 and M
<66.50
Miscellaneous M >=46.50 and M
VerDate Sep<11>2014
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Average Length of Stay
No
Tier Tier Tier
Comorbidity
1
2
3
Tier
12
11
13
13
15
15
14
13
18
17
16
15
11
11
10
10
13
13
12
12
15
14
14
13
20
17
15
15
10
11
10
9
Tier 1
Tier 2
Tier 3
1.4376
1.7346
2.0201
1.2446
1.5082
1.7761
2.0391
1.1312
1.1706
1.4125
1.6450
1.0612
1.2859
1.5143
1.7385
0.8992
1.0792
1.3021
1.5165
0.9769
1.1838
1.3940
1.6005
0.8492
No
Comorbidity
Tier
0.9698
1.1702
1.3628
0.9280
1.1245
1.3242
1.5203
0.7836
1.3963
1.1099
1.0482
0.9672
11
11
12
11
1.6234
1.8910
1.2904
1.5031
1.2187
1.4196
1.1245
1.3098
13
14
14
15
13
15
13
14
1.4098
1.1015
1.0310
0.9404
12
12
12
11
1.7293
1.3512
1.2647
1.1536
15
14
14
13
2.0092
1.5699
1.4694
1.3403
17
17
16
15
2.2231
1.7369
1.6258
1.4829
20
18
17
17
2.4140
1.8861
1.7654
1.6103
21
20
19
17
1.1788
0.9975
0.8908
0.8151
13
11
10
10
1.5258
1.2911
1.1530
1.0551
15
15
13
12
1.8891
1.5984
1.4275
1.3063
19
18
15
15
2.1888
1.8521
1.6541
1.5136
26
21
18
16
2.5760
2.1797
1.9467
1.7813
27
22
20
20
3.4401
1.2297
2.9109
0.9638
2.5996
0.9258
2.3788
0.9026
40
13
31
11
28
11
25
11
1.7299
1.3558
1.3024
1.2697
17
17
14
15
2.6270
3.7274
1.2127
2.0589
2.9213
0.9812
1.9778
2.8063
0.9107
1.9282
2.7359
0.8268
26
44
11
23
30
11
22
29
10
21
30
10
1.4948
1.7515
1.2094
1.4171
1.1225
1.3152
1.0192
1.1942
13
15
13
15
12
14
12
13
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Relative Weight
CMG
BILLING CODE 4120–01–C
V. Facility-Level Adjustment Factors
Section 1886(j)(3)(A)(v) of the Act
confers broad authority upon the
Secretary to adjust the per unit payment
rate by such factors as the Secretary
determines are necessary to properly
reflect variations in necessary costs of
treatment among rehabilitation
facilities. Under this authority, we
currently adjust the prospective
payment amount associated with a CMG
to account for facility-level
characteristics such as an IRF’s LIP,
teaching status, and location in a rural
area, if applicable, as described in
§ 412.624(e).
Based on the substantive changes to
the facility-level adjustment factors that
were adopted in the FY 2014 IRF PPS
final rule (78 FR 47860, 47868 through
47872), in the FY 2015 IRF PPS final
rule (79 FR 45872, 45882 through
45883), we froze the facility-level
adjustment factors at the FY 2014 levels
for FY 2015 and all subsequent years
(unless and until we propose to update
them again through future notice-andcomment rulemaking). For FY 2020, we
will continue to hold the adjustment
factors at the FY 2014 levels as we
continue to monitor the most current
IRF claims data available and continue
to evaluate and monitor the effects of
the FY 2014 changes.
jbell on DSK3GLQ082PROD with RULES2
VI. FY 2020 IRF PPS Payment Update
A. Background
Section 1886(j)(3)(C) of the Act
requires the Secretary to establish an
increase factor that reflects changes over
time in the prices of an appropriate mix
of goods and services included in the
VerDate Sep<11>2014
17:22 Aug 07, 2019
Jkt 247001
covered IRF services. According to
section 1886(j)(3)(A)(i) of the Act, the
increase factor shall be used to update
the IRF prospective payment rates for
each FY. Section 1886(j)(3)(C)(ii)(I) of
the Act requires the application of a
productivity adjustment. Thus, in the
FY 2020 IRF proposed rule, we
proposed to update the IRF PPS
payments for FY 2020 by a market
basket increase factor as required by
section 1886(j)(3)(C) of the Act based
upon the most current data available,
with a productivity adjustment as
required by section 1886(j)(3)(C)(ii)(I) of
the Act (84 FR 17261).
We have utilized various market
baskets through the years in the IRF
PPS. For a discussion of these market
baskets, we refer readers to the FY 2016
IRF PPS final rule (80 FR 47046).
Beginning with FY 2016, we finalized
the use of a 2012-based IRF market
basket, using Medicare cost report
(MCR) data for both freestanding and
hospital-based IRFs (80 FR 47049
through 47068). Beginning with FY
2020, we proposed to rebase and revise
the IRF market basket to reflect a 2016
base year. In the following discussion,
we provide an overview of the proposed
market basket and describe the
methodologies used to determine the
operating and capital portions of the
proposed 2016-based IRF market basket.
B. Overview of the 2016-Based IRF
Market Basket
The 2016-based IRF market basket is
a fixed-weight, Laspeyres-type price
index. A Laspeyres price index
measures the change in price, over time,
of the same mix of goods and services
purchased in the base period. Any
PO 00000
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39071
changes in the quantity or mix of goods
and services (that is, intensity)
purchased over time relative to a base
period are not measured.
The index itself is constructed in
three steps. First, a base period is
selected (for the proposed IRF market
basket, the base period is 2016), total
base period costs are estimated for a set
of mutually exclusive and exhaustive
cost categories, and each category is
calculated as a proportion of total costs.
These proportions are called cost
weights. Second, each cost category is
matched to an appropriate price or wage
variable, referred to as a price proxy. In
nearly every instance where we have
selected price proxies for the various
market baskets, these price proxies are
derived from publicly available
statistical series that are published on a
consistent schedule (preferably at least
on a quarterly basis). In cases where a
publicly available price series is not
available (for example, a price index for
malpractice insurance), we have
collected price data from other sources
and subsequently developed our own
index to capture changes in prices for
these types of costs. Finally, the cost
weight for each cost category is
multiplied by the established price
proxy. The sum of these products (that
is, the cost weights multiplied by their
price levels) for all cost categories yields
the composite index level of the market
basket for the given time period.
Repeating this step for other periods
produces a series of market basket levels
over time. Dividing the composite index
level of one period by the composite
index level for an earlier period
produces a rate of growth in the input
price index over that timeframe.
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As previously noted, the market
basket is described as a fixed-weight
index because it represents the change
in price over time of a constant mix
(quantity and intensity) of goods and
services needed to furnish IRF services.
The effects on total costs resulting from
changes in the mix of goods and
services purchased after the base period
are not measured. For example, an IRF
hiring more nurses after the base period
to accommodate the needs of patients
would increase the volume of goods and
services purchased by the IRF, but
would not be factored into the price
change measured by a fixed-weight IRF
market basket. Only when the index is
rebased would changes in the quantity
and intensity be captured, with those
changes being reflected in the cost
weights. Therefore, we rebase the
market basket periodically so that the
cost weights reflect recent changes in
the mix of goods and services that IRFs
purchase to furnish inpatient care
between base periods.
C. Rebasing and Revising of the IRF PPS
Market Basket
As discussed in the FY 2016 IRF PPS
final rule (80 FR 47050), the 2012-based
IRF market basket reflects the Medicare
cost reports for both freestanding and
hospital-based facilities.
Beginning with FY 2020, we proposed
to rebase and revise the 2012-based IRF
market basket to a 2016 base year
reflecting both freestanding and
hospital-based IRFs. Below we provide
a detailed description of our
methodology used to develop the
proposed 2016-based IRF market basket.
This proposed methodology is generally
similar to the methodology used to
develop the 2012-based IRF market
basket with the exception of the
proposed derivation of the Home Office
Contract Labor cost weight using the
MCR data as described in section
VI.C.a.(6) of this final rule.
1. Development of Cost Categories and
Weights for the 2016-Based IRF Market
Basket
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a. Use of Medicare Cost Report Data
We proposed a 2016-based IRF market
basket that consists of seven major cost
categories and a residual derived from
the 2016 Medicare cost reports (CMS
Form 2552–10) for freestanding and
hospital-based IRFs. The seven cost
categories are Wages and Salaries,
Employee Benefits, Contract Labor,
Pharmaceuticals, Professional Liability
Insurance (PLI), Home Office Contract
Labor, and Capital. The residual
category reflects all remaining costs not
captured in the seven cost categories.
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The 2016 cost reports include providers
whose cost reporting period began on or
after October 1, 2015, and prior to
September 30, 2016. We selected 2016
as the base year because we believe that
the Medicare cost reports for this year
represent the most recent, complete set
of MCR data available for developing
the IRF market basket at the time of the
proposed rule.
Since our goal is to establish cost
weights that were reflective of case mix
and practice patterns associated with
the services IRFs provide to Medicare
beneficiaries, as we did for the 2012based IRF market basket, we proposed
to limit the cost reports used to establish
the 2016-based IRF market basket to
those from facilities that had a Medicare
average LOS that was relatively similar
to their facility average LOS. We believe
that this requirement eliminates
statistical outliers and ensures a more
accurate market basket that reflects the
costs generally incurred during a
Medicare-covered stay. The Medicare
average LOS for freestanding IRFs is
calculated from data reported on line 14
of Worksheet S–3, part I. The Medicare
average LOS for hospital-based IRFs is
calculated from data reported on line 17
of Worksheet S–3, part I. We proposed
to include the cost report data from IRFs
with a Medicare average LOS within 15
percent (that is, 15 percent higher or
lower) of the facility average LOS to
establish the sample of providers used
to estimate the 2016-based IRF market
basket cost weights. We proposed to
apply this LOS edit to the data for IRFs
to exclude providers that serve a
population whose LOS would indicate
that the patients served are not
consistent with a LOS of a typical
Medicare patient. We note that this is
the same LOS edit that we applied to
develop the 2012-based IRF market
basket. This process resulted in the
exclusion of about eight percent of the
freestanding and hospital-based IRF
Medicare cost reports. Of those
excluded, about 18 percent were
freestanding IRFs and 82 percent were
hospital-based IRFs. This ratio is
relatively consistent with the ratio of the
universe of freestanding to hospitalbased IRF providers.
We then used the cost reports for IRFs
that met this requirement to calculate
the costs for the seven major cost
categories (Wages and Salaries,
Employee Benefits, Contract Labor,
Professional Liability Insurance,
Pharmaceuticals, Home Office Contract
Labor, and Capital) for the market
basket. For comparison, the 2012-based
IRF market basket utilized the Bureau of
Economic Analysis Benchmark InputOutput data rather than MCR data to
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derive the Home Office Contract Labor
cost weight. A more detailed discussion
of this methodological change is
provided in section VI.C.1.a.(6). of this
final rule.
Similar to the 2012-based IRF market
basket major cost weights, the proposed
2016-based IRF market basket cost
weights reflect Medicare allowable costs
(routine, ancillary and capital)—costs
that are eligible for reimbursement
through the IRF PPS.
For freestanding IRFs, total Medicare
allowable costs would be equal to the
total costs as reported on Worksheet B,
part I, column 26, lines 30 through 35,
50 through 76 (excluding 52 and 75), 90
through 91, and 93. For hospital-based
IRFs, total Medicare allowable costs
would be equal to the total costs for the
IRF inpatient unit after the allocation of
overhead costs (Worksheet B, part I,
column 26, line 41) and a proportion of
total ancillary costs reported on
Worksheet B, part I, column 26, lines 50
through 76 (excluding 52 and 75), 90
through 91, and 93. We proposed to
calculate the portion of ancillary costs
attributable to the hospital-based IRF for
a given ancillary cost center by
multiplying total facility ancillary costs
for the specific cost center (as reported
on Worksheet B, part I, column 26) by
the ratio of IRF Medicare ancillary costs
for the cost center (as reported on
Worksheet D–3, column 3 for hospitalbased IRFs) to total Medicare ancillary
costs for the cost center (equal to the
sum of Worksheet D–3, column 3 for all
relevant PPS [that is, IPPS, IRF, IPF and
skilled nursing facility (SNF)]). We
proposed to use these methods to derive
levels of total costs for IRF providers.
This is the same methodology used for
the 2012-based IRF market basket. With
this work complete, we then set about
deriving cost levels for the seven major
cost categories and then derive a
residual cost weight reflecting all other
costs not classified.
(1) Wages and Salaries Costs
For freestanding IRFs, we proposed to
derive Wages and Salaries costs as the
sum of routine inpatient salaries,
ancillary salaries, and a proportion of
overhead (or general service cost centers
in the Medicare cost reports) salaries as
reported on Worksheet A, column 1.
Since overhead salary costs are
attributable to the entire IRF, we only
include the proportion attributable to
the Medicare allowable cost centers. We
proposed to estimate the proportion of
overhead salaries that are attributed to
Medicare allowable costs centers by
multiplying the ratio of Medicare
allowable area salaries (Worksheet A,
column 1, lines 50 through 76
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(excluding 52 and 75), 90 through 91,
and 93) to total salaries (Worksheet A,
column 1, line 200) times total overhead
salaries (Worksheet A, column 1, lines
4 through 18). This is the same
methodology used in the 2012-based
IRF market basket.
For hospital-based IRFs, we proposed
to derive Wages and Salaries costs as the
sum of inpatient routine salary costs
(Worksheet A, column 1, line 41) for the
hospital-based IRF and the overhead
salary costs attributable to this IRF
inpatient unit; and ancillary salaries
plus a portion of overhead salary costs
attributable to the ancillary departments
utilized by the hospital-based IRF.
We proposed to calculate hospitalbased ancillary salary costs for a specific
cost center (Worksheet A, column 1,
lines 50 through 76 (excluding 52 and
75), 90 through 91, and 93) using salary
costs from Worksheet A, column 1,
multiplied by the ratio of IRF Medicare
ancillary costs for the cost center (as
reported on Worksheet D–3, column 3,
for IRF subproviders) to total Medicare
ancillary costs for the cost center (equal
to the sum of Worksheet D–3, column 3,
for all relevant PPS units [that is, IPPS,
IRF, IPF and a SNF]). For example, if
hospital-based IRF Medicare physical
therapy costs represent 30 percent of the
total Medicare physical therapy costs for
the entire facility, then 30 percent of
total facility physical therapy salaries
(as reported in Worksheet A, column 1,
line 66) would be attributable to the
hospital-based IRF. We believe it is
appropriate to use only a portion of the
ancillary costs in the market basket cost
weight calculations since the hospitalbased IRF only utilizes a portion of the
facility’s ancillary services. We believe
the ratio of reported IRF Medicare costs
to reported total Medicare costs
provides a reasonable estimate of the
ancillary services utilized, and costs
incurred, by the hospital-based IRF.
We proposed to calculate the portion
of overhead salary costs attributable to
hospital-based IRFs by first calculating
total noncapital overhead costs
(Worksheet B, part I, columns 4–18, line
41, less Worksheet B, part II, columns
4–18, line 41). We then multiply total
noncapital overhead costs by an
overhead ratio equal to the ratio of total
facility overhead salaries (as reported on
Worksheet A, column 1, lines 4–18) to
total facility noncapital overhead costs
(as reported on Worksheet A, column 1
and 2, lines 4–18). This methodology
assumes the proportion of total costs
related to salaries for the overhead cost
center is similar for all inpatient units
(that is, acute inpatient or inpatient
rehabilitation).
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We proposed to calculate the portion
of overhead salaries attributable to each
ancillary department by first calculating
total noncapital overhead costs
attributable to each specific ancillary
department (Worksheet B, part I,
columns 4–18 less, Worksheet B, part II,
columns 4–18). We then identify the
portion of these noncapital overhead
costs attributable to Wages and Salaries
by multiplying these costs by the
overhead ratio defined as the ratio of
total facility overhead salaries (as
reported on Worksheet A, column 1,
lines 4–18) to total overhead costs (as
reported on Worksheet A, column 1 &
2, lines 4–18). Finally, we identified the
portion of these overhead salaries for
each ancillary department that is
attributable to the hospital-based IRF by
multiplying by the ratio of IRF Medicare
ancillary costs for the cost center (as
reported on Worksheet D–3, column 3,
for hospital-based IRFs) to total
Medicare ancillary costs for the cost
center (equal to the sum of Worksheet
D–3, column 3, for all relevant PPS
units [that is, IPPS, IRF, IPF and SNF]).
This is the same methodology used to
derive the 2012-based IRF market
basket.
(2) Employee Benefits Costs
Effective with the implementation of
CMS Form 2552–10, we began
collecting Employee Benefits and
Contract Labor data on Worksheet S–3,
part V.
For 2016 MCR data, the majority of
providers did not report data on
Worksheet S–3, part V; particularly,
approximately 48 percent of
freestanding IRFs and 40 percent of
hospital-based IRFs reported data on
Worksheet S–3, part V. However, we
believe we have a large enough sample
to enable us to produce a reasonable
Employee Benefits cost weight. Again,
we continue to encourage all providers
to report these data on the Medicare cost
report.
For freestanding IRFs, we proposed
Employee Benefits costs would be equal
to the data reported on Worksheet S–3,
part V, column 2, line 2. We note that
while not required to do so, freestanding
IRFs also may report Employee Benefits
data on Worksheet S–3, part II, which is
applicable to only IPPS providers. For
those freestanding IRFs that report
Worksheet S–3, part II, data, but not
Worksheet S–3, part V, we proposed to
use the sum of Worksheet S–3, part II,
lines 17, 18, 20, and 22, to derive
Employee Benefits costs. This proposed
method allows us to obtain data from
about 30 more freestanding IRFs than if
we were to only use the Worksheet S–
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3, part V, data as was done for the 2012based IRF market basket.
For hospital-based IRFs, we proposed
to calculate total benefit costs as the
sum of inpatient unit benefit costs, a
portion of ancillary benefits, and a
portion of overhead benefits attributable
to the routine inpatient unit and a
portion of overhead benefits attributable
to the ancillary departments. We
proposed inpatient unit benefit costs be
equal to Worksheet S–3, part V, column
2, line 4. We proposed that the portion
of overhead benefits attributable to the
routine inpatient unit and ancillary
departments be calculated by
multiplying ancillary salaries for the
hospital-based IRF and overhead
salaries attributable to the hospitalbased IRF (determined in the derivation
of hospital-based IRF Wages and
Salaries costs as described above) by the
ratio of total facility benefits to total
facility salaries. Total facility benefits is
equal to the sum of Worksheet S–3, part
II, column 4, lines 17–25, and total
facility salaries is equal to Worksheet S–
3, part II, column 4, line 1.
(3) Contract Labor Costs
Contract Labor costs are primarily
associated with direct patient care
services. Contract labor costs for other
services such as accounting, billing, and
legal are calculated separately using
other government data sources as
described in section VI.C.3. of this final
rule. To derive contract labor costs
using Worksheet S–3, part V, data, for
freestanding IRFs, we proposed Contract
Labor costs be equal to Worksheet S–3,
part V, column 1, line 2. As we noted
for Employee Benefits, freestanding IRFs
also may report Contract Labor data on
Worksheet S–3, part II, which is
applicable to only IPPS providers. For
those freestanding IRFs that report
Worksheet S–3, part II data, but not
Worksheet S–3, part V, we proposed to
use the sum of Worksheet S–3, part II,
lines 11 and 13, to derive Contract Labor
costs.
For hospital-based IRFs, we proposed
that Contract Labor costs would be
equal to Worksheet S–3, part V, column
1, line 4. As previously noted, for 2016
MCR data, while there were providers
that did report data on Worksheet S–3,
part V, many providers did not
complete this worksheet. However, we
believe we have a large enough sample
to enable us to produce a reasonable
Contract Labor cost weight. We continue
to encourage all providers to report
these data on the Medicare cost report.
(4) Pharmaceuticals Costs
For freestanding IRFs, we proposed to
calculate pharmaceuticals costs using
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non-salary costs reported on Worksheet
A, column 7, less Worksheet A, column
1, for the pharmacy cost center (line 15)
and drugs charged to patients cost
center (line 73).
For hospital-based IRFs, we proposed
to calculate pharmaceuticals costs as the
sum of a portion of the non-salary
pharmacy costs and a portion of the
non-salary drugs charged to patient
costs reported for the total facility. We
proposed that non-salary pharmacy
costs attributable to the hospital-based
IRF would be calculated by multiplying
total pharmacy costs attributable to the
hospital-based IRF (as reported on
Worksheet B, part I, column 15, line 41)
by the ratio of total non-salary pharmacy
costs (Worksheet A, column 2, line 15)
to total pharmacy costs (sum of
Worksheet A, columns 1 and 2 for line
15) for the total facility. We proposed
that non-salary drugs charged to patient
costs attributable to the hospital-based
IRF would be calculated by multiplying
total non-salary drugs charged to patient
costs (Worksheet B, part I, column 0,
line 73 plus Worksheet B, part I, column
15, line 73, less Worksheet A, column
1, line 73) for the total facility by the
ratio of Medicare drugs charged to
patient ancillary costs for the IRF unit
(as reported on Worksheet D–3 for
hospital-based IRFs, column 3, line 73)
to total Medicare drugs charged to
patient ancillary costs for the total
facility (equal to the sum of Worksheet
D–3, column 3, line 73 for all relevant
PPS [that is, IPPS, IRF, IPF and SNF]).
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(5) Professional Liability Insurance
Costs
For freestanding IRFs, we proposed
that Professional Liability Insurance
(PLI) costs (often referred to as
malpractice costs) would be equal to
premiums, paid losses and selfinsurance costs reported on Worksheet
S–2, part I, columns 1 through 3, line
118. For hospital-based IRFs, we
proposed to assume that the PLI weight
for the total facility is similar to the
hospital-based IRF unit since the only
data reported on this worksheet is for
the entire facility, as we currently have
no means to identify the proportion of
total PLI costs that are only attributable
to the hospital-based IRF. Therefore,
hospital-based IRF PLI costs are equal to
total facility PLI (as reported on
Worksheet S–2, part I, columns 1
through 3, line 118) divided by total
facility costs (as reported on Worksheet
A, columns 1 and 2, line 200) times
hospital-based IRF Medicare allowable
total costs. Our assumption is that the
same proportion of expenses are used
among each unit of the hospital.
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(6) Home Office/Related Organization
Contract Labor Costs
For the 2016-based IRF market basket,
we proposed to determine the home
office/related organization contract
labor costs using MCR data. The 2012based IRF market basket used the 2007
Benchmark Input-Output (I–O) expense
data published by the Bureau of
Economic Analysis (BEA) to derive
these costs (80 FR 47057). A more
detailed explanation of the general
methodology using the BEA I–O data is
provided in section VI.C.3. of this final
rule. For freestanding and hospitalbased IRFs, we proposed to calculate the
home office contract labor cost weight
(using data reported on Worksheet S–3,
part II, column 4, lines 14, 1401, 1402,
2550, and 2551) and total facility costs
(Worksheet B, part I, column 26, line
202). We proposed to use total facility
costs as the denominator for calculating
the home office contract labor cost
weight as these expenses reported on
Worksheet S–3, part II reflect the entire
hospital facility. Our assumption is that
the same proportion of expenses are
used among each unit of the hospital.
For the 2012-based IRF market basket,
we calculated the home office cost
weight using expense data for North
American Industry Classification
System (NAICS) code 55, Management
of Companies and Enterprises (80 FR
47067).
(7) Capital Costs
For freestanding IRFs, we proposed
that capital costs would be equal to
Medicare allowable capital costs as
reported on Worksheet B, part II,
column 26, lines 30 through 35, 50
through 76 (excluding 52 and 75), 90
through 91, and 93.
For hospital-based IRFs, we proposed
that capital costs would be equal to IRF
inpatient capital costs (as reported on
Worksheet B, part II, column 26, line 41)
and a portion of IRF ancillary capital
costs. We calculate the portion of
ancillary capital costs attributable to the
hospital-based IRF for a given cost
center by multiplying total facility
ancillary capital costs for the specific
ancillary cost center (as reported on
Worksheet B, part II, column 26) by the
ratio of IRF Medicare ancillary costs for
the cost center (as reported on
Worksheet D–3, column 3 for hospitalbased IRFs) to total Medicare ancillary
costs for the cost center (equal to the
sum of Worksheet D–3, column 3 for all
relevant PPS [that is, IPPS, IRF, IPF and
SNF]). For example, if hospital-based
IRF Medicare physical therapy costs
represent 30 percent of the total
Medicare physical therapy costs for the
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entire facility, then 30 percent of total
facility physical therapy capital costs (as
reported in Worksheet B, part II, column
26, line 66) would be attributable to the
hospital-based IRF.
b. Final Major Cost Category
Computation
After we derive costs for the major
cost categories for each provider using
the MCR data as previously described,
we proposed to trim the data for
outliers. For the Wages and Salaries,
Employee Benefits, Contract Labor,
Pharmaceuticals, Professional Liability
Insurance, and Capital cost weights, we
first divide the costs for each of these
six categories by total Medicare
allowable costs calculated for the
provider to obtain cost weights for the
universe of IRF providers. We then
remove those providers whose derived
cost weights fall in the top and bottom
5 percent of provider specific derived
cost weights to ensure the exclusion of
outliers. After the outliers have been
excluded, we sum the costs for each
category across all remaining providers.
We then divide this by the sum of total
Medicare allowable costs across all
remaining providers to obtain a cost
weight for the 2016-based IRF market
basket for the given category.
The proposed trimming methodology
for the Home Office Contract Labor cost
weight is slightly different than the
proposed trimming methodology for the
other six cost categories as described
above. For the Home Office Contract
Labor cost weight, since we are using
total facility data rather than Medicareallowable costs associated with IRF
services, we proposed to trim the
freestanding and hospital-based IRF cost
weights separately. For each of the
providers, we first divide the home
office contract labor costs by total
facility costs to obtain a Home Office
Contract Labor cost weight for the
universe of IRF providers. We then
proposed to trim only the top 1 percent
of providers to exclude outliers while
also allowing providers who have
reported zero home office costs to
remain in the Home Office Contract
Labor cost weight calculations as not all
providers will incur home office costs.
After removing these outliers, we are
left with a trimmed data set for both
freestanding and hospital-based
providers. We then proposed to sum the
costs for each category (freestanding and
hospital-based) across all remaining
providers. We next divide this by the
sum of total facility costs across all
remaining providers to obtain a
freestanding and hospital-based cost
weight. Lastly, we proposed to weight
these two cost weights together using
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the Medicare-allowable costs to derive a
Home Office Contract Labor cost weight
for the 2016-based IRF market basket.
Finally, we proposed to calculate the
residual ‘‘All Other’’ cost weight that
reflects all remaining costs that are not
captured in the seven cost categories
listed.
We received a few comments on our
proposed derivation of the Home Office
Contract Labor cost weight from the
Medicare cost reports, which are
summarized below.
Comment: Commenters expressed
concern with the proposed methodology
change to the Home Office Contract
Labor cost weight. These commenters
stated that CMS had not provided
sufficient rationale for this change in
methodology nor has CMS provided a
discussion of how these data points
were reasonably validated and tested.
One commenter requested that CMS
provide stakeholders with more
information on the rationale and the
data validation methodologies
employed in the final rule.
The commenters expressed concern
with the sample of IRFs reporting the
home office cost data and found based
on their analysis that reporting was
between 50 to 65 percent. These
commenters suggested that this was due
to these cost report line items being an
optional category for IRFs under
Medicare cost reporting requirements.
One of the commenters further
expressed concern with the
methodology and approach that CMS
applied in determining IRF unit Home
Office Contract Labor amounts,
specifically the assumption that
hospital-based IRFs utilize the same
proportion of home office expenses as
the rest of the acute care hospital in
which it is located. The commenter
stated that typically IRF units are a very
small part of the larger parent acute care
hospital and that the larger systems do
not spend the same proportional time
and resources on these units compared
to hospital system as a whole. They
stated that this assumption likely
overstates the Home Office Contract
Labor cost weight.
Based on these concerns, the
commenters requested that CMS not
finalize its proposed changes to the
Home Office Contract Labor cost
category and instead finalize use of the
previous methodology relating to this
category that was used for the 2012based market basket. One commenter
also requested that CMS revisit this
potential change with adequate
explanation and data in future
rulemaking.
Response: We appreciate the
commenters’ concerns on the proposed
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methodological change for the Home
Office Contract Labor cost weight. We
proposed to revise our methodology and
use the 2016 IRF MCR data to calculate
the Home Office Contract Labor costs
rather than the 2012 Benchmark I–O
data because it reflected more up-to-date
data and we believe it to be an
improvement over the use of the BEA
Benchmark I–O data that is not specific
to IRFs. The MCR data allows us to
calculate Home Office Contract Labor
Costs for freestanding and IRF hospitalbased facilities.
We disagree with the commenters’
concern that the MCR data completion
rates for the Home Office Contract Labor
costs are inadequate to obtain a cost
weight. When developing the proposed
2016-based IRF market basket, we
conducted a thorough analysis of the
MCR data and our proposed Home
Office Contract Labor cost weight
methodology. We found that
approximately 90 percent of
freestanding IRFs reported having a
home office, of which over 50 percent
reported home office compensation data
on Worksheet S–3, part II. The
composition of the providers (by
ownership-type and region) that
reported both wage index data
(including those who do not have a
home office) and home office contract
labor cost data were similarly
representative to all freestanding IRFs.
A sensitivity analysis of calculating a
reweighted Home Office Contract Labor
cost weight based on ownership-type
and region produced a Home Office
Contract Labor cost weight similar to the
proposed 3.7 percent weight.
For additional sensitivity testing,
recognizing that some of the
freestanding IRFs with home offices
may not have completed the applicable
fields on the MCR, we calculated a
weight using only freestanding IRFs that
reported having a home office
(Worksheet S–2, part I, line 140). This
produced a Home Office Contract Labor
cost weight nearly identical to the
freestanding IRF 2016 cost weight using
our proposed methodology. Based on
this analysis, we believe that the sample
of providers included in the Home
Office Contract Labor cost weight are a
technically representative sample of all
IRF providers.
Regarding IRF units, we recognize the
commenter’s concern that they
represent a small proportion of the total
facility. We believe that the assumption
that IRFs utilize the same proportion of
home office expenses as the rest of the
acute care hospital is reasonable. The
use of total facility data assumes the
facility Home Office Contract Labor cost
weight is equal to the Home Office
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Contract Labor cost weight for the IRF
unit. Further analysis of the MCR data
shows IRF unit direct patient care costs
(as reported on Worksheet B, part I,
column 0, line 41) account for about one
percent of total facility costs (excluding
capital, Administrative and General
(A&G), and Employee Benefit
department costs). Similarly, A&G costs
(Worksheet B, part I, column 0, line 5),
where Home Office Contract Labor costs
are likely captured, allocated to the IRF
unit account for a similar proportion of
direct patient care costs with about one
percent of total A&G costs. We also
found the proportion of allocated A&G
costs for other larger, more medicallycomplex hospital units (such as the
intensive care, surgical care, and
operating room) were consistent with
direct patient care cost proportions and
the proportions for these units were
higher than the proportion of the A&G
expenses allocated to the IRF unit. This
supports the commenter’s claim that
hospitals allocate less A&G costs to less
medically-complex services (as
measured by costs). Our proposed
calculation would adhere to this
assumption as well since the facility
level cost weight is applied to the IRF
Medicare allowable total costs
representing these relatively less
medically-complex services.
Furthermore, the Benchmark I–O
methodology used in the 2012-based
IRF market basket also assumes that the
IRF relative costs are the same as those
of the hospital total facility. We invite
the commenters to submit additional
data that would help in this area for
consideration in future rulemaking.
We disagree with the commenters’
request to use the Benchmark I–O data
to calculate the Home Office Contract
Labor cost weight rather than the
proposed 2016 MCR data. We believe
the proposed methodology is a technical
improvement over the prior
methodology because it represents more
recent data that is representative
compositionally and geographically of
IRFs. It is also is the same data used to
determine the other major cost weights
in the 2016-based market basket and the
proportion of the Home Office Contract
Labor cost weight that is allocated to the
Professional Fees: Labor-related and
Professional Fees: Nonlabor-related cost
weights. We believe the assumptions
made by using the total facility data for
the hospital-based IRFs are reasonable
and supported by the MCR data on A&G
cost allocation. Finally, we note that the
methodological change accounts for
only 0.2 percentage point of the 2.0
percentage points change in the laborrelated share.
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After careful consideration of
comments, we are finalizing our
methodology for deriving the major cost
weights as proposed.
Table 4 presents the cost weights for
these major cost categories calculated
from the Medicare cost reports for the
2016-based IRF market basket, as well as
for the 2012-based IRF market basket.
As we did for the 2012-based IRF
market basket, we proposed to allocate
the Contract Labor cost weight to the
Wages and Salaries and Employee
Benefits cost weights based on their
relative proportions under the
assumption that contract labor costs are
comprised of both wages and salaries
and employee benefits. The Contract
Labor allocation proportion for Wages
and Salaries is equal to the Wages and
Salaries cost weight as a percent of the
sum of the Wages and Salaries cost
weight and the Employee Benefits cost
weight. For the proposed rule, this
rounded percentage is 81 percent;
therefore, we proposed to allocate 81
percent of the Contract Labor cost
weight to the Wages and Salaries cost
weight and 19 percent to the Employee
Benefits cost weight. The 2012-based
IRF market basket percentage was also
81 percent (80 FR 47056). We did not
receive any specific public comments
on our proposed allocation of Contract
Labor. Therefore, we are finalizing our
method of allocating Contract Labor as
proposed.
Table 5 shows the Wages and Salaries
and Employee Benefit cost weights after
Contract Labor cost weight allocation for
both the 2016-based IRF market basket
and 2012-based IRF market basket.
c. Derivation of the Detailed Operating
Cost Weights
economic accounts. Thus, they
represent the most comprehensive and
complete set of data on the economic
processes or mechanisms by which
output is produced and distributed.1
BEA also produces Annual I–O
estimates; however, while based on a
similar methodology, these estimates
reflect less comprehensive and less
detailed data sources and are subject to
revision when benchmark data becomes
available. Instead of using the less
detailed Annual I–O data, we proposed
to inflate the 2012 Benchmark I–O data
forward to 2016 by applying the annual
price changes from the respective price
proxies to the appropriate market basket
cost categories that are obtained from
the 2012 Benchmark I–O data. We
repeat this practice for each year. We
then proposed to calculate the cost
shares that each cost category represents
of the inflated 2012 data. These
resulting 2016 cost shares are applied to
the All Other residual cost weight to
obtain the detailed cost weights for the
2016-based IRF market basket. For
example, the cost for Food: Direct
Purchases represents 5.0 percent of the
sum of the ‘‘All Other’’ 2012 Benchmark
I–O Hospital Expenditures inflated to
2016; therefore, the Food: Direct
Purchases cost weight represents 5.0
percent of the 2016-based IRF market
basket’s ‘‘All Other’’ cost category (22.2
percent), yielding a ‘‘final’’ Food: Direct
Purchases cost weight of 1.1 percent in
the 2016-based IRF market basket (0.05
* 22.2 percent = 1.1 percent).
Using this methodology, we proposed
to derive seventeen detailed IRF market
To further divide the ‘‘All Other’’
residual cost weight estimated from the
2016 MCR data into more detailed cost
categories, we proposed to use the 2012
Benchmark I–O ‘‘Use Tables/Before
Redefinitions/Purchaser Value’’ for
NAICS 622000, Hospitals, published by
the BEA. This data is publicly available
at https://www.bea.gov/industry/io_
annual.htm. For the 2012-based IRF
market basket, we used the 2007
Benchmark I–O data, the most recent
data available at the time (80 FR 47057).
The BEA Benchmark I–O data are
scheduled for publication every 5 years
with the most recent data available for
2012. The 2007 Benchmark I–O data are
derived from the 2012 Economic Census
and are the building blocks for BEA’s
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basket cost category weights from the
2016-based IRF market basket residual
cost weight (22.2 percent). These
categories are: (1) Electricity; (2) Fuel,
Oil, and Gasoline; (3) Food: Direct
Purchases; (4) Food: Contract Services;
(5) Chemicals; (6) Medical Instruments;
(7) Rubber & Plastics; (8) Paper and
Printing Products; (9) Miscellaneous
Products; (10) Professional Fees: Laborrelated; (11) Administrative and
Facilities Support Services; (12)
Installation, Maintenance, and Repair;
(13) All Other Labor-related Services;
(14) Professional Fees: Nonlabor-related;
(15) Financial Services; (16) Telephone
Services; and (17) All Other Nonlaborrelated Services. We note that for the
2012-based IRF market basket, we had a
Water and Sewerage cost weight. For the
2016-based IRF market basket, we
proposed to include Water and
Sewerage costs in the Electricity cost
weight due to the small amount of costs
in this category.
For the 2012-based IRF market basket,
we used the I–O data for NAICS 55
Management of Companies to derive the
Home Office Contract Labor cost weight,
which were classified in the
Professional Fees: Labor-related and
Professional Fees: Nonlabor-related cost
weights. As previously discussed, we
proposed to use the MCR data to derive
the Home Office Contract Labor cost
weight, which we would further classify
into the Professional Fees: Labor-related
or Professional Fees: Nonlabor-related
categories.
We did not receive any specific
comments on the derivation of the
detailed operating cost weights. In this
final rule, we are finalizing our
methodology for deriving the detailed
operating cost weights as proposed.
d. Derivation of the Detailed Capital
Cost Weights
As described in section VI.C.1.a.(6) of
this final rule, we proposed a CapitalRelated cost weight of 9.0 percent as
obtained from the 2016 Medicare cost
reports for freestanding and hospitalbased IRF providers. We proposed to
then separate this total Capital-Related
cost weight into more detailed cost
categories.
Using 2016 Medicare cost reports, we
were able to group Capital-Related costs
into the following categories:
Depreciation, Interest, Lease, and Other
Capital-Related costs. For each of these
categories, we proposed to determine
separately for hospital-based IRFs and
freestanding IRFs what proportion of
total capital-related costs the category
represents.
For freestanding IRFs, we proposed to
derive the proportions for Depreciation,
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Interest, Lease, and Other Capitalrelated costs using the data reported by
the IRF on Worksheet A–7, which is
similar to the methodology used for the
2012-based IRF market basket.
For hospital-based IRFs, data for these
four categories were not reported
separately for the hospital-based IRF;
therefore, we proposed to derive these
proportions using data reported on
Worksheet A–7 for the total facility. We
assumed the cost shares for the overall
hospital are representative for the
hospital-based IRF unit. For example, if
depreciation costs make up 60 percent
of total capital costs for the entire
facility, we believe it is reasonable to
assume that the hospital-based IRF
would also have a 60 percent proportion
because it is a unit contained within the
total facility. This is the same
methodology used for the 2012-based
IRF market basket (80 FR 47057).
To combine each detailed capital cost
weight for freestanding and hospitalbased IRFs into a single capital cost
weight for the 2016-based IRF market
basket, we proposed to weight together
the shares for each of the categories
(Depreciation, Interest, Lease, and Other
Capital-related costs) based on the share
of total capital costs each provider type
represents of the total capital costs for
all IRFs for 2016. Applying this
methodology results in proportions of
total capital-related costs for
Depreciation, Interest, Lease and Other
Capital-related costs that are
representative of the universe of IRF
providers. This is the same methodology
used for the 2012-based IRF market
basket (80 FR 47057 through 47058).
Lease costs are unique in that they are
not broken out as a separate cost
category in the 2016-based IRF market
basket. Rather, we proposed to
proportionally distribute these costs
among the cost categories of
Depreciation, Interest, and Other
Capital-Related, reflecting the
assumption that the underlying cost
structure of leases is similar to that of
capital-related costs in general. As was
done under the 2012-based IRF market
basket, we proposed to assume that 10
percent of the lease costs as a proportion
of total capital-related costs represents
overhead and assign those costs to the
Other Capital-Related cost category
accordingly. We proposed to distribute
the remaining lease costs proportionally
across the three cost categories
(Depreciation, Interest, and Other
Capital-Related) based on the proportion
that these categories comprise of the
sum of the Depreciation, Interest, and
Other Capital-related cost categories
(excluding lease expenses). This
resulted in three primary capital-related
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cost categories in the 2016-based IRF
market basket: Depreciation, Interest,
and Other Capital-Related costs. This is
the same methodology used for the
2012-based IRF market basket (80 FR
47058). The allocation of these lease
expenses are shown in Table 6.
Finally, we proposed to further divide
the Depreciation and Interest cost
categories. We proposed to separate
Depreciation into the following two
categories: (1) Building and Fixed
Equipment; and (2) Movable Equipment.
We proposed to separate Interest into
the following two categories: (1)
Government/Nonprofit; and (2) Forprofit.
To disaggregate the Depreciation cost
weight, we need to determine the
percent of total Depreciation costs for
IRFs that are attributable to Building
and Fixed Equipment, which we
hereafter refer to as the ‘‘fixed
percentage.’’ For the 2016-based IRF
market basket, we proposed to use
slightly different methods to obtain the
fixed percentages for hospital-based
IRFs compared to freestanding IRFs.
For freestanding IRFs, we proposed to
use depreciation data from Worksheet
A–7 of the 2016 Medicare cost reports.
However, for hospital-based IRFs, we
determined that the fixed percentage for
the entire facility may not be
representative of the hospital-based IRF
unit due to the entire facility likely
employing more sophisticated movable
assets that are not utilized by the
hospital-based IRF. Therefore, for
hospital-based IRFs, we proposed to
calculate a fixed percentage using: (1)
Building and fixture capital costs
allocated to the hospital-based IRF unit
as reported on Worksheet B, part I, line
41; and (2) building and fixture capital
costs for the top five ancillary cost
centers utilized by hospital-based IRFs.
We proposed to weight these two fixed
percentages (inpatient and ancillary)
using the proportion that each capital
cost type represents of total capital costs
in the 2016-based IRF market basket. We
proposed to then weight the fixed
percentages for hospital-based and
freestanding IRFs together using the
proportion of total capital costs each
provider type represents. For both
freestanding and hospital-based IRFs,
this is the same methodology used for
the 2012-based IRF market basket (80 FR
47058).
To disaggregate the Interest cost
weight, we determined the percent of
total interest costs for IRFs that are
attributable to government and
nonprofit facilities, which is hereafter
referred to as the ‘‘nonprofit
percentage,’’ as price pressures
associated with these types of interest
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costs tend to differ from those for forprofit facilities. For the 2016-based IRF
market basket, we proposed to use
interest costs data from Worksheet A–7
of the 2016 Medicare cost reports for
both freestanding and hospital-based
IRFs. We proposed to determine the
percent of total interest costs that are
attributed to government and nonprofit
IRFs separately for hospital-based and
freestanding IRFs. We then proposed to
weight the nonprofit percentages for
hospital-based and freestanding IRFs
together using the proportion of total
capital costs that each provider type
represents.
We did not receive any specific public
comments on the derivation of the
detailed capital cost weights. In this
final rule, we are finalizing our
e. 2016-Based IRF Market Basket Cost
Categories and Weights
market basket compared to the 2012based IRF market basket.
methodology for deriving the detailed
capital cost weights as proposed. Table
6 provides the detailed capital cost
share composition estimated from the
2016 IRF Medicare cost reports. These
detailed capital cost share composition
percentages are applied to the total
Capital-Related cost weight of 9.0
percent explained in detail in section
VI.C.1.a.(6) of this final rule.
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Table 7 compares the cost categories
and weights for the final 2016-based IRF
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2. Selection of Price Proxies
After developing the cost weights for
the 2016-based IRF market basket, we
selected the most appropriate wage and
price proxies currently available to
represent the rate of price change for
each expenditure category. For the
majority of the cost weights, we base the
price proxies on U.S. Bureau of Labor
Statistics (BLS) data and group them
into one of the following BLS categories:
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• Employment Cost Indexes.
Employment Cost Indexes (ECIs)
measure the rate of change in
employment wage rates and employer
costs for employee benefits per hour
worked. These indexes are fixed-weight
indexes and strictly measure the change
in wage rates and employee benefits per
hour. ECIs are superior to Average
Hourly Earnings (AHE) as price proxies
for input price indexes because they are
not affected by shifts in occupation or
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industry mix, and because they measure
pure price change and are available by
both occupational group and by
industry. The industry ECIs are based
on the NAICS and the occupational ECIs
are based on the Standard Occupational
Classification System (SOC).
• Producer Price Indexes. Producer
Price Indexes (PPIs) measure the average
change over time in the selling prices
received by domestic producers for their
output. The prices included in the PPI
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are from the first commercial
transaction for many products and some
services (https://www.bls.gov/ppi/).
• Consumer Price Indexes. Consumer
Price Indexes (CPIs) measure the
average change over time in the prices
paid by urban consumers for a market
basket of consumer goods and services
(https://www.bls.gov/cpi/). CPIs are only
used when the purchases are similar to
those of retail consumers rather than
purchases at the producer level, or if no
appropriate PPIs are available.
We evaluate the price proxies using
the criteria of reliability, timeliness,
availability, and relevance:
• Reliability. Reliability indicates that
the index is based on valid statistical
methods and has low sampling
variability. Widely accepted statistical
methods ensure that the data were
collected and aggregated in a way that
can be replicated. Low sampling
variability is desirable because it
indicates that the sample reflects the
typical members of the population.
(Sampling variability is variation that
occurs by chance because only a sample
was surveyed rather than the entire
population.)
• Timeliness. Timeliness implies that
the proxy is published regularly,
preferably at least once a quarter. The
market baskets are updated quarterly,
and therefore, it is important for the
underlying price proxies to be up-todate, reflecting the most recent data
available. We believe that using proxies
that are published regularly (at least
quarterly, whenever possible) helps to
ensure that we are using the most recent
data available to update the market
basket. We strive to use publications
that are disseminated frequently,
because we believe that this is an
optimal way to stay abreast of the most
current data available.
• Availability. Availability means that
the proxy is publicly available. We
prefer that our proxies are publicly
available because this will help ensure
that our market basket updates are as
transparent to the public as possible. In
addition, this enables the public to be
able to obtain the price proxy data on
a regular basis.
• Relevance. Relevance means that
the proxy is applicable and
representative of the cost category
weight to which it is applied. The CPIs,
PPIs, and ECIs that we have selected
meet these criteria. Therefore, we
believe that they continue to be the best
measure of price changes for the cost
categories to which they would be
applied.
Table 10 lists all price proxies that we
proposed to use for the 2016-based IRF
market basket. Below is a detailed
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explanation of the price proxies we
proposed for each cost category weight.
We did not receive any specific
comments on our proposed price
proxies for the 2016-based IRF market
basket. Therefore, in this final rule, we
are finalizing the price proxies as
proposed.
30 blend of these price proxies,
reflecting the 2007 I–O data (80 FR
47060). We believe that these two price
proxies continue to be the most
technically appropriate indices
available to measure the price growth of
the Fuel, Oil, and Gasoline cost category
in the 2016-based IRF market basket.
a. Price Proxies for the Operating
Portion of the 2016-Based IRF Market
Basket
(5) Professional Liability Insurance
We proposed to continue to use the
CMS Hospital Professional Liability
Index to measure changes in PLI
premiums. To generate this index, we
collect commercial insurance premiums
for a fixed level of coverage while
holding non-price factors constant (such
as a change in the level of coverage).
This is the same proxy used in the 2012based IRF market basket (80 FR 47060).
(1) Wages and Salaries
We proposed to continue to use the
ECI for Wages and Salaries for All
Civilian workers in Hospitals (BLS
series code CIU1026220000000I) to
measure the wage rate growth of this
cost category. This is the same price
proxy used in the 2012-based IRF
market basket (80 FR 47060).
(2) Benefits
We proposed to continue to use the
ECI for Total Benefits for All Civilian
workers in Hospitals to measure price
growth of this category. This ECI is
calculated using the ECI for Total
Compensation for All Civilian workers
in Hospitals (BLS series code
CIU1016220000000I) and the relative
importance of wages and salaries within
total compensation. This is the same
price proxy used in the 2012-based IRF
market basket (80 FR 47060).
(3) Electricity
We proposed to continue to use the
PPI Commodity Index for Commercial
Electric Power (BLS series code
WPU0542) to measure the price growth
of this cost category. This is the same
price proxy used in the 2012-based IRF
market basket (80 FR 47060).
(4) Fuel, Oil, and Gasoline
Similar to the 2012-based IRF market
basket, for the 2016-based IRF market
basket, we proposed to use a blend of
the PPI for Petroleum Refineries and the
PPI Commodity for Natural Gas. Our
analysis of the Bureau of Economic
Analysis’ 2012 Benchmark Input-Output
data (use table before redefinitions,
purchaser’s value for NAICS 622000
[Hospitals]), shows that Petroleum
Refineries expenses account for
approximately 90 percent and Natural
Gas expenses account for approximately
10 percent of Hospitals’ (NAICS 622000)
total Fuel, Oil, and Gasoline expenses.
Therefore, we proposed to use a blend
of 90 percent of the PPI for Petroleum
Refineries (BLS series code
PCU324110324110) and 10 percent of
the PPI Commodity Index for Natural
Gas (BLS series code WPU0531) as the
price proxy for this cost category. The
2012-based IRF market basket used a 70/
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(6) Pharmaceuticals
We proposed to continue to use the
PPI for Pharmaceuticals for Human Use,
Prescription (BLS series code
WPUSI07003) to measure the price
growth of this cost category. This is the
same proxy used in the 2012-based IRF
market basket (80 FR 47060).
(7) Food: Direct Purchases
We proposed to continue to use the
PPI for Processed Foods and Feeds (BLS
series code WPU02) to measure the
price growth of this cost category. This
is the same proxy used in the 2012based IRF market basket (80 FR 47060).
(8) Food: Contract Purchases
We proposed to continue to use the
CPI for Food Away From Home (BLS
series code CUUR0000SEFV) to measure
the price growth of this cost category.
This is the same proxy used in the 2012based IRF market basket (80 FR 47060
through 47061).
(9) Chemicals
Similar to the 2012-based IRF market
basket, we proposed to use a four part
blended PPI as the proxy for the
chemical cost category in the 2016based IRF market basket. The proposed
blend is composed of the PPI for
Industrial Gas Manufacturing, Primary
Products (BLS series code
PCU325120325120P), the PPI for Other
Basic Inorganic Chemical
Manufacturing (BLS series code
PCU32518–32518–), the PPI for Other
Basic Organic Chemical Manufacturing
(BLS series code PCU32519–32519–),
and the PPI for Other Miscellaneous
Chemical Product Manufacturing (BLS
series code PCU325998325998). We
note that the four part blended PPI used
in the 2012-based IRF market basket is
composed of the PPI for Industrial Gas
Manufacturing (BLS series code
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PCU325120325120P), the PPI for Other
Basic Inorganic Chemical
Manufacturing (BLS series code
PCU32518–32518–), the PPI for Other
Basic Organic Chemical Manufacturing
(BLS series code PCU32519–32519–),
and the PPI for Soap and Cleaning
Compound Manufacturing (BLS series
code PCU32561–32561–). For the 2016based IRF market basket, we proposed
to derive the weights for the PPIs using
the 2012 Benchmark I–O data. The
2012-based IRF market basket used the
2007 Benchmark I–O data to derive the
weights for the four PPIs (80 FR 47061).
Table 8 shows the weights for each of
the four PPIs used to create the
proposed blended Chemical proxy for
the 2016 IRF market basket compared to
the 2012-based blended Chemical
proxy.
(10) Medical Instruments
the 2012-based IRF market basket (80 FR
47061).
(18) Professional Fees: Nonlabor-Related
We proposed to continue to use the
ECI for Total Compensation for Private
Industry workers in Professional and
Related (BLS series code
CIU2010000120000I) to measure the
price growth of this category. This is the
same proxy used in the 2012-based IRF
market basket (80 FR 47061).
(11) Rubber and Plastics
We proposed to continue to use the
PPI for Rubber and Plastic Products
(BLS series code WPU07) to measure
price growth of this cost category. This
is the same proxy used in the 2012based IRF market basket (80 FR 47061).
(12) Paper and Printing Products
We proposed to continue to use the
PPI for Converted Paper and Paperboard
Products (BLS series code WPU0915) to
measure the price growth of this cost
category. This is the same proxy used in
the 2012-based IRF market basket (80 FR
47061).
(13) Miscellaneous Products
We proposed to continue to use the
PPI for Finished Goods Less Food and
Energy (BLS series code WPUFD4131)
to measure the price growth of this cost
category. This is the same proxy used in
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(14) Professional Fees: Labor-Related
We proposed to continue to use the
ECI for Total Compensation for Private
Industry workers in Professional and
Related (BLS series code
CIU2010000120000I) to measure the
price growth of this category. This is the
same proxy used in the 2012-based IRF
market basket (80 FR 47061).
(15) Administrative and Facilities
Support Services
We proposed to continue to use the
ECI for Total Compensation for Private
Industry workers in Office and
Administrative Support (BLS series
code CIU2010000220000I) to measure
the price growth of this category. This
is the same proxy used in the 2012based IRF market basket (80 FR 47061).
(16) Installation, Maintenance, and
Repair
We proposed to continue to use the
ECI for Total Compensation for Civilian
workers in Installation, Maintenance,
and Repair (BLS series code
CIU1010000430000I) to measure the
price growth of this cost category. This
is the same proxy used in the 2012based IRF market basket (80 FR 47061).
(17) All Other: Labor-Related Services
We proposed to continue to use the
ECI for Total Compensation for Private
Industry workers in Service
Occupations (BLS series code
CIU2010000300000I) to measure the
price growth of this cost category. This
is the same proxy used in the 2012based IRF market basket (80 FR 47061).
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(19) Financial Services
We proposed to continue to use the
ECI for Total Compensation for Private
Industry workers in Financial Activities
(BLS series code CIU201520A000000I)
to measure the price growth of this cost
category. This is the same proxy used in
the 2012-based IRF market basket (80 FR
47061).
(20) Telephone Services
We proposed to continue to use the
CPI for Telephone Services (BLS series
code CUUR0000SEED) to measure the
price growth of this cost category. This
is the same proxy used in the 2012based IRF market basket (80 FR 47061).
(21) All Other: Nonlabor-Related
Services
We proposed to continue to use the
CPI for All Items Less Food and Energy
(BLS series code CUUR0000SA0L1E) to
measure the price growth of this cost
category. This is the same proxy used in
the 2012-based IRF market basket (80 FR
47061).
b. Price Proxies for the Capital Portion
of the 2016-Based IRF Market Basket
(1) Capital Price Proxies Prior to Vintage
Weighting
We proposed to continue to use the
same price proxies for the capitalrelated cost categories in the 2016-based
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We proposed to continue to use a
blend of two PPIs for the Medical
Instruments cost category. The 2012
Benchmark Input-Output data shows an
approximate 57/43 split between
Surgical and Medical Instruments and
Medical and Surgical Appliances and
Supplies for this cost category.
Therefore, we proposed a blend
composed of 57 percent of the
commodity-based PPI for Surgical and
Medical Instruments (BLS series code
WPU1562) and 43 percent of the
commodity-based PPI for Medical and
Surgical Appliances and Supplies (BLS
series code WPU1563). The 2012-based
IRF market basket used a 50/50 blend of
these PPIs based on the 2007
Benchmark I–O data (80 FR 47061).
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IRF market basket as were used in the
2012-based IRF market basket (80 FR
47062), which are provided in Table 10
and described below. Specifically, we
proposed to proxy:
• Depreciation: Building and Fixed
Equipment cost category by BEA’s
Chained Price Index for Nonresidential
Construction for Hospitals and Special
Care Facilities (BEA Table 5.4.4. Price
Indexes for Private Fixed Investment in
Structures by Type).
• Depreciation: Movable Equipment
cost category by the PPI for Machinery
and Equipment (BLS series code
WPU11).
• Nonprofit Interest cost category by
the average yield on domestic municipal
bonds (Bond Buyer 20-bond index).
• For-profit Interest cost category by
the average yield on Moody’s Aaa bonds
(Federal Reserve).
• Other Capital-Related cost category
by the CPI–U for Rent of Primary
Residence (BLS series code
CUUS0000SEHA).
We believe these are the most
appropriate proxies for IRF capitalrelated costs that meet our selection
criteria of relevance, timeliness,
availability, and reliability. We
proposed to continue to vintage weight
the capital price proxies for
Depreciation and Interest to capture the
long-term consumption of capital. This
vintage weighting method is similar to
the method used for the 2012-based IRF
market basket (80 FR 47062) and is
described below.
(2) Vintage Weights for Price Proxies
Because capital is acquired and paid
for over time, capital-related expenses
in any given year are determined by
both past and present purchases of
physical and financial capital. The
vintage-weighted capital-related portion
of the 2016-based IRF market basket is
intended to capture the long-term
consumption of capital, using vintage
weights for depreciation (physical
capital) and interest (financial capital).
These vintage weights reflect the
proportion of capital-related purchases
attributable to each year of the expected
life of building and fixed equipment,
movable equipment, and interest. We
proposed to use vintage weights to
compute vintage-weighted price
changes associated with depreciation
and interest expenses.
Capital-related costs are inherently
complicated and are determined by
complex capital-related purchasing
decisions, over time, based on such
factors as interest rates and debt
financing. In addition, capital is
depreciated over time instead of being
consumed in the same period it is
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purchased. By accounting for the
vintage nature of capital, we are able to
provide an accurate and stable annual
measure of price changes. Annual nonvintage price changes for capital are
unstable due to the volatility of interest
rate changes, and therefore, do not
reflect the actual annual price changes
for IRF capital-related costs. The capitalrelated component of the 2016-based
IRF market basket reflects the
underlying stability of the capitalrelated acquisition process.
The methodology used to calculate
the vintage weights for the 2016-based
IRF market basket is the same as that
used for the 2012-based IRF market
basket (80 FR 47062 through 47063)
with the only difference being the
inclusion of more recent data. To
calculate the vintage weights for
depreciation and interest expenses, we
first need a time series of capital-related
purchases for building and fixed
equipment and movable equipment. We
found no single source that provides an
appropriate time series of capital-related
purchases by hospitals for all of the
above components of capital purchases.
The early Medicare cost reports did not
have sufficient capital-related data to
meet this need. Data we obtained from
the American Hospital Association
(AHA) do not include annual capitalrelated purchases. However, we are able
to obtain data on total expenses back to
1963 from the AHA. Consequently, we
proposed to use data from the AHA
Panel Survey and the AHA Annual
Survey to obtain a time series of total
expenses for hospitals. We then
proposed to use data from the AHA
Panel Survey supplemented with the
ratio of depreciation to total hospital
expenses obtained from the Medicare
cost reports to derive a trend of annual
depreciation expenses for 1963 through
2016. We proposed to separate these
depreciation expenses into annual
amounts of building and fixed
equipment depreciation and movable
equipment depreciation as determined
earlier. From these annual depreciation
amounts, we derive annual end-of-year
book values for building and fixed
equipment and movable equipment
using the expected life for each type of
asset category. While data is not
available that is specific to IRFs, we
believe this information for all hospitals
serves as a reasonable alternative for the
pattern of depreciation for IRFs.
To continue to calculate the vintage
weights for depreciation and interest
expenses, we also need to account for
the expected lives for Building and
Fixed Equipment, Movable Equipment,
and Interest for the 2016-based IRF
market basket. We proposed to calculate
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the expected lives using MCR data from
freestanding and hospital-based IRFs.
The expected life of any asset can be
determined by dividing the value of the
asset (excluding fully depreciated
assets) by its current year depreciation
amount. This calculation yields the
estimated expected life of an asset if the
rates of depreciation were to continue at
current year levels, assuming straightline depreciation. We proposed to
determine the expected life of building
and fixed equipment separately for
hospital-based IRFs and freestanding
IRFs, and then weight these expected
lives using the percent of total capital
costs each provider type represents. We
proposed to apply a similar method for
movable equipment. Using these
methods, we determined the average
expected life of building and fixed
equipment to be equal to 22 years, and
the average expected life of movable
equipment to be equal to 11 years. For
the expected life of interest, we believe
vintage weights for interest should
represent the average expected life of
building and fixed equipment because,
based on previous research described in
the FY 1997 IPPS final rule (61 FR
46198), the expected life of hospital
debt instruments and the expected life
of buildings and fixed equipment are
similar. We note that for the 2012-based
IRF market basket, the expected life of
building and fixed equipment is 23
years, and the expected life of movable
equipment is 11 years (80 FR 47062).
Multiplying these expected lives by
the annual depreciation amounts results
in annual year-end asset costs for
building and fixed equipment and
movable equipment. We then calculate
a time series, beginning in 1964, of
annual capital purchases by subtracting
the previous year’s asset costs from the
current year’s asset costs.
For the building and fixed equipment
and movable equipment vintage
weights, we proposed to use the real
annual capital-related purchase
amounts for each asset type to capture
the actual amount of the physical
acquisition, net of the effect of price
inflation. These real annual capitalrelated purchase amounts are produced
by deflating the nominal annual
purchase amount by the associated price
proxy as provided earlier in this final
rule. For the interest vintage weights,
we proposed to use the total nominal
annual capital-related purchase
amounts to capture the value of the debt
instrument (including, but not limited
to, mortgages and bonds). Using these
capital-related purchase time series
specific to each asset type, we proposed
to calculate the vintage weights for
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building and fixed equipment, for
movable equipment, and for interest.
The vintage weights for each asset
type are deemed to represent the
average purchase pattern of the asset
over its expected life (in the case of
building and fixed equipment and
interest, 22 years, and in the case of
movable equipment, 11 years). For each
asset type, we used the time series of
annual capital-related purchase
amounts available from 2016 back to
1964. These data allow us to derive 32,
22-year periods of capital-related
purchases for building and fixed
equipment and interest, and 43, 11-year
periods of capital-related purchases for
movable equipment. For each 22-year
period for building and fixed equipment
and interest, or 11-year period for
movable equipment, we calculate
annual vintage weights by dividing the
capital-related purchase amount in any
given year by the total amount of
purchases over the entire 22-year or 11year period. This calculation is done for
each year in the 22-year or 11-year
period and for each of the periods for
which we have data. We then calculate
the average vintage weight for a given
year of the expected life by taking the
average of these vintage weights across
the multiple periods of data.
We did not receive any specific public
comments on our proposed calculation
of the vintage weights for the 2016based IRF market basket. Therefore, in
this final rule, we are finalizing the
vintage weights as proposed. The
vintage weights for the capital-related
portion of the 2016-based IRF market
basket and the 2012-based IRF market
basket are presented in Table 9.
The process of creating vintageweighted price proxies requires
applying the vintage weights to the
price proxy index where the last applied
vintage weight in Table 8 is applied to
the most recent data point. We have
provided on the CMS website an
example of how the vintage weighting
price proxies are calculated, using
example vintage weights and example
price indices. The example can be found
at https://www.cms.gov/ResearchStatistics-Data-and-Systems/StatisticsTrends-and-Reports/MedicareProgram
RatesStats/MarketBasketResearch.html
in the zip file titled ‘‘Weight
Calculations as described in the IPPS FY
2010 Proposed Rule.’’
c. Summary of Price Proxies of the 2016Based IRF Market Basket
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Table 10 shows both the operating
and capital price proxies for the 2016based IRF market basket.
BILLING CODE 4120–01–P
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TABLE 10: Price Proxies and Cost Share Weights for Use in the Final2016-based IRF
Market Basket
Professional Fees: Labor-related
Administrative and Facilities Support
Services
Maintenance &
Professional Fees: Nonlabor-related
Financial services
ECI for Total compensation for Private industry workers in
Professional and related
ECI for Total compensation for Private industry workers in Office
and administrative onT"'"..tECI for Total compensation for Civilian workers in Installation,
·
and
·
ECI for Total compensation for Private industry workers in
Service
ECI for Total compensation for Private industry workers in
Professional and related
ECI for Total compensation for Private industry workers in
Financial activities
5.0%
0.7%
1.6%
1.8%
5.4%
0.9%
0.9%
Note: Totals may not sum to 100.0 percent due to rounding.
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BILLING CODE 4120–01–C
D. FY 2020 Market Basket Update and
Productivity Adjustment
1. FY 2020 Market Basket Update
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For FY 2020 (that is, beginning
October 1, 2019 and ending September
30, 2020), we proposed to use the 2016based IRF market basket increase factor
described in section V.C. of the
proposed rule to update the IRF PPS
base payment rate. Consistent with
historical practice, we proposed to
estimate the market basket update for
the IRF PPS based on IHS Global Inc.’s
(IGI’s) forecast using the most recent
available data. IGI is a nationallyrecognized economic and financial
forecasting firm with which we contract
to forecast the components of the market
baskets and MFP. In the FY 2020 IRF
PPS proposed rule (84 FR 17274), we
proposed a market basket increase factor
of 3.0 percent for FY 2020, which was
based on IGI’s first quarter 2019 forecast
with historical data through fourth
quarter 2018.
In the FY 2020 IRF PPS proposed
rule, we also proposed that if more
recent data were subsequently available
(for example, a more recent estimate of
the market basket and MFP adjustment),
we would use such data to determine
the FY 2020 update in the final rule.
Incorporating more recent data, the
projected 2016-based IRF market basket
increase factor for FY 2020 is 2.9
percent, which is based on IGI’s second
quarter 2019 forecast with historical
data through first quarter 2019.
We received several comments on our
proposed market basket update and
productivity adjustment, which are
summarized below.
Comment: Commenters supported the
proposal to update the market basket
and MFP adjustment using the latest
available data, and encouraged CMS to
update these factors using the latest
available data as part of the release of
the FY 2020 IRF PPS final rule.
Response: We appreciate the
commenters’ support for updating the
market basket and MFP adjustments
using the latest available data.
Comment: A few commenters
expressed concern about the lack of
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transparency of the market basket and
MFP payment updates. The commenters
stated that the IGI forecast appears to be
procured specifically for the purpose of
CMS updating the IRF market basket
and productivity adjustment. The
commenters also noted that it is
concerning that CMS does not provide
IGI’s analyses or report to the public
given the key role the market basket and
productivity adjustment play in
updating the payment system each year
and that without such information
stakeholders are unable to evaluate the
accuracy of the update. The commenters
also mentioned that the same comment
was submitted in the FY 2019
rulemaking process but they do not
believe that the response was adequate
since the actual analysis or report used
to create the forecasts was not provided
(83 FR 38525). The commenters
requested that CMS release an IGI report
and analysis used to update the IRF
market basket and standard payment
conversion factor.
Response: IGI regularly produces and
publishes a wide variety of forecasted
series on a monthly or quarterly basis.
These forecasts are derived using a
framework of proprietary economic
models that are created and updated
regularly by IGI. IGI provides these
forecasts to a wide array of clients in
addition to CMS. We use a contractor
for the price forecasts so that the
forecasts are independent and reflect a
complete economic forecasting model, a
capability that we do not have. IGI has
received multiple awards for their
macroeconomic forecast accuracy of
major economic indicators. We use IGI’s
price forecasts in all of the FFS market
baskets used for payment updates and
has used the forecasts produced by this
company for many years.
We select approximately 30
individual price proxies as inputs to the
IRF market basket calculation. The price
series are discussed in detail as part of
the rulemaking process. In order to
derive a forecast of the IRF market
basket index, we contract with IGI to
procure the forecasts of these individual
price proxies on a quarterly basis. We
then combine these price proxies with
the market basket base year cost weights
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to derive the levels of the IRF market
basket. The data sources and methods
used to derive these cost weights are
discussed in detail as part of the
rulemaking process.
As provided in our previous response
to this comment in the FY 2019 IRF PPS
final rule (83 FR 38525), the market
basket update is derived using: (1) The
market basket base year cost weights as
finalized by CMS through rulemaking;
and (2) the most up-to-date forecast of
the price proxies used in the market
basket as forecasted by IGI. Specifically,
for each cost category in the market
basket (for example, Wages and Salaries,
Pharmaceuticals), the level of each of
these price proxies are multiplied by the
cost weight for that cost category. The
sum of these products (that is, weights
multiplied by proxied index levels) for
all cost categories yields the composite
index level in the market basket in a
given year.
As acknowledged by the commenters,
we provided a link from the CMS
website to the top-line market basket
updates. We also indicated that more
detailed forecasts of the IRF market
basket calculations are readily available
by request by sending an email to
CMSDNHS@cms.hhs.gov to request this
information (83 FR 38525). Using these
detailed data, the commenter would be
able to replicate the levels of the IRF
market basket update in the history and
the forecast period. We encourage
stakeholders to utilize these data, which
we believe will address the commenters’
concerns.
Incorporating more recent data, the
projected 2016-based IRF market basket
update for FY 2020 is 2.9 percent. After
careful consideration of the comments,
consistent with our historical practice of
estimating market basket increases
based on the best available data, we are
finalizing a market basket increase
factor of 2.9 percent for FY 2020. For
comparison, the current 2012-based IRF
market basket is also projected to
increase by 2.9 percent in FY 2020
based on IGI’s second quarter 2019
forecast.
Table 11 compares the 2016-based IRF
market basket and the 2012-based IRF
market basket percent changes.
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2. Productivity Adjustment
According to section 1886(j)(3)(C)(i) of
the Act, the Secretary shall establish an
increase factor based on an appropriate
percentage increase in a market basket
of goods and services. As described in
sections VI.C and VI.D.1. of this final
rule, we are finalizing an estimate of the
IRF PPS increase factor for FY 2020
based on the 2016-based IRF market
basket. Section 1886(j)(3)(C)(ii) of the
Act then requires that, after establishing
the increase factor for a FY, the
Secretary shall reduce such increase
factor for FY 2012 and each subsequent
FY, by the productivity adjustment
described in section 1886(b)(3)(B)(xi)(II)
of the Act. Section 1886(b)(3)(B)(xi)(II)
of the Act sets forth the definition of
this productivity adjustment. The
statute defines the productivity
adjustment to be equal to the 10-year
moving average of changes in annual
economy-wide private nonfarm business
MFP (as projected by the Secretary for
the 10-year period ending with the
applicable FY, year, cost reporting
period, or other annual period) (the
‘‘MFP adjustment’’). The BLS publishes
the official measure of private nonfarm
business MFP. Please see https://
www.bls.gov/mfp for the BLS historical
published MFP data.
MFP is derived by subtracting the
contribution of labor and capital input
growth from output growth. The
projections of the components of MFP
are currently produced by IGI, a
nationally recognized economic
forecasting firm with which CMS
contracts to forecast the components of
the market basket and MFP. For more
information on the productivity
adjustment, we refer reader to the
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discussion in the FY 2016 IRF PPS final
rule (80 FR 47065).
Using IGI’s first quarter 2019 forecast,
the proposed MFP adjustment for FY
2020 (the 10-year moving average of
MFP for the period ending FY 2020) was
0.5 percent (84 FR 17274). Thus, in
accordance with section 1886(j)(3)(C) of
the Act, we proposed to base the FY
2020 market basket update, which is
used to determine the applicable
percentage increase for the IRF
payments, on the most recent estimate
of the 2016-based IRF market basket. We
proposed to then reduce this percentage
increase by the current estimate of the
proposed MFP adjustment for FY 2020
of 0.5 percentage point (the 10-year
moving average of MFP for the period
ending FY 2020 based on IGI’s first
quarter 2019 forecast). Therefore, the
proposed FY 2020 IRF update was 2.5
percent (3.0 percent market basket
update, less 0.5 percentage point MFP
adjustment). Furthermore, we proposed
that if more recent data are subsequently
available (for example, a more recent
estimate of the market basket and MFP
adjustment), we would use such data to
determine the FY 2020 market basket
update and MFP adjustment in the final
rule.
We received a few comments on the
application of the productivity
adjustment, which are summarized
below.
Comment: Commenters continue to be
concerned about the application of the
productivity adjustment to IRFs. One of
the commenters stated that they
understood CMS is bound by statute to
reduce the market basket update by a
productivity adjustment factor in
accordance with the PPACA, but they
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believe that IRFs are unable to generate
additional productivity gains at a pace
matching the productivity of the
economy at large on an ongoing,
consistent basis. The commenter noted
that the services provided in IRFs are
labor-intensive and the services do not
lend themselves to continuous
productivity improvements. The
commenter also noted that IRFs are
bound by unchanging labor-intensive
standards such as the 3-hour therapy
rule and other regulatory requirements
that reduce flexibility and restrict the
pursuit of certain efficiencies. The
commenter noted that continued
application of a productivity adjustment
to payments could results in decreased
beneficiary access to IRF services. The
commenter requested that CMS
continue to monitor the impact that the
multi-factor productivity adjustments
have on the IRF sector, provide feedback
to Congress as appropriate, and reduce
the productivity adjustment. One
commenter requested that, in addition
to monitoring its effects on overall
payments, CMS should evaluate
whether IRFs are able to achieve the
same level of productivity improvement
as workers across the U.S. economy.
Response: We acknowledge the
commenters’ concerns regarding
productivity growth at the economywide level and its application to IRFs.
As the commenter acknowledges,
section 1886(j)(3)(C)(ii)(I) of the Act
requires the application of a
productivity adjustment to the IRF PPS
market basket increase factor.
We will continue to monitor the
impact of the payment updates,
including the effects of the productivity
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adjustment, on IRF finances, as well as
beneficiary access to care.
We note that each year, MedPAC
makes an annual update
recommendation to Congress based on a
variety of measures related to payment
adequacy, including a detailed margin
analysis and analysis of beneficiary
access to care for IRF services. For FY
2020, MedPAC recommended that
Congress reduce the IRF PPS base rate
by 5 percent and found that beneficiary
access to care was not a concern. The
‘‘March 2019 Report to the Congress:
Medicare Payment Policy’’, chapter 10
is publicly available at https://
www.medpac.gov/-documents-/reports.
We would be very interested in better
understanding IRF-specific
productivity; however, the data
elements required to estimate IRF
specific multi-factor productivity are
not produced at the level of detail that
would allow this analysis. We have
estimated hospital-sector multi-factor
productivity and have published the
findings on the CMS website at https://
www.cms.gov/Research-Statistics-Dataand-Systems/Statistics-Trends-andReports/ReportsTrustFunds/Downloads/
ProductivityMemo2016.pdf.
After careful consideration of
comments, we are incorporating more
recent data to determine the market
basket update and MFP adjustment for
FY 2020. Using IGI’s second quarter
2019 forecast, the current estimate of the
MFP adjustment for FY 2020 (the 10year moving average of MFP for the
period ending FY 2020) is 0.4 percent.
Thus, in accordance with section
1886(j)(3)(C) of the Act, we are
finalizing a FY 2020 market basket
update of 2.9 percent. We then reduce
this percentage increase by the most
recent estimate of the MFP adjustment
for FY 2020 of 0.4 percentage point (the
10-year moving average of MFP for the
period ending FY 2020 based on IGI’s
second quarter 2019 forecast).
Therefore, the final FY 2020 IRF
productivity-adjusted market basket
update is equal to 2.5 percent (2.9
percent market basket update, less 0.4
percentage point MFP adjustment).
For FY 2020, the Medicare Payment
Advisory Commission (MedPAC)
recommends that a decrease of 5 percent
be applied to IRF PPS payment rates. As
discussed, and in accordance with
section 1886(j)(3)(C) of the Act, we are
finalizing an update to IRF PPS payment
rates for FY 2020 by a productivityadjusted market basket increase factor of
2.5 percent, as section 1886(j)(3)(C) of
the Act does not provide the Secretary
with the authority to apply a different
update factor to IRF PPS payment rates
for FY 2020.
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Comment: One commenter (MedPAC)
stated that they understand that CMS is
required to implement the statutory
update of market basket less
productivity adjustment, but that their
analysis of beneficiary access to
rehabilitative services, the supply of
providers, and aggregate IRF Medicare
margins, which have been above 11
percent since 2012, indicates that the
Congress should reduce the IRF
payment rate by 5 percent for FY 2020.
Response: We appreciate MedPAC’s
interest in the IRF increase factor.
However, we are required to update IRF
PPS payments by the market basket
reduced by the productivity adjustment,
as directed by section 1886(j)(3)(C) of
the Act.
E. Labor-Related Share for FY 2020
Section 1886(j)(6) of the Act specifies
that the Secretary is to adjust the
proportion (as estimated by the
Secretary from time to time) of
rehabilitation facilities’ costs which are
attributable to wages and wage-related
costs, of the prospective payment rates
computed under section 1886(j)(3) of
the Act for area differences in wage
levels by a factor (established by the
Secretary) reflecting the relative hospital
wage level in the geographic area of the
rehabilitation facility compared to the
national average wage level for such
facilities. The labor-related share is
determined by identifying the national
average proportion of total costs that are
related to, influenced by, or vary with
the local labor market. We proposed to
continue to classify a cost category as
labor-related if the costs are laborintensive and vary with the local labor
market. As stated in the FY 2016 IRF
PPS final rule (80 FR 47068), the laborrelated share was defined as the sum of
the relative importance of Wages and
Salaries, Employee Benefits,
Professional Fees: Labor-related
Services, Administrative and Facilities
Support Services, Installation,
Maintenance, and Repair, All Other:
Labor-related Services, and a portion of
the Capital Costs from the 2012-based
IRF market basket.
Based on our definition of the laborrelated share and the cost categories in
the 2016-based IRF market basket, we
proposed to include in the labor-related
share for FY 2020 the sum of the FY
2020 relative importance of Wages and
Salaries, Employee Benefits,
Professional Fees: Labor-related,
Administrative and Facilities Support
Services, Installation, Maintenance, and
Repair, All Other: Labor-related
Services, and a portion of the CapitalRelated cost weight from the 2016-based
IRF market basket.
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Similar to the 2012-based IRF market
basket (80 FR 47067), the 2016-based
IRF market basket includes two cost
categories for nonmedical Professional
Fees (including, but not limited to,
expenses for legal, accounting, and
engineering services). These are
Professional Fees: Labor-related and
Professional Fees: Nonlabor-related. For
the 2016-based IRF market basket, we
proposed to estimate the labor-related
percentage of non-medical professional
fees (and assign these expenses to the
Professional Fees: Labor-related services
cost category) based on the same
method that was used to determine the
labor-related percentage of professional
fees in the 2012-based IRF market
basket.
As was done in the 2012-based IRF
market basket (80 FR 47067), we
proposed to determine the proportion of
legal, accounting and auditing,
engineering, and management
consulting services that meet our
definition of labor-related services based
on a survey of hospitals conducted by
us in 2008, a discussion of which can
be found in the FY 2010 IPPS/LTCH
PPS final rule (74 FR 43850 through
43856). Based on the weighted results of
the survey, we determined that
hospitals purchase, on average, the
following portions of contracted
professional services outside of their
local labor market:
• 34 percent of accounting and
auditing services.
• 30 percent of engineering services.
• 33 percent of legal services.
• 42 percent of management
consulting services.
We proposed to apply each of these
percentages to the respective
Benchmark I–O cost category
underlying the professional fees cost
category to determine the Professional
Fees: Nonlabor-related costs. The
Professional Fees: Labor-related costs
were determined to be the difference
between the total costs for each
Benchmark I–O category and the
Professional Fees: Nonlabor-related
costs. This is the same methodology that
we used to separate the 2012-based IRF
market basket professional fees category
into Professional Fees: Labor-related
and Professional Fees: Nonlabor-related
cost categories (80 FR 47067).
In the 2016-based IRF market basket,
nonmedical professional fees that are
subject to allocation based on these
survey results represent 4.4 percent of
total costs (and are limited to those fees
related to Accounting & Auditing, Legal,
Engineering, and Management
Consulting services). Based on our
survey results, we proposed to
apportion 2.8 percentage points of the
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4.4 percentage point figure into the
Professional Fees: Labor-related share
cost category and designate the
remaining 1.6 percentage point into the
Professional Fees: Nonlabor-related cost
category.
In addition to the professional
services listed, for the 2016-based IRF
market basket, we proposed to allocate
a proportion of the Home Office
Contract Labor cost weight, calculated
using the Medicare cost reports as stated
above, into the Professional Fees: Laborrelated and Professional Fees: Nonlaborrelated cost categories. We proposed to
classify these expenses as labor-related
and nonlabor-related as many facilities
are not located in the same geographic
area as their home office, and therefore,
do not meet our definition for the laborrelated share that requires the services
to be purchased in the local labor
market. For the 2012-based IRF market
basket, we used the BEA I–O expense
data for NAICS 55, Management of
Companies and Enterprises, to estimate
the Home Office Contract Labor cost
weight (80 FR 47067). We then allocated
these expenses into the Professional
Fess: Labor-related and Professional
Fees: Nonlabor-related cost categories.
Similar to the 2012-based IRF market
basket, we proposed for the 2016-based
IRF market basket to use the Medicare
cost reports for both freestanding IRF
providers and hospital-based IRF
providers to determine the home office
labor-related percentages. The MCR
requires a hospital to report information
regarding their home office provider.
For the 2016-based IRF market basket,
we proposed to start with the sample of
IRF providers that passed the top 1
percent trim used to derive the Home
Office Contract Labor cost weight as
described in section VI.B. of this final
rule. For both freestanding and hospitalbased providers, we proposed to
multiply each provider’s Home Office
Contract Labor cost weight (calculated
using data from the total facility) by
Medicare allowable total costs. This
results in an amount of Medicare
allowable home office compensation
costs for each IRF. Using information on
the Medicare cost report, we then
compare the location of the IRF with the
location of the IRF’s home office. We
proposed to classify an IRF with a home
office located in their respective local
labor market if the IRF and its home
office are located in the same
Metropolitan Statistical Area. We then
calculate the proportion of Medicare
allowable home office compensation
costs that these IRFs represent of total
Medicare allowable home office
compensation costs. We proposed to
multiply this percentage (42 percent) by
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the Home Office Contract Labor cost
weight (3.7 percent) to determine the
proportion of costs that should be
allocated to the labor-related share.
Therefore, we allocated 1.6 percentage
points of the Home Office Contract
Labor cost weight (3.7 percent times 42
percent) to the Professional Fees: Laborrelated cost weight and 2.1 percentage
points of the Home Office Contract
Labor cost weight to the Professional
Fees: Nonlabor-related cost weight (3.7
percent times 58 percent). For the 2012based IRF market basket, we used a
similar methodology but we relied on
provider counts rather than home office/
related organization contract labor
compensation costs to determine the
labor-related percentage (80 FR 47067).
In summary, we apportioned 2.8
percentage points of the non-medical
professional fees and 1.6 percentage
points of the home office/related
organization contract labor cost weights
into the Professional Fees: Labor-related
cost category. This amount was added to
the portion of professional fees that was
identified to be labor-related using the
I–O data such as contracted advertising
and marketing costs (approximately 0.6
percentage point of total costs) resulting
in a Professional Fees: Labor-related
cost weight of 5.0 percent.
We received several comments on the
proposed labor-related share, which are
summarized below.
Comment: A few commenters noted
that the cost weight for Home Office
Contract Labor costs is 3.7 percent of all
IRFs’ costs and influences changes in
other payment areas, such as the total
labor-related share. The commenters
stated that they believe the proposed
changes to the methodology are
responsible, at least in large part, to the
notable proposed increase of
approximately 2 percent of the laborrelated share. Some of the commenters
also stated that the increase in the laborrelated share will adversely impact rural
IRFs and IRFs with a wage index below
1.0.
Response: The labor-related share for
IRFs is derived from the relative
importance of the labor-related cost
categories. The relative importance for
FY 2020 reflects the different rates of
price change for each of the individual
cost categories between the base year
and FY 2020. For the FY 2020 final rule,
as proposed, the final labor-related
share for FY 2020 is based on a more
recent forecast of the 2016-based IRF
market basket. Using the more recent
forecast, the total difference between the
FY 2020 labor-related share using the
2016-based IRF market basket and 2012based IRF market basket is 2.0
percentage points (72.7 percent using
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2016-based IRF market basket and 70.7
percent using 2012-based IRF market
basket). This difference can be separated
into two primary components: (1)
Revision to the base year cost weights
(1.4 percentage points); and (2) revision
to starting point of calculation of
relative importance (base year) from
2012 to 2016 (0.6 percentage point). Of
the 1.4-percentage points difference in
the base year cost weights, just 0.2
percentage point is attributable to
deriving the Home Office Contract Labor
cost weight using the MCR data rather
than the I–O data; the remainder is due
to the increase in Compensation and
Capital cost weights (calculated using
the MCR data) and the incorporation of
the 2012 Benchmark I–O data.
The impact of using the MCR data to
calculate the Home Office Contract
Labor cost weight is minimal because it
also lowers the residual ‘‘All Other’’
cost weight from 25.8 percent (using the
I–O data to calculate the Home Office
Contract Labor cost weight) to 22.2
percent (using the MCR data to calculate
the Home Office Contract labor cost
weight). The lower residual ‘‘All Other’’
cost weight then leads to relatively
lower cost weights for Administrative
and Business Support Services,
Installation, Maintenance and Repair
Services, and All Other: Labor-related
Services (which are calculated using the
Benchmark I–O data), each of which is
also reflected in the labor-related share.
After careful consideration of
comments, in this final rule, we are
finalizing the 2016-based IRF market
basket labor-related share cost weights
as proposed.
As stated previously, we proposed to
include in the labor-related share the
sum of the relative importance of Wages
and Salaries, Employee Benefits,
Professional Fees: Labor-Related,
Administrative and Facilities Support
Services, Installation, Maintenance, and
Repair, All Other: Labor-related
Services, and a portion of the CapitalRelated cost weight from the 2016-based
IRF market basket. The relative
importance reflects the different rates of
price change for these cost categories
between the base year (2016) and FY
2020. Based on IGI’s 2nd quarter 2019
forecast for the 2016-based IRF market
basket, the sum of the FY 2020 relative
importance for Wages and Salaries,
Employee Benefits, Professional Fees:
Labor-related, Administrative and
Facilities Support Services, Installation
Maintenance & Repair Services, and All
Other: Labor-related Services is 68.7
percent. The portion of Capital costs
that are influenced by the local labor
market is estimated to be 46 percent,
which is the same percentage applied to
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the 2012-based IRF market basket (80 FR
47068). Since the relative importance
for Capital is 8.6 percent of the 2016based IRF market basket in FY 2020, we
took 46 percent of 8.6 percent to
determine the labor-related share of
Capital for FY 2020 of 4.0 percent.
Therefore, we are finalizing a total
labor-related share for FY 2020 of 72.7
percent (the sum of 68.7 percent for the
operating costs and 4.0 percent for the
labor-related share of Capital).
Table 12 shows the FY 2020 laborrelated share using the final 2016-based
IRF market basket relative importance
and the FY 2019 labor-related share
which was based on the 2012-based IRF
market basket relative importance.
F. Update to the IRF Wage Index To Use
Concurrent IPPS Wage Index Beginning
With FY 2020
For this reason, we believed, and
continue to believe, that the wage data
of IPPS hospitals accurately captures the
relationship of wages and wage-related
costs of IRFs in an area as compared
with the national average. Therefore, in
the FY 2002 IRF PPS final rule, we
finalized use of the FY 1997 IPPS wage
data to develop the wage index for the
IRF PPS, as that was the most recent
final data available.
For all subsequent years in which the
IRF PPS wage index has been updated,
we have continued to use the most
recent final IPPS data available, which
has led us to use the pre-floor, prereclassified FY IPPS wage index values
from the prior fiscal year.
In the FY 2018 IRF PPS proposed rule
(82 FR 20742 through 20743), we
included a request for information (RFI)
to solicit comments from stakeholders
requesting information on CMS
flexibilities and efficiencies. The
purpose of the RFI was to receive
feedback regarding ways in which we
could reduce burden for hospitals and
physicians, improve quality of care,
decrease costs and ensure that patients
receive the best care. We received
comments from IRF industry
associations, state and national hospital
associations, industry groups,
representing hospitals, and individual
IRF providers in response to the
solicitation. One of the responses we
received to the RFI suggested that there
is concern among IRF stakeholders
about the different wage index data used
in the different post-acute care (PAC)
settings. For the IRF PPS, we use a 1year lag of the pre-floor, pre-reclassified
FY IPPS wage index, meaning that for
the IRF PPS for FY 2019, we finalized
use of the FY 2018 IPPS wage index (83
FR 38527). However, we base the wage
indexes for the SNF PPS and the LTCH
PPS on the concurrent IPPS wage index
((83 FR 39172 through 39178) and (83
FR 41731), respectively).
As we look towards a more unified
PACpayment system, we believe that
standardizing the wage index data
across PAC settings is necessary.
Therefore, we proposed to change the
IRF wage index methodology to align
with other PAC settings. Specifically,
we proposed changing from our
established policy of using the pre-floor,
pre-reclassified FY IPPS wage index
(that is, for FY 2020 we proposed to use
the concurrent FY 2020 pre-floor, prereclassified IPPS wage index under the
IRF PPS). This proposed change would
use the concurrent IPPS pre-floor, prereclassified wage index for the IRF wage
index beginning with FY 2020 and
continuing for all subsequent years.
Thus, for the FY 2020 IRF wage index,
we proposed to use the FY 2020 prefloor, pre-reclassified IPPS wage index,
which is based on data submitted for
hospital cost reporting periods
beginning in FY 2016. We proposed to
implement these revisions in a budget
neutral manner. For more information
1. Background
Section 1886(j)(6) of the Act requires
the Secretary to adjust the proportion of
rehabilitation facilities’ costs
attributable to wages and wage-related
costs (as estimated by the Secretary from
time to time) by a factor (established by
the Secretary) reflecting the relative
hospital wage level in the geographic
area of the rehabilitation facility
compared to the national average wage
level for those facilities. The Secretary
is required to update the IRF PPS wage
index on the basis of information
available to the Secretary on the wages
and wage-related costs to furnish
rehabilitation services. Any adjustment
or updates made under section
1886(j)(6) of the Act for a FY are made
in a budget-neutral manner.
2. Update to the IRF Wage Index To Use
Concurrent IPPS Wage Index Beginning
with FY 2020
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When the IRF PPS was implemented
in the FY 2002 IRF PPS final rule (66
FR 41358), we finalized the use of the
FY IPPS wage data in the creation of an
IRF wage index. We believed that a
wage index based on FY IPPS wage data
was the best proxy and most appropriate
wage index to use in adjusting payments
to IRFs, since both IPPS hospitals and
IRFs compete in the same labor markets.
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on the distributional impacts of this
proposal, we refer readers to the FY
2020 IRF PPS proposed rule (84 FR
17278).
Using the current pre-floor, prereclassified FY IPPS wage index would
result in the most up-to-date wage data
being the basis for the IRF wage index.
It would also result in more consistency
and equity in the wage index
methodology used by Medicare.
We received 7 comments on this
proposal to align the data timeframes
with that of the IPPS by using the FY
2020 pre-floor, pre-reclassified FY IPPS
wage index as the basis for the FY 2020
IRF wage index, which are summarized
below.
Comment: All of the commenters
supported CMS’ proposal to use the FY
2020 pre-floor, pre-reclassified FY IPPS
wage index for the FY 2020 IRF wage
index. Commenters agreed that the
proposed change to use the concurrent
FY IPPS wage index data would align
the wage index data across PAC settings
and move in the direction of unified
PAC payment. A few commenters
recommended that CMS adopt other
wage index policies for IRFs that apply
to or have been proposed for IPPS
hospitals, such as geographic
reclassifications, suggesting that this
would increase consistency and
alignment across settings.
Response: We appreciate the
commenter’s support for the proposal.
We agree that finalizing this proposal is
necessary as we move towards a more
unified PAC payment system. We plan
to monitor the use of the concurrent FY
IPPS wage index data before we
consider any other potential wage index
policy changes.
After careful consideration of the
comments we received, we are
finalizing our proposal to align the data
timeframes with that of the IPPS by
using the concurrent pre-floor, prereclassified IPPS wage index for the IRF
wage index beginning with FY 2020 and
continuing for all subsequent years.
Thus, we will use the FY 2020 pre-floor,
pre-reclassified IPPS wage index as the
basis for the FY 2020 IRF wage index
(that is, for all IRF discharges beginning
on or after October 1, 2019). We will
implement these revisions in a budget
neutral manner. We refer readers to
Table 20 in section XIII.C of this final
rule for more information on the
distributional effects of this change.
3. Wage Adjustment for FY 2020 Using
Concurrent IPPS Wage Index Labor
Market Area Definitions and the
Due to our proposal to use the
concurrent IPPS wage index beginning
with FY 2020, for FY 2020, we proposed
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using the policy and methodologies
described in section VI. of this final rule
related to the labor market area
definitions and the wage index
methodology for areas with wage data.
Thus, we proposed using the CBSA
labor market area definitions and the FY
2020 pre-reclassification and pre-floor
IPPS wage index data. In accordance
with section 1886(d)(3)(E) of the Act,
the FY 2020 pre-reclassification and
pre-floor IPPS wage index is based on
data submitted for hospital cost
reporting periods beginning on or after
October 1, 2015 and before October 1,
2016 (that is, FY 2016 cost report data).
The labor market designations made
by the OMB include some geographic
areas where there are no hospitals and,
thus, no hospital wage index data on
which to base the calculation of the IRF
PPS wage index. We proposed to
continue to use the same methodology
discussed in the FY 2008 IRF PPS final
rule (72 FR 44299) to address those
geographic areas where there are no
hospitals and, thus, no hospital wage
index data on which to base the
calculation for the FY 2020 IRF PPS
wage index.
We received one comment on this
proposal, which is summarized below.
Comment: One commenter requested
that, until a new wage index system is
implemented, CMS should establish a
smoothing variable to be applied to the
current IRF wage index to reduce the
fluctuations IRFs experience annually.
Response: Under section 1886(j)(6) of
the Act, we adjust IRF PPS rates to
account for differences in area wage
levels. Any perceived volatility in the
wage index is predicated upon volatility
in actual wages in that area and reflects
real differences in area wage levels. As
we believe that the application of a
smoothing variable would make the
wage index values less reflective of the
area wage levels, we do not believe it
would be appropriate to implement
such a change to the IRF wage index
policy.
After careful consideration of the
comments we received, we are
finalizing our proposal to use the policy
and methodologies described in section
VI. of this final rule related to the labor
market area definitions and the wage
index methodology for areas with wage
data. Thus, we are finalizing the use of
the CBSA labor market area definitions
and the FY 2020 pre-reclassification and
pre-floor IPPS wage index data. We are
finalizing the continued use of the same
methodology discussed in the FY 2008
IRF PPS final rule (72 FR 44299) to
address those geographic areas where
there are no hospitals and, thus, no
hospital wage index data on which to
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base the calculation for the FY 2020 IRF
PPS wage index.
4. Core-Based Statistical Areas (CBSAs)
for the FY 2020 IRF Wage Index
The wage index used for the IRF PPS
is calculated using the prereclassification and pre-floor IPPS wage
index data and is assigned to the IRF on
the basis of the labor market area in
which the IRF is geographically located.
IRF labor market areas are delineated
based on the CBSAs established by the
OMB. The current CBSA delineations
(which were implemented for the IRF
PPS beginning with FY 2016) are based
on revised OMB delineations issued on
February 28, 2013, in OMB Bulletin No.
13–01. OMB Bulletin No. 13–01
established revised delineations for
Metropolitan Statistical Areas,
Micropolitan Statistical Areas, and
Combined Statistical Areas in the
United States and Puerto Rico based on
the 2010 Census, and provided guidance
on the use of the delineations of these
statistical areas using standards
published in the June 28, 2010 Federal
Register (75 FR 37246 through 37252).
We refer readers to the FY 2016 IRF PPS
final rule (80 FR 47068 through 47076)
for a full discussion of our
implementation of the OMB labor
market area delineations beginning with
the FY 2016 wage index.
Generally, OMB issues major
revisions to statistical areas every 10
years, based on the results of the
decennial census. However, OMB
occasionally issues minor updates and
revisions to statistical areas in the years
between the decennial censuses. On
July 15, 2015, OMB issued OMB
Bulletin No. 15–01, which provides
minor updates to and supersedes OMB
Bulletin No. 13–01 that was issued on
February 28, 2013. The attachment to
OMB Bulletin No. 15–01 provides
detailed information on the update to
statistical areas since February 28, 2013.
The updates provided in OMB Bulletin
No. 15–01 are based on the application
of the 2010 Standards for Delineating
Metropolitan and Micropolitan
Statistical Areas to Census Bureau
population estimates for July 1, 2012
and July 1, 2013.
In the FY 2018 IRF PPS final rule (82
FR 36250 through 36251), we adopted
the updates set forth in OMB Bulletin
No. 15–01 effective October 1, 2017,
beginning with the FY 2018 IRF wage
index. For a complete discussion of the
adoption of the updates set forth in
OMB Bulletin No. 15–01, we refer
readers to the FY 2018 IRF PPS final
rule. In the FY 2019 IRF PPS final rule
(83 FR 38527), we continued to use the
OMB delineations that were adopted
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beginning with FY 2016 to calculate the
area wage indexes, with updates set
forth in OMB Bulletin No. 15–01 that
we adopted beginning with the FY 2018
wage index.
On August 15, 2017, OMB issued
OMB Bulletin No. 17–01, which
provided updates to and superseded
OMB Bulletin No. 15–01 that was issued
on July 15, 2015. The attachments to
OMB Bulletin No. 17–01 provide
detailed information on the update to
statistical areas since July 15, 2015, and
are based on the application of the 2010
Standards for Delineating Metropolitan
and Micropolitan Statistical Areas to
Census Bureau population estimates for
July 1, 2014 and July 1, 2015. In OMB
Bulletin No. 17–01, OMB announced
that one Micropolitan Statistical Area
now qualifies as a Metropolitan
Statistical Area. The new urban CBSA is
as follows:
• Twin Falls, Idaho (CBSA 46300).
This CBSA is comprised of the principal
city of Twin Falls, Idaho in Jerome
County, Idaho and Twin Falls County,
Idaho.
The OMB bulletin is available on the
OMB website at https://
www.whitehouse.gov/sites/
whitehouse.gov/files/omb/bulletins/
2017/b-17-01.pdf.
As we indicated in the FY 2019 IRF
PPS final rule (83 FR 38528), we believe
that it is important for the IRF PPS to
use the latest labor market area
delineations available as soon as is
reasonably possible to maintain a more
accurate and up-to-date payment system
that reflects the reality of population
shifts and labor market conditions. As
discussed in the FY 2019 IPPS and
LTCH PPS final rule (83 FR 20591),
these updated labor market area
definitions were implemented under the
IPPS beginning on October 1, 2018.
Therefore, we proposed to implement
these revisions for the IRF PPS
beginning October 1, 2019, consistent
with our historical practice of modeling
IRF PPS adoption of the labor market
area delineations after IPPS adoption of
these delineations.
We received 2 comments on this
proposal, which are summarized below.
Comment: Commenters expressed
concern that the IRF wage index values
published in the FY 2020 IRF PPS
proposed rule were not consistent with
the values published in the FY 2020
IPPS proposed rule wage index public
use file. These commenters suggested
that CMS examine these wage index
values and correct them if we find that
they are in error prior to finalizing the
use of the concurrent IPPS wage index
data for the IRF PPS.
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Response: We identified a slight error
in the proposed rule wage index values
after the FY 2020 IRF PPS proposed rule
was published. A programming error
caused the data for all providers in a
single county to be included twice,
which affected the national average
hourly rate, and therefore, affected
nearly all wage index values. We have
corrected the programming logic so this
error cannot occur again. We also
standardized our procedures for
rounding, to ensure consistency. The
correction to the proposed rule wage
index data was not completed until after
the comment period closed on June 17,
2019. This final rule reflects the
corrected and updated wage index data.
We are finalizing and implementing
these revisions for the IRF PPS
beginning October 1, 2019, consistent
with our historical practice of modeling
IRF PPS adoption of the labor market
area delineations after IPPS adoption of
these delineations.
5. Wage Adjustment
The FY 2020 wage index tables
(which, as discussed in section VI.F
above, we base on the FY 2020 prereclassified, pre-floor FY 2020 IPPS
wage index) are available on the CMS
website at https://www.cms.gov/
Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/IRFRules-and-Related-Files.html. Table A is
for urban areas, and Table B is for rural
areas.
To calculate the wage-adjusted facility
payment for the payment rates set forth
in this final rule, we would multiply the
unadjusted federal payment rate for
IRFs by the FY 2020 labor-related share
based on the 2016-based IRF market
basket (72.7 percent) to determine the
labor-related portion of the standard
payment amount. A full discussion of
the calculation of the labor-related share
is located in section VI.E of this final
rule. We would then multiply the laborrelated portion by the applicable IRF
wage index from the tables in the
addendum to this final rule. These
tables are available on the CMS website
at https://www.cms.gov/Medicare/
Medicare-Fee-for-Service-Payment/
InpatientRehabFacPPS/IRF-Rules-andRelated-Files.html. Adjustments or
updates to the IRF wage index made
under section 1886(j)(6) of the Act must
be made in a budget-neutral manner. We
proposed to calculate a budget-neutral
wage adjustment factor as established in
the FY 2004 IRF PPS final rule (68 FR
45689), codified at § 412.624(e)(1), as
described in the steps below. We
proposed to use the listed steps to
ensure that the FY 2020 IRF standard
payment conversion factor reflects the
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updates to the IRF wage index (based on
the FY 2020 IPPS wage index) and the
labor-related share in a budget-neutral
manner:
Step 1. Determine the total amount of
the estimated FY 2019 IRF PPS
payments, using the FY 2019 standard
payment conversion factor and the
labor-related share and the wage
indexes from FY 2019 (as published in
the FY 2019 IRF PPS final rule (83 FR
38514)).
Step 2. Calculate the total amount of
estimated IRF PPS payments using the
FY 2020 standard payment conversion
factor and the FY 2020 labor-related
share and CBSA urban and rural wage
indexes.
Step 3. Divide the amount calculated
in step 1 by the amount calculated in
step 2. The resulting quotient is the FY
2020 budget-neutral wage adjustment
factor of 1.0076.
Step 4. Apply the FY 2020 budgetneutral wage adjustment factor from
step 3 to the FY 2020 IRF PPS standard
payment conversion factor after the
application of the increase factor to
determine the FY 2020 standard
payment conversion factor.
We note that we have updated our
data between the FY 2020 IRF PPS
proposed and final rules to ensure that
we use the most recent available data in
calculating IRF PPS payments. This
updated data includes a more complete
set of claims for FY 2018 and updated
wage index data. Based on our analysis
using this updated data, we now
estimate a budget-neutral wage
adjustment factor of 1.0031 for FY 2020.
We discuss the calculation of the
standard payment conversion factor for
FY 2020 in section VI.H. of this final
rule.
We invited public comments on this
proposal. However, we did not receive
any comments on the proposed
methodology for calculating the budgetneutral wage adjustment factor.
As we did not receive any comments
on the proposed methodology for
calculating the budget-neutral wage
adjustment factor, we are finalizing this
policy as proposed for FY 2020.
G. Wage Index Comment Solicitation
Historically, we have calculated the
IRF wage index values using unadjusted
wage index values from another
provider setting. Stakeholders have
frequently commented on certain
aspects of the IRF wage index values
and their impact on payments.
Therefore, we solicited public
comments in the FY 2020 IRF PPS
proposed rule (84 FR 17280) on
concerns stakeholders may have
regarding the wage index used to adjust
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IRF payments and suggestions for
possible updates and improvements to
the geographic adjustment of IRF
payments.
We appreciate the commenters’
responses to this solicitation and will
take them into consideration for
possible future policy development.
H. Description of the IRF Standard
Payment Conversion Factor and
Payment Rates for FY 2020
To calculate the standard payment
conversion factor for FY 2020, as
illustrated in Table 13, we begin by
applying the increase factor for FY 2020,
as adjusted in accordance with sections
1886(j)(3)(C) of the Act, to the standard
payment conversion factor for FY 2019
($16,021). Applying the 2.5 percent
increase factor for FY 2020 to the
standard payment conversion factor for
FY 2019 of $16,021 yields a standard
payment amount of $16,422. Then, we
apply the budget neutrality factor for the
FY 2020 wage index and labor-related
share of 1.0031, which results in a
standard payment amount of $16,472.
We next apply the budget neutrality
factor for the revised CMGs and CMG
relative weights of 1.0010, which results
in the standard payment conversion
factor of $16,489 for FY 2020.
We received one comment on the
proposed FY 2020 standard payment
conversion factor, which is summarized
below.
Comment: One commenter stated that
the proposed rate update fails to cover
the cost of medical inflation or payment
reductions due to sequestration. As a
result, this commenter expressed
concern that their hospitals’ financial
viability and their ability to care for
their patients will be threatened.
Response: We appreciate this
commenter’s concerns. However, we
note that the IRF PPS payment rates are
updated annually by an increase factor
that reflects changes over time in the
prices of an appropriate mix of goods
and services included in the covered
IRF services, as required by section
1886(j)(3)(C) of the Act.
After careful consideration of the
comment we received, we are finalizing
the IRF standard payment conversion
factor of $16,489 for FY 2020.
After the application of the CMG
relative weights described in section IV.
of this final rule to the FY 2020 standard
payment conversion factor ($16,489),
the resulting unadjusted IRF prospective
payment rates for FY 2020 are shown in
Table 14.
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TABLE 14: FY 2020 Payment Rates
0101
0102
0103
0104
0105
0106
0201
0202
0203
0204
0205
0301
0302
0303
0304
0305
0401
0402
0403
0404
0405
0406
0407
0501
0502
0503
0504
0505
0601
0602
0603
0604
0701
0702
0703
0704
0801
0802
0803
0804
0805
0901
0902
0903
0904
1001
1002
1003
1004
1101
1102
1103
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Payment Rate
Tier 1
$ 17,067.76
$ 21,683.04
$ 27,685.03
$ 36,206.55
$ 40,068.27
$ 46,762.80
$ 19,115.70
$ 23,688.10
$ 28,834.31
$ 35,185.88
$ 43,911.86
$ 20,248.49
$ 25,727.79
$ 31,022.40
$ 34,786.84
$ 37,741.67
$ 22,593.23
$ 29,658.76
$ 35,861.93
$ 52,672.46
$ 44,859.97
$ 54,852.31
$ 67,939.63
$ 20,934.43
$ 26,149.91
$ 30,130.35
$ 36,620.42
$ 46,766.10
$ 22,146.38
$ 27,439.34
$ 32,328.33
$ 37,157.96
$ 20,629.39
$ 25,821.77
$ 31,263.14
$ 35,357.36
$ 17,496.48
$ 20,621.14
$ 23,130.77
$ 26,601.70
$ 31,662.18
$ 19,895.63
$ 25,165.51
$ 29,576.32
$ 33,568.31
$ 21,194.96
$ 26,413.73
$ 30,476.62
$ 35,418.37
$ 23,417.68
$ 29,075.05
$ 33,345.70
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Payment Rate
Tier 2
$ 14,782.39
$ 18,779.32
$ 23,976.65
$ 31,357.13
$ 34,702.75
$ 40,500.28
$ 15,664.55
$ 19,410.85
$ 23,628.74
$ 28,834.31
$ 35,983.94
$ 16,480.76
$ 20,941.03
$ 25,249.61
$ 28,313.26
$ 30,719.01
$ 19,371.28
$ 25,430.98
$ 30,750.34
$ 45,163.37
$ 38,465.54
$ 47,031.57
$ 58,255.64
$ 17,100.74
$ 21,359.85
$ 24,611.48
$ 29,912.69
$ 38,198.42
$ 17,216.16
$ 21,331.82
$ 25,132.53
$ 28,887.08
$ 16,647.29
$ 20,835.50
$ 25,226.52
$ 28,530.92
$ 14,553.19
$ 17,151.86
$ 19,241.01
$ 22,126.59
$ 26,337.88
$ 15,897.04
$ 20,109.98
$ 23,633.68
$ 26,824.31
$ 18,058.75
$ 22,504.19
$ 25,966.88
$ 30,176.52
$ 19,460.32
$ 24,161.33
$ 27,711.41
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Payment Rate
Tier3
$ 13,685.87
$ 17,387.65
$ 22,200.79
$ 29,033.83
$ 32,132.11
$ 37,499.28
$ 14,127.78
$ 17,508.02
$ 21,310.38
$ 26,006.45
$ 32,455.30
$ 15,199.56
$ 19,311.92
$ 23,287.41
$ 26,111.98
$ 28,331.40
$ 17,730.62
$ 23,277.52
$ 28,146.72
$ 41,337.92
$ 35,207.31
$ 43,049.48
$ 53,320.48
$ 15,852.52
$ 19,801.64
$ 22,815.83
$ 27,729.55
$ 35,413.43
$ 16,073.48
$ 19,915.41
$ 23,463.85
$ 26,969.41
$ 15,901.99
$ 19,905.52
$ 24,098.67
$ 27,254.67
$ 13,178.01
$ 15,530.99
$ 17,422.28
$ 20,035.78
$ 23,848.04
$ 14,757.66
$ 18,667.20
$ 21,938.61
$ 24,900.04
$ 16,348.84
$ 20,375.46
$ 23,510.02
$ 27,322.27
$ 17,615.20
$ 21,871.01
$ 25,083.07
Sfmt 4725
Payment Rate No
Comorbidity
$
13,036.20
$
16,563.20
$
21,147.14
$
27,655.35
$
30,606.88
$
35,720.12
$
13,178.01
$
16,329.06
$
19,877.49
$
24,255.32
$
30,270.51
$
14,210.22
$
18,055.46
$
21,770.43
$
24,411.96
$
26,486.28
$
16,258.15
$
21,343.36
$
25,808.58
$
37,904.91
$
32,282.16
$
39,473.02
$
48,891.53
$
14,507.02
$
18,121.41
$
20,880.02
$
25,376.57
$
32,407.48
$
14,615.85
$
18,109.87
$
21,336.77
$
24,524.09
$
14,462.50
$
18,101.62
$
21,915.53
$
24,786.26
$
12,257.92
$
14,447.66
$
16,207.04
$
18,639.17
$
22,184.30
$
13,591.88
$
17,193.08
$
20,205.62
$
22,932.90
$
15,021.48
$
18,719.96
$
21,600.59
$
25,102.85
$
14,746.11
$
18,307.74
$
20,997.09
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CMG
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1201
1202
1203
1204
1301
1302
1303
1304
1305
1401
1402
1403
1404
1501
1502
1503
1504
1601
1602
1603
1604
1701
1702
1703
1704
1705
1801
1802
1803
1804
1805
1806
1901
1902
1903
1904
2001
2002
2003
2004
2005
2101
2102
5001
5101
5102
5103
5104
Payment Rate
Tier 1
$ 20,410.08
$ 25,975.12
$ 29,676.90
$ 31,573.14
$ 19,237.72
$ 23,528.15
$ 27,727.90
$ 31,388.46
$ 30,946.56
$ 18,838.68
$ 23,704.59
$ 28,601.82
$ 33,309.43
$ 20,522.21
$ 24,868.71
$ 29,286.11
$ 33,622.72
$ 18,652.36
$ 23,023.59
$ 26,768.24
$ 31,180.70
$ 23,246.19
$ 28,514.43
$ 33,129.70
$ 36,656.70
$ 39,804.45
$ 19,437.23
$ 25,158.92
$ 31,149.37
$ 36,091.12
$ 42,475.66
$ 56,723.81
$ 20,276.52
$ 28,524.32
$ 43,316.60
$ 61,461.10
$ 19,996.21
$ 24,647.76
$ 28,880.48
$ 32,448.70
$ 34,659.88
$ 25,430.98
$ 36,335.16
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Payment Rate
Tier2
$ 15,717.31
$ 20,002.81
$ 22,853.75
$ 24,314.68
$ 16,210.34
$ 19,824.72
$ 23,363.26
$ 26,448.36
$ 26,075.70
$ 15,339.72
$ 19,302.02
$ 23,290.71
$ 27,124.41
$ 17,498.13
$ 21,203.21
$ 24,969.29
$ 28,666.13
$ 14,826.91
$ 18,301.14
$ 21,277.41
$ 24,784.62
$ 18,162.63
$ 22,279.94
$ 25,886.08
$ 28,639.74
$ 31,099.90
$ 16,447.78
$ 21,288.95
$ 26,356.02
$ 30,539.28
$ 35,941.07
$ 47,997.83
$ 15,892.10
$ 22,355.79
$ 33,949.20
$ 48,169.32
$ 16,179.01
$ 19,941.80
$ 23,366.56
$ 26,253.79
$ 28,042.84
$ 20,978.95
$ 29,975.35
Payment Rate
Tier3
$ 15,262.22
$ 19,424.04
$ 22,192.55
$ 23,608.95
$ 15,359.50
$ 18,784.27
$ 22,136.48
$ 25,059.98
$ 24,707.12
$ 14,140.97
$ 17,794.93
$ 21,470.33
$ 25,005.57
$ 16,108.10
$ 19,519.68
$ 22,985.67
$ 26,390.64
$ 14,002.46
$ 17,283.77
$ 20,095.14
$ 23,407.78
$ 17,000.16
$ 20,853.64
$ 24,228.94
$ 26,807.82
$ 29,109.68
$ 14,688.40
$ 19,011.82
$ 23,538.05
$ 27,274.45
$ 32,099.14
$ 42,864.80
$ 15,265.52
$ 21,475.27
$ 32,611.94
$ 46,273.08
$ 15,016.53
$ 18,508.90
$ 21,686.33
$ 24,367.44
$ 26,027.89
$ 19,471.86
$ 27,821.89
H. Example of the Methodology for
Adjusting the Prospective Payment
Rates
Table 15 illustrates the methodology
for adjusting the prospective payments
(as described in section VI. of this final
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Payment Rate No
Comorbidity
$
14,180.54
$
18,045.56
$
20,619.49
$
21,935.32
$
14,145.91
$
17,300.26
$
20,388.65
$
23,079.65
$
22,754.82
$
12,708.07
$
15,991.03
$
19,295.43
$
22,471.21
$
15,301.79
$
18,541.88
$
21,834.73
$
25,068.23
$
12,920.78
$
15,948.16
$
18,541.88
$
21,597.29
$
15,506.26
$
19,021.71
$
22,100.21
$
24,451.54
$
26,552.24
$
13,440.18
$
17,397.54
$
21,539.58
$
24,957.75
$
29,371.86
$
39,224.03
$
14,882.97
$
20,936.08
$
31,794.09
$
45,112.26
$
13,633.11
$
16,805.59
$
19,691.16
$
22,123.29
$
23,632.03
$
17,501.42
$
25,005.57
$
2,994.40
$
9,403.68
$
29,579.62
$
11,113.59
$
36,203.25
rule). The following examples are based
on two hypothetical Medicare
beneficiaries, both classified into CMG
0104 (without comorbidities). The
unadjusted prospective payment rate for
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CMG 0104 (without comorbidities)
appears in Table 14.
Example: One beneficiary is in
Facility A, an IRF located in rural
Spencer County, Indiana, and another
beneficiary is in Facility B, an IRF
located in urban Harrison County,
Indiana. Facility A, a rural non-teaching
hospital has a Disproportionate Share
Hospital (DSH) percentage of 5 percent
(which would result in a LIP adjustment
of 1.0156), a wage index of 0.8319, and
a rural adjustment of 14.9 percent.
Facility B, an urban teaching hospital,
has a DSH percentage of 15 percent
(which would result in a LIP adjustment
of 1.0454 percent), a wage index of
0.8844, and a teaching status adjustment
of 0.0784.
To calculate each IRF’s labor and nonlabor portion of the prospective
payment, we begin by taking the
unadjusted prospective payment rate for
CMG 0104 (without comorbidities) from
Table 14. Then, we multiply the laborrelated share for FY 2020 (72.7 percent)
described in section VI.E. of this final
rule by the unadjusted prospective
payment rate. To determine the nonlabor portion of the prospective
payment rate, we subtract the labor
portion of the federal payment from the
unadjusted prospective payment.
To compute the wage-adjusted
prospective payment, we multiply the
labor portion of the federal payment by
the appropriate wage index located in
Tables A and B. These tables are
available on the CMS website at https://
www.cms.gov/Medicare/Medicare-Feefor-Service-Payment/InpatientRehab
FacPPS/IRF-Rules-and-RelatedFiles.html.
The resulting figure is the wageadjusted labor amount. Next, we
compute the wage-adjusted federal
payment by adding the wage-adjusted
labor amount to the non-labor portion of
the federal payment.
Adjusting the wage-adjusted federal
payment by the facility-level
adjustments involves several steps.
First, we take the wage-adjusted
prospective payment and multiply it by
the appropriate rural and LIP
adjustments (if applicable). Second, to
determine the appropriate amount of
additional payment for the teaching
status adjustment (if applicable), we
multiply the teaching status adjustment
(0.0784, in this example) by the wageadjusted and rural-adjusted amount (if
applicable). Finally, we add the
additional teaching status payments (if
applicable) to the wage, rural, and LIPadjusted prospective payment rates.
Table 15 illustrates the components of
the adjusted payment calculation.
Thus, the adjusted payment for
Facility A would be $28,327.82, and the
adjusted payment for Facility B would
be $28,467.16.
the adjusted outlier threshold. We
calculate the adjusted outlier threshold
by adding the IRF PPS payment for the
case (that is, the CMG payment adjusted
by all of the relevant facility-level
adjustments) and the adjusted threshold
amount (also adjusted by all of the
relevant facility-level adjustments).
Then, we calculate the estimated cost of
a case by multiplying the IRF’s overall
CCR by the Medicare allowable covered
charge. If the estimated cost of the case
is higher than the adjusted outlier
threshold, we make an outlier payment
for the case equal to 80 percent of the
difference between the estimated cost of
the case and the outlier threshold.
In the FY 2002 IRF PPS final rule (66
FR 41362 through 41363), we discussed
our rationale for setting the outlier
threshold amount for the IRF PPS so
that estimated outlier payments would
equal 3 percent of total estimated
payments. For the 2002 IRF PPS final
rule, we analyzed various outlier
policies using 3, 4, and 5 percent of the
total estimated payments, and we
concluded that an outlier policy set at
3 percent of total estimated payments
would optimize the extent to which we
could reduce the financial risk to IRFs
VII. Update to Payments for High-Cost
Outliers Under the IRF PPS for FY 2020
A. Update to the Outlier Threshold
Amount for FY 2020
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Section 1886(j)(4) of the Act provides
the Secretary with the authority to make
payments in addition to the basic IRF
prospective payments for cases
incurring extraordinarily high costs. A
case qualifies for an outlier payment if
the estimated cost of the case exceeds
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of caring for high-cost patients, while
still providing for adequate payments
for all other (non-high cost outlier)
cases.
Subsequently, we updated the IRF
outlier threshold amount in the FYs
2006 through 2019 IRF PPS final rules
and the FY 2011 and FY 2013 notices
(70 FR 47880, 71 FR 48354, 72 FR
44284, 73 FR 46370, 74 FR 39762, 75 FR
42836, 76 FR 47836, 76 FR 59256, 77 FR
44618, 78 FR 47860, 79 FR 45872, 80 FR
47036, 81 FR 52056, 82 FR 36238, and
83 FR 38514, respectively) to maintain
estimated outlier payments at 3 percent
of total estimated payments. We also
stated in the FY 2009 final rule (73 FR
46370 at 46385) that we would continue
to analyze the estimated outlier
payments for subsequent years and
adjust the outlier threshold amount as
appropriate to maintain the 3 percent
target.
To update the IRF outlier threshold
amount for FY 2020, we proposed to use
FY 2018 claims data and the same
methodology that we used to set the
initial outlier threshold amount in the
FY 2002 IRF PPS final rule (66 FR 41316
and 41362 through 41363), which is also
the same methodology that we used to
update the outlier threshold amounts for
FYs 2006 through 2019. The outlier
threshold is calculated by simulating
aggregate payments and using an
iterative process to determine a
threshold that results in outlier
payments being equal to 3 percent of
total payments under the simulation. To
determine the outlier threshold for FY
2020, we estimate the amount of FY
2020 IRF PPS aggregate and outlier
payments using the most recent claims
available (FY 2018) and the FY 2020
standard payment conversion factor,
labor-related share, and wage indexes,
incorporating any applicable budgetneutrality adjustment factors. The
outlier threshold is adjusted either up or
down in this simulation until the
estimated outlier payments equal 3
percent of the estimated aggregate
payments. Based on an analysis of the
preliminary data used for the proposed
rule, we estimated that IRF outlier
payments as a percentage of total
estimated payments would be
approximately 3.2 percent in FY 2019.
Therefore, we proposed to update the
outlier threshold amount from $9,402
for FY 2019 to $9,935 for FY 2020 to
maintain estimated outlier payments at
approximately 3 percent of total
estimated aggregate IRF payments for
FY 2020.
We note that, as we typically do, we
updated our data between the FY 2020
IRF PPS proposed and final rules to
ensure that we use the most recent
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available data in calculating IRF PPS
payments. This updated data includes a
more complete set of claims for FY
2018. Based on our analysis using this
updated data, we now estimate that IRF
outlier payments as a percentage of total
estimated payments are approximately
3.0 percent in FY 2019. Although our
analysis shows that we achieved our
goal to have estimated outlier payments
equal 3.0 percent of total estimated
aggregate IRF payments for FY 2019, we
still need to adjust the IRF outlier
threshold to reflect changes in estimated
costs and payments for IRFs in FY 2020.
That is, as discussed in section VI. of
this final rule, we are finalizing our
proposal to increase IRF PPS payment
rates by 2.5 percent, in accordance with
section 1886(j)(3)(C) of the Act to
account for changes over time in the
prices of an appropriate mix of goods
and services included in the covered
IRF services. Similarly, we estimate
costs for IRFs in FY 2020 are expected
to increase to account for changes over
time in the prices of goods and services
included in the covered IRF services.
Therefore, we will update the outlier
threshold amount from $9,402 for FY
2019 to $9,300 for FY 2020 to account
for the increases in IRF PPS payments
and estimated costs and to maintain
estimated outlier payments at
approximately 3 percent of total
estimated aggregate IRF payments for
FY 2020.
We received three comments on the
proposed update to the FY 2020 outlier
threshold, which are summarized
below.
Comment: Commenters suggested that
historical outlier reconciliation dollars
should be included in the calculation of
the fixed loss threshold under the IRF
PPS.
Response: As we did not propose a
change to the methodology used to
establish an outlier threshold for IRF
PPS payments, these comments are
outside the scope of this rule. However,
we will continue to monitor our IRF
outlier policies to ensure that they
continue to compensate IRFs
appropriately for treating unusually
high-cost patients and do not limit
access to care for patients who are likely
to require unusually high-cost care.
Comment: A few commenters
suggested that CMS consider
implementing a cap on the amount of
outlier payments an individual IRF can
receive under the IRF PPS. One
commenter was supportive of
maintaining estimated payments for
outlier payments at approximately 3
percent while other commenters
expressed concern with maintaining the
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3 percent target and suggested reducing
the outlier pool below 3 percent.
Response: As we did not propose to
implement a cap on the amount of
outlier payments an individual IRF can
receive under the IRF PPS, these
comments are outside the scope of this
rule. However, we note that any future
consideration given to imposing a limit
on outlier payments would have to
carefully analyze and take into
consideration the effect on access to IRF
care for certain high-cost populations.
As most recently discussed in the FY
2019 IRF PPS final rule (83 FR 38532),
we analyzed various outlier policies
using 3, 4, and 5 percent of the total
estimated payments for the FY 2002 IRF
PPS final rule, and we concluded that
an outlier policy set at 3 percent of total
estimated payments would optimize the
extent to which we could reduce the
financial risk to IRFs of caring for highcost patients, while still providing for
adequate payments for all other (nonhigh cost outlier) cases. We continue to
believe that the outlier policy of 3
percent of total estimated aggregate
payments accomplishes this objective.
We refer readers to the FY 2002 IRF PPS
final rule (66 FR 41316, 41362 through
41363) for more information regarding
the rationale for setting the outlier
threshold amount for the IRF PPS so
that estimated outlier payments would
equal 3 percent of total estimated
payments.
Comment: One commenter requested
that CMS update the outlier threshold
amount in the final rule using the latest
available data.
Response: We agree that we should
use the most recent data available to
calculate the outlier threshold.
Therefore, as previously stated, we
updated the data used to calculate the
outlier threshold between the FY 2020
IRF PPS proposed and final rules.
Having carefully considered the
public comments received and also
taking into account the most recent
available data, we are finalizing the
outlier threshold amount of $9,300 to
maintain estimated outlier payments at
approximately 3 percent of total
estimated aggregate IRF payments for
FY 2020.
B. Update to the IRF Cost-to-Charge
Ratio Ceiling and Urban/Rural Averages
for FY 2020
Cost-to-charge ratios are used to
adjust charges from Medicare claims to
costs and are computed annually from
facility-specific data obtained from
Medicare cost reports. IRF specific costto-charge ratios are used in the
development of the CMG relative
weights and the calculation of outlier
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payments under the IRF prospective
payment system. In accordance with the
methodology stated in the FY 2004 IRF
PPS final rule (68 FR 45674, 45692
through 45694), we proposed to apply a
ceiling to IRFs’ CCRs. Using the
methodology described in that final
rule, we proposed to update the national
urban and rural CCRs for IRFs, as well
as the national CCR ceiling for FY 2020,
based on analysis of the most recent
data that is available. We apply the
national urban and rural CCRs in the
following situations:
• New IRFs that have not yet
submitted their first Medicare cost
report.
• IRFs whose overall CCR is in excess
of the national CCR ceiling for FY 2020,
as discussed below in this section.
• Other IRFs for which accurate data
to calculate an overall CCR are not
available.
Specifically, for FY 2020, we
proposed to estimate a national average
CCR of 0.500 for rural IRFs, which we
calculated by taking an average of the
CCRs for all rural IRFs using their most
recently submitted cost report data.
Similarly, we proposed to estimate a
national average CCR of 0.406 for urban
IRFs, which we calculated by taking an
average of the CCRs for all urban IRFs
using their most recently submitted cost
report data. We apply weights to both of
these averages using the IRFs’ estimated
costs, meaning that the CCRs of IRFs
with higher total costs factor more
heavily into the averages than the CCRs
of IRFs with lower total costs. For this
final rule, we have used the most recent
available cost report data (FY 2017).
This includes all IRFs whose cost
reporting periods begin on or after
October 1, 2016, and before October 1,
2017. If, for any IRF, the FY 2017 cost
report was missing or had an ‘‘as
submitted’’ status, we used data from a
previous fiscal year’s (that is, FY 2004
through FY 2016) settled cost report for
that IRF. We do not use cost report data
from before FY 2004 for any IRF because
changes in IRF utilization since FY 2004
resulting from the 60 percent rule and
IRF medical review activities suggest
that these older data do not adequately
reflect the current cost of care. Using
updated FY 2017 cost report data for
this final rule, we estimate a national
average CCR of 0.500 for rural IRFs, and
a national average CCR of 0.405 for
urban IRFs.
In accordance with past practice, we
proposed to set the national CCR ceiling
at 3 standard deviations above the mean
CCR. Using this method, we proposed a
national CCR ceiling of 1.31 for FY
2020. This means that, if an individual
IRF’s CCR were to exceed this ceiling of
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1.31 for FY 2020, we would replace the
IRF’s CCR with the appropriate
proposed national average CCR (either
rural or urban, depending on the
geographic location of the IRF). We
calculated the proposed national CCR
ceiling by:
Step 1. Taking the national average
CCR (weighted by each IRF’s total costs,
as previously discussed) of all IRFs for
which we have sufficient cost report
data (both rural and urban IRFs
combined).
Step 2. Estimating the standard
deviation of the national average CCR
computed in step 1.
Step 3. Multiplying the standard
deviation of the national average CCR
computed in step 2 by a factor of 3 to
compute a statistically significant
reliable ceiling.
Step 4. Adding the result from step 3
to the national average CCR of all IRFs
for which we have sufficient cost report
data, from step 1.
Using the updated FY 2017 cost
report data for this final rule, we
estimate a national average CCR ceiling
of 1.31, using the same methodology.
We did not receive comments on the
proposed update to the IRF CCR ceiling
and the urban/rural averages for FY
2020.
As we did not receive any comments
on the proposed update to the IRF CCR
ceiling and the urban/rural averages for
FY 2020, we are finalizing the national
average urban CCR at 0.405, the national
average rural CCR at 0.500, and the
national average CCR ceiling at 1.31 for
FY 2020.
VIII. Amendments to § 412.622 To
Clarify the Definition of a
Rehabilitation Physician
Under § 412.622(a)(3)(iv), a
rehabilitation physician is defined as ‘‘a
licensed physician with specialized
training and experience in inpatient
rehabilitation.’’ The term rehabilitation
physician is used in several other places
in § 412.622, with corresponding
references to § 412.622(a)(3)(iv). The
definition at § 412.622(a)(3)(iv) does not
specify the level or type of training and
experience required for a licensed
physician to be designated as a
rehabilitation physician because we
believe that the IRFs are in the best
position to make this determination for
purposes of § 412.622.
Therefore, we proposed to amend the
definition of a rehabilitation physician
to clarify that the determination as to
whether a physician qualifies as a
rehabilitation physician (that is, a
licensed physician with specialized
training and experience in inpatient
rehabilitation) is made by the IRF (84 FR
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17284 through 17285). For clarity, we
also proposed to remove this definition
from § 412.622(a)(3)(iv) and move it to
a new paragraph (§ 412.622(c)). We also
proposed to make corresponding
technical corrections elsewhere in
§ 412.622(a)(3)(iv), (a)(4)(i)(A),
(a)(4)(iii)(A), and (a)(5)(i) to remove the
references to § 412.622(a)(3)(iv) in those
paragraphs, so as to reflect the new
location of the definition.
We received 1,163 comments on the
proposal to clarify the definition of a
rehabilitation physician, to move the
definition from § 412.622(a)(3)(iv) to
§ 412.622(c), and to make corresponding
technical corrections elsewhere in
§ 412.622 to remove references to the
current location of the definition in
§ 412.622(a)(3)(iv). The majority of these
comments consisted of form letters, in
which we received multiple copies of
two types of identically-worded letters
that had been signed and submitted by
different individuals. The comments we
received on this are summarized below.
Comment: Many of the commenters
noted appreciation and support for the
proposal to amend the definition of a
rehabilitation physician to clarify that
the determination as to whether a
physician qualifies as a rehabilitation
physician (that is, a licensed physician
with specialized training and
experience in inpatient rehabilitation) is
made by the IRF. One commenter stated
that while board-certified physiatrists
play a crucial caregiver and leadership
role in rehabilitation hospitals, they are
not alone in doing so. Physicians
representing other specialties can and
do also display the leadership and
caregiving skills and experience that
clearly qualify them as a rehabilitation
physician. One commenter indicated
that CMS’ proposal is consistent with
CMS’ previously stated position from
2010. Some commenters also stated that
clarifying the regulation would reduce
the number of claims denials by
promoting a shared understanding of
the requirements between IRFs and
Medicare contractors.
Response: We appreciate the
commenters’ support and agree that this
clarification in our regulations supports
our longstanding position that the
responsibility is, and always has been,
on the IRF to ensure that the
rehabilitation physician(s) who are
making the admission decisions and
treating the patients have the necessary
training and experience.
Comment: Many commenters stated
that they do not support CMS’ proposal
and suggested that CMS not finalize the
proposed amendments to § 412.622.
These commenters requested that CMS
delay any changes to current regulations
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until CMS and stakeholders can work
together to develop a consensus
approach for protecting the quality and
integrity of IRF care. These commenters
stated that they believe that allowing the
IRF to determine whether an individual
physician meets the regulatory
standards for a rehabilitation physician
could increase the risks that some IRFs
will hire or contract with unqualified or
underqualified physicians, reduce the
quality of care that patients receive in
IRFs, and reduce the value of
physiatrists. These commenters also
stated that reducing the value of
physiatrists could also deter students
from wanting to pursue this specialty in
the future. Some commenters also
indicated that CMS’ proposal, if
finalized, would undermine CMS’
ability to engage in appropriate program
integrity oversight by not reviewing an
IRF’s decision to hire a particular
physician to fill a rehabilitation
physician role.
Response: While we appreciate and
share the commenters’ desire to ensure
that Medicare beneficiaries in IRFs
receive the highest-quality care from
trained and qualified physicians, we do
not believe that merely clarifying our
existing policy would reduce quality of
care. The regulation will continue to
require a rehabilitation physician to be
a licensed physician with specialized
training and experience in inpatient
rehabilitation. We are not lowering
these requirements. However, we
continue to believe that we need to
clarify our existing policy that the IRF
makes the determination as to whether
a given physician qualifies as a
rehabilitation physician in order to
eliminate any unnecessary uncertainty
on this issue. Over the past year, we
have received questions regarding how
this provision can be enforced, and we
believe that this clarification will
promote a shared understanding of how
we intend the enforcement to occur. We
expect that IRFs will continue to ensure
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that the rehabilitation physicians
treating patients in their facilities have
the necessary training and experience in
inpatient rehabilitation. To this end, we
will continue to work with stakeholders
to refine Medicare’s IRF payment
policies in the future so that they
support IRFs in providing the highest
quality care to beneficiaries.
After careful consideration of the
comments we received, we are
finalizing our proposal to amend the
definition of a rehabilitation physician
to clarify that the determination as to
whether a physician qualifies as a
rehabilitation physician (that is, a
licensed physician with specialized
training and experience in inpatient
rehabilitation) is made by the IRF.
However, based on the stakeholder
feedback, we will continue to assess
whether future refinements to this
policy may be needed.
For clarity, we are also removing this
definition from § 412.622(a)(3)(iv) and
moving it to a new paragraph
(§ 412.622(c)). We are also making
corresponding technical corrections
elsewhere in § 412.622(a)(3)(iv),
(a)(4)(i)(A), (a)(4)(iii)(A), and (a)(5)(i) to
remove the references to
§ 412.622(a)(3)(iv) in those paragraphs,
so as to reflect the new location of the
definition.
IX. Updates to the IRF Quality
Reporting Program (QRP)
A. Background
The IRF QRP is authorized by section
1886(j)(7) of the Act, and it applies to
freestanding IRFs, as well as inpatient
rehabilitation units of hospitals or
critical access hospitals (CAHs) paid by
Medicare under the IRF PPS. Under the
IRF QRP, the Secretary must reduce the
annual increase factor for discharges
occurring during such fiscal year by 2
percentage points for any IRF that does
not submit data in accordance with the
requirements established by the
Secretary. For more information on the
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background and statutory authority for
the IRF QRP, we refer readers to the FY
2012 IRF PPS final rule (76 FR 47873
through 47874), the CY 2013 Hospital
Outpatient Prospective Payment
System/Ambulatory Surgical Center
(OPPS/ASC) Payment Systems and
Quality Reporting Programs final rule
(77 FR 68500 through 68503), the FY
2014 IRF PPS final rule (78 FR 47902),
the FY 2015 IRF PPS final rule (79 FR
45908), the FY 2016 IRF PPS final rule
(80 FR 47080 through 47083), the FY
2017 IRF PPS final rule (81 FR 52080
through 52081), the FY 2018 IRF PPS
final rule (82 FR 36269 through 36270),
and the FY 2019 IRF PPS final rule (83
FR 38555 through 38556).
While we did not solicit comments on
previously finalized IRF QRP policies,
we received comments, which are
summarized below.
Comment: A few commenters stated
that the IRF QRP compliance threshold
of 95 percent for assessment-based items
is too high given the number of data
elements that have been added to the
IRF–PAI, and requested that CMS lower
it to 80 percent in alignment with other
programs.
Response: We did not propose any
changes to the compliance threshold,
which has been codified at § 412.634(f).
While these comments were out of
scope for this rule, we will take these
comments under consideration.
B. General Considerations Used for the
Selection of Measures for the IRF QRP
For a detailed discussion of the
considerations we use for the selection
of IRF QRP quality, resource use, and
other measures, we refer readers to the
FY 2016 IRF PPS final rule (80 FR 47083
through 47084).
C. Quality Measures Currently Adopted
for the FY 2021 IRF QRP
The IRF QRP currently has 15
measures for the FY 2020 program year,
which are set out in Table 16.
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While we did not solicit comments on
currently adopted measures (with the
exception of the Discharge to
Community Measure discussed in
section IX.D.3 of this rule and the
policies regarding public display of the
Drug Regimen Review Conducted With
Follow-Up for Identified Issues—PAC
IRF QRP in section IX.I of this rule), we
received several comments.
Comment: A few commenters had
suggestions for removing measures they
believe were ‘‘topped out’’ according to
the Hospital Inpatient Quality Reporting
(IQR) Program definition (83 FR 20408)
and did not demonstrate variation
across facilities, including Application
of Percent of Residents Experiencing
One or More Falls with Major Injury
(Long Stay) (NQF #0674) and
Application of Percent of Long-Term
Care Hospital Patients with an
Admission and Discharge Functional
Assessment and a Care Plan That
Addresses Function (NQF #2631), and
Changes in Skin Integrity Post-Acute
Care: Pressure Ulcer/Injury. One
commenter had suggestions for
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improving the training manual for the
Drug Regimen Review measure in terms
of considered clinically significant
medication issue.
Response: We did not propose any
changes to these previously finalized
measures, nor did we propose measure
removals from the IRF QRP. We wish to
clarify that the IRF QRP has not adopted
the Hospital Inpatient Quality Reporting
(IQR) definition of ‘‘topped out’’ in the
measure removal criteria finalized for
the IRF QRP at § 412.634(2). We also
note that we do not automatically
remove high performing measures, and
wish to reiterate that such measures
may be retained for other specified
reasons. For example, a particular
measure with high performance rates
may be retained if the measure
addresses a topic related to quality that
is so significant that we do not want to
risk a decline in quality that could
result if we removed the measure, or if
the measure addresses a topic that is
statutorily required. We will continue to
monitor and evaluate the data from all
IRF QRP measures.
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With regard to the commenter’s
suggestions about the Drug Regimen
Review measure, we interpret that the
commenter is requesting additional
clarification for coding. We will take
these comments into account as we
develop training materials for the IRF
QRP.
D. Adoption of Two New Quality
Measures and Updated Specifications
for a Third Quality Measure Beginning
With the FY 2022 IRF QRP
In the FY 2020 IRF PPS proposed rule
(84 FR 17286 through 17291), we
proposed to adopt two process measures
for the IRF QRP that would satisfy
section 1899B(c)(1)(E)(ii) of the Act,
which requires that the quality
measures specified by the Secretary
include measures with respect to the
quality measure domain titled
‘‘Accurately communicating the
existence of and providing for the
transfer of health information and care
preferences of an individual to the
individual, family caregiver of the
individual, and providers of services
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furnishing items and services to the
individual when the individual
transitions from a PAC provider to
another applicable setting, including a
different PAC provider, a hospital, a
critical access hospital, or the home of
the individual.’’ Given the length of this
domain title, hereafter, we will refer to
this quality measure domain as
‘‘Transfer of Health Information.’’
The two measures we proposed to
adopt are: (1) Transfer of Health
Information to the Provider—Post-Acute
Care (PAC); and (2) Transfer of Health
Information to the Patient—Post-Acute
Care (PAC). Both of these measures
support our Meaningful Measures
priority of promoting effective
communication and coordination of
care, specifically the Meaningful
Measure area of the transfer of health
information and interoperability.
In addition to the two measure
proposals, we proposed to update the
specifications for the Discharge to
Community—Post Acute Care (PAC) IRF
QRP measure to exclude baseline
nursing facility (NF) residents from the
measure.
We sought public comment on each of
these proposals. These comments are
summarized after each proposal below.
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1. Transfer of Health Information to the
Provider—Post-Acute Care (PAC)
Measure
The Transfer of Health Information to
the Provider—Post-Acute Care (PAC)
Measure that we proposed to adopt
beginning with the FY 2022 IRF QRP is
a process-based measure that assesses
whether or not a current reconciled
medication list is given to the
subsequent provider when a patient is
discharged or transferred from his or her
current PAC setting.
a. Background
In 2013, 22.3 percent of all acute
hospital discharges were discharged to
PAC settings, including 11 percent who
were discharged to home under the care
of a home health agency, and 9 percent
who were discharged to SNFs.2 The
proportion of patients being discharged
from an acute care hospital to a PAC
setting was greater among beneficiaries
enrolled in Medicare FFS. Among
Medicare FFS patients discharged from
an acute hospital, 42 percent went
directly to PAC settings. Of that 42
percent, 20 percent were discharged to
a SNF, 18 percent were discharged to a
home health agency (HHA), 3 percent
were discharged to an IRF, and 1
2 Tian, W. ‘‘An all-payer view of hospital
discharge to post-acute care,’’ May 2016. Available
at https://www.hcup-us.ahrq.gov/reports/statbriefs/
sb205-Hospital-Discharge-Postacute-Care.jsp.
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percent were discharged to an LTCH.3
Of the Medicare FFS beneficiaries with
an IRF stay in FYs 2016 and 2017, an
estimated 10 percent were discharged or
transferred to an acute care hospital, 51
percent discharged home with home
health services, 16 percent discharged
or transferred to a SNF, and one percent
discharged or transferred to another
PAC setting (for example, another IRF,
a hospice, or an LTCH).4
The transfer and/or exchange of
health information from one provider to
another can be done verbally (for
example, clinician-to-clinician
communication in-person or by
telephone), paper-based (for example,
faxed or printed copies of records), and
via electronic communication (for
example, through a health information
exchange network using an electronic
health/medical record, and/or secure
messaging). Health information, such as
3 Ibid.
4 RTI International analysis of Medicare claims
data for index stays in IRF 2016/2017. (RTI program
reference: MM150).
5 Kwan, J.L., Lo, L., Sampson, M., & Shojania,
K.G., ‘‘Medication reconciliation during transitions
of care as a patient safety strategy: a systematic
review,’’ Annals of Internal Medicine, 2013, Vol.
158(5), pp. 397–403.
6 Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ‘‘Effect
of admission medication reconciliation on adverse
drug events from admission medication changes,’’
Archives of Internal Medicine, 2011, Vol. 171(9),
pp. 860–861.
7 Bell, C.M., Brener, S.S., Gunraj, N., Huo, C.,
Bierman, A.S., Scales, D.C., & Urbach, D.R.,
‘‘Association of ICU or hospital admission with
unintentional discontinuation of medications for
chronic diseases,’’ JAMA, 2011, Vol. 306(8), pp.
840–847.
8 Basey, A.J., Krska, J., Kennedy, T.D., &
Mackridge, A.J., ‘‘Prescribing errors on admission to
hospital and their potential impact: a mixedmethods study,’’ BMJ Quality & Safety, 2014, Vol.
23(1), pp. 17–25.
9 Desai, R., Williams, C.E., Greene, S.B., Pierson,
S., & Hansen, R.A., ‘‘Medication errors during
patient transitions into nursing homes:
characteristics and association with patient harm,’’
The American Journal of Geriatric
Pharmacotherapy, 2011, Vol. 9(6), pp. 413–422.
10 Boling, P.A., ‘‘Care transitions and home health
care,’’ Clinical Geriatric Medicine, 2009, Vol.25(1),
pp. 135–48.
11 Barnsteiner, J.H., ‘‘Medication Reconciliation:
Transfer of medication information across
settings—keeping it free from error,’’ The American
Journal of Nursing, 2005, Vol. 105(3), pp. 31–36.
12 Arbaje, A.I., Kansagara, D.L., Salanitro, A.H.,
Englander, H.L., Kripalani, S., Jencks, S.F., &
Lindquist, L.A., ‘‘Regardless of age: incorporating
principles from geriatric medicine to improve care
transitions for patients with complex needs,’’
Journal of General Internal Medicine, 2014, Vol.
29(6), pp. 932–939.
13 Jencks, S.F., Williams, M.V., & Coleman, E.A.,
‘‘Rehospitalizations among patients in the Medicare
fee-for-service program,’’ New England Journal of
Medicine, 2009, Vol. 360(14), pp. 1418–1428.
14 Institute of Medicine. ‘‘Preventing medication
errors: quality chasm series,’’ Washington, DC: The
National Academies Press 2007. Available at
https://www.nap.edu/read/11623/chapter/1.
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medication information, that is
incomplete or missing increases the
likelihood of a patient or resident safety
risk, and is often lifethreatening.5 6 7 8 9 10 Poor
communication and coordination across
health care settings contributes to
patient complications, hospital
readmissions, emergency department
visits, and medication
errors.11 12 13 14 15 16 17 18 19 20
Communication has been cited as the
third most frequent root cause in
sentinel events, which The Joint
Commission defines 21 as a patient
safety event that results in death,
permanent harm, or severe temporary
harm. Failed or ineffective patient
handoffs are estimated to play a role in
20 percent of serious preventable
adverse events.22 When care transitions
are enhanced through care coordination
activities, such as expedited patient
information flow, these activities can
reduce duplication of care services and
costs of care, resolve conflicting care
plans, and prevent medical
errors.23 24 25 26 27
15 Kitson, N.A., Price, M., Lau, F.Y., & Showler,
G., ‘‘Developing a medication communication
framework across continuums of care using the
Circle of Care Modeling approach,’’ BMC Health
Services Research, 2013, Vol. 13(1), pp. 1–10.
16 Mor, V., Intrator, O., Feng, Z., & Grabowski,
D.C., ‘‘The revolving door of rehospitalization from
skilled nursing facilities,’’ Health Affairs, 2010, Vol.
29(1), pp. 57–64.
17 Institute of Medicine. ‘‘Preventing medication
errors: quality chasm series,’’ Washington, DC: The
National Academies Press 2007. Available at
https://www.nap.edu/read/11623/chapter/1.
18 Kitson, N.A., Price, M., Lau, F.Y., & Showler,
G., ‘‘Developing a medication communication
framework across continuums of care using the
Circle of Care Modeling approach,’’ BMC Health
Services Research, 2013, Vol. 13(1), pp. 1–10.
19 Forster, A.J., Murff, H.J., Peterson, J.F., Gandhi,
T.K., & Bates, D.W., ‘‘The incidence and severity of
adverse events affecting patients after discharge
from the hospital.’’ Annals of Internal Medicine,
2003,138(3), pp. 161–167.
20 King, B.J., Gilmore-Bykovskyi, A.L., Roiland,
R.A., Polnaszek, B.E., Bowers, B.J., & Kind, A.J.
‘‘The consequences of poor communication during
transitions from hospital to skilled nursing facility:
a qualitative study,’’ Journal of the American
Geriatrics Society, 2013, Vol. 61(7), 1095–1102.
21 The Joint Commission, ‘‘Sentinel Event Policy’’
available at https://www.jointcommission.org/
sentinel_event_policy_and_procedures/.
22 The Joint Commission. ‘‘Sentinel Event Data
Root Causes by Event Type 2004–2015.’’ 2016.
Available at https://www.jointcommission.org/
assets/1/23/jconline_Mar_2_2016.pdf.
23 Mor, V., Intrator, O., Feng, Z., & Grabowski,
D.C., ‘‘The revolving door of rehospitalization from
skilled nursing facilities,’’ Health Affairs, 2010, Vol.
29(1), pp. 57–64.
24 Institute of Medicine, ‘‘Preventing medication
errors: quality chasm series,’’ Washington, DC: The
National Academies Press, 2007. Available at
https://www.nap.edu/read/11623/chapter/1.
25 Starmer, A.J., Sectish, T.C., Simon, D.W.,
Keohane, C., McSweeney, M.E., Chung, E.Y., Yoon,
C.S., Lipsitz, S.R., Wassner, A.J., Harper, M.B., &
Landrigan, C.P., ‘‘Rates of medical errors and
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Care transitions across health care
settings have been characterized as
complex, costly, and potentially
hazardous, and may increase the risk for
multiple adverse outcomes.28 29 The
rising incidence of preventable adverse
events, complications, and hospital
readmissions have drawn attention to
the importance of the timely transfer of
health information and care preferences
at the time of transition. Failures of care
coordination, including poor
communication of information, were
estimated to cost the U.S. health care
system between $25 billion and $45
billion in wasteful spending in 2011.30
The communication of health
information and patient care preferences
is critical to ensuring safe and effective
transitions from one health care setting
to another.31 32
Patients in PAC settings often have
complicated medication regimens and
require efficient and effective
preventable adverse events among hospitalized
children following implementation of a resident
handoff bundle,’’ JAMA, 2013, Vol. 310(21), pp.
2262–2270.
26 Pronovost, P., M.M.E. Johns, S. Palmer, R.C.
Bono, D.B. Fridsma, A. Gettinger, J. Goldman, W.
Johnson, M. Karney, C. Samitt, R.D. Sriram, A.
Zenooz, and Y.C. Wang, Editors. Procuring
Interoperability: Achieving High-Quality,
Connected, and Person-Centered Care. Washington,
DC, 2018 National Academy of Medicine. Available
at https://nam.edu/wp-content/uploads/2018/10/
Procuring-Interoperability_web.pdf.
27 Balaban RB, Weissman JS, Samuel PA, &
Woolhandler, S., ‘‘Redefining and redesigning
hospital discharge to enhance patient care: a
randomized controlled study,’’ J Gen Intern Med,
2008, Vol. 23(8), pp. 1228–33.
28 Arbaje, A.I., Kansagara, D.L., Salanitro, A.H.,
Englander, H.L., Kripalani, S., Jencks, S.F., &
Lindquist, L.A., ‘‘Regardless of age: incorporating
principles from geriatric medicine to improve care
transitions for patients with complex needs,’’
Journal of General Internal Medicine, 2014, Vol
29(6), pp. 932–939.
29 Simmons, S., Schnelle, J., Slagle, J., Sathe,
N.A., Stevenson, D., Carlo, M., & McPheeters, M.L.,
‘‘Resident safety practices in nursing home
settings.’’ Technical Brief No. 24 (Prepared by the
Vanderbilt Evidence-based Practice Center under
Contract No. 290–2015–00003–I.) AHRQ
Publication No. 16–EHC022–EF. Rockville, MD:
Agency for Healthcare Research and Quality. May
2016. Available at https://www.ncbi.nlm.nih.gov/
books/NBK384624/.
30 Berwick, D.M. & Hackbarth, A.D. ‘‘Eliminating
Waste in US Health Care,’’ JAMA, 2012, Vol.
307(14), pp.1513–1516.
31 McDonald, K.M., Sundaram, V., Bravata, D.M.,
Lewis, R., Lin, N., Kraft, S.A. & Owens, D.K. Care
Coordination. Vol. 7 of: Shojania K.G., McDonald
K.M., Wachter R.M., Owens D.K., editors. ‘‘Closing
the quality gap: A critical analysis of quality
improvement strategies.’’ Technical Review 9
(Prepared by the Stanford University-UCSF
Evidence-based Practice Center under contract 290–
02–0017). AHRQ Publication No. 04(07)–0051–7.
Rockville, MD: Agency for Healthcare Research and
Quality. June 2006. Available at https://
www.ncbi.nlm.nih.gov/books/NBK44015/.
32 Lattimer, C., ‘‘When it comes to transitions in
patient care, effective communication can make all
the difference,’’ Generations, 2011, Vol. 35(1), pp.
69–72.
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communication and coordination of
care between settings, including
detailed transfer of medication
information.33 34 35 Individuals in PAC
settings may be vulnerable to adverse
health outcomes due to insufficient
medication information on the part of
their health care providers, and the
higher likelihood for multiple comorbid
chronic conditions, polypharmacy, and
complicated transitions between care
settings.36 37 Preventable adverse drug
events (ADEs) may occur after hospital
discharge in a variety of settings
including PAC.38 A 2014 Office of
Inspector General report found that 10
percent of Medicare patients in IRFs
experienced adverse events, with most
of those events being medication
related. Over 45 percent of the adverse
events and temporary harm events were
clearly or likely preventable.39
Medication errors and one-fifth of ADEs
occur during transitions between
settings, including admission to or
discharge from a hospital to home or a
PAC setting, or transfer between
hospitals.40 41
33 Starmer A.J, Spector N.D., Srivastava R., West,
D.C., Rosenbluth, G., Allen, A.D., Noble, E.L., &
Landrigen, C.P., ‘‘Changes in medical errors after
implementation of a handoff program,’’ N Engl J
Med, 2014, Vol. 37(1), pp. 1803–1812.
34 Kruse, C.S. Marquez, G., Nelson, D., &
Polomares, O., ‘‘The use of health information
exchange to augment patient handoff in long-term
care: a systematic review,’’ Applied Clinical
Informatics, 2018, Vol. 9(4), pp. 752–771.
35 Brody, A.A., Gibson, B., Tresner-Kirsch, D.,
Kramer, H., Thraen, I., Coarr, M.E., & Rupper, R.,
‘‘High prevalence of medication discrepancies
between home health referrals and Centers for
Medicare and Medicaid Services home health
certification and plan of care and their potential to
affect safety of vulnerable elderly adults,’’ Journal
of the American Geriatrics Society, 2016, Vol.
64(11), pp. e166–e170.
36 Chhabra, P.T., Rattinger, G.B., Dutcher, S.K.,
Hare, M.E., Parsons, K., L., & Zuckerman, I.H.,
‘‘Medication reconciliation during the transition to
and from long-term care settings: a systematic
review,’’ Res Social Adm Pharm, 2012, Vol. 8(1),
pp. 60–75.
37 Levinson, D.R., & General, I., ‘‘Adverse events
in skilled nursing facilities: national incidence
among Medicare beneficiaries.’’ Washington, DC:
U.S. Department of Health and Human Services,
Office of the Inspector General, February 2014.
Available at https://oig.hhs.gov/oei/reports/oei-0611-00370.pdf.
38 Battles J., Azam I., Grady M., & Reback K.,
‘‘Advances in patient safety and medical liability,’’
AHRQ Publication No. 17–0017–EF. Rockville, MD:
Agency for Healthcare Research and Quality,
August 2017. Available at https://www.ahrq.gov/
sites/default/files/publications/files/advancescomplete_3.pdf.
39 Health and Human Services Office of Inspector
General. Adverse Events in Rehabilitation
Hospitals: National Incidence Among Medicare
Beneficiaries. (OEI–06–14–00110). 2018. Available
at https://oig.hhs.gov/oei/reports/oei-06-1400110.asp.
40 Barnsteiner, J.H., ‘‘Medication Reconciliation:
Transfer of medication information across
settings—keeping it free from error,’’ The American
Journal of Nursing, 2005, Vol. 105(3), pp. 31–36.
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Patients in PAC settings are often
taking multiple medications.
Consequently, PAC providers regularly
are in the position of starting complex
new medication regimens with little
knowledge of the patients or their
medication history upon admission.
Furthermore, inter-facility
communication barriers delay resolving
medication discrepancies during
transitions of care.42 Medication
discrepancies are common 43 and found
to occur in 86 percent of all transitions,
increasing the likelihood of ADEs.44 45 46
Up to 90 percent of patients experience
at least one medication discrepancy in
the transition from hospital to home
care, and discrepancies occur within all
therapeutic classes of medications.47 48
Transfer of a medication list between
providers is necessary for medication
reconciliation interventions, which have
been shown to be a cost-effective way to
avoid ADEs by reducing errors 49 50 51
41 Gleason, K.M., Groszek, J.M., Sullivan, C.,
Rooney, D., Barnard, C., Noskin, G.A.,
‘‘Reconciliation of discrepancies in medication
histories and admission orders of newly
hospitalized patients,’’ American Journal of Health
System Pharmacy, 2004, Vol. 61(16), pp. 1689–
1694.
42 Patterson M., Foust J.B., Bollinger, S., Coleman,
C., Nguyen, D., ‘‘Inter-facility communication
barriers delay resolving medication discrepancies
during transitions of care,’’ Research in Social &
Administrative Pharmacy (2018), doi: 10.1016/
j.sapharm.2018.05.124.
43 Manias, E., Annaikis, N., Considine, J.,
Weerasuriya, R., & Kusljic, S. ‘‘Patient-, medicationand environment-related factors affecting
medication discrepancies in older patients,’’
Collegian, 2017, Vol. 24, pp. 571–577.
44 Tjia, J., Bonner, A., Briesacher, B.A., McGee, S.,
Terrill, E., Miller, K., ‘‘Medication discrepancies
upon hospital to skilled nursing facility
transitions,’’ J Gen Intern Med, 2009, Vol. 24(5), pp.
630–635.
45 Sinvani, L.D., Beizer, J., Akerman, M.,
Pekmezaris, R., Nouryan, C., Lutsky, L., Cal, C.,
Dlugacz, Y., Masick, K., Wolf-Klein, G.,’’Medication
reconciliation in continuum of care transitions: a
moving target,’’ J Am Med Dir Assoc, 2013, Vol.
14(9), 668–672.
46 Coleman E.A., Parry C., Chalmers S., & Min,
S.J., ‘‘The Care Transitions Intervention: results of
a randomized controlled trial,’’ Arch Intern Med,
2006, Vol. 166, pp. 1822–28.
47 Corbett C.L., Setter S.M., Neumiller J.J., &
Wood, l.D., ‘‘Nurse identified hospital to home
medication discrepancies: implications for
improving transitional care,’’ Geriatr Nurs, 2011,
Vol. 31(3), pp. 188–96.
48 Setter S.M., Corbett C.F., Neumiller J.J., Gates,
B.J., Sclar, D.A., & Sonnett, T.E., ‘‘Effectiveness of
a pharmacist-nurse intervention on resolving
medication discrepancies in older patients
transitioning from hospital to home care: impact of
a pharmacy/nursing intervention,’’ Am J Health
Syst Pharm, 2009, Vol. 66, pp. 2027–31.
49 Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ‘‘Effect
of admission medication reconciliation on adverse
drug events from admission medication changes,’’
Archives of Internal Medicine, 2011, Vol. 171(9),
pp. 860–861.
50 Kwan, J.L., Lo, L., Sampson, M., & Shojania,
K.G., ‘‘Medication reconciliation during transitions
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especially when medications are
reviewed by a pharmacist using
electronic medical records.52
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b. Stakeholder and Technical Expert
Panel (TEP) Input
The proposed measure was developed
after consideration of feedback we
received from stakeholders and four
TEPs convened by our contractors.
Further, the proposed measure was
developed after evaluation of data
collected during two pilot tests we
conducted in accordance with the CMS
Measures Management System
Blueprint.
Our measure development contractors
constituted a TEP which met on
September 27, 2016,53 January 27,
2017,54 and August 3, 2017 55 to provide
input on a prior version of this measure.
Based on this input, we updated the
measure concept in late 2017 to include
the transfer of a specific component of
health information—medication
information. Our measure development
contractors reconvened this TEP on
April 20, 2018 for the purpose of
obtaining expert input on the proposed
measure, including the measure’s
reliability, components of face validity,
and feasibility of being implemented
across PAC settings. Overall, the TEP
of care as a patient safety strategy: a systematic
review,’’ Annals of Internal Medicine, 2013, Vol.
158(5), pp. 397–403.
51 Chhabra, P.T., Rattinger, G.B., Dutcher, S.K.,
Hare, M.E., Parsons, K., L., & Zuckerman, I.H.,
‘‘Medication reconciliation during the transition to
and from long-term care settings: a systematic
review,’’ Res Social Adm Pharm, 2012, Vol. 8(1),
pp. 60–75.
52 Agrawal A, Wu WY. ‘‘Reducing medication
errors and improving systems reliability using an
electronic medication reconciliation system,’’ The
Joint Commission Journal on Quality and Patient
Safety, 2009, Vol. 35(2), pp. 106–114.
53 Technical Expert Panel Summary Report:
Development of two quality measures to satisfy the
Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain
of Transfer of health Information and Care
Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient
Rehabilitation Facilities (IRFs), Long Term Care
Hospitals (LTCHs) and Home Health Agencies
(HHAs). Available at https://www.cms.gov/
Medicare/Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-Quality-Initiatives/
Downloads/Transfer-of-Health-Information-TEP_
Summary_Report_Final-June-2017.pdf.
54 Technical Expert Panel Summary Report:
Development of two quality measures to satisfy the
Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain
of Transfer of health Information and Care
Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient
Rehabilitation Facilities (IRFs), Long Term Care
Hospitals (LTCHs) and Home Health Agencies
(HHAs). Available at https://www.cms.gov/
Medicare/Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-Quality-Initiatives/
Downloads/Transfer-of-Health-Information-TEPMeetings-2-3-Summary-Report_Final_Feb2018.pdf.
55 Ibid.
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was supportive of the proposed
measure, affirming that the measure
provides an opportunity to improve the
transfer of medication information. A
summary of the April 20, 2018 TEP
proceedings titled ‘‘Transfer of Health
Information TEP Meeting 4—June 2018’’
is available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
Our measure development contractors
solicited stakeholder feedback on the
proposed measure by requesting
comment on the CMS Measures
Management System Blueprint website,
and accepted comments that were
submitted from March 19, 2018 to May
3, 2018. The comments received noted
overall support for the measure. Several
commenters suggested ways to improve
the measure, primarily related to what
types of information should be included
at transfer. We incorporated this input
into development of the proposed
measure. The summary report for the
March 19 to May 3, 2018 public
comment period titled ‘‘IMPACT
Medication Profile Transferred Public
Comment Summary Report’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
c. Pilot Testing
The proposed measure was tested
between June and August 2018 in a pilot
test that involved 24 PAC facilities/
agencies, including five IRFs, six SNFs,
six LTCHs, and seven HHAs. The 24
pilot sites submitted a total of 801
records. Analysis of agreement between
coders within each participating facility
(266 qualifying pairs) indicated a 93
percent agreement for this measure.
Overall, pilot testing enabled us to
verify its reliability, components of face
validity, and feasibility of being
implemented across PAC settings.
Further, more than half of the sites that
participated in the pilot test stated
during the debriefing interviews that the
measure could distinguish facilities or
agencies with higher quality medication
information transfer from those with
lower quality medication information
transfer at discharge. The pilot test
summary report titled ‘‘Transfer of
Health Information 2018 Pilot Test
Summary Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
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IMPACT-Act-Downloads-andVideos.html.
d. Measure Applications Partnership
(MAP) Review and Related Measures
We included the proposed measure in
the IRF QRP section of the 2018
Measures Under Consideration (MUC)
List. The MAP conditionally supported
this measure pending NQF
endorsement, noting that the measure
can promote the transfer of important
medication information. The MAP also
suggested that we consider a measure
that can be adapted to capture bidirectional information exchange, and
recommended that the medication
information transferred include
important information about
supplements and opioids. More
information about the MAP’s
recommendations for this measure is
available at https://www.qualityforum
.org/Publications/2019/02/MAP_2019_
Considerations_for_Implementing_
Measures_Final_Report_-_PACLTC.aspx.
As part of the measure development
and selection process, we also identified
one NQF-endorsed quality measure
similar to the proposed measure, titled
Documentation of Current Medications
in the Medical Record (NQF #0419,
CMS eCQM ID: CMS68v8). This
measure was adopted as one of the
recommended adult core clinical quality
measures for eligible professionals for
the EHR Incentive Program beginning in
2014 and was also adopted under the
Merit-based Incentive Payment System
(MIPS) quality performance category
beginning in 2017. The measure is
calculated based on the percentage of
visits for patients aged 18 years and
older for which the eligible professional
or eligible clinician attests to
documenting a list of current
medications using all resources
immediately available on the date of the
encounter.
The proposed Transfer of Health
Information to the Provider—Post-Acute
Care (PAC) measure addresses the
transfer of information whereas the
NQF-endorsed measure #0419 assesses
the documentation of medications, but
not the transfer of such information.
This is important as the proposed
measure assesses for the transfer of
medication information for the
proposed measure calculation. Further,
the proposed measure utilizes
standardized patient assessment data
elements (SPADEs), which is a
requirement for measures specified
under the Transfer of Health
Information measure domain under
section 1899B(c)(1)(E) of the Act,
whereas NQF #0419 does not.
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After review of the NQF-endorsed
measure, we determined that the
proposed Transfer of Health Information
to the Provider—Post-Acute Care (PAC)
measure better addresses the Transfer of
Health Information measure domain,
which requires that at least some of the
data used to calculate the measure be
collected as standardized patient
assessment data through the post-acute
care assessment instruments. Section
1886(j)(7)(D)(i) of the Act requires that
any measure specified by the Secretary
be endorsed by the entity with a
contract under section 1890(a) of the
Act, which is currently the National
Quality Form (NQF). However, when a
feasible and practical measure has not
been NQF endorsed for a specified area
or medical topic determined appropriate
by the Secretary, section 1886(j)(7)(D)(ii)
of the Act allows the Secretary to
specify a measure that is not NQF
endorsed as long as due consideration is
given to the measures that have been
endorsed or adopted by a consensus
organization identified by the Secretary.
For the reasons discussed previously,
we believe that there is currently no
feasible NQF-endorsed measure that we
could adopt under section
1886(j)(7)(D)(ii) of the Act. However, we
note that we intend to submit the
proposed measure to the NQF for
consideration of endorsement when
feasible.
e. Quality Measure Calculation
The proposed Transfer of Health
Information to the Provider—Post-Acute
Care (PAC) quality measure is
calculated as the proportion of patient
stays with a discharge assessment
indicating that a current reconciled
medication list was provided to the
subsequent provider at the time of
discharge. The proposed measure
denominator is the total number of IRF
patient stays ending in discharge to a
subsequent provider, which is defined
as a short-term general acute-care
hospital, intermediate care (intellectual
and developmental disabilities
providers), home under care of an
organized home health service
organization or hospice, hospice in an
institutional facility, a SNF, an LTCH,
another IRF, an IPF, or a CAH. These
health care providers were selected for
inclusion in the denominator because
they are identified as subsequent
providers on the discharge destination
item that is currently included on the
IRF PAI. The proposed measure
numerator is the number of IRF patient
stays with an IRF–PAI discharge
assessment indicating a current
reconciled medication list was provided
to the subsequent provider at the time
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of discharge. For additional technical
information about this proposed
measure, we refer readers to the
document titled, ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html. The data source for the
proposed quality measure is the IRF–
PAI assessment instrument for IRF
patients.
For more information about the data
submission requirements we proposed
for this measure, we refer readers to
section VIII.G.3. of this final rule.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the IRF QRP Quality
Measure Proposals beginning with the
FY 2022 IRF QRP. A discussion of these
comments, along with our responses,
appears below. We also address
comments on the proposed Transfer of
Health Information to the Patient—PostAcute Care measure (discussed further
in a subsequent section of this final
rule) in this section because
commenters frequently addressed both
Transfer of Health Information measures
together.
Response: We thank the commenters
for their support of the Transfer of
Health Information measures.
Comment: One commenter suggested
that other providers, such as outpatient
physical therapists, should be included
in the definition of a subsequent
provider for the Transfer of Health
Information to the Provider—Post-Acute
Care measure.
Response: We appreciate the
suggestion to expand the Transfer of
Health Information to the Provider—
Post-Acute Care measure outcome to
assess the transfer of health information
to other providers such as outpatient
physical therapists. We recognize that
sharing medication information with
outpatient providers is important, and
will take into consideration additional
providers in future measure
modifications. Through our measure
development and pilot testing we
learned that outpatient providers cannot
always be readily identified by the PAC
provider. For this process measure,
which serves as a building block for
improving the transfer of medication
information, we specified providers
who will be involved in the care of the
patient and medication management
after discharge and can be readily
identified through the discharge
location item on the IRF–PAI. The clear
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39103
delineation of the recipient of the
medication list in the measure
specifications will improve measure
reliability and validity.
Comment: A commenter
recommended that the Transfer of
Health Information to the Provider—
Post-Acute Care measure be expanded
to include the transfer of information
that would help prevent infections and
facilitate appropriate infection
prevention and control interventions
during care transitions in addition to the
medication information in the finalized
measure.
Response: The Transfer of Health
Information to the Provider—Post-Acute
Care measure focuses on the transfer of
a reconciled medication list. The
measure was designed after input from
TEPs, public comment, and other
stakeholders that suggested the quality
measures focus on the transfer of the
most critical pieces of information to
support patient safety and care
coordination. However, we
acknowledge that the transfer of many
other forms of health information is
important, and while the focus of this
measure is on a reconciled medication
list, we hope to expand our measures in
the future.
Comment: Several commenters raised
concerns about both of the Transfer of
Health Information measures not being
endorsed by the National Quality Forum
(NQF). A few commenters requested
that we consider delaying rollout of
these two new measures until endorsed
by NQF. A few commenters
recommended that we only adopt
measures that have NQF approval. One
commenter was opposed to the
measures because they have not been
endorsed by NQF.
Response: While this measure is not
currently NQF-endorsed, we recognize
that the NQF endorsement process is an
important part of measure development.
As discussed in the FY 2020 IRF PPS
proposed rule (84 FR 17286 through
17291), we believe the measures better
address the Transfer of Health
Information measure domain, which
requires that at least some of the data
used to calculate the measure be
collected as standardized patient
assessment data through the post-acute
care assessment instruments, than any
endorsed measures. While section
1886(j)(7)(D)(i) of the Act requires that
any measure specified by the Secretary
be endorsed by the entity with a
contract under section 1890(a) of the
Act, which is currently the National
Quality Form (NQF), when a feasible
and practical measure has not been NQF
endorsed for a specified area or medical
topic determined appropriate by the
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Secretary, section 1886(j)(7)(D)(ii) of the
Act allows the Secretary to specify a
measure that is not NQF endorsed as
long as due consideration is given to the
measures that has been endorsed or
adopted by a consensus organization
identified by the Secretary. We plan to
submit the measure for NQF
endorsement consideration as soon as
feasible.
Comment: Several commenters stated
that the Transfer of Health Information
measures will add burden. Two
commenters did not support the
measures for this reason. One
commenter stated that achieving high
performance on the measures will add
administrative burden. Another
commenter stated that the measures will
add burden with no added value.
Another commenter stated that while
there will be additional burden on IRFs
to collect and report data for these new
measures, the benefit to patients and the
CMS program outweighs the additional
burden on providers.
Response: We agree that the benefit to
patients outweighs any additional
burden on providers. We are also very
mindful of burden that may occur from
the collection and reporting of our
measures, as supported by the
Meaningful Measures and Patients over
Paperwork initiatives. We emphasize
that both measures are comprised of one
item, and further, the activities
associated with the measure align with
existing requirements related to
transferring information at the time of
discharge to safeguard patients.
Additionally, TEP feedback and pilot
test found that the burden of reporting
will not be significant. We believe that
these measures will likely drive
improvements in the transfer of
medication information between
providers and with patients, families,
and caregivers.
Comment: One commenter stated that
there will be no additional burden to
IRFs, because providing medication
information as part of discharge
planning is a Condition of Participation
requirement for Medicaid and Medicare,
and the medication list can be generated
from the electronic medical record.
Response: We believe that the
Transfer of Health Information measures
will not substantially increase burden
because we understand that many
hospitals already generate medication
lists as a best practice.
Comment: We received comments
related to the validity and reliability of
both Transfer of Health Information
measures. One commenter suggested
that CMS should ensure accuracy of
these measures. Other commenters
suggested that additional testing is
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needed to ensure that these measures
will be able to differentiate among IRF
providers. Another commenter
questioned if the measures would be
topped out shortly after adoption, since
medication reconciliation is already
completed by facilities at discharge.
Response: Elements of validity and
reliability were analyzed during pilot
testing of these measures, with good
results, including inter-rater reliability
of at least 87 percent for all tested items.
Pilot testing also indicated that there is
room for improvement for IRFs and
other settings, so we do not expect the
measure to be topped out shortly after
adoption. As we monitor the outcomes
of these measures, we will ensure that
reliability and validity of the measures
meet acceptable standards.
Comment: Some commenters
recommended ways in which the
Transfer of Health Information measures
specifications could be updated or
changed. A few commenters suggested
that the ‘‘not applicable’’ (NA) answer
choice available in the home health
version of the measure be made
available in all settings, including IRFs.
A few commenters also requested
clarification about why patients
discharged home under the care of an
organized home health service or
hospice would be captured in the
denominators of both Transfer of Health
information measures.
Response: We are appreciative of the
measure modification suggestions and
clarify why the response option of N/A
was considered only for the HH version
of this measure. The coding response N/
A, or ‘‘not applicable’’ is used when the
HHA was not made aware of the transfer
in a timely manner and, therefore, the
HHA is not able to provide the
medication list at the time of transfer to
the subsequent provider. For example, a
HHA may not be immediately aware
when a patient is taken to the
emergency room. For facility settings,
such as the IRF setting, where 24-hour
care is being provided, the facility
should always be aware and actively
involved in the discharge of the patient,
and therefore, able to provide the
current reconciled medication list at the
time of discharge. Therefore, we
believed the coding option of ‘‘N/A’’
would not be useful in the facility-based
measure as the facility is aware and
involved in the discharge. We wish to
note that while the N/A option is
considered for the HHA version of the
measure, the measure specifications
indicate that these patients are not
removed from the denominator. In
addition, discharge to home under the
care of an organized HHA or hospice is
captured in the denominator of both the
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Transfer of Health Information to
Provider and Transfer of Health
Information to Patient measures because
this type of discharge represents two
opportunities to transfer the medication
list. These measures aim to assure that
each of these transfers is taking place.
We refer readers to the measure
specifications where updates or changes
can be found and are available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Comment: One commenter suggested
that the Transfer of Health Information
measures should include a measure of
the timeliness of the transfer. The
commenter stated that, as currently
specified, the measures give equal credit
for information that is sent immediately
and information sent days later.
Response: We appreciate the
suggestions that CMS develop and adopt
measures that assess for the timeliness
of transfer. We agree that measure
concepts of this type are important and
would complement the measures that
focus for whether information was
transferred at the time the patient leaves
the facility. We clarify that the measures
do not give credit for when information
was sent, whether immediately or days
later. This is because there may be
circumstances where information may
not be sent at the immediate time of
discharge. However, the measures do
require that information be shared with
the subsequent provider and/or the
patient as close to the time of discharge
as this is actionable, allows for shared
decision making, and will increase
coordinated care. We are not
establishing a new standard of transfer
at discharge; we are simply assessing if
information was sent at the time a
patient leaves the facility. As we move
through future measure development
work, we will consider a ‘‘timeliness’’
component for these measure concepts.
Comment: A commenter noted that
although CMS provided guidelines
regarding what should be included in
the transfer of medication information,
the data collection on this measure does
not require that these guidelines be met.
The commenter questioned if CMS
intends to audit IRFs to ensure that the
measure values are consistent with the
information being shared.
Response: The Transfer of Health
Information measures serve as a check
to ensure that a reconciled medication
list is provided as the patient changes
care settings. Defining the completeness
of that medication list is left to the
discretion of the providers and patient
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who are coordinating this care. We
interpret the comment about audits to
be referring to data validation. While we
do not have a data validation program
in place at this time, we are exploring
such a program akin to that of the
hospital QRPs. For all measures and
data collected for the IRF QRP, we
monitor and evaluate our data to assess
for coding patterns, errors, reliability,
and soundness of the data. Through data
monitoring, we are able to assess if
measure outcomes are consistent with
the information that is collected. We
note that all data are subject to review
and audit.
Comment: A few comments included
concerns that the Transfer of Health
Information measures are not indicative
of provider quality and questioned the
ability of the measures to improve
patient outcomes. Two commenters did
not support the measures for this
reason. Commenters noted that the
measures assess whether a medication
list was transferred and not whether that
medication list was accurate and
received by the subsequent provider.
Response: The Transfer of Health
Information to the Provider—Post-Acute
Care and Transfer of Health Information
to the Patient—Post-Acute Care
measures are process measures designed
to address and improve an important
aspect of care quality. Lack of timely
transfer of medication information at
transitions has been demonstrated to
lead to increased risk of adverse events,
medication errors, and hospitalizations.
In addition, public commenters and our
TEP members identified many problems
and gaps in the timely transfer of
medication information at transitions.
Process measures, such as these, are
building blocks toward improved
coordinated care and discharge
planning, providing information that
will improve shared decision making
and coordination. Further, process
measures hold a lot of value as they
delineate negative and/or positive
aspects of the health care process. These
measures will capture the quality of the
process of medication information
transfer and, we believe, help to
improve those processes. When
developing future measures, we will
take into consideration suggestions
about measures that assess the accuracy
of the medication list and whether it
was received by the subsequent
provider.
Comment: One commenter suggested
that CMS work to identify
interoperability solutions as a means of
decreasing opportunities for errors by
providing clinicians and patients secure
access to the most up-to-date
medication-related information. The
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commenter also suggests that if CMS is
required by the IMPACT Act to adopt
these measures, that we do so as an
interim step, within a defined
timeframe, while interoperability
solutions are explored and tested.
Response: We agree with the
comments on the importance of
interoperability solutions to support
health information transfer. CMS and
ONC are focused on improving
interoperability and the timely sharing
of information between providers,
patients, families and caregivers. We
believe that PAC provider health
information exchange supports the goals
of high quality, personalized, efficient
healthcare, care coordination, personcentered care, and supports real-time,
data driven, clinical decision making.
We are optimistic that this measure will
encourage the electronic transfer of
current and important medication
information at transitions. These
measures and related efforts may help
accelerate interoperability solutions.
The Transfer of Health Information
measures assess the process of
medication transfer, which can occur
through both electronic and nonelectronic means. We clarify that these
measures are an interim step in
improving coordinated care, and we
also believe that other interoperable
solutions should be explored. Finalizing
these Transfer of Health measures will
be a first step in measuring the transfer
of this medication-related information.
After consideration of the public
comments, we are finalizing our
proposal to adopt the Transfer of Health
Information to the Provider—Post Acute
Care (PAC) measure, under section
1899B(c)(1)(E) of the Act, with data
collection for discharges beginning
October 1, 2020.
2. Transfer of Health Information to the
Patient—Post-Acute Care (PAC)
Measure
Beginning with the FY 2022 IRF QRP,
we proposed to adopt the Transfer of
Health Information to the Patient—Post
Acute Care (PAC) measure, a measure
that satisfies the IMPACT Act domain of
Transfer of Health Information, with
data collection for discharges beginning
October 1, 2020. This process-based
measure assesses whether or not a
current reconciled medication list was
provided to the patient, family, or
caregiver when the patient was
discharged from a PAC setting to a
private home/apartment, a board and
care home, assisted living, a group
home, transitional living or home under
care of an organized home health
service organization, or a hospice.
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a. Background
In 2013, 22.3 percent of all acute
hospital discharges were discharged to
PAC settings, including 11 percent who
were discharged to home under the care
of a home health agency.56 Of the
Medicare FFS beneficiaries with an IRF
stay in FYs 2016 and 2017, an estimated
51 percent were discharged home with
home health services, 21 percent were
discharged home with self-care, and 0.5
percent were discharged with home
hospice services.57
The communication of health
information, such as a reconciled
medication list, is critical to ensuring
safe and effective patient transitions
from health care settings to home and/
or other community settings. Incomplete
or missing health information, such as
medication information, increases the
likelihood of a patient safety risk, often
life-threatening.58 59 60 61 62 Individuals
who use PAC care services are
particularly vulnerable to adverse health
outcomes due to their higher likelihood
of having multiple comorbid chronic
conditions, polypharmacy, and
complicated transitions between care
settings.63 64 Upon discharge to home,
56 Tian, W. ‘‘An all-payer view of hospital
discharge to postacute care,’’ May 2016. Available
at https://www.hcup-us.ahrq.gov/reports/statbriefs/
sb205-Hospital-Discharge-Postacute-Care.jsp.
57 RTI International analysis of Medicare claims
data for index stays in IRF 2016/2017. (RTI program
reference: MM150).
58 Kwan, J.L., Lo, L., Sampson, M., & Shojania,
K.G., ‘‘Medication reconciliation during transitions
of care as a patient safety strategy: A systematic
review,’’ Annals of Internal Medicine, 2013, Vol.
158(5), pp. 397–403.
59 Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ‘‘Effect
of admission medication reconciliation on adverse
drug events from admission medication changes,’’
Archives of Internal Medicine, 2011, Vol. 171(9),
pp. 860–861.
60 Bell, C.M., Brener, S.S., Gunraj, N., Huo, C.,
Bierman, A.S., Scales, D.C., & Urbach, D.R.,
‘‘Association of ICU or hospital admission with
unintentional discontinuation of medications for
chronic diseases,’’ JAMA, 2011, Vol. 306(8), pp.
840–847.
61 Basey, A.J., Krska, J., Kennedy, T.D., &
Mackridge, A.J., ‘‘Prescribing errors on admission to
hospital and their potential impact: A mixedmethods study,’’ BMJ Quality & Safety, 2014, Vol.
23(1), pp. 17–25.
62 Desai, R., Williams, C.E., Greene, S.B., Pierson,
S., & Hansen, R.A., ‘‘Medication errors during
patient transitions into nursing homes:
Characteristics and association with patient harm,’’
The American Journal of Geriatric
Pharmacotherapy, 2011, Vol. 9(6), pp. 413–422.
63 Brody, A.A., Gibson, B., Tresner-Kirsch, D.,
Kramer, H., Thraen, I., Coarr, M.E., & Rupper, R.
‘‘High prevalence of medication discrepancies
between home health referrals and Centers for
Medicare and Medicaid Services home health
certification and plan of care and their potential to
affect safety of vulnerable elderly adults,’’ Journal
of the American Geriatrics Society, 2016, Vol.
64(11), pp. e166–e170.
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individuals in PAC settings may be
faced with numerous medication
changes, new medication regimes, and
follow-up details.65 66 67 The efficient
and effective communication and
coordination of medication information
may be critical to prevent potentially
deadly adverse effects. When care
coordination activities enhance care
transitions, these activities can reduce
duplication of care services and costs of
care, resolve conflicting care plans, and
prevent medical errors.68 69
Finally, the transfer of a patient’s
discharge medication information to the
patient, family, or caregiver is common
practice and supported by discharge
planning requirements for participation
in Medicare and Medicaid
programs.70 71 Most PAC EHR systems
generate a discharge medication list to
promote patient participation in
medication management, which has
been shown to be potentially useful for
64 Chhabra, P.T., Rattinger, G.B., Dutcher, S.K.,
Hare, M.E., Parsons, K., L., & Zuckerman, I.H.,
‘‘Medication reconciliation during the transition to
and from long-term care settings: A systematic
review,’’ Res Social Adm Pharm, 2012, Vol. 8(1),
pp. 60–75.
65 Brody, A.A., Gibson, B., Tresner-Kirsch, D.,
Kramer, H., Thraen, I., Coarr, M.E., & Rupper, R.
‘‘High prevalence of medication discrepancies
between home health referrals and Centers for
Medicare and Medicaid Services home health
certification and plan of care and their potential to
affect safety of vulnerable elderly adults,’’ Journal
of the American Geriatrics Society, 2016, Vol.
64(11), pp. e166–e170.
66 Bell, C.M., Brener, S.S., Gunraj, N., Huo, C.,
Bierman, A.S., Scales, D.C., & Urbach, D.R.,
‘‘Association of ICU or hospital admission with
unintentional discontinuation of medications for
chronic diseases,’’ JAMA, 2011, Vol. 306(8), pp.
840–847.
67 Sheehan, O.C., Kharrazi, H., Carl, K.J., Leff, B.,
Wolff, J.L., Roth, D.L., Gabbard, J., & Boyd, C.M.,
‘‘Helping older adults improve their medication
experience (HOME) by addressing medication
regimen complexity in home healthcare,’’ Home
Healthcare Now. 2018, Vol. 36(1) pp. 10–19.
68 Mor, V., Intrator, O., Feng, Z., & Grabowski, D.
C., ‘‘The revolving door of rehospitalization from
skilled nursing facilities,’’ Health Affairs, 2010, Vol.
29(1), pp. 57–64.
69 Starmer, A.J., Sectish, T.C., Simon, D.W.,
Keohane, C., McSweeney, M.E., Chung, E.Y., Yoon,
C.S., Lipsitz, S.R., Wassner, A.J., Harper, M.B., &
Landrigan, C.P., ‘‘Rates of medical errors and
preventable adverse events among hospitalized
children following implementation of a resident
handoff bundle,’’ JAMA, 2013, Vol. 310(21), pp.
2262–2270.
70 CMS, ‘‘Revision to state operations manual
(SOM), Hospital Appendix A—Interpretive
Guidelines for 42 CFR 482.43, Discharge Planning’’
May 17, 2013. Available at https://www.cms.gov/
Medicare/Provider-Enrollment-and-Certification/
SurveyCertificationGenInfo/Downloads/Surveyand-Cert-Letter-13-32.pdf.
71 The State Operations Manual Guidance to
Surveyors for Long Term Care Facilities (Guidance
§ 483.21(c)(1) Rev. 11–22–17) for discharge
planning process. Available at https://
www.cms.gov/Regulations-and-Guidance/
Guidance/Manuals/downloads/som107ap_pp_
guidelines_ltcf.pdf.
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improving patient outcomes and
transitional care.72
b. Stakeholder and Technical Expert
Panel (TEP) Input
The proposed measure was developed
after consideration of feedback we
received from stakeholders and four
TEPs convened by our contractors.
Further, the proposed measure was
developed after evaluation of data
collected during two pilot tests we
conducted in accordance with the CMS
Measures Management System
Blueprint.
Our measure development contractors
constituted a TEP which met on
September 27, 2016,73 January 27,
2017,74 and August 3, 2017 75 to provide
input on a prior version of this measure.
Based on this input, we updated the
measure concept in late 2017 to include
the transfer of a specific component of
health information—medication
information. Our measure development
contractors reconvened this TEP on
April 20, 2018 to seek expert input on
the measure. Overall, the TEP members
supported the proposed measure,
affirming that the measure provides an
opportunity to improve the transfer of
medication information. Most of the
TEP members believed that the measure
could improve the transfer of
medication information to patients,
families, and caregivers. Several TEP
members emphasized the importance of
transferring information to patients and
their caregivers in a clear manner using
plain language. A summary of the April
72 Toles, M., Colon-Emeric, C., Naylor, M.D.,
Asafu-Adjei, J., Hanson, L.C., ‘‘Connect-home:
Transitional care of skilled nursing facility patients
and their caregivers,’’ Am Geriatr Soc., 2017, Vol.
65(10), pp. 2322–2328.
73 Technical Expert Panel Summary Report:
Development of two quality measures to satisfy the
Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain
of Transfer of health Information and Care
Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient
Rehabilitation Facilities (IRFs), Long Term Care
Hospitals (LTCHs) and Home Health Agencies
(HHAs). Available at https://www.cms.gov/
Medicare/Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-Quality-Initiatives/
Downloads/Transfer-of-Health-Information-TEP_
Summary_Report_Final-June-2017.pdf.
74 Technical Expert Panel Summary Report:
Development of two quality measures to satisfy the
Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain
of Transfer of health Information and Care
Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient
Rehabilitation Facilities (IRFs), Long Term Care
Hospitals (LTCHs) and Home Health Agencies
(HHAs). Available at https://www.cms.gov/
Medicare/Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-Quality-Initiatives/
Downloads/Transfer-of-Health-Information-TEPMeetings-2-3-Summary-Report_Final_Feb2018.pdf.
75 Ibid.
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20, 2018 TEP proceedings titled
‘‘Transfer of Health Information TEP
Meeting 4—June 2018’’ is available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Our measure development contractors
solicited stakeholder feedback on the
proposed measure by requesting
comment on the CMS Measures
Management System Blueprint website,
and accepted comments that were
submitted from March 19, 2018 to May
3, 2018. Several commenters noted the
importance of ensuring that the
instruction provided to patients and
caregivers is clear and understandable
to promote transparent access to
medical record information and meet
the goals of the IMPACT Act. The
summary report for the March 19 to May
3, 2018 public comment period titled
‘‘IMPACT—Medication Profile
Transferred Public Comment Summary
Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
c. Pilot Testing
Between June and August 2018, we
held a pilot test involving 24 PAC
facilities/agencies, including five IRFs,
six SNFs, six LTCHs, and seven HHAs.
The 24 pilot sites submitted a total of
801 assessments. Analysis of agreement
between coders within each
participating facility (241 qualifying
pairs) indicated an 87 percent
agreement for this measure. Overall,
pilot testing enabled us to verify its
reliability, components of face validity,
and feasibility of being implemented
across PAC settings. Further, more than
half of the sites that participated in the
pilot test stated, during debriefing
interviews, that the measure could
distinguish facilities or agencies with
higher quality medication information
transfer from those with lower quality
medication information transfer at
discharge. The pilot test summary report
titled ‘‘Transfer of Health Information
2018 Pilot Test Summary Report’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
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d. Measure Applications Partnership
(MAP) Review and Related Measures
We included the proposed measure in
the IRF QRP section of the 2018 MUC
list. The MAP conditionally supported
this measure pending NQF
endorsement, noting that the measure
can promote the transfer of important
medication information to the patient.
The MAP recommended that providers
transmit medication information to
patients that is easy to understand
because health literacy can impact a
person’s ability to take medication as
directed. More information about the
MAP’s recommendations for this
measure is available at https://
www.qualityforum.org/Publications/
2019/02/MAP_2019_Considerations_
for_Implementing_Measures_Final_
Report_-_PAC-LTC.aspx.
Section 1886(j)(7)(D)(i) of the Act,
requires that any measure specified by
the Secretary be endorsed by the entity
with a contract under section 1890(a) of
the Act, which is currently the NQF.
However, when a feasible and practical
measure has not been NQF endorsed for
a specified area or medical topic
determined appropriate by the
Secretary, section 1886(j)(7)(D)(ii) of the
Act allows the Secretary to specify a
measure that is not NQF endorsed as
long as due consideration is given to the
measures that have been endorsed or
adopted by a consensus organization
identified by the Secretary. Therefore, in
the absence of any NQF-endorsed
measures that address the proposed
Transfer of Health Information to the
Patient—Post-Acute Care (PAC), which
requires that at least some of the data
used to calculate the measure be
collected as standardized patient
assessment data through PAC
assessment instruments, we believe that
there is currently no feasible NQFendorsed measure that we could adopt
under section 1886(j)(7)(D)(ii) of the
Act. However, we note that we intend
to submit the proposed measure to the
NQF for consideration of endorsement
when feasible.
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e. Quality Measure Calculation
The calculation of the proposed
Transfer of Health Information to the
Patient—Post-Acute Care (PAC) measure
would be based on the proportion of
patient stays with a discharge
assessment indicating that a current
reconciled medication list was provided
to the patient, family, or caregiver at the
time of discharge.
The proposed measure denominator is
the total number of IRF patient stays
ending in discharge to a private home/
apartment, a board and care home,
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assisted living, a group home,
transitional living or home under care of
an organized home health service
organization, or a hospice. These
locations were selected for inclusion in
the denominator because they are
identified as home locations on the
discharge destination item that is
currently included on the IRF–PAI. The
proposed measure numerator is the
number of IRF patient stays with an
IRF–PAI discharge assessment
indicating a current reconciled
medication list was provided to the
patient, family, or caregiver at the time
of discharge. For technical information
about this proposed measure, we refer
readers to the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html. Data for the proposed
quality measure would be calculated
using data from the IRF–PAI assessment
instrument for IRF patients.
For more information about the data
submission requirements we proposed
for this measure, we refer readers to
section VIII.G.3. of this rule.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the IRF QRP Quality
Measure Proposals Beginning with the
FY 2022 IRF QRP. A discussion of these
comments, along with our responses,
appears below. We received many
comments that addressed both of the
Transfer of Health Information
measures. Comments that applied to
both measures are discussed above in
IX.D.1 of this rule.
Comment: One commenter suggested
that CMS use the field’s experience with
transferring information to patients and
reporting on this measure to
disseminate best practices about how to
best convey the medication list and
suggested this include formats and
informational elements helpful to
patients and families.
Response: We have interpreted ‘‘the
field’’ to mean PAC providers. Facilities
and clinicians should use clinical
judgement to guide their practices
around transferring information to
patients and how to best convey the
medication list, including identifying
the best formats and informational
elements. This may be determined by
the patient’s individualized needs in
response to their medical condition. We
do not determine clinical best practices
standards and facilities are advised to
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refer to other sources, such as
professional guidelines.
Comment: One commenter suggested
that the Transfer of Health Information
to the Patient—Post-Acute Care (PAC)
Measure require transfer of the
medication list to both the patient and
family or caregiver.
Response: We agree there are times
when it is appropriate for the IRF to
provide the medication list to the
patient and family and this decision
should be based on clinical judgement.
However, because it is not always
necessary or appropriate to provide the
medication list to both the patient and
family, we are not requiring this for the
measure.
After consideration of the public
comments, we are finalizing our
proposal to adopt the Transfer of Health
Information to the Patient—Post Acute
Care (PAC) measure, under section
1899B(c)(1)(E) of the Act, with data
collection for discharges beginning
October 1, 2020.
3. Update to the Discharge to
Community—Post Acute Care (PAC)
Inpatient Rehabilitation Facility (IRF)
Quality Reporting Program (QRP)
Measure
In the FY 2020 IRF PPS proposed rule
(84 FR 17291), we proposed to update
the specifications for the Discharge to
Community—PAC IRF QRP measure to
exclude baseline nursing facility (NF)
residents from the measure. This
measure reports an IRF’s riskstandardized rate of Medicare FFS
patients who are discharged to the
community following an IRF stay, do
not have an unplanned readmission to
an acute care hospital or LTCH in the 31
days following discharge to community,
and who remain alive during the 31
days following discharge to community.
We adopted this measure in the FY 2017
IRF PPS final rule (81 FR 52095 through
52103).
In the FY 2017 IRF PPS final rule (81
FR 52099), we addressed public
comments recommending exclusion of
IRF patients who were baseline NF
residents, as these patients lived in a NF
prior to their IRF stay, as these patients
may not be expected to return to the
community following their IRF stay. In
the FY 2018 IRF PPS final rule (82 FR
36285), we addressed public comments
expressing support for a potential future
modification of the measure that would
exclude baseline NF residents;
commenters stated that the exclusion
would result in the measure more
accurately portraying quality of care
provided by IRFs, while controlling for
factors outside of IRF control.
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We assessed the impact of excluding
baseline NF residents from the measure
using CY 2015 and CY 2016 data, and
found that this exclusion impacted both
patient- and facility-level discharge to
community rates. We defined baseline
NF residents as IRF patients who had a
long-term NF stay in the 180 days
preceding their hospitalization and IRF
stay, with no intervening community
discharge between the NF stay and
qualifying hospitalization for measure
inclusion. Baseline NF residents
represented 0.3 percent of the measure
population after all measure exclusions
were applied. Observed patient-level
discharge to community rates were
significantly lower for baseline NF
residents (20.82 percent) compared with
non-NF residents (64.52 percent). The
national observed patient-level
discharge to community rate was 64.41
percent when baseline NF residents
were included in the measure,
increasing to 64.52 percent when they
were excluded from the measure. After
excluding baseline NF residents, 26.9
percent of IRFs had an increase in their
risk-standardized discharge to
community rate that exceeded the
increase in the national observed
patient-level discharge to community
rate.
Based on public comments received
and our impact analysis, we proposed to
exclude baseline NF residents from the
Discharge to Community—PAC IRF QRP
measure beginning with the FY 2020
IRF QRP, with baseline NF residents
defined as IRF patients who had a longterm NF stay in the 180 days preceding
their hospitalization and IRF stay, with
no intervening community discharge
between the NF stay and
hospitalization.
For additional technical information
regarding the Discharge to
Community—PAC IRF QRP measure,
including technical information about
the proposed exclusion, we refer readers
to the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
We sought public comment on this
proposal and received several
comments. A discussion of these
comments, along with our responses,
appears below.
Comment: Several commenters
supported the proposed exclusion of
baseline NF residents from the
Discharge to Community—PAC IRF QRP
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measure. Commenters referred to their
recommendation of this exclusion in
prior years and appreciated CMS’
willingness to consider and implement
stakeholder feedback. One commenter
stated they did not foresee any negative
impacts of the exclusion. One
commenter suggested that CMS instead
consider other quality measures for NF
residents, such as functional status
measures, to determine whether
residents receive the appropriate
standard of care they need in a longterm NF stay.
Response: We thank the commenters
for their support of the proposed
exclusion of baseline nursing facility
residents from this measure and for
recommending other measures for
consideration for baseline NF residents.
Comment: MedPAC did not support
the proposed exclusion of baseline
nursing facility residents from the
Discharge to Community—PAC IRF QRP
measure. They suggested that CMS
instead expand their definition of
‘‘return to the community’’ to include
baseline nursing home residents
returning to the nursing home where
they live, as this represents their home
or community. MedPAC also stated that
providers should be held accountable
for the quality of care they provide for
as much of their Medicare patient
population as feasible.
Response: We agree that providers
should be accountable for quality of care
for as much of their Medicare
population as feasible; we endeavor to
do this as much as possible, only
specifying exclusions we believe are
necessary for measure validity. We also
believe that monitoring quality of care
and outcomes is important for all PAC
patients, including baseline NF
residents who return to a NF after their
PAC stay. We publicly report several
long-stay resident quality measures on
Nursing Home Compare including
measures of hospitalization and
emergency department visits.
Community is traditionally
understood as representing noninstitutional settings by policy makers,
providers, and other stakeholders.
Including long-term care NF in the
definition of community would confuse
this long-standing concept of
community and would misalign with
CMS’ definition of community in
patient assessment instruments. We
conceptualized this measure using the
traditional definition of ‘‘community’’
and specified the measure as a discharge
to community measure, rather than a
discharge to baseline residence measure.
Baseline NF residents represent an
inherently different patient population
with not only a significantly lower
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likelihood of discharge to community
settings, but also a higher likelihood of
post-discharge readmissions and death
compared with PAC patients who did
not live in a NF at baseline. The
inherent differences in patient
characteristics and PAC processes and
goals of care for baseline NF residents
and non-NF residents are significant
enough that we do not believe risk
adjustment using a NF flag would
provide adequate control. While we
acknowledge that a return to nursing
home for baseline NF residents
represents a return to their home, this
outcome does not align with our
measure concept. Thus, we have chosen
to exclude baseline NF residents from
the measure.
Comment: One commenter suggested
the definition of ‘‘long-term’’ NF stay in
the proposed measure exclusion,
requesting further clarification in the
measure specifications.
Response: We have further clarified
the definition of long-term NF stay in
the final measure specifications, Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html. A long-term NF stay is
identified by the presence of a non-SNF
PPS MDS assessment in the 180 days
preceding the qualifying prior acute care
admission and index SNF stay.
Comment: One commenter questioned
whether the methodology for calculating
confidence intervals for performance
categories used in public display of the
Discharge to Community—PAC
measures has been updated.
Response: On May 31, 2019, we
announced an update to the
methodology used for calculating
confidence intervals for provider
assignment to performance categories
for public display of the Discharge to
Community—PAC measures. For more
information, we refer readers to the
‘‘Fact Sheet for Discharge to Community
Post-Acute Care Measures’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/LTCH-Quality-Reporting/
Downloads/Fact-Sheet-for-Discharge-toCommunity-Post-Acute-CareMeasures.pdf and the ‘‘FAQ for
Discharge to Community Post-Acute
Care Measures’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/LTCH-Quality-Reporting/
Downloads/FAQ-for-Discharge-to-
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After consideration of the public
comments, we are finalizing our
proposal to exclude baseline NF
residents from the Discharge to
Community—PAC IRF QRP measure as
proposed beginning with the FY 2020
IRF QRP.
E. IRF QRP Quality Measures, Measure
Concepts, and Standardized Patient
Assessment Data Elements Under
Consideration for Future Years: Request
for Information
While we will not be responding to
specific comments submitted in
response to this Request for Information,
we intend to use this input to inform
our future measure and SPADE
development efforts.
We received several comments on this
RFI, which are summarized below.
Comment: Several commenters
supported the inclusion of all of the
proposed measures and SPADEs listed
in Table 17. One commenter agreed that
the SPADE categories will provide a
fuller picture of the patients in the IRF
setting and could be used for creating
and risk adjusting quality measures.
Many commenters supported the
dementia SPADE, since dementia can
affect a beneficiary’s ability to
participate in his or her care in the PAC
setting, in addition to managing chronic
conditions and medications after
discharge. One commenter also agreed
that regularly assessing cognitive
function and mental health status
presents opportunities for better care
and quality of life.
One commenter did not support the
cognitive complexity SPADEs, since
there is no singular assessment tool
designed to assess executive function
and memory, and it would be overly
burdensome for IRFs to conduct testing
on every patient. The commenter
recommended that CMS work with
stakeholders to prioritize which patient
conditions would benefit from a
cognitive complexity assessment and
screen for those cases.
Many commenters supported the
caregiver status SPADE; one commenter
stated that regular assessment of
caregivers will result in better care for
the beneficiary and quality of life for
both individuals. Another commenter
encouraged CMS to capture caregiver
status, along with the caregiver’s
willingness and ability, and account for
it in discharge disposition outcomes.
With regard to an opioids-based
quality measure, providers had some
concerns about unintended
consequences of reporting of opioid use,
including the over- or under-prescribing
of opioids or limiting patients access to
critical treatments for pain management.
Many commenters were supportive of
SPADEs focused on bowel and bladder
continence. One commenter noted that
this is already collected on admission
and did not support a bowel and
bladder SPADE on discharge, citing that
IRFs already communicate continence
needs at discharge and this would be
duplicative. A few commenters had
concerns about the burden of future
measures and SPADEs. One commenter
recommended that prior to adding
measures or data elements, CMS
reassess and analyze all of the measures
and data elements currently collected to
limit administrative burden and create a
meaningful set of measures and data
elements. Another commenter
supported utilization of data from the
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standardized patient assessment data
elements (SPADEs), and concepts under
consideration listed in the Table 17 for
future years in the IRF QRP.
We sought input on the importance,
relevance, appropriateness, and
applicability of each of the measures,
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suggested measures and SPADEs and
suggested using existing data sources,
such a Medicare claims data. One
commenter did not support any future
SPADE concepts that were not required
by the IMPACT Act. Another
commenter suggested that CMS should
explore beneficiary-matching methods
with the Department of Veteran’s Affairs
to collect veteran status without
additional IRF data collection burden.
Response: We appreciate the input
provided by commenters. While we will
not be responding to specific comments
submitted in response to this Request
for Information, we intend to use this
input to inform our future measure and
SPADE development efforts.
F. Standardized Patient Assessment
Data Reporting Beginning With the FY
2022 IRF QRP
Section 1886(j)(7)(F)(ii) of the Act
requires that, for FY 2019 and each
subsequent fiscal year, IRFs must report
standardized patient assessment data
required under section 1899B(b)(1) of
the Act. Section 1899B(a)(1)(C) of the
Act requires, in part, the Secretary to
modify the PAC assessment instruments
in order for PAC providers, including
IRFs, to submit SPADEs under the
Medicare program. Section
1899B(b)(1)(A) of the Act requires PAC
providers to submit SPADEs under
applicable reporting provisions (which,
for IRFs, is the IRF QRP) with respect to
the admission and discharge of an
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individual (and more frequently as the
Secretary deems appropriate), and
section 1899B(b)(1)(B) of the Act defines
standardized patient assessment data as
data required for at least the quality
measures described in section
1899B(c)(1) of the Act and that is with
respect to the following categories: (1)
Functional status, such as mobility and
self-care at admission to a PAC provider
and before discharge from a PAC
provider; (2) cognitive function, such as
ability to express ideas and to
understand, and mental status, such as
depression and dementia; (3) special
services, treatments, and interventions,
such as need for ventilator use, dialysis,
chemotherapy, central line placement,
and total parenteral nutrition; (4)
medical conditions and comorbidities,
such as diabetes, congestive heart
failure, and pressure ulcers; (5)
impairments, such as incontinence and
an impaired ability to hear, see, or
swallow; and (6) other categories
deemed necessary and appropriate by
the Secretary.
In the FY 2018 IRF PPS proposed rule
(82 FR 20722 through 20739), we
proposed to adopt SPADEs that would
satisfy the first five categories. In the FY
2018 IRF PPS final rule (82 FR 36287
through 36289), we summarized
comments that supported our adoption
of SPADEs, including support for our
broader standardization goal and
support for the clinical usefulness of
specific proposed SPADEs. However,
we did not finalize the majority of our
SPADE proposals in recognition of the
concern raised by many commenters
that we were moving too fast to adopt
the SPADEs and modify our assessment
instruments in light of all of the other
requirements we were also adopting
under the IMPACT Act at that time (82
FR 36292 through 36294). In addition,
commenters noted that we should
conduct further testing of the data
elements we have proposed (82 FR
36288).
However, we finalized the adoption of
SPADEs for two of the categories
described in section 1899B(b)(1)(B) of
the Act: (1) Functional status: Data
elements currently reported by IRFs to
calculate the measure Application of
Percent of Long-Term Care Hospital
Patients with an Admission and
Discharge Functional Assessment and a
Care Plan That Addresses Function
(NQF #2631); and (2) Medical
conditions and comorbidities: The data
elements used to calculate the pressure
ulcer measures, Percent of Residents or
Patients with Pressure Ulcers That Are
New or Worsened (Short Stay) (NQF
#0678) and the replacement measure,
Changes in Skin Integrity Post-Acute
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Care: Pressure Ulcer/Injury. We stated
that these data elements were important
for care planning, known to be valid and
reliable, and already being reported by
IRFs for the calculation of quality
measures.
Since we issued the FY 2018 IRF PPS
final rule, IRFs have had an opportunity
to familiarize themselves with other
new reporting requirements that we
have adopted under the IMPACT Act.
We have also conducted further testing
of the SPADEs, as described more fully
below, and believe that this testing
supports the use of the SPADEs in our
PAC assessment instruments. Therefore,
we proposed to adopt many of the same
SPADEs that we previously proposed to
adopt, along with other SPADEs.
We proposed that IRFs would be
required to report these SPADEs
beginning with the FY 2022 IRF QRP. If
finalized as proposed, IRFs would be
required to report these data with
respect to admission and discharge for
Medicare Part A and Medicare
Advantage patients discharged between
October 1, 2020, and December 31, 2020
for the FY 2022 IRF QRP. Beginning
with the FY 2023 IRF QRP, we proposed
that IRFs must report data with respect
to Medicare Part A and Medicare
Advantage admissions and discharges
that occur during the subsequent
calendar year (for example, CY 2021 for
the FY 2023 IRF QRP, CY 2022 for the
FY 2024 IRF QRP).
We also proposed that IRFs that
submit the Hearing, Vision, Race, and
Ethnicity SPADEs with respect to
admission will be deemed to have
submitted those SPADEs with respect to
both admission and discharge, because
it is unlikely that the assessment of
those SPADEs at admission will differ
from the assessment of the same
SPADEs at discharge.
In selecting the proposed SPADEs
below, we considered the burden of
assessment-based data collection and
aimed to minimize additional burden by
evaluating whether any data that is
currently collected through one or more
PAC assessment instruments could be
collected as SPADEs. In selecting the
SPADEs below, we also took into
consideration the following factors with
respect to each data element:
(1) Overall clinical relevance;
(2) Interoperable exchange to facilitate
care coordination during transitions in
care;
(3) Ability to capture medical
complexity and risk factors that can
inform both payment and quality; and
(4) Scientific reliability and validity,
general consensus agreement for its
usability.
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In identifying the SPADEs proposed
below, we additionally drew on input
from several sources, including TEPs
held by our data element contractor,
public input, and the results of a recent
National Beta Test of candidate data
elements conducted by our data element
contractor (hereafter ‘‘National Beta
Test’’).
The National Beta Test collected data
from 3,121 patients and residents across
143 PAC facilities (26 LTCHs, 60 SNFs,
22 IRFs, and 35 HHAs) from November
2017 to August 2018 to evaluate the
feasibility, reliability, and validity of the
candidate data elements across PAC
settings. The 3,121 patients and
residents with an admission assessment
included 507 in LTCHs, 1,167 in SNFs,
794 in IRFs, and 653 in HHAs. The
National Beta Test also gathered
feedback on the candidate data elements
from staff who administered the test
protocol in order to understand
usability and workflow of the candidate
data elements. More information on the
methods, analysis plan, and results for
the National Beta Test can be found in
the document titled, ‘‘Development and
Evaluation of Candidate Standardized
Patient Assessment Data Elements:
Findings from the National Beta Test
(Volume 2),’’ available in the document
titled, ‘‘Development and Evaluation of
Candidate Standardized Patient
Assessment Data Elements: Findings
from the National Beta Test (Volume
2),’’ available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
Further, to inform the proposed
SPADEs, we took into account feedback
from stakeholders, as well as from
technical and clinical experts, including
feedback on whether the candidate data
elements would support the factors
described above. Where relevant, we
also took into account the results of the
Post-Acute Care Payment Reform
Demonstration (PAC PRD) that took
place from 2006 to 2012.
Comment: Several commenters were
supportive of the SPADE proposals. A
commenter recognized that the
proposed SPADEs may influence care,
impact case mix and risk adjustment
scores, and drive planning for future
management. Other commenters
supported the proposals to add the
proposed SPADEs to the IRF–PAI, with
one noting that many of the data
elements are already collected and
reported on, and the other stating that
the items are important to describing
current IRF patients and are applicable
to determining patient acuity. Another
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commenter stated that data
standardization as accomplished by the
SPADEs will help facilitate appropriate
payment reforms and appropriate
quality measures.
Response: We thank the commenters
for their support. We selected the
proposed SPADEs in part because of the
attributes that the commenters noted,
such as their ability to describe IRF
patients and to support future quality
measurement.
Comment: Some commenters stated
support but noted reservations. One
commenter described the SPADEs as an
appropriate start, but noted that the
SPADEs cannot stand alone, and must
be built upon in order to be useful for
risk adjustment and quality
measurement. Similarly, another
commenter suggested CMS continue
working with clinicians and researchers
to ensure that the SPADEs are collecting
valid, reliable, and useful data, and to
continue to refine and explore new data
elements for standardization.
Response: We agree with the
commenter’s statement that the SPADEs
are an appropriate start for
standardization, but we disagree that
they cannot stand alone. While we
intend to evaluate the SPADEs as they
are submitted and explore additional
opportunities for standardization, we
also believe that the SPADEs as
proposed represent an important core
set of information about clinical status
and patient characteristics and they will
be useful for quality measurement. We
will continue to explore the use of the
SPADEs across our PAC setting,
continuing our efforts to explore the
feasibility, reliability, validity, and
usability of the data elements in our
measure models and QRPs. We would
welcome continued input,
recommendations, and feedback from
stakeholders about ways to improve
assessment and quality measurement for
PAC providers, including ways that the
SPADEs could be used in the IRF QRP.
Input can be shared with CMS through
our PAC Quality Initiatives email
address PACQualityInitiative@
cms.hhs.gov.
Comment: One commenter noted
support for the goals of the IMPACT
Act, but expressed concern about the
scope and timing of proposed changes,
including the SPADEs. The same
commenter suggested that CMS share
with the public a data use strategy and
analysis plan for the SPADEs so that
providers better understand how CMS
will assess the potential usability of the
SPADEs to support changes to payment
and quality programs.
Response: We thank the commenter
for the support and appreciate their
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concern about the proposed changes.
We intend to monitor and evaluate
SPADEs as they are submitted, and to
continue to engage stakeholders around
ways the SPADEs could be best used in
the PAC quality programs. We will
continue to communicate and
collaborate with stakeholders by
soliciting input on use of the SPADEs in
the IRF QRP through future rulemaking.
Comment: One commenter was
generally critical of the set of SPADEs
proposed, stating they fail to adequately
describe a patient’s clinical situation
with regard to their level of
independence, including swallowing
function, communication, and cognitive
function.
Response: The proposed SPADEs
were selected based on their overall
clinical relevance to PAC providers,
including IRFs, their ability to facilitate
care coordination during transitions,
their ability to capture medical
complexity and risk factors, and their
scientific reliability and validity. We
have strived to balance the scope and
level of detail of the data elements
against the potential burden placed on
patients and providers. At this time,
SPADEs focused on impairments are
limited to sensory impairments (that is,
hearing and vision) and do not include
swallowing. The patient’s ability to
communicate is also not captured with
a SPADE, although we note that the
IRF–PAI includes two data elements on
communication: Expression of Ideas and
Wants, and Understanding Verbal and
Non-Verbal Content. However, in
combination with other sections of the
IRF–PAI that have been standardized
across PAC providers, we believe the
proposed SPADEs capture key clinical
information (for example, cognitive
function for patients who are able to
communicate, as collected by the BIMS)
and form an important foundation of
standardized assessment on which to
build.
Comment: One commenter described
several concerns about the scope and
implementation of the National Beta
Test, including the representativeness of
IRFs included in the sample, the share
of total IRF patients included in the
National Beta Test, the reported
exclusion of patients with
communication and cognitive
impairments, and the exclusion of nonEnglish speaking patients, and
described how these concerns
compromise their confidence in the
findings of the National Beta Test.
Response: In a supplementary
document to the proposed rule, we
described key findings from the
National Beta Test related to the
proposed SPADEs. We also referred
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39111
readers to an initial volume of the
National Beta Test report that details the
methodology of the field test
(‘‘Development and Evaluation of
Candidate Standardized Patient
Assessment Data Elements: Findings
from the National Beta Test (Volume
2),’’ available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html). Additional volumes of the
National Beta Test report will be
available in late 2019.
To address the commenter’s specific
concerns, we note that the National Beta
Test was designed to generate valid and
robust national SPADE performance
estimates for each of the four PAC
provider types, which required
acceptable geographic diversity,
sufficient sample size, and reasonable
coverage of the range of clinical
characteristics. To meet these
requirements, the National Beta Test
was carefully designed so that data
could be collected from a wide range of
environments, allowing for thorough
evaluation of candidate SPADE
performance in all PAC settings. The
approach included a stratified random
sample, to maximize generalizability,
and subsequent analyses included
extensive checks on the sampling
design.
The commenter further implied that
the small share of overall IRF
admissions included in the Beta test is
indicative of inadequate
representativeness. The objective of the
National Beta Test was to evaluate the
performance of candidate SPADEs for
cross-setting use. It is true that the
proportion of IRFs may not reflect actual
proportion in the United States, but our
sampling design ensured that sufficient
spread of IRFs across randomly selected
markets, and adequate numbers to
provide ample data with which to
evaluate SPADE performance in IRFs
relative to other settings.
The National Beta Test did not
exclude non-communicative patients/
residents; rather, it had two distinct
samples, one of which focused on
patients/residents who were able to
communicate, and one of which focused
on patient/residents who were not able
to communicate. The assessment of noncommunicative patients/residents
differed primarily in that observational
assessments were substituted for some
interview assessments. Non-Englishspeaking patients were excluded from
the National Beta Test due to feasibility
constraints during the field test.
Including limited English proficiency
patients/residents in the sample would
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have required the Beta test facilities to
engage or involve translators during the
test assessments. We anticipated that
this would have added undue
complexity to what facilities/agencies
were being requested to do, and would
have undermined the ability of facility/
agency staff to complete the requested
number of assessments during the study
period. Moreover, there is strong
existing evidence for the feasibility of
all clinical patient/resident interview
SPADEs included in this final rule
(BIMS [section IX.G.1 in this final rule],
Pain Interference [section IX.G.3 in this
final rule], PHQ [section IX.G.1 in this
final rule]) when administered in other
languages, either through standard PAC
workflow, as tested and currently
collected in the MDS 3.0, or through
rigorous translation and testing, such as
the PHQ. For all these reasons, we
determined that the performance of
translated versions of these patient/
resident interview SPADEs did not need
to be further evaluated. In addition,
because their exclusion did not threaten
our ability to achieve acceptable
geographic diversity, sufficient sample
size, and reasonable coverage of the
range of PAC patient/resident clinical
characteristics, the exclusion of limited
English proficiency patients/residents
was not considered a limitation to
interpretation of the National Beta Test
results.
Comment: Two commenters wanted
CMS to share more information from the
National Beta Test. One of the
commenters remarked on the lack of
information about clinical
characteristics that has been shared with
stakeholders, limiting their ability to
draw conclusions about the data, and
requested that CMS release the data
from the National Beta Test to be
analyzed by third parties. The other
commenter noted that CMS has not
shared quantitative results of the
National Beta Test which has limited
the ability of stakeholders to determine
if these items will yield useful
information for quality and/or payment
purposes, and suggested CMS release
additional information, such as
response frequencies, and analysis from
the field test to provide evidence of the
validity and utility of the SPADEs for
quality and payment.
Response: We shared both
quantitative and qualitative findings
from the National Beta Test with
stakeholders at a public meeting on
November 27, 2018. For each SPADE
proposed in this rule within the clinical
categories in the IMPACT Act, we
provided information in the
supplementary documents to the
proposed rule (the document titled
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‘‘Proposed Specifications for IRF QRP
Quality Measures and Standardized
Patient Assessment Data Elements,’’
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html) on the feasibility and
reliability based on findings from the
National Beta Test.
We are in the process of writing the
final report for the National Beta Test,
which includes the clinical SPADEs in
this rule as well as additional data
elements. Volume 2 of that report
(‘‘Development and Evaluation of
Candidate Standardized Patient
Assessment Data Elements. Findings
from the National Beta Test (Volume
2)’’) was posted on CMS’ website in
March 2019. The other volumes will be
available in late 2019. In addition, we
are committed to making data available
for researchers and the public to
analyze, and to doing so in a way that
protects the privacy of patients and
providers who participated in the
National Beta Test. We are in the
process of creating research identifiable
files that we anticipate will be available
through a data use agreement sometime
in 2019.
Comment: Many commenters
expressed concerns with respect to the
standardized patient assessment data
proposals. Several commenters stated
that the standardized patient assessment
data reporting requirements will impose
significant burden on providers, given
the volume of new standardized patient
assessment data elements, and
corresponding sub-elements, that were
proposed to be added to the IRF–PAI.
One commenter noted that the addition
of the proposed standardized patient
assessment data elements would require
an expanded timeline to implement to
ensure necessary operational and
workflow revisions.
Response: We acknowledge the
additional burden that the SPADEs will
impose on providers and patients. Our
development and selection process for
the SPADEs we are adopting in this
final rule prioritized data elements that
are essential to comprehensive patient
care. We maintain that there will be
significant benefit associated with each
of the SPADEs to providers and
patients, in that they are clinically
useful (for example, for care planning),
they support patient-centered care, and
they will promote interoperability and
data exchange between providers.
During the SPADE development
process, we were cognizant of the
changes that providers will need to
make to implement these additions to
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the IRF–PAI. In the last two rules (82 FR
36287 through 36289, 83 FR 38555), we
provided information about goals,
scope, and timeline for implementing
SPADEs, as well as updated IRFs about
ongoing development and testing of data
elements through other public forums.
We believe that IRFs have had an
opportunity to familiarize themselves
with other new reporting requirements
that we have adopted under the
IMPACT Act and prepare for additional
changes.
Comment: Some commenters
expressed concern that this additional
burden was not justified because, in
their view, there was limited or no
evidence for the SPADEs to describe
case mix, measure quality, or improve
care. One of these commenters noted
that CMS has provided evidence of
validity, reliability, and feasibility
through documents related to the
National Beta Test, but stated that CMS
has not provided any evidence that the
proposed SPADEs have the ‘‘potential
for improving quality’’ or ‘‘utility for
describing case mix.’’
Response: The clinical SPADEs
proposed in this rule were the result of
an extensive consensus vetting process
in which experts and stakeholders were
engaged through Technical Expert
Panels, Special Open Door Forums, and
posting of interim reports and other
documents on the CMS website. Results
of these activities provide evidence that
experts and providers believe that the
proposed SPADEs have the potential for
measuring quality, for describing case
mix, and improving care. We refer the
commenter to the most recent TEP
report: A summary of the September 17,
2018 TEP meeting titled ‘‘SPADE
Technical Expert Panel Summary (Third
Convening)’’, which is available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html. In this report, we
summarize the TEP’s discussion of
individual SPADEs in which they
reflect on the clinical usefulness and
importance of the SPADEs for
describing patient acuity (case mix) and
providing high-quality clinical care
(improving quality). Therefore, we have
provided evidence that the SPADEs
have the potential for improving quality
and utility for describing case mix.
Comment: One commenter believes
that the expansion of the IRF–PAI
assessment will prove to be intrusive
and prove challenging for patients who
are elderly, frail, in pain, or have
cognitive deficits, causing the patients
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to lose focus, and thus, impact the
accuracy of the data.
Response: We acknowledge that
several SPADEs in this rule require the
patient to be asked questions directly.
We believe that direct patient
assessment and patient-reported
outcomes on these topics have benefits
for providers and patients. These data
elements support patient-centered care
by soliciting the patient’s perspective,
and better information on a patient’s
status is expected to improve the care
the patient receives.76 77 78 The burden
the patient-interview data elements
place on patients is necessary for
accurate assessment of the patient’s
status. Regarding the validity and
performance of interview-based data
elements, we note that many of these
data elements (for example, the BIMS,
PHQ, and Pain Interference data
elements) are currently used in the MDS
in SNFs. Evidence from that setting, as
well as from the National Beta Test,
demonstrates feasibility of these data
elements for even very sick patients,
such as many patients receiving care
from IRFs.
Comment: Commenters also stated
that the time burden (as in, ‘‘time-tocomplete’’) associated with the clinical
SPADEs was underestimated, with some
commenters noting that it did not
account for clinician time to review
charts and update treatment plans or
that test conditions do not represent
conditions of day-to-day operation. One
commenter stated that the estimated
time to complete reported in the
National Beta Test was based only on
the time needed to enter a value on a
tablet and did not include the time to
evaluate the patient on each item.
Another commenter stated that because
testing conditions focused on
cognitively intact, English-speaking
patients with no speech or language
deficits, the estimates of impact to
providers’ time and resources is
inadequate.
Response: We disagree with the
commenters that the National Beta Test
time-to-complete estimates are
underestimates. Contrary to what one
76 Boyce MB, Browne JP, Greenhalgh J The
experiences of professionals with using information
from patient-reported outcome measures to improve
the quality of healthcare: A systematic review of
qualitative research BMJ Quality & Safety
2014;23:508–518.
77 Chen J, Ou L, Hollis SJ. A systematic review of
the impact of routine collection of patient reported
outcome measures on patients, providers and health
organizations in an oncologic setting. BMC Health
Services Research 2013;13:211.
78 Marshall, S., Haywood, K. and Fitzpatrick, R.
(2006), Impact of patient-reported outcome
measures on routine practice: A structured review.
Journal of Evaluation in Clinical Practice, 12: 559–
568. doi:10.1111/j.1365–2753.2006.00650.x.
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commenter noted, we wish to clarify
that time-to-complete estimates from the
National Beta Test included the time
spent both to collect data, including the
review of the medical record, if needed,
and to enter the data elements into a
tablet. We note that time-to-complete
estimates were calculated using the data
from Facility/Agency Staff only, and not
Research Nurses, who completed more
training and conducted more
assessments overall than the Facility/
Agency staff. This decision to calculate
time-to-complete estimates from
Facility/Agency Staff only supports our
claim that the time-to-complete
estimates are accurate reflections of the
time the SPADEs will require when
implemented by PAC providers in dayto-day operations. Contrary to another
commenter’s statement, we also wish to
clarify that National Beta Test did
exclude patients/residents who were not
able to communicate in English, but did
not categorically exclude patients with
cognitive impairment or patients with
speech or language deficits. Therefore,
we believe that our estimates of time-tocomplete capture the general population
of IRF patients, including those with
communication impairments.
Comment: Some commenters
recommended changes to when and
how SPADEs would be collected in
order to reduce administrative burden.
These recommendations included
collecting data only at admission when
answers are unlikely to change between
admission and discharge, adopting a
staged implementation or only a subset
of the proposed data elements, and that
CMS explore options for obtaining these
data via claims or voluntary reporting
only, particularly as many of the
proposed SPADEs are not relevant to
IRF patients.
Response: We appreciate the
commenters’ recommendations. To
support data exchange between settings,
and to support quality measurement,
section 1899B(b)(1)(A) of the Act
requires that the SPADEs be collected
with respect to both admission and
discharge. In the FY 2020 IRF PPS
proposed rule (84 FR 17292), we
proposed that IRFs that submit four
SPADEs with respect to admission will
be deemed to have submitted those
SPADEs with respect to both admission
and discharge, because we stated that it
is unlikely that the assessment of those
SPADEs at admission would differ from
the assessment of the same SPADEs at
discharge. We note that a patient’s
ability to hear or ability to see are more
likely to change between admission and
discharge than, for example, a patient’s
self-report of his or her race, ethnicity,
preferred language, or need for
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interpreter services. The Hearing and
Vision SPADEs are also different from
the other SPADEs (that is, Race,
Ethnicity, Preferred Language, and
Interpreter Services) because evaluation
of sensory status is a fundamental part
of the ongoing nursing assessment
conducted for IRF patients. Therefore,
clinically significant changes that occur
in a patient’s hearing or vision status
during the IRF stay would be captured
as part of the clinical record and
communicated to the next setting of
care, as well as taken into account
during discharge planning as a part of
standard best practice.
After consideration of public
comments discussed in sections IX.G.4
and IX.G.4.b in this final rule, we will
deem IRFs that submit the Hearing,
Vision, Race, Ethnicity, Preferred
Language, and Interpreter Services
SPADEs with respect to admission to
have submitted with respect to both
admission and discharge. We will take
into consideration the recommendation
to obtain patient data from claims data
in future work.
Comment: A commenter
recommended that CMS limit the
number and type of data elements
implemented in the coming year,
continue ongoing dialogue with
stakeholders, and develop and
implement a process to assess the value
of specific indicators for all patient
types. Another commenter
recommended that CMS conduct a
thorough analysis of SPADEs currently
collected to determine if any current
data elements could be eliminated. One
commenter believed that CMS should
not finalize the implementation of the
SPADEs until they evaluate alternative
means of data collection (such as via
billing/claims data), or measures to
reduce burden (such as removal of
duplicative data elements and
elimination of data collection at
discharge).
Response: We note that we adopted
SPADEs in the last two rule cycles to
support the adoption of the IRF
Functional Outcomes Measures
(Application of Percent of Long-Term
Care Hospital Patients with an
Admission and Discharge Functional
Assessment and a Care Plan That
Addresses Function (80 FR 47111);
Change in Self-Care for Medical
Rehabilitation Patients (80 FR 47117);
Change in Mobility Score for Medical
Rehabilitation Patients (80 FR 47118);
Discharge Self-Care Score for Medical
Rehabilitation Patients (80 FR 47119);
Discharge Mobility Score for Medical
Rehabilitation Patients (80 FR 47120))
and drug regimen review (Drug Regimen
Review Conducted with Follow-Up for
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Identified Issues (81 FR 52111)). We
have also communicated about the
SPADE development work with
stakeholders over the last 2 years
through SODFs held on June 20, 2017,
September 28, 2017, December 12, 2017,
March 28, 2018, June 19, 2018, and July
25, 2018, and at a public meeting of
stakeholders on November 27, 2018.
Therefore, our implementation to date
has been incremental while we have
strived to keep stakeholders apprised as
to the status of ongoing SPADE
development. We have also conducted a
large-scale test of feasibility and
reliability—the National Beta Test,
described in the proposed rule (84 FR
17293)—which, along with the
consensus vetting activities described in
the proposals for each SPADE, provide
evidence of the value of the SPADEs for
patients across PAC settings, including
IRF patients. We will monitor and
conduct analysis on the SPADEs as they
are submitted in order to identify any
problems and to identify any
unnecessary burden or duplication.
Comment: One commenter
recommended that CMS focus on
providing funding and administrative
support to allow improvements and
standardization to the electronic
medical record to allow effective
interoperability across all post-acute
sites.
Response: We appreciate the
commenter’s recommendation. At this
time, funding for electronic medical
record adoption and support is not
currently authorized for PAC providers.
Final decisions on the SPADEs are
given below, following more detailed
comments on each SPADE proposal.
G. Standardized Patient Assessment
Data by Category
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1. Cognitive Function and Mental Status
Data
A number of underlying conditions,
including dementia, stroke, traumatic
brain injury, side effects of medication,
metabolic and/or endocrine imbalances,
delirium, and depression, can affect
cognitive function and mental status in
PAC patient and resident populations.79
The assessment of cognitive function
and mental status by PAC providers is
important because of the high
percentage of patients and residents
with these conditions,80 and because
79 National Institute on Aging. (2014). Assessing
Cognitive Impairment in Older Patients. A Quick
Guide for Primary Care Physicians. Retrieved from
https://www.nia.nih.gov/alzheimers/publication/
assessing-cognitive-impairment-older-patients.
80 Gage B., Morley M., Smith L., et al. (2012).
Post-Acute Care Payment Reform Demonstration
(Final report, Volume 4 of 4). Research Triangle
Park, NC: RTI International.
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these assessments provide opportunity
for improving quality of care.
Symptoms of dementia may improve
with pharmacotherapy, occupational
therapy, or physical activity,81 82 83 and
promising treatments for severe
traumatic brain injury are currently
being tested.84 For older patients and
residents diagnosed with depression,
treatment options to reduce symptoms
and improve quality of life include
antidepressant medication and
psychotherapy,85 86 87 88 and targeted
services, such as therapeutic recreation,
exercise, and restorative nursing, to
increase opportunities for psychosocial
interaction.89
In alignment with our Meaningful
Measures Initiative, accurate assessment
of cognitive function and mental status
of patients and residents in PAC is
expected to make care safer by reducing
harm caused in the delivery of care;
promote effective prevention and
treatment of chronic disease; strengthen
person and family engagement as
partners in their care; and promote
effective communication and
coordination of care. For example,
standardized assessment of cognitive
function and mental status of patients
and residents in PAC will support
establishing a baseline for identifying
81 Casey D.A., Antimisiaris D., O’Brien J. (2010).
Drugs for Alzheimer’s Disease: Are They Effective?
Pharmacology & Therapeutics, 35, 208–11.
82 Graff M.J., Vernooij-Dassen M.J., Thijssen M.,
Dekker J., Hoefnagels W.H., Rikkert M.G.O. (2006).
Community Based Occupational Therapy for
Patients with Dementia and their Care Givers:
Randomised Controlled Trial. BMJ, 333(7580):
1196.
83 Bherer L., Erickson K.I., Liu-Ambrose T. (2013).
A Review of the Effects of Physical Activity and
Exercise on Cognitive and Brain Functions in Older
Adults. Journal of Aging Research, 657508.
84 Giacino J.T., Whyte J., Bagiella E., et al. (2012).
Placebo-controlled trial of amantadine for severe
traumatic brain injury. New England Journal of
Medicine, 366(9), 819–826.
85 Alexopoulos G.S., Katz I.R., Reynolds C.F. 3rd,
Carpenter D., Docherty J.P., Ross R.W. (2001).
Pharmacotherapy of depression in older patients: A
summary of the expert consensus guidelines.
Journal of Psychiatric Practice, 7(6), 361–376.
86 Arean P.A., Cook B.L. (2002). Psychotherapy
and combined psychotherapy/pharmacotherapy for
late life depression. Biological Psychiatry, 52(3),
293–303.
87 Hollon S.D., Jarrett R.B., Nierenberg A.A.,
Thase M.E., Trivedi M., Rush A.J. (2005).
Psychotherapy and medication in the treatment of
adult and geriatric depression: Which monotherapy
or combined treatment? Journal of Clinical
Psychiatry, 66(4), 455–468.
88 Wagenaar D, Colenda CC, Kreft M, Sawade J,
Gardiner J, Poverejan E. (2003). Treating depression
in nursing homes: Practice guidelines in the real
world. J Am Osteopath Assoc. 103(10), 465–469.
89 Crespy SD, Van Haitsma K, Kleban M, Hann CJ.
Reducing Depressive Symptoms in Nursing Home
Residents: Evaluation of the Pennsylvania
Depression Collaborative Quality Improvement
Program. J Healthc Qual. 2016. Vol. 38, No. 6, pp.
e76–e88.
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changes in cognitive function and
mental status (for example, delirium),
anticipating the patient’s or resident’s
ability to understand and participate in
treatments during a PAC stay, ensuring
patient and resident safety (for example,
risk of falls), and identifying appropriate
support needs at the time of discharge
or transfer. Standardized patient
assessment data elements will enable or
support clinical decision-making and
early clinical intervention; personcentered, high quality care through
facilitating better care continuity and
coordination; better data exchange and
interoperability between settings; and
longitudinal outcome analysis.
Therefore, reliable standardized patient
assessment data elements assessing
cognitive function and mental status are
needed to initiate a management
program that can optimize a patient’s or
resident’s prognosis and reduce the
possibility of adverse events.
The data elements related to cognitive
function and mental status were first
proposed as standardized patient
assessment data elements in the FY
2018 IRF PPS proposed rule (82 FR
20723 through 20726). In response to
our proposals, a few commenters noted
that the proposed data elements did not
capture some dimensions of cognitive
function and mental status, such as
functional cognition, communication,
attention, concentration, and agitation.
One commenter also suggested that
other cognitive assessments should be
considered for standardization. Another
commenter stated support for the
standardized assessment of cognitive
function and mental status, because it
could support appropriate use of skilled
therapy for beneficiaries with
degenerative conditions, such as
dementia, and appropriate use of
medications for behavioral and
psychological symptoms of dementia.
We sought comment on our proposals
to collect as standardized patient
assessment data the following data with
respect to cognitive function and mental
status.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the cognitive function and
mental status data elements.
Comment: A few commenters were
supportive of the proposal to adopt the
BIMS, CAM, and PHQ–2 to 9 as SPADEs
on the topic of cognitive function and
mental status. One commenter agreed
that standardizing cognitive assessments
will allow providers to identify changes
in status, support clinical decisionmaking, and improve care continuity
and interventions.
Response: We thank the commenters
for their support. We selected the
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Cognitive Function and Mental Status
data elements for proposal as
standardized data in part because of the
attributes that the commenters noted.
Comment: A few commenters noted
limitations of these SPADEs to fully
assess all areas of cognition and mental
status, particularly mild to moderate
cognitive impairment, and performance
deficits that may be related to cognitive
impairment. Some commenters
suggested CMS continue exploring
assessment tools on the topic of
cognition and to include a more
comprehensive assessment of cognitive
function for use in PAC settings, noting
that highly vulnerable patients with a
mild cognitive impairment cannot be
readily identified through the current
SPADEs.
Response: We have strived to balance
the scope and level of detail of the data
elements against the potential burden
placed on patients and providers. In our
past work, we evaluated the potential of
several different cognition assessments
for use as standardized data elements in
PAC settings. We ultimately decided on
the BIMS, CAM, and PHQ–2 to 9 data
elements in our proposal as a starting
point. We would welcome continued
input, recommendations, and feedback
from stakeholders about additional data
elements for standardization, which can
be shared with CMS through our PAC
Quality Initiatives email address:
PACQualityInitiative@cms.hhs.gov.
Comment: A commenter stated that
cognitive assessment should be
individualized, rather than
standardized, and performed as
determined by patient needs.
Response: We believe that the
standardized assessment of cognitive
function is essential to achieving the
goals of the IMPACT Act. We also wish
to clarify that the proposed SPADEs are
not intended to replace comprehensive
clinical evaluation and in no way
preclude providers from conducting
further patient evaluation or
assessments in their settings as they
believe are necessary and useful.
Comment: Regarding future use of
these data elements, one commenter
recommended that CMS monitor the use
of the cognition and mental status
SPADEs as risk adjustors and make
appropriate adjustments to methodology
as needed.
Response: We intend to monitor data
submitted via the proposed SPADEs and
will consider these uses in the future.
We will also continue to review
recommendation and feedback from
stakeholders regarding data elements
that would both satisfy the categories
listed in the IMPACT Act and provide
meaningful data.
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Final decisions on the SPADEs are
given below, following more detailed
comments on each SPADE proposal.
• Brief Interview for Mental Status
(BIMS)
In the FY 2020 IRF PPS proposed rule
(84 FR 17294 through 17295), we
proposed that the data elements that
comprise the BIMS meet the definition
of standardized patient assessment data
with respect to cognitive function and
mental status under section
1899B(b)(1)(B)(ii) of the Act.
As described in the FY 2018 IRF PPS
Proposed Rule (82 FR 20723 through
20724), dementia and cognitive
impairment are associated with longterm functional dependence and,
consequently, poor quality of life and
increased healthcare costs and
mortality.90 This makes assessment of
mental status and early detection of
cognitive decline or impairment critical
in the PAC setting. The intensity of
routine nursing care is higher for
patients and residents with cognitive
impairment than those without, and
dementia is a significant variable in
predicting readmission after discharge
to the community from PAC
providers.91
The BIMS is a performance-based
cognitive assessment screening tool that
assesses repetition, recall with and
without prompting, and temporal
orientation. The data elements that
make up the BIMS are seven questions
on the repetition of three words,
temporal orientation, and recall that
result in a cognitive function score. The
BIMS was developed to be a brief,
objective screening tool, with a focus on
learning and memory. As a brief
screener, the BIMS was not designed to
diagnose dementia or cognitive
impairment, but rather to be a relatively
quick and easy to score assessment that
could identify cognitively impaired
patients, as well as those who may be
at risk for cognitive decline and require
further assessment. It is currently in use
in two of the PAC assessments: The
MDS used by SNFs and the IRF–PAI
used by IRFs. For more information on
the BIMS, we refer readers to the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
90 Agu
¨ ero-Torres, H., Fratiglioni, L., Guo, Z.,
Viitanen, M., von Strauss, E., & Winblad, B. (1998).
‘‘Dementia is the major cause of functional
dependence in the elderly: 3-year follow-up data
from a population-based study.’’ Am J of Public
Health 88(10): 1452–1456.
91 RTI International. Proposed Measure
Specifications for Measures Proposed in the FY
2017 IRF QRP NPRM. Research Triangle Park, NC.
2016.
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www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The data elements that comprise the
BIMS were first proposed as
standardized patient assessment data
elements in the FY 2018 IRF PPS
proposed rule (82 FR 20723 through
20724). In that proposed rule, we stated
that the proposal was informed by input
we received through a call for input
published on the CMS Measures
Management System Blueprint website.
Input submitted from August 12 to
September 12, 2016, noted support for
use of the BIMS, noting that it is
reliable, feasible to use across settings,
and will provide useful information
about patients and residents. We also
stated that the data collected through
the BIMS will provide a clearer picture
of patient or resident complexity, help
with the care planning process, and be
useful during care transitions and when
coordinating across providers. A
summary report for the August 12 to
September 12, 2016 public comment
period titled ‘‘SPADE August 2016
Public Comment Summary Report’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
the use of the BIMS, especially in its
capacity to inform care transitions, but
other commenters were critical, noting
the limitations of the BIMS to assess
mild cognitive impairment and
‘‘functional’’ cognition, and that the
BIMS cannot be completed by patients
and residents who are unable to
communicate. They also stated that
other cognitive assessments available in
the public domain should be considered
for standardization. One commenter
suggested that CMS require use of the
BIMS with respect to discharge, as well
as admission.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the BIMS was
included in the National Beta Test of
candidate data elements conducted by
our data element contractor from
November 2017 to August 2018. Results
of this test found the BIMS to be feasible
and reliable for use with PAC patients
and residents. More information about
the performance of the BIMS in the
National Beta Test can be found in the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
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Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements and the TEP supported the
assessment of patient or resident
cognitive status with respect to both
admission and discharge. A summary of
the September 17, 2018 TEP meeting
titled ‘‘SPADE Technical Expert Panel
Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held Special Open Door
Forums (SODFs) and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019.
Some commenters also expressed
concern that the BIMS, if used alone,
may not be sensitive enough to capture
the range of cognitive impairments,
including mild cognitive impairment. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
We understand the concerns raised by
stakeholders that BIMS, if used alone,
may not be sensitive enough to capture
the range of cognitive impairments,
including functional cognition and MCI,
but note that the purpose of the BIMS
data elements as SPADEs is to screen for
cognitive impairment in a broad
population. We also acknowledge that
further cognitive tests may be required
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based on a patient’s condition and will
take this feedback into consideration in
the development of future standardized
patient assessment data elements.
However, taking together the
importance of assessing for cognitive
status, stakeholder input, and strong test
results, we proposed that the BIMS data
elements meet the definition of
standardized patient assessment data
with respect to cognitive function and
mental status under section
1899B(b)(1)(B)(ii) of the Act and to
adopt the BIMS data elements as
standardized patient assessment data for
use in the IRF QRP.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the BIMS data elements.
Comment: One commenter supported
the collection of BIMS at both
admission and discharge and believes it
will result in more complete data and
better care.
Response: We thank the commenter
for the support of the BIMS data
element.
Comment: One commenter stated that
the BIMS fails to detect mild cognitive
impairment, differentiate cognitive
impairment from a language
impairment, link impairment to
functional limitation, or identify issues
with problem solving and executive
function. This commenter
recommended use of the Development
of Outpatient Therapy Payment
Alternatives (DOTPA) items for PAC, as
well as a screener targeting functional
cognition. Another commenter also
recommended CMS identify a better
cognitive assessment and not to move
forward with the proposal.
Response: We recognize that the BIMS
assesses components of cognition and
does not, alone, provide a
comprehensive assessment of potential
cognitive impairment. We clarify that
any SPADE is intended as a minimum
assessment and does not limit the
ability of providers to conduct a more
comprehensive assessment of cognition
to identify the complexities or potential
impacts of cognitive impairment that
the commenter describes.
We evaluated the suitability of the
DOTPA, as well as other screening tools
that targeted functional cognition, by
engaging our TEP, through ‘‘alpha’’
feasibility testing, and through soliciting
input from stakeholders. At the second
meeting of TEP in March 2017, members
questioned the use of data elements that
rely on assessor observation and
judgment, such as DOTPA CARE tool
items, and favored other assessments of
cognition that required patient
interview or patient actions. The TEP
also discussed performance-based
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assessment of functional cognition.
These are assessments that require
patients to respond by completing a
simulated task, such as ordering from a
menu, or reading medication
instructions and simulating the taking of
medications, as required by the
Performance Assessment of Self-Care
Skills (PASS) items.
In Alpha 2 feasibility testing, which
was conducted between April and July
2017, we included a subset of items
from the DOTPA as well as the PASS.
Findings of that test identified several
limitations of the DOTPA items for use
as SPADEs, such as relatively long to
administer (5 to 7 minutes), especially
in the LTCH setting. Assessors also
indicated that these items had low
relevance for SNF and LTCH patients. In
addition, interrater reliability was
highly variable among the DOTPA
items, both overall and across settings,
with some items showing very low
agreement (as low as 0.34) and others
showing excellent agreement (as high as
0.81). Similarly, findings of the Alpha 2
feasibility test identified several
limitations of the PASS for use as
SPADEs. The PASS was relatively timeintensive to administer (also 5 to 7
minutes), many patients in HHAs and
IRFs needed assistance completing the
PASS tasks, and missing data were
prevalent. Unlike the DOTPA items,
interrater reliability was consistently
high overall for PASS (ranging from 0.78
to 0.92), but the high reliability was not
deemed to outweigh fundamental
feasibility concerns related to
administration challenges. A summary
report for the Alpha 2 feasibility testing
titled ‘‘Development and Maintenance
of Standardized Cross Setting Patient
Assessment Data for Post-Acute Care:
Summary Report of Findings from
Alpha 2 Pilot Testing’’ is available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/Downloads/Alpha-2-SPADEPilot-Summary-Document.pdf.
Feedback was obtained on the DOTPA
and other assessments of functional
cognition through a call for input that
was open from April 26, 2017 to June
26, 2017. While we received support for
the DOTPA, PASS, and other
assessments of functional cognition,
commenters also raised concerns about
the reliability of the DOTPA, given that
it is based on staff evaluation, and the
feasibility of the PASS, given that the
simulated medication task requires
props, such as a medication bottle with
printed label and pill box, which may
not be accessible in all settings. A
summary report for the April 26 to June
26, 2017 public comment period titled
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‘‘Public Comment Summary Report 2’’
is available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/Downloads/
Public-Comment-Summary-Report_
Standardized-Patient-Assessment-DataElement-Work_PC2_Jan-2018.pdf.
Based on the input from our TEP,
results of alpha feasibility testing, and
input from stakeholders, we decided to
propose the BIMS for standardization at
this time due to the body of research
literature supporting its feasibility and
validity, its relative brevity, and its
existing use in the MDS and IRF–PAI.
Comment: A few commenters noted
that BIMS is currently collected by IRFs
and has not been demonstrated to
predict costs or differentiate case-mix
and believes that CMS has not provided
any evidence that the BIMS is capable
of being utilized for quality purposes to
support the collection of these data
elements at discharge. Another
commenter stated that CMS has not
provided quantitative evidence that the
BIMS data elements are capable of
measuring provider performance for
quality or of differentiating case-mix for
payment.
Response: We reiterate that the
purpose of standardizing data elements,
in accordance with the IMPACT Act, is
to support care planning, clinical
decision support, inform case-mix and
quality measurement, support care
transitions, and enable interoperable
data exchange and data sharing between
PAC settings. Before being identified as
a SPADE, the BIMS underwent an
extensive consensus vetting process in
which experts and stakeholders were
engaged through TEPs, SODFs, and
posting of interim reports and other
documents on the CMS.gov website. A
summary of the most recent TEP
meeting (September 17, 2018) titled
‘‘SPADE Technical Expert Panel
Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html. Results of these activities
provide evidence that experts and
providers believe that the BIMS data
elements have the potential for
measuring quality, describing case mix,
and improving care.
Comment: A commenter believes that
assessing BIMS at discharge would not
be clinically useful and would not
contribute to improved patient care or
outcomes. The commenter noted that
assessing BIMS at discharge was not
evaluated during the National Beta Test,
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and objected to the BIMS being
proposed for use at discharge.
Response: We maintain that a
standardized cognitive assessment using
the BIMS is clinically useful and has the
potential to improve patient care and
outcomes. The commenter stated that
the BIMS was not administered at
discharge in the National Beta Test.
However, the BIMS was in fact assessed
at both admission and discharge in the
National Beta Test. Moreover, to support
data exchange between settings, and to
support quality measurement, the
IMPACT Act requires that the SPADEs
be collected with respect to both
admission and discharge. After careful
consideration of the public comments
we received, we are finalizing our
proposal to adopt the BIMS as
standardized patient assessment data
beginning with the FY 2022 IRF QRP as
proposed.
• Confusion Assessment Method (CAM)
In the FY 2020 IRF PPS proposed rule
(84 FR 17295), we proposed that the
data elements that comprise the
Confusion Assessment Method (CAM)
meet the definition of standardized
patient assessment data with respect to
cognitive function and mental status
under section 1899B(b)(1)(B)(ii) of the
Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20724), the CAM
was developed to identify the signs and
symptoms of delirium. It results in a
score that suggests whether a patient or
resident should be assigned a diagnosis
of delirium. Because patients and
residents with multiple comorbidities
receive services from PAC providers, it
is important to assess delirium, which is
associated with a high mortality rate
and prolonged duration of stay in
hospitalized older adults.92 Assessing
these signs and symptoms of delirium is
clinically relevant for care planning by
PAC providers.
The CAM is a patient assessment that
screens for overall cognitive
impairment, as well as distinguishes
delirium or reversible confusion from
other types of cognitive impairment.
The CAM is currently in use in two of
the PAC assessments: A four-item
version of the CAM is used in the MDS
in SNFs; and a six-item version of the
CAM is used in the LTCH CARE Data
Set (LCDS) in LTCHs. We proposed the
four-item version of the CAM that
assesses acute change in mental status,
inattention, disorganized thinking, and
92 Fick, D.M., Steis, M.R., Waller, J.L., & Inouye,
S.K. (2013). ‘‘Delirium superimposed on dementia
is associated with prolonged length of stay and poor
outcomes in hospitalized older adults.’’ J of
Hospital Med 8(9): 500–505.
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altered level of consciousness. For more
information on the CAM, we refer
readers to the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The data elements that comprise the
CAM were first proposed as
standardized patient assessment data
elements in the FY 2018 IRF PPS
proposed rule (82 FR 20724). In that
proposed rule, we stated that the
proposal was informed by public input
we received on the CAM through a call
for input published on the CMS
Measures Management System
Blueprint website. Input submitted from
August 12 to September 12, 2016 noted
support for use of the CAM, noting that
it would provide important information
for care planning and care coordination,
and therefore, contribute to quality
improvement. We also stated that those
commenters had noted the CAM is
particularly helpful in distinguishing
delirium and reversible confusion from
other types of cognitive impairment. A
summary report for the August 12 to
September 12, 2016 public comment
period titled ‘‘SPADE August 2016
Public Comment Summary Report’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, one
commenter supported use of the CAM
for standardized patient assessment
data. However, some commenters
expressed concerns that the CAM data
elements assess: The presence of
behavioral symptoms, but not the cause;
the possibility of a false positive for
delirium due to patient cognitive or
communication impairments; and the
lack of specificity of the assessment
specifications. In addition, other
commenters noted that the CAM is not
necessary because: Delirium is easily
diagnosed without a tool; the CAM and
BIMS assessments are redundant; and
some CAM response options are not
meaningful.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the CAM was
included in the National Beta Test of
candidate data elements conducted by
our data element contractor from
November 2017 to August 2018. Results
of this test found the CAM to be feasible
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and reliable for use with PAC patients
and residents. More information about
the performance of the CAM in the
National Beta Test can be found in the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although they did not
specifically discuss the CAM data
elements, the TEP supported the
assessment of patient or resident
cognitive status with respect to both
admission and discharge. A summary of
the September 17, 2018 TEP meeting
titled ‘‘SPADE Technical Expert Panel
Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for delirium, stakeholder
input, and strong test results, we
proposed that the CAM data elements
meet the definition of standardized
patient assessment data with respect to
cognitive function and mental status
under section 1899B(b)(1)(B)(ii) of the
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Act and to adopt the CAM data elements
as standardized patient assessment data
for use in the IRF QRP.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the proposed CAM data
elements.
Comment: A few commenters stated
that the CAM would be redundant with
other cognitive assessments, such as
BIMS. One commenter stated that
delirium would be assessed prior to
discharge from the acute care setting,
making the assessment of delirium at
admission to the IRF redundant.
Another commenter stated that concerns
about burden outweighed the value that
the CAM might have for some
populations, and noted that daily
physician visits and daily assessments
of patients by the interdisciplinary team
were sufficient to assess cognitive
needs.
Response: The CAM specifically
screens for change in mental status,
inattention, disorganized thinking and
altered level of consciousness, which
can indicate symptoms of delirium.
These symptoms are not assessed by
other cognitive assessments in the IRF–
PAI. We believe the assessment of
delirium at admission and discharge is
important to informing patient care.
Delirium occurs in up to half of
patients/residents receiving PAC
services,93 and signs and symptoms of
delirium are associated with poor
functional recovery,94 rehospitalization, and mortality.95
Because the majority of delirium
episodes are transient,96 we would not
expect assessment of delirium prior to
discharge from the acute care setting to
capture all cases of delirium in PAC, as
there may be an acute change in mental
status from the patient’s baseline or
93 Dan K. Kiely et al., ‘‘Characteristics Associated
with Delirium Persistence Among Newly Admitted
Post-Acute Facility Patients,’’ Journals of
Gerontology: Series A (Biological Sciences and
Medical Sciences), Vol. 59, No. 4, April 2004;
Edward R. Marcantonio et al., ‘‘Delirium Symptoms
in Post-Acute Care: Prevalent, Persistent, and
Associated with Poor Functional Recovery,’’ Journal
of the American Geriatrics Society, Vol. 51, No. 1,
January 2003.
94 Marcantonio, Edward R., Samuel E. Simon,
Margaret A. Bergmann, Richard N. Jones, Katharine
M. Murphy, and John N. Morris, ‘‘Delirium
Symptoms in Post-Acute Care: Prevalent, Persistent,
and Associated with Poor Functional Recovery,’’
Journal of the American Geriatrics Society, Vol. 51,
No. 1, January 2003, pp. 4–9.
95 Edward R. Marcantonio et al., Outcomes of
Older People Admitted to Postacute Facilities with
Delirium,’’ Journal of the American Geratrics
Society, Vol. 53, No. 6, June 2005.
96 Cole MG, Ciampi A, Belzile E, Zhong L.
Persistent delirium in older hospital patients: A
systematic review of frequency and prognosis. Age
Ageing 2009;38:19–26.
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fluctuations in the patient’s behaviors
that are identified after PAC admission.
Comment: Several commenters noted
doubts about the usefulness of the CAM.
One commenter was unsure if CAM will
identify differences in cognitive status
or measure changes during the stay
resulting from therapeutic interventions.
A few commenters stated that the CAM
would not provide information that
would be useful clinically, that it was
not specific enough or too narrowly
focused, and that it should not be
required at discharge. Another
commenter suggested that CMS not
include the CAM as SPADE because
they believe delirium is clinically
apparent, and therefore, doubt that a
standardized assessment of delirium
will contribute to improving patient
care or outcomes. Another commenter
expressed concern that the CAM data
elements would not identify cognitive
needs that would impact quality in
therapeutic intervention across
facilities.
Response: As with any brief screening
tool, we believe that the CAM has value
as a universal assessment to identify
patients in need of further clinical
evaluation. Delirium occurs in up to 50
percent of patients/residents in PAC 97
and is associated with poor
outcomes.98 99 Hyperactive delirium—
the type of delirium that manifests with
agitation—makes up only a quarter of
delirium cases.100 101 Delirium more
commonly manifests as hypoactive, or
‘‘quiet’’ delirium,102 suggesting that
brief, universal screening is appropriate.
Moreover, because there are treatments
for delirium that can be developed
based on medication review, physical
examination, laboratory tests, and
evaluation of environmental factors,103
97 Kiely DK, Jones RN, Bergmann MA,
Marcantonio ER. Association between psychomotor
activity delirium subtypes and mortality among
newly admitted post-acute facility patients. J
Gerontol A Biol Sci Med Sci 2007;62:174–179.
98 Marcantonio, Edward R., Samuel E. Simon,
Margaret A. Bergmann, Richard N. Jones, Katharine
M. Murphy, and John N. Morris, ‘‘Delirium
Symptoms in Post-Acute Care: Prevalent, Persistent,
and Associated with Poor Functional Recovery,’’
Journal of the American Geriatrics Society, Vol. 51,
No. 1, January 2003, pp. 4–9.
99 Edward R. Marcantonio et al., Outcomes of
Older People Admitted to Postacute Facilities with
Delirium,’’ Journal of the American Geratrics
Society, Vol. 53, No. 6, June 2005.
100 Inouye SK, Westendorp RG, Saczynski JS.
Delirium in elderly people. Lancet 2014;383:911–
922.
101 Marcantonio ER. In the clinic: Delirium. Ann
Intern Med 2011;154:ITC6–1–ITC6–1.
102 Yang FM, Marcantonio ER, Inouye SK, et al.
Phenomenological subtypes of delirium in older
persons: Patterns, prevalence, and prognosis.
Psychosomatics 2009;50:248–254.
103 Marcantonio ER. Delirium in Hospitalized
Older Adults. N Engl J Med. 2017 Oct
12;377(15):1456–1466.
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we believe that screening for delirium
would support care planning and care
transitions for these patients.
Comment: A few commenters believe
the CAM would be difficult to
administer and raised concerns about
the training that staff would receive in
order to ensure that administration is
consistent and valid.
Response: We appreciate the
commenters’ recommendation to
provide clear training for administering
the CAM, and will take it into
consideration as we revise the current
training for the IRF–PAI. We intend to
reinforce assessment tips and item
rationale through training, open door
forums, and future rulemaking efforts.
Comment: One commenter disagreed
that delirium assesses a dimension of
cognitive function.
Response: The CAM data elements
were proposed to meet the definition of
the standardized patient assessment
data with respect to cognitive function
and mental status. Section
1899B(b)(1)(B)(ii) of the Act specifies
that PAC providers shall be required to
submit standardized patient assessment
data for the category of cognitive
function, such as the ability to express
ideas and to understand, and mental
status, such as depression and
dementia. A recent deterioration in
cognitive function or present and
fluctuating behaviors of inattention,
disorganized thinking, or altered level of
consciousness may indicate delirium.104
Delirium can also be misdiagnosed as
dementia.105
Comment: A commenter stated that
CMS has not provided quantitative
evidence that the CAM data elements
are capable of measuring provider
performance for quality or of
differentiating case-mix for payment.
Response: The clinical SPADEs
proposed in this rule, including CAM,
were the result of an extensive
consensus vetting process. Over the past
several years, we have engaged experts
and a wide range of stakeholders
through TEPs, Special Open Door
Forums, and documents made available
on the CMS.gov website. A summary of
the most recent TEP meeting (September
17, 2018) titled ‘‘SPADE Technical
Expert Panel Summary (Third
Convening)’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-Assessment104 Inouye
SK, van Dyck CH, Alessi CA, Balkin S,
Siegal AP, Horwitz RI. Clarifying confusion: The
confusion assessment method. A new method for
detection of delirium. Ann Intern Med. 1990 Dec
15;113(12):941–8.
105 Marcantonio ER. Delirium in Hospitalized
Older Adults. N Engl J Med. 2017 Oct
12;377(15):1456–1466.
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Instruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html. Results of these activities
provide evidence that experts and
providers believe that the proposed
SPADEs, including the CAM data
elements, have the potential for
measuring quality, describing case mix,
and improving care.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the
CAM as standardized patient
assessment data beginning with the FY
2022 IRF QRP as proposed.
• Patient Health Questionnaire–2 to 9
(PHQ–2 to 9)
In the FY 2020 IRF PPS proposed rule
(84 FR 17296 through 17297), we
proposed that the Patient Health
Questionnaire–2 to 9 (PHQ–2 to 9) data
elements meet the definition of
standardized patient assessment data
with respect to cognitive function and
mental status under section
1899B(b)(1)(B)(ii) of the Act. The
proposed data elements are based on the
PHQ–2 mood interview, which focuses
on only the two cardinal symptoms of
depression, and the longer PHQ–9 mood
interview, which assesses presence and
frequency of nine signs and symptoms
of depression. The name of the data
element, the PHQ–2 to 9, refers to an
embedded skip pattern that transitions
patients with a threshold level of
symptoms in the PHQ–2 to the longer
assessment of the PHQ–9. The skip
pattern is described further below. As
described in the FY 2018 IRF PPS
proposed rule (82 FR 20725 through
20726), depression is a common and
under-recognized mental health
condition. Assessments of depression
help PAC providers better understand
the needs of their patients and residents
by: Prompting further evaluation after
establishing a diagnosis of depression;
elucidating the patient’s or resident’s
ability to participate in therapies for
conditions other than depression during
their stay; and identifying appropriate
ongoing treatment and support needs at
the time of discharge.
The proposed PHQ–2 to 9 is based on
the PHQ–9 mood interview. The PHQ–
2 consists of questions about only the
first two symptoms addressed in the
PHQ–9: Depressed mood and anhedonia
(inability to pleasure), which are the
cardinal symptoms of depression. The
PHQ–2 has performed well as both a
screening tool for identifying
depression, to assess depression
severity, and to monitor patient mood
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over time.106 107 If a patient
demonstrates signs of depressed mood
and anhedonia under the PHQ–2, then
the patient is administered the lengthier
PHQ–9. This skip pattern (also referred
to as a gateway) is designed to reduce
the length of the interview assessment
for patients who fail to report the
cardinal symptoms of depression. The
design of the PHQ–2 to 9 reduces the
burden that would be associated with
requiring the full PHQ–9, while
ensuring that patients and residents
with indications of depressive
symptoms based on the PHQ–2 receive
the longer assessment.
Components of the proposed data
elements are currently used in the
OASIS for HHAs (PHQ–2) and the MDS
for SNFs (PHQ–9). For more information
on the PHQ–2 to 9, we refer readers to
the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
We proposed the PHQ–2 data
elements as SPADEs in the FY 2018 IRF
proposed rule (82 FR 20725 through
20726). In that proposed rule, we stated
that the proposal was informed by input
we received from the TEP convened by
our data element contractor on April 6
and 7, 2016. The TEP members
particularly noted that the brevity of the
PHQ–2 made it feasible to administer
with low burden for both assessors and
PAC patients or residents. A summary
of the April 6 and 7, 2016 TEP meeting
titled ‘‘SPADE Technical Expert Panel
Summary (First Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
The rule proposal was also informed
by public input that we received
through a call for input published on
the CMS Measures Management System
Blueprint website. Input was submitted
from August 12 to September 12, 2016
on three versions of the PHQ depression
screener: The PHQ–2; the PHQ–9; and
106 Li, C., Friedman, B., Conwell, Y., & Fiscella,
K. (2007). ‘‘Validity of the Patient Health
Questionnaire 2 (PHQ–2) in identifying major
depression in older people.’’ J of the A Geriatrics
Society, 55(4): 596–602.
107 Lo
¨ we, B., Kroenke, K., & Gra¨fe, K. (2005).
‘‘Detecting and monitoring depression with a twoitem questionnaire (PHQ–2).’’ J of Psychosomatic
Research, 58(2): 163–171.
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the PHQ–2 to 9 with the skip pattern
design. Many commenters were
supportive of the standardized
assessment of mood in PAC settings,
given the role that depression plays in
well-being. Several commenters noted
support for an approach that would use
PHQ–2 as a gateway to the longer PHQ–
9 while still potentially reducing burden
on most patients and residents, as well
as test administrators, and ensuring the
administration of the PHQ–9, which
exhibits higher specificity,108 for
patients and residents who showed
signs and symptoms of depression on
the PHQ–2. A summary report for the
August 12 to September 12, 2016 public
comment period titled ‘‘SPADE August
2016 Public Comment Summary
Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In response to our proposal to use the
PHQ–2 in the FY 2018 IRF PPS
proposed rule (82 FR 20725 through
20726), we received comments agreeing
to the importance of a standardized
assessment of depression in patients
and residents receiving PAC services.
Commenters also raised concerns about
the ability of the PHQ–2 to correctly
identify all patients and residents with
signs and symptoms of depression. One
commenter supported using the PHQ–2
as a gateway assessment and conducting
a more thorough evaluation of
depression symptoms with the PHQ–9 if
the PHQ–2 is positive. Another
commenter expressed concern that
standardized assessment of signs and
symptoms of depression via the PHQ–2
is not appropriate in the IRF setting, as
patients may have recently experienced
acute illness or injury, and routine
screening may lead to overprescribing of
antidepressant medications. Another
commenter expressed concern about
potential conflicts between the results of
screening assessments and documented
diagnoses based on the expertise of
physicians and other clinicians. In
response to these comments, we carried
out additional testing, and we provide
our findings below.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the PHQ–2
to 9 was included in the National Beta
Test of candidate data elements
108 Arroll B, Goodyear-Smith F, Crengle S, Gunn
J, Kerse N, Fishman T, et al. Validation of PHQ–2
and PHQ–9 to screen for major depression in the
primary care population. Annals of family
medicine. 2010;8(4):348–53. doi: 10.1370/afm.1139
pmid:20644190; PubMed Central PMCID:
PMC2906530.
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conducted by our data element
contractor from November 2017 to
August 2018. Results of this test found
the PHQ–2 to 9 to be feasible and
reliable for use with PAC patients and
residents. More information about the
performance of the PHQ–2 to 9 in the
National Beta Test can be found in the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the PHQ–2 to 9. The
TEP was supportive of the PHQ–2 to 9
data element set as a screener for signs
and symptoms of depression. The TEP’s
discussion noted that symptoms
evaluated by the full PHQ–9 (for
example, concentration, sleep, appetite)
had relevance to care planning and the
overall well-being of the patient or
resident, but that the gateway approach
of the PHQ–2 to 9 would be appropriate
as a depression screening assessment, as
it depends on the well-validated PHQ–
2 and focuses on the cardinal symptoms
of depression. A summary of the
September 17, 2018 TEP meeting titled
‘‘SPADE Technical Expert Panel
Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our on-going SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-Quality-
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Taking together the importance of
assessing for depression, stakeholder
input, and test results, we proposed that
the PHQ–2 to 9 data elements meet the
definition of standardized patient
assessment data with respect to
cognitive function and mental status
under section 1899B(b)(1)(B)(ii) of the
Act and to adopt the PHQ–2 to 9 data
elements as standardized patient
assessment data for use in the IRF QRP.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the PHQ–2 to 9 data
elements.
Comment: Some commenters
supported the inclusion of the PHQ–2 to
9. One of these commenters was
particularly supportive of the use of the
2-item gateway in the PHQ–2 to 9
approach to improve efficiency.
Response: We thank the commenters
for their support of the PHQ–2 to 9,
including the gateway approach as a
way to decrease burden for providers
and patients.
Comment: One commenter was
unsure if PHQ–2 to 9 will identify
differences in cognitive status or
measure changes during the stay
resulting from therapeutic interventions.
Another commenter expressed concern
that the PHQ–2 to 9 data elements
would not identify cognitive needs that
would impact quality in therapeutic
intervention across facilities.
Response: As with any brief screening
tool, we believe that the PHQ–2 to 9 has
value as a universal assessment to
identify patients in need of further
clinical evaluation. We believe that
applying a brief, standardized
assessment of depression across PAC
settings, including IRFs, will improve
detection based on the PHQ–2 to 9
interview. A universal depression
screening is expected to improve patient
outcomes by increasing the likelihood
that depression will be identified and
treated in IRF patients. The proposal of
the PHQ–2 to 9 was the result of an
extensive consensus vetting process in
which experts and stakeholders were
engaged through TEPs, SODFs, and
posting of interim reports and other
documents on CMS.gov. These experts
and stakeholders were supportive of the
clinical usefulness of the PHQ–2 to 9
assessment. A summary of the most
recent TEP meeting (September 17,
2018) titled ‘‘SPADE Technical Expert
Panel Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of-
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2014/IMPACT-Act-Downloads-andVideos.html.
Comment: A few commenters raised
concerns about administration of the
PHQ–2 to 9 to IRF patients. One
commenter noted that patients in acute
rehabilitation may have limited
attention and working memory that
affects their ability to complete the
PHQ–2 to 9. Another commenter noted
doubts that PHQ–9 is a good tool for
IRFs because of the likelihood of false
positives, given patients who are
adjusting to recent injuries, surgeries,
conditions, and various disabilities.
Rather, the commenter believes that
assessment by rehabilitation
psychologists, who have specialty
training in working with rehabilitation
populations, would provide a
comprehensive evaluation and informed
treatment plan. Another commenter
expressed concerns about the use of the
PHQ in short-stay IRF patients,
suggesting that being assessed for
depression, especially if assessed
multiple times, will affect the patient’s
perception of how they should be
experiencing their situation.
Response: We recognize the
challenges faced by patients receiving
care from IRF providers. We believe that
the PHQ–2 to 9 is the most accurate and
appropriate depression screening for the
PAC population, including patients in
IRFs, and that assessing for depression
is necessary for high-quality clinical
care. As stated in our proposal above,
the PHQ–2 has performed well as a
screening tool for identifying
depression, to assess depression
severity, and to monitor patient mood
over time.109 110 Additionally, the PHQ–
2 and PHQ–9 instruments have been
validated in primary care populations
against a gold standard diagnostic
interview.111 We believe this prior
validation research generalizes to the
IRF population. We also note that,
regardless of the LOS of patients, the
timeframe over which they may have
been experiencing signs and symptoms
of depression, and the types of
circumstances that have led to their IRF
stay, it is the responsibility of the IRF
to deliver high quality care for all the
109 Li, C., Friedman, B., Conwell, Y., & Fiscella,
K. (2007). ‘‘Validity of the Patient Health
Questionnaire 2 (PHQ–2) in identifying major
depression in older people.’’ J of the A Geriatrics
Society, 55(4): 596–602.
110 Lo
¨ we, B., Kroenke, K., & Gra¨fe, K. (2005).
‘‘Detecting and monitoring depression with a twoitem questionnaire (PHQ–2).’’ J of Psychosomatic
Research, 58(2): 163–171.
111 Arroll B, Goodyear-Smith F, Crengle S, Gunn
J, Kerse N, Fishman T, et al. Validation of PHQ–2
and PHQ–9 to screen for major depression in the
primary care population. Annals of family
medicine. 2010;8(4):348–353.
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symptoms or conditions a patient may
have. The expectation that the episode
of care will be short does not exempt an
IRF from screening and treating patients
for the full range of physical and mental
health problems. Similarly, if a patient
self-reports a significant number of
depressive symptoms, we do not believe
that they should be considered to be a
‘‘false positive’’ because of, for example,
a recent trauma or acute care stay. As a
screening tool, the PHQ–2 to 9 is
intended to capture likely depression to
have those patients referred for further
evaluation, which will ascertain if their
condition is consistent with the full
diagnostic criteria for a major depressive
disorder. Moreover, standardized
screening for the signs and symptoms of
depression with the PHQ–2 to 9 does
not preclude or provide a substitute for
assessment by rehabilitation
psychologist or other clinicians, as
deemed appropriate by a patient’s care
team.
Comment: Several commenters cited
concerns related to the findings from the
National Beta Test related to the PHQ–
2 to 9, namely, that testing found it to
be burdensome for staff and patients
and the wording difficult to understand.
Response: We acknowledge that some
assessors in the National Beta Test
noted concerns regarding the burden of
the PHQ–2 to 9 for staff and patients
and that the wording of some items was
challenging for patients to understand.
In the National Beta Test, the PHQ–2 to
9 was one of a collection of mood
assessments, meaning that assessors and
patients completed additional questions
about depressed mood and well-being
immediately before and after the PHQ–
2 to 9. We believe that the perception
of burden of the PHQ–2 to 9 was in part
due to the larger mood assessment
section included in the National Beta
Test. Despite the burden and
administration challenges noted by
National Beta Test assessors, assessors
generally appreciated the clinical utility
and relevance of the PHQ–2 to 9 and
noted the importance of standardizing
the assessment of depressive symptoms.
Comment: Additional concerns about
administration focused on the patient
interview format of the PHQ–2 to 9.
Some commenters raised concerns
about administering the PHQ–2 to 9 to
patients with severe cognitive deficits,
prior mental health issues, or noncommunicative conditions. One
commenter suggested that CMS develop
exemptions from repeated screenings for
short stay patients, and for patients
whose medical or cognitive status make
it inappropriate to administer the PHQ–
2 to 9. Another commenter suggested
that the PHQ–2 to 9 have an option to
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be self-administered by the patient via
a patient-friendly paper and pencil
layout, which would reduce time
burden placed on assessors.
Response: We appreciate commenters’
concerns that administering the PHQ–2
to 9 to patients whose medical or
cognitive status make it inappropriate to
administer. The guidance for
completing the data elements will
include instructions that if the patient is
rarely or never understood verbally, in
writing, or using another method, the
PHQ–2 to 9 interview will not be
completed and the assessor code the
responses to the first two items (Little
interest or pleasure in doing things;
Feeling down, depressed, or hopeless)
as 9 (no response). We will take the
suggestion to explore the possibility for
patient self-administration of the PHQ–
2 to 9 into consideration in future
SPADE development work.
Comment: One commenter noted
confusion about how depression relates
to cognitive function.
Response: Section 1899(b)(1)(B)(ii) of
the Act specifies the category of
‘‘cognitive function, such as ability to
express ideas and to understand, and
mental status, such as depression and
dementia.’’ We proposed the PHQ–2 to
9 data elements to meet the definition
of the standardized patient assessment
data with respect to cognitive function
and mental status, particularly the
‘‘mental status’’ topic within that
category.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the
PHQ–2 to 9 data elements as
standardized patient assessment data
beginning with the FY 2022 IRF QRP as
proposed.
2. Special Services, Treatments, and
Interventions Data
Special services, treatments, and
interventions performed in PAC can
have a major effect on an individual’s
health status, self-image, and quality of
life. The assessment of these special
services, treatments, and interventions
in PAC is important to ensure the
continuing appropriateness of care for
the patients and residents receiving
them, and to support care transitions
from one PAC provider to another, an
acute care hospital, or discharge. In
alignment with our Meaningful
Measures Initiative, accurate assessment
of special services, treatments, and
interventions of patients and residents
served by PAC providers is expected to
make care safer by reducing harm
caused in the delivery of care; promote
effective prevention and treatment of
chronic disease; strengthen person and
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family engagement as partners in their
care; and promote effective
communication and coordination of
care.
For example, standardized assessment
of special services, treatments, and
interventions used in PAC can promote
patient and resident safety through
appropriate care planning (for example,
mitigating risks such as infection or
pulmonary embolism associated with
central intravenous access), and
identifying life-sustaining treatments
that must be continued, such as
mechanical ventilation, dialysis,
suctioning, and chemotherapy, at the
time of discharge or transfer.
Standardized assessment of these data
elements will enable or support:
Clinical decision-making and early
clinical intervention; person-centered,
high quality care through, for example,
facilitating better care continuity and
coordination; better data exchange and
interoperability between settings; and
longitudinal outcome analysis.
Therefore, reliable data elements
assessing special services, treatments,
and interventions are needed to initiate
a management program that can
optimize a patient’s or resident’s
prognosis and reduce the possibility of
adverse events.
A TEP convened by our data element
contractor provided input on the
proposed data elements for special
services, treatments, and interventions.
In a meeting held on January 5 and 6,
2017, this TEP found that these data
elements are appropriate for
standardization because they would
provide useful clinical information to
inform care planning and care
coordination. The TEP affirmed that
assessment of these services and
interventions is standard clinical
practice, and that the collection of these
data by means of a list and checkbox
format would conform with common
workflow for PAC providers. A
summary of the January 5 and 6, 2017
TEP meeting titled ‘‘SPADE Technical
Expert Panel Summary (Second
Convening)’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Comments on the category of special
services, treatments, and interventions
were also submitted by stakeholders
during the FY 2018 IRF PPS proposed
rule (82 FR 20726 through 20736) public
comment period. One commenter
supported adding the SPADEs for
special services, treatments, and
interventions. Others stated labor costs
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and staff burden would increase for data
collection. The Medicare Payment
Advisory Commission (MedPAC)
suggested that a few other high-cost
services, such as cardiac monitoring and
specialty bed/surfaces, may warrant
consideration for inclusion in future
collection efforts. One commenter
believes that the low frequency of the
special services, treatments, and
interventions in the IRF setting makes
them not worth assessing for patients
given the cost of data collection and
reporting. A few commenters noted that
many of these data elements should be
obtainable from administrative data
(that is, coding and Medicare claims),
and therefore, assessing them through
patient record review would be
duplicated effort.
Information on data element
performance in the National Beta Test,
which collected data between November
2017 and August 2018, is reported
within each data element proposal
below. Clinical staff who participated in
the National Beta Test supported these
data elements because of their
importance in conveying patient or
resident significant health care needs,
complexity, and progress. However,
clinical staff also noted that, despite the
simple ‘‘check box’’ format of these data
element, they sometimes needed to
consult multiple information sources to
determine a patient’s or resident’s
treatments.
We sought comment on our proposals
to collect as standardized patient
assessment data the following data with
respect to special services, treatments,
and interventions.
Commenters submitted the following
comments related to the proposed rule’s
discussion of special services,
treatments, and interventions data
elements.
Comment: One commenter was
supportive of collecting these data
elements, noting that collection will
help to better inform CMS and IRF
providers on the severity and needs of
patients in this setting.
Response: We thank the commenter
for the support of these items. We
selected the Special Services,
Treatments, and Interventions data
elements for proposal as standardized
data in part because of the attributes
noted.
Comment: Some commenters were
concerned about the reliability of the
Special Services, Treatments, and
Interventions data elements, noting that
the results of the National Beta Test
indicated that these data elements had
a low interrater reliability kappa
statistic relative to other data elements
in the test.
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Response: In the category of Special
Services, Treatments, and Interventions,
for SPADEs where kappas could be
calculated, 1 data element and 2 subelements demonstrated overall
reliabilities in the moderate range (0.41–
0.60) and only 1 sub-element
demonstrated an overall reliability in
the slight/poor range (0.00–0.20). These
overall reliabilities were as follows: 0.60
for the Therapeutic Diet data element;
0.55 for the ‘‘Continuous’’ sub-element
of Oxygen Therapy; 0.46 for the ‘‘Other’’
sub-element of IV Medications; and 0.13
for the ‘‘Anticoagulant’’ sub-element of
IV Medications. However, the overall
reliabilities for all other data elements
and sub-elements where kappas could
be calculated were substantial/good or
excellent/almost perfect. When looking
at percent agreement—an alternative
measure of interrater agreement—values
of overall percent agreement for all
Special Services, Treatments, and
Interventions SPADEs and sub-elements
ranged from 80 to 100 percent.
Comment: Commenters also noted
concern around the burden of
completing these data elements, in
particular because of their low
frequency of occurrence in IRF settings.
To reduce burden around collection of
this information, commenters
recommended that CMS explore
obtaining this data via claims.
Additionally, one commenter added
that if these data elements are finalized,
they should be collected at discharge
only, to reduce administrative burden.
Response: We appreciate the
commenters’ concern for burden on
clinical staff due to completing
assessments with respect to both
admission and discharge. We believe
that assessment of various special
services, treatments, and interventions
received by patients in the IRF setting
will provide important information for
care planning and resource use in IRFs.
The assessments of the special services,
treatments, and interventions with
multiple responses are formatted as a
‘‘check all that apply’’ format.
Therefore, when treatments do not
apply—as the commenters note, this is
the case for many IRF patients—the
assessor need only check one row for
‘‘None of the Above.’’ We will take
under consideration the commenters’
recommendation to explore the
feasibility of collecting information on
special services, treatments, and
interventions through claims-based
data. Regarding the recommendation to
collect these SPADEs at discharge only,
we state that it is clinically appropriate
and important to the ultimate usefulness
of these SPADEs that they are collected
with respect to both admission and
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discharge. For example, for patients
coming from acute care or from the
community, the admission assessment
establishes a baseline for the IRF stay.
For all patients, the admission
assessment ensures that each patient is
systematically assessed for a broad
range of health and well-being issues,
which we expect to inform care
planning.
Comment: One commenter expressed
concern that the Special Services,
Treatments, and Interventions data
elements assess the presence or absence
of something rather than the clinical
rationale or patient outcomes. This
commenter stressed the importance of
bringing this assessment to ‘‘the next
level’’ in order to determine impact of
these treatments on patients’ outcomes.
Response: We agree with commenter’s
concern that recording the presence or
absence of certain treatments is only a
first step in characterizing the
complexity that is often the cause of a
patient’s receipt of special services,
treatments, and interventions. We
clarify that all the SPADEs we proposed
were intended as a minimum
assessment and do not limit the ability
of providers to conduct a more
comprehensive evaluation of a patient’s
situation to identify the potential
impacts on outcomes that the
commenter describes.
Comment: One commenter noted that
the item numbering in the Special
Services, Treatments, and Interventions
data elements is extremely confusing
and needs to be reworked.
Response: Several patient assessment
tools have traditionally combined letters
and numbers, along with labels, to
distinguish between data elements. The
proposed data elements in the Special
Services, Treatments, and Interventions
section follow the conventions
established by CMS. However, we will
take this feedback into consideration in
our evaluation and refinement of patient
assessment instruments.
Final decisions on the SPADEs are
given below, following more detailed
comments on each SPADE proposal.
• Cancer Treatment: Chemotherapy (IV,
Oral, Other)
In the FY 2020 IRF PPS proposed rule
(84 FR 17297 through 17299), we
proposed that the Chemotherapy (IV,
Oral, Other) data element meets the
definition of standardized patient
assessment data with respect to special
services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20726 through
20727), chemotherapy is a type of
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cancer treatment that uses drugs to
destroy cancer cells. It is sometimes
used when a patient has a malignancy
(cancer), which is a serious, often lifethreatening or life-limiting condition.
Both intravenous (IV) and oral
chemotherapy have serious side effects,
including nausea/vomiting, extreme
fatigue, risk of infection due to a
suppressed immune system, anemia,
and an increased risk of bleeding due to
low platelet counts. Oral chemotherapy
can be as potent as chemotherapy given
by IV and can be significantly more
convenient and less resource-intensive
to administer. Because of the toxicity of
these agents, special care must be
exercised in handling and transporting
chemotherapy drugs. IV chemotherapy
is administered either peripherally, or
more commonly, given via an
indwelling central line, which raises the
risk of bloodstream infections. Given the
significant burden of malignancy, the
resource intensity of administering
chemotherapy, and the side effects and
potential complications of these highlytoxic medications, assessing the receipt
of chemotherapy is important in the
PAC setting for care planning and
determining resource use. The need for
chemotherapy predicts resource
intensity, both because of the
complexity of administering these
potent, toxic drug combinations under
specific protocols, and because of what
the need for chemotherapy signals about
the patient’s underlying medical
condition. Furthermore, the resource
intensity of IV chemotherapy is higher
than for oral chemotherapy, as the
protocols for administration and the
care of the central line (if present) for IV
chemotherapy require significant
resources.
The Chemotherapy (IV, Oral, Other)
data element consists of a principal data
element (Chemotherapy) and three
response option sub-elements: IV
chemotherapy, which is generally
resource-intensive; Oral chemotherapy,
which is less invasive and generally
requires less intensive administration
protocols; and a third category, Other,
provided to enable the capture of other
less common chemotherapeutic
approaches. This third category is
potentially associated with higher risks
and is more resource intensive due to
delivery by other routes (for example,
intraventricular or intrathecal). If the
assessor indicates that the patient is
receiving chemotherapy on the
principal Chemotherapy data element,
the assessor would then indicate by
which route or routes (for example, IV,
Oral, Other) the chemotherapy is
administered.
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A single Chemotherapy data element
that does not include the proposed three
sub-elements is currently in use in the
MDS in SNFs. For more information on
the Chemotherapy (IV, Oral, Other) data
element, we refer readers to the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Chemotherapy data element was
first proposed as a standardized patient
assessment data element in the FY 2018
IRF PPS proposed rule (82 FR 20726
through 20727). In that proposed rule,
we stated that the proposal was
informed by input we received through
a call for input published on the CMS
Measures Management System
Blueprint website. Input submitted from
August 12 to September 12, 2016 noted
support for the IV Chemotherapy data
element and suggested it be included as
standardized patient assessment data.
We also stated that those commenters
had noted that assessing the use of
chemotherapy services is relevant to
share across the care continuum to
facilitate care coordination and care
transitions and noted the validity of the
data element. Commenters also noted
the importance of capturing all types of
chemotherapy, regardless of route, and
stated that collecting data only on
patients and residents who received
chemotherapy by IV would limit the
usefulness of this standardized data
element. A summary report for the
August 12 to September 12, 2016 public
comment period titled ‘‘SPADE August
2016 Public Comment Summary
Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general;
no additional comments were received
that were specific to the Chemotherapy
data element.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the
Chemotherapy data element was
included in the National Beta Test of
candidate data elements conducted by
our data element contractor from
November 2017 to August 2018. Results
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of this test found the Chemotherapy
data element to be feasible and reliable
for use with PAC patients and residents.
More information about the
performance of the Chemotherapy data
element in the National Beta Test can be
found in the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP members
did not specifically discuss the
Chemotherapy data element, the TEP
members supported the assessment of
the special services, treatments, and
interventions included in the National
Beta Test with respect to both admission
and discharge. A summary of the
September 17, 2018 TEP meeting titled
‘‘SPADE Technical Expert Panel
Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for chemotherapy, stakeholder
input, and strong test results, we
proposed that the Chemotherapy (IV,
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Oral, Other) data element with a
principal data element and three subelements meet the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and
to adopt the Chemotherapy (IV, Oral,
Other) data element as standardized
patient assessment data for use in the
IRF QRP.
A commenter submitted the following
comment related to the proposed rule’s
discussion of the Chemotherapy data
element.
Comment: One commenter agreed that
it is important to know if a patient is
receiving chemotherapy for cancer and
the method of administration, but also
expressed concern about the lack of an
association with a patient outcome. This
commenter noted that implications of
chemotherapy for patients needing
speech-language pathology services
include chemotherapy-related cognitive
impairment, dysphagia, and speech- and
voice-related deficits.
Response: We appreciate the
commenter’s concern. We agree with the
commenter that chemotherapy can
create related treatment needs for
patients, such as the examples noted by
the commenter. However, we believe
that it is not feasible for SPADEs to
capture all of a patient’s needs related
to any given treatment, and we maintain
that the Special Services, Treatments,
and Interventions SPADEs provide a
common foundation of clinical
assessment, which can be built on by
the individual provider or a patient’s
care team.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the
Chemotherapy (IV, Oral, Other) data
element as standardized patient
assessment data beginning with the FY
2022 IRF QRP as proposed.
• Cancer Treatment: Radiation
In the FY 2020 IRF PPS proposed rule
(84 FR 17299), we proposed that the
Radiation data element meets the
definition of standardized patient
assessment data with respect to special
services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20727 through
20728), radiation is a type of cancer
treatment that uses high-energy
radioactivity to stop cancer by damaging
cancer cell DNA, but it can also damage
normal cells. Radiation is an important
therapy for particular types of cancer,
and the resource utilization is high,
with frequent radiation sessions
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required, often daily for a period of
several weeks. Assessing whether a
patient or resident is receiving radiation
therapy is important to determine
resource utilization because PAC
patients and residents will need to be
transported to and from radiation
treatments, and monitored and treated
for side effects after receiving this
intervention. Therefore, assessing the
receipt of radiation therapy, which
would compete with other care
processes given the time burden, would
be important for care planning and care
coordination by PAC providers.
The proposed data element consists of
the single Radiation data element. The
Radiation data element is currently in
use in the MDS in SNFs. For more
information on the Radiation data
element, we refer readers to the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Radiation data element was first
proposed as a standardized patient
assessment data element in the FY 2018
IRF PPS proposed rule (82 FR 20727
through 20728). In that proposed rule,
we stated that the proposal was
informed by input we received through
a call for input published on the CMS
Measures Management System
Blueprint website. Input submitted from
August 12 to September 12, 2016 noted
support for the Radiation data element,
noting its importance and clinical
usefulness for patients and residents in
PAC settings, due to the side effects and
consequences of radiation treatment on
patients and residents that need to be
considered in care planning and care
transitions, the feasibility of the item,
and the potential for it to improve
quality. A summary report for the
August 12 to September 12, 2016 public
comment period titled ‘‘SPADE August
2016 Public Comment Summary
Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general;
no additional comments were received
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that were specific to the Radiation data
element.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the Radiation
data element was included in the
National Beta Test of candidate data
elements conducted by our data element
contractor from November 2017 to
August 2018. Results of this test found
the Radiation data element to be feasible
and reliable for use with PAC patients
and residents. More information about
the performance of the Radiation data
element in the National Beta Test can be
found in the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP members
did not specifically discuss the
Radiation data element, the TEP
members supported the assessment of
the special services, treatments, and
interventions included in the National
Beta Test with respect to both admission
and discharge. A summary of the
September 17, 2018 TEP meeting titled
‘‘SPADE Technical Expert Panel
Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present results of the National Beta Test
and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-Assessment-
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Instruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for radiation, stakeholder
input, and strong test results, we
proposed that the Radiation data
element meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and
to adopt the Radiation data element as
standardized patient assessment data for
use in the IRF QRP.
A commenter submitted the following
comment related to the proposed rule’s
discussion of the Radiation data
element.
Comment: One commenter expressed
concern that the Radiation data element
assesses whether a patient is receiving
radiation for cancer treatment, but does
not identify the rationale for and
outcomes associated with radiation. The
commenter noted that implications of
radiation for patients needing speechlanguage pathology services include
reduced head and neck range of motion
due to radiation or severe fibrosis, scar
bands, and reconstructive surgery
complications and that these can impact
both communication and swallowing
abilities.
Response: We appreciate the
commenter’s concern. We agree with the
commenter that radiation can create
related treatment needs for patients,
such as the examples noted by the
commenter. However, we believe that it
is not feasible for SPADEs to capture all
of a patient’s needs related to any given
treatment, and we maintain that the
Special Services, Treatments, and
Interventions SPADEs provide a
common foundation of clinical
assessment, which can be built on by
the individual provider or a patient’s
care team.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the
Radiation data element as standardized
patient assessment data beginning with
the FY 2022 IRF QRP as proposed.
• Respiratory Treatment: Oxygen
Therapy (Intermittent, Continuous,
High-concentration Oxygen Delivery
System)
In the FY 2020 IRF PPS proposed rule
(84 FR 17299 through 17300), we
proposed that the Oxygen Therapy
(Intermittent, Continuous, Highconcentration Oxygen Delivery System)
data element meets the definition of
standardized patient assessment data
with respect to special services,
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treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20728), we
proposed a similar data element related
to oxygen therapy. Oxygen therapy
provides a patient or resident with extra
oxygen when medical conditions such
as chronic obstructive pulmonary
disease, pneumonia, or severe asthma
prevent the patient or resident from
getting enough oxygen from breathing.
Oxygen administration is a resourceintensive intervention, as it requires
specialized equipment such as a source
of oxygen, delivery systems (for
example, oxygen concentrator, liquid
oxygen containers, and high-pressure
systems), the patient interface (for
example, nasal cannula or mask), and
other accessories (for example,
regulators, filters, tubing). The data
element proposed here captures patient
or resident use of three types of oxygen
therapy (intermittent, continuous, and
high-concentration oxygen delivery
system), which reflects the intensity of
care needed, including the level of
monitoring and bedside care required.
Assessing the receipt of this service is
important for care planning and
resource use for PAC providers.
The proposed data element, Oxygen
Therapy, consists of the principal
Oxygen Therapy data element and three
response option sub-elements:
Continuous (whether the oxygen was
delivered continuously, typically
defined as > =14 hours per day);
Intermittent; or High-concentration
Oxygen Delivery System. Based on
public comments and input from expert
advisors about the importance and
clinical usefulness of documenting the
extent of oxygen use, we added a third
sub-element, high-concentration oxygen
delivery system, to the sub-elements,
which previously included only
intermittent and continuous. If the
assessor indicates that the patient is
receiving oxygen therapy on the
principal oxygen therapy data element,
the assessor then would indicate the
type of oxygen the patient receives (for
example, Intermittent, Continuous,
High-concentration oxygen delivery
system).
These three proposed sub-elements
were developed based on similar data
elements that assess oxygen therapy,
currently in use in the MDS in SNFs
(‘‘Oxygen Therapy’’), previously used in
the OASIS (‘‘Oxygen (intermittent or
continuous)’’), and a data element tested
in the PAC PRD that focused on
intensive oxygen therapy (‘‘High O2
Concentration Delivery System with
FiO2 > 40 percent’’). For more
information on the proposed Oxygen
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Therapy (Continuous, Intermittent,
High-concentration oxygen delivery
system) data element, we refer readers
to the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Oxygen Therapy (Intermittent,
Continuous) data element was first
proposed as standardized patient
assessment data in the FY 2018 IRF PPS
proposed rule (82 FR 20728). In that
proposed rule, we stated that the
proposal was informed by input we
received on the single data element,
Oxygen (inclusive of intermittent and
continuous oxygen use), through a call
for input published on the CMS
Measures Management System
Blueprint website. Input submitted from
August 12 to September 12, 2016, noted
the importance of the Oxygen data
element, noting feasibility of this item
in PAC, and the relevance of it to
facilitating care coordination and
supporting care transitions, but
suggesting that the extent of oxygen use
be documented. A summary report for
the August 12 to September 12, 2016
public comment period titled ‘‘SPADE
August 2016 Public Comment Summary
Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general;
no additional comments were received
that were specific to the Oxygen
Therapy (Intermittent, Continuous) data
element.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the Oxygen
Therapy data element was included in
the National Beta Test of candidate data
elements conducted by our data element
contractor from November 2017 to
August 2018. Results of this test found
the Oxygen Therapy data element to be
feasible and reliable for use with PAC
patients and residents. More
information about the performance of
the Oxygen Therapy data element in the
National Beta Test can be found in the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
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Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP did not
specifically discuss the Oxygen Therapy
data element, the TEP supported the
assessment of the special services,
treatments, and interventions included
in the National Beta Test with respect to
both admission and discharge. A
summary of the September 17, 2018 TEP
meeting titled ‘‘SPADE Technical Expert
Panel Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing oxygen therapy, stakeholder
input, and strong test results, we
proposed that the Oxygen Therapy
(Intermittent, Continuous, Highconcentration Oxygen Delivery System)
data element with a principal data
element and three sub-elements meets
the definition of standardized patient
assessment data with respect to special
services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Oxygen Therapy
(Intermittent, Continuous, High-
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concentration Oxygen Delivery System)
data element as standardized patient
assessment data for use in the IRF QRP.
We invited public comment on this
proposal. While we received support
from some commenters on the Special
Services, Treatments, and Interventions
section (IX.G.2 in this final rule) and its
proposals as a whole (section IX.F in
this final rule), we did not receive any
specific comments on the Oxygen
Therapy (Intermittent, Continuous,
High-concentration Oxygen Delivery
System) data element in particular.
After careful consideration of the
public comments we received on the
category of Special Services,
Treatments, and Interventions, we are
finalizing our proposal to adopt the
Oxygen Therapy (Intermittent,
Continuous, High-Concentration
Oxygen Delivery System) data element
as standardized patient assessment data
beginning with the FY 2022 IRF QRP as
proposed.
• Respiratory Treatment: Suctioning
(Scheduled, as Needed)
In the FY 2020 IRF PPS proposed rule
(84 FR 17300 through 17302), we
proposed that the Suctioning
(Scheduled, As needed) data element
meets the definition of standardized
patient assessment data with respect to
special services, treatments, and
interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20728 through
20729), suctioning is a process used to
clear secretions from the airway when a
person cannot clear those secretions on
his or her own. It is done by aspirating
secretions through a catheter connected
to a suction source. Types of suctioning
include oropharyngeal and
nasopharyngeal suctioning, nasotracheal
suctioning, and suctioning through an
artificial airway such as a tracheostomy
tube. Oropharyngeal and
nasopharyngeal suctioning are a key
part of many patients’ or residents’ care
plans, both to prevent the accumulation
of secretions than can lead to aspiration
pneumonias (a common condition in
patients and residents with inadequate
gag reflexes), and to relieve obstructions
from mucus plugging during an acute or
chronic respiratory infection, which
often lead to desaturations and
increased respiratory effort. Suctioning
can be done on a scheduled basis if the
patient is judged to clinically benefit
from regular interventions, or can be
done as needed when secretions become
so prominent that gurgling or choking is
noted, or a sudden desaturation occurs
from a mucus plug. As suctioning is
generally performed by a care provider
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rather than independently, this
intervention can be quite resource
intensive if it occurs every hour, for
example, rather than once a shift. It also
signifies an underlying medical
condition that prevents the patient from
clearing his/her secretions effectively
(such as after a stroke, or during an
acute respiratory infection). Generally,
suctioning is necessary to ensure that
the airway is clear of secretions which
can inhibit successful oxygenation of
the individual. The intent of suctioning
is to maintain a patent airway, the loss
of which can lead to death or
complications associated with hypoxia.
The Suctioning (Scheduled, As
needed) data element consists of a
principal data element, and two subelements: Scheduled and As needed.
These sub-elements capture two types of
suctioning. Scheduled indicates
suctioning based on a specific
frequency, such as every hour. As
needed means suctioning only when
indicated. If the assessor indicates that
the patient is receiving suctioning on
the principal Suctioning data element,
the assessor would then indicate the
frequency (for example, Scheduled, As
needed). The proposed data element is
based on an item currently in use in the
MDS in SNFs which does not include
our proposed two sub-elements, as well
as data elements tested in the PAC PRD
that focused on the frequency of
suctioning required for patients and
residents with tracheostomies (‘‘Trach
Tube with Suctioning: Specify most
intensive frequency of suctioning during
stay [Every __hours]’’). For more
information on the Suctioning data
element, we refer readers to the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Suctioning data element was first
proposed as standardized patient
assessment data elements in the FY
2018 IRF PPS proposed rule (82 FR
20728 through 20729). In that proposed
rule, we stated that the proposal was
informed by input we received through
a call for input published on the CMS
Measures Management System
Blueprint website. Input submitted from
August 12 to September 12, 2016 noted
support for the Suctioning data element.
The input noted the feasibility of this
item in PAC, and the relevance of this
data element to facilitating care
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coordination and supporting care
transitions.
We also stated that those commenters
had suggested that we examine the
frequency of suctioning to better
understand the use of staff time, the
impact on a patient or resident’s
capacity to speak and swallow, and
intensity of care required. Based on
these comments, we decided to add two
sub-elements (Scheduled and As
needed) to the suctioning element. The
proposed Suctioning data element
includes both the principal Suctioning
data element that is included on the
MDS in SNFs and two sub-elements,
Scheduled and As needed. A summary
report for the August 12 to September
12, 2016 public comment period titled
‘‘SPADE August 2016 Public Comment
Summary Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general;
no additional comments were received
that were specific to the Suctioning data
element. Subsequent to receiving
comments on the FY 2018 IRF PPS rule,
the Suctioning data element was
included in the National Beta Test of
candidate data elements conducted by
our data element contractor from
November 2017 to August 2018. Results
of this test found the Suctioning data
element to be feasible and reliable for
use with PAC patients and residents.
More information about the
performance of the Suctioning data
element in the National Beta Test can be
found in the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP did not
specifically discuss the Suctioning data
element, the TEP supported the
assessment of the special services,
treatments, and interventions included
in the National Beta Test with respect to
both admission and discharge. A
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39127
summary of the September 17, 2018 TEP
meeting titled ‘‘SPADE Technical Expert
Panel Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicited additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for suctioning, stakeholder
input, and strong test results, we
proposed that the Suctioning
(Scheduled, As needed) data element
with a principal data element and two
sub-elements meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and
to adopt the Suctioning (Scheduled, As
needed) data element as standardized
patient assessment data for use in the
IRF QRP.
A commenter submitted the following
comment related to the proposed rule’s
discussion of the Suctioning data
element.
Comment: One commenter requested
that this data element also assess the
frequency of suctioning, as it can impact
resource utilization and potential
medication changes in the plan of care.
Response: We appreciate the
commenter’s feedback that the response
options for this data element may not
fully capture impacts to resource
utilization and care plans. The
Suctioning data element does include
sub-elements to identify if suctioning is
performed on a ‘‘Scheduled’’ or ‘‘As
Needed’’ basis, but it does not directly
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assess the frequency of suctioning by,
for example, asking an assessor to
specify how often suctioning is
scheduled. As finalized, this data
element differentiates between patients
who only occasionally need suctioning,
and patients for whom assessment of
suctioning needs is a frequent and
routine part of the care (that is, where
suctioning is performed on a schedule
according to physician instructions). In
our work to identify standardized data
elements, we have strived to balance the
scope and level of detail of the data
elements against the potential burden
placed on patients and providers.
However, we clarify that any SPADE is
intended as a minimum assessment and
does not limit the ability of providers to
conduct a more comprehensive
evaluation of a patient’s situation to
identify the potential impacts on
outcomes that the commenter describes.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the
Suctioning (Scheduled, As needed) data
element as standardized patient
assessment data beginning with the FY
2022 IRF QRP as proposed.
• Respiratory Treatment: Tracheostomy
Care
In the FY 2020 IRF PPS proposed rule
(84 FR 17302), we proposed that the
Tracheostomy Care data element meets
the definition of standardized patient
assessment data with respect to special
services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20729 through
20730), a tracheostomy provides an air
passage to help a patient or resident
breathe when the usual route for
breathing is obstructed or impaired.
Generally, in all of these cases,
suctioning is necessary to ensure that
the tracheostomy is clear of secretions,
which can inhibit successful
oxygenation of the individual. Often,
individuals with tracheostomies are also
receiving supplemental oxygenation.
The presence of a tracheostomy, albeit
permanent or temporary, warrants
careful monitoring and immediate
intervention if the tracheostomy
becomes occluded or if the device used
becomes dislodged. While in rare cases
the presence of a tracheostomy is not
associated with increased care demands
(and in some of those instances, the care
of the ostomy is performed by the
patient) in general the presence of such
as device is associated with increased
patient risk, and clinical care services
will necessarily include close
monitoring to ensure that no life-
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threatening events occur as a result of
the tracheostomy. In addition,
tracheostomy care, which primarily
consists of cleansing, dressing changes,
and replacement of the tracheostomy
cannula (tube), is a critical part of the
care plan. Regular cleansing is
important to prevent infection, such as
pneumonia, and to prevent any
occlusions with which there are risks
for inadequate oxygenation.
The proposed data element consists of
the single Tracheostomy Care data
element. The proposed data element is
currently in use in the MDS in SNFs
(‘‘Tracheostomy care’’). For more
information on the Tracheostomy Care
data element, we refer readers to the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Tracheostomy Care data element
was first proposed as a standardized
patient assessment data element in the
FY 2018 IRF PPS proposed rule (82 FR
20729 through 20730). In that proposed
rule, we stated that the proposal was
informed by input we received on the
Tracheostomy Care data element
through a call for input published on
the CMS Measures Management System
Blueprint website. Input submitted from
August 12 to September 12, 2016 noted
support for this data element, noting the
feasibility of this item in PAC, and the
relevance of this data element to
facilitating care coordination and
supporting care transitions. A summary
report for the August 12 to September
12, 2016 public comment period titled
‘‘SPADE August 2016 Public Comment
Summary Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general;
no additional comments were received
that were specific to the Tracheostomy
Care data element.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the
Tracheostomy Care data element was
included in the National Beta Test of
candidate data elements conducted by
our data element contractor from
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November 2017 to August 2018. Results
of this test found the Tracheostomy Care
data element to be feasible and reliable
for use with PAC patients and residents.
More information about the
performance of the Tracheostomy Care
data element in the National Beta Test
can be found in the document titled
‘‘Final Specifications for IRF QRP
Quality Measures and Standardized
Patient Assessment Data Elements,’’ at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP did not
specifically discuss the Tracheostomy
Care data element, the TEP supported
the assessment of the special services,
treatments, and interventions included
in the National Beta Test with respect to
both admission and discharge. A
summary of the September 17, 2018 TEP
meeting titled ‘‘SPADE Technical Expert
Panel Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for tracheostomy care,
stakeholder input, and strong test
results, we proposed that the
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Tracheostomy Care data element meets
the definition of standardized patient
assessment data with respect to special
services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Tracheostomy Care
data element as standardized patient
assessment data for use in the IRF QRP.
We invited public comment on this
proposal. While we received support
from some commenters on Special
Services, Treatments, and Interventions
as a whole (section IX.G.2 in this final
rule), we did not receive any specific
comments on Tracheostomy Care data
element.
After careful consideration of the
public comments we received on the
category of Special Services,
Treatments, and Interventions, we are
finalizing our proposal to adopt the
Tracheostomy Care data element as
standardized patient assessment data
beginning with the FY 2022 IRF QRP as
proposed.
• Respiratory Treatment: Non-Invasive
Mechanical Ventilator (BiPAP, CPAP)
In the FY 2020 IRF PPS proposed rule
(84 FR 17303), we proposed that the
Non-invasive Mechanical Ventilator
(Bilevel Positive Airway Pressure
[BiPAP], Continuous Positive Airway
Pressure [CPAP]) data element meets the
definition of standardized patient
assessment data with respect to special
services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20730), BiPAP and
CPAP are respiratory support devices
that prevent the airways from closing by
delivering slightly pressurized air via
electronic cycling throughout the
breathing cycle (BiPAP) or through a
mask continuously (CPAP). Assessment
of non-invasive mechanical ventilation
is important in care planning, as both
CPAP and BiPAP are resource-intensive
(although less so than invasive
mechanical ventilation) and signify
underlying medical conditions about
the patient or resident who requires the
use of this intervention. Particularly
when used in settings of acute illness or
progressive respiratory decline,
additional staff (for example, respiratory
therapists) are required to monitor and
adjust the CPAP and BiPAP settings and
the patient or resident may require more
nursing resources.
The proposed data element, Noninvasive Mechanical Ventilator (BiPAP,
CPAP), consists of the principal Noninvasive Mechanical Ventilator data
element and two response option subelements: BiPAP and CPAP. If the
assessor indicates that the patient is
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receiving non-invasive mechanical
ventilation on the principal Noninvasive Mechanical Ventilator data
element, the assessor would then
indicate which type (for example,
BiPAP, CPAP). Data elements that assess
non-invasive mechanical ventilation are
currently included on LCDS for the
LTCH setting (‘‘Non-invasive Ventilator
(BiPAP, CPAP)’’), and the MDS for the
SNF setting (‘‘Non-invasive Mechanical
Ventilator (BiPAP/CPAP)’’). For more
information on the Non-invasive
Mechanical Ventilator (BiPAP, CPAP)
data element, we refer readers to the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Non-invasive Mechanical
Ventilator data element was first
proposed as standardized patient
assessment data elements in the FY
2018 IRF PPS proposed rule (82 FR
20730). In that proposed rule, we stated
that the proposal was informed by input
we received through a call for input
published on the CMS Measures
Management System Blueprint website.
Input submitted from August 12 to
September 12, 2016 on a single data
element, BiPAP/CPAP, that captures
equivalent clinical information but uses
a different label than the data element
currently used in the MDS in SNFs and
LCDS, noted support for this data
element, noting the feasibility of these
items in PAC, and the relevance of this
data element for facilitating care
coordination and supporting care
transitions. In addition, we also stated
that some commenters supported
separating out BiPAP and CPAP as
distinct sub-elements, as they are
therapies used for different types of
patients and residents. A summary
report for the August 12 to September
12, 2016 public comment period titled
‘‘SPADE August 2016 Public Comment
Summary Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general.
One commenter noted appreciation of
the revisions to the Non-invasive
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39129
Mechanical Ventilator data element in
response to comments submitted during
a public input period held from August
12 to September 12, 2016.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the Noninvasive Mechanical Ventilator data
element was included in the National
Beta Test of candidate data elements
conducted by our data element
contractor from November 2017 to
August 2018. Results of this test found
the Non-invasive Mechanical Ventilator
data element to be feasible and reliable
for use with PAC patients and residents.
More information about the
performance of the Non-invasive
Mechanical Ventilator data element in
the National Beta Test can be found in
the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP did not
specifically discuss the Non-invasive
Mechanical Ventilator data element, the
TEP supported the assessment of the
special services, treatments, and
interventions included in the National
Beta Test with respect to both admission
and discharge. A summary of the
September 17, 2018 TEP meeting titled
‘‘SPADE Technical Expert Panel
Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
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Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for non-invasive mechanical
ventilation, stakeholder input, and
strong test results, we proposed that the
Non-invasive Mechanical Ventilator
(BiPAP, CPAP) data element with a
principal data element and two subelements meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and
to adopt the Non-invasive Mechanical
Ventilator (BiPAP, CPAP) data element
as standardized patient assessment data
for use in the IRF QRP.
We invited public comment on this
proposal. While we received support
from some commenters on Special
Services, Treatments, and Interventions
as a whole (section IX.G.2 in this final
rule), we did not receive any specific
comments on the Non-invasive
Mechanical Ventilator (BiPAP, CPAP)
data element.
After careful consideration of the
public comments we received on the
category of Special Services,
Treatments, and Interventions, we are
finalizing our proposal to adopt the
Non-invasive Mechanical Ventilator
(BiPAP, CPAP) data element as
standardized patient assessment data
beginning with the FY 2022 IRF QRP as
proposed.
• Respiratory Treatment: Invasive
Mechanical Ventilator
In the FY 2020 IRF PPS proposed rule
(84 FR 17304), we proposed that the
Invasive Mechanical Ventilator data
element meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20730 through
20731), invasive mechanical ventilation
includes ventilators and respirators that
ventilate the patient through a tube that
extends via the oral airway into the
pulmonary region or through a surgical
opening directly into the trachea. Thus,
assessment of invasive mechanical
ventilation is important in care planning
and risk mitigation. Ventilation in this
manner is a resource-intensive therapy
associated with life-threatening
conditions without which the patient or
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resident would not survive. However,
ventilator use has inherent risks
requiring close monitoring. Failure to
adequately care for the patient or
resident who is ventilator dependent
can lead to iatrogenic events such as
death, pneumonia, and sepsis.
Mechanical ventilation further signifies
the complexity of the patient’s
underlying medical or surgical
condition. Of note, invasive mechanical
ventilation is associated with high daily
and aggregate costs.112
The proposed data element, Invasive
Mechanical Ventilator, consists of a
single data element. Data elements that
capture invasive mechanical ventilation
are currently in use in the MDS in SNFs
and LCDS in LTCHs. For more
information on the Invasive Mechanical
Ventilator data element, we refer readers
to the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Invasive Mechanical Ventilator
data element was first proposed as a
standardized patient assessment data
element in the FY 2018 IRF PPS
proposed rule (82 FR 20730 through
20731). In that proposed rule, we stated
that the proposal was informed by input
we received on data elements that assess
invasive ventilator use and weaning
status that were tested in the PAC PRD
(‘‘Ventilator—Weaning’’ and
‘‘Ventilator—Non-Weaning’’) through a
call for input published on the CMS
Measures Management System
Blueprint website. Input submitted from
August 12 to September 12, 2016, noted
support for this data element,
highlighting the importance of this
information in supporting care
coordination and care transitions. We
also stated that some commenters had
expressed concern about the
appropriateness for standardization
given: The prevalence of ventilator
weaning across PAC providers; the
timing of administration; how weaning
is defined; and how weaning status in
particular relates to quality of care.
These public comments guided our
decision to propose a single data
element focused on current use of
invasive mechanical ventilation only,
which does not attempt to capture
112 Wunsch, H., Linde-Zwirble, W.T., Angus,
D.C., Hartman, M.E., Milbrandt, E.B., & Kahn, J.M.
(2010). ‘‘The epidemiology of mechanical
ventilation use in the United States.’’ Critical Care
Med 38(10): 1947–1953.
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weaning status. A summary report for
the August 12 to September 12, 2016
public comment period titled ‘‘SPADE
August 2016 Public Comment Summary
Report’’ we received is available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general.
Two commenters noted their
appreciation of the revisions to the
Invasive Mechanical Ventilator data
element in response to comments
submitted during a public input period
held from August 12 to September 12,
2016.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the Invasive
Mechanical Ventilator data element was
included in the National Beta Test of
candidate data elements conducted by
our data element contractor from
November 2017 to August 2018. Results
of this test found the Invasive
Mechanical Ventilator data element to
be feasible and reliable for use with PAC
patients and residents. More
information about the performance of
the Invasive Mechanical Ventilator data
element in the National Beta Test can be
found in the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
element. Although the TEP did not
specifically discuss the Invasive
Mechanical Ventilator data element, the
TEP supported the assessment of the
special services, treatments, and
interventions included in the National
Beta Test with respect to both admission
and discharge. A summary of the
September 17, 2018 TEP meeting titled
‘‘SPADE Technical Expert Panel
Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
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We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present results of the National Beta Test
and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for invasive mechanical
ventilation, stakeholder input, and
strong test results, we proposed that the
Invasive Mechanical Ventilator data
element that assesses the use of an
invasive mechanical ventilator meets
the definition of standardized patient
assessment data with respect to special
services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Invasive
Mechanical Ventilator data element as
standardized patient assessment data for
use in the IRF QRP.
A commenter submitted the following
comment related to the proposed rule’s
discussion of the Invasive Mechanical
Ventilator data element.
Comment: One commenter noted
disappointment over seeing that the
SPADE for invasive mechanical
ventilator only assesses whether or not
a patient is on a mechanical ventilator.
The commenter suggested CMS consider
collecting data to track functional
outcomes related to progress towards
independence in communication and
swallowing.
Response: We have attempted to
balance the scope and level of detail of
the data elements against the potential
burden placed on patients and
providers. We believe that assessing the
use of an invasive mechanical ventilator
will be a useful point of information to
inform care planning and further
assessment, such as related to functional
outcomes, as the commenter suggests.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the
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Invasive Mechanical Ventilator data
element as standardized patient
assessment data beginning with the FY
2022 IRF QRP as proposed.
• Intravenous (IV) Medications
(Antibiotics, Anticoagulants, Vasoactive
Medications, Other)
In the FY 2020 IRF PPS proposed rule
(84 FR 17305 through 17306), we
proposed that the IV Medications
(Antibiotics, Anticoagulants, Vasoactive
Medications, Other) data element meets
the definition of standardized patient
assessment data with respect to special
services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20731 through
20732), when we proposed a similar
data element related to IV medications,
IV medications are solutions of a
specific medication (for example,
antibiotics, anticoagulants)
administered directly into the venous
circulation via a syringe or intravenous
catheter. IV medications are
administered via intravenous push,
single, intermittent, or continuous
infusion through a catheter placed into
the vein. Further, IV medications are
more resource intensive to administer
than oral medications, and signify a
higher patient complexity (and often
higher severity of illness).
The clinical indications for each of
the sub-elements of the IV Medications
data element (Antibiotics,
Anticoagulants, Vasoactive Medications,
and Other) are very different. IV
antibiotics are used for severe infections
when the bioavailability of the oral form
of the medication would be inadequate
to kill the pathogen or an oral form of
the medication does not exist. IV
anticoagulants refer to anti-clotting
medications (that is, ‘‘blood thinners’’).
IV anticoagulants are commonly used
for hospitalized patients who have deep
venous thrombosis, pulmonary
embolism, or myocardial infarction, as
well as those undergoing interventional
cardiac procedures. Vasoactive
medications refer to the IV
administration of vasoactive drugs,
including vasopressors, vasodilators,
and continuous medication for
pulmonary edema, which increase or
decrease blood pressure or heart rate.
The indications, risks, and benefits of
each of these classes of IV medications
are distinct, making it important to
assess each separately in PAC. Knowing
whether or not patients and residents
are receiving IV medication and the type
of medication provided by each PAC
provider will improve quality of care.
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The IV Medications (Antibiotics,
Anticoagulants, Vasoactive Medications,
and Other) data element we proposed
consists of a principal data element (IV
Medications) and four response option
sub-elements: Antibiotics,
Anticoagulants, Vasoactive Medications,
and Other. The Vasoactive Medications
sub-element was not proposed in the FY
2018 IRF PPS proposed rule (82 FR
20731 through 20732). We added the
Vasoactive Medications sub-element to
our proposal in order to harmonize the
proposed IV Mediciations element with
the data currently collected in the
LCDS.
If the assessor indicates that the
patient is receiving IV medications on
the principal IV Medications data
element, the assessor would then
indicate which types of medications (for
example, Antibiotics, Anticoagulants,
Vasoactive Medications, Other). An IV
Medications data element is currently in
use on the MDS in SNFs and there is a
related data element in OASIS that
collects information on Intravenous and
Infusion Therapies. For more
information on the IV Medications
(Antibiotics, Anticoagulants, Vasoactive
Medications, Other) data element, we
refer readers to the document titled
‘‘Final Specifications for IRF QRP
Quality Measures and Standardized
Patient Assessment Data Elements,’’
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
An IV Medications data element was
first proposed as standardized patient
assessment data element in the FY 2018
IRF PPS proposed rule (82 FR 20731
through 20732). In that proposed rule,
we stated that the proposal was
informed by input we received on
Vasoactive Medications through a call
for input published on the CMS
Measures Management System
Blueprint website. Input submitted from
August 12 to September 12, 2016
supported this data element with one
noting the importance of this data
element in supporting care transitions.
We also stated that those commenters
had criticized the need for collecting
specifically Vasoactive Medications,
giving feedback that the data element
was too narrowly focused. In addition,
public comment received indicated that
the clinical significance of vasoactive
medications administration alone was
not high enough in PAC to merit
mandated assessment, noting that
related and more useful information
could be captured in an item that
assessed all IV medication use. A
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summary report for the August 12 to
September 12, 2016 public comment
period titled ‘‘SPADE August 2016
Public Comment Summary Report’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general;
no additional comments were received
that were specific to the IV Medications
data element.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the IV
Medications data element was included
in the National Beta Test of candidate
data elements conducted by our data
element contractor from November 2017
to August 2018. Results of this test
found the IV Medications data element
to be feasible and reliable for use with
PAC patients and residents. More
information about the performance of
the IV Medications data element in the
National Beta Test can be found in the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP did not
specifically discuss the IV Medications
data element, the TEP supported the
assessment of the special services,
treatments, and interventions included
in the National Beta Test with respect to
both admission and discharge. A
summary of the September 17, 2018 TEP
meeting titled ‘‘SPADE Technical Expert
Panel Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
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public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for IV medications,
stakeholder input, and strong test
results, we proposed that the IV
Medications (Antibiotics,
Anticoagulants, Vasoactive Medications,
Other) data element with a principal
data element and four sub-elements
meets the definition of standardized
patient assessment data with respect to
special services, treatments, and
interventions under section
1899B(b)(1)(B)(iii) of the Act and to
adopt the IV Medications (Antibiotics,
Anticoagulants, Vasoactive Medications,
Other) data element as standardized
patient assessment data for use in the
IRF QRP.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the IV Medications data
elements.
Comment: One commenter noted that
the IV Medications data elements seem
redundant of the proposed High-Risk
Drug Classes: Use and Indication data
elements.
Response: We wish to clarify that the
IV Medications data element collects
information on medications received by
IV only, with sub-elements specific to
antibiotics, anticoagulants, and
vasoactive medications only. In
contrast, the High Risk Drug Classes:
Use and Indication data element collects
information on medications received by
any route, only for six specific drug
classes, and collects information on the
presence of an indication. We believe
the overlap between these SPADEs is
minimal, as it would only occur when
a medication in a high-risk drug class is
delivered by IV. Additionally, in this
case, the High-Risk Drug Classes: Use
and Indication data element would
assess the presence of an indication in
the patient’s medical record, which the
IV Medications data element does not
do.
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Comment: Commenters were
concerned about the performance of the
IV Medications data element in the
National Beta Test, noting that its
reliability was only fair to good and
poor for the anticoagulation subelement.
Response: The kappa for the
overarching IV Medications data
element was 0.70 across settings, which
falls in the range of ‘‘substantial/good’’
agreement. The IV Medications subelement that had a ‘‘slight/poor’’
reliability (in the range of 0.00–0.20)
was the IV Anticoagulants sub-element
(kappa = 0.13). The Other IV
Medications sub-element had
‘‘moderate’’ reliability (kappa = 0.46).
Consultation with assessors suggested
that the low kappa for the IV
Anticoagulants sub-element was likely
due to inconsistent interpretation of the
coding instructions. Having identified
the likely source of the relatively lower
interrater reliability, we are confident
that with proper training of IRFs on how
to report the data elements, the
reliability of these sub-elements will be
improved.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the IV
Medications (Antibiotics,
Anticoagulants, Vasoactive Medications,
Other) data element as standardized
patient assessment data beginning with
the FY 2022 IRF QRP as proposed.
• Transfusions
In the FY 2020 IRF PPS proposed rule
(84 FR 17306), we proposed that the
Transfusions data element meets the
definition of standardized patient
assessment data with respect to special
services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20732),
transfusion refers to introducing blood
or blood products into the circulatory
system of a person. Blood transfusions
are based on specific protocols, with
multiple safety checks and monitoring
required during and after the infusion in
case of adverse events. Coordination
with the provider’s blood bank is
necessary, as well as documentation by
clinical staff to ensure compliance with
regulatory requirements. In addition, the
need for transfusions signifies
underlying patient complexity that is
likely to require care coordination and
patient monitoring, and impacts
planning for transitions of care, as
transfusions are not performed by all
PAC providers.
The proposed data element consists of
the single Transfusions data element. A
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data element on transfusion is currently
in use in the MDS in SNFs
(‘‘Transfusions’’) and a data element
tested in the PAC PRD (‘‘Blood
Transfusions’’) was found feasible for
use in each of the four PAC settings. For
more information on the Transfusions
data element, we refer readers to the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Transfusions data element was
first proposed as a standardized patient
assessment data element in the FY 2018
IRF PPS proposed rule (82 FR 20732). In
response to our proposal in the FY 2018
IRF PPS proposed rule, we received
public comments in support of the
special services, treatments, and
interventions data elements in general;
no additional comments were received
that were specific to the Transfusions
data element.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the
Transfusions data element was included
in the National Beta Test of candidate
data elements conducted by our data
element contractor from November 2017
to August 2018. Results of this test
found the Transfusions data element to
be feasible and reliable for use with PAC
patients and residents. More
information about the performance of
the Transfusions data element in the
National Beta Test can be found in the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP did not
specifically discuss the Transfusions
data element, the TEP supported the
assessment of the special services,
treatments, and interventions included
in the National Beta Test with respect to
both admission and discharge. A
summary of the September 17, 2018 TEP
meeting titled ‘‘SPADE Technical Expert
Panel Summary (Third Convening)’’ is
available at https://www.cms.gov/
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Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for transfusions, stakeholder
input, and strong test results, we
proposed that the Transfusions data
element meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and
to adopt the Transfusions data element
as standardized patient assessment data
for use in the IRF QRP.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the Transfusions data
element.
Comment: One commenter applauded
CMS for including the Transfusions data
element, noting that it will provide
information on care planning, clinical
decision making, patient safety, care
transitions, and resource use in IRFs
and will contribute to higher quality
and coordinated care for patients who
rely on these life-saving treatments.
Response: We thank the commenter
for their support. We selected the
Transfusions data element for proposal
as standardized data in part because of
the attributes that the commenter noted.
Comment: One commenter was
concerned that IRFs will not have the
resources needed to provide patients
with access to blood transfusions and
requested that CMS consider whether
payments to IRFs are adequate to cover
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the cost of this resource intensive,
specialized service.
Response: We wish to clarify that this
item is finalized only to collect
information on the complexity of the
patient and resources the patient
requires. At this time, this item will not
be used for any payment purposes, and
thus we are not able to comment on cost
of this service. We wish to clarify that
this SPADE is not intended to measure
the ability of an IRF to provide in-house
transfusions, only to capture the
services a given patient may be
receiving. Further, for patients who
require services related to blood
transfusions, information collected by
this data element is a part of common
clinical workflow, and thus, we believe
that burden on resource intensity would
not be affected by the standardization of
this data element.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the
Transfusions data element as
standardized patient assessment data
beginning with the FY 2022 IRF QRP as
proposed.
• Dialysis (Hemodialysis, Peritoneal
Dialysis)
In the FY 2020 IRF PPS proposed rule
(84 FR 17306 through 17307), we
proposed that the Dialysis
(Hemodialysis, Peritoneal dialysis) data
element meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20732 through
20733), dialysis is a treatment primarily
used to provide replacement for lost
kidney function. Both forms of dialysis
(hemodialysis and peritoneal dialysis)
are resource intensive, not only during
the actual dialysis process but before,
during, and following. Patients and
residents who need and undergo
dialysis procedures are at high risk for
physiologic and hemodynamic
instability from fluid shifts and
electrolyte disturbances, as well as
infections that can lead to sepsis.
Further, patients or residents receiving
hemodialysis are often transported to a
different facility, or at a minimum, to a
different location in the same facility for
treatment. Close monitoring for fluid
shifts, blood pressure abnormalities, and
other adverse effects is required prior to,
during, and following each dialysis
session. Nursing staff typically perform
peritoneal dialysis at the bedside, and as
with hemodialysis, close monitoring is
required.
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The proposed data element, Dialysis
(Hemodialysis, Peritoneal dialysis)
consists of the principal Dialysis data
element and two response option subelements: Hemodialysis and Peritoneal
dialysis. If the assessor indicates that
the patient is receiving dialysis on the
principal Dialysis data element, the
assessor would then indicate which
type (Hemodialysis or Peritoneal
dialysis). The principal Dialysis data
element is currently included on the
MDS in SNFs and the LCDS for LTCHs
and assesses the overall use of dialysis.
As the result public feedback
described below, in the proposed rule,
we proposed a data element that
includes the principal Dialysis data
element and two sub-elements
(Hemodialysis and Peritoneal dialysis).
For more information on the Dialysis
data element, we refer readers to the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Dialysis data element was first
proposed as standardized patient
assessment data in the FY 2018 IRF PPS
proposed rule (82 FR 20732 through
20733). In that proposed rule, we stated
that the proposal was informed by input
we received on a singular Hemodialysis
data element through a call for input
published on the CMS Measures
Management System Blueprint website.
Input submitted from August 12 to
September 12, 2016 supported the
assessment of hemodialysis and
recommended that the data element be
expanded to include peritoneal dialysis.
We also stated that those commenters
had supported the singular
Hemodialysis data element, noting the
relevance of this information for sharing
across the care continuum to facilitate
care coordination and care transitions,
the potential for this data element to be
used to improve quality, and the
feasibility for use in PAC. In addition,
we received comments that the item
would be useful in improving patient
and resident transitions of care. We also
noted that several commenters had
stated that peritoneal dialysis should be
included in a standardized data element
on dialysis and recommended collecting
information on peritoneal dialysis in
addition to hemodialysis. The rationale
for including peritoneal dialysis from
commenters included the fact that
patients and residents receiving
peritoneal dialysis will have different
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needs at post-acute discharge compared
to those receiving hemodialysis or not
having any dialysis. Based on these
comments, the Hemodialysis data
element was expanded to include a
principal Dialysis data element and two
sub-elements, Hemodialysis and
Peritoneal dialysis. We proposed the
version of the Dialysis element that
includes two types of dialysis. A
summary report for the August 12 to
September 12, 2016 public comment
period titled ‘‘SPADE August 2016
Public Comment Summary Report’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received comments in support of the
special services, treatments, and
interventions data elements in general.
One commenter noted that they
appreciated the revisions to the Dialysis
data element in response to comments
submitted during a public input period
held from August 12 to September 12,
2016.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the Dialysis
data element was included in the
National Beta Test of candidate data
elements conducted by our data element
contractor from November 2017 to
August 2018. Results of this test found
the Dialysis data element to be feasible
and reliable for use with PAC patients
and residents. More information about
the performance of the Dialysis data
element in the National Beta Test can be
found in the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although they did not
specifically discuss the Dialysis data
element, the TEP supported the
assessment of the special services,
treatments, and interventions included
in the National Beta Test with respect to
both admission and discharge. A
summary of the September 17, 2018 TEP
meeting titled ‘‘SPADE Technical Expert
Panel Summary (Third Convening)’’ is
available at https://www.cms.gov/
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We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for dialysis, stakeholder input,
and strong test results, we proposed that
the Dialysis (Hemodialysis, Peritoneal
dialysis) data element with a principal
data element and two sub-elements
meets the definition of standardized
patient assessment data with respect to
special services, treatments, and
interventions under section
1899B(b)(1)(B)(iii) of the Act and to
adopt the Dialysis (Hemodialysis,
Peritoneal dialysis) data element as
standardized patient assessment data for
use in the IRF QRP.
We invited public comment on this
proposal. While we received support
from some commenters on this Special
Services, Treatments, and Interventions
as a whole (section IX.G.2 in this final
rule), we did not receive any specific
comments on the Dialysis
(Hemodialysis, Peritoneal dialysis) data
element.
After careful consideration of the
public comments we received on the
category of Special Services,
Treatments, and Interventions, we are
finalizing our proposal to adopt the
Dialysis (Hemodialysis, Peritoneal
dialysis) data element as standardized
patient assessment data beginning with
the FY 2022 IRF QRP as proposed.
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• Intravenous (IV) Access (Peripheral
IV, Midline, Central Line)
In the FY 2020 IRF PPS proposed rule
(84 FR 17307 through 17308), we
proposed that the IV Access (Peripheral
IV, Midline, Central line) data element
meets the definition of standardized
patient assessment data with respect to
special services, treatments, and
interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20733 through
20734), patients or residents with
central lines, including those
peripherally inserted or who have
subcutaneous central line ‘‘port’’ access,
always require vigilant nursing care to
keep patency of the lines and ensure
that such invasive lines remain free
from any potentially life-threatening
events such as infection, air embolism,
or bleeding from an open lumen.
Clinically complex patients and
residents are likely to be receiving
medications or nutrition intravenously.
The sub-elements included in the IV
Access data elements distinguish
between peripheral access and different
types of central access. The rationale for
distinguishing between a peripheral IV
and central IV access is that central
lines confer higher risks associated with
life-threatening events such as
pulmonary embolism, infection, and
bleeding.
The proposed data element, IV Access
(Peripheral IV, Midline, Central line),
consists of the principal IV Access data
element and three response option subelements: Peripheral IV, Midline, and
Central line. The proposed IV Access
data element is not currently included
on any of the PAC assessment
instruments. For more information on
the IV Access data element, we refer
readers to the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The IV Access data element was first
proposed as standardized patient
assessment data elements in the FY
2018 IRF PPS proposed rule (82 FR
20733 through 20734). In that proposed
rule, we stated that the proposal was
informed by input we received on one
of the PAC PRD data elements, Central
Line Management, through a call for
input published on the CMS Measures
Management System Blueprint website.
A central line is a type of IV access.
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Input submitted from August 12 to
September 12, 2016 supported the
assessment of central line management
and recommended that the data element
be broadened to also include other types
of IV access. Several commenters noted
feasibility and importance for
facilitating care coordination and care
transitions. However, a few commenters
recommended that the definition of this
data element be broadened to include
peripherally inserted central catheters
(‘‘PICC lines’’) and midline IVs. Based
on public comment feedback and in
consultation with expert input,
described below, we created an
overarching IV Access data element
with sub-elements for other types of IV
access in addition to central lines (that
is, peripheral IV and midline). This
expanded version of IV Access is the
data element being proposed. A
summary report for the August 12 to
September 12, 2016 public comment
period titled ‘‘SPADE August 2016
Public Comment Summary Report’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general.
One commenter noted appreciation of
the revisions to the IV Access data
element in response to comments
submitted during a public input period
held from August 12 to September 12,
2016.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the IV Access
data element was included in the
National Beta Test of candidate data
elements conducted by our data element
contractor from November 2017 to
August 2018. Results of this test found
the IV Access data element to be feasible
and reliable for use with PAC patients
and residents. More information about
the performance of the IV Access data
element in the National Beta Test can be
found in the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
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39135
standardized patient assessment data
elements. Although the TEP did not
specifically discuss the IV Access data
element, the TEP supported the
assessment of the special services,
treatments, and interventions included
in the National Beta Test with respect to
both admission and discharge. A
summary of the September 17, 2018 TEP
meeting titled ‘‘SPADE Technical Expert
Panel Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present results of the National Beta Test
and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for IV access, stakeholder
input, and strong test results, we
proposed that the IV access (Peripheral
IV, Midline, Central line) data element
with a principal data element and three
sub-elements meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and
to adopt the IV Access (Peripheral IV,
Midline, Central line) data element as
standardized patient assessment data for
use in the IRF QRP.
We invited public comment on this
proposal. While we received support
from some commenters on this Special
Services, Treatments, and Interventions
as a whole (section IX.G.2 in this final
rule), we did not receive any specific
comments on the IV Access (Peripheral
IV, Midline, Central line) data element.
After careful consideration of the
public comments we received on the
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category of Special Services,
Treatments, and Interventions, we are
finalizing our proposal to adopt the IV
Access (Peripheral IV, Midline, Central
line) data element as standardized
patient assessment data beginning with
the FY 2022 IRF QRP as proposed.
• Nutritional Approach: Parenteral/IV
Feeding
In the FY 2020 IRF PPS proposed rule
(84 FR 17308 through 17309), we
proposed that the Parenteral/IV Feeding
data element meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20734), parenteral
nutrition/IV feeding refers to a patient
or resident being fed intravenously
using an infusion pump, bypassing the
usual process of eating and digestion.
The need for IV/parenteral feeding
indicates a clinical complexity that
prevents the patient or resident from
meeting his or her nutritional needs
enterally, and is more resource intensive
than other forms of nutrition, as it often
requires monitoring of blood
chemistries and the maintenance of a
central line. Therefore, assessing a
patient’s or resident’s need for
parenteral feeding is important for care
planning and resource use. In addition
to the risks associated with central and
peripheral intravenous access, total
parenteral nutrition is associated with
significant risks, such as air embolism
and sepsis.
The proposed data element consists of
the single Parenteral/IV Feeding data
element. The proposed Parenteral/IV
Feeding data element is currently in use
in the MDS in SNFs, and equivalent or
related data elements are in use in the
LCDS, IRF–PAI, and OASIS. We
proposed to rename the existing Tube/
Parenteral feeding item in the IRF–PAI
to be the Parenteral/IV Feeding data
element. For more information on the
Parenteral/IV Feeding data element, we
refer readers to the document titled
‘‘Final Specifications for IRF QRP
Quality Measures and Standardized
Patient Assessment Data Elements,’’
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
The Parenteral/IV Feeding data
element was first proposed as a
standardized patient assessment data
element in the FY 2018 IRF PPS
proposed rule (82 FR 20734). In that
proposed rule, we stated that the
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proposal was informed by input we
received on Total Parenteral Nutrition
(an item with nearly the same meaning
as the proposed data element, but with
the label used in the PAC PRD), through
a call for input published on the CMS
Measures Management System
Blueprint website. Input submitted from
August 12 to September 12, 2016
supported this data element, noting its
relevance to facilitating care
coordination and supporting care
transitions. After the public comment
period, the Total Parenteral Nutrition
data element was renamed Parenteral/IV
Feeding, to be consistent with how this
data element is referred to in the MDS
in SNFs. A summary report for the
August 12 to September 12, 2016 public
comment period titled ‘‘SPADE August
2016 Public Comment Summary
Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received comments in support of the
special services, treatments, and
interventions data elements in general;
no additional comments were received
that were specific to the Parenteral/IV
Feeding data element.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the
Parenteral/IV Feeding data element was
included in the National Beta Test of
candidate data elements conducted by
our data element contractor from
November 2017 to August 2018. Results
of this test found the Parenteral/IV
Feeding data element to be feasible and
reliable for use with PAC patients and
residents. More information about the
performance of the Parenteral/IV
Feeding data element in the National
Beta Test can be found in the document
titled ‘‘Final Specifications for IRF QRP
Quality Measures and Standardized
Patient Assessment Data Elements,’’
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP did not
specifically discuss the Parenteral/IV
Feeding data element, the TEP
supported the assessment of the special
services, treatments, and interventions
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included in the National Beta Test with
respect to both admission and
discharge. A summary of the September
17, 2018 TEP meeting titled ‘‘SPADE
Technical Expert Panel Summary (Third
Convening)’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for parenteral/IV feeding,
stakeholder input, and strong test
results, we proposed that the Parenteral/
IV Feeding data element meets the
definition of standardized patient
assessment data with respect to special
services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Parenteral/IV
Feeding data element as standardized
patient assessment data for use in the
IRF QRP.
A commenter submitted the following
comment related to the proposed rule’s
discussion of the Parenteral/IV Feeding
data element.
Comment: One commenter was
supportive of collecting this data
element, but noted that it should not be
a substitute for capturing information
related to swallowing which reflects
additional patient complexity and
resource use.
Response: We thank the commenter
for their support and appreciate the
concerns raised. We agree that the
Parenteral/IV Feeding SPADE should
not be used as a substitute for an
assessment of a patient’s swallowing
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function. The proposed SPADEs are not
intended to replace comprehensive
clinical evaluation and in no way
preclude providers from conducting
further patient evaluation or
assessments in their settings as they
believe are necessary and useful. We
agree that information related to
swallowing can capture patient
complexity. However, we also note that
Parenteral/IV Feeding data element
captures a different construct than an
evaluation of swallowing. That is, the
Parenteral/IV Feeding data element
captures a patient’s need to receive
calories and nutrients intravenously,
while an assessment of swallowing
would capture a patient’s functional
ability to safely consume food/liquids
orally for digestion in their
gastrointestinal tract.
After careful consideration of the
public comments we received on the
category of Special Services,
Treatments, and Interventions, we are
finalizing our proposal to adopt the
Parenteral/IV Feeding data element as
standardized patient assessment data
beginning with the FY 2022 IRF QRP as
proposed.
• Nutritional Approach: Feeding Tube
In the FY 2020 IRF PPS proposed rule
(84 FR 17309 through 17310), we
proposed that the Feeding Tube data
element meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20734 through
20735), the majority of patients
admitted to acute care hospitals
experience deterioration of their
nutritional status during their hospital
stay, making assessment of nutritional
status and method of feeding if unable
to eat orally very important in PAC. A
feeding tube can be inserted through the
nose or the skin on the abdomen to
deliver liquid nutrition into the stomach
or small intestine. Feeding tubes are
resource intensive, and therefore, are
important to assess for care planning
and resource use. Patients with severe
malnutrition are at higher risk for a
variety of complications.113 In PAC
settings, there are a variety of reasons
that patients and residents may not be
able to eat orally (including clinical or
cognitive status).
The proposed data element consists of
the single Feeding Tube data element.
113 Dempsey, D.T., Mullen, J.L., & Buzby, G.P.
(1988). ‘‘The link between nutritional status and
clinical outcome: can nutritional intervention
modify it?’’ Am J of Clinical Nutrition, 47(2): 352–
356.
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The Feeding Tube data element is
currently included in the MDS for SNFs,
and in the OASIS for HHAs, where it is
labeled Enteral Nutrition. A related data
element, collected in the IRF–PAI for
IRFs (Tube/Parenteral Feeding), assesses
use of both feeding tubes and parenteral
nutrition. We proposed to rename the
existing Tube/Parenteral feeding item in
the IRF–PAI to the Feeding Tube data
element. For more information on the
Feeding Tube data element, we refer
readers to the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Feeding Tube data element was
first proposed as a standardized patient
assessment data element in the FY 2018
IRF PPS proposed rule (82 FR 20734
through 20735). In that proposed rule,
we stated that the proposal was
informed by input we received on an
Enteral Nutrition data element (the
Enteral Nutrition data item is the same
as the data element we proposed, but is
used in the OASIS under a different
name) through a call for input published
on the CMS Measures Management
System Blueprint website. Input
submitted from August 12 to September
12, 2016 supported the data element,
noting the importance of assessing
enteral nutrition status for facilitating
care coordination and care transitions.
After the public comment period, the
Enteral Nutrition data element used in
public comment was renamed Feeding
Tube, indicating the presence of an
assistive device. A summary report for
the August 12 to September 12, 2016
public comment period titled ‘‘SPADE
August 2016 Public Comment Summary
Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general.
In addition, a commenter recommended
that the term ‘‘enteral feeding’’ be used
instead of ‘‘feeding tube’’.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the Feeding
Tube data element was included in the
National Beta Test of candidate data
elements conducted by our data element
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39137
contractor from November 2017 to
August 2018. Results of this test found
the Feeding Tube data element to be
feasible and reliable for use with PAC
patients and residents. More
information about the performance of
the Feeding Tube data element in the
National Beta Test can be found in the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP did not
specifically discuss the Feeding Tube
data element, the TEP supported the
assessment of the special services,
treatments, and interventions included
in the National Beta Test with respect to
both admission and discharge. A
summary of the September 17, 2018 TEP
meeting titled ‘‘SPADE Technical Expert
Panel Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for feeding tubes, stakeholder
input, and strong test results, we
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proposed that the Feeding Tube data
element meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and
to adopt the Feeding Tube data element
as standardized patient assessment data
for use in the IRF QRP.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the Feeding Tube data
element.
Comment: One commenter noted that
in addition to identifying if the patient
is on a feeding tube or not, it would be
important to assess the patient’s
progression towards oral feeding within
this data element, as this impacts the
tube feeding regimen.
Response: We agree that progression
to oral feeding is important for care
planning and transfer. At this time, we
are finalizing a singular Feeding Tube
SPADE, which assesses the nutritional
approach only and does not capture the
patient’s prognosis with regard to oral
feeding. We wish to clarify that the
proposed SPADEs are not intended to
replace comprehensive clinical
evaluation and in no way preclude
providers from conducting further
patient evaluation or assessments in
their settings as they believe are
necessary and useful. We will take this
recommendation into consideration in
future work on standardized data
elements.
Comment: One commenter noted that
this data element should designate
between percutaneous endoscopic
gastrostomy (PEG) tube and nasogastric
(NG) tube because the different routes of
access have different levels of resource
requirements.
Response: We appreciate the
commenter’s suggestion, but we have
decided to maintain the singular
Feeding Tube SPADE. We agree that
different routes of access may have
different levels of resource
requirements. However, we do not
believe collecting this level of
information about nutritional therapies
via a SPADE would be significantly
more clinically useful or supportive of
care transitions than the singular
Feeding Tube SPADE. However, we will
take this suggestion into consideration
in future refinement of the clinical
SPADEs.
After careful consideration of the
public comments we received on the
category of Special Services,
Treatments, and Interventions, we are
finalizing our proposal to adopt the
Feeding Tube data element as
standardized patient assessment data
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beginning with the FY 2022 IRF QRP as
proposed.
• Nutritional Approach: Mechanically
Altered Diet
In the FY 2020 IRF PPS proposed rule
(84 FR 17310 through 17311), we
proposed that the Mechanically Altered
Diet data element meets the definition
of standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20735 through
20736), the Mechanically Altered Diet
data element refers to food that has been
altered to make it easier for the patient
or resident to chew and swallow, and
this type of diet is used for patients and
residents who have difficulty
performing these functions. Patients
with severe malnutrition are at higher
risk for a variety of complications.114
In PAC settings, there are a variety of
reasons that patients and residents may
have impairments related to oral
feedings, including clinical or cognitive
status. The provision of a mechanically
altered diet may be resource intensive,
and can signal difficulties associated
with swallowing/eating safety,
including dysphagia. In other cases, it
signifies the type of altered food source,
such as ground or puree that will enable
the safe and thorough ingestion of
nutritional substances and ensure safe
and adequate delivery of nourishment to
the patient. Often, patients and
residents on mechanically altered diets
also require additional nursing support,
such as individual feeding or direct
observation, to ensure the safe
consumption of the food product.
Therefore, assessing whether a patient
or resident requires a mechanically
altered diet is important for care
planning and resource identification.
The proposed data element consists of
the single Mechanically Altered Diet
data element. The proposed data
element is currently included on the
MDS for SNFs. A related data element
(‘‘Modified food consistency/
supervision’’) is currently included on
the IRF–PAI for IRFs. Another related
data element is included in the OASIS
for HHAs that collects information
about independent eating that requires
‘‘a liquid, pureed or ground meat diet.’’
We proposed to replace the existing
Modified food consistency/supervision
data element in the IRF–PAI to the
Mechanically Altered Diet data element.
114 Dempsey, D.T., Mullen, J.L., & Buzby, G.P.
(1988). ‘‘The link between nutritional status and
clinical outcome: can nutritional intervention
modify it?’’ Am J of Clinical Nutrition, 47(2): 352–
356.
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For more information on the
Mechanically Altered Diet data element,
we refer readers to the document titled
‘‘Final Specifications for IRF QRP
Quality Measures and Standardized
Patient Assessment Data Elements,’’
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
The Mechanically Altered Diet data
element was first proposed as a
standardized patient assessment data
element in the FY 2018 IRF PPS
proposed rule (82 FR 20735 through
20736). In response to our proposal in
the FY 2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general;
no additional comments were received
that were specific to the Mechanically
Altered Diet data element.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the
Mechanically Altered Diet data element
was included in the National Beta Test
of candidate data elements conducted
by our data element contractor from
November 2017 to August 2018. Results
of this test found the Mechanically
Altered Diet data element to be feasible
and reliable for use with PAC patients
and residents. More information about
the performance of the Mechanically
Altered Diet data element in the
National Beta Test can be found in the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP did not
specifically discuss the Mechanically
Altered Diet data element, the TEP
supported the assessment of the special
services, treatments, and interventions
included in the National Beta Test with
respect to both admission and
discharge. A summary of the September
17, 2018 TEP meeting titled ‘‘SPADE
Technical Expert Panel Summary (Third
Convening)’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
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IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for mechanically altered diet,
stakeholder input, and strong test
results, we proposed that the
Mechanically Altered Diet data element
meets the definition of standardized
patient assessment data with respect to
special services, treatments, and
interventions under section
1899B(b)(1)(B)(iii) of the Act and to
adopt the Mechanically Altered Diet
data element as standardized patient
assessment data for use in the IRF QRP.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the Mechanically Altered
Diet data element.
Comment: Commenters were
concerned about the performance of this
data element in the National Beta Test,
noting that its reliability was only
moderate in IRF settings.
Response: We provided
supplementary information with the
proposed rule on the reliability of the
SPADEs, described by the kappa
statistic and by the ‘‘percent agreement’’
between assessor, another measure of
reliability that is in some cases more
accurate than the kappa statistic,
depending on the underlying
distribution. (The document titled
‘‘Proposed Specifications for IRF QRP
Quality Measures and Standardized
Patient Assessment Data Elements,’’
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-and-
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Videos.html). In this document, we
stated that the interrater reliability for
Mechanically Altered Diet data element,
as measured by kappa, was ‘‘substantial/
good’’ across the four PAC provider
types (LTCH, SNF, HHA, and IRF) in
which it was tested (kappa = 0.65) and
‘‘moderate’’ in the IRF setting (kappa =
0.53). However, percent agreement for
the data element was 93 percent across
all PAC settings in the National Beta
Test (that is, HHA, IRF, LTCH, and SNF)
and 89 percent in the IRF setting. That
is, when assessing if patients required a
mechanically altered diet, the facility
staff and the external research nurse
agreed 89 percent of the time for IRF
patients.
Comment: One commenter was
concerned that the Mechanically
Altered Diet data element does not
capture clinical complexity and does
not provide any insight into resource
allocation because it only measures
whether the patient needs a
mechanically altered diet and not, for
example, the extent of help a patient
needs in consuming his or her meal.
Response: We believe that assessing
patients’ needs for mechanically altered
diets captures one piece of information
about resource intensity. That is,
patients with this special nutritional
requirement may require additional
nutritional planning services, special
meals, and staff to ensure that meals are
prepared and served in the way the
patient needs. Additional factors that
would affect resource allocation, such as
those noted by the commenter, are not
captured by this data element. We have
attempted to balance the scope and level
of detail of the data elements against the
potential burden placed on providers
who must complete the assessment. We
will take this suggestion into
consideration in future refinement of
the clinical SPADEs.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the
Mechanically Altered Diet data element
as standardized patient assessment data
beginning with the FY 2022 IRF QRP as
proposed.
• Nutritional Approach: Therapeutic
Diet
In the FY 2020 IRF PPS proposed rule
(84 FR 17311 through 17312), we
proposed that the Therapeutic Diet data
element meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20736), a
therapeutic diet refers to meals planned
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39139
to increase, decrease, or eliminate
specific foods or nutrients in a patient’s
or resident’s diet, such as a low-salt
diet, for the purpose of treating a
medical condition. The use of
therapeutic diets among patients and
residents in PAC provides insight on the
clinical complexity of these patients and
residents and their multiple
comorbidities. Therapeutic diets are less
resource intensive from the bedside
nursing perspective, but do signify one
or more underlying clinical conditions
that preclude the patient from eating a
regular diet. The communication among
PAC providers about whether a patient
is receiving a particular therapeutic diet
is critical to ensure safe transitions of
care.
The proposed data element consists of
the single Therapeutic Diet data
element. This data element is currently
in use in the MDS in SNFs. For more
information on the Therapeutic Diet
data element, we refer readers to the
document titled ‘‘Final Specifications
for IRF QRP Quality Measures and
Standardized Patient Assessment Data
Elements,’’ available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Therapeutic Diet data element
was first proposed as a standardized
patient assessment data element in the
FY 2018 IRF PPS proposed rule (82 FR
20736). In response to our proposal in
the FY 2018 IRF PPS proposed rule, we
received public comments in support of
the special services, treatments, and
interventions data elements in general.
One commenter recommended that the
definition of Therapeutic Diet be
aligned with the Academy of Nutrition
and Dietetics’ definition and that
‘‘medically altered diet’’ be added to the
list of nutritional approaches.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the
Therapeutic Diet data element was
included in the National Beta Test of
candidate data elements conducted by
our data element contractor from
November 2017 to August 2018. Results
of this test found the Therapeutic Diet
data element to be feasible and reliable
for use with PAC patients and residents.
More information about the
performance of the Therapeutic Diet
data element in the National Beta Test
can be found in the document titled
‘‘Final Specifications for IRF QRP
Quality Measures and Standardized
Patient Assessment Data Elements,’’
available at https://www.cms.gov/
Medicare/Quality-Initiatives-Patient-
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In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. Although the TEP did not
specifically discuss the Therapeutic Diet
data element, the TEP supported the
assessment of the special services,
treatments, and interventions included
in the National Beta Test with respect to
both admission and discharge. A
summary of the September 17, 2018 TEP
meeting titled ‘‘SPADE Technical Expert
Panel Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019. A
summary of the public input received
from the November 27, 2018 stakeholder
meeting titled ‘‘Input on Standardized
Patient Assessment Data Elements
(SPADEs) Received After November 27,
2018 Stakeholder Meeting’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for therapeutic diet,
stakeholder input, and strong test
results, we proposed that the
Therapeutic Diet data element meets the
definition of standardized patient
assessment data with respect to special
services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Therapeutic Diet
data element as standardized patient
assessment data for use in the IRF QRP.
We invited public comment on this
proposal. While we received support
from some commenters on Special
Services, Treatments, and Interventions
as a whole (section IX.G.2 in this final
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rule), we did not receive any specific
comments on the Therapeutic Diet data
element.
After careful consideration of the
public comments we received on the
category of Special Services,
Treatments, and Interventions, we are
finalizing our proposal to adopt the
Therapeutic Diet data element as
standardized patient assessment data
beginning with the FY 2022 IRF QRP as
proposed.
• High-Risk Drug Classes: Use and
Indication
In the FY 2020 IRF PPS proposed rule
(84 FR 17312 through 17314), we
proposed that the High-Risk Drug
Classes: Use and Indication data
element meets the definition of
standardized patient assessment data
with respect to special services,
treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
Most patients and residents receiving
PAC services depend on short- and
long-term medications to manage their
medical conditions. However, as a
treatment, medications are not without
risk; medications are, in fact, a leading
cause of adverse events. A study by the
U.S. Department of Health and Human
Services found that 31 percent of
adverse events that occurred in 2008
among hospitalized Medicare
beneficiaries were related to
medication.115 Moreover, changes in a
patient’s condition, medications, and
transitions between care settings put
patients at risk of medication errors and
adverse drug events (ADEs). ADEs may
be caused by medication errors such as
drug omissions, errors in dosage, and
errors in dosing frequency.116
ADEs are known to occur across
different types of healthcare settings.
For example, the incidence of ADEs in
the outpatient setting has been
estimated at 1.15 ADEs per 100 personmonths,117 while the rate of ADEs in the
long-term care setting is approximately
9.80 ADEs per 100 resident-months.118
115 U.S. Department of Health and Human
Services. Office of Inspector General. Daniel R.
Levinson. Adverse Events in Hospitals: National
Incidence Among Medicare Beneficiaries. OEI–06–
09–00090. November 2010.
116 Boockvar KS, Liu S, Goldstein N, Nebeker J,
Siu A, Fried T. Prescribing discrepancies likely to
cause adverse drug events after patient transfer.
Qual Saf Health Care. 2009;18(1):32–6.
117 Gandhi TK, Seger AC, Overhage JM, et al.
Outpatient adverse drug events identified by
screening electronic health records. J Patient Saf
2010;6:91–6.doi:10.1097/PTS.0b013e3181dcae06.
118 Gurwitz JH, Field TS, Judge J, Rochon P,
Harrold LR, Cadoret C, et al. The incidence of
adverse drug events in two large academic longterm care facilities. Am J Med. 2005; 118(3):251±8.
Epub 2005/03/05. https://doi.org/10.1016/
j.amjmed.2004.09.018 PMID: 15745723.
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In the hospital setting, the incidence has
been estimated at 15 ADEs per 100
admissions.119 In addition,
approximately half of all hospitalrelated medication errors and 20 percent
of ADEs occur during transitions within,
admission to, transfer to, or discharge
from a hospital.120 121 122 ADEs are more
common among older adults, who make
up most patients receiving PAC
services. The rate of emergency
department visits for ADEs is three
times higher among adults 65 years of
age and older compared to that among
those younger than age 65.123
Understanding the types of
medication a patient is taking, and the
reason for its use, are key facets of a
patient’s treatment with respect to
medication. Some classes of drugs are
associated with more risk than
others.124 We proposed one High-Risk
Drug Class data element with six subelements. The response options that
correspond to the six medication classes
are: Anticoagulants, antiplatelets,
hypoglycemics (including insulin),
opioids, antipsychotics, and antibiotics.
These drug classes are high-risk due to
the adverse effects that may result from
use. In particular, bleeding risk is
associated with anticoagulants and
antiplatelets; 125 126 fluid retention,
heart failure, and lactic acidosis are
associated with hypoglycemics; 127
119 Hug BL, Witkowski DJ, Sox CM, Keohane CA,
Seger DL, Yoon C, Matheny ME, Bates DW.
Occurrence of adverse, often preventable, events in
community hospitals involving nephrotoxic drugs
or those excreted by the kidney. Kidney Int. 2009;
76:1192–1198. [PubMed: 19759525].
120 Barnsteiner JH. Medication reconciliation:
transfer of medication information across settingskeeping it free from error. J Infus Nurs. 2005;28(2
Suppl):31–36.
121 Rozich J, Roger, R. Medication safety: one
organization’s approach to the challenge. Journal of
Clinical Outcomes Management. 2001(8):27–34.
122 Gleason KM, Groszek JM, Sullivan C, Rooney
D, Barnard C, Noskin GA. Reconciliation of
discrepancies in medication histories and
admission orders of newly hospitalized patients.
Am J Health Syst Pharm. 2004;61(16):1689–1695.
123 Shehab N, Lovegrove MC, Geller AI, Rose KO,
Weidle NJ, Budnitz DS. US emergency department
visits for outpatient adverse drug events, 2013–
2014. JAMA. doi: 10.1001/jama.2016.16201.
124 Ibid.
125 Shoeb M, Fang MC. Assessing bleeding risk in
patients taking anticoagulants. J Thromb
Thrombolysis. 2013;35(3):312–319. doi: 10.1007/
s11239–013–0899–7.
126 Melkonian M, Jarzebowski W, Pautas E.
Bleeding risk of antiplatelet drugs compared with
oral anticoagulants in older patients with atrial
fibrillation: a systematic review and meta-analysis.
J Thromb Haemost. 2017;15:1500–1510. DOI:
10.1111/jth.13697.
127 Hamnvik OP, McMahon GT. Balancing Risk
and Benefit with Oral Hypoglycemic Drugs. The
Mount Sinai journal of medicine, New York. 2009;
76:234–243.
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misuse is associated with opioids; 128
fractures and strokes are associated with
antipsychotics; 129 130 and various
adverse events, such as central nervous
systems effects and gastrointestinal
intolerance, are associated with
antimicrobials,131 the larger category of
medications that include antibiotics.
Moreover, some medications in five of
the six drug classes included in this
data element are included in the 2019
Updated Beers Criteria® list as
potentially inappropriate medications
for use in older adults.132 Finally,
although a complete medication list
should record several important
attributes of each medication (for
example, dosage, route, stop date),
recording an indication for the drug is
of crucial importance.133
The High-Risk Drug Classes: Use and
Indication data element requires an
assessor to record whether or not a
patient is taking any medications within
the six drug classes. The six response
options for this data element are highrisk drug classes with particular
relevance to PAC patients and residents,
as identified by our data element
contractor. The six data element
response options are Anticoagulants,
Antiplatelets, Hypoglycemics, Opioids,
Antipsychotics, and Antibiotics. For
each drug class, the assessor is required
to indicate if the patient is taking any
medications within the class, and, for
drug classes in which medications were
being taken, whether indications for all
drugs in the class are noted in the
medical record. For example, for the
response option Anticoagulants, if the
assessor indicates that the patient has
received anticoagulant medication, the
assessor would then indicate if an
indication is recorded in the medication
record for the anticoagulant(s).
128 Naples JG, Gellad WF, Hanlon JT. The Role of
Opioid Analgesics in Geriatric Pain Management.
Clin Geriatr Med. 2016;32(4):725–735.
129 Rigler SK, Shireman TI, Cook-Wiens GJ,
Ellerbeck EF, Whittle JC, Mehr DR, Mahnken JD.
Fracture risk in nursing home residents initiating
antipsychotic medications. J Am Geriatr Soc. 2013;
61(5):715–722. [PubMed: 23590366].
130 Wang S, Linkletter C, Dore D et al. Age,
antipsychotics, and the risk of ischemic stroke in
the Veterans Health Administration. Stroke
2012;43:28–31. doi:10.1161/
STROKEAHA.111.617191.
131 Faulkner CM, Cox HL, Williamson JC. Unique
aspects of antimicrobial use in older adults. Clin
Infect Dis. 2005;40(7):997–1004.
132 American Geriatrics Society 2019 Beers
Criteria Update Expert Panel. American Geriatrics
Society 2019 Updated Beers Criteria for Potentially
Inappropriate Medication Use in Older Adults. J
Am Geriatr Soc 2019; 00:1–21.
133 Li Y, Salmasian H, Harpaz R, Chase H,
Friedman C. Determining the reasons for
medication prescriptions in the EHR using
knowledge and natural language processing. AMIA
Annu Symp Proc. 2011; 2011:768–76.
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The High-Risk Drug Classes: Use and
Indication data element that is being
proposed as a SPADE was developed as
part of a larger set of data elements to
assess medication reconciliation, the
process of obtaining a patient’s multiple
medication lists and reconciling any
discrepancies. For more information on
the High-Risk Drug Classes: Use and
Indication data element, we refer
readers to the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
We sought public input on the
relevance of conducting assessments on
medication reconciliation and
specifically on the proposed High-Risk
Drug Classes: Use and Indication data
element. Our data element contractor
presented data elements related to
medication reconciliation to the TEP
convened on April 6 and 7, 2016. The
TEP supported a focus on high-risk
drugs, because of higher potential for
harm to patients and residents, and
were in favor of a data element to
capture whether or not indications for
medications were recorded in the
medical record. A summary of the April
6 and 7, 2016 TEP meeting titled
‘‘SPADE Technical Expert Panel
Summary (First Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html. Medication reconciliation
data elements were also discussed at a
second TEP meeting on January 5 and
6, 2017, convened by our data element
contractor. At this meeting, the TEP
agreed about the importance of
evaluating the medication reconciliation
process, but disagreed about how this
could be accomplished through
standardized assessment. The TEP also
disagreed about the usability and
appropriateness of using the Beers
Criteria to identify high-risk
medications.134 A summary of the
January 5 and 6, 2017 TEP meeting
titled ‘‘SPADE Technical Expert Panel
Summary (Second Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-Patient134 American Geriatrics Society 2015 Beers
Criteria Update Expert Panel. American Geriatrics
Society. Updated Beers Criteria for Potentially
Inappropriate Medication Use in Older Adults. J
Am Geriatr Soc 2015; 63:2227–2246.
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Assessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also solicited public input on data
elements related to medication
reconciliation during a public input
period from April 26 to June 26, 2017.
Several commenters noted support for
the medication reconciliation data
elements that were put on display,
noting the importance of medication
reconciliation in preventing medication
errors and stated that the items seemed
feasible and clinically useful. A few
commenters were critical of the choice
of 10 drug classes posted during that
comment period, stating that ADEs are
not limited to high-risk drugs, and
raised issues related to training
assessors to correctly complete a valid
assessment of medication reconciliation.
A summary report for the April 26 to
June 26, 2017 public comment period
titled ‘‘SPADE May–June 2017 Public
Comment Summary Report’’ is available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The High-Risk Drug Classes: Use and
Indication data element was included in
the National Beta Test of candidate data
elements conducted by our data element
contractor from November 2017 to
August 2018. Results of this test found
the High-Risk Drug Classes: Use and
Indication data element to be feasible
and reliable for use with PAC patients
and residents. More information about
the performance of the High-Risk Drug
Classes: Use and Indication data
element in the National Beta Test can be
found in the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018, for the purpose of
soliciting input on the proposed
standardized patient assessment data
elements. The TEP acknowledged the
challenges of assessing medication
safety, but were supportive of some of
the data elements focused on
medication reconciliation that were
tested in the National Beta Test. The
TEP was especially supportive of the
focus on the six high-risk drug classes
and using these classes to assess
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whether the indication for a drug is
recorded. A summary of the September
17, 2018 TEP meeting titled ‘‘SPADE
Technical Expert Panel Summary (Third
Convening)’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. These activities provided
updates on the field-testing work and
solicited feedback on data elements
considered for standardization,
including the High-Risk Drug Classes:
Use and Indication data element. One
stakeholder group was critical of the six
drug classes included as response
options in the High-Risk Drug Classes:
Use and Indication data element, noting
that potentially risky medications (for
example, muscle relaxants) are not
included in this list; that there may be
important differences between drugs
within classes (for example, more recent
versus older style antidepressants); and
that drug allergy information is not
captured. Finally, on November 27,
2018, our data element contractor
hosted a public meeting of stakeholders
to present the results of the National
Beta Test and solicit additional
comments. General input on the testing
and item development process and
concerns about burden were received
from stakeholders during this meeting
and via email through February 1, 2019.
Additionally, one commenter
questioned whether the time to
complete the High-Risk Drug Classes:
Use and Indication data element would
differ across settings. A summary of the
public input received from the
November 27, 2018 stakeholder meeting
titled ‘‘Input on Standardized Patient
Assessment Data Elements (SPADEs)
Received After November 27, 2018
Stakeholder Meeting’’ is available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing high-risk drugs and for
whether or not indications are noted for
high-risk drugs, stakeholder input, and
strong test results, we proposed that the
High-Risk Drug Classes: Use and
Indication data element meets the
definition of standardized patient
assessment data with respect to special
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services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the High-Risk Drug
Classes: Use and Indication data
element as standardized patient
assessment data for use in the IRF QRP.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the High-Risk Drug
Classes: Use and Indication data
element.
Comment: Some commenters noted
that the proposed High-Risk Drug
Classes: Use and Indication data
elements are redundant of the existing
standards in the Hospital Conditions of
Participation (CoPs) and that requiring
the collection of these data elements
would be duplicative, unnecessary, and
at odds with the Meaningful Measures
framework.
Response: We disagree that assessing
the extent to which medications from
certain drug classes are being taken and
the extent to which indications are
recorded for medications in these
classes is redundant with the existing
CoPs. The CoPs provide guidance on
clinical practice, while the proposed
SPADEs attempt to collect information
about individual patients in order to
understand clinical acuity and to
populate a core set of information that
can be exchanged with the patient
across care transitions.
Comment: Commenters noted that
because adverse drug events (ADEs) are
not limited to high-risk drugs, this data
element has limited utility.
Response: We acknowledge that not
all ADEs are associated with ‘‘high-risk’’
drugs, and we also note that
medications in the named drug classes
are mostly used in a safe manner.
Prescribed high-risk medications are
defined as a ‘‘proximate factor’’ to
preventable ADEs by the Joint
Commission.135 However, the Joint
Commission’s conceptual model of
preventable ADEs also includes
provider, patient, health care system,
organization, and technical factors, all
of which present many opportunities for
disrupting preventable ADEs. We have
decided to focus on a selection of drug
classes that are commonly used by older
adults and are related to ADEs which
are clinically significant, preventable,
and measurable. Anticoagulants,
antibiotics, and diabetic agents have
been implicated in an estimated 46.9
percent (95 percent CI, 44.2 percent–
49.7 percent) of emergency department
135 Chang A, Schyve PM, Croteau RJ, O’Leary DS,
Loeb JM. The JCAHO patient safety event
taxonomy: A standardized terminology and
classification schema for near misses and adverse
events. Int J Qual Health Care. 2005;17(2):95–105.
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visits for adverse drug events.136 Among
older adults (aged ≥65 years), three drug
classes (anticoagulants, diabetic agents,
and opioid analgesics) have been
implicated in an estimated 59.9 percent
(95 percent CI, 56.8 percent–62.9
percent) of ED visits for adverse drug
events.137 Further, antipsychotic
medications have been identified as a
drug class for which there is a need for
increased outreach and educational
efforts to reduce use among older adults.
Comment: One commenter was
concerned with the addition of the
High-Risk Drug Classes: Use and
Indication data elements, noting that
providers should be granted clinical
judgment to effectively treat patients
without CMS monitoring of medications
used for treatment.
Response: The proposed SPADEs
attempt to collect information about
individual patients to understand
clinical acuity and to populate a core set
of information that can be exchanged
with the patient across care transitions.
The intent of these data elements is not
to monitor prescribing practices, but
rather to assess the extent to which
indications are noted for medications in
certain drug classes.
Comment: A few commenters noted
that the High-Risk Drug Class: Use and
Indication data elements seemed
redundant with other SPADEs (that is,
IV Medications) and measures (that is,
Provision of Current Reconciled
Medication List to Subsequent Provider
at Discharge), or duplicative of existing
standards in the Hospital CoPs related
to procurement, preparation, and
administration of drugs, which creates
unnecessary burden.
Response: The High-Risk Drugs: Use
and Indications data element captures
unique information compared to the
other SPADEs and measures to which
the commenters referred. With regard to
the reference to the measure Provision
of Current Reconciled Medication List
to Subsequent Provider at Discharge, we
wish to clarify that the High-Risk Drug
Classes: Use and Indication data
elements capture medications taken by
any route and focuses on a select set of
drug classes, not the act of
communicating a complete medication
list. To the extent that the activities
captured by the High-Risk Drugs: Use
and Indications data element are already
being performed by providers as part of
136 Shehab N, Lovegrove MC, Geller AI, Rose KO,
Weidle NJ, Budnitz DS. US emergency department
visits for outpatient adverse drug events, 2013–
2014. JAMA 2016;316(2):2115–2125.
137 Shehab N, Lovegrove MC, Geller AI, Rose KO,
Weidle NJ, Budnitz DS. US emergency department
visits for outpatient adverse drug events, 2013–
2014. JAMA 2016;316(2):2115–2125.
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the Hospital CoPs, we believe that
reporting of this data elements should
be easily integrated into existing
workflow.
Comment: One commenter noted that
medication indications are typically
documented in narrative notes by the
medical staff and would therefore be
difficult to collect.
Response: We maintain that collecting
information on the presence of
indications in the medical record is
clinically important information that
can inform care planning and support
care transitions. It is the responsibility
of IRF providers to record patient data
in a way that is useful and appropriate
to meet clinical and administrative
needs. It is possible that the adoption of
this SPADE and related reporting
requirement will promote a more
efficient method for documenting the
clinical indication for each medication.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the
High-Risk Drug Classes: Use and
Indication data element as standardized
patient assessment data beginning with
the FY 2022 IRF QRP as proposed.
3. Medical Condition and Comorbidity
Data
Assessing medical conditions and
comorbidities is critically important for
care planning and safety for patients
and residents receiving PAC services,
and the standardized assessment of
selected medical conditions and
comorbidities across PAC providers is
important for managing care transitions
and understanding medical complexity.
In this section we discuss our
proposals for data elements related to
the medical condition of pain as
standardized patient assessment data.
Appropriate pain management begins
with a standardized assessment, and
thereafter establishing and
implementing an overall plan of care
that is person-centered, multi-modal,
and includes the treatment team and the
patient. Assessing and documenting the
effect of pain on sleep, participation in
therapy, and other activities may
provide information on undiagnosed
conditions and comorbidities and the
level of care required, and do so more
objectively than subjective numerical
scores. With that, we assess that taken
separately and together, these proposed
data elements are essential for care
planning, consistency across transitions
of care, and identifying medical
complexities including undiagnosed
conditions. We also conclude that it is
the standard of care to always consider
the risks and benefits associated with a
personalized care plan, including the
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risks of any pharmacological therapy,
especially opioids.138 We also conclude
that in addition to assessing and
appropriately treating pain through the
optimum mix of pharmacologic, nonpharmacologic, and alternative
therapies, while being cognizant of
current prescribing guidelines,
clinicians in partnership with patients
are best able to mitigate factors that
contribute to the current opioid
crisis.139 140 141
In alignment with our Meaningful
Measures Initiative, accurate assessment
of medical conditions and comorbidities
of patients and residents in PAC is
expected to make care safer by reducing
harm caused in the delivery of care;
promote effective prevention and
treatment of chronic disease; strengthen
person and family engagement as
partners in their care; and promote
effective communication and
coordination of care. The SPADEs will
enable or support: Clinical decisionmaking and early clinical intervention;
person-centered, high quality care
through: facilitating better care
continuity and coordination; better data
exchange and interoperability between
settings; and longitudinal outcome
analysis. Therefore, reliable data
elements assessing medical conditions
and comorbidities are needed to initiate
a management program that can
optimize a patient’s or resident’s
prognosis and reduce the possibility of
adverse events.
We sought comment that applies
specifically to the standardized patient
assessment data for the category of
medical conditions and co-morbidities.
We did not receive any comments on
the category of medical conditions and
co-morbidities.
138 Department of Health and Human Services:
Pain Management Best Practices Inter-Agency Task
Force. Draft Report on Pain Management Best
Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://
www.hhs.gov/sites/default/files/final-pmtf-draftreport-on-pain-management%20-best-practices2018-12-12-html-ready-clean.pdf.
139 Department of Health and Human Services:
Pain Management Best Practices Inter-Agency Task
Force. Draft Report on Pain Management Best
Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://
www.hhs.gov/sites/default/files/final-pmtf-draftreport-on-pain-management%20-best-practices2018-12-12-html-ready-clean.pdf.
140 Fishman SM, Carr DB, Hogans B, et al. Scope
and Nature of Pain- and Analgesia-Related Content
of the United States Medical Licensing Examination
(USMLE). Pain Med Malden Mass. 2018;19(3):449–
459. doi:10.1093/pm/pnx336.
141 Fishman SM, Young HM, Lucas Arwood E, et
al. Core competencies for pain management: results
of an interprofessional consensus summit. Pain
Med Malden Mass. 2013;14(7):971–981.
doi:10.1111/pme.12107.
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Final decisions on the SPADEs are
given below, following more detailed
comments on each SPADE proposal.
• Pain Interference (Pain Effect on
Sleep, Pain Interference With Therapy
Activities, and Pain Interference With
Day-to-Day Activities)
In acknowledgement of the opioid
crisis, we specifically sought comment
on whether or not we should add these
pain items in light of those concerns.
Commenters were asked to address to
what extent the collection of the
SPADEs described below through
patient queries might encourage
providers to prescribe opioids.
In the FY 2020 IRF PPS proposed rule
(84 FR 17314 through 17316), we
proposed that a set of three data
elements on the topic of Pain
Interference (Pain Effect on Sleep, Pain
Interference with Therapy Activities,
and Pain Interference with Day-to-Day
Activities) meet the definition of
standardized patient assessment data
with respect to medical condition and
comorbidity data under section
1899B(b)(1)(B)(iv) of the Act.
The practice of pain management
began to undergo significant changes in
the 1990s because the inadequate, nonstandardized, non-evidence-based
assessment and treatment of pain
became a public health issue.142 In pain
management, a critical part of providing
comprehensive care is performance of a
thorough initial evaluation, including
assessment of both the medical and any
biopsychosocial factors causing or
contributing to the pain, with a
treatment plan to address the causes of
pain and to manage pain that persists
over time.143 Quality pain management,
based on current guidelines and
evidence-based practices, can minimize
unnecessary opioid prescribing both by
offering alternatives or supplemental
treatment to opioids and by clearly
stating when they may be appropriate,
and how to utilize risk-benefit analysis
for opioid and non-opioid treatment
modalities.144
142 Institute of Medicine. Relieving Pain in
America: A Blueprint for Transforming Prevention,
Care, Education, and Research. Washington (DC):
National Academies Press (US); 2011. https://
www.ncbi.nlm.nih.gov/books/NBK91497/.
143 Department of Health and Human Services:
Pain Management Best Practices Inter-Agency Task
Force. Draft Report on Pain Management Best
Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://
www.hhs.gov/sites/default/files/final-pmtf-draftreport-on-pain-management%20-best-practices2018-12-12-html-ready-clean.pdf.
144 National Academies. Pain Management and
the Opioid Epidemic: Balancing Societal and
Individual Benefits and Risks of Prescription Opioid
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Pain is not a surprising symptom in
PAC patients and residents, where
healing, recovery, and rehabilitation
often require regaining mobility and
other functions after an acute event.
Standardized assessment of pain that
interferes with function is an important
first step towards appropriate pain
management in PAC settings. The
National Pain Strategy called for refined
assessment items on the topic of pain,
and describes the need for these
improved measures to be implemented
in PAC assessments.145 Further, the
focus on pain interference, as opposed
to pain intensity or pain frequency, was
supported by the TEP convened by our
data element contractor as an
appropriate and actionable metric for
assessing pain. A summary of the
September 17, 2018 TEP meeting titled
‘‘SPADE Technical Expert Panel
Summary (Third Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We appreciate the important concerns
related to the misuse and overuse of
opioids in the treatment of pain and to
that end we note that in the proposed
rule we have also proposed a SPADE
that assess for the use of, as well as
importantly the indication for the use
of, high-risk drugs, including opioids.
Further, in the FY 2017 IRF PPS final
rule (81 FR 52111) we adopted the Drug
Regimen Review Conducted With
Follow-Up for Identified Issues—Post
Acute Care (PAC) IRF QRP measure
which assesses whether PAC providers
were responsive to potential or actual
clinically significant medication
issue(s), which includes issues
associated with use and misuse of
opioids for pain management, when
such issues were identified.
We also note that the proposed
SPADE related to pain assessment are
not associated with any particular
approach to management. Since the use
of opioids is associated with serious
complications, particularly in the
elderly,146 147 148 an array of successful
Use. Washington DC: National Academies of
Sciences, Engineering, and Medicine.; 2017.
145 National Pain Strategy: A Comprehensive
Population-Health Level Strategy for Pain. https://
iprcc.nih.gov/sites/default/files/HHSNational_
Pain_Strategy_508C.pdf.
146 Chau, D. L., Walker, V., Pai, L., & Cho, L. M.
(2008). Opiates and elderly: use and side effects.
Clinical interventions in aging, 3(2), 273–8.
147 Fine, P. G. (2009). Chronic Pain Management
in Older Adults: Special Considerations. Journal of
Pain and Symptom Management, 38(2): S4–S14.
148 Solomon, D. H., Rassen, J. A., Glynn, R. J.,
Garneau, K., Levin, R., Lee, J., & Schneeweiss, S.
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non-pharmacologic and non-opioid
approaches to pain management may be
considered. PAC providers have
historically used a range of pain
management strategies, including nonsteroidal anti-inflammatory drugs, ice,
transcutaneous electrical nerve
stimulation (TENS) therapy, supportive
devices, acupuncture, and the like. In
addition, non-pharmacological
interventions for pain management
include, but are not limited to,
biofeedback, application of heat/cold,
massage, physical therapy, stretching
and strengthening exercises,
chiropractic, electrical stimulation,
radiotherapy, and ultrasound.149 150 151
We believe that standardized
assessment of pain interference will
support PAC clinicians in applying bestpractices in pain management for
chronic and acute pain, consistent with
current clinical guidelines. For example,
the standardized assessment of both
opioids and pain interference would
support providers in successfully
tapering the dosage regimens in
patients/residents who arrive in the
PAC setting with long-term opioid use
off of opioids onto non-pharmacologic
treatments and non-opioid medications,
as recommended by the Society for PostAcute and Long-Term Care Medicine,152
and consistent with HHS’s 5-Point
Strategy To Combat the Opioid Crisis 153
which includes ‘‘Better Pain
Management.’’
The Pain Interference data elements
consist of three data elements: Pain
Effect on Sleep, Pain Interference with
Therapy Activities, and Pain
Interference with Day-to-Day Activities.
Pain Effect on Sleep assesses the
frequency with which pain affects a
resident’s sleep. Pain Interference with
Therapy Activities assesses the
frequency with which pain interferes
with a resident’s ability to participate in
therapies. The Pain Interference with
(2010). Archives Internal Medicine, 170(22):1979–
1986.
149 Byrd L. Managing chronic pain in older adults:
a long-term care perspective. Annals of Long-Term
Care: Clinical Care and Aging. 2013;21(12):34–40.
150 Kligler, B., Bair, M.J., Banerjea, R. et al. (2018).
Clinical Policy Recommendations from the VHA
State-of-the-Art Conference on NonPharmacological Approaches to Chronic
Musculoskeletal Pain. Journal of General Internal
Medicine, 33(Suppl 1): 16. https://doi.org/10.1007/
s11606-018-4323-z.
151 Chou, R., Deyo, R., Friedly, J., et al. (2017).
Nonpharmacologic Therapies for Low Back Pain: A
Systematic Review for an American College of
Physicians Clinical Practice Guideline. Annals of
Internal Medicine, 166(7):493–505.
152 Society for Post-Acute and Long-Term Care
Medicine (AMDA). (2018). Opioids in Nursing
Homes: Position Statement. https://paltc.org/
opioids%20in%20nursing%20homes.
153 https://www.hhs.gov/opioids/about-theepidemic/hhs-response/.
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Day-to-Day Activities assesses the extent
to which pain interferes with a
resident’s ability to participate in dayto-day activities excluding therapy.
A similar data element on the effect
of pain on activities is currently
included in the OASIS. A similar data
element on the effect on sleep is
currently included in the MDS
instrument. For more information on the
Pain Interference data elements, we
refer readers to the document titled
‘‘Final Specifications for IRF QRP
Quality Measures and Standardized
Patient Assessment Data Elements,’’
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We sought public input on the
relevance of conducting assessments on
pain and specifically on the larger set of
Pain Interview data elements included
in the National Beta Test. The proposed
data elements were supported by
comments from the TEP meeting held
by our data element contractor on April
7 to 8, 2016. The TEP affirmed the
feasibility and clinical utility of pain as
a concept in a standardized assessment.
The TEP agreed that data elements on
pain interference with ability to
participate in therapies versus other
activities should be addressed. Further,
during a more recent convening of the
same TEP on September 17, 2018, the
TEP supported the interview-based pain
data elements included in the National
Beta Test. The TEP members were
particularly supportive of the items that
focused on how pain interferes with
activities (that is, Pain Interference data
elements), because understanding the
extent to which pain interferes with
function would enable clinicians to
determine the need for appropriate pain
treatment. A summary of the September
17, 2018 TEP meeting titled ‘‘SPADE
Technical Expert Panel Summary (Third
Convening)’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
We held a public input period in 2016
to solicit feedback on the
standardization of pain and several
other items that were under
development in prior efforts. From the
prior public comment period, we
included several pain data elements
(Pain Effect on Sleep; Pain
Interference—Therapy Activities; Pain
Interference—Other Activities) in a
second call for public input, open from
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April 26 to June 26, 2017. The items we
sought comment on were modified from
all stakeholder and test efforts.
Commenters provided general
comments about pain assessment in
general in addition to feedback on the
specific pain items. A few commenters
shared their support for assessing pain,
the potential for pain assessment to
improve the quality of care, and for the
validity and reliability of the data
elements. Commenters affirmed that the
item of pain and the effect on sleep
would be suitable for PAC settings.
Commenters’ main concerns included
redundancy with existing data elements,
feasibility and utility for cross-setting
use, and the applicability of interviewbased items to patients and residents
with cognitive or communication
impairments, and deficits. A summary
report for the April 26 to June 26, 2017
public comment period titled ‘‘SPADE
May–June 2017 Public Comment
Summary Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Pain Interference data elements
were included in the National Beta Test
of candidate data elements conducted
by our data element contractor from
November 2017 to August 2018. Results
of this test found the Pain Interference
data elements to be feasible and reliable
for use with PAC patients and residents.
More information about the
performance of the Pain Interference
data elements in the National Beta Test
can be found in the document titled
‘‘Final Specifications for IRF QRP
Quality Measures and Standardized
Patient Assessment Data Elements,’’
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on
September 17, 2018 for the purpose of
soliciting input on the standardized
patient assessment data elements. The
TEP supported the interview-based pain
data elements included in the National
Beta Test. The TEP members were
particularly supportive of the items that
focused on how pain interferes with
activities (that is, Pain Interference data
elements), because understanding the
extent to which pain interferes with
function would enable clinicians to
determine the need for pain treatment.
A summary of the September 17, 2018
TEP meeting titled ‘‘SPADE Technical
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Expert Panel Summary (Third
Convening)’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
We also held Special Open Door
Forums and small-group discussions
with PAC providers and other
stakeholders in 2018 for the purpose of
updating the public about our on-going
SPADE development efforts. Finally, on
November 27, 2018, our data element
contractor hosted a public meeting of
stakeholders to present the results of the
National Beta Test and solicit additional
comments. General input on the testing
and item development process and
concerns about burden were received
from stakeholders during this meeting
and via email through February 1, 2019.
Additionally, one commenter noted
strong support for the Pain data
elements and was encouraged by the
fact that this portion of the assessment
goes beyond merely measuring the
presence of pain. A summary of the
public input received from the
November 27, 2018 stakeholder meeting
titled ‘‘Input on Standardized Patient
Assessment Data Elements (SPADEs)
Received After November 27, 2018
Stakeholder Meeting’’ is available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Taking together the importance of
assessing for the effect of pain on
function, stakeholder input, and strong
test results, we proposed that the three
Pain Interference data elements (Pain
Effect on Sleep, Pain Interference with
Therapy Activities, and Pain
Interference with Day-to-Day Activities)
meet the definition of standardized
patient assessment data with respect to
medical conditions and comorbidities
under section 1899B(b)(1)(B)(iv) of the
Act and to adopt the Pain Interference
data elements (Pain Effect on Sleep;
Pain Interference with Therapy
Activities; and Pain Interference with
Day-to-Day Activities) as standardized
patient assessment data for use in the
IRF QRP.
Commenters submitted the following
comments related to our proposal to
adopt the Pain Interference (Pain Effect
on Sleep, Pain Interference with
Therapy Activities, and Pain
Interference with Day-to-Day Activities)
data elements.
Comment: A few commenters noted
support for the Pain Interference data
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39145
element, noting that the data element
will provide a useful and more accurate
assessment of a patient’s ability to
function, and that understanding the
impact of pain on therapy and other
activities, including sleep, can improve
the quality of care, which in turn will
support providers in their ability to
provide effective pain management
services.
Response: We thank the commenters
for their support of the Pain Interference
data element.
Comment: A commenter noted that
the proposed Pain Interference SPADEs
document pain frequency, but stated
that it is important to identify both pain
frequency and pain intensity.
Response: We wish to clarify, the Pain
Interference interview data elements
question the patient on the frequency
with which pain interferes with sleep,
therapy, or non-therapy activities. These
data elements therefore combine the
concepts of frequency and intensity,
with the measure of intensity being
interference with the named activities.
Self-reported measures of pain intensity
are often criticized for being infeasible
to standardize. In these data elements,
we use interference with activities as an
alternative to inquiring about intensity.
Comment: A commenter expressed
concerns about the suitability of the
Pain Interference data elements for use
in patients with cognitive and
communication deficits and
recommended CMS consider the use of
non-verbal means to allow patients to
respond to SPADEs related to pain.
Response: We appreciate the
commenter’s concern surrounding pain
assessment with patients with cognitive
and communication deficits. The Pain
Interference interview SPADEs require
that a patient be able to communicate,
whether verbally, in writing, or using
another method; assessors may use nonverbal means to administer the
questions (for example, providing the
questions and response in writing for a
patient with severe hearing
impairment). Patients who are unable to
communicate by any means would not
be required to complete the Pain
Interference interview SPADEs.
However, evidence suggests that pain
presence can be reliably assessed in
non-communicative patients through
structural observational protocols. To
that end, we tested observational pain
presence elements in the National Beta
Test, but have chosen not to propose
those data elements as SPADEs at this
time. We will take the commenter’s
concern into consideration as the
SPADEs are monitored and refined in
the future.
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Comment: A commenter expressed
concerns about how CMS might use
these data elements, noting particular
concern that collection of these data
elements may inappropriately translate
into an assessment of quality, and that
data collection on this topic could
create incentives that directly or
indirectly interfere with treatment
decisions.
Response: We appreciate the
commenter’s concern related to wanting
to understand how we will use the
SPADEs in the future. We intend to
continue to communicate and
collaborate with stakeholders about how
the SPADEs will be used in the IRF
QRP, as those plans are developed, by
soliciting input during the development
process and establishing use of the
SPADEs in payment and quality
programs through future rulemaking.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the Pain
Interference (Pain Effect on Sleep, Pain
Interference with Therapy Activities,
and Pain Interference with Day-to-Day
Activities) data elements as
standardized patient assessment data
beginning with the FY 2022 IRF QRP as
proposed.
4. Impairment Data
Hearing and vision impairments are
conditions that, if unaddressed, affect
activities of daily living,
communication, physical functioning,
rehabilitation outcomes, and overall
quality of life. Sensory limitations can
lead to confusion in new settings,
increase isolation, contribute to mood
disorders, and impede accurate
assessment of other medical conditions.
Failure to appropriately assess,
accommodate, and treat these
conditions increases the likelihood that
patients and residents will require more
intensive and prolonged treatment.
Onset of these conditions can be
gradual, so individualized assessment
with accurate screening tools and
follow-up evaluations are essential to
determining which patients and
residents need hearing- or visionspecific medical attention or assistive
devices and accommodations, including
auxiliary aids and/or services, and to
ensure that person-directed care plans
are developed to accommodate a
patient’s or resident’s needs. Accurate
diagnosis and management of hearing or
vision impairment would likely
improve rehabilitation outcomes and
care transitions, including transition
from institutional-based care to the
community. Accurate assessment of
hearing and vision impairment would
be expected to lead to appropriate
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treatment, accommodations, including
the provision of auxiliary aids and
services during the stay, and ensure that
patients and residents continue to have
their vision and hearing needs met
when they leave the facility.
In alignment with our Meaningful
Measures Initiative, we expect accurate
and individualized assessment,
treatment, and accommodation of
hearing and vision impairments of
patients and residents in PAC to make
care safer by reducing harm caused in
the delivery of care; promote effective
prevention and treatment of chronic
disease; strengthen person and family
engagement as partners in their care;
and promote effective communication
and coordination of care. For example,
standardized assessment of hearing and
vision impairments used in PAC will
support ensuring patient safety (for
example, risk of falls), identifying
accommodations needed during the
stay, and appropriate support needs at
the time of discharge or transfer.
Standardized assessment of these data
elements will: Enable or support clinical
decision-making and early clinical
intervention; person-centered, high
quality care (for example, facilitating
better care continuity and coordination);
better data exchange and
interoperability between settings; and
longitudinal outcome analysis.
Therefore, reliable data elements
assessing hearing and vision
impairments are needed to initiate a
management program that can optimize
a patient’s or resident’s prognosis and
reduce the possibility of adverse events.
Comments on the category of
impairments were also submitted by
stakeholders during the FY 2018 IRF
PPS proposed rule (82 FR 20737
through 20739) public comment period.
A commenter stated hearing and vision
assessments should be administered at
the beginning of the assessment process
to provide evidence about any sensory
deficits that may affect the patient’s
ability to participate in the assessment
and to allow the assessor to offer an
assistive device.
We sought comment on our proposals
to collect as standardized patient
assessment data the following data with
respect to impairments. We did not
receive any comments on the category of
impairments.
Final decisions on the SPADEs are
given below, following more detailed
comments on each SPADE proposal.
• Hearing
In the FY 2020 IRF PPS proposed rule
(84 FR 17317 through 17318), we
proposed that the Hearing data element
meets the definition of standardized
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patient assessment data with respect to
impairments under section
1899B(b)(1)(B)(v) of the Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20737 through
20738), accurate assessment of hearing
impairment is important in the PAC
setting for care planning and resource
use. Hearing impairment has been
associated with lower quality of life,
including poorer physical, mental,
social functioning, and emotional
health.154 155 Treatment and
accommodation of hearing impairment
led to improved health outcomes
including, but not limited to, quality of
life.156 For example, hearing loss in
elderly individuals has been associated
with depression and cognitive
impairment,157 158 159 higher rates of
incident cognitive impairment and
cognitive decline,160 and less time in
occupational therapy.161 Accurate
assessment of hearing impairment is
important in the PAC setting for care
planning and defining resource use.
The proposed data element consists of
the single Hearing data element. This
data consists of one question that
assesses level of hearing impairment.
This data element is currently in use in
the MDS in SNFs. For more information
on the Hearing data element, we refer
readers to the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-Assessment154 Dalton DS, Cruickshanks KJ, Klein BE, Klein
R, Wiley TL, Nondahl DM. The impact of hearing
loss on quality of life in older adults. Gerontologist.
2003;43(5):661–668.
155 Hawkins K, Bottone FG, Jr., Ozminkowski RJ,
et al. The prevalence of hearing impairment and its
burden on the quality of life among adults with
Medicare Supplement Insurance. Qual Life Res.
2012;21(7):1135–1147.
156 Horn KL, McMahon NB, McMahon DC, Lewis
JS, Barker M, Gherini S. Functional use of the
Nucleus 22-channel cochlear implant in the elderly.
The Laryngoscope. 1991;101(3):284–288.
157 Sprinzl GM, Riechelmann H. Current trends in
treating hearing loss in elderly people: a review of
the technology and treatment options—a minireview. Gerontology. 2010;56(3):351–358.
158 Lin FR, Thorpe R, Gordon-Salant S, Ferrucci
L. Hearing Loss Prevalence and Risk Factors Among
Older Adults in the United States. The Journals of
Gerontology Series A: Biological Sciences and
Medical Sciences. 2011;66A(5):582–590.
159 Hawkins K, Bottone FG, Jr., Ozminkowski RJ,
et al. The prevalence of hearing impairment and its
burden on the quality of life among adults with
Medicare Supplement Insurance. Qual Life Res.
2012;21(7):1135–1147.
160 Lin FR, Metter EJ, O’Brien RJ, Resnick SM,
Zonderman AB, Ferrucci L. Hearing Loss and
Incident Dementia. Arch Neurol. 2011;68(2):214–
220.
161 Cimarolli VR, Jung S. Intensity of
Occupational Therapy Utilization in Nursing Home
Residents: The Role of Sensory Impairments. J Am
Med Dir Assoc. 2016;17(10):939–942.
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Instruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The Hearing data element was first
proposed as a standardized patient
assessment data element in the FY 2018
IRF PPS proposed rule (82 FR 20737
through 20738). In that proposed rule,
we stated that the proposal was
informed by input we received on the
PAC PRD form of the data element
(‘‘Ability to Hear’’) through a call for
input published on the CMS Measures
Management System Blueprint website.
Input submitted from August 12 to
September 12, 2016 recommended that
hearing, vision, and communication
assessments be administered at the
beginning of patient assessment process.
A summary report for the August 12 to
September 12, 2016 public comment
period titled ‘‘SPADE August 2016
Public Comment Summary Report’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
received public comments in support of
adopting the Hearing data element for
standardized cross-setting use, noting
that it would help address the needs of
patient and residents with disabilities
and that failing to identify impairments
during the initial assessment can result
in inaccurate diagnoses of impaired
language or cognition and can invalidate
other information obtained from patient
assessment.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the Hearing
data element was included in the
National Beta Test of candidate data
elements conducted by our data element
contractor from November 2017 to
August 2018. Results of this test found
the Hearing data element to be feasible
and reliable for use with PAC patients
and residents. More information about
the performance of the Hearing data
element in the National Beta Test can be
found in the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on January 5
and 6, 2017 for the purpose of soliciting
input on all the SPADEs, including the
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Hearing data element. The TEP affirmed
the importance of standardized
assessment of hearing impairment in
PAC patients and residents. A summary
of the January 5 and 6, 2017 TEP
meeting titled ‘‘SPADE Technical Expert
Panel Summary (Second Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held Special Open Door
Forums and small-group discussions
with PAC providers and other
stakeholders in 2018 for the purpose of
updating the public about our ongoing
SPADE development efforts. Finally, on
November 27, 2018, our data element
contractor hosted a public meeting of
stakeholders to present the results of the
National Beta Test and solicit additional
comments. General input on the testing
and item development process and
concerns about burden were received
from stakeholders during this meeting
and via email through February 1, 2019.
Additionally, a commenter noted
support for the Hearing data element
and suggested administration at the
beginning of the patient assessment to
maximize utility. A summary of the
public input received from the
November 27, 2018 stakeholder meeting
titled ‘‘Input on Standardized Patient
Assessment Data Elements (SPADEs)
Received After November 27, 2018
Stakeholder Meeting’’ is available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Due to the relatively stable nature of
hearing impairment, it is unlikely that a
patient’s score on this assessment would
change between the start and end of the
IRF stay. Therefore, we proposed that
IRFs that submit the Hearing data
element with respect to admission will
be deemed to have submitted with
respect to both admission and
discharge.
Taking together the importance of
assessing for hearing, stakeholder input,
and strong test results, we proposed that
the Hearing data element meets the
definition of standardized patient
assessment data with respect to
impairments under section
1899B(b)(1)(B)(v) of the Act and to
adopt the Hearing data element as
standardized patient assessment data for
use in the IRF QRP.
Commenters submitted the following
comments related to our proposal for
the Hearing data element.
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Comment: A few commenters
supported the collection of information
on hearing impairment. One of these
commenters also suggested that CMS
consider how hearing impairment
impacts a patient’s ability to respond to
the assessment tool in general.
Response: We thank the commenters
for their support of the Hearing data
element. We intend to reinforce
assessment tips and item rationale
through training, open door forums, and
future rulemaking efforts.
In the existing guidance manual for
the IRF–PAI, we offer tips for
administration that direct assessors to
take appropriate steps to accommodate
sensory and communication
impairments when conducting the
assessment.
Comment: Some commenters
expressed concern that severely
impaired hearing occurs infrequently in
IRF patients, thereby limiting the utility
of the data collected.
Response: The Hearing SPADE
consists of one data element completed
by the assessor based primarily on
interacting with the patient and
reviewing the medical record. Given the
low burden of reporting the Hearing
data element, and despite severe hearing
impairment occurring in a small
proportion of IRF patients, we believe it
is important to systematically assess for
hearing impairment in order to improve
clinical care and care transitions.
Comment: One commenter
recommended adding ‘‘unable to
assess’’ as a response option, which the
commenter believes would be the
appropriate choice if the patient is
comatose or is unable to effectively
answer questions related to an
assessment of their hearing.
Response: We appreciate the
commenter’s recommendation. The
assessment of hearing is completed
based on observing the patient during
assessment, patient interactions with
others, reviewing medical record
documentation, and consulting with
patient’s family and other staff, in
addition to interviewing the patient, so
it can be completed when the patient is
unable to effectively answer questions
related to an assessment of their
hearing.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the
Hearing data element as standardized
patient assessment data beginning with
the FY 2022 IRF QRP as proposed.
• Vision
In the FY 2020 IRF PPS proposed rule
(84 FR 17318 through 17319), we
proposed that the Vision data element
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meets the definition of standardized
patient assessment data with respect to
impairments under section
1899B(b)(1)(B)(v) of the Act.
As described in the FY 2018 IRF PPS
proposed rule (82 FR 20738 through
20739), evaluation of an individual’s
ability to see is important for assessing
for risks such as falls and provides
opportunities for improvement through
treatment and the provision of
accommodations, including auxiliary
aids and services, which can safeguard
patients and residents and improve their
overall quality of life. Further, vision
impairment is often a treatable risk
factor associated with adverse events
and poor quality of life. For example,
individuals with visual impairment are
more likely to experience falls and hip
fracture, have less mobility, and report
depressive
symptoms.162 163 164 165 166 167 168
Individualized initial screening can lead
to life-improving interventions such as
accommodations, including the
provision of auxiliary aids and services,
during the stay and/or treatments that
can improve vision and prevent or slow
further vision loss.
In addition, vision impairment is
often a treatable risk factor associated
with adverse events which can be
prevented and accommodated during
the stay. Accurate assessment of vision
impairment is important in the IRF
setting for care planning and defining
resource use.
The proposed data element consists of
the single Vision data element (Ability
To See in Adequate Light) that consists
of one question with five response
categories. The Vision data element that
we proposed for standardization was
tested as part of the development of the
162 Colon-Emeric CS, Biggs DP, Schenck AP, Lyles
KW. Risk factors for hip fracture in skilled nursing
facilities: Who should be evaluated? Osteoporos Int.
2003;14(6):484–489.
163 Freeman EE, Munoz B, Rubin G, West SK.
Visual field loss increases the risk of falls in older
adults: The Salisbury eye evaluation. Invest
Ophthalmol Vis Sci. 2007;48(10):4445–4450.
164 Keepnews D, Capitman JA, Rosati RJ.
Measuring patient-level clinical outcomes of home
health care. J Nurs Scholarsh. 2004;36(1):79–85.
165 Nguyen HT, Black SA, Ray LA, Espino DV,
Markides KS. Predictors of decline in MMSE scores
among older Mexican Americans. J Gerontol A Biol
Sci Med Sci. 2002;57(3):M181–185.
166 Prager AJ, Liebmann JM, Cioffi GA, Blumberg
DM. Self-reported Function, Health Resource Use,
and Total Health Care Costs Among Medicare
Beneficiaries With Glaucoma. JAMA
ophthalmology. 2016;134(4):357–365.
167 Rovner BW, Ganguli M. Depression and
disability associated with impaired vision: The
MoVies Project. J Am Geriatr Soc. 1998;46(5):617–
619.
168 Tinetti ME, Ginter SF. The nursing home lifespace diameter. A measure of extent and frequency
of mobility among nursing home residents. J Am
Geriatr Soc. 1990;38(12):1311–1315.
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MDS and is currently in use in that
assessment in SNFs. Similar data
elements, but with different wording
and fewer response option categories,
are in use in the OASIS. For more
information on the Vision data element,
we refer readers to the document titled
‘‘Final Specifications for IRF QRP
Quality Measures and Standardized
Patient Assessment Data Elements,’’
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
The Vision data element was first
proposed as a standardized patient
assessment data element in the FY 2018
IRF PPS proposed rule (82 FR 20738
through 20739).
In that proposed rule, we stated that
the proposal was informed by input we
received on the Ability to See in
Adequate Light data element (version
tested in the PAC PRD with three
response categories) through a call for
input published on the CMS Measures
Management System Blueprint website.
Although the data element in public
comment differed from the proposed
data element, input submitted from
August 12 to September 12, 2016
supported assessing vision in PAC
settings and the useful information a
vision data element would provide.
We also stated that commenters had
noted that the Ability to See item would
provide important information that
would facilitate care coordination and
care planning, and consequently
improve the quality of care. Other
commenters suggested it would be
helpful as an indicator of resource use
and noted that the item would provide
useful information about the abilities of
patients and residents to care for
themselves. Additional commenters
noted that the item could feasibly be
implemented across PAC providers and
that its kappa scores from the PAC PRD
support its validity. Some commenters
noted a preference for MDS version of
the Vision data element in SNFs over
the form put forward in public
comment, citing the widespread use of
this data element. A summary report for
the August 12 to September 12, 2016
public comment period titled ‘‘SPADE
August 2016 Public Comment Summary
Report’’ is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In response to our proposal in the FY
2018 IRF PPS proposed rule, we
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received a comment supporting having
a standardized patient assessment data
element for vision across PAC settings,
but it stated the proposed data element
captures only basic information for risk
adjustment, and more detailed
information would need to be collected
to use it as an outcome measure.
Subsequent to receiving comments on
the FY 2018 IRF PPS rule, the Vision
data element was included in the
National Beta Test of candidate data
elements conducted by our data element
contractor from November 2017 to
August 2018. Results of this test found
the Vision data element to be feasible
and reliable for use with PAC patients
and residents. More information about
the performance of the Vision data
element in the National Beta Test can be
found in the document titled ‘‘Final
Specifications for IRF QRP Quality
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In addition, our data element
contractor convened a TEP on January 5
and 6, 2017 for the purpose of soliciting
input on all the SPADEs including the
Vision data element. The TEP affirmed
the importance of standardized
assessment of vision impairment in PAC
patients and residents. A summary of
the January 5 and 6, 2017 TEP meeting
titled ‘‘SPADE Technical Expert Panel
Summary (Second Convening)’’ is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We also held SODFs and small-group
discussions with PAC providers and
other stakeholders in 2018 for the
purpose of updating the public about
our ongoing SPADE development
efforts. Finally, on November 27, 2018,
our data element contractor hosted a
public meeting of stakeholders to
present the results of the National Beta
Test and solicit additional comments.
General input on the testing and item
development process and concerns
about burden were received from
stakeholders during this meeting and
via email through February 1, 2019.
Additionally, a commenter noted
support for the Vision data element and
suggested administration at the
beginning of the patient assessment to
maximize utility. A summary of the
public input received from the
November 27, 2018 stakeholder meeting
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titled ‘‘Input on Standardized Patient
Assessment Data Elements (SPADEs)
Received After November 27, 2018
Stakeholder Meeting’’ is available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Due to the relatively stable nature of
vision impairment, it is unlikely that a
patient’s score on this assessment would
change between the start and end of the
IRF stay. Therefore, we proposed that
IRFs that submit the Vision data
element with respect to admission will
be deemed to have submitted with
respect to both admissions and
discharge.
Taking together the importance of
assessing for vision, stakeholder input,
and strong test results, we proposed that
the Vision data element meets the
definition of standardized patient
assessment data with respect to
impairments under section
1899B(b)(1)(B)(v) of the Act and to
adopt the Vision data element as
standardized patient assessment data for
use in the IRF QRP.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the Vision data element.
Comment: A few commenters
supported the collection of information
on vision impairment. One of the
commenters noted that the collection of
information on vision impairment
would support the identification and
appropriate treatment of vision
problems, which they stated were
prevalent and undertreated.
Response: We thank the commenters
for their support.
Comment: One commenter
recommended that a doctor of
optometry should play a lead role in
conducting vision assessments, and that
vision assessments done by other
clinicians should also obtain the
patient’s own assessment of his or her
vision, such as used by the Centers for
Disease Control and Prevention (CDC)
Behavioral Risk Factors Surveillance
System survey, which questions
patients ‘‘Do you have serious difficulty
seeing, even when wearing glasses?’’
This commenter expressed concerns
about the proposed SPADE being
subjective and risks of mis-categorizing
patients.
Response: We appreciate the
commenter’s recommendation about
how to assess for vision impairment. We
do not require that a certain type of
clinician complete assessments; the
SPADEs have been developed so that
any clinician who is trained in the
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administration of the assessment will be
able to administer it correctly. The
proposed item relies on the assessor’s
evaluation of the patient’s vision, which
has the advantage of reducing burden
placed on the patient. We will take the
recommendation to use patient-reported
vision impairment assessment into
consideration in the development of
future assessments.
Comment: Some commenters
expressed concern that severely
impaired vision occurs infrequently in
IRF patients, thereby limiting the utility
of the data collected.
Response: The Vision SPADE consists
of one data element completed by the
assessor based primarily on interacting
with the patient and reviewing the
medical record. Given the low burden of
the Vision data element, and despite
severe vision impairment occurring in a
small proportion of IRF patients, we
believe it is important to systematically
assess for vision impairment in order to
improve clinical care and care
transitions.
Comment: A commenter
recommended that CMS require a vision
assessment at discharge, noting that
vision impairment could be related to
challenges in medication management
and compliance with written follow-up
instructions for care.
Response: We appreciate the
commenter’s feedback. We agree that
adequate vision—or the
accommodations and assistive
technology needed to compensate for
vision impairment—is important to
patient safety in the community, in part
for the reasons the commenter
mentions. In the FY 2020 IRF PPS
proposed rule (84 FR 17292), we
proposed that IRFs that submitted the
Vision SPADE with respect to
admission will be deemed to have
submitted with respect to both
admission and discharge; we stated that
it is unlikely that the assessment of this
SPADEs at admission would differ from
the assessment at discharge. Vision
assessment, collected via the Vision
SPADE with respect to admission, will
provide information that will support
the patient’s care while in the IRF. Out
of consideration for the burden of data
collection, and with an understanding
that significant clinical changes to a
patient’s vision will be documented in
the medical record as part of routine
clinical practice, we are finalizing our
proposal that IRFs that submit the
Vision SPADE with respect to
admission will be deemed to have
submitted with respect to both
admission and discharge. We note that
during the discharge planning process,
it is incumbent on IRF providers to
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make reasonable assurances that the
patient’s needs will be met in the next
care setting, including in the home.
Comment: One commenter
recommended adding ‘‘unable to
assess’’ as a response option, which the
commenter believes would be the
appropriate choice if the patient is
comatose or is unable to effectively
answer questions related to an
assessment of their vision.
Response: We appreciate the
commenter’s recommendation.
However, the assessment of vision is
completed based on consulting with
patient’s family and other staff,
observing the patient including
requesting the patient to read text or
examine pictures or numbers in
addition to interviewing the patient
about their vision abilities. These other
sources/methods can be used to
complete the assessment of vision when
the patient is unable to effectively
answer questions related to an
assessment of their vision.
After careful consideration of the
public comments we received, we are
finalizing our proposal to adopt the
Vision data element as standardized
patient assessment data beginning with
the FY 2022 IRF QRP as proposed.
4. New Category: Social Determinants of
Health
a. Social Determinants of Health Data
Collection To Inform Measures and
Other Purposes
Section 2(d)(2)(A) of the IMPACT Act
requires CMS to assess appropriate
adjustments to quality measures,
resource measures and other measures,
and to assess and implement
appropriate adjustments to payment
under Medicare, based on those
measures, after taking into account
studies conducted by ASPE on social
risk factors (described below) and other
information, and based on an
individual’s health status and other
factors. Paragraph (C) of section 2(d)(2)
of the IMPACT Act further requires the
Secretary to carry out periodic analyses,
at least every 3 years, based on the
factors referred to paragraph (A) so as to
monitor changes in possible
relationships. Paragraph (B) of section
2(d)(2) of the IMPACT Act requires CMS
to collect or otherwise obtain access to
data necessary to carry out the
requirement of the paragraph (both
assessing adjustments described above
in such paragraph (A) and for periodic
analyses in such paragraph (C)).
Accordingly we proposed to use our
authority under paragraph (B) of section
2(d)(2) of the IMPACT Act to establish
a new data source for information to
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meet the requirements of paragraphs (A)
and (C) of section 2(d)(2) of the IMPACT
Act. In this rule, we proposed to collect
and access data about social
determinants of health (SDOH) in order
to perform CMS’ responsibilities under
paragraphs (A) and (C) of section 2(d)(2)
of the IMPACT Act, as explained in
more detail below. Social determinants
of health, also known as social risk
factors, or health-related social needs,
are the socioeconomic, cultural and
environmental circumstances in which
individuals live that impact their health.
We proposed to collect information on
seven proposed SDOH SPADE data
elements relating to race, ethnicity,
preferred language, interpreter services,
health literacy, transportation, and
social isolation; a detailed discussion of
each of the proposed SDOH data
elements is found in section VII.G.5.b.
of this rule.
We also proposed to use the
assessment instrument for the IRF QRP,
the IRF–PAI, described as a PAC
assessment instrument under section
1899B(a)(2)(B) of the Act, to collect
these data via an existing data collection
mechanism. We believe this approach
will provide CMS with access to data
with respect to the requirements of
section 2(d)(2) of the IMPACT Act,
while minimizing the reporting burden
on PAC health care providers by relying
on a data reporting mechanism already
used and an existing system to which
PAC health care providers are already
accustomed.
The IMPACT Act includes several
requirements applicable to the
Secretary, in addition to those imposing
new data reporting obligations on
certain PAC providers as discussed in
IX.G.4.b. of this final rule. Paragraphs
(A) and (B) of sections 2(d)(1) of the
IMPACT Act require the Secretary,
acting through the Office of the
Assistant Secretary for Planning and
Evaluation (ASPE), to conduct two
studies that examine the effect of risk
factors, including individuals’
socioeconomic status, on quality,
resource use and other measures under
the Medicare program. The first ASPE
study was completed in December 2016
and is discussed below, and the second
study is to be completed in the fall of
2019. We recognize that ASPE, in its
studies, is considering a broader range
of social risk factors than the SDOH data
elements in this proposal, and address
both PAC and non-PAC settings. We
acknowledge that other data elements
may be useful to understand, and that
some of those elements may be of
particular interest in non-PAC settings.
For example, for beneficiaries receiving
care in the community, as opposed to an
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in-patient facility, housing stability and
food insecurity may be more relevant.
We will continue to take into account
the findings from both of ASPE’s reports
in future policy making.
One of the ASPE’s first actions under
the IMPACT Act was to commission the
National Academies of Sciences,
Engineering, and Medicine (NASEM) to
define and conceptualize socioeconomic
status for the purposes of ASPE’s two
studies under section 2(d)(1) of the
IMPACT Act. The NASEM convened a
panel of experts in the field and
conducted an extensive literature
review. Based on the information
collected, the 2016 NASEM panel report
titled, ‘‘Accounting for Social Risk
Factors in Medicare Payment:
Identifying Social Risk Factors’’,
concluded that the best way to assess
how social processes and social
relationships influence key healthrelated outcomes in Medicare
beneficiaries is through a framework of
social risk factors instead of
socioeconomic status. Social risk factors
discussed in the NASEM report include
socioeconomic position, race, ethnicity,
gender, social context, and community
context. These factors are discussed at
length in chapter 2 of the NASEM
report, titled ‘‘Social Risk Factors.’’ 169
Consequently NASEM framed the
results of its report in terms of ‘‘social
risk factors’’ rather than ‘‘socioeconomic
status’’ or ‘‘sociodemographic status.’’
The full text of the ‘‘Social Risk Factors’’
NASEM report is available for reading
on the website at https://www.nap.edu/
read/21858/chapter/1.
Each of the data elements we
proposed to collect and access under
our authority under section 2(d)(2)(B) of
the IMPACT Act is identified in the
2016 NASEM report as a social risk
factor that has been shown to impact
care use, cost and outcomes for
Medicare beneficiaries. CMS uses the
term social determinants of health
(SDOH) to denote social risk factors,
which is consistent with the objectives
of Healthy People 2020.170
ASPE issued its first Report to
Congress, titled ‘‘Social Risk Factors and
Performance Under Medicare’s ValueBased Purchasing Programs,’’ under
section 2(d)(1)(A) of the IMPACT Act on
December 21, 2016.171 Using NASEM’s
169 National Academies of Sciences, Engineering,
and Medicine. 2016. Accounting for social risk
factors in Medicare payment: Identifying social risk
factors. Chapter 2. Washington, DC: The National
Academies Press.
170 Social Determinants of Health. Healthy People
2020. https://www.healthypeople.gov/2020/topicsobjectives/topic/social-determinants-of-health.
(February 2019).
171 U.S. Department of Health and Human
Services, Office of the Assistant Secretary for
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social risk factors framework, ASPE
focused on the following social risk
factors, in addition to disability: (1)
Dual enrollment in Medicare and
Medicaid as a marker for low income;
(2) residence in a low-income area; (3)
Black race; (4) Hispanic ethnicity; and
(5) residence in a rural area. ASPE
acknowledged that the social risk factors
examined in its report were limited due
to data availability. The report also
noted that the data necessary to
meaningfully attempt to reduce
disparities and identify and reward
improved outcomes for beneficiaries
with social risk factors have not been
collected consistently on a national
level in PAC settings. Where these data
have been collected, the collection
frequently involves lengthy
questionnaires. More information on the
Report to Congress on Social Risk
Factors and Performance under
Medicare’s Value-Based Purchasing
Programs, including the full report, is
available on the website at https://
aspe.hhs.gov/social-risk-factors-andmedicares-value-based-purchasingprograms-reports.
Section 2(d)(2) of the IMPACT Act
relates to CMS activities and imposes
several responsibilities on the Secretary
relating to quality, resource use, and
other measures under Medicare. As
mentioned previously, under paragraph
(A) of section 2(d)(2) of the IMPACT
Act, the Secretary is required, on an
ongoing basis, taking into account the
ASPE studies and other information,
and based on an individual’s health
status and other factors, to assess
appropriate adjustments to quality,
resource use, and other measures, and to
assess and implement appropriate
adjustments to Medicare payments
based on those measures. Section
2(d)(2)(A)(i) of the IMPACT Act applies
to measures adopted under sections (c)
and (d) of section 1899B of the Act and
to other measures under Medicare.
However, CMS’ ability to perform these
analyses, and assess and make
appropriate adjustments is hindered by
limits of existing data collections on
SDOH data elements for Medicare
beneficiaries. In its first study in 2016,
in discussing the second study, ASPE
noted that information relating to many
of the specific factors listed in the
IMPACT Act, such as health literacy,
limited English proficiency, and
Medicare beneficiary activation, are not
available in Medicare data.
Planning and Evaluation. 2016. Report to Congress:
Social Risk Factors and Performance Under
Medicare’s Value-Based Payment Programs.
Washington, DC.
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Paragraph 2(d)(2)(A) of the IMPACT
Act specifically requires the Secretary to
take the studies and considerations from
ASPE’s reports to Congress, as well as
other information as appropriate, into
account in assessing and implementing
adjustments to measures and related
payments based on measures in
Medicare. The results of the ASPE’s first
study demonstrated that Medicare
beneficiaries with social risk factors
tended to have worse outcomes on
many quality measures, and providers
who treated a disproportionate share of
beneficiaries with social risk factors
tended to have worse performance on
quality measures. As a result of these
findings, ASPE suggested a threepronged strategy to guide the
development of value-based payment
programs under which all Medicare
beneficiaries receive the highest quality
healthcare services possible. The three
components of this strategy are to: (1)
Measure and report quality of care for
beneficiaries with social risk factors; (2)
set high, fair quality standards for care
provided to all beneficiaries; and (3)
reward and support better outcomes for
beneficiaries with social risk factors. In
discussing how measuring and reporting
quality for beneficiaries with social risk
factors can be applied to Medicare
quality payment programs, the report
offered nine considerations across the
three-pronged strategy, including
enhancing data collection and
developing statistical techniques to
allow measurement and reporting of
performance for beneficiaries with
social risk factors on key quality and
resource use measures.
Congress, in section 2(d)(2)(B) of the
IMPACT Act, required the Secretary to
collect or otherwise obtain access to the
data necessary to carry out the
provisions of paragraph (2) of section
2(d) of the IMPACT Act through both
new and existing data sources. Taking
into consideration NASEM’s conceptual
framework for social risk factors
discussed above, ASPE’s study, and
considerations under section 2(d)(1)(A)
of the IMPACT Act, as well as the
current data constraints of ASPE’s first
study and its suggested considerations,
we proposed to collect and access data
about SDOH under section 2(d)(2) of the
IMPACT Act. Our collection and use of
the SDOH data described in section
IX.G.4.b. of this final rule, under section
2(d)(2) of the IMPACT Act would be
independent of our proposal below (in
section IX.G.4.b. of this final rule) and
our authority to require submission of
that data for use as SPADE under
section 1899B(a)(1)(B) of the Act.
Accessing standardized data relating
to the SDOH data elements on a national
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level is necessary to permit CMS to
conduct periodic analyses, to assess
appropriate adjustments to quality
measures, resource use measures, and
other measures, and to assess and
implement appropriate adjustments to
Medicare payments based on those
measures. We agree with ASPE’s
observations, in the value-based
purchasing context, that the ability to
measure and track quality, outcomes,
and costs for beneficiaries with social
risk factors over time is critical as
policymakers and providers seek to
reduce disparities and improve care for
these groups. Collecting the data as
proposed will provide the basis for our
periodic analyses of the relationship
between an individual’s health status
and other factors and quality, resource
use, and other measures, as required by
section 2(d)(2) of the IMPACT Act, and
to assess appropriate adjustments. These
data will also permit us to develop the
statistical tools necessary to maximize
the value of Medicare data, reduce costs
and improve the quality of care for all
beneficiaries. Collecting and accessing
SDOH data in this way also supports the
three-part strategy put forth in the first
ASPE report, specifically ASPE’s
consideration to enhance data collection
and develop statistical techniques to
allow measurement and reporting of
performance for beneficiaries with
social risk factors on key quality and
resource use measures.
For the reasons discussed above, we
proposed under section 2(d)(2) of the
IMPACT Act, to collect the data on the
following SDOH: (1) Race, as described
in section VII.G.4.b.(1) of this rule; (2)
Ethnicity, as described in section
VII.G.4.b.(1) of this rule; (3) Preferred
Language, as described in section
VII.G.4.b.(2) of this rule; (4) Interpreter
Services, as described in section
VII.G.4.b.(2) of this rule; (5) Health
Literacy, as described in section
VII.G.4.b.(3) of this rule; (6)
Transportation, as described in section
VII.G.4.b.(4) of this rule; and (7) Social
Isolation, as described in section
VII.G.4.b.(5) of this rule. These data
elements are discussed in more detail
below in section VII.G.4.b of this rule.
A detailed discussion of the comments
we received, along with our responses is
included in each section.
b. Standardized Patient Assessment
Data
Section 1899B(b)(1)(B)(vi) of the Act
authorizes the Secretary to collect
SPADEs with respect to other categories
deemed necessary and appropriate.
Below we proposed to create a Social
Determinants of Health SPADE category
under section 1899B(b)(1)(B)(vi) of the
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39151
Act. In addition to collecting SDOH data
for the purposes outlined above under
section 2(d)(2)(B), we also proposed to
collect as SPADE these same data
elements (race, ethnicity, preferred
language, interpreter services, health
literacy, transportation, and social
isolation) under section
1899B(b)(1)(B)(vi) of the Act. We believe
that this proposed new category of
Social Determinants of Health will
inform provider understanding of
individual patient risk factors and
treatment preferences, facilitate
coordinated care and care planning, and
improve patient outcomes. We proposed
to deem this category necessary and
appropriate, for the purposes of SPADE,
because using common standards and
definitions for PAC data elements is
important in ensuring interoperable
exchange of longitudinal information
between PAC providers and other
providers to facilitate coordinated care,
continuity in care planning, and the
discharge planning process from PAC
settings.
All of the Social Determinants of
Health data elements we proposed
under section 1899B(b)(1)(B)(vi) of the
Act have the capacity to take into
account treatment preferences and care
goals of patients, and to inform our
understanding of patient complexity
and risk factors that may affect care
outcomes. While acknowledging the
existence and importance of additional
social determinants of health, we
proposed to assess some of the factors
relevant for patients receiving PAC that
PAC settings are in a position to impact
through the provision of services and
supports, such as connecting patients
with identified needs with
transportation programs, certified
interpreters, or social support programs.
We proposed to adopt the following
seven data elements as SPADE under
the proposed Social Determinants of
Health category: Race, ethnicity,
preferred language, interpreter services,
health literacy, transportation, and
social isolation. To select these data
elements, we reviewed the research
literature, a number of validated
assessment tools and frameworks for
addressing SDOH currently in use (for
example, Health Leads,172 NASEM,
Protocol for Responding to and
Assessing Patients’ Assets, Risks, and
Experiences (PRAPARE), and ICD–10),
and we engaged in discussions with
stakeholders. We also prioritized
balancing the reporting burden for PAC
providers with our policy objective to
collect SPADEs that will inform care
172 Health Leads. Available at https://
healthleadsusa.org/.
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planning and coordination and quality
improvement across care settings.
Furthermore, incorporating SDOH data
elements into care planning has the
potential to reduce readmissions and
help beneficiaries achieve and maintain
their health goals.
We also considered feedback received
during a listening session that we held
on December 13, 2018. The purpose of
the listening session was to solicit
feedback from health systems, research
organizations, advocacy organizations
and state agencies and other members of
the public on collecting patient-level
data on SDOH across care settings,
including consideration of race,
ethnicity, spoken language, health
literacy, social isolation, transportation,
sex, gender identity, and sexual
orientation. We also gave participants
an option to submit written comments.
A full summary of the listening session,
titled ‘‘Listening Session on Social
Determinants of Health Data Elements:
Summary of Findings,’’ includes a list of
participating stakeholders and their
affiliations, and is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
We solicited comment on these
proposals.
Commenters submitted the following
comments related to the proposed rule’s
discussion of SDOH SPADEs. A
discussion of these comments, along
with our responses, appears below.
Comment: One commenter supported
the incorporation of SDOH in the IRF
QRP, in the interest of promoting access
and assuring high-quality care for all
beneficiaries. The commenter also
encouraged CMS to be mindful of
meaningful data collection and the
potential impact for data overload.
Since SDOH have impacts far beyond
the post-acute care setting, the
commenter cautioned data collection
that cannot be readily gathered, shared,
or replicated beyond the PAC setting.
The commenter also encouraged CMS
to consider leveraging data points
collected during primary care visits by
using social risk factor data captured
during those encounters. They pointed
out that the ability to have a hospital’s
or physician’s EHR also collect, capture,
and exchange segments of this
information is powerful. The
commenter recommended that CMS
take a holistic view of SDOH across the
care continuum so that all care settings
may gather, collect or leverage this data
efficiently and in way that maximizes
its impact.
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Response: We agree that collecting
SDOH data elements can be useful in
identifying and addressing health
disparities. We also agree that CMS
should be mindful that data elements
selected are useful. The proposed SDOH
SPADEs are aligned with SDOH
identified in the 2016 NASEM report,
which was commissioned by ASPE.
Regarding the commenter’s suggestion
that CMS consider how it can align
existing and future SDOH data
collection to minimize burden on
providers, we agree that it is important
to minimize duplication of effort and
will take this under advisement for
future policy development.
Comment: One commenter
recommended that CMS consider
admission assessment for certain
SPADEs as also fulfilling the discharge
assessment requirement. The
commenter supported the inclusion of
the SDOH SPADEs and recommended
that CMS require these items be
assessed at some point during the
patient’s stay instead of during the
admission assessment time window.
The commenter recommended that any
SDOH SPADES finalized should be
assessed at any point during the stay.
Response: We disagree with the
commenters regarding SDOH SPADES
should be assessed at any point during
the stay. Each of the SDOH SPADE data
elements will assist with care planning
when the patient is admitted. It is
important for providers to identify a
patient’s needs in order to better inform
the patient’s care decisions made during
and after the stay, including a patient’s
unique risk factors and treatment
preferences.
Comment: Commenters were
generally in favor of the concept of
collecting SDOH data elements and
provided that, if implemented
appropriately, the data could be useful
in identifying and addressing health
care disparities, as well as refining the
risk adjustment of outcome measures.
However, some of the commenters
suggested CMS not to finalize the
proposed policy until CMS can address
important issues around the potential
future uses of these elements and the
requirements around data collection for
certain elements. The commenters
provided that CMS did not state
explicitly in the rule whether it
anticipates the SDOH SPADEs will be
used in adjusting measures and believe
that the IMPACT Act’s requirements
make it likely the SPADEs will be
considered for use in future
adjustments. The commenters
recommended CMS to be circumspect
and transparent in its approaches to
incorporating the data elements
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proposed in payment and quality
adjustments, such as by collecting
stakeholder feedback before
implementing any adjustments.
Response: We appreciate the
commenters for recognizing that
collecting SDOH data elements can be
useful in identifying and address health
disparities. We intend to use this data
to assess the impact that the social
determinants of health have on health
outcomes. We will continue to work
with stakeholders to promote
transparency and support providers
who serve vulnerable populations,
promote high quality care, and refine
and further implement SDOH SPADE.
We appreciate the comment on
collecting stakeholder feedback before
implementing any adjustments to
measures based on the SDOH SPADE.
Collection of this data will help us in
identifying potential disparities,
conducting analyses, and assessing
whether any adjustments are needed.
Any future policy development based
on this data would be done
transparently, and involve solicitation
of stakeholder feedback through the
notice and comment rulemaking process
as appropriate.
Comment: Several commenters
recommended that CMS include
disability status as a SDOH that
contributes to overall patient access to
care, health status, outcomes, and many
other determinants of health since it is
already included in some Medicare risk
adjustment. The commenters stated that
ASPE’s report to Congress entitled
‘‘Social Risk Factors and Performance
Under Medicare’s Value-Based
Purchasing Programs’’ reported that
disability is an independent predictor of
poor mental and physical health
outcomes and that individuals with
disabilities may receive lower-quality
preventive care.
Response: We appreciate the
comments and suggestions provided by
the commenters. We agree that it is
important to understand and meet the
needs of patients with disabilities.
While disability is not being currently
assessed through the SPADE, it is
comprehensively assessed as part of
existing protocols around care plans and
health goals. However, as we continue
to evaluate SDOH SPADEs, we will keep
commenters’ feedback in mind and may
consider these suggestions in future
rulemaking.
Comment: One commenter supported
CMS’s proposal to collect SDOH data
within SPADEs but was concerned that
all of these new elements may be
burdensome. The commenter
recommended that CMS require data
collection on race, ethnicity, preferred
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language, and interpreter services, and
make data collection on health literacy,
transportation, and social isolation
voluntary for now and have the
requirement phased into future
rulemaking. The commenter noted that
this would give IRFs an opportunity to
adjust to the new data collection
methods, while signaling their
importance as entities that are currently
collecting information on SDOH are
experiencing various workflow, privacy,
and other challenges. The commenter
recommended that CMS consider
including the collection of housing
status in the future as individuals with
unmet housing needs, such as
homelessness or substandard housing,
have higher health care costs and can be
at risk for readmissions.
Response: We thank the commenter
for their comment. As discussed above,
section 2(d)(2)(B) of the IMPACT Act
requires the Secretary to collect or
otherwise obtain access to the data
necessary to carry out the provisions of
paragraph (2) of section 2(d) of the
IMPACT Act through both new and
existing data sources. Accessing
standardized data relating to the SDOH
data elements on a national level is
necessary to permit CMS to conduct
periodic analyses, to assess appropriate
adjustments to quality measures,
resource use measures, and other
measures, and to assess and implement
appropriate adjustments to Medicare
payments based on those measures.
Collecting the data as proposed will
provide the basis for our periodic
analyses of the relationship between an
individual’s health status and other
factors and quality, resource use, and
other measures, as required by section
2(d)(2) of the IMPACT Act, and to assess
appropriate adjustments. Regarding the
suggestion that CMS consider a housing
status SPADE data element in future
rulemaking efforts, we appreciate this
feedback and will consider this
suggestion in future rulemaking efforts
on SPADE SDOH data elements.
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(1) Race and Ethnicity
The persistence of racial and ethnic
disparities in health and health care is
widely documented, including in PAC
settings.173 174 175 176 177 Despite the trend
173 2017 National Healthcare Quality and
Disparities Report. Rockville, MD: Agency for
Healthcare Research and Quality; September 2018.
AHRQ Pub. No. 18–0033–EF.
174 Fiscella, K. and Sanders, M.R. Racial and
Ethnic Disparities in the Quality of Health Care.
(2016). Annual Review of Public Health. 37:375–
394.
175 2018 National Impact Assessment of the
Centers for Medicare & Medicaid Services (CMS)
Quality Measures Reports. Baltimore, MD: U.S.
Department of Health and Human Services, Centers
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toward overall improvements in quality
of care and health outcomes, the Agency
for Healthcare Research and Quality, in
its National Healthcare Quality and
Disparities Reports, consistently
indicates that racial and ethnic
disparities persist, even after controlling
for factors such as income, geography,
and insurance.178 For example, racial
and ethnic minorities tend to have
higher rates of infant mortality, diabetes
and other chronic conditions, and visits
to the emergency department, and lower
rates of having a usual source of care
and receiving immunizations such as
the flu vaccine.179 Studies have also
shown that African Americans are
significantly more likely than white
Americans to die prematurely from
heart disease and stroke.180 However,
our ability to identify and address racial
and ethnic health disparities has
historically been constrained by data
limitations, particularly for smaller
populations groups such as Asians,
American Indians and Alaska Natives,
and Native Hawaiians and other Pacific
Islanders.181
The ability to improve understanding
of and address racial and ethnic
disparities in PAC outcomes requires
the availability of better data. There is
currently a Race and Ethnicity data
element, collected in the MDS, LCDS,
IRF–PAI, and OASIS, that consists of a
single question, which aligns with the
1997 Office of Management and Budget
(OMB) minimum data standards for
federal data collection efforts.182 The
for Medicare and Medicaid Services; February 28,
2018.
176 Smedley, B.D., Stith, A.Y., & Nelson, A.R.
(2003). Unequal treatment: Confronting racial and
ethnic disparities in health care. Washington, DC,
National Academy Press.
177 Chase, J., Huang, L. and Russell, D. (2017).
Racial/ethnic disparities in disability outcomes
among post-acute home care patients. J of Aging
and Health. 30(9):1406–1426.
178 National Healthcare Quality and Disparities
Reports. (December 2018). Agency for Healthcare
Research and Quality, Rockville, MD. https://
www.ahrq.gov/research/findings/nhqrdr/
index.html.
179 National Center for Health Statistics. Health,
United States, 2017: With special feature on
mortality. Hyattsville, Maryland. 2018.
180 HHS. Heart disease and African Americans.
2016b. (October 24, 2016). https://
minorityhealth.hhs.gov/omh/browse.aspx?
lvl=4&lvlid=19.
181 National Academies of Sciences, Engineering,
and Medicine; Health and Medicine Division; Board
on Population Health and Public Health Practice;
Committee on Community-Based Solutions to
Promote Health Equity in the United States; Baciu
A, Negussie Y, Geller A, et al., editors.
Communities in Action: Pathways to Health Equity.
Washington (DC): National Academies Press (US);
2017 Jan 11. 2, The State of Health Disparities in
the United States. Available at https://
www.ncbi.nlm.nih.gov/books/NBK425844/.
182 ‘‘Revisions to the Standards for the
Classification of Federal Data on Race and Ethnicity
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39153
1997 OMB Standard lists five minimum
categories of race: (1) American Indian
or Alaska Native; (2) Asian; (3) Black or
African American; (4) Native Hawaiian
or Other Pacific Islander; (5) and White.
The 1997 OMB Standard also lists two
minimum categories of ethnicity: (1)
Hispanic or Latino; and (2) Not Hispanic
or Latino. The 2011 HHS Data Standards
requires a two-question format when
self-identification is used to collect data
on race and ethnicity. Large federal
surveys such as the National Health
Interview Survey, Behavioral Risk
Factor Surveillance System, and the
National Survey on Drug Use and
Health, have implemented the 2011
HHS race and ethnicity data standards.
CMS has similarly updated the
Medicare Current Beneficiary Survey,
Medicare Health Outcomes Survey, and
the Health Insurance Marketplace
Application for Health Coverage with
the 2011 HHS data standards. More
information about the HHS Race and
Ethnicity Data Standards are available
on the website at https://
minorityhealth.hhs.gov/omh/
browse.aspx?lvl=3&lvlid=54.
We proposed to revise the current
Race and Ethnicity data element for
purposes of this proposal to conform to
the 2011 HHS Data Standards for
person-level data collection, while also
meeting the 1997 OMB minimum data
standards for race and ethnicity. Rather
than one data element that assesses both
race and ethnicity, we proposed two
separate data elements: One for Race
and one for Ethnicity, that would
conform with the 2011 HHS Data
Standards and the 1997 OMB Standard.
In accordance with the 2011 HHS Data
Standards a two-question format would
be used for the proposed race and
ethnicity data elements.
The proposed Race data element asks,
‘‘What is your race? We proposed to
include fourteen response options under
the race data element: (1) White; (2)
Black or African American; (3)
American Indian or Alaska Native; (4)
Asian Indian; (5) Chinese; (6) Filipino;
(7) Japanese; (8) Korean; (9) Vietnamese;
(10) Other Asian; (11) Native Hawaiian;
(12) Guamanian or Chamorro; (13)
Samoan; and (14) Other Pacific Islander.
The proposed Ethnicity data element
asks, ‘‘Are you Hispanic, Latino/a, or
Spanish origin?’’ We proposed to
include five response options under the
ethnicity data element: (1) Not of
Hispanic, Latino/a, or Spanish origin;
(2) Mexican, Mexican American,
(Notice of Decision)’’. Federal Register 62:210
(October 30, 1997) pp. 58782–58790. Available at
https://www.govinfo.gov/content/pkg/FR-1997-1030/pdf/97-28653.pdf.
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Chicano/a; (3) Puerto Rican; (4) Cuban;
and (5) Another Hispanic, Latino, or
Spanish Origin. We are including the
addition of ‘‘of’’ to the Ethnicity data
element to read, ‘‘Are you of Hispanic,
Latino/a, or Spanish origin?’’
We believe that the two proposed data
elements for race and ethnicity conform
to the 2011 HHS Data Standards for
person-level data collection, while also
meeting the 1997 OMB minimum data
standards for race and ethnicity,
because under those standards, more
detailed information on population
groups can be collected if those
additional categories can be aggregated
into the OMB minimum standard set of
categories.
In addition, we received stakeholder
feedback during the December 13, 2018
SDOH listening session on the
importance of improving response
options for race and ethnicity as a
component of health care assessments
and for monitoring disparities. Some
stakeholders emphasized the
importance of allowing for selfidentification of race and ethnicity for
more categories than are included in the
2011 HHS Standard to better reflect
state and local diversity, while
acknowledging the burden of coding an
open-ended health care assessment
question across different settings.
We believe that the proposed
modified race and ethnicity data
elements more accurately reflect the
diversity of the U.S. population than the
current race/ethnicity data element
included in MDS, LCDS, IRF–PAI, and
OASIS.183 184 185 186 We believe, and
research consistently shows, that
improving how race and ethnicity data
are collected is an important first step
in improving quality of care and health
outcomes. Addressing disparities in
access to care, quality of care, and
health outcomes for Medicare
beneficiaries begins with identifying
and analyzing how SDOH, such as race
and ethnicity, align with disparities in
183 Penman-Aguilar, A., Talih, M., Huang, D.,
Moonesinghe, R., Bouye, K., Beckles, G. (2016).
Measurement of Health Disparities, Health
Inequities, and Social Determinants of Health to
Support the Advancement of Health Equity. J Public
Health Manag Pract. 22 Suppl 1: S33–42.
184 Ramos, R., Davis, J.L., Ross, T., Grant, C.G.,
Green, B.L. (2012). Measuring health disparities and
health inequities: Do you have REGAL data? Qual
Manag Health Care. 21(3):176–87.
185 IOM (Institute of Medicine). 2009. Race,
Ethnicity, and Language Data: Standardization for
Health Care Quality Improvement. Washington, DC:
The National Academies Press.
186 ‘‘Revision of Standards for Maintaining,
Collecting, and Presenting Federal Data on Race and
Ethnicity: Proposals From Federal Interagency
Working Group (Notice and Request for
Comments).’’ Federal Register 82: 39 (March 1,
2017) p. 12242.
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these areas.187 Standardizing selfreported data collection for race and
ethnicity allows for the equal
comparison of data across multiple
healthcare entities.188 By collecting and
analyzing these data, CMS and other
healthcare entities will be able to
identify challenges and monitor
progress. The growing diversity of the
U.S. population and knowledge of racial
and ethnic disparities within and across
population groups supports the
collection of more granular data beyond
the 1997 OMB minimum standard for
reporting categories. The 2011 HHS race
and ethnicity data standard includes
additional detail that may be used by
PAC providers to target quality
improvement efforts for racial and
ethnic groups experiencing disparate
outcomes. For more information on the
Race and Ethnicity data elements, we
refer readers to the document titled
‘‘Final Specifications for IRF QRP
Measures and Standardized Patient
Assessment Data Elements,’’ available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In an effort to standardize the
submission of race and ethnicity data
among IRFs, HHAs, SNFs and LTCHs,
for the purposes outlined in section
1899B(a)(1)(B) of the Act, while
minimizing the reporting burden, we
proposed to adopt the Race and
Ethnicity data elements described above
as SPADEs with respect to the proposed
Social Determinants of Health category.
Specifically, we proposed to replace
the current Race/Ethnicity data element
with the proposed Race and Ethnicity
data elements on the IRF–PAI. We also
proposed that IRFs that submit the Race
and Ethnicity data elements with
respect to admission will be considered
to have submitted with respect to
discharge as well, because it is unlikely
that the results of these assessment
findings will change between the start
and end of the IRF stay, making the
information submitted with respect to a
187 National Academies of Sciences, Engineering,
and Medicine; Health and Medicine Division; Board
on Population Health and Public Health Practice;
Committee on Community-Based Solutions to
Promote Health Equity in the United States; Baciu
A, Negussie Y, Geller A, et al., editors.
Communities in Action: Pathways to Health Equity.
Washington (DC): National Academies Press (US);
2017 Jan 11. 2, The State of Health Disparities in
the United States. Available at https://
www.ncbi.nlm.nih.gov/books/NBK425844/.
188 IOM (Institute of Medicine). 2009. Race,
Ethnicity, and Language Data: Standardization for
Health Care Quality Improvement. Washington, DC:
The National Academies Press.
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patient’s admission the same with
respect to a patient’s discharge.
We solicited comment on these
proposals.
Commenters submitted the following
comments related to the proposed rule’s
discussion of the Race and Ethnicity
SPADEs. A discussion of these
comments, along with our responses,
appears below.
Comment: Some commenters noted
that the response options for race do not
align with those used in other
government data, such as the U.S.
Census or the Office of Management and
Budget (OMB). The commenters also
stated these responses are not consistent
with the recommendations made in the
2009 Institute of Medicine report. The
commenters pointed out that IOM report
recommended using broader OMB race
categories and granular ethnicities
chosen from a national standard set that
can be ‘‘rolled up’’ into the broader
categories. The commenters stated that
it is unclear how CMS chose the 14
response options under the race data
element and the five options under the
ethnicity element and worried that these
response options would add to the
confusion that already may exist for
patients about what terms like ‘‘race’’
and ‘‘ethnicity’’ mean for the purposes
of health care data collection. The
commenters also noted that CMS should
confer directly with experts on the issue
to ensure patient assessments are
collecting the right data in the right way
before these SDOH SPADEs are
finalized.
Response: The proposed Race and
Ethnicity categories align with and are
rolled up into the 1997 OMB minimum
data standards and conforming with the
2011 HHS Data Standards as described
in the implementation guidance titled
‘‘U.S. Department of Health and Human
Services Implementation Guidance on
Data Collection Standards for Race,
Ethnicity, Sex, Primary Language, and
Disability Status’’ at https://
aspe.hhs.gov/basic-report/hhsimplementation-guidance-datacollection-standards-race-ethnicity-sexprimary-language-and-disability-status.
As stated in the proposed rule, the 14
race categories and the 5 ethnicity
categories conform with the 2011 HHS
Data Standards for person-level data
collection, which were developed in
fulfillment of section 4302 of the
Affordable Care Act that required the
Secretary of HHS to establish data
collection standards for race, ethnicity,
sex, primary language, and disability
status. Through the HHS Data Council,
which is the principal, senior internal
Departmental forum and advisory body
to the Secretary on health and human
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services data policy and coordinates
HHS data collection and analysis
activities, the Section 4302 Standards
Workgroup was formed. The Workgroup
included representatives from HHS, the
OMB, and the Census Bureau. The
Workgroup examined current federal
data collection standards, adequacy of
prior testing, and quality of the data
produced in prior surveys; consulted
with statistical agencies and programs;
reviewed OMB data collection standards
and the Institute of Medicine (IOM)
Report Race, Ethnicity, and Language
Data Collection: Standardization for
Health Care Quality Improvement;
sought input from national experts; and
built on its members’ experience with
collecting and analyzing demographic
data. As a result of this Workgroup, a set
of data collection standards were
developed, and then published for
public comment. This set of data
collection standards is referred to as the
2011 HHS Data Standards.189 As
described in the implementation
guidance provided above, the categories
of race and ethnicity under the 2011
HHS Data Standards allow for more
detailed information to be collected and
the additional categories under the 2011
HHS Data Standards can be aggregated
into the OMB minimum standards set of
categories.
As noted in the FY 2020 IRF PPS
proposed rule (84 FR 17321 through
17323), we conferred with experts by
conducting a listening session regarding
the proposed SDOH data elements
regarding the importance of improving
response options for race and ethnicity
as a component of health care
assessments and for monitoring
disparities. Some stakeholders
emphasized the importance of allowing
for self-identification of race and
ethnicity for more categories than are
included in the 2011 HHS Data
Standards to better reflect state and
local diversity.
Comment: A commenter
recommended that CMS consider the
implications of having PAC providers
collect Race and Ethnicity codes that
vary from the Race and Ethnicity codes
collected by other healthcare providers,
specifically acute-care hospitals. The
commenter noted that unless all care
providers are expected to utilize the
uniform 2011 HHS Data Standards, the
consistency and accuracy of race and
ethnicity data across settings will likely
be unreliable and problematic. Another
commenter provided that the proposed
189 HHS Data Standards. Available at https://
aspe.hhs.gov/basic-report/hhs-implementationguidance-data-collection-standards-race-ethnicitysex-primary-language-and-disability-status.
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list of response options for Race may not
include all races that should be
reflected, for example, Native African
and Middle Eastern. In addition, the
item should include ‘‘check all that
apply’’ to ensure accurate and complete
data collection. The commenter
encouraged CMS to refine the list of
response options for Race and provide
a rationale for the final list of response
options.
Response: We thank the commenter
and agree that it is important to collect
race and ethnicity data in a consistent
way. The race and ethnicity categories
that were proposed align with the 2011
HHS Data Standards and are rolled up
into the 1997 OMB minimum data
standards, which can be found at
https://aspe.hhs.gov/basic-report/hhsimplementation-guidance-datacollection-standards-race-ethnicity-sexprimary-language-and-disability-status.
For example, the 1997 OMB minimum
data standard for Hispanic is the roll up
category for the following response
options on the 2011 HHS Data
Standards: Mexican, Mexican American,
Chicano/a; Puerto Rican; Cuban; another
Hispanic, Latino, or Spanish origin.
However, we will take the comment
under advisement for future
consideration. We also note that the
option for ‘‘check all that apply’’ is
available for providers to choose from
the list of response options.
Comment: A commenter supported
the opportunities to better account for
SDOH in the diagnosis and treatment of
patients but is concerned by the
specificity of several of the seven
proposed element for data collection for
example, collection of race by Japanese,
Chinese, Korean, etc. The commenter’s
concern is with the added burden in
collecting the level of specificity
outlined, and the commenter requested
that CMS provide more detailed
guidance in the final rule regarding how
this information should be collected and
shared in compliance with HIPAA.
Further, the commenter asked that the
agency outlines its expectations for how
this newly collected information will be
used by Medicare for payment and
public reporting.
Response: For the Race and Ethnicity
SPADE, this data should be completed
based on the response of the patient. It
is important to ask the patient to select
the category or categories that most
closely correspond to their race and
ethnicity. Respondents should be
offered the option of selecting one or
more race and ethnicity categories.
Observer identification or medical
record documentation may not be used.
The SDOH data elements that will be
collected will assist with care
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coordination and with evaluating the
impact of disparities. With respect to
how the data will be used for payment
and public reporting, any potential
future use of the data for these purposes
would be done through future
rulemaking.
SDOH data elements should be
treated the same as other data collected
on the assessment tool. As to any
specific HIPAA questions, we
appreciate the commenter’s
commitment to compliance with the
HIPAA requirements, but note that the
Office for Civil Rights (OCR) is tasked
with implementing and enforcing
HIPAA, not CMS. Commenters should
consult appropriate counsel in instances
in which they are unsure of their HIPAA
status, or the permissibility of a
disclosure under the HIPAA Privacy
Rule. In doing so, commenters may wish
to consult 45 CFR 164.103 (definition of
‘‘required by law’’) and § 164.512(a)
(allowing ‘‘required by law’’
disclosures).
(2) Preferred Language and Interpreter
Services
More than 64 million Americans
speak a language other than English at
home, and nearly 40 million of those
individuals have limited English
proficiency (LEP).190 Individuals with
LEP have been shown to receive worse
care and have poorer health outcomes,
including higher readmission
rates.191 192 193 Communication with
individuals with LEP is an important
component of high quality health care,
which starts by understanding the
population in need of language services.
Unaddressed language barriers between
a patient and provider care team
negatively affects the ability to identify
and address individual medical and
non-medical care needs, to convey and
understand clinical information, as well
as discharge and follow up instructions,
all of which are necessary for providing
high quality care. Understanding the
communication assistance needs of
patients with LEP, including
individuals who are Deaf or hard of
190 U.S. Census Bureau, 2013–2017 American
Community Survey 5-Year Estimates.
191 Karliner LS, Kim SE, Meltzer DO, Auerbach
AD. Influence of language barriers on outcomes of
hospital care for general medicine inpatients. J
Hosp Med. 2010 May–Jun;5(5):276–82. doi:
10.1002/jhm.658.
192 Kim EJ, Kim T, Paasche-Orlow MK, et al.
Disparities in Hypertension Associated with
Limited English Proficiency. J Gen Intern Med. 2017
Jun;32(6):632–639. doi: 10.1007/s11606–017–3999–
9.
193 National Academies of Sciences, Engineering,
and Medicine. 2016. Accounting for social risk
factors in Medicare payment: Identifying social risk
factors. Washington, DC: The National Academies
Press.
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hearing, is critical for ensuring good
outcomes.
Presently, the preferred language of
patients and residents and need for
interpreter services are assessed in two
PAC assessment tools. The LCDS and
the MDS use the same two data
elements to assess preferred language
and whether a patient or resident needs
or wants an interpreter to communicate
with health care staff. The MDS initially
implemented preferred language and
interpreter services data elements to
assess the needs of SNF residents and
patients and inform care planning. For
alignment purposes, the LCDS later
adopted the same data elements for
LTCHs. The 2009 NASEM (formerly
Institute of Medicine) report on
standardizing data for health care
quality improvement emphasizes that
language and communication needs
should be assessed as a standard part of
health care delivery and quality
improvement strategies.194
In developing our proposal for a
standardized language data element
across PAC settings, we considered the
current preferred language and
interpreter services data elements that
are in LCDS and MDS. We also
considered the 2011 HHS Primary
Language Data Standard and peerreviewed research. The current
preferred language data element in
LCDS and MDS asks, ‘‘What is your
preferred language?’’ Because the
preferred language data element is openended, the patient or resident is able to
identify their preferred language,
including American Sign Language
(ASL). Finally, we considered the
recommendations from the 2009
NASEM (formerly Institute of Medicine)
report, ‘‘Race, Ethnicity, and Language
Data: Standardization for Health Care
Quality Improvement.’’ In it, the
committee recommended that
organizations evaluating a patient’s
language and communication needs for
health care purposes, should collect
data on the preferred spoken language
and on an individual’s assessment of
his/her level of English proficiency.
A second language data element in
LCDS and MDS asks, ‘‘Do you want or
need an interpreter to communicate
with a doctor or health care staff?’’ and
includes yes or no response options. In
contrast, the 2011 HHS Primary
Language Data Standard recommends
either a single question to assess how
well someone speaks English or, if more
granular information is needed, a two194 IOM (Institute of Medicine). 2009. Race,
Ethnicity, and Language Data: Standardization for
Health Care Quality Improvement. Washington, DC:
The National Academies Press.
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part question to assess whether a
language other than English is spoken at
home and if so, identify that language.
However, neither option allows for a
direct assessment of a patient’s or
resident’s preferred spoken or written
language nor whether they want or need
interpreter services for communication
with a doctor or care team, both of
which are an important part of assessing
patient/resident needs and the care
planning process. More information
about the HHS Data Standard for
Primary Language is available on the
website at https://
minorityhealth.hhs.gov/omh/
browse.aspx?lvl=3&lvlid=54.
Research consistently recommends
collecting information about an
individual’s preferred spoken language
and evaluating those responses for
purposes of determining language
access needs in health care.195 However,
using ‘‘preferred spoken language’’ as
the metric does not adequately account
for people whose preferred language is
ASL, which would necessitate adopting
an additional data element to identify
visual language. The need to improve
the assessment of language preferences
and communication needs across PAC
settings should be balanced with the
burden associated with data collection
on the provider and patient. Therefore
we proposed to retain the Preferred
Language and Interpreter Services data
elements currently in use on the MDS
and LCDS on the IRF–PAI.
In addition, we received feedback
during the December 13, 2018 listening
session on the importance of evaluating
and acting on language preferences early
to facilitate communication and
allowing for patient self-identification of
preferred language. Although the
discussion about language was focused
on preferred spoken language, there was
general consensus among participants
that stated language preferences may or
may not accurately indicate the need for
interpreter services, which supports
collecting and evaluating data to
determine language preference, as well
as the need for interpreter services. An
alternate suggestion was made to
inquire about preferred language
specifically for discussing health or
health care needs. While this suggestion
does allow for ASL as a response option,
we do not have data indicating how
195 Guerino, P. and James, C. Race, Ethnicity, and
Language Preference in the Health Insurance
Marketplaces 2017 Open Enrollment Period.
Centers for Medicare & Medicaid Services, Office of
Minority Health. Data Highlight: Volume 7—April
2017. Available at https://www.cms.gov/AboutCMS/Agency-Information/OMH/Downloads/DataHighlight-Race-Ethnicity-and-Language-PreferenceMarketplace.pdf.
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useful this question might be for
assessing the desired information and
thus we are not including this question
in our proposal.
Improving how preferred language
and need for interpreter services data
are collected is an important component
of improving quality by helping PAC
providers and other providers
understand patient needs and develop
plans to address them. For more
information on the Preferred Language
and Interpreter Services data elements,
we refer readers to the document titled
‘‘Final Specifications for IRF QRP
Measures and Standardized Patient
Assessment Data Elements,’’ available
on the website at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
In an effort to standardize the
submission of language data among
IRFs, HHAs, SNFs and LTCHs, for the
purposes outlined in section
1899B(a)(1)(B) of the Act, while
minimizing the reporting burden, we
proposed to adopt the Preferred
Language and Interpreter Services data
elements currently used on the MDS
and LCDS, and described above, as
SPADEs with respect to the Social
Determinants of Health category. We
proposed to add the current Preferred
Language and Interpreter Services data
elements from the MDS and LCDS to the
IRF–PAI.
We solicited comment on these
proposals.
Commenters submitted the following
comments related to the proposed rule’s
discussion of Preferred Language and
Interpreter Services SPADEs. A
discussion of these comments, along
with our responses, appears below.
Comment: Some commenters noted
that, if finalized, IRFs should only need
to submit data on the race and ethnicity
SPADEs with respect to admission and
would not need to collect and report
again at discharge, as it is unlikely that
patient status for these elements will
change. The commenters believe that a
patient’s preferred language and need
for an interpreter also are unlikely to
change between admission and
discharge; thus, the commenter urged
CMS to require collection of these
SDOH SPADEs with respect to
admission only.
Response: We thank the commenters
for the comment. With regard to the
submission of the Preferred Language
SPADE and the Interpreter Services
SPADE, we agree with the commenters
that it is unlikely that the assessment of
Preferred Language and Interpreter
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Services at admission would differ from
assessment at discharge. As discussed in
previous response for Vision and
Hearing, we believe that the submission
of preferred language and the need for
an interpreter is similar to the
submission of Race, Ethnicity, Hearing,
and Vision SPADES.
We account for this change to the
Collection of Information requirements
for the IRF QRP in XIV.C of this final
rule. Based on the comments received,
and for the reasons discussed, we are
finalizing that the Preferred Language
and Interpreter Services SPADEs be
collected as proposed with the
modification that we will deem IRFs
that submit these two SPADEs with
respect to admission to have submitted
with respect to both admission and
discharge.
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(3) Health Literacy
The Department of Health and Human
Services defines health literacy as ‘‘the
degree to which individuals have the
capacity to obtain, process, and
understand basic health information
and services needed to make
appropriate health decisions.’’ 196
Similar to language barriers, low health
literacy can interfere with
communication between the provider
and patient and the ability for patients
or their caregivers to understand and
follow treatment plans, including
medication management. Poor health
literacy is linked to lower levels of
knowledge about health, worse health
outcomes, and the receipt of fewer
preventive services, but higher medical
costs and rates of emergency department
use.197
Health literacy is prioritized by
Healthy People 2020 as an SDOH.198
Healthy People 2020 is a long-term,
evidence-based effort led by the
Department of Health and Human
Services that aims to identify
nationwide health improvement
priorities and improve the health of all
Americans. Although not designated as
a social risk factor in NASEM’s 2016
report on accounting for social risk
factors in Medicare payment, the
NASEM noted that health literacy is
impacted by other social risk factors and
196 U.S. Department of Health and Human
Services, Office of Disease Prevention and Health
Promotion. National action plan to improve health
literacy. Washington (DC): Author; 2010.
197 National Academies of Sciences, Engineering,
and Medicine. 2016. Accounting for social risk
factors in Medicare payment: Identifying social risk
factors. Washington, DC: The National Academies
Press.
198 Social Determinants of Health. Healthy People
2020. https://www.healthypeople.gov/2020/topicsobjectives/topic/social-determinants-of-health.
(February 2019).
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can affect access to care, as well as
quality of care and health outcomes.199
Assessing for health literacy across PAC
settings would facilitate better care
coordination and discharge planning. A
significant challenge in assessing the
health literacy of individuals is avoiding
excessive burden on patients and health
care providers. The majority of existing,
validated health literacy assessment
tools use multiple screening items,
generally with no fewer than four,
which would make them burdensome if
adopted in MDS, LCDS, IRF–PAI, and
OASIS. The Single Item Literacy
Screener (SILS) question questions,
‘‘How often do you need to have
someone help you when you read
instructions, pamphlets, or other written
material from your doctor or
pharmacy?’’ Possible response options
are: (1) Never; (2) Rarely; (3) Sometimes;
(4) Often; and (5) Always. The SILS
question, which assesses reading ability,
(a primary component of health
literacy), tested reasonably well against
the 36 item Short Test of Functional
Health Literacy in Adults (S–TOFHLA),
a thoroughly vetted and widely adopted
health literacy test, in assessing the
likelihood of low health literacy in an
adult sample from primary care
practices participating in the Vermont
Diabetes Information System.200 201 The
S–TOFHLA is a more complex
assessment instrument developed using
actual hospital related materials such as
prescription bottle labels and
appointment slips, and often considered
the instrument of choice for a detailed
evaluation of health literacy.202
Furthermore, the S–TOFHLA
instrument is proprietary and subject to
purchase for individual entities or
users.203 Given that SILS is publicly
199 U.S. Department of Health & Human Services,
Office of the Assistant Secretary for Planning and
Evaluation. Report to Congress: Social Risk Factors
and Performance Under Medicare’s Value-Based
Purchasing Programs. Available at https://
aspe.hhs.gov/pdf-report/report-congress-social-riskfactors-and-performance-under-medicares-valuebased-purchasing-programs. Washington, DC: 2016.
200 Morris, N.S., MacLean, C.D., Chew, L.D., &
Littenberg, B. (2006). The Single Item Literacy
Screener: evaluation of a brief instrument to
identify limited reading ability. BMC family
practice, 7, 21. doi:10.1186/1471–2296–7–21.
201 Brice, J.H., Foster, M.B., Principe, S., Moss, C.,
Shofer, F.S., Falk, R.J., Ferris, M.E., DeWalt, D.A.
(2013). Single-item or two-item literacy screener to
predict the S–TOFHLA among adult hemodialysis
patients. Patient Educ Couns. 94(1):71–5.
202 University of Miami, School of Nursing &
Health Studies, Center of Excellence for Health
Disparities Research. Test of Functional Health
Literacy in Adults (TOFHLA). (March 2019).
Available at https://elcentro.sonhs.miami.edu/
research/measures-library/tofhla/.
203 Nurss, J.R., Parker, R.M., Williams, M.V.,
&Baker, D.W. David W. (2001). TOFHLA.
Peppercorn Books & Press. Available at https://
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available, shorter and easier to
administer than the full health literacy
screen, and research found that a
positive result on the SILS demonstrates
an increased likelihood that an
individual has low health literacy, we
proposed to use the single-item reading
question for health literacy in the
standardized data collection across PAC
settings. We believe that use of this data
element will provide sufficient
information about the health literacy of
IRF patients to facilitate appropriate
care planning, care coordination, and
interoperable data exchange across PAC
settings.
In addition, we received feedback
during the December 13, 2018 SDOH
listening session on the importance of
recognizing health literacy as more than
understanding written materials and
filling out forms, as it is also important
to evaluate whether patients understand
their conditions. However, the NASEM
recently recommended that health care
providers implement health literacy
universal precautions instead of taking
steps to ensure care is provided at an
appropriate literacy level based on
individualized assessment of health
literacy.204 Given the dearth of Medicare
data on health literacy and gaps in
addressing health literacy in practice,
we recommend the addition of a health
literacy data element.
The proposed Health Literacy data
element is consistent with
considerations raised by NASEM and
other stakeholders and research on
health literacy, which demonstrates an
impact on health care use, cost, and
outcomes.205 For more information on
the proposed Health Literacy data
element, we refer readers to the
document titled ‘‘Final Specifications
for IRF QRP Measures and Standardized
Patient Assessment Data Elements,’’
available on the website at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In an effort to standardize the
submission of health literacy data
among IRFs, HHAs, SNFs and LTCHs,
for the purposes outlined in section
www.peppercornbooks.com/catalog/
information.php?info_id=5.
204 Hudson, S., Rikard, R.V., Staiculescu, I. &
Edison, K. (2017). Improving health and the bottom
line: The case for health literacy. In Building the
case for health literacy: Proceedings of a workshop.
Washington, DC: The National Academies Press.
205 National Academies of Sciences, Engineering,
and Medicine. 2016. Accounting for Social Risk
Factors in Medicare Payment: Identifying Social
Risk Factors. Washington, DC: The National
Academies Press.
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1899B(a)(1)(B) of the Act, while
minimizing the reporting burden, we
proposed to adopt SILS question
described above for the Health Literacy
data element as SPADE under the Social
Determinants of Health Category. We
proposed to add the Health Literacy
data element to the IRF–PAI.
We solicited comment on this
proposals. A discussion of these
comments, along with our responses,
appears below.
Comment: Some commenters noted
that, if finalized, IRFs should only need
to submit data on the race and ethnicity
SPADEs with respect to admission and
would not need to collect and report
again at discharge, as it is unlikely that
patient status for these elements will
change. The commenters believe that a
patient’s health literacy is unlikely to
change between admission and
discharge; thus, the commenter urged
CMS to require collection of all SDOH
SPADEs with respect to admission only.
Response: We disagree with the
commenters that it is unlikely patient
status for health literacy will change
from admission to discharge. Unlike the
Vision, Hearing, Race, Ethnicity,
Preferred Language, and Interpreter
Services SPADEs, we believe that the
response to this data element may
change from admission to discharge for
some patients. Health literacy can
impact a patient’s ability to manage
their conditions, and it something that
should be taken into account when
developing care plans. The collection of
the Health Literacy SPADE at discharge
is to support patients, whose
circumstances may have changed over
the duration of their admission, in
having the appropriate supports postdischarge. Therefore, the health literacy
data element should be collected at both
admission and discharge given the
impact this could have on health
outcomes and care planning.
Comment: One commenter stated that
the health literacy question could be
improved to capture whether the patient
can read, understand, and implement/
respond to the information. In addition,
the commenter stated that the question
does not take into account whether a
patient’s need for help is due to limited
vision, which is different from the
purpose of the separate Vision
Impairment data element. Another
possible question the commenter
suggested was ‘‘How often do you have
difficulty?’’ The commenter suggested
that a single construct may not be
sufficient for this area, depending on the
aspect of health literacy that CMS
intends to identify.
Response: We thank the commenters
for the comment on the health literacy
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data element. We agree that knowing
whether a patient has a reading or
comprehension challenge, or limited
vision would be helpful. However, we
specifically proposed data elements that
have been tested. We were also mindful
to try and limit the potential burden of
asking additional questions related to
health literacy. The SILS Health
Literacy data element that we proposed
performed well when tested, and it
minimizes concerns related to burden
by requiring one instead of multiple
questions on health literacy.206 207 If
commenters have examples of SDOH
questions that have been cognitively
tested, we would welcome that feedback
as we seek to refine SDOH SPADE data
elements in future rulemaking.
(4) Transportation
Transportation barriers commonly
affect access to necessary health care,
causing missed appointments, delayed
care, and unfilled prescriptions, all of
which can have a negative impact on
health outcomes.208 Access to
transportation for ongoing health care
and medication access needs,
particularly for those with chronic
diseases, is essential to successful
chronic disease management. Adopting
a data element to collect and analyze
information regarding transportation
needs across PAC settings would
facilitate the connection to programs
that can address identified needs. We
therefore proposed to adopt as SPADE a
single transportation data element that
is from the Protocol for Responding to
and Assessing Patients’ Assets, Risks,
and Experiences (PRAPARE) assessment
tool and currently part of the
Accountable Health Communities
(AHC) Screening Tool.
The proposed Transportation data
element from the PRAPARE tool
questions, ‘‘Has lack of transportation
kept you from medical appointments,
meetings, work, or from getting things
needed for daily living?’’ The three
response options are: (1) Yes, it has kept
me from medical appointments or from
getting my medications; (2) Yes, it has
kept me from non-medical meetings,
appointments, work, or from getting
206 Morris, N.S., MacLean, C.D., Chew, L.D., &
Littenberg, B. (2006). The Single Item Literacy
Screener: Evaluation of a brief instrument to
identify limited reading ability. BMC family
practice, 7, 21. doi:10.1186/1471–2296–7–21.
207 Brice, J.H., Foster, M.B., Principe, S., Moss, C.,
Shofer, F.S., Falk, R.J., Ferris, M.E., DeWalt, D.A.
(2013). Single-item or two-item literacy screener to
predict the S–TOFHLA among adult hemodialysis
patients. Patient Educ Couns. 94(1):71–5.
208 Syed, S.T., Gerber, B.S., and Sharp, L.K.
(2013). Traveling Towards Disease: Transportation
Barriers to Health Care Access. J Community
Health. 38(5): 976–993.
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things that I need; and (3) No. The
patient would be given the option to
select all responses that apply. We
proposed to use the transportation data
element from the PRAPARE Tool, with
permission from National Association of
Community Health Centers (NACHC),
after considering research on the
importance of addressing transportation
needs as a critical SDOH.209
The proposed data element is
responsive to research on the
importance of addressing transportation
needs as a critical SDOH and would
adopt the Transportation item from the
PRAPARE tool.210 This data element
comes from the national PRAPARE
social determinants of health
assessment protocol, developed and
owned by NACHC, in partnership with
the Association of Asian Pacific
Community Health Organization, the
Oregon Primary Care Association, and
the Institute for Alternative Futures.
Similarly the Transportation data
element used in the AHC Screening
Tool was adapted from the PRAPARE
tool. The AHC screening tool was
implemented by the Center for Medicare
and Medicaid Innovation’s AHC Model
and developed by a panel of
interdisciplinary experts that looked at
evidence-based ways to measure SDOH,
including transportation. While the
transportation access data element in
the AHC screening tool serves the same
purposes as our proposed SPADE
collection about transportation barriers,
the AHC tool has binary yes or no
response options that do not
differentiate between challenges for
medical versus non-medical
appointments and activities. We believe
that this is an important nuance for
informing PAC discharge planning to a
community setting, as transportation
needs for non-medical activities may
differ than for medical activities and
should be taken into account.211 We
believe that use of this data element will
provide sufficient information about
transportation barriers to medical and
non-medical care for IRF patients to
facilitate appropriate discharge planning
and care coordination across PAC
settings. As such, we proposed to adopt
the Transportation data element from
PRAPARE. More information about
209 Health Research & Educational Trust. (2017,
November). Social determinants of health series:
Transportation and the role of hospitals. Chicago,
IL. Available at www.aha.org/
transportation.www.aha.org/transportation.
210 Health Research & Educational Trust. (2017,
November). Social determinants of health series:
Transportation and the role of hospitals. Chicago,
IL. Available at www.aha.org/transportation.
211 Northwestern University. (2017). PROMIS
Item Bank v. 1.0—Emotional Distress—Anger—
Short Form 1.
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development of the PRAPARE tool is
available on the website at https://
protect2.fireeye.com/url?k=7cb6eb4420e2f238-7cb6da7b-0cc47adc5fa21751cb986c8c2f8c&u=https://
www.nachc.org/prapare.
In addition, we received stakeholder
feedback during the December 13, 2018
SDOH listening session on the impact of
transportation barriers on unmet care
needs. While recognizing that there is
no consensus in the field about whether
providers should have responsibility for
resolving patient transportation needs,
discussion focused on the importance of
assessing transportation barriers to
facilitate connections with available
community resources.
Adding a Transportation data element
to the collection of SPADE would be an
important step to identifying and
addressing SDOH that impact health
outcomes and patient experience for
Medicare beneficiaries. For more
information on the Transportation data
element, we refer readers to the
document titled ‘‘Final Specifications
for IRF QRP Measures and Standardized
Patient Assessment Data Elements,’’
available on the website at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
In an effort to standardize the
submission of transportation data
among IRFs, HHAs, SNFs and LTCHs,
for the purposes outlined in section
1899B(a)(1)(B) of the Act, while
minimizing the reporting burden, we
proposed to adopt the Transportation
data element described above as SPADE
with respect to the proposed Social
Determinants of Health category. If
finalized as proposed, we would add the
Transportation data element to the IRF–
PAI.
We solicited comment on these
proposals. A discussion of these
comments, along with our responses,
appears below.
Comment: One commenter supported
the collection of data to capture the
reason(s) transportation affects a
patient’s access to health care. The
commenter appreciated the inclusion of
these items on the IRF–PAI and
encouraged exploration of quality
measures in this area as transportation
is an extremely important instrumental
activity of daily living to effectively
transition to the community.
Response: We thank the commenter
and we will consider this feedback as
we continue to improve and refine the
SPADEs.
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Comment: Some commenters noted
that, if finalized, IRFs should only need
to submit data on the race and ethnicity
SPADEs with respect to admission and
would not need to collect and report
again at discharge, as it is unlikely that
patient status for these elements will
change. The commenters believe that a
patient’s access to transportation is
unlikely to change between admission
and discharge; thus, the commenter
suggested CMS to require collection of
all SDOH SPADEs with respect to
admission only.
Response: We disagree with the
commenters that stated that access to
transportation will always be the same
from admission to discharge. Unlike the
Vision, Hearing, Race, Ethnicity,
Preferred Language, and Interpreter
Services SPADEs, we believe that the
response to this data element is likely to
change from admission to discharge for
some patients. For example, a patient
could lose a family member or caregiver
between admission and discharge,
which could impact his or her access to
transportation and impact how the
patient responds to the access to
transportation SPADE data element.
Therefore, we believe that the response
to this SDOH data element is likely to
change from admission to discharge for
some patients and we proposed to
collect this SPADE data element with
respect to both admission and
discharge.
As outlined in the FY 2020 IRF PPS
proposed rule, multiple studies have
demonstrated that access to
transportation has an impact on the
health of patients (84 FR 17325).
Therefore, it is important for providers
to be able to identify a patient’s needs
when the patient is admitted and when
the patient is discharged in order to
better inform the patient’s care
decisions made during and after the
stay, including understanding the
patient’s unique risk factors and
treatment preferences. Because of this,
we are requiring that the Access to
Transportation data element be assessed
with respect to both admission and
discharge.
(5) Social Isolation
Distinct from loneliness, social
isolation refers to an actual or perceived
lack of contact with other people, such
as living alone or residing in a remote
area.212 213 Social isolation tends to
212 Tomaka, J., Thompson, S., and Palacios, R.
(2006). The Relation of Social Isolation, Loneliness,
and Social Support to Disease Outcomes Among the
Elderly. J of Aging and Health. 18(3): 359–384.
213 Social Connectedness and Engagement
Technology for Long-Term and Post-Acute Care: A
Primer and Provider Selection Guide. (2019).
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increase with age, is a risk factor for
physical and mental illness, and a
predictor of mortality.214 215 216 PAC
providers are well-suited to design and
implement programs to increase social
engagement of patients, while also
taking into account individual needs
and preferences. Adopting a data
element to collect and analyze
information about social isolation in
IRFs and across PAC settings would
facilitate the identification of patients
who are socially isolated and who may
benefit from engagement efforts.
We proposed to adopt as SPADE a
single social isolation data element that
is currently part of the AHC Screening
Tool. The AHC item was selected from
the Patient-Reported Outcomes
Measurement Information System
(PROMIS®) Item Bank on Emotional
Distress and questions, ‘‘How often do
you feel lonely or isolated from those
around you?’’ The five response options
are: (1) Never; (2) Rarely; (3) Sometimes;
(4) Often; and (5) Always.217 The AHC
Screening Tool was developed by a
panel of interdisciplinary experts that
looked at evidence-based ways to
measure SDOH, including social
isolation. More information about the
AHC Screening Tool is available on the
website at https://innovation.cms.gov/
Files/worksheets/ahcmscreeningtool.pdf.
In addition, we received stakeholder
feedback during the December 13, 2018
SDOH listening session on the value of
receiving information on social isolation
for purposes of care planning. Some
stakeholders also recommended
assessing social isolation as an SDOH as
opposed to social support.
The proposed Social Isolation data
element is consistent with NASEM
considerations about social isolation as
a function of social relationships that
impacts health outcomes and increases
mortality risk, as well as the current
work of a NASEM committee examining
how social isolation and loneliness
Leading Age. Available at https://
www.leadingage.org/white-papers/socialconnectedness-and-engagement-technology-longterm-and-post-acute-care-primer-and#1.1.
214 Landeiro, F., Barrows, P., Nuttall Musson, E.,
Gray, A.M., and Leal, J. (2017). Reducing Social
Loneliness in Older People: A Systematic Review
Protocol. BMJ Open. 7(5): e013778.
215 Ong, A.D., Uchino, B.N., and Wethington, E.
(2016). Loneliness and Health in Older Adults: A
Mini-Review and Synthesis. Gerontology. 62:443–
449.
216 Leigh-Hunt, N., Bagguley, D., Bash, K., Turner,
V., Turnbull, S., Valtorta, N., and Caan, W. (2017).
An overview of systematic reviews on the public
health consequences of social isolation and
loneliness. Public Health. 152:157–171.
217 Northwestern University. (2017). PROMIS
Item Bank v. 1.0—Emotional Distress—Anger—
Short Form 1.
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impact health outcomes in adults 50
years and older. We believe that adding
a Social Isolation data element would be
an important component of better
understanding patient complexity and
the care goals of patients, thereby
facilitating care coordination and
continuity in care planning across PAC
settings. For more information on the
Social Isolation data element, we refer
readers to the document titled ‘‘Final
Specifications for IRF QRP Measures
and Standardized Patient Assessment
Data Elements,’’ available on the
website at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
In an effort to standardize the
submission of social isolation data
among IRFs, HHAs, SNFs and LTCHs,
for the purposes outlined in section
1899B(a)(1)(B) of the Act, while
minimizing the reporting burden, we
proposed to adopt the Social Isolation
data element described above as SPADE
with respect to the proposed Social
Determinants of Health category. We
proposed to add the Social Isolation
data element to the IRF–PAI.
We sought public comment on this
proposal. A discussion of these
comments, along with our responses,
appears below.
Comment: Commenters agreed with
CMS that SDOH data could provide
Medicare with valuable information
about the role that non-clinical factors
play in PAC patient outcomes and that
the addition of the SDOH SPADEs will
facilitate communication between PAC
settings and other health care providers.
A commenter noted that common
standards and definitions are important
for interoperability and communication
across providers and encouraged CMS
to ensure that the SDOH elements
collected in IRF settings are aligned
with future proposed SDOH data
collection requirements in other
settings. One commenter stated that
there is increasing attention on the
critical role that social factors play in
individual and population health and
that addressing health-related social
needs through enhanced clinicalcommunity linkages can improve health
outcomes and reduce costs. Another
commenter was also pleased that CMS
is looking at SDOH and believes it is a
positive step toward identifying
disparities in health care.
Response: We thank the commenters
for the comments.
Comment: Some commenters noted
that, if finalized, IRFs should only need
to submit data on the race and ethnicity
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SPADEs with respect to admission and
would not need to collect and report
again at discharge, as it is unlikely that
patient status for these elements will
change. The commenters believe that a
patient’s response to social isolation is
unlikely to change between admission
and discharge; thus, the commenter
suggested CMS to require collection of
all SDOH SPADEs with respect to
admission only.
Response: We disagree with the
commenters that stated that the
response to the Social Isolation data
element will be the same from
admission to discharge. Unlike the
Vision, Hearing, Race, Ethnicity,
Preferred Language, and Interpreter
Services SPADEs, we believe that the
response to this data element is likely to
change from admission to discharge for
some patients. For example, a patient
could lose a family member or caregiver
between admission and discharge,
which could impact their response to
the Social Isolation data element.
Therefore, we proposed to collect this
SPADE data element with respect to
both admission and discharge. As
outlined in the FY 2020 IRF PPS
proposed rule, multiple studies have
demonstrated that social isolation has
an impact on the health of patients (84
FR 17325 through 17326). Therefore, it
is important for providers to be able to
identify a patient’s needs when the
patient is admitted and when the
patient is discharged in order to better
inform the patient’s care decisions made
during and after the stay, including
understanding the patient’s unique risk
factors and treatment preferences.
Because of this, we are requiring that
the Social Isolation data element be
assessed at both admission and
discharge.
Comment: One commenter stated that
the proposed question on social
isolation may have a very different
answer based on the time horizon
considered by the beneficiary as
beneficiaries who are newly admitted to
an IRF may have experienced differing
levels of social isolation over the
preceding week due to interactions with
health care providers, emergency
providers, and friends or family visiting
due to hospitalization. The commenter
believes this question could be
improved by adding a timeframe to the
question. For example, ‘‘How often have
you felt lonely or isolated from those
around you in the past 6 months?’’
Response: We thank the commenter
for this comment. The Social Isolation
data element assesses whether a patient
has experienced social isolation in the
past 6 months to a year. The social
isolation question proposed is currently
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part of the Accountable Health
Communities (AHC) Screening Tool.
The AHC item was selected from the
Patient-Reported Outcomes
Measurement Information System
(PROMIS®) Item Bank on Emotional
Distress.
Comment: A commenter suggested
that collecting SDOH SPADEs that have
no clinical value, such as transportation
and social isolation during an assigned
period of either admission or discharge,
is a significant concern. The commenter
stated that at admission, the focus
should be on assessing the patient’s
medical needs and plan of care, and at
discharge, the focus shifts to patient’s
transition plan and caregiver education.
As there are already multiple required
assessments on the IRF–PAI, the SDOH
SPADEs would add burden and
recommended that any SDOH SPADEs
finalized should be assessed at any
point during the stay.
Response: We disagree with the
commenters that the Social Isolation
and Transportation data elements have
no value. As proposed in the
transportation and social isolation
section, multiple studies have
demonstrated that access to
transportation and social isolation have
an impact on the health of
patients.218 219 For example, access to
transportation is important to
medication access. Similarly, social
isolation is a predictor of mortality.
Therefore, it is important for providers
to identify a patient’s needs both at
admission and discharge in order to
better inform the patient’s care
decisions made during and after the
stay, including a patient’s unique risk
factors and treatment preferences. To
minimize burden, we proposed to
collect this data element with respect to
admission and discharge, rather than
more frequently.
After consideration of the public
comments, we are finalizing our
proposals to collect SDOH data for the
purposes of section 2(d)(2)(B) of the
IMPACT Act and section
1899B(b)(1)(B)(vi) of the Act as follows.
With regard to Race, Ethnicity, Health
Literacy, Transportation, and Social
Isolation, we are finalizing our
proposals as proposed. In response to
stakeholder comments, we are revising
our proposed policies and finalizing
218 Syed, S.T., Gerber, B.S., and Sharp, L.K.
(2013). Traveling Towards Disease: Transportation
Barriers to Health Care Access. J Community
Health. 38(5): 976–993.
219 Leigh-Hunt, N., Bagguley, D., Bash, K., Turner,
V., Turnbull, S., Valtorta, N., and Caan, W. (2017).
An overview of systematic reviews on the public
health consequences of social isolation and
loneliness. Public Health. 152:157–171.
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that IRFs that submit the Preferred
Language and Interpreter Services
SPADEs with respect to admission will
be deemed to have submitted with
respect to both admission and
discharge.
H. Form, Manner, and Timing of Data
Submission Under the IRF QRP
1. Background
We refer readers to § 412.634(b) for
information regarding the current
policies for reporting IRF QRP data.
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2. Update to the CMS System for
Reporting Quality Measures and
Standardized Patient Assessment Data
and Associated Procedural Proposals
IRFs are currently required to submit
IRF–PAI data to CMS using the Quality
Improvement and Evaluation System
(QIES) Assessment and Submission
Processing (ASAP) system. We will be
migrating to a new internet Quality
Improvement and Evaluation System
(iQIES) that will enable real-time
upgrades, and we proposed to designate
that system as the data submission
system for the IRF QRP beginning
October 1, 2019. We proposed to revise
§ 412.634(a)(1) by replacing
‘‘Certification and Survey Provider
Enhanced Reports (CASPER)’’ with
‘‘CMS designated data submission’’. We
proposed to revise § 412.634(d)(1) by
replacing the reference to ‘‘Quality
Improvement and Evaluation System
Assessment Submission and Processing
(QIES ASAP) system’’ with ‘‘CMS
designated data submission system’’.
We proposed to revise § 412.634(d)(5)
by replacing reference to the ‘‘QIES
ASAP’’ with ‘‘CMS designated data
submission’’. We proposed to revise
§ 412.634(f)(1) by replacing ‘‘QIES’’ with
‘‘CMS designated data submission
system’’. In addition, we proposed to
notify the public of any future changes
to the CMS designated system using
subregulatory mechanisms, such as
website postings, listserv messaging,
and webinars.
We invited public comment on our
proposals.
Comment: One commenter supported
this proposal and recommended that
CMS begin educating and preparing
IRFs for the transition as soon as
possible.
Response: We thank the commenter
for their support and appreciate the
importance of educating for this
transition. Information regarding the
transition to iQIES and instructions for
onboarding has been provided to IRFs
and will be ongoing. Training resources
are currently available on You-Tube at
https://go.cms.gov/iQIES_Training and
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additional help content for users is
available within iQIES. Ongoing
technical support via email is also
available at help@QTSO.com.
After consideration of the public
comments, we are finalizing our
proposal to revise § 412.634(a)(1),
§ 412.634(d)(1), § 412.634(d)(5), and
§ 412.634(f)(1) as proposed. We are also
finalizing our proposal to notify the
public of any future changes to the CMS
designated system using subregulatory
mechanisms, such as website postings,
listserv messaging, and webinars.
3. Schedule for Reporting the Transfer
of Health Information Quality Measures
Beginning With the FY 2022 IRF QRP
As discussed in section VIII.D. of this
final rule, we proposed to adopt the
Transfer of Health Information to the
Provider—Post-Acute Care (PAC) and
Transfer of Health Information to the
Patient—Post-Acute Care (PAC) quality
measures beginning with the FY 2022
IRF QRP. We also proposed that IRFs
would report the data on those measures
using the IRF–PAI. IRFs would be
required to collect data on both
measures for Medicare Part A and
Medicare Advantage patients beginning
with patients discharged on or after
October 1, 2020. We refer readers to the
FY 2018 IRF PPS final rule (82 FR 36291
through 36292) for the data collection
and submission timeframes that we
finalized for the IRF QRP.
We sought public comment on this
proposal and did not receive any
comments.
We are finalizing our proposal that
IRFs report the data on Transfer of
Health Information to the Provider—
Post-Acute Care (PAC) and Transfer of
Health Information to the Patient—PostAcute Care (PAC) quality measures
using the IRF–PAI as proposed. IRFs
will be required to collect data on both
measures for Medicare Part A and
Medicare Advantage patients beginning
with patients discharged on or after
October 1, 2020.
4. Schedule for Reporting Standardized
Patient Assessment Data Elements
Beginning With the FY 2022 IRF QRP
As discussed in section IV.F. of the
proposed rule, we proposed to adopt
SPADEs beginning with the FY 2022
IRF QRP. We proposed that IRFs would
report the data using the IRF–PAI.
Similar to the proposed schedule for
reporting the Transfer of Health
Information to the Provider—Post-Acute
Care (PAC) and Transfer of Health
Information to the Patient—Post-Acute
Care (PAC) quality measures, IRFs
would be required to collect the
SPADEs for all Medicare Part A and
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Medicare Advantage patients discharged
on or after October 1, 2020, at both
admission and discharge. IRFs that
submit data with respect to admission
for the Hearing, Vision, Race, and
Ethnicity SPADEs would be considered
to have submitted data with respect to
discharges. We refer readers to the FY
2018 IRF PPS final rule (82 FR 36291
through 36292) for the data collection
and submission timeframes that we
finalized for the IRF QRP.
We sought public comment on this
proposal and did not receive any
comments.
We are finalizing our proposal that
IRFs must submit the SPADEs for all
Medicare Part A and Medicare
Advantage patients discharged on or
after October 1, 2020, with respect to
both admission and discharge, using the
IRF–PAI. IRFs that submit data with
respect to admission for the Hearing,
Vision, Preferred Language, Interpreter
Services, Race, and Ethnicity SPADEs
will be considered to have submitted
data with respect to discharges.
5. Data Reporting on Patients for the IRF
Quality Reporting Program Beginning
With the FY 2022 IRF QRP
We received public input suggesting
that the quality measures used in the
IRF QRP should be calculated using
data collected from all IRF patients,
regardless of the patients’ payer. This
input was provided to us via comments
requested about quality measure
development on the CMS Measures
Management System Blueprint
website,220 as well as through comments
we received from stakeholders via our
IRF QRP mailbox, and feedback
received from the NQF-convened MAP
as part of their recommendations on
Coordination Strategy for Post-Acute
Care and Long-Term Care Performance
Measurement.221 Further, in the FY
2018 IRF PPS proposed rule (82 FR
20740), we sought input on expanding
the reporting of quality measures to
include all patients, regardless of payer,
so as to ensure that the IRF QRP makes
publicly available information regarding
the quality of the services furnished to
the IRF population as a whole, rather
220 Public Comment Summary Report Posting for
Transfer of Health Information and Care
Preferences. https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-Assessment-Instruments/
Post-Acute-Care-Quality-Initiatives/Downloads/
Development-of-Cross-Setting-Transfer-of-HealthInformation-Quality-Meas.pdf.
221 MAP Coordination Strategy for Post-Acute
Care and Long-Term Care Performance
Measurement. Feb 2012. https://
www.qualityforum.org/Publications/2012/02/MAP_
Coordination_Strategy_for_Post-Acute_Care_and_
Long-Term_Care_Performance_Measurement.aspx.
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than just those patients who have
Medicare.
In response to that request for public
input, several commenters, including
MedPAC, submitted comments stating
that they would be supportive of an
effort to collect data specified under the
IRF QRP from all IRF patients regardless
of their payer. Many commenters noted
that this would not be overly
burdensome, as most of their
organizations’ members currently
complete the IRF–PAI on all patients,
regardless of their payer. A few
commenters had concerns, including
recommending that CMS continue to
align the patient assessment instruments
across PAC settings and whether the use
of the data would outweigh any
additional reporting burden. For a more
detailed discussion, we refer readers to
the FY 2018 IRF final rule (82 FR
36292). We have taken these concerns
under consideration in proposing this
policy.
Further, given that we do not have
access to other payer claims, we believe
that the most accurate representation of
the quality provided in IRFs would be
best conveyed using data collected via
the IRF–PAI on all IRF patients,
regardless of payer, for the purposes of
the IRF QRP. Medicare is the primary
payer for approximately 60 percent of
IRF patients.222
We also believe that data reporting on
standardized patient assessment data
elements using IRF–PAI should include
all IRF patients for the same reasons for
collecting data on all residents for the
IRF QRP’s quality measures: To promote
higher quality and more efficient health
care for Medicare beneficiaries and all
patients receiving IRF services, for
example through the exchange of
information and longitudinal analysis of
the data. With that, we believe that
collecting quality measure and
standardized patient assessment data
via the IRF–PAI on all IRF patients
ensures that quality care is provided for
Medicare beneficiaries, and patients
receiving IRF services as a whole. While
we appreciate that collecting quality
data on all patients regardless of payer
may create additional burden, we also
note that the effort to separate out
Medicare beneficiaries from other
patients is also burdensome.
Collecting data on all IRF patients
will provide us with the most robust,
accurate reflection of the quality of care
delivered to Medicare beneficiaries as
compared with non-Medicare patients
222 National Academies of Sciences, Engineering,
and Medicine. 2016. Accounting for social risk
factors in Medicare payment: Identifying social risk
factors. Washington, DC: The National Acadiemies
Press.
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and residents, and we intend to display
the calculation of this data on IRF
Compare in the future. Accordingly, we
proposed that IRFs collect data on all
IRF patients to ensure that all patients,
regardless of their payer, are receiving
the same care and that provider metrics
measure performance across the
spectrum of patients.
Therefore, to meet the quality
reporting requirements for IRFs for the
FY 2022 payment determination and
each subsequent year, we proposed to
expand the reporting of IRF–PAI data
used for the IRF QRP to include data on
all patients, regardless of their payer,
beginning with patients discharged on
or after October 1, 2020 for the FY 2022
IRF QRP and the IRF–PAI V4.0, effective
October 1, 2020.
We sought public comment on this
proposal and received several
comments, which are discussed below.
Comment: Many commenters,
including MedPAC, supported the
proposal to expand the reporting of
quality measures to all patients
regardless of payer, agreeing that quality
care should be a goal for all patients.
Several commenters agreed that most
providers already complete an IRF–PAI
for all patients. MedPAC also cautioned
that any future Medicare payment
adjustments related to performance
should be based only on outcomes for
Medicare beneficiaries. One commenter
stated that this approach is consistent
with other quality programs and offers
consumers a fuller picture of quality of
care. One commenter recommended
including quality data about all payers
on IRF Compare, and another
commenter supported the proposal but
suggested CMS to allow adequate time
to review and validate data before it is
made public and allow data on IRF
Compare to be analyzed by payer.
Response: We thank commenters for
their support and appreciate suggestions
for implementing this policy.
Comment: A few commenters
requested additional details about how
this proposal would be implemented.
One commenter suggested that CMS
verify comprehensive data submission
on all patients to avoid ‘‘cherry-picking’’
patients. A few commenters
recommended that CMS delay this
proposal and study how this additional
data affects quality measure
performance.
Response: We appreciate the
commenters’ request for more details
regarding the implementation of this
proposal, how data submission will be
verified to avoid cherry-picking, and
how this data will affect quality
measure performance. We acknowledge
the commenters’ concerns about the
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proposal’s implementation timeline and
the request to delay the proposal;
however instead of delaying, we plan to
use the comments received during this
rulemaking cycle to bring a new allpayer policy proposal in the future.
Therefore, after consideration of the
public comments we received on these
issues, we have decided that at this
time, we will not finalize this proposal.
We agree that it would be useful to
assess further how to best implement
the collection of data for all payers for
the IRF QRP.
Comment: Many commenters had
concerns about the burden of collecting
quality data on all patients regardless of
payer, citing that it contradicted the
Patients over Paperwork initiative. One
commenter suggested that CMS make
this requirement voluntary and to
conduct an analysis on the
administrative burden on IRFs. Another
commenter suggested that the Collection
of Information section should contain
an estimate of burden required for this
reporting.
Response: We do not believe that that
the intent of this policy contradicts the
Patients over Paperwork initiative,
which aims to simplify the
documentation required for our
programs. However, the all payer
proposal would have imposed a new
reporting burden on IRFs. We are
sensitive to the issue of burden
associated with data collection and
acknowledge the commenters’ concerns
about the additional burden required to
collect quality data on all patients.
Although we believe that the reporting
of all-payer data under the IRF QRP
would add value to the program and
provide a more accurate representation
of the quality provided by IRFs, we
believe we need to better quantify the
new reporting burden on IRFs from this
proposal for stakeholders to submit
comments. Therefore, after
consideration of the public comments,
we received on these issues, we have
decided that at this time, we will not
finalize this proposal. We agree that this
burden should be accounted for and we
will estimate this burden in future
rulemaking.
Comment: One commenter questioned
whether IRFs support this proposal.
Another commenter was concerned that
this proposal would add complexity to
CMS’ administration of the IRF QRP
compliance determination process. One
commenter was concerned that quality
data would be skewed because younger,
non-Medicare patients have more room
for improvement compared to older
patients.
Response: We do not believe this will
add complexity to the IRF QRP
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compliance determination process,
since adding more patients will not
change the overall process that we
follow with regard to determining
compliance. With regard to IRF support
for this proposal, we sought input on
this topic in the FY 2018 IRF PPS
proposed rule (82 FR 20740) and we
received several supportive comments.
With regard to the commenter’s
concerns that quality data would be
skewed because younger non-Medicare
patients have more room for
improvement, we note that risk
adjustment is currently used for many
quality measures, including measures
that focus on improvement, such as the
functional outcome measures. We take
patient characteristics, such as age, into
consideration when developing
measures, and these are included as risk
adjustors for the functional outcome
measures.
Comment: Several commenters did
not support the proposal, citing
concerns about patient privacy. Some
commenters suggested that collecting
quality data from non-Medicare
beneficiaries would be a violation of the
Health Insurance Portability and
Accountability Act of 1996 (HIPAA)
since it is not required for
reimbursement purposes. Another
commenter was concerned that CMS’
collection of, and possible disclosing of,
sensitive health information from nonMedicare patients without consent may
violate the Privacy Act of 1974, the EGovernment Act of 2002, and other state
level privacy acts. The commenter
suggests amending § 412.608(a) to
require the clinician at the IRF to
provide the Privacy Act Statement and
other information to non-Medicare
patients.
Other commenters questioned how
CMS would keep this non-Medicare
data secure and were concerned that
CMS could work with other payers to
de-identify this data. A few commenters
recommended informing non-Medicare
beneficiaries of this reporting and to use
only de-identified data. A few
commenters requested more details
from CMS about the scope of data
collection, including non-quality
information on the IRF–PAI.
Response: We appreciate the
commenters’ concerns but disagree that
this proposal is a violation of HIPAA,
Privacy Act of 1974, and e-Government
Act of 2002. IRF–PAI data is collected
under an existing system of records
notice (66 FR 56682). Any disclosure of
the data will be made in accordance
with the Privacy Act and those routine
uses outlined in the SORN. Medicare
patients are currently given a Privacy
Act Statement and would be given to
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every patient under the IRF QRP.
Section 208 of the e-Government Act of
2002 requires federal agencies to
perform Privacy Impact Assessments
when acquiring or developing new
information technology or making
substantial changes to existing
information technology that involves
the collection maintenance, or
dissemination of information in
identifiable form. Because we are not
acquiring or developing new
information technology, or making
substantial changes to existing
information technology under this
proposal, we disagree that this policy
violates the e-Government Act.
With regard to questions about how
CMS would keep data non-Medicare
data secure, we safeguard the IRF–PAI
data in a secure data system. The system
limits data access to authorized users
and monitors such users to ensure
against unauthorized data access or
disclosures. This system conforms to all
applicable federal laws and regulations
as well as federal government,
Department of Health & Human Services
(HHS), and CMS policies and standards
as they relate to information security
and data privacy. The applicable laws
and regulations include, but are not
limited to: The Privacy Act of 1974; the
Federal Information Security
Management Act of 2002; the Computer
Fraud and Abuse Act of 1986; the
Health Insurance Portability and
Accountability Act of 1996; the EGovernment Act of 2002; the ClingerCohen Act of 1996; the Medicare
Modernization Act of 2003; and the
corresponding implementing
regulations. With regard to the scope of
data collection, IRFs would be required
to submit quality measure and
standardized patient assessment data
elements required by the IRF QRP. After
consideration of the public comments
we received on these issues, we have
decided that at this time, we will not
finalize this proposal. We appreciate
concerns raised by providers and will
take them into consideration for future
rulemaking.
Comment: One commenter questioned
whether CMS has the statutory authority
to require IRFs to submit IRF–PAI data
for the IRF QRP for all patients,
regardless of payer, citing that it is
inconsistent with section 1886(j)(2)(D)
of the Act because data from nonMedicare IRF patients are not
‘‘necessary’’ for administering the IRF
PPS. The commenter further noted that
§ 412.604(c) currently requires IRFs to
complete an IRF–PAI for all Medicare
Part A and Part C patients that an IRF
admits or discharges and does not
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address reporting for non-Medicare
patients.
Response: We believe that we
generally have authority to collect all
payer data for the IRF QRP under
section 1886(j)(7) of the Act. We also
note that with respect to the data
submitted in accordance with section
1886(j)(7)(F) of the Act, the statute
expressly requires that data on quality
measures specified under section
1899B(c)(1) of the Act be submitted
using the IRF PAI, to the extent
possible, and that SPADE required
under section 1899B(b)(1) of the Act be
submitted using the IRF PAI. No all
payer data collected for the IRF QRP
would be used for purposes of
administering the IRF PPS.
We appreciate the support offered by
some commenters for our proposal to
collect data on all IRF patients
regardless of payer so as to ensure that
the IRF QRP makes publicly available
information regarding the quality of the
services furnished to Medicare
beneficiaries, as well as to the IRF
population as a whole. However, we
also acknowledge the concerns raised by
some commenters with respect to the
administrative challenges of
implementing all payer data collection,
the need to account for the burden
related to this policy, as well as the
need for us to provide further detail and
training to IRFs. We continue to believe
that the collection of quality data to
include all patients would help to
ensure that Medicare patients receive
the same quality of care as other
patients who are treated by IRFs.
Therefore, after careful consideration
of the public comments we received, we
will not finalize the proposal to expand
the reporting of IRF quality data to
include all patients, regardless of payer,
at this time. We plan to use the
comments we received on this proposal
to help inform a future all payer
proposal.
I. Policies Regarding Public Display of
Measure Data for the IRF QRP
Section 1886(j)(7)(E) of the Act
requires the Secretary to establish
procedures for making the IRF QRP data
available to the public after ensuring
that IRFs have the opportunity to review
their data prior to public display.
Measure data are currently displayed on
the Inpatient Rehabilitation Facility
Compare website, an interactive web
tool that assists individuals by
providing information on IRF quality of
care. For more information on IRF
Compare, we refer readers to the website
at https://www.medicare.gov/inpatient
rehabilitationfacilitycompare/. For a
more detailed discussion about our
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policies regarding public display of IRF
QRP measure data and procedures for
the opportunity to review and correct
data and information, we refer readers
to the FY 2017 IRF PPS final rule (81 FR
52125 through 52131).
In the proposed rule, we proposed to
begin publicly displaying data for the
Drug Regimen Review Conducted With
Follow-Up for Identified Issues—PAC
IRF QRP measure beginning CY 2020 or
as soon as technically feasible. We
finalized the Drug Regimen Review
Conducted With Follow-Up for
Identified Issues—PAC IRF QRP
measure in the FY 2017 IRF PPS final
rule (81 FR 52111 through 52116).
Data collection for this assessmentbased measure began with patients
discharged on or after October 1, 2018.
We proposed to display data based on
four rolling quarters, initially using
discharges from January 1, 2019 through
December 31, 2019 (Quarter 1 2019
through Quarter 4 2019). To ensure the
statistical reliability of the data, we
proposed that we would not publicly
report an IRF’s performance on the
measure if the IRF had fewer than 20
eligible cases in any four consecutive
rolling quarters. IRFs that have fewer
than 20 eligible cases would be
distinguished with a footnote that states,
‘‘The number of cases/patient stays is
too small to publicly report.’’
We sought public comment on these
proposals and received several, which
are summarized below.
Comment: Several commenters
supported the proposal to begin
publicly displaying data for the Drug
Regimen Review Conducted With
Follow-Up for Identified Issues—PAC
IRF QRP measure in CY 2020 or as soon
as technically feasible, including the
exception for IRFs with fewer than 20
eligible cases. One commenter clarified
that its support is contingent on the
measure not utilizing performance
categories.
Response: We appreciate the
commenter’s support.
After consideration of the public
comments, we are finalizing our
proposal to begin publicly displaying
data for the Drug Regimen Review
Conducted With Follow-Up for
Identified Issues—PAC IRF QRP
measure beginning CY 2020 or as soon
as technically feasible.
J. Removal of the List of Compliant IRFs
In the FY 2016 IRF PPS final rule (80
FR 47125 through 47127), we finalized
that we would publish a list of IRFs that
successfully met the reporting
requirements for the applicable payment
determination on the IRF QRP website
and update the list on an annual basis.
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We have received feedback from
stakeholders that this list offers minimal
benefit. Although the posting of
successful providers was the final step
in the applicable payment
determination process, it does not
provide new information or clarification
to the providers regarding their annual
payment update status. Therefore, we
proposed that we will no longer publish
a list of compliant IRFs on the IRF QRP
website, effective beginning with the FY
2020 payment determination.
We sought public comment on this
proposal and received several
comments.
Comment: One commenter supported
this proposal, but suggested that CMS
make this information available to
stakeholders upon request in the
interest of transparency.
Response: We thank commenters for
their support. At this time, we do not
plan to make the list of compliant IRFs
available upon request, in alignment
with other QRPs that do not provide this
list. We believe stakeholders can find
sufficient quality information about
IRFs on the IRF compare website.
Comment: Several commenters did
not support the proposal removal of the
list of compliant IRFs. One commenter
agreed that the list was not relevant to
IRF providers in reviewing their own
compliance status, but stated that it
could be of interest to patients and other
IRFs. Other commenters recommended
posting the list because it is helpful for
large health systems to quickly
determine which hospitals are
compliant. One commenter further
suggested that the list continue to be
posted in a standardized manner across
the various QRPs to improve
transparency.
Response: We acknowledge
commenters’ concerns about removing
the requirement to post the list of
compliant IRFs. Patients and consumers
can still find information about IRF
quality on the IRF Compare website. We
do not believe that removing this list
will have a negative impact for IRFs,
since the list does not give any new
information to IRF providers or health
providers about their own compliance
status. We also note that other QRPs do
not require posting of a list of compliant
facilities.
After consideration of the comments,
we are finalizing our proposal and will
no longer publish a list of compliant
IRFs on the IRF QRP website, beginning
with the FY 2020 payment
determination.
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K. Method for Applying the Reduction to
the FY 2020 IRF Increase Factor for IRFs
That Fail To Meet the Quality Reporting
Requirements
As previously noted, section
1886(j)(7)(A)(i) of the Act requires the
application of a 2-percentage point
reduction of the applicable market
basket increase factor for payments for
discharges occurring during such fiscal
year for IRFs that fail to comply with the
quality data submission requirements.
We proposed to apply a 2-percentage
point reduction to the applicable FY
2020 proposed market basket increase
factor in calculating an adjusted FY
2020 proposed standard payment
conversion factor to apply to payments
for only those IRFs that failed to comply
with the data submission requirements.
As previously noted, application of the
2-percentage point reduction may result
in an update that is less than 0.0 for a
fiscal year and in payment rates for a
fiscal year being less than such payment
rates for the preceding fiscal year. Also,
reporting-based reductions to the market
basket increase factor will not be
cumulative; they will only apply for the
FY involved.
We invited public comment on the
proposed method for applying the
reduction to the FY 2020 IRF increase
factor for IRFs that fail to meet the
quality reporting requirements, which
are summarized below.
Comment: Some commenters
suggested that CMS provide flexibility
in its application of the IRF QRP
payment penalty for IRFs who make a
good-faith effort to comply and submit
quality reporting data.
Response: We interpret the
commenter’s suggestion that we take
into consideration case by case
exceptions and apply leniency for
providers have attempted but failed to
submit their quality reporting data for
the IRF QRP. We are unable to provide
flexibility with respect to the 2 percent
payment penalty; as noted previously,
section 1886(j)(7) of the Act requires the
Secretary to reduce the annual increase
factor for IRFs that fail to comply with
the quality data submission
requirements. While we did not seek
comment on flexibilities on which the
penalty is applied, we note that we have
provided flexibility where the failure of
the IRF to comply with the requirements
of the IRF QRP stemmed from
circumstances beyond its control. For
example, we have finalized policies that
grant exceptions or extensions for IRFs
if we determine that a systemic problem
with one of our data collection systems
affected the ability of IRFs to submit
data (79 FR 45920). We have also
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adopted policies (78 FR 47920) that
allow us to grant exemptions or
extensions to an IRF if it has
experienced an extraordinary
circumstance beyond its control. In
addition, we set the reporting
compliance threshold at 95 percent
rather than at 100 percent to data to for
account for the rare instances when
assessment data collection and
submission maybe impossible, such as
when patients have been discharged
emergently, or against medical advice.
Table 18 shows the calculation of the
adjusted FY 2020 standard payment
conversion factor that will be used to
compute IRF PPS payment rates for any
IRF that failed to meet the quality
reporting requirements for the
applicable reporting period.
After consideration of the comments,
we are finalizing our proposal to apply
a 2-percentage point reduction to the
applicable FY 2020 proposed market
basket increase factor in calculating an
adjusted FY 2020 proposed standard
payment conversion factor to apply to
payments for only those IRFs that failed
to comply with the data submission
requirements.
relative weights and average LOS values
for FY 2020, in a budget neutral manner,
as discussed in section IV. of this final
rule.
• We will rebase and revise the IRF
market basket to reflect a 2016 base year
rather than the current 2012 base year
as discussed in section VI. of this FY
2020 IRF PPS final rule.
• We will update the IRF PPS
payment rates for FY 2020 by the market
basket increase factor, based upon the
most current data available, with a
productivity adjustment required by
section 1886(j)(3)(C)(ii)(I) of the Act, as
described in section VI. of this final
rule.
• We will update to the IRF wage
index to use the concurrent FY IPPS
wage index and the FY 2020 laborrelated share in a budget-neutral
manner, as described in section VI. of
this final rule.
• The facility-level adjustments will
remain frozen at the FY 2014 levels for
FY 2015 and all subsequent years, as
discussed in section V. of this final rule.
• We will calculate the final IRF
standard payment conversion factor for
FY 2020, as discussed in section VI. of
this final rule.
• We will update the outlier
threshold amount for FY 2020, as
discussed in section VII. of this final
rule.
• We will update the CCR ceiling and
urban/rural average CCRs for FY 2020,
as discussed in section VII. of this final
rule.
• We will amend the regulations at
§ 412.622 to clarify that the
determination as to whether a physician
qualifies as a rehabilitation physician
(that is, a licensed physician with
specialized training and experience in
inpatient rehabilitation) is made by the
IRF, as discussed in section VIII. of this
final rule.
• We will adopt updates
requirements to the IRF QRP, as
discussed in section IX. of this final
rule.
XI. Provisions of the Final Regulations
In this final rule, we are adopting the
provisions set forth in the FY 2020 IRF
PPS proposed rule (84 FR 17244).
Specifically:
• We will adopt an unweighted motor
score to assign patients to CMGs, the
removal of one item from the score, and
revisions to the CMGs beginning on
October 1, 2019, based on analysis of 2
years of data (FYs 2017 and 2018) using
the Quality Indicator items in the IRF–
PAI. This includes revisions to the CMG
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XII. Collection of Information
Requirements
A. Statutory Requirement for
Solicitation of Comments
Under the Paperwork Reduction Act
of 1995 (PRA), we are required to
provide 60-day notice in the Federal
Register and solicit public comment
before a collection of information
requirement is submitted to the OMB for
review and approval. To fairly evaluate
whether an information collection
should be approved by OMB, section
3506(c)(2)(A) of the PRA requires that
we solicit comment on the following
issues:
• The need for the information
collection and its usefulness in carrying
out the proper functions of our agency.
• The accuracy of our estimate of the
information collection burden.
• The quality, utility, and clarity of
the information to be collected.
• Recommendations to minimize the
information collection burden on the
affected public, including automated
collection techniques.
This final rule makes reference to
associated information collections that
are not discussed in the regulation text
contained in this document.
B. Collection of Information
Requirements for Updates Related to the
IRF QRP
An IRF that does not meet the
requirements of the IRF QRP for a fiscal
year will receive a 2 percentage point
reduction to its otherwise applicable
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X. Miscellaneous Comments
We received several comments that
were outside the scope of the FY 2020
IRF PPS proposed rule. Specifically, we
received comments regarding the
processes for updating the IRF facilitylevel adjustment factors and the
transparency of these updates, the
application of a cost-of-living
adjustment for IRFs located in Alaska
and Hawaii, the need for CMS education
and instruction on the appropriate IGC/
ICD coding on the IRF–PAI, reevaluating and phasing out the 60
percent rule as criteria for IRF
admission, and federal funding for
universal health care. We thank
commenters for bringing these issues to
our attention, and we will take these
comments into consideration for
potential policy refinements.
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annual increase factor for that fiscal
year. Information is not currently
available to determine the precise
number of IRFs that will receive less
than the full annual increase factor for
FY 2020 due to non-compliance with
the requirements of the IRF QRP.
We believe that the burden associated
with the IRF QRP is the time and effort
associated with complying with the
requirements of the IRF QRP. As of July
15, 2019, there are approximately 1,122
IRFs reporting quality data to CMS. For
the purposes of calculating the costs
associated with the collection of
information requirements, we obtained
mean hourly wages for these staff from
the U.S. Bureau of Labor Statistics’ May
2018 National Occupational
Employment and Wage Estimates
(https://www.bls.gov/oes/current/oes_
nat.htm). To account for overhead and
fringe benefits, we have doubled the
hourly wage. These amounts are
detailed in Table 19.
As discussed in section VIII.D. of this
final rule, we are adopting two new
measures, (1) Transfer of Health
Information to the Provider—Post-Acute
Care (PAC); and (2) Transfer of Health
Information to the Patient—Post-Acute
Care (PAC), beginning with the FY 2022
IRF QRP. As a result, the estimated
burden and cost for IRFs for complying
with requirements of the FY 2022 IRF
QRP will increase. Specifically, we
believe that there will be a 1.2 minute
addition in clinical staff time to report
data per patient stay. We estimate
411,622 discharges from 1,122 IRFs
annually. This equates to an increase of
8,232 hours in burden for all IRFs (0.02
hours per assessment × 411,622
discharges). Given 0.7 minutes of RN
time at $70.72 per hour and 0.5 minutes
of LVN time at $43.96 per hour, we
estimate that the total cost will be
increased by $437 per IRF annually, or
$490,314 for all IRFs annually. This
increase in burden will be accounted for
in the information collection under
OMB control number (0938–0842),
which expires December 31, 2021.
In addition, we are finalizing our
proposal to add the standardized patient
assessment data elements described in
section VIII.F of this final rule beginning
with the FY 2022 IRF QRP. As a result,
the estimated burden and cost for IRFs
for complying with requirements of the
FY 2022 IRF QRP will be increased.
Specifically, we believe that there will
be an addition of 7.8 minutes on
admission, and 10.95 minutes on
discharge, for a total of 18.8 minutes of
additional clinical staff time to report
data per patient stay. Note that this is a
decrease from the proposed 11.1
minutes at discharge because of the
changes in section XIII.G.4.2 of this final
rule. We estimate 411,622 discharges
from 1,122 IRFs annually. This equates
to an increase of 122,995 hours in
burden for all IRFs (0.3 hours per
assessment × 409,982 discharges). Given
11.3 minutes of RN time at $70.72 per
hour and 7.5 minutes of LVN time at
$43.96 per hour, we estimate that the
total cost will be increased by $6,902
per IRF annually, or $7,744,044 for all
IRFs. This increase in burden will be
accounted for in the information
collection under OMB control number
(0938–0842), which expires December
31, 2021.
In summary, the newly adopted IRF
QRP quality measures and standardized
patient assessment data elements will
result in a burden addition of $7,339 per
IRF annually, and $8,234,450 for all
IRFs annually.
2012 base year, revising the CMGs,
making a technical correction to the
regulatory language to indicate that the
determination of whether a treating
physician has specialized training and
experience in inpatient rehabilitation is
made by the IRF and updating
regulatory language related to IRF QRP
data collection.
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XIII. Regulatory Impact Analysis
A. Statement of Need
This final rule updates the IRF
prospective payment rates for FY 2020
as required under section 1886(j)(3)(C)
of the Act. It responds to section
1886(j)(5) of the Act, which requires the
Secretary to publish in the Federal
Register on or before the August 1 that
precedes the start of each fiscal year, the
classification and weighting factors for
the IRF PPS’s CMGs, and a description
of the methodology and data used in
computing the prospective payment
rates for that fiscal year.
This final rule also implements
sections 1886(j)(3)(C) of the Act. Section
1886(j)(3)(C)(ii)(I) of the Act requires the
Secretary to apply a MFP adjustment to
the market basket increase factor. The
productivity adjustment applies to FYs
from 2012 forward.
Furthermore, this final rule also
adopts policy changes under the
statutory discretion afforded to the
Secretary under section 1886(j)(7) of the
Act. Specifically, we are rebasing and
revising the IRF market basket to reflect
a 2016 base year rather than the current
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B. Overall Impact
We have examined the impacts of this
rule as required by Executive Order
12866 on Regulatory Planning and
Review (September 30, 1993), Executive
Order 13563 on Improving Regulation
and Regulatory Review (January 18,
2011), the Regulatory Flexibility Act
(RFA) (September 19, 1980, Pub. L. 96–
354), section 1102(b) of the Act, section
202 of the Unfunded Mandates Reform
Act of 1995 (March 22, 1995; Pub. L.
104–4), Executive Order 13132 on
Federalism (August 4, 1999), the
Congressional Review Act (5 U.S.C.
804(2) and Executive Order 13771 on
Reducing Regulation and Controlling
Regulatory Costs (January 30, 2017).
Executive Orders 12866 and 13563
direct agencies to assess all costs and
benefits of available regulatory
alternatives and, if regulation is
necessary, to select regulatory
approaches that maximize net benefits
(including potential economic,
environmental, public health and safety
effects, distributive impacts, and
equity). Section 3(f) of Executive Order
12866 defines a ‘‘significant regulatory
action’’ as an action that is likely to
result in a rule: (1) Having an annual
effect on the economy of $100 million
or more in any 1 year, or adversely and
materially affecting a sector of the
economy, productivity, competition,
jobs, the environment, public health or
safety, or state, local or tribal
governments or communities (also
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referred to as ‘‘economically
significant’’); (2) creating a serious
inconsistency or otherwise interfering
with an action taken or planned by
another agency; (3) materially altering
the budgetary impacts of entitlement
grants, user fees, or loan programs or the
rights and obligations of recipients
thereof; or (4) raising novel legal or
policy issues arising out of legal
mandates, the President’s priorities, or
the principles set forth in the Executive
Order.
A regulatory impact analysis (RIA)
must be prepared for major rules with
economically significant effects ($100
million or more in any 1 year). We
estimate the total impact of the policy
updates described in this final rule by
comparing the estimated payments in
FY 2020 with those in FY 2019. This
analysis results in an estimated $210
million increase for FY 2020 IRF PPS
payments. Additionally we estimate that
costs associated with the proposals to
update the reporting requirements
under the IRF QRP result in an
estimated $8.2 million addition in costs
in FY 2020 for IRFs. We estimate that
this rulemaking is ‘‘economically
significant’’ as measured by the $100
million threshold, and hence also a
major rule under the Congressional
Review Act. Also, the rule has been
reviewed by OMB. Accordingly, we
have prepared a Regulatory Impact
Analysis that, to the best of our ability,
presents the costs and benefits of the
rulemaking.
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C. Anticipated Effects
1. Effects on IRFs
The RFA requires agencies to analyze
options for regulatory relief of small
entities, if a rule has a significant impact
on a substantial number of small
entities. For purposes of the RFA, small
entities include small businesses,
nonprofit organizations, and small
governmental jurisdictions. Most IRFs
and most other providers and suppliers
are small entities, either by having
revenues of $7.5 million to $38.5
million or less in any 1 year depending
on industry classification, or by being
nonprofit organizations that are not
dominant in their markets. (For details,
see the Small Business Administration’s
final rule that set forth size standards for
health care industries, at 65 FR 69432 at
https://www.sba.gov/sites/default/files/
files/Size_Standards_Table.pdf,
effective March 26, 2012 and updated
on February 26, 2016.) Because we lack
data on individual hospital receipts, we
cannot determine the number of small
proprietary IRFs or the proportion of
IRFs’ revenue that is derived from
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Medicare payments. Therefore, we
assume that all IRFs (an approximate
total of 1,120 IRFs, of which
approximately 55 percent are nonprofit
facilities) are considered small entities
and that Medicare payment constitutes
the majority of their revenues. The HHS
generally uses a revenue impact of 3 to
5 percent as a significance threshold
under the RFA. As shown in Table 20,
we estimate that the net revenue impact
of this final rule on all IRFs is to
increase estimated payments by
approximately 2.5 percent. The rates
and policies set forth in this final rule
will not have a significant impact (not
greater than 3 percent) on a substantial
number of small entities. Medicare
Administrative Contractors are not
considered to be small entities.
Individuals and states are not included
in the definition of a small entity.
In addition, section 1102(b) of the Act
requires us to prepare a regulatory
impact analysis if a rule may have a
significant impact on the operations of
a substantial number of small rural
hospitals. This analysis must conform to
the provisions of section 604 of the
RFA. For purposes of section 1102(b) of
the Act, we define a small rural hospital
as a hospital that is located outside of
a Metropolitan Statistical Area and has
fewer than 100 beds. As discussed in
detail below in this section, the rates
and policies set forth in this final rule
will not have a significant impact (not
greater than 3 percent) on a substantial
number of rural hospitals based on the
data of the 136 rural units and 11 rural
hospitals in our database of 1,122 IRFs
for which data were available.
Section 202 of the Unfunded
Mandates Reform Act of 1995 (Pub. L.
104–04, enacted March 22, 1995)
(UMRA) also requires that agencies
assess anticipated costs and benefits
before issuing any rule whose mandates
require spending in any 1 year of $100
million in 1995 dollars, updated
annually for inflation. In 2019, that
threshold is approximately $154
million. This final rule does not
mandate any requirements for State,
local, or tribal governments, or for the
private sector.
Executive Order 13132 establishes
certain requirements that an agency
must meet when it issues a proposed
rule (and subsequent final rule) that
imposes substantial direct requirement
costs on state and local governments,
preempts state law, or otherwise has
federalism implications. As stated, this
final rule will not have a substantial
effect on state and local governments,
preempt state law, or otherwise have a
federalism implication.
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Executive Order 13771, titled
Reducing Regulation and Controlling
Regulatory Costs, was issued on January
30, 2017 and requires that the costs
associated with significant new
regulations ‘‘shall, to the extent
permitted by law, be offset by the
elimination of existing costs associated
with at least two prior regulations.’’
This final rule is considered an E.O.
13771 regulatory action. We estimate
that this rule would generate $6.18
million in annualized cost, discounted
at 7 percent relative to year 2016, over
a perpetual time horizon. Details on the
estimated costs of this rule can be found
in the preceding analyses.
2. Detailed Economic Analysis
This final rule updates to the IRF PPS
rates contained in the FY 2019 IRF PPS
final rule (83 FR 38514). Specifically,
this final rule updates the CMG relative
weights and average LOS values, the
wage index, and the outlier threshold
for high-cost cases. This final rule
applies a MFP adjustment to the FY
2020 IRF market basket increase factor
in accordance with section
1886(j)(3)(C)(ii)(I) of the Act. Further,
this final rule rebases and revises the
IRF market basket to reflect a 2016 base
year rather than the current 2012 base
year, revises the CMGs based on FYs
2017 and 2018 data and amends the
regulatory language to clarify that the
determination of whether a treating
physician has specialized training and
experience in inpatient rehabilitation is
made by the IRF.
We estimate that the impact of the
changes and updates described in this
final rule will be a net estimated
increase of $210 million in payments to
IRF providers. This estimate does not
include the implementation of the
required 2 percentage point reduction of
the market basket increase factor for any
IRF that fails to meet the IRF quality
reporting requirements (as discussed in
section IX.K. of this final rule). The
impact analysis in Table 20 of this final
rule represents the projected effects of
the updates to IRF PPS payments for FY
2020 compared with the estimated IRF
PPS payments in FY 2019. We
determine the effects by estimating
payments while holding all other
payment variables constant. We use the
best data available, but we do not
attempt to predict behavioral responses
to these changes, and we do not make
adjustments for future changes in such
variables as number of discharges or
case-mix.
We note that certain events may
combine to limit the scope or accuracy
of our impact analysis, because such an
analysis is future-oriented and, thus,
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susceptible to forecasting errors because
of other changes in the forecasted
impact time period. Some examples
could be legislative changes made by
the Congress to the Medicare program
that would impact program funding, or
changes specifically related to IRFs.
Although some of these changes may
not necessarily be specific to the IRF
PPS, the nature of the Medicare program
is such that the changes may interact,
and the complexity of the interaction of
these changes could make it difficult to
predict accurately the full scope of the
impact upon IRFs.
In updating the rates for FY 2020, we
are adopting standard annual revisions
described in this final rule (for example,
the update to the wage and market
basket indexes used to adjust the federal
rates). We are also implementing a
productivity adjustment to the FY 2020
IRF market basket increase factor in
accordance with section
1886(j)(3)(C)(ii)(I) of the Act. We
estimate the total increase in payments
to IRFs in FY 2020, relative to FY 2019,
will be approximately $210 million.
This estimate is derived from the
application of the FY 2020 IRF market
basket increase factor, as reduced by a
productivity adjustment in accordance
with section 1886(j)(3)(C)(ii)(I) of the
Act, which yields an estimated increase
in aggregate payments to IRFs of $210
million. Outlier payments are estimated
to remain at 3 percent in FY 2020.
Therefore, we estimate that these
updates will result in a net increase in
estimated payments of $210 million
from FY 2019 to FY 2020.
The effects of the updates that impact
IRF PPS payment rates are shown in
Table 20. The following updates that
affect the IRF PPS payment rates are
discussed separately below:
• The effects of the update to the
outlier threshold amount, from
approximately 3.0 percent to 3.0 percent
of total estimated payments for FY 2020,
consistent with section 1886(j)(4) of the
Act.
• The effects of the annual market
basket update (using the IRF market
basket) to IRF PPS payment rates, as
required by sections 1886(j)(3)(A)(i) and
(j)(3)(C) of the Act, including a
productivity adjustment in accordance
with section 1886(j)(3)(C)(i)(I) of the
Act.
• The effects of applying the budgetneutral labor-related share and wage
index adjustment, as required under
section 1886(j)(6) of the Act.
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• The effects of the budget-neutral
changes to the CMGs, relative weights
and average LOS values, under the
authority of section 1886(j)(2)(C)(i) of
the Act.
• The total change in estimated
payments based on the FY 2020
payment changes relative to the
estimated FY 2019 payments.
3. Description of Table 20
Table 20 shows the overall impact on
the 1,122 IRFs included in the analysis.
The next 12 rows of Table 20 contain
IRFs categorized according to their
geographic location, designation as
either a freestanding hospital or a unit
of a hospital, and by type of ownership;
all urban, which is further divided into
urban units of a hospital, urban
freestanding hospitals, and by type of
ownership; and all rural, which is
further divided into rural units of a
hospital, rural freestanding hospitals,
and by type of ownership. There are 975
IRFs located in urban areas included in
our analysis. Among these, there are 697
IRF units of hospitals located in urban
areas and 278 freestanding IRF hospitals
located in urban areas. There are 147
IRFs located in rural areas included in
our analysis. Among these, there are 136
IRF units of hospitals located in rural
areas and 11 freestanding IRF hospitals
located in rural areas. There are 393 forprofit IRFs. Among these, there are 357
IRFs in urban areas and 36 IRFs in rural
areas. There are 616 non-profit IRFs.
Among these, there are 526 urban IRFs
and 90 rural IRFs. There are 113
government-owned IRFs. Among these,
there are 92 urban IRFs and 21 rural
IRFs.
The remaining four parts of Table 20
show IRFs grouped by their geographic
location within a region, by teaching
status, and by DSH PP. First, IRFs
located in urban areas are categorized
for their location within a particular one
of the nine Census geographic regions.
Second, IRFs located in rural areas are
categorized for their location within a
particular one of the nine Census
geographic regions. In some cases,
especially for rural IRFs located in the
New England, Mountain, and Pacific
regions, the number of IRFs represented
is small. IRFs are then grouped by
teaching status, including non-teaching
IRFs, IRFs with an intern and resident
to average daily census (ADC) ratio less
than 10 percent, IRFs with an intern and
resident to ADC ratio greater than or
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equal to 10 percent and less than or
equal to 19 percent, and IRFs with an
intern and resident to ADC ratio greater
than 19 percent. Finally, IRFs are
grouped by DSH PP, including IRFs
with zero DSH PP, IRFs with a DSH PP
less than 5 percent, IRFs with a DSH PP
between 5 and less than 10 percent,
IRFs with a DSH PP between 10 and 20
percent, and IRFs with a DSH PP greater
than 20 percent.
The estimated impacts of each policy
described in this rule to the facility
categories listed are shown in the
columns of Table 20. The description of
each column is as follows:
• Column (1) shows the facility
classification categories.
• Column (2) shows the number of
IRFs in each category in our FY 2020
analysis file.
• Column (3) shows the number of
cases in each category in our FY 2020
analysis file.
• Column (4) shows the estimated
effect of the adjustment to the outlier
threshold amount.
• Column (5) shows the estimated
effect of the update to the IRF laborrelated share and wage index, in a
budget-neutral manner.
• Column (6) shows the estimated
effect of the update to the CMGs,
relative weights, and average LOS
values, in a budget-neutral manner.
• Column (7) compares our estimates
of the payments per discharge,
incorporating all of the policies
reflected in this final rule for FY 2020
to our estimates of payments per
discharge in FY 2019.
The average estimated increase for all
IRFs is approximately 2.5 percent. This
estimated net increase includes the
effects of the IRF market basket increase
factor for FY 2020 of 2.9 percent,
reduced by a productivity adjustment of
0.4 percentage point in accordance with
section 1886(j)(3)(C)(ii)(I) of the Act.
There is no change in estimated IRF
outlier payments from the update to the
outlier threshold amount. Since we are
making the updates to the IRF wage
index and the CMG relative weights in
a budget-neutral manner, they will not
be expected to affect total estimated IRF
payments in the aggregate. However, as
described in more detail in each section,
they will be expected to affect the
estimated distribution of payments
among providers.
BILLING CODE 4120–01–P
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TABLE 20: IRF Impact Table for FY 2020 (Columns 4 through 7 in percentage)
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Percent
ER08AU19.024
FY2020 CBSA
Number of
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BILLING CODE 4120–01–C
updates to have small distributional
effects.
4. Impact of the Update to the Outlier
Threshold Amount
The estimated effects of the update to
the outlier threshold adjustment are
presented in column 4 of Table 20. In
the FY 2019 IRF PPS final rule (83 FR
38531 through 38532), we used FY 2017
IRF claims data (the best, most complete
data available at that time) to set the
outlier threshold amount for FY 2019 so
that estimated outlier payments would
equal 3 percent of total estimated
payments for FY 2019.
For the FY 2020 IRF PPS proposed
rule (84 FR 17244), we used preliminary
FY 2018 IRF claims data, and, based on
that preliminary analysis, we estimated
that IRF outlier payments as a
percentage of total estimated IRF
payments would be 3.2 percent in FY
2019. As we typically do between the
proposed and final rules each year, we
updated our FY 2018 IRF claims data to
ensure that we are using the most recent
available data in setting IRF payments.
Therefore, based on updated analysis of
the most recent IRF claims data for this
final rule, we now estimate that IRF
outlier payments as a percentage of total
IRF payments as 3.0 in FY 2019. Thus,
we are adjusting the outlier threshold
amount in this final rule to maintain
total estimated outlier payments equal
to 3 percent of total estimated payments
in FY 2020.
The impact of this outlier adjustment
update (as shown in column 4 of Table
20) is to maintain estimated overall
payments to IRFs at 3 percent.
5. Impact of the CBSA Wage Index and
Labor-Related Share
In column 5 of Table 20, we present
the effects of the budget-neutral update
of the wage index and labor-related
share. The changes to the wage index
and the labor-related share are
discussed together because the wage
index is applied to the labor-related
share portion of payments, so the
changes in the two have a combined
effect on payments to providers. As
discussed in section VI.E. of this final
rule, we are updating the labor-related
share from 70.5 percent in FY 2019 to
72.7 percent in FY 2020.
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6. Impact of the Update to the CMG
Relative Weights and Average LOS
Values
In column 6 of Table 20, we present
the effects of the budget-neutral update
of the CMGs, relative weights and
average LOS values. In the aggregate, we
do not estimate that these updates will
affect overall estimated payments of
IRFs. However, we do expect these
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7. Effects of the Requirements for the
IRF QRP for FY 2020
In accordance with section
1886(j)(7)(A) of the Act, the Secretary
must reduce by 2 percentage points the
market basket increase factor otherwise
applicable to an IRF for a fiscal year if
the IRF does not comply with the
requirements of the IRF QRP for that
fiscal year. In section VIII.J of this final
rule, we discuss the method for
applying the 2 percentage point
reduction to IRFs that fail to meet the
IRF QRP requirements.
As discussed in section VIII.D. of this
final rule, we are finalizing our proposal
to add two measures to the IRF QRP: (1)
Transfer of Health Information to the
Provider—Post-Acute Care (PAC); and
(2) Transfer of Health Information to the
Patient—Post-Acute Care (PAC),
beginning with the FY 2022 IRF QRP.
We are also finalizing our proposal to
add standardized patient assessment
data elements, as discussed in section
IV.G of this final rule. We describe the
estimated burden and cost reductions
for both of these measures in section
VIII.C of this final rule. In summary, the
changes to the IRF QRP will result in a
burden addition of $7,339 per IRF
annually, and $8,234,450 for all IRFs
annually.
We intend to continue to closely
monitor the effects of the IRF QRP on
IRFs and to help perpetuate successful
reporting outcomes through ongoing
stakeholder education, national
trainings, IRF announcements, website
postings, CMS Open Door Forums, and
general and technical help desks.
8. Effects of the Amending
§ 412.622(a)(3)(iv) To Clarify the
Definition of a Rehabilitation Physician
As discussed in section VIII. of this
final rule, we are amending
§ 412.622(a)(3)(iv) to clarify that the
determination as to whether a physician
qualifies as a rehabilitation physician
(that is, a licensed physician with
specialized training and experience in
inpatient rehabilitation) is made by the
IRF. We do not expect this to have any
effect on the quality of care that
beneficiaries receive in IRFs because we
continue to require that the
rehabilitation physicians caring for
patients in IRFs be licensed physicians
with specialized training and
experience in inpatient rehabilitation.
We expect IRFs to continue ensuring
that the rehabilitation physicians meet
these requirements. Although we do not
currently collect data from IRFs on the
physicians specialties that are providing
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care to patients in IRFs, we do not
expect this to change as a result of the
amendments we are making to
§ 412.622(a)(3)(iv). However, we will
continue to monitor the quality of care
beneficiaries receive in IRFs, and will
initiate appropriate actions through
future rulemaking if we observe any
declines in quality of care in IRFs.
As this is merely clarifying our
existing policy regarding the definition
of a rehabilitation physician in
§ 412.622(a)(3)(iv), we do not expect this
to result in any financial impacts for the
Medicare contractors, IRFs, other
providers, or for the Medicare program.
However, we expect that this
clarification may ease some
administrative burden for IRFs and for
Medicare contractors by making it easier
for IRF providers to document their
decisions regarding the licensed
physicians in their facilities that meet
the regulatory definition of a
rehabilitation physician and for the
Medicare contractors to continue to
accept the IRFs’ decisions in this regard.
We are unable at this time to quantify
how much administrative burden may
have existed because of the previous
ambiguity surrounding the definition of
a rehabilitation physician, but we are
hopeful that this clarification will
alleviate any administrative burden that
might have existed before.
We expect this clarification to
enhance Medicare’s program integrity
efforts in this area by eliminating
uncertainty surrounding the definition
of a rehabilitation physician.
D. Alternatives Considered
The following is a discussion of the
alternatives considered for the IRF PPS
updates contained in this final rule.
Section 1886(j)(3)(C) of the Act
requires the Secretary to update the IRF
PPS payment rates by an increase factor
that reflects changes over time in the
prices of an appropriate mix of goods
and services included in the covered
IRF services.
We are adopting a market basket
increase factor for FY 2020 that is based
on a rebased and revised market basket
reflecting a 2016 base year. We
considered the alternative of continuing
to use the IRF market basket without
rebasing to determine the market basket
increase factor for FY 2020. However,
we typically rebase and revise the
market baskets for the various PPS every
4 to 5 years so that the cost weights and
price proxies reflect more recent data.
Therefore, we believe it is more
technically appropriate to use a 2016based IRF market basket since it allows
for the FY 2020 market basket increase
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factor to reflect a more up-to-date cost
structure experienced by IRFs.
As noted previously in this final rule,
section 1886(j)(3)(C)(ii)(I) of the Act
requires the Secretary to apply a
productivity adjustment to the market
basket increase factor for FY 2020. Thus,
in accordance with section 1886(j)(3)(C)
of the Act, we are updating the IRF
prospective payments in this final rule
by 2.5 percent (which equals the 2.9
percent estimated IRF market basket
increase factor for FY 2020 reduced by
a 0.4 percentage point productivity
adjustment as determined under section
1886(b)(3)(B)(xi)(II) of the Act (as
required by section 1886(j)(3)(C)(ii)(I) of
the Act)).
As we finalized in the FY 2019 IRF
PPS final rule (83 FR 38514) use of the
Quality Indicators items in determining
payment and the associated CMG and
CMG relative weight revisions using 2
years of data (FYs 2017 and 2018)
beginning with FY 2020, we did not
consider any alternative to proposing
these changes.
However, we did consider whether or
not to apply a weighting methodology to
the IRF motor score that was finalized
in the FY 2019 IRF PPS final rule (83
FR 38514) to assign patients to CMGs
beginning in FY 2020. As described in
the FY 2020 IRF PPS proposed rule (84
FR 17244, 17249 through 17260), we
explored the use of a weighted motor
score, as requested by stakeholders. Our
analysis showed that weighting the
motor score would improve the
accuracy of payments under the IRF
PPS. The improved accuracy combined
with the requests from stakeholders to
explore a weighted methodology led us
to propose to use a weighted motor
score to assign patients to CMGs
beginning on October 1, 2019. However,
in light of the many concerned
stakeholder comments on the FY 2020
IRF PPS proposed rule that requested
that we go back to an unweighted motor
score methodology until we can more
fully analyze a weighted motor score,
the fact that the improvement in
accuracy using the weighted motor
score is small, and the greater simplicity
achieved through the use of an
unweighted motor score, we are
finalizing an unweighted motor score, in
which each of the 18 items have a
weight of 1, beginning October 1, 2019.
We will continue to analyze weighted
motor score approaches and will
consider possible revisions to the motor
score for future rulemaking.
We considered not removing the item
GG0170A1 Roll left and right from the
composition of the motor score.
However, this item was found to be very
collinear with other items in the motor
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score and did not behave as expected in
the models. Therefore, we believe it is
appropriate to remove this item from the
construction of the motor score.
We considered updating facility-level
adjustment factors for FY 2020.
However, as discussed in more detail in
the FY 2015 final rule (79 FR 45872), we
believe that freezing the facility-level
adjustments at FY 2014 levels for FY
2015 and all subsequent years (unless
and until the data indicate that they
need to be further updated) will allow
us an opportunity to monitor the effects
of the substantial changes to the
adjustment factors for FY 2014, and will
allow IRFs time to adjust to the previous
changes.
We considered not updating the IRF
wage index to use the concurrent fiscal
year’s IPPS wage index and instead
continuing to use a 1-year lag of the
IPPS wage index. However, we believe
that updating the IRF wage index based
on the concurrent fiscal year’s IPPS
wage index will better align the data
across acute and PAC settings in
support of our efforts to move toward
more unified Medicare payments across
PAC settings.
We considered maintaining the
existing outlier threshold amount for FY
2020. However, the outlier threshold
must be adjusted to reflect changes in
estimated costs and payments for IRFs
in FY 2020. Consequently, we are
adjusting the outlier threshold amount
in this final rule to maintain total outlier
payments equal to 3 percent of aggregate
estimated payments in FY 2020.
We considered not amending
§ 412.622(a)(3)(iv) to clarify that the
determination as to whether a physician
qualifies as a rehabilitation physician
(that is, a licensed physician with
specialized training and experience in
inpatient rehabilitation) is made by the
IRF. Instead, we considered addressing
this issue through subregulatory means,
such as issuing guidance to the
Medicare contractors. However, we
believe that it is important to clarify this
definition in regulation to ensure that
IRF providers and Medicare contractors
have a shared understanding of these
regulatory requirements and to make
certain that there is no room for further
ambiguity on this point.
In addition, we considered addressing
this issue by amending
§ 412.622(a)(3)(iv) to add further
specificity to the definition of a
rehabilitation physician. However, we
did not take this approach because we
continue to believe that the IRFs are in
the best position to make the
determination as to which licensed
physicians meet the requirements for
purposes of § 412.622, and we did not
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39171
want to inadvertently affect access to
IRF care for beneficiaries. However, we
will continue to monitor this policy and
engage with stakeholders to determine if
further specificity of these requirements
may be warranted in the future.
E. Regulatory Review Costs
If regulations impose administrative
costs on private entities, such as the
time needed to read and interpret this
final rule, we should estimate the cost
associated with regulatory review. Due
to the uncertainty involved with
accurately quantifying the number of
entities that will review the rule, we
assume that the total number of unique
commenters on the FY 2020 IRF PPS
proposed rule will be the number of
reviewers of this final rule. We
acknowledge that this assumption may
understate or overstate the costs of
reviewing this final rule. It is possible
that not all commenters reviewed the
FY 2020 IRF PPS proposed rule in
detail, and it is also possible that some
reviewers chose not to comment on the
proposed rule. For these reasons we
thought that the number of past
commenters would be a fair estimate of
the number of reviewers of this final
rule.
We also recognize that different types
of entities are in many cases affected by
mutually exclusive sections of this final
rule, and therefore, for the purposes of
our estimate we assume that each
reviewer reads approximately 50
percent of the rule. We sought
comments on this assumption.
Using the wage information from the
BLS for medical and health service
managers (Code 11–9111), we estimate
that the cost of reviewing this rule is
$107.38 per hour, including overhead
and fringe benefits (https://www.bls.gov/
oes/current/oes_nat.htm). Assuming an
average reading speed, we estimate that
it would take approximately 2 hours for
the staff to review half of this final rule.
For each IRF that reviews the rule, the
estimated cost is $218.72 (2 hours ×
$109.36). Therefore, we estimate that
the total cost of reviewing this
regulation is $274,931.04 ($218.72 ×
1,257 reviewers).
We received one comment on the
proposed methodology for estimating
the total cost of reviewing this
regulation which is summarized below.
Comment: One commenter suggested
that CMS should take into consideration
the number of times the proposed rule
has been downloaded in estimating the
cost of reviewing this regulation.
Response: The regulatory review cost
is an estimate that makes several
assumptions such as average reading
speed and number of the people who
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read the document, etc. For more than
2 years, we have used the number of
comments received as a proxy for the
number of staff members who review
the document. This assumption is well
accepted by the general public. The
number of comments received is a more
reasonable proxy than the number of
downloads since those who provide
comments must actually read the rule,
as those that download the rule may not
read the rule.
G. Conclusion
Overall, the estimated payments per
discharge for IRFs in FY 2020 are
projected to increase by 2.5 percent,
compared with the estimated payments
in FY 2019, as reflected in column 7 of
Table 20.
IRF payments per discharge are
estimated to increase by 2.4 percent in
urban areas and 4.4 percent in rural
areas, compared with estimated FY 2019
payments. Payments per discharge to
rehabilitation units are estimated to
increase 5.0 percent in urban areas and
5.7 percent in rural areas. Payments per
discharge to freestanding rehabilitation
hospitals are estimated to increase 0.2
percent in urban areas and decrease 2.1
percent in rural areas.
Overall, IRFs are estimated to
experience a net increase in payments
as a result of the policies in this final
rule. The largest payment increase is
estimated to be a 6.8 percent increase
for rural government IRFs and rural IRFs
located in the West South Central
region. The analysis above, together
with the remainder of this preamble,
provides a Regulatory Impact Analysis.
In accordance with the provisions of
Executive Order 12866, this regulation
was reviewed by the Office of
Management and Budget.
PART 412—PROSPECTIVE PAYMENT
SYSTEMS FOR INPATIENT HOSPITAL
SERVICES
List of Subjects in 42 CFR Part 412
Administrative practice and
procedure, Health facilities, Medicare,
Puerto Rico, Reporting and
recordkeeping requirements.
For the reasons set forth in the
preamble, the Centers for Medicare &
Medicaid Services amends 42 CFR
chapter IV as set forth below:
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17:22 Aug 07, 2019
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F. Accounting Statement and Table
As required by OMB Circular A–4
(available at https://
www.whitehouse.gov/sites/default/files/
omb/assets/omb/circulars/a004/a4.pdf), in Table 21, we have prepared an
accounting statement showing the
1. The authority citation for part 412
is revised to read as follows:
■
Authority: 42 U.S.C. 1302 and 1395hh.
2. Section 412.622 is amended by
revising paragraphs (a)(3)(iv),
(a)(4)(i)(A), (a)(4)(iii)(A), and (a)(5)(i)
and adding paragraph (c) to read as
follows:
■
§ 412.622
Basis of payment.
(a) * * *
(3) * * *
(iv) Requires physician supervision by
a rehabilitation physician. The
requirement for medical supervision
means that the rehabilitation physician
must conduct face-to-face visits with the
patient at least 3 days per week
throughout the patient’s stay in the IRF
to assess the patient both medically and
functionally, as well as to modify the
course of treatment as needed to
maximize the patient’s capacity to
benefit from the rehabilitation process.
The post-admission physician
evaluation described in paragraph
(a)(4)(ii) of this section may count as
one of the face-to-face visits.
(4) * * *
(i) * * *
(A) It is conducted by a licensed or
certified clinician(s) designated by a
rehabilitation physician within the 48
hours immediately preceding the IRF
admission. A preadmission screening
that includes all of the required
elements, but that is conducted more
than 48 hours immediately preceding
the IRF admission, will be accepted as
PO 00000
Frm 00120
Fmt 4701
Sfmt 4700
classification of the expenditures
associated with the provisions of this
final rule. Table 21 provides our best
estimate of the increase in Medicare
payments under the IRF PPS as a result
of the updates presented in this final
rule based on the data for 1,122 IRFs in
our database. In addition, Table 21
presents the costs associated with the
new IRF QRP requirements for FY 2020.
long as an update is conducted in
person or by telephone to update the
patient’s medical and functional status
within the 48 hours immediately
preceding the IRF admission and is
documented in the patient’s medical
record.
*
*
*
*
*
(iii) * * *
(A) It is developed by a rehabilitation
physician with input from the
interdisciplinary team within 4 days of
the patient’s admission to the IRF.
*
*
*
*
*
(5) * * *
(i) The team meetings are led by a
rehabilitation physician and further
consist of a registered nurse with
specialized training or experience in
rehabilitation; a social worker or case
manager (or both); and a licensed or
certified therapist from each therapy
discipline involved in treating the
patient. All team members must have
current knowledge of the patient’s
medical and functional status. The
rehabilitation physician may lead the
interdisciplinary team meeting remotely
via a mode of communication such as
video or telephone conferencing.
*
*
*
*
*
(c) Definitions. As used in this
section—
Rehabilitation physician means a
licensed physician who is determined
by the IRF to have specialized training
and experience in inpatient
rehabilitation.
3. Section 412.634 is amended by
revising paragraphs (a)(1), (d)(1) and (5),
and (f)(1) to read as follows:
■
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39172
Federal Register / Vol. 84, No. 153 / Thursday, August 8, 2019 / Rules and Regulations
§ 412.634 Requirements under the
Inpatient Rehabilitation Facility (IRF) Quality
Reporting Program (QRP).
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(a) * * *
(1) For the FY 2018 payment
determination and subsequent years, an
IRF must begin reporting data under the
IRF QRP requirements no later than the
first day of the calendar quarter
subsequent to 30 days after the date on
its CMS Certification Number (CCN)
notification letter, which designates the
IRF as operating in the CMS designated
data submission system.
*
*
*
*
*
(d) * * *
(1) IRFs that do not meet the
requirement in paragraph (b) of this
section for a program year will receive
a written notification of non-compliance
VerDate Sep<11>2014
17:22 Aug 07, 2019
Jkt 247001
through at least one of the following
methods: The CMS designated data
submission system, the United States
Postal Service, or via an email from the
Medicare Administrative Contractor
(MAC).
*
*
*
*
*
(5) CMS will notify IRFs, in writing,
of its final decision regarding any
reconsideration request through at least
one of the following methods: CMS
designated data submission system, the
United States Postal Service, or via an
email from the Medicare Administrative
Contractor (MAC).
*
*
*
*
*
(f) * * *
(1) IRFs must meet or exceed two
separate data completeness thresholds:
One threshold set at 95 percent for
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39173
completion of required quality measures
data and standardized patient
assessment data collected using the
IRF–PAI submitted through the CMS
designated data submission system; and
a second threshold set at 100 percent for
measures data collected and submitted
using the CDC NHSN.
*
*
*
*
*
Dated: July 23, 2019.
Seema Verma,
Administrator, Centers for Medicare &
Medicaid Services.
Dated: July 25, 2019.
Alex M. Azar II,
Secretary, Department of Health and Human
Services.
[FR Doc. 2019–16603 Filed 7–31–19; 4:15 pm]
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Agencies
[Federal Register Volume 84, Number 153 (Thursday, August 8, 2019)]
[Rules and Regulations]
[Pages 39054-39173]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2019-16603]
[[Page 39053]]
Vol. 84
Thursday,
No. 153
August 8, 2019
Part II
Department of Health and Human Services
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Centers for Medicare & Medicaid Services
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42 CFR Part 412
Medicare Program; Inpatient Rehabilitation Facility (IRF) Prospective
Payment System for Federal Fiscal Year 2020 and Updates to the IRF
Quality Reporting Program; Final Rule
Federal Register / Vol. 84 , No. 153 / Thursday, August 8, 2019 /
Rules and Regulations
[[Page 39054]]
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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Medicare & Medicaid Services
42 CFR Part 412
[CMS-1710-F]
RIN 0938-AT67
Medicare Program; Inpatient Rehabilitation Facility (IRF)
Prospective Payment System for Federal Fiscal Year 2020 and Updates to
the IRF Quality Reporting Program
AGENCY: Centers for Medicare & Medicaid Services (CMS), HHS.
ACTION: Final rule.
-----------------------------------------------------------------------
SUMMARY: This final rule updates the prospective payment rates for
inpatient rehabilitation facilities (IRFs) for federal fiscal year (FY)
2020. As required by the statute, this final rule includes the
classification and weighting factors for the IRF prospective payment
system's (PPS) case-mix groups (CMGs) and a description of the
methodologies and data used in computing the prospective payment rates
for FY 2020. This final rule rebases and revises the IRF market basket
to reflect a 2016 base year rather than the current 2012 base year.
Additionally, this final rule revises the CMGs and updates the CMG
relative weights and average length of stay (LOS) values beginning with
FY 2020, based on analysis of 2 years of data (FYs 2017 and 2018).
Although we proposed to use a weighted motor score to assign patients
to CMGs, we are finalizing based on public comments the use of an
unweighted motor score to assign patients to CMGs beginning with FY
2020. Additionally, we are finalizing the removal of one item from the
motor score. We are updating the IRF wage index to use the concurrent
fiscal year inpatient prospective payment system (IPPS) wage index
beginning with FY 2020. We are amending the regulations to clarify that
the determination as to whether a physician qualifies as a
rehabilitation physician (that is, a licensed physician with
specialized training and experience in inpatient rehabilitation) is
made by the IRF. For the IRF Quality Reporting Program (QRP), we are
adopting two new measures, modifying an existing measure, and adopting
new standardized patient assessment data elements. We are also making
updates to reflect our migration to a new data submission system.
DATES:
Effective date: These regulations are effective on October 1, 2019.
Applicability dates: The updated IRF prospective payment rates are
applicable for IRF discharges occurring on or after October 1, 2019,
and on or before September 30, 2020 (FY 2020). The new and updated
quality measures and reporting requirements under the IRF QRP are
applicable for IRF discharges occurring on or after October 1, 2020.
FOR FURTHER INFORMATION CONTACT:
Gwendolyn Johnson, (410) 786-6954, for general information.
Catie Kraemer, (410) 786-0179, for information about the IRF
payment policies and payment rates.
Kadie Derby, (410) 786-0468, for information about the IRF coverage
policies.
Kate Brooks, (410) 786-7877, for information about the IRF quality
reporting program.
SUPPLEMENTARY INFORMATION:
Inspection of Public Comments: All comments received before the
close of the comment period are available for viewing by the public,
including any personally identifiable or confidential business
information that is included in a comment. We post all comments
received before the close of the comment period as soon as possible
after they have been received at https://www.regulations.gov. Follow the
search instructions on that website to view public comments.
The IRF PPS Addenda along with other supporting documents and
tables referenced in this final rule are available through the internet
on the CMS website at https://www.cms.hhs.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/.
Executive Summary
A. Purpose
This final rule updates the prospective payment rates for IRFs for
FY 2020 (that is, for discharges occurring on or after October 1, 2019,
and on or before September 30, 2020) as required under section
1886(j)(3)(C) of the Social Security Act (the Act). As required by
section 1886(j)(5) of the Act, this final rule includes the
classification and weighting factors for the IRF PPS's case-mix groups
(CMGs) and a description of the methodologies and data used in
computing the prospective payment rates for FY 2020. This final rule
also rebases and revises the IRF market basket to reflect a 2016 base
year, rather than the current 2012 base year. Additionally, this final
rule revises the CMGs and updates the CMG relative weights and average
LOS values beginning with FY 2020, based on analysis of 2 years of data
(FYs 2017 and 2018). Although we proposed to use a weighted motor score
to assign patients to CMGs, we are finalizing based on public comments
the use of an unweighted motor score to assign patients to CMGs
beginning with FY 2020. Additionally, we are finalizing the removal of
one item from the motor score. We are also updating the IRF wage index
to use the concurrent FY IPPS wage index for the IRF PPS beginning with
FY 2020. We are also amending the regulations at 42 CFR 412.622 to
clarify that the determination as to whether a physician qualifies as a
rehabilitation physician (that is, a licensed physician with
specialized training and experience in inpatient rehabilitation) is
made by the IRF. For the IRF QRP, we are adopting two new measures,
modifying an existing measure, and adopting new standardized patient
assessment data elements. We also include updates related to the system
used for the submission of data and related regulation text. We are not
finalizing our proposal requiring that IRFs submit data on measures and
standardized patient assessment data for which the source of the data
is the IRF-PAI to all patients, regardless of payer, but plan to
propose this policy in future rulemaking.
B. Summary of Major Provisions
In this final rule, we use the methods described in the FY 2019 IRF
PPS final rule (83 FR 38514) to update the prospective payment rates
for FY 2020 using updated FY 2018 IRF claims and the most recent
available IRF cost report data, which is FY 2017 IRF cost report data.
This final rule also rebases and revises the IRF market basket to
reflect a 2016 base year rather than the current 2012 base year.
Additionally, this final rule revises the CMGs and updates the CMG
relative weights and average LOS values beginning with FY 2020, based
on analysis of 2 years of data (FYs 2017 and 2018). Although we
proposed to use a weighted motor score to assign patients to CMGs, we
are finalizing based on public comments the use of an unweighted motor
score to assign patients to CMGs beginning with FY 2020. Additionally,
we are finalizing the removal of one item from the motor score. We are
also updating the IRF wage index to use the concurrent FY IPPS wage
index for the IRF PPS beginning in FY 2020. We are also amending the
regulations at Sec. 412.622 to clarify that the determination as to
whether a physician qualifies as a rehabilitation physician (that is, a
licensed physician with specialized
[[Page 39055]]
training and experience in inpatient rehabilitation) is made by the
IRF. We also update requirements for the IRF QRP.
C. Summary of Impacts
[GRAPHIC] [TIFF OMITTED] TR08AU19.000
I. Background
A. Historical Overview of the IRF PPS
Section 1886(j) of the Act provides for the implementation of a
per-discharge PPS for inpatient rehabilitation hospitals and inpatient
rehabilitation units of a hospital (collectively, hereinafter referred
to as IRFs). Payments under the IRF PPS encompass inpatient operating
and capital costs of furnishing covered rehabilitation services (that
is, routine, ancillary, and capital costs), but not direct graduate
medical education costs, costs of approved nursing and allied health
education activities, bad debts, and other services or items outside
the scope of the IRF PPS. Although a complete discussion of the IRF PPS
provisions appears in the original FY 2002 IRF PPS final rule (66 FR
41316) and the FY 2006 IRF PPS final rule (70 FR 47880), we are
providing a general description of the IRF PPS for FYs 2002 through
2019.
Under the IRF PPS from FY 2002 through FY 2005, the prospective
payment rates were computed across 100 distinct CMGs, as described in
the FY 2002 IRF PPS final rule (66 FR 41316). We constructed 95 CMGs
using rehabilitation impairment categories (RICs), functional status
(both motor and cognitive), and age (in some cases, cognitive status
and age may not be a factor in defining a CMG). In addition, we
constructed five special CMGs to account for very short stays and for
patients who expire in the IRF.
For each of the CMGs, we developed relative weighting factors to
account for a patient's clinical characteristics and expected resource
needs. Thus, the weighting factors accounted for the relative
difference in resource use across all CMGs. Within each CMG, we created
tiers based on the estimated effects that certain comorbidities would
have on resource use.
We established the federal PPS rates using a standardized payment
conversion factor (formerly referred to as the budget-neutral
conversion factor). For a detailed discussion of the budget-neutral
conversion factor, please refer to our FY 2004 IRF PPS final rule (68
FR 45684 through 45685). In the FY 2006 IRF PPS final rule (70 FR
47880), we discussed in detail the methodology for determining the
standard payment conversion factor.
We applied the relative weighting factors to the standard payment
conversion factor to compute the unadjusted prospective payment rates
under the IRF PPS from FYs 2002 through 2005. Within the structure of
the payment system, we then made adjustments to account for interrupted
stays, transfers, short stays, and deaths. Finally, we applied the
applicable adjustments to account for geographic variations in wages
(wage index), the percentage of low-income patients, location in a
rural area (if applicable), and outlier payments (if applicable) to the
IRFs' unadjusted prospective payment rates.
For cost reporting periods that began on or after January 1, 2002,
and before October 1, 2002, we determined the final prospective payment
amounts using the transition methodology prescribed in section
1886(j)(1) of the Act. Under this provision, IRFs transitioning into
the PPS were paid a blend of the federal IRF PPS rate and the payment
that the IRFs would have received had the IRF PPS not been implemented.
This provision also allowed IRFs to elect to bypass this blended
payment and immediately be paid 100 percent of the federal IRF PPS
rate. The transition methodology expired as of cost reporting periods
beginning on or after October 1, 2002 (FY 2003), and payments for all
IRFs now consist of 100 percent of the federal IRF PPS rate.
Section 1886(j) of the Act confers broad statutory authority upon
the Secretary to propose refinements to the IRF PPS. In the FY 2006 IRF
PPS final rule (70 FR 47880) and in correcting amendments to the FY
2006 IRF PPS final rule (70 FR 57166), we finalized a number of
refinements to the IRF PPS case-mix classification system (the CMGs and
the corresponding relative weights) and the case-level and facility-
level adjustments. These refinements included the adoption of the
Office of Management and Budget's (OMB) Core-Based Statistical Area
(CBSA) market definitions; modifications to the CMGs, tier
comorbidities; and CMG relative weights, implementation of a new
teaching status adjustment for IRFs; rebasing and revising the market
basket index used to update IRF payments, and updates to the rural,
low-income percentage (LIP), and high-cost outlier adjustments.
Beginning with the FY 2006 IRF PPS final rule (70 FR 47908 through
47917), the market basket index used to update IRF payments was a
market basket reflecting the operating and capital cost structures for
freestanding IRFs, freestanding inpatient psychiatric facilities
(IPFs), and long-term care hospitals (LTCHs) (hereinafter referred to
as the rehabilitation, psychiatric, and long-term care (RPL) market
basket). Any reference to the FY 2006 IRF PPS final rule in this final
rule also includes the provisions effective in the correcting
amendments. For a detailed discussion of the final key policy changes
for FY 2006, please refer to the FY 2006 IRF PPS final rule.
In the FY 2007 IRF PPS final rule (71 FR 48354), we further refined
the IRF PPS case-mix classification system (the CMG relative weights)
and the case-level adjustments, to ensure that IRF PPS payments would
continue to reflect as accurately as possible the costs of care. For a
detailed discussion of the FY 2007 policy revisions, please refer to
the FY 2007 IRF PPS final rule.
In the FY 2008 IRF PPS final rule (72 FR 44284), we updated the
prospective payment rates and the outlier threshold, revised the IRF
wage index policy, and clarified how we determine high-cost outlier
payments for transfer cases. For more information on the policy changes
[[Page 39056]]
implemented for FY 2008, please refer to the FY 2008 IRF PPS final
rule.
After publication of the FY 2008 IRF PPS final rule (72 FR 44284),
section 115 of the Medicare, Medicaid, and SCHIP Extension Act of 2007
(Pub. L. 110-173, enacted December 29, 2007) (MMSEA) amended section
1886(j)(3)(C) of the Act to apply a zero percent increase factor for
FYs 2008 and 2009, effective for IRF discharges occurring on or after
April 1, 2008. Section 1886(j)(3)(C) of the Act required the Secretary
to develop an increase factor to update the IRF prospective payment
rates for each FY. Based on the legislative change to the increase
factor, we revised the FY 2008 prospective payment rates for IRF
discharges occurring on or after April 1, 2008. Thus, the final FY 2008
IRF prospective payment rates that were published in the FY 2008 IRF
PPS final rule (72 FR 44284) were effective for discharges occurring on
or after October 1, 2007, and on or before March 31, 2008, and the
revised FY 2008 IRF prospective payment rates were effective for
discharges occurring on or after April 1, 2008, and on or before
September 30, 2008. The revised FY 2008 prospective payment rates are
available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Data-Files.html.
In the FY 2009 IRF PPS final rule (73 FR 46370), we updated the CMG
relative weights, the average LOS values, and the outlier threshold;
clarified IRF wage index policies regarding the treatment of ``New
England deemed'' counties and multi-campus hospitals; and revised the
regulation text in response to section 115 of the MMSEA to set the IRF
compliance percentage at 60 percent (the ``60 percent rule'') and
continue the practice of including comorbidities in the calculation of
compliance percentages. We also applied a zero percent market basket
increase factor for FY 2009 in accordance with section 115 of the
MMSEA. For more information on the policy changes implemented for FY
2009, please refer to the FY 2009 IRF PPS final rule.
In the FY 2010 IRF PPS final rule (74 FR 39762) and in correcting
amendments to the FY 2010 IRF PPS final rule (74 FR 50712), we updated
the prospective payment rates, the CMG relative weights, the average
LOS values, the rural, LIP, teaching status adjustment factors, and the
outlier threshold; implemented new IRF coverage requirements for
determining whether an IRF claim is reasonable and necessary; and
revised the regulation text to require IRFs to submit patient
assessments on Medicare Advantage (MA) (formerly called Medicare Part
C) patients for use in the 60 percent rule calculations. Any reference
to the FY 2010 IRF PPS final rule in this final rule also includes the
provisions effective in the correcting amendments. For more information
on the policy changes implemented for FY 2010, please refer to the FY
2010 IRF PPS final rule.
After publication of the FY 2010 IRF PPS final rule (74 FR 39762),
section 3401(d) of the Patient Protection and Affordable Care Act (Pub.
L. 111-148, enacted March 23, 2010), as amended by section 10319 of the
same Act and by section 1105 of the Health Care and Education
Reconciliation Act of 2010 (Pub. L. 111-152, enacted March 30, 2010)
(collectively, hereinafter referred to as ``PPACA''), amended section
1886(j)(3)(C) of the Act and added section 1886(j)(3)(D) of the Act.
Section 1886(j)(3)(C) of the Act requires the Secretary to estimate a
multifactor productivity (MFP) adjustment to the market basket increase
factor, and to apply other adjustments as defined by the Act. The
productivity adjustment applies to FYs from 2012 forward. The other
adjustments apply to FYs 2010 to 2019.
Sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(i) of the Act
defined the adjustments that were to be applied to the market basket
increase factors in FYs 2010 and 2011. Under these provisions, the
Secretary was required to reduce the market basket increase factor in
FY 2010 by a 0.25 percentage point adjustment. Notwithstanding this
provision, in accordance with section 3401(p) of the PPACA, the
adjusted FY 2010 rate was only to be applied to discharges occurring on
or after April 1, 2010. Based on the self-implementing legislative
changes to section 1886(j)(3) of the Act, we adjusted the FY 2010
prospective payment rates as required, and applied these rates to IRF
discharges occurring on or after April 1, 2010, and on or before
September 30, 2010. Thus, the final FY 2010 IRF prospective payment
rates that were published in the FY 2010 IRF PPS final rule (74 FR
39762) were used for discharges occurring on or after October 1, 2009,
and on or before March 31, 2010, and the adjusted FY 2010 IRF
prospective payment rates applied to discharges occurring on or after
April 1, 2010, and on or before September 30, 2010. The adjusted FY
2010 prospective payment rates are available on the CMS website at
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
In addition, sections 1886(j)(3)(C) and (D) of the Act also
affected the FY 2010 IRF outlier threshold amount because they required
an adjustment to the FY 2010 RPL market basket increase factor, which
changed the standard payment conversion factor for FY 2010.
Specifically, the original FY 2010 IRF outlier threshold amount was
determined based on the original estimated FY 2010 RPL market basket
increase factor of 2.5 percent and the standard payment conversion
factor of $13,661. However, as adjusted, the IRF prospective payments
were based on the adjusted RPL market basket increase factor of 2.25
percent and the revised standard payment conversion factor of $13,627.
To maintain estimated outlier payments for FY 2010 equal to the
established standard of 3 percent of total estimated IRF PPS payments
for FY 2010, we revised the IRF outlier threshold amount for FY 2010
for discharges occurring on or after April 1, 2010, and on or before
September 30, 2010. The revised IRF outlier threshold amount for FY
2010 was $10,721.
Sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(i) of the Act also
required the Secretary to reduce the market basket increase factor in
FY 2011 by a 0.25 percentage point adjustment. The FY 2011 IRF PPS
notice (75 FR 42836) and the correcting amendments to the FY 2011 IRF
PPS notice (75 FR 70013) described the required adjustments to the FY
2010 and FY 2011 IRF PPS prospective payment rates and outlier
threshold amount for IRF discharges occurring on or after April 1,
2010, and on or before September 30, 2011. It also updated the FY 2011
prospective payment rates, the CMG relative weights, and the average
LOS values. Any reference to the FY 2011 IRF PPS notice in this final
rule also includes the provisions effective in the correcting
amendments. For more information on the FY 2010 and FY 2011 adjustments
or the updates for FY 2011, please refer to the FY 2011 IRF PPS notice.
In the FY 2012 IRF PPS final rule (76 FR 47836), we updated the IRF
prospective payment rates, rebased and revised the RPL market basket,
and established a new QRP for IRFs in accordance with section
1886(j)(7) of the Act. We also consolidated, clarified, and revised
existing policies regarding IRF hospitals and IRF units of hospitals to
eliminate unnecessary confusion and enhance consistency. For more
information on the policy changes implemented for FY 2012, please refer
to the FY 2012 IRF PPS final rule.
The FY 2013 IRF PPS notice (77 FR 44618) described the required
adjustments to the FY 2013 prospective
[[Page 39057]]
payment rates and outlier threshold amount for IRF discharges occurring
on or after October 1, 2012, and on or before September 30, 2013. It
also updated the FY 2013 prospective payment rates, the CMG relative
weights, and the average LOS values. For more information on the
updates for FY 2013, please refer to the FY 2013 IRF PPS notice.
In the FY 2014 IRF PPS final rule (78 FR 47860), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also updated the facility-level adjustment factors
using an enhanced estimation methodology, revised the list of diagnosis
codes that count toward an IRF's 60 percent rule compliance calculation
to determine ``presumptive compliance,'' revised sections of the IRF
patient assessment instrument (IRF-PAI), revised requirements for acute
care hospitals that have IRF units, clarified the IRF regulation text
regarding limitation of review, updated references to previously
changed sections in the regulations text, and updated requirements for
the IRF QRP. For more information on the policy changes implemented for
FY 2014, please refer to the FY 2014 IRF PPS final rule.
In the FY 2015 IRF PPS final rule (79 FR 45872) and the correcting
amendments to the FY 2015 IRF PPS final rule (79 FR 59121), we updated
the prospective payment rates, the CMG relative weights, and the
outlier threshold amount. We also revised the list of diagnosis codes
that count toward an IRF's 60 percent rule compliance calculation to
determine ``presumptive compliance,'' revised sections of the IRF-PAI,
and updated requirements for the IRF QRP. Any reference to the FY 2015
IRF PPS final rule in this final rule also includes the provisions
effective in the correcting amendments. For more information on the
policy changes implemented for FY 2015, please refer to the FY 2015 IRF
PPS final rule.
In the FY 2016 IRF PPS final rule (80 FR 47036), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also adopted an IRF-specific market basket that
reflects the cost structures of only IRF providers, a blended 1-year
transition wage index based on the adoption of new OMB area
delineations, a 3-year phase-out of the rural adjustment for certain
IRFs due to the new OMB area delineations, and updates for the IRF QRP.
For more information on the policy changes implemented for FY 2016,
please refer to the FY 2016 IRF PPS final rule.
In the FY 2017 IRF PPS final rule (81 FR 52056) and the correcting
amendments to the FY 2017 IRF PPS final rule (81 FR 59901), we updated
the prospective payment rates, the CMG relative weights, and the
outlier threshold amount. We also updated requirements for the IRF QRP.
Any reference to the FY 2017 IRF PPS final rule in this final rule also
includes the provisions effective in the correcting amendments. For
more information on the policy changes implemented for FY 2017, please
refer to the FY 2017 IRF PPS final rule.
In the FY 2018 IRF PPS final rule (82 FR 36238), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also revised the International Classification of
Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis
codes that are used to determine presumptive compliance under the ``60
percent rule,'' removed the 25 percent payment penalty for IRF-PAI late
transmissions, removed the voluntary swallowing status item (Item 27)
from the IRF-PAI, summarized comments regarding the criteria used to
classify facilities for payment under the IRF PPS, provided for a
subregulatory process for certain annual updates to the presumptive
methodology diagnosis code lists, adopted the use of height/weight
items on the IRF-PAI to determine patient body mass index (BMI) greater
than 50 for cases of single-joint replacement under the presumptive
methodology, and updated requirements for the IRF QRP. For more
information on the policy changes implemented for FY 2018, please refer
to the FY 2018 IRF PPS final rule.
In the FY 2019 IRF PPS final rule (83 FR 38514), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also alleviated administrative burden for IRFs by
removing the FIMTM instrument and associated Function
Modifiers from the IRF-PAI beginning in FY 2020 and revised certain IRF
coverage requirements to reduce the amount of required paperwork in the
IRF setting beginning in FY 2019. Additionally, we incorporated certain
data items located in the Quality Indicators section of the IRF-PAI
into the IRF case-mix classification system using analysis of 2 years
of data (FYs 2017 and 2018) beginning in FY 2020. For the IRF QRP, we
adopted a new measure removal factor, removed two measures from the IRF
QRP measure set, and codified a number of program requirements in our
regulations. For more information on the policy changes implemented for
FY 2019, please refer to the FY 2019 IRF PPS final rule.
B. Provisions of the PPACA Affecting the IRF PPS in FY 2012 and Beyond
The PPACA included several provisions that affect the IRF PPS in
FYs 2012 and beyond. In addition to what was previously discussed,
section 3401(d) of the PPACA also added section 1886(j)(3)(C)(ii)(I) of
the Act (providing for a ``productivity adjustment'' for FY 2012 and
each subsequent fiscal year). The productivity adjustment for FY 2020
is discussed in section VI.D. of this final rule. Section
1886(j)(3)(C)(ii)(II) of the Act provides that the application of the
productivity adjustment to the market basket update may result in an
update that is less than 0.0 for a fiscal year and in payment rates for
a fiscal year being less than such payment rates for the preceding
fiscal year.
Sections 3004(b) of the PPACA and section 411(b) of the Medicare
Access and CHIP Reauthorization Act of 2015 (Pub. L. 114-10, enacted
April 16, 2015) (MACRA) also addressed the IRF PPS. Section 3004(b) of
PPACA reassigned the previously designated section 1886(j)(7) of the
Act to section 1886(j)(8) of the Act and inserted a new section
1886(j)(7) of the Act, which contains requirements for the Secretary to
establish a QRP for IRFs. Under that program, data must be submitted in
a form and manner and at a time specified by the Secretary. Beginning
in FY 2014, section 1886(j)(7)(A)(i) of the Act requires the
application of a 2 percentage point reduction to the market basket
increase factor otherwise applicable to an IRF (after application of
paragraphs (C)(iii) and (D) of section 1886(j)(3) of the Act) for a
fiscal year if the IRF does not comply with the requirements of the IRF
QRP for that fiscal year. Application of the 2 percentage point
reduction may result in an update that is less than 0.0 for a fiscal
year and in payment rates for a fiscal year being less than such
payment rates for the preceding fiscal year. Reporting-based reductions
to the market basket increase factor are not cumulative; they only
apply for the FY involved. Section 411(b) of MACRA amended section
1886(j)(3)(C) of the Act by adding paragraph (iii), which required us
to apply for FY 2018, after the application of section
1886(j)(3)(C)(ii) of the Act, an increase factor of 1.0 percent to
update the IRF prospective payment rates.
[[Page 39058]]
C. Operational Overview of the Current IRF PPS
As described in the FY 2002 IRF PPS final rule (66 FR 41316), upon
the admission and discharge of a Medicare Part A Fee-for-Service (FFS)
patient, the IRF is required to complete the appropriate sections of a
PAI, designated as the IRF-PAI. In addition, beginning with IRF
discharges occurring on or after October 1, 2009, the IRF is also
required to complete the appropriate sections of the IRF-PAI upon the
admission and discharge of each Medicare Advantage (MA) patient, as
described in the FY 2010 IRF PPS final rule (74 FR 39762 and 74 FR
50712). All required data must be electronically encoded into the IRF-
PAI software product. Generally, the software product includes patient
classification programming called the Grouper software. The Grouper
software uses specific IRF-PAI data elements to classify (or group)
patients into distinct CMGs and account for the existence of any
relevant comorbidities.
The Grouper software produces a five-character CMG number. The
first character is an alphabetic character that indicates the
comorbidity tier. The last four characters are numeric characters that
represent the distinct CMG number. Free downloads of the Inpatient
Rehabilitation Validation and Entry (IRVEN) software product, including
the Grouper software, are available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Software.html.
Once a Medicare Part A FFS patient is discharged, the IRF submits a
Medicare claim as a Health Insurance Portability and Accountability Act
of 1996 (Pub. L. 104-191, enacted August 21, 1996) (HIPAA) compliant
electronic claim or, if the Administrative Simplification Compliance
Act of 2002 (Pub. L. 107-105, enacted December 27, 2002) (ASCA)
permits, a paper claim (a UB-04 or a CMS-1450 as appropriate) using the
five-character CMG number and sends it to the appropriate Medicare
Administrative Contractor (MAC). In addition, once a MA patient is
discharged, in accordance with the Medicare Claims Processing Manual,
chapter 3, section 20.3 (Pub. 100-04), hospitals (including IRFs) must
submit an informational-only bill (Type of Bill (TOB) 111), which
includes Condition Code 04 to their MAC. This will ensure that the MA
days are included in the hospital's Supplemental Security Income (SSI)
ratio (used in calculating the IRF LIP adjustment) for fiscal year 2007
and beyond. Claims submitted to Medicare must comply with both ASCA and
HIPAA.
Section 3 of the ASCA amended section 1862(a) of the Act by adding
paragraph (22), which requires the Medicare program, subject to section
1862(h) of the Act, to deny payment under Part A or Part B for any
expenses for items or services for which a claim is submitted other
than in an electronic form specified by the Secretary. Section 1862(h)
of the Act, in turn, provides that the Secretary shall waive such
denial in situations in which there is no method available for the
submission of claims in an electronic form or the entity submitting the
claim is a small provider. In addition, the Secretary also has the
authority to waive such denial in such unusual cases as the Secretary
finds appropriate. For more information, see the ``Medicare Program;
Electronic Submission of Medicare Claims'' final rule (70 FR 71008).
Our instructions for the limited number of Medicare claims submitted on
paper are available at https://www.cms.gov/manuals/downloads/clm104c25.pdf.
Section 3 of the ASCA operates in the context of the administrative
simplification provisions of HIPAA, which include, among others, the
requirements for transaction standards and code sets codified in 45 CFR
part 160 and part 162, subparts A and I through R (generally known as
the Transactions Rule). The Transactions Rule requires covered
entities, including covered health care providers, to conduct covered
electronic transactions according to the applicable transaction
standards. (See the CMS program claim memoranda at https://www.cms.gov/ElectronicBillingEDITrans/ and listed in the addenda to the Medicare
Intermediary Manual, Part 3, section 3600).
The MAC processes the claim through its software system. This
software system includes pricing programming called the ``Pricer''
software. The Pricer software uses the CMG number, along with other
specific claim data elements and provider-specific data, to adjust the
IRF's prospective payment for interrupted stays, transfers, short
stays, and deaths, and then applies the applicable adjustments to
account for the IRF's wage index, percentage of low-income patients,
rural location, and outlier payments. For discharges occurring on or
after October 1, 2005, the IRF PPS payment also reflects the teaching
status adjustment that became effective as of FY 2006, as discussed in
the FY 2006 IRF PPS final rule (70 FR 47880).
D. Advancing Health Information Exchange
The Department of Health and Human Services (HHS) has a number of
initiatives designed to encourage and support the adoption of
interoperable health information technology and to promote nationwide
health information exchange to improve health care. The Office of the
National Coordinator for Health Information Technology (ONC) and CMS
work collaboratively to advance interoperability across settings of
care, including post-acute care.
To further interoperability in post-acute care, we developed a Data
Element Library (DEL) to serve as a publicly-available centralized,
authoritative resource for standardized data elements and their
associated mappings to health IT standards. The DEL furthers CMS' goal
of data standardization and interoperability. These interoperable data
elements can reduce provider burden by allowing the use and exchange of
healthcare data, support provider exchange of electronic health
information for care coordination, person-centered care, and support
real-time, data driven, clinical decision making. Standards in the Data
Element Library (https://del.cms.gov/) can be referenced on the CMS
website and in the ONC Interoperability Standards Advisory (ISA). The
2019 ISA is available at https://www.healthit.gov/isa.
The 21st Century Cures Act (Pub. L. 114-255, enacted December 13,
2016) (Cures Act), requires HHS to take new steps to enable the
electronic sharing of health information ensuring interoperability for
providers and settings across the care continuum. In another important
provision, Congress defined ``information blocking'' as practices
likely to interfere with, prevent, or materially discourage access,
exchange, or use of electronic health information, and established new
authority for HHS to discourage these practices. In March 2019, ONC and
CMS published the proposed rules, ``21st Century Cures Act:
Interoperability, Information Blocking, and the ONC Health IT
Certification Program,'' (84 FR 7424) and ``Interoperability and
Patient Access'' (84 FR 7610) to promote secure and more immediate
access to health information for patients and healthcare providers
through the implementation of information blocking provisions of the
Cures Act and the use of standardized application programming
interfaces (APIs) that enable easier access to electronic health
information. We solicited comment on the two proposed rules. We invited
providers to
[[Page 39059]]
learn more about these important developments and how they are likely
to affect IRFs.
II. Summary of Provisions of the Proposed Rule
In the FY 2020 IRF PPS proposed rule, we proposed to update the IRF
prospective payment rates for FY 2020 and to rebase and revise the IRF
market basket to reflect a 2016 base year rather than the current 2012
base year. We also proposed to replace the previously finalized
unweighted motor score with a weighted motor score to assign patients
to CMGs and remove one item from the score beginning with FY 2020 and
to revise the CMGs and update the CMG relative weights and average LOS
values beginning with FY 2020, based on analysis of 2 years of data
(FYs 2017 and 2018). We also proposed to use the concurrent FY IPPS
wage index for the IRF PPS beginning with FY 2020. We also solicited
comments on stakeholder concerns regarding the appropriateness of the
wage index used to adjust IRF payments. We proposed to amend the
regulations at Sec. 412.622 to clarify that the determination as to
whether a physician qualifies as a rehabilitation physician (that is, a
licensed physician with specialized training and experience in
inpatient rehabilitation) is made by the IRF.
The proposed policy changes and updates to the IRF prospective
payment rates for FY 2020 are as follows:
Describe a proposed weighted motor score to replace the
previously finalized unweighted motor score to assign a patient to a
CMG, the removal of one item from the score, and revisions to the CMGs
beginning on October 1, 2019, based on analysis of 2 years of data (FYs
2017 and 2018) using the Quality Indicator items in the IRF-PAI. This
includes proposed revisions to the CMG relative weights and average LOS
values for FY 2020, in a budget neutral manner, as discussed in section
III. of the FY 2020 IRF PPS proposed rule (84 FR 17244, 17249 through
17260).
Describe the proposed rebased and revised IRF market
basket to reflect a 2016 base year rather than the current 2012 base
year as discussed in section V. of the FY 2020 IRF PPS proposed rule
(84 FR 17244, 17261 through 17273).
Update the IRF PPS payment rates for FY 2020 by the
proposed market basket increase factor, based upon the most current
data available, with a proposed productivity adjustment required by
section 1886(j)(3)(C)(ii)(I) of the Act, as described in section V. of
the FY 2020 IRF PPS proposed rule (84 FR 17244, 17274 through 17275).
Describe the proposed update to the IRF wage index to use
the concurrent FY IPPS wage index and the FY 2020 proposed labor-
related share in a budget-neutral manner, as described in section V. of
the FY 2020 IRF PPS proposed rule (84 FR 17244, 17276 through 17279).
Describe the continued use of FY 2014 facility-level
adjustment factors, as discussed in section IV. of the FY 2020 IRF PPS
proposed rule (84 FR 17244, 17260 through 17261).
Describe the calculation of the IRF standard payment
conversion factor for FY 2020, as discussed in section V. of the FY
2020 IRF PPS proposed rule (84 FR 17244, 17280 through 17282).
Update the outlier threshold amount for FY 2020, as
discussed in section VI. of the FY 2020 IRF PPS proposed rule (84 FR
17244, 17283 through 17284).
Update the cost-to-charge ratio (CCR) ceiling and urban/
rural average CCRs for FY 2020, as discussed in section VI. of the FY
2020 IRF PPS proposed rule (84 FR 17244 at 17284).
Describe the proposed amendments to the regulations at
Sec. 412.622 to clarify that the determination as to whether a
physician qualifies as a rehabilitation physician (that is, a licensed
physician with specialized training and experience in inpatient
rehabilitation) is made by the IRF, as discussed in section VII. of the
FY 2020 IRF PPS proposed rule (84 FR 17244, 17284 through 17285).
Updates to the requirements for the IRF QRP, as discussed
in section VIII. of the FY 2020 IRF PPS proposed rule (84 FR 17244,
17285 through 17330).
III. Analysis and Response to Public Comments
We received 1,257 timely responses from the public, many of which
contained multiple comments on the FY 2020 IRF PPS proposed rule (84 FR
17244). The majority consisted of form letters, in which we received
multiple copies of two types of identically-worded letters that had
been signed and submitted by different individuals. We received
comments from various trade associations, IRFs, individual physicians,
therapists, clinicians, health care industry organizations, and health
care consulting firms. The following sections, arranged by subject
area, include a summary of the public comments that we received, and
our responses.
IV. Refinements to the Case-Mix Classification System Beginning With FY
2020
A. Background
Section 1886(j)(2)(A) of the Act requires the Secretary to
establish CMGs for payment under the IRF PPS and a method of
classifying specific IRF patients within these groups. Under section
1886(j)(2)(B) of the Act, the Secretary must assign each CMG an
appropriate weighting factor that reflects the relative facility
resources used for patients classified within the group as compared to
patients classified within other groups. Additionally, section
1886(j)(2)(C)(i) of the Act requires the Secretary from time to time to
adjust the established classifications and weighting factors as
appropriate to reflect changes in treatment patterns, technology, case-
mix, number of payment units for which payment is made under title
XVIII of the Act, and other factors which may affect the relative use
of resources. Such adjustments must be made in a manner so that changes
in aggregate payments under the classification system are a result of
real changes and are not a result of changes in coding that are
unrelated to real changes in case mix.
In the FY 2019 IRF PPS final rule (83 FR 38533 through 38549), we
finalized the removal of the Functional Independence Measure
(FIMTM) instrument and associated Function Modifiers from
the IRF-PAI and the incorporation of an unweighted additive motor score
derived from 19 data items located in the Quality Indicators section of
the IRF-PAI beginning with FY 2020 (83 FR 38535 through 38536, 38549).
As discussed in section IV.B of this final rule, based on further
analysis to examine the potential impact of weighting the motor score,
we proposed to replace the previously finalized unweighted motor score
with a weighted motor score and remove one item from the score
beginning with FY 2020.
Additionally, as noted in the FY 2019 IRF PPS final rule (83 FR
38534), the incorporation of the data items from the Quality Indicator
section of the IRF-PAI into the IRF case-mix classification system
necessitates revisions to the CMGs to ensure that IRF payments are
calculated accurately. We finalized the use of data items from the
Quality Indicators section of the IRF-PAI to construct the functional
status scores used to classify IRF patients in the IRF case-mix
classification system for purposes of establishing payment under the
IRF PPS beginning with FY 2020, but modified our proposal based on
[[Page 39060]]
public comments to incorporate 2 years of data (FYs 2017 and 2018) into
our analyses used to revise the CMG definitions (83 FR 38549). We
stated that any changes to the proposed CMG definitions resulting from
the incorporation of an additional year of data (FY 2018) into the
analysis would be addressed in future rulemaking prior to their
implementation beginning in FY 2020. As discussed in section III.C of
the FY 2020 IRF PPS proposed rule (84 FR 17244, 17250 through 17260),
we proposed to revise the CMGs based on analysis of 2 years of data
(FYs 2017 and 2018) beginning with FY 2020. We also proposed to update
the relative weights and average LOS values associated with the revised
CMGs beginning with FY 2020.
B. Proposed Use of a Weighted Motor Score Beginning With FY 2020
As noted in the FY 2019 IRF PPS final rule (83 FR 38535), the IRF
case-mix classification system currently uses a weighted motor score
based on FIMTM data items to assign patients to CMGs under
the IRF PPS through FY 2019. More information on the development and
implementation of this motor score can be found in the FY 2006 IRF PPS
final rule (70 FR 47896 through 47900). In the FY 2019 IRF PPS final
rule (83 FR 38535 through 38536, 38549), we finalized the incorporation
of an unweighted additive motor score derived from 19 data items
located in the Quality Indicators section of the IRF-PAI beginning with
FY 2020. We did not propose a weighted motor score at the time, because
we believed that the unweighted motor score would facilitate greater
understanding among the provider community, as it is less complex.
However, we also noted that we would take comments in favor of a
weighted motor score into consideration in future analysis. In response
to feedback we received from various stakeholders and professional
organizations regarding the use of an unweighted motor score and
requesting that we consider weighting the motor score, we extended our
contract with Research Triangle Institute, International (RTI) to
examine the potential impact of weighting the motor score. Based on
this analysis, discussed further below, we believed that a weighted
motor score would improve the accuracy of payments to IRFs and proposed
to replace the previously finalized unweighted motor score with a
weighted motor score to assign patients to CMGs beginning with FY 2020.
The previously finalized motor score is calculated by summing the
scores of the 19 data items, with equal weight applied to each item.
The 19 data items are (83 FR 38535):
GG0130A1 Eating.
GG0130B1 Oral hygiene.
GG0130C1 Toileting hygiene.
GG0130E1 Shower/bathe self.
GG0130F1 Upper-body dressing.
GG0130G1 Lower-body dressing.
GG0130H1 Putting on/taking off footwear.
GG0170A1 Roll left and right.
GG0170B1 Sit to lying.
GG0170C1 Lying to sitting on side of bed.
GG0170D1 Sit to stand.
GG0170E1 Chair/bed-to-chair transfer.
GG0170F1 Toilet transfer.
GG0170I1 Walk 10 feet.
GG0170J1 Walk 50 feet with two turns.
GG0170K1 Walk 150 feet.
GG0170M1 One step curb.
H0350 Bladder continence.
H0400 Bowel continence.
In response to feedback we received from various stakeholders and
professional organizations requesting that we consider applying weights
to the motor score, we extended our contract with RTI to explore the
potential of applying unique weights to each of the 19 items in the
motor score.
As part of their analysis, RTI examined the degree to which the
items used to construct the motor score were related to one another and
adjusted their weighting methodology to account for their findings. RTI
considered a number of different weighting methodologies to develop a
weighted index that would increase the predictive power of the IRF
case-mix classification system while at the same time maintaining
simplicity. RTI used regression analysis to explore the relationship of
the motor score items to costs. This analysis was undertaken to
determine the impact of each of the items on cost and then to weight
each item in the index according to its relative impact on cost. Based
on findings from this analysis, we proposed to remove the item GG0170A1
Roll left and right from the motor score as this item was found to have
a high degree of multicollinearity with other items in the motor score
and would have resulted in either a negative or non-significant
coefficient. As such, we did not believe it would be appropriate to
include this item in the motor score calculation. Using the revised
motor score composed of the remaining 18 items identified above, RTI
designed a weighting methodology for the motor score that could be
applied uniformly across all RICs. For a more detailed discussion of
the analysis used to construct the weighted motor score, we refer
readers to the March 2019 technical report entitled ``Analyses to
Inform the Use of Standardized Patient Assessment Data Elements in the
Inpatient Rehabilitation Facility Prospective Payment System'',
available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research.html. Findings from this analysis
suggested that the use of a weighted motor score index slightly
improves the ability of the IRF PPS to predict patient costs. Based on
this analysis, we proposed to use a weighted motor score for the
purpose of determining IRF payments.
Table 1 shows the proposed weights for each component of the motor
score, averaged to 1, obtained through the regression analysis.
[[Page 39061]]
[GRAPHIC] [TIFF OMITTED] TR08AU19.001
We proposed to determine the motor score by applying each of the
weights indicated in Table 1 to the score of each corresponding item,
as finalized in the FY 2019 IRF PPS final rule (83 FR 38535 through
38537), and then summing the weighted scores for each of the 18 items
that compose the motor score.
We received several comments on the proposal to replace the
previously finalized unweighted motor score with a weighted motor score
to assign patients to CMGs under the IRF PPS and our proposal to remove
the item GG0170A1 Roll left and right from the calculation of the motor
score beginning with FY 2020, that is, for all discharges beginning on
or after October 1, 2019. As summarized in more detail below, with the
exception of one comment from MedPAC, the commenters overwhelmingly
requested that CMS delay implementation of a weighted motor score and
use an unweighted motor score to assign patients to CMGs until we can
more fully analyze and work with stakeholders on developing a weighted
motor score methodology.
In response to public comments, we carefully considered whether to
finalize the proposed weighted motor score or go back to using an
unweighted motor score to assign patients to CMGs. Although the
proposed weighted motor score results in a slight improvement in the
ability of the IRF PPS to predict patient costs and thus the accuracy
of IRF PPS payments (less than 0.18 difference in accuracy between the
weighted and the unweighted motor scores), we acknowledge the
unweighted motor score is conceptually simpler and, as such, believe it
will ease providers' transition to the use of the data items located in
the Quality Indicators section of the IRF-PAI (also referred to as
section GG items). Thus, we are finalizing based on public comments the
use of an unweighted motor score to assign patients to CMGs beginning
with FY 2020. We appreciate the commenters' suggestions on the
weighting methodology and will take them into consideration as we
explore possible refinements to the case-mix classification system in
the future.
Comment: Although several commenters noted appreciation for the
fact that we analyzed a weighted motor score in response to their
comments on the FY 2019 IRF PPS proposed rule (83 FR 38546), these same
commenters expressed concerns with the actual weight values that CMS
proposed for FY 2020, as indicated in Table 1, and stated that we
should go back to an unweighted motor score so that we can do further
analysis and collaborate with stakeholders to further refine the
weighting methodology. Some commenters expressed concern that CMS might
be proposing higher weights for the self-care items than for the
mobility items, in contrast to the current weighted motor score, which
weights mobility items higher than self-care items. Some commenters
specifically requested that CMS explain why the weight for the eating
item increased from 0.6 under the current weighting methodology to 2.7
under the proposed methodology, and requested we explain what we
believe this change will mean for patients with eating deficits.
Commenters were also generally concerned by what they suggested were
large differences in the weight value assignments between the current
and proposed motor score.
Response: We used simple ordinary least squares regression analysis
of the data that IRFs submitted to us in FYs 2017 and 2018 to calculate
the proposed weight values for the motor score, in response to
stakeholder feedback on the FY 2019 IRF PPS proposed rule (83 FR
38546). Commenters are correct that the proposed weights for the motor
score items, in comparison with the current weights, shift some of the
weight from the mobility to the self-care items. We also note that the
proposed weights assigned to the bowel and bladder function items
increased compared with the current weights. These changes are all
reflective of the data the IRFs submitted to us in FYs 2017 and 2018.
Regarding the proposed increase in the weight for the eating item,
it is important to note key differences in the coding guidelines
between the FIMTM eating item and the section GG eating item
that may have contributed to the change in the relative importance of
this item for predicting IRF costs. For item GG0130A, Eating,
assistance with tube feedings is not considered when coding this item.
If a patient does not eat or drink by mouth but is instead tube fed,
item GG0130A must be coded as 88--``Not attempted due to medical
condition or safety concerns'' or 09--``Not applicable''. Both of these
responses would be recoded to a 01--``Dependent'' for the purposes of
assigning the patient to a CMG. This
[[Page 39062]]
differs from the coding instructions for the FIMTM eating
item used in the current motor score, which takes into consideration
assistance with tube feedings in scoring the item. For example,
according to the FIMTM instructions, a patient who could
administer the tube feeding completely independently could receive a
score of 7-Complete independence on the eating item.
In regards to the suggested differences in the weight value
assignments between the current and proposed methodologies, we note
that in certain cases the proposed weights were divided among multiple
items in the motor score that were found to be highly correlated to
avoid overweighting any particular measure of function. For instance,
the three items (GG0170I1, GG0170J1, and GG0170K1) that assess walking
function were each assigned a proposed weight of 0.8. When summed
together, the weight value for walking under the proposed methodology
is 2.4, which is slightly higher than the weight value of 1.6 for the
single walking item used in the current motor score.
Comment: One commenter disagreed with the removal of item GG0170A1
roll left and right from the motor score and noted it is an important
functional task in the IRF setting. Some commenters questioned the use
of averaging values across pairs of items that were correlated and
inquired why the roll left and right item was removed from the motor
score while other correlated items were not removed. Commenters also
inquired about the use of the item ``walk 10 feet'' to derive the
weights for the ``walk 50 feet'' and ``walk 150 feet'' items.
Response: We appreciate the commenter's concerns regarding the
removal of item GG0170A1 from the motor score. As described in detail
in the technical report, ``Analyses to Inform the Use of Standardized
Patient Assessment Data Elements in the Inpatient Rehabilitation
Facility Prospective Payment System,'' the roll left and right item was
found to have a high degree of multicollinearity with other
standardized patient assessment elements and to be inversely correlated
with costs after controlling for each of the other self-care and
mobility items. This relationship persisted when this item was paired
with the other correlated items. The continued inclusion of this item
in the motor score would have resulted in either a negative or non-
significant coefficient. As such, we do not believe it is appropriate
to include this item in the construction of the motor score. The other
item pairs that were found to be correlated did not generate negative
or non-significant coefficients, and were therefore maintained in the
calculation of the motor score.
Unlike the FIMTM instrument, the items from the quality
indicator section of the IRF-PAI sometimes use more than one item to
measure functional areas. As discussed in more detail in the technical
report, we noted that a few items were found to be highly correlated.
Because of the correlation, we proposed to use an average score for
some items so as to avoid introducing bias or inappropriately
overweighting any particular functional area. We note this methodology
is consistent with the methodology used under the Patient Driven
Payment Model (PDPM), as described in more detail in the FY 2019 SNF
final rule (83 FR 39204) and the accompanying technical report entitled
``Skilled Nursing Facilities Patient-Driven Payment Model Technical
Report'' available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/therapyresearch.html.
Regarding the ``walk 10 feet'' item, that item was used to derive
the weights for the ``walk 50 feet'' and ``walk 150 feet'' items as
these three items were found to be highly correlated and the ``walk 150
feet'' item had a high proportion of observations coded on admission
with ``activity not attempted'' codes.
Comment: Some commenters requested that CMS apply the current motor
score weights associated with the FIMTM items to the revised
motor score while other commenters requested that CMS postpone
weighting the motor score until additional data can be collected and
analyzed. While a few commenters were supportive of using a weighted
motor score, other commenters suggested that CMS use a 1-year payment
model or phase in the use of a weighted motor score.
Response: We do not believe it would be appropriate to apply the
weight values associated with the FIMTM items to the
components of the revised motor score, as these weights would not
accurately reflect how the various components of the revised motor
score contribute to predicting patient costs. We used simple ordinary
least squares regression analysis of the data that IRFs submitted to us
in FYs 2017 and 2018 to calculate the proposed weight values for the
revised motor score. Changes in patient demographics, treatment
practices, technology, and other factors that may affect the relative
use of resources in an IRF since the motor score weights were
originally calculated have likely contributed to changes in the weight
values applied across the self-care and mobility items. We proposed to
apply weights to the motor score items because RTI's analysis indicated
that a weighted motor score would improve the classification of
patients into CMGs, which in turn would improve the accuracy of
payments to IRFs. However, as discussed above, in response to public
comments, we carefully considered whether to finalize the proposed
weighted motor score or go back to using an unweighted motor score to
assign patients to CMGs. Although the proposed weighted motor score
results in a slight improvement in the ability of the IRF PPS to
predict patient costs and thus the accuracy of IRF PPS payments (less
than 0.18 difference in accuracy between the weighted and the
unweighted motor scores), we acknowledge the unweighted motor score is
conceptually simpler and, as such, believe it will ease providers'
transition to the use of the data items located in the Quality
Indicators section of the IRF-PAI (also referred to as section GG
items). Thus, we are finalizing based on public comments the use of an
unweighted motor score, in which each of the 18 items have a weight of
1, to assign patients to CMGs beginning with FY 2020.
Comment: Commenters expressed concern that the analysis performed
by RTI did not explicitly follow the analysis conducted by RAND when
the motor score weights were developed for FY 2006 (70 FR 47896 through
47900) and that RTI based their analyses on 2 years of data instead of
several years of data. Additionally, commenters requested more
information on the other weighting methodologies that RTI considered.
Response: We disagree with the commenters that the RAND analysis
for FY 2006 used more years of data than RTI's analysis for the FY 2020
proposed rule. As discussed in the FY 2006 IRF PPS final rule (70 FR
47897), RAND performed regression analysis on less than 2 full years of
data (calendar year (CY) 2002 and FY 2003) to derive the current motor
score weights. In contrast, RTI used 2 full years of data (FYs 2017 and
2018) to perform the analysis for the weighted motor score proposed in
the FY 2020 IRF PPS proposed rule. As the FYs 2017 and 2018 data
portrays the most recent and complete picture of patients under the IRF
PPS, we believe it was sufficient and appropriate to utilize for the
analysis for the proposed rule.
While RTI utilized a different weighting methodology than was used
by RAND in 2006, the overall model
[[Page 39063]]
prediction using the weighted motor score developed by RAND and the
weighted motor score developed by RTI is extremely similar. The model
using the CMGs based on the standardized patient assessment data
elements and comorbidity tiers to predict wage-adjusted costs of care
has an r-squared value is 0.3358, while the r-squared value is 0.3169
for the CMGs in the current IRF PPS. This is indicative of similar
model performance regardless of model specification. The item weights
that the RAND work notes as ``optimally weighted'' are weights that
were constructed separately for each RIC. These were not the weights
that were used in the final weights developed by RAND.
RTI also examined weighing methodologies utilizing a general linear
model (GLM) and log transformed ordinary least squares (OLS) regression
models, as well as the OLS model described in more detail in the
technical report. All three models had comparable model fit and
generated similar item weights. Based on the greater simplicity
achieved through the use of the OLS regression model we believe using
the OLS regression was appropriate to maintain simplicity and
transparency in the payment system.
Comment: Commenters disagreed with the omission of the wheelchair
mobility items from the items used to construct the motor score.
Response: We appreciate the commenters' concerns about wheelchair-
dependent patients. As most recently discussed in the FY 2019 IRF PPS
final rule (83 FR 38546) in response to similar stakeholder comments,
we explained our rationale for not including the wheelchair mobility
items in the construction of the finalized motor score. We continue to
believe that the higher resource needs of wheelchair dependent patients
in IRFs will be better accounted for by not including a wheelchair item
in the motor score at this time. Patients that are considered
wheelchair dependent or unable to walk will be accounted for through
the ``not attempted'' response codes captured through other items,
especially some of the walking items, that are included in the motor
score. In this way, we ensure that IRFs will be appropriately
compensated for the higher costs they incur in treating wheelchair-
dependent patients. We refer readers to the FY 2019 IRF PPS final rule
(83 FR 38546) and the technical report entitled ``Analyses to Inform
the Use of Standardized Patient Assessment Data Elements in the
Inpatient Rehabilitation Facility Prospective Payment System'' for more
information on the rationale as to why this item was not included in
the calculation of the motor score.
Comment: Commenters expressed concern with the weighted motor score
and questioned the reliability and validity of the weighted motor
score. Some commenters stated that they believe the weighted and
unweighted motor scores have shown little to no correlation with the
weighted motor score currently in use, and therefore, questioned if the
weighted motor score could accurately measure patient severity.
Response: We disagree with the commenters' suggestion that
unweighted and weighted motor scores have shown little to no
correlation with the weighted motor score currently in use as our
analysis shows a strong correlation between the scores. In addition,
each of the proposed Quality Indicators data items that were included
in the motor score were found to have statistically significant
correlation with IRF costs. As discussed in the technical report
``Analyses to Inform the Use of Standardized Patient Assessment Data
Elements in the Inpatient Rehabilitation Facility Prospective Payment
System'' the use of a weighted motor score was found to increase the
predictive ability of the payment model.
Comment: Commenters requested that CMS make available the data
utilized in the analyses including patient assessment data, matching
claims data, and additional facility and cost report data to enable
stakeholders to replicate the analyses.
Response: We appreciate the commenters' feedback regarding the
types of information that would be most useful to them in replicating
our analyses. We are unable to make patient assessment and claims data
publicly available on the CMS website because these data contain
personally identifiable information. However, we believe that we
released sufficient information in the proposed rule, the accompanying
data files, and the technical report entitled ``Analyses to Inform the
Use of Standardized Patient Assessment Data Elements in the Inpatient
Rehabilitation Facility Prospective Payment System,'' to enable
stakeholders to submit meaningful comments on the underlying analyses
and methodologies used to revise the IRF case-mix classification
system, to pose alternative approaches, and to assess the impacts of
the proposed revisions.
Comment: A few commenters noted that they did not believe that CMS
has performed the thorough data analyses and engagement with the
provider community that are necessary prior to making significant
changes to the existing IRF PPS. These commenters requested that we
solicit additional feedback from the stakeholder community, including
convening technical advisory panels (TEPs), to provide additional
transparency into the underlying analyses and to delay implementation
of a weighted motor score until we conduct additional engagements with
stakeholders.
Response: We value transparency in our processes and will continue
to engage stakeholders in future development of payment policies. We
appreciate the offers from stakeholders to assist in the development of
future revisions to payment policies and we recognize the value from
these partnerships. However, for something as analytically simple as
running a regression analysis to determine the weights for the motor
score items that best reflect patients' resource needs in the IRF, we
do not believe that a TEP is necessary.
As noted above, although the proposed weighted motor score results
in a slight improvement in the ability of the IRF PPS to predict
patient costs and thus the accuracy of IRF PPS payments (less than 0.18
difference in accuracy between the weighted and the unweighted motor
scores), we acknowledge the unweighted motor score is conceptually
simpler and, as such, believe it will ease providers' transition to the
use of the data items located in the Quality Indicators section of the
IRF-PAI (also referred to as section GG items). Thus, we are finalizing
based on public comments the use of an unweighted motor score to assign
patients to CMGs beginning with FY 2020. We appreciate the
stakeholders' comments on this topic and will take them into
consideration for future analysis.
Comment: A few commenters requested that CMS provide additional
information regarding the provider specific impact analysis file that
accompanied the rule, such as a data dictionary describing the data
used to calculate the impacts.
Response: In conjunction with the release of the FY 2020 IRF PPS
proposed rule, we posted a provider-specific impact analysis file that
compared estimated payments to providers for FY 2020 without the
proposed revisions to the CMGs with estimated payments to providers for
FY 2020 with the proposed revisions to the CMGs. We believe that this
file gives IRFs added information to enable them to see how their
individual payments would be affected by the proposed changes to the
CMGs. We updated this
[[Page 39064]]
provider specific impact analysis file shortly after it was initially
posted to include additional information regarding the underlying data
used to calculate the provider specific impacts, and we believe that
this additional information is responsive to commenters' requests. The
file can be downloaded from the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html. We appreciate the commenters' suggestions
regarding the additional types of information that would be most useful
to them to further facilitate understanding of our analyses.
As previously discussed, we proposed a weighted motor score as it
was found to slightly improve the predicative ability of the case-mix
system and thus the accuracy of IRF PPS payments. However, nearly all
of the comments we received requested that we revert to an unweighted
motor score for the various reasons discussed above. While we continue
to believe that a weighted motor score is slightly more accurate, the
difference is small, and in light of the conceptual simplicity achieved
through the use of an unweighted motor score, which we believe will
ease providers' transition to the use of the data items located in the
Quality Indicators section of the IRF-PAI, we are finalizing the use of
an unweighted motor score, in which each of the 18 items used in the
score have an equal weight of 1, to assign patients to CMGs beginning
with FY 2020. Additionally, we are finalizing the proposed removal of
one item (GG0170A1 Roll left to right) from the motor score beginning
with FY 2020. Effective for all discharges beginning on or after
October 1, 2019, we will use an unweighted motor score as indicated in
Table 2 to determine a beneficiary's CMG placement.
[GRAPHIC] [TIFF OMITTED] TR08AU19.002
C. Revisions to the CMGs and Updates to the CMG Relative Weights and
Average Length of Stay Values Beginning With FY 2020
In the FY 2019 IRF PPS final rule (83 FR 38549), we finalized the
use of data items from the Quality Indicators section of the IRF-PAI to
construct the functional status scores used to classify IRF patients in
the IRF case-mix classification system for purposes of establishing
payment under the IRF PPS beginning with FY 2020, but modified our
proposal based on public comments to incorporate 2 years of data (FYs
2017 and 2018) into our analyses used to revise the CMG definitions. We
stated that any changes to the proposed CMG definitions resulting from
the incorporation of an additional year of data (FY 2018) into the
analysis would be addressed in future rulemaking prior to their
implementation beginning in FY 2020. Additionally, we stated that we
would also update the relative weights and average LOS values
associated with any revised CMG definitions in future rulemaking.
As noted in the FY 2020 IRF PPS proposed rule (84 FR 17251), we
continued our contract with RTI to support us in developing proposed
revisions to the CMGs used under the IRF PPS based on analysis of 2
years of data (FYs 2017 and 2018). The process RTI uses for its
analysis, which is based on a Classification and Regression Tree (CART)
algorithm, is described in detail in the FY 2019 IRF PPS final rule (83
FR 38536 through 38540). RTI used this analysis to revise the CMGs
utilizing FYs 2017 and 2018 claim and assessment data and to develop
revised CMGs that reflect the use of the data items collected in the
Quality Indicators section of the IRF-PAI, incorporating the proposed
weighted motor score described in the FY 2020 IRF PPS proposed rule.
However, as discussed in section IV.B of this final rule, we are
finalizing based on public comments the use of an unweighted motor
score to assign patients to a CMGs beginning in with FY 2020.
To develop the proposed revised CMGs, RTI used CART analysis to
divide patients into payment groups based on similarities in their
clinical characteristics and relative costs. As part of this analysis,
RTI imposed some typically-used constraints on the payment group
divisions (for example, on the minimum number of cases that could be in
the resulting payment groups and the minimum dollar payment amount
differences between groups) to identify the optimal set of payment
groups. For a more detailed discussion of the analysis used to revise
[[Page 39065]]
the CMGs for FY 2020, we refer readers to the March 2019 technical
report entitled, ``Analyses to Inform the Use of Standardized Patient
Assessment Data Elements in the Inpatient Rehabilitation Facility
Prospective Payment System'' available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research.html.
Additionally, we refer readers to the FY 2020 IRF PPS proposed rule (84
FR 17250 through 17260) for more information on the proposed revisions
to the CMGs.
As noted above, we are finalizing the use of an unweighted motor
score beginning with FY 2020. As the motor score is a key input in the
CART analysis used to revise the CMGs, the use of the unweighted motor
score required that the CART analysis be rerun utilizing the unweighted
motor score. RTI utilized the same methodology described in the FY 2020
IRF PPS proposed rule (84 FR 17250 through 17260) to support us in
developing revisions to the CMGs, incorporating the unweighted motor
score, as described in section IV.B of this final rule. The revised
CMGs can be found in Table 3.
After developing the revised CMGs, RTI then calculated the relative
weights and average LOS values for each revised CMG using the same
methodologies that we have used to update the CMG relative weights and
average LOS values each fiscal year since 2009 (when we implemented an
update to this methodology). More information about the methodology
used to update the CMG relative weights can be found in the FY 2009 IRF
PPS final rule (73 FR 46372 through 46374). For FY 2020, we proposed to
use the FYs 2017 and 2018 IRF claims and FY 2017 IRF cost report data
to update the CMG relative weights and average LOS values. In
calculating the CMG relative weights, we use a hospital-specific
relative value method to estimate operating (routine and ancillary
services) and capital costs of IRFs. As noted in the FY 2019 IRF PPS
final rule (83 FR 38521), this is the same methodology that we have
used to update the CMG relative weights and average LOS values each
fiscal year since we implemented an update to the methodology in the FY
2009 IRF PPS final rule (73 FR 46372 through 46374). More information
on the methodology used to update calculate the CMG relative weights
and average LOS values can found in the March 2019 technical report
entitled ``Analyses to Inform the Use of Standardized Patient
Assessment Data Elements in the Inpatient Rehabilitation Facility
Prospective Payment System'' available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research.html.
Consistent with the methodology that we have used to update the IRF
classification system in each instance in the past, we proposed to
update the relative weights associated with the revised CMGs for FY
2020 in a budget neutral manner by applying a budget neutrality factor
to the standard payment amount. To calculate the appropriate budget
neutrality factor for use in updating the FY 2020 CMG relative weights,
we used the following steps:
Step 1. Calculate the estimated total amount of IRF PPS payments
for FY 2020 (with no changes to the CMG relative weights).
Step 2. Calculate the estimated total amount of IRF PPS payments
for FY 2020 by applying the changes to the CMGs and the associated CMG
relative weights (as described in this final rule).
Step 3. Divide the amount calculated in step 1 by the amount
calculated in step 2 to determine the budget neutrality factor (1.0016)
that would maintain the same total estimated aggregate payments in FY
2020 with and without the changes to the CMGs and the associated CMG
relative weights.
Step 4. Apply the budget neutrality factor (1.0016) to the FY 2019
IRF PPS standard payment amount after the application of the budget-
neutral wage adjustment factor.
We note that, as we typically do, we updated our data between the
FY 2020 IRF PPS proposed and final rules to ensure that we use the most
recent available data in calculating IRF PPS payments. Additionally, we
are finalizing the use of unweighted motor score beginning in with FY
2020 which generated revisions to the CMGs and relative weights. Based
on our analysis using this updated data and an unweighted motor score,
we now estimate a budget neutrality factor of (1.0010) to maintain the
same total estimated aggregate payments in FY 2020 with and without the
changes to the CMGs and the associated CMG relative weights. For FY
2020 we will apply the budget neutrality factor (1.0010) to the FY 2019
IRF PPS standard payment amount after the application of the budget-
neutral wage adjustment factor.
The relative weights and average LOS values for those revised CMGs
(found in Table 3) were calculated using the same methodology described
in the FY 2020 IRF PPS proposed rule, which is the same methodology
that we have used to update the CMG relative weights and average LOS
values each fiscal year since we implemented an update to the
methodology in FY 2009. The revised CMGs (reflecting the unweighted
motor score) and their respective descriptions, as well as the
comorbidity tiers, corresponding relative weights and the average LOS
values for each CMG and tier for FY 2020 are shown in Table 3. The
average LOS for each CMG is used to determine when an IRF discharge
meets the definition of a short-stay transfer, which results in a per
diem case level adjustment. In section V.H. of this final rule, we
discuss the proposed use of the existing methodology to calculate the
standard payment conversion factor for FY 2020.
We received a number of comments on the proposed revisions to the
CMGs based on analysis of 2 years of data (FYs 2017 and 2018) and the
proposed updates to the relative weights and average LOS values
associated with the revised CMGs beginning with FY 2020, that is, for
all discharges beginning on or after October 1, 2019, which are
summarized below.
Comment: A number of commenters were appreciative of the use of 2
years of data to revise the CMGs; however, commenters expressed concern
with the proposed CMG revisions and suggested that these changes could
result in payment rate compression or a misalignment between payments
and the costs of caring for patients. Commenters suggested payment
compression would result in reduced payments for higher acuity patients
and increased payments for lower acuity patients which could compromise
access to care for patients with certain impairments. Additionally,
some commenters questioned why there would be fewer CMGs within some
RICs and suggested having fewer CMGs would also contribute to payment
rate compression.
Response: We disagree with the commenters that revisions to CMGs
will lead to payment rate compression or could compromise access to
care for any particular group of patients. As the revised CMGs are
reflective of the data that IRFs submitted to us in FYs 2017 and 2018,
we believe the revised CMGs reflect the distinct resource needs of the
current Medicare IRF population. We believe the revised CMGs more
accurately predict resource use in IRFs and better align payments with
the expected costs of treating patients in the IRF setting. As such, we
believe that the revised CMGs may in fact improve access to and quality
of care for IRF patients by increasing the accuracy of IRF payments to
providers.
Regarding why some RICs would have fewer CMGs, we refer the
commenters to the Technical Report entitled ``Analyses
[[Page 39066]]
to Inform the Use of Standardized Patient Assessment Data Elements in
the Inpatient Rehabilitation Facility Prospective Payment System'' that
describes in detail the analysis used to derive the CMGs and the
criteria required to generate additional payment groups. As noted in
the FY 2020 IRF PPS proposed rule (84 FR 17250 through 17252), RTI
imposed some typically-used constraints in their analysis to identify
the proposed set of payment groups. These constraints consisted of a
minimum number of stays within a node, a 0.5 percentage point increase
of explanatory power, and monotonicity across the CMGs within each RIC.
We do not believe it would be appropriate to generate additional CMGs
that did not improve the predicative ability of the model beyond what
was produced through the CART analysis utilizing the constraints above.
We note that while the CART analysis generated fewer CMGs within some
RICs, it generated a greater number of CMGs within other RICs and that
the overall number of CMGs increases through these revisions to the
case-mix classification system. We do not believe having fewer CMGs
within any RIC will contribute to payment rate compression as we
believe these revisions better align payments with the expected costs
of treating patients in IRFs.
Additionally, we disagree with the commenters' statements that the
CMG revisions will result in higher payments for lower acuity patients
and reduced payments for higher acuity patients. Our analysis has found
that higher function is associated with a slight reduction in payment
under the revised CMGs and that lower function is associated with a
slight increase in payments. The purpose of the proposed revisions to
the CMGs is to align payments more appropriately with the costs of
caring for all types of patients in IRFs. As such, we do not believe
that the revisions will result in higher payments for lower acuity
patients. We appreciate the commenters' concerns and will continue to
monitor the IRF data closely to ensure that IRF payments are
appropriately aligned with costs of care and that Medicare patients
continue to have appropriate access to IRF services.
Comment: Several commenters expressed concerns that the proposed
CMG revisions could cause a significant redistribution of payments
among IRF provides. These commenters indicated that they believe the
section GG items make patients appear to be less severe and requested
additional information on how patients would be redistributed among the
revised CMGs. Additionally, commenters encouraged CMS to monitor the
data based on these changes and to update the model if necessary in the
future.
Response: We agree with the commenters that the revisions to the
CMGs may result in some redistribution of payments among providers. As
noted in the FY 2019 IRF PPS final rule (83 FR 38547), the scales and
coding instructions are slightly different between the item sets used
to derive the existing CMGs and those used to derive the revised CMGs.
As such, these differences may result in some patients grouping into
different CMGs that more accurately account for the expected resource
needs of the patient. While we cannot make individual Medicare
beneficiary data publically available, we believe we released adequate
information for stakeholders to determine how beneficiaries could be
distributed across the revised CMGs. We appreciate the commenters'
suggestions to conduct monitoring activities and make future updates to
the case-mix classification system and will take this into
consideration in the future.
Comment: Commenters expressed concern with the use of section GG
items to assign a patient to a CMG and suggested that these items are
not sensitive enough and do not capture patients' true burden of care.
Commenters also expressed concern with the reliability of the data
collected through these items and suggested that the data is not
accurate or valid.
Response: As discussed in detail in the FY 2019 IRF PPS final rule
(83 FR 38541), we believe that the data items located in the Quality
Indicators section of the IRF-PAI are sensitive and accurately capture
the functional and cognitive status of patients and can also be used to
accurately assess changes in patients' functional status. As noted
above, RTI found that the model predicting costs using the CMGs derived
from the items located in the Quality Indicators section of the IRF-PAI
had a slightly higher R-squared value than models using the current
CMGs which are derived from items in the FIMTM instrument,
indicating that the revised CMGs more accurately predict resource use
in IRFs than the CMGs that are currently utilized. As the data
collected in the Quality Indicators section of the IRF-PAI have been
collected nationally for all IRFs since October 1, 2016, we believe the
data to be accurate and valid at this time. We also believe it is the
responsibility of the IRF to submit accurate and valid data that
adheres to the coding guidelines detailed in the IRF-PAI training
manual.
Comment: Commenters expressed concern with the cognition items
collected on the IRF-PAI and their omission from the revised CMGs. A
few commenters noted the importance of cognitive impairment in the IRF
setting and encouraged CMS to conduct further analysis of the
relationship between cognitive function and resource use in the IRF
setting and to improve the items that are used to measure cognitive
function.
Response: We appreciate the commenters' concerns with the cognitive
items that are collected on the IRF-PAI. As we discussed in the FY 2019
IRF PPS final rule (83 FR 38546), the cognitive items that we used for
this analysis are the best ones that we have for use at the present
time. Unfortunately, we found that including these cognitive items in
generating the CMGs would have resulted in lower payments for patients
with higher cognitive deficits. This result does not make sense from a
clinical perspective, and could have the unintended consequence of
reducing access to IRF care for more cognitively impaired
beneficiaries. Thus, we determined that it would be better at this time
to remove the CMG splits that were generated by the cognitive items. We
appreciate the commenters' suggestion to incorporate improved cognition
measures into the IRF-PAI and will take this into consideration in the
future.
Comment: Commenters suggested that CMS has not provided sufficient
education, training materials, or supporting documentation regarding
the functional items to support their use in developing a payment
model. Some commenters suggested revisions to the existing training
materials while other commenters requested that CMS provide additional
training, monitor the data, and modify the case mix groupings as
needed.
Response: We disagree with the commenters that we have provided
insufficient training or guidance on proper coding of this data. We
believe we have provided adequate training opportunities for IRFs on
coding the Quality Indicator data items, including multiple in-person
training opportunities, webinars, on-line training and on-going help
desk guidance. We are committed to providing information and support
that will allow providers to accurately interpret and complete quality
reporting items and we will continue to provide these types of
opportunities to the IRF community. We thank the commenters for their
suggestions to improve the training materials and we appreciate the
commenters' suggestions to continue to monitor the data and make
updates to
[[Page 39067]]
the case-mix classification system when necessary.
After careful consideration of the comments received, we are
finalizing revisions to the CMGs based on analysis of 2 years of data
(FYs 2017 and 2018) and the incorporation of the unweighted motor score
described in section IV.B of this final rule. The revised CMGs that
will be effective October 1, 2019 are presented below in Table 3. We
refer readers to Table 20 in section XIII.C of this final rule for more
information on the distributional effects of revisions to the CMGs. For
a provider specific impact analysis for this change, we refer readers
to the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
We are also updating the relative weights and average LOS values
associated with the revised CMGs (reflecting an unweighted motor score)
beginning with FY 2020.
BILLING CODE 4120-01-P
[[Page 39068]]
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[[Page 39069]]
[GRAPHIC] [TIFF OMITTED] TR08AU19.004
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[GRAPHIC] [TIFF OMITTED] TR08AU19.005
[[Page 39071]]
[GRAPHIC] [TIFF OMITTED] TR08AU19.006
BILLING CODE 4120-01-C
V. Facility-Level Adjustment Factors
Section 1886(j)(3)(A)(v) of the Act confers broad authority upon
the Secretary to adjust the per unit payment rate by such factors as
the Secretary determines are necessary to properly reflect variations
in necessary costs of treatment among rehabilitation facilities. Under
this authority, we currently adjust the prospective payment amount
associated with a CMG to account for facility-level characteristics
such as an IRF's LIP, teaching status, and location in a rural area, if
applicable, as described in Sec. 412.624(e).
Based on the substantive changes to the facility-level adjustment
factors that were adopted in the FY 2014 IRF PPS final rule (78 FR
47860, 47868 through 47872), in the FY 2015 IRF PPS final rule (79 FR
45872, 45882 through 45883), we froze the facility-level adjustment
factors at the FY 2014 levels for FY 2015 and all subsequent years
(unless and until we propose to update them again through future
notice-and-comment rulemaking). For FY 2020, we will continue to hold
the adjustment factors at the FY 2014 levels as we continue to monitor
the most current IRF claims data available and continue to evaluate and
monitor the effects of the FY 2014 changes.
VI. FY 2020 IRF PPS Payment Update
A. Background
Section 1886(j)(3)(C) of the Act requires the Secretary to
establish an increase factor that reflects changes over time in the
prices of an appropriate mix of goods and services included in the
covered IRF services. According to section 1886(j)(3)(A)(i) of the Act,
the increase factor shall be used to update the IRF prospective payment
rates for each FY. Section 1886(j)(3)(C)(ii)(I) of the Act requires the
application of a productivity adjustment. Thus, in the FY 2020 IRF
proposed rule, we proposed to update the IRF PPS payments for FY 2020
by a market basket increase factor as required by section 1886(j)(3)(C)
of the Act based upon the most current data available, with a
productivity adjustment as required by section 1886(j)(3)(C)(ii)(I) of
the Act (84 FR 17261).
We have utilized various market baskets through the years in the
IRF PPS. For a discussion of these market baskets, we refer readers to
the FY 2016 IRF PPS final rule (80 FR 47046).
Beginning with FY 2016, we finalized the use of a 2012-based IRF
market basket, using Medicare cost report (MCR) data for both
freestanding and hospital-based IRFs (80 FR 47049 through 47068).
Beginning with FY 2020, we proposed to rebase and revise the IRF market
basket to reflect a 2016 base year. In the following discussion, we
provide an overview of the proposed market basket and describe the
methodologies used to determine the operating and capital portions of
the proposed 2016-based IRF market basket.
B. Overview of the 2016-Based IRF Market Basket
The 2016-based IRF market basket is a fixed-weight, Laspeyres-type
price index. A Laspeyres price index measures the change in price, over
time, of the same mix of goods and services purchased in the base
period. Any changes in the quantity or mix of goods and services (that
is, intensity) purchased over time relative to a base period are not
measured.
The index itself is constructed in three steps. First, a base
period is selected (for the proposed IRF market basket, the base period
is 2016), total base period costs are estimated for a set of mutually
exclusive and exhaustive cost categories, and each category is
calculated as a proportion of total costs. These proportions are called
cost weights. Second, each cost category is matched to an appropriate
price or wage variable, referred to as a price proxy. In nearly every
instance where we have selected price proxies for the various market
baskets, these price proxies are derived from publicly available
statistical series that are published on a consistent schedule
(preferably at least on a quarterly basis). In cases where a publicly
available price series is not available (for example, a price index for
malpractice insurance), we have collected price data from other sources
and subsequently developed our own index to capture changes in prices
for these types of costs. Finally, the cost weight for each cost
category is multiplied by the established price proxy. The sum of these
products (that is, the cost weights multiplied by their price levels)
for all cost categories yields the composite index level of the market
basket for the given time period. Repeating this step for other periods
produces a series of market basket levels over time. Dividing the
composite index level of one period by the composite index level for an
earlier period produces a rate of growth in the input price index over
that timeframe.
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As previously noted, the market basket is described as a fixed-
weight index because it represents the change in price over time of a
constant mix (quantity and intensity) of goods and services needed to
furnish IRF services. The effects on total costs resulting from changes
in the mix of goods and services purchased after the base period are
not measured. For example, an IRF hiring more nurses after the base
period to accommodate the needs of patients would increase the volume
of goods and services purchased by the IRF, but would not be factored
into the price change measured by a fixed-weight IRF market basket.
Only when the index is rebased would changes in the quantity and
intensity be captured, with those changes being reflected in the cost
weights. Therefore, we rebase the market basket periodically so that
the cost weights reflect recent changes in the mix of goods and
services that IRFs purchase to furnish inpatient care between base
periods.
C. Rebasing and Revising of the IRF PPS Market Basket
As discussed in the FY 2016 IRF PPS final rule (80 FR 47050), the
2012-based IRF market basket reflects the Medicare cost reports for
both freestanding and hospital-based facilities.
Beginning with FY 2020, we proposed to rebase and revise the 2012-
based IRF market basket to a 2016 base year reflecting both
freestanding and hospital-based IRFs. Below we provide a detailed
description of our methodology used to develop the proposed 2016-based
IRF market basket. This proposed methodology is generally similar to
the methodology used to develop the 2012-based IRF market basket with
the exception of the proposed derivation of the Home Office Contract
Labor cost weight using the MCR data as described in section VI.C.a.(6)
of this final rule.
1. Development of Cost Categories and Weights for the 2016-Based IRF
Market Basket
a. Use of Medicare Cost Report Data
We proposed a 2016-based IRF market basket that consists of seven
major cost categories and a residual derived from the 2016 Medicare
cost reports (CMS Form 2552-10) for freestanding and hospital-based
IRFs. The seven cost categories are Wages and Salaries, Employee
Benefits, Contract Labor, Pharmaceuticals, Professional Liability
Insurance (PLI), Home Office Contract Labor, and Capital. The residual
category reflects all remaining costs not captured in the seven cost
categories. The 2016 cost reports include providers whose cost
reporting period began on or after October 1, 2015, and prior to
September 30, 2016. We selected 2016 as the base year because we
believe that the Medicare cost reports for this year represent the most
recent, complete set of MCR data available for developing the IRF
market basket at the time of the proposed rule.
Since our goal is to establish cost weights that were reflective of
case mix and practice patterns associated with the services IRFs
provide to Medicare beneficiaries, as we did for the 2012-based IRF
market basket, we proposed to limit the cost reports used to establish
the 2016-based IRF market basket to those from facilities that had a
Medicare average LOS that was relatively similar to their facility
average LOS. We believe that this requirement eliminates statistical
outliers and ensures a more accurate market basket that reflects the
costs generally incurred during a Medicare-covered stay. The Medicare
average LOS for freestanding IRFs is calculated from data reported on
line 14 of Worksheet S-3, part I. The Medicare average LOS for
hospital-based IRFs is calculated from data reported on line 17 of
Worksheet S-3, part I. We proposed to include the cost report data from
IRFs with a Medicare average LOS within 15 percent (that is, 15 percent
higher or lower) of the facility average LOS to establish the sample of
providers used to estimate the 2016-based IRF market basket cost
weights. We proposed to apply this LOS edit to the data for IRFs to
exclude providers that serve a population whose LOS would indicate that
the patients served are not consistent with a LOS of a typical Medicare
patient. We note that this is the same LOS edit that we applied to
develop the 2012-based IRF market basket. This process resulted in the
exclusion of about eight percent of the freestanding and hospital-based
IRF Medicare cost reports. Of those excluded, about 18 percent were
freestanding IRFs and 82 percent were hospital-based IRFs. This ratio
is relatively consistent with the ratio of the universe of freestanding
to hospital-based IRF providers.
We then used the cost reports for IRFs that met this requirement to
calculate the costs for the seven major cost categories (Wages and
Salaries, Employee Benefits, Contract Labor, Professional Liability
Insurance, Pharmaceuticals, Home Office Contract Labor, and Capital)
for the market basket. For comparison, the 2012-based IRF market basket
utilized the Bureau of Economic Analysis Benchmark Input-Output data
rather than MCR data to derive the Home Office Contract Labor cost
weight. A more detailed discussion of this methodological change is
provided in section VI.C.1.a.(6). of this final rule.
Similar to the 2012-based IRF market basket major cost weights, the
proposed 2016-based IRF market basket cost weights reflect Medicare
allowable costs (routine, ancillary and capital)--costs that are
eligible for reimbursement through the IRF PPS.
For freestanding IRFs, total Medicare allowable costs would be
equal to the total costs as reported on Worksheet B, part I, column 26,
lines 30 through 35, 50 through 76 (excluding 52 and 75), 90 through
91, and 93. For hospital-based IRFs, total Medicare allowable costs
would be equal to the total costs for the IRF inpatient unit after the
allocation of overhead costs (Worksheet B, part I, column 26, line 41)
and a proportion of total ancillary costs reported on Worksheet B, part
I, column 26, lines 50 through 76 (excluding 52 and 75), 90 through 91,
and 93. We proposed to calculate the portion of ancillary costs
attributable to the hospital-based IRF for a given ancillary cost
center by multiplying total facility ancillary costs for the specific
cost center (as reported on Worksheet B, part I, column 26) by the
ratio of IRF Medicare ancillary costs for the cost center (as reported
on Worksheet D-3, column 3 for hospital-based IRFs) to total Medicare
ancillary costs for the cost center (equal to the sum of Worksheet D-3,
column 3 for all relevant PPS [that is, IPPS, IRF, IPF and skilled
nursing facility (SNF)]). We proposed to use these methods to derive
levels of total costs for IRF providers. This is the same methodology
used for the 2012-based IRF market basket. With this work complete, we
then set about deriving cost levels for the seven major cost categories
and then derive a residual cost weight reflecting all other costs not
classified.
(1) Wages and Salaries Costs
For freestanding IRFs, we proposed to derive Wages and Salaries
costs as the sum of routine inpatient salaries, ancillary salaries, and
a proportion of overhead (or general service cost centers in the
Medicare cost reports) salaries as reported on Worksheet A, column 1.
Since overhead salary costs are attributable to the entire IRF, we only
include the proportion attributable to the Medicare allowable cost
centers. We proposed to estimate the proportion of overhead salaries
that are attributed to Medicare allowable costs centers by multiplying
the ratio of Medicare allowable area salaries (Worksheet A, column 1,
lines 50 through 76
[[Page 39073]]
(excluding 52 and 75), 90 through 91, and 93) to total salaries
(Worksheet A, column 1, line 200) times total overhead salaries
(Worksheet A, column 1, lines 4 through 18). This is the same
methodology used in the 2012-based IRF market basket.
For hospital-based IRFs, we proposed to derive Wages and Salaries
costs as the sum of inpatient routine salary costs (Worksheet A, column
1, line 41) for the hospital-based IRF and the overhead salary costs
attributable to this IRF inpatient unit; and ancillary salaries plus a
portion of overhead salary costs attributable to the ancillary
departments utilized by the hospital-based IRF.
We proposed to calculate hospital-based ancillary salary costs for
a specific cost center (Worksheet A, column 1, lines 50 through 76
(excluding 52 and 75), 90 through 91, and 93) using salary costs from
Worksheet A, column 1, multiplied by the ratio of IRF Medicare
ancillary costs for the cost center (as reported on Worksheet D-3,
column 3, for IRF subproviders) to total Medicare ancillary costs for
the cost center (equal to the sum of Worksheet D-3, column 3, for all
relevant PPS units [that is, IPPS, IRF, IPF and a SNF]). For example,
if hospital-based IRF Medicare physical therapy costs represent 30
percent of the total Medicare physical therapy costs for the entire
facility, then 30 percent of total facility physical therapy salaries
(as reported in Worksheet A, column 1, line 66) would be attributable
to the hospital-based IRF. We believe it is appropriate to use only a
portion of the ancillary costs in the market basket cost weight
calculations since the hospital-based IRF only utilizes a portion of
the facility's ancillary services. We believe the ratio of reported IRF
Medicare costs to reported total Medicare costs provides a reasonable
estimate of the ancillary services utilized, and costs incurred, by the
hospital-based IRF.
We proposed to calculate the portion of overhead salary costs
attributable to hospital-based IRFs by first calculating total
noncapital overhead costs (Worksheet B, part I, columns 4-18, line 41,
less Worksheet B, part II, columns 4-18, line 41). We then multiply
total noncapital overhead costs by an overhead ratio equal to the ratio
of total facility overhead salaries (as reported on Worksheet A, column
1, lines 4-18) to total facility noncapital overhead costs (as reported
on Worksheet A, column 1 and 2, lines 4-18). This methodology assumes
the proportion of total costs related to salaries for the overhead cost
center is similar for all inpatient units (that is, acute inpatient or
inpatient rehabilitation).
We proposed to calculate the portion of overhead salaries
attributable to each ancillary department by first calculating total
noncapital overhead costs attributable to each specific ancillary
department (Worksheet B, part I, columns 4-18 less, Worksheet B, part
II, columns 4-18). We then identify the portion of these noncapital
overhead costs attributable to Wages and Salaries by multiplying these
costs by the overhead ratio defined as the ratio of total facility
overhead salaries (as reported on Worksheet A, column 1, lines 4-18) to
total overhead costs (as reported on Worksheet A, column 1 & 2, lines
4-18). Finally, we identified the portion of these overhead salaries
for each ancillary department that is attributable to the hospital-
based IRF by multiplying by the ratio of IRF Medicare ancillary costs
for the cost center (as reported on Worksheet D-3, column 3, for
hospital-based IRFs) to total Medicare ancillary costs for the cost
center (equal to the sum of Worksheet D-3, column 3, for all relevant
PPS units [that is, IPPS, IRF, IPF and SNF]). This is the same
methodology used to derive the 2012-based IRF market basket.
(2) Employee Benefits Costs
Effective with the implementation of CMS Form 2552-10, we began
collecting Employee Benefits and Contract Labor data on Worksheet S-3,
part V.
For 2016 MCR data, the majority of providers did not report data on
Worksheet S-3, part V; particularly, approximately 48 percent of
freestanding IRFs and 40 percent of hospital-based IRFs reported data
on Worksheet S-3, part V. However, we believe we have a large enough
sample to enable us to produce a reasonable Employee Benefits cost
weight. Again, we continue to encourage all providers to report these
data on the Medicare cost report.
For freestanding IRFs, we proposed Employee Benefits costs would be
equal to the data reported on Worksheet S-3, part V, column 2, line 2.
We note that while not required to do so, freestanding IRFs also may
report Employee Benefits data on Worksheet S-3, part II, which is
applicable to only IPPS providers. For those freestanding IRFs that
report Worksheet S-3, part II, data, but not Worksheet S-3, part V, we
proposed to use the sum of Worksheet S-3, part II, lines 17, 18, 20,
and 22, to derive Employee Benefits costs. This proposed method allows
us to obtain data from about 30 more freestanding IRFs than if we were
to only use the Worksheet S-3, part V, data as was done for the 2012-
based IRF market basket.
For hospital-based IRFs, we proposed to calculate total benefit
costs as the sum of inpatient unit benefit costs, a portion of
ancillary benefits, and a portion of overhead benefits attributable to
the routine inpatient unit and a portion of overhead benefits
attributable to the ancillary departments. We proposed inpatient unit
benefit costs be equal to Worksheet S-3, part V, column 2, line 4. We
proposed that the portion of overhead benefits attributable to the
routine inpatient unit and ancillary departments be calculated by
multiplying ancillary salaries for the hospital-based IRF and overhead
salaries attributable to the hospital-based IRF (determined in the
derivation of hospital-based IRF Wages and Salaries costs as described
above) by the ratio of total facility benefits to total facility
salaries. Total facility benefits is equal to the sum of Worksheet S-3,
part II, column 4, lines 17-25, and total facility salaries is equal to
Worksheet S-3, part II, column 4, line 1.
(3) Contract Labor Costs
Contract Labor costs are primarily associated with direct patient
care services. Contract labor costs for other services such as
accounting, billing, and legal are calculated separately using other
government data sources as described in section VI.C.3. of this final
rule. To derive contract labor costs using Worksheet S-3, part V, data,
for freestanding IRFs, we proposed Contract Labor costs be equal to
Worksheet S-3, part V, column 1, line 2. As we noted for Employee
Benefits, freestanding IRFs also may report Contract Labor data on
Worksheet S-3, part II, which is applicable to only IPPS providers. For
those freestanding IRFs that report Worksheet S-3, part II data, but
not Worksheet S-3, part V, we proposed to use the sum of Worksheet S-3,
part II, lines 11 and 13, to derive Contract Labor costs.
For hospital-based IRFs, we proposed that Contract Labor costs
would be equal to Worksheet S-3, part V, column 1, line 4. As
previously noted, for 2016 MCR data, while there were providers that
did report data on Worksheet S-3, part V, many providers did not
complete this worksheet. However, we believe we have a large enough
sample to enable us to produce a reasonable Contract Labor cost weight.
We continue to encourage all providers to report these data on the
Medicare cost report.
(4) Pharmaceuticals Costs
For freestanding IRFs, we proposed to calculate pharmaceuticals
costs using
[[Page 39074]]
non-salary costs reported on Worksheet A, column 7, less Worksheet A,
column 1, for the pharmacy cost center (line 15) and drugs charged to
patients cost center (line 73).
For hospital-based IRFs, we proposed to calculate pharmaceuticals
costs as the sum of a portion of the non-salary pharmacy costs and a
portion of the non-salary drugs charged to patient costs reported for
the total facility. We proposed that non-salary pharmacy costs
attributable to the hospital-based IRF would be calculated by
multiplying total pharmacy costs attributable to the hospital-based IRF
(as reported on Worksheet B, part I, column 15, line 41) by the ratio
of total non-salary pharmacy costs (Worksheet A, column 2, line 15) to
total pharmacy costs (sum of Worksheet A, columns 1 and 2 for line 15)
for the total facility. We proposed that non-salary drugs charged to
patient costs attributable to the hospital-based IRF would be
calculated by multiplying total non-salary drugs charged to patient
costs (Worksheet B, part I, column 0, line 73 plus Worksheet B, part I,
column 15, line 73, less Worksheet A, column 1, line 73) for the total
facility by the ratio of Medicare drugs charged to patient ancillary
costs for the IRF unit (as reported on Worksheet D-3 for hospital-based
IRFs, column 3, line 73) to total Medicare drugs charged to patient
ancillary costs for the total facility (equal to the sum of Worksheet
D-3, column 3, line 73 for all relevant PPS [that is, IPPS, IRF, IPF
and SNF]).
(5) Professional Liability Insurance Costs
For freestanding IRFs, we proposed that Professional Liability
Insurance (PLI) costs (often referred to as malpractice costs) would be
equal to premiums, paid losses and self-insurance costs reported on
Worksheet S-2, part I, columns 1 through 3, line 118. For hospital-
based IRFs, we proposed to assume that the PLI weight for the total
facility is similar to the hospital-based IRF unit since the only data
reported on this worksheet is for the entire facility, as we currently
have no means to identify the proportion of total PLI costs that are
only attributable to the hospital-based IRF. Therefore, hospital-based
IRF PLI costs are equal to total facility PLI (as reported on Worksheet
S-2, part I, columns 1 through 3, line 118) divided by total facility
costs (as reported on Worksheet A, columns 1 and 2, line 200) times
hospital-based IRF Medicare allowable total costs. Our assumption is
that the same proportion of expenses are used among each unit of the
hospital.
(6) Home Office/Related Organization Contract Labor Costs
For the 2016-based IRF market basket, we proposed to determine the
home office/related organization contract labor costs using MCR data.
The 2012-based IRF market basket used the 2007 Benchmark Input-Output
(I-O) expense data published by the Bureau of Economic Analysis (BEA)
to derive these costs (80 FR 47057). A more detailed explanation of the
general methodology using the BEA I-O data is provided in section
VI.C.3. of this final rule. For freestanding and hospital-based IRFs,
we proposed to calculate the home office contract labor cost weight
(using data reported on Worksheet S-3, part II, column 4, lines 14,
1401, 1402, 2550, and 2551) and total facility costs (Worksheet B, part
I, column 26, line 202). We proposed to use total facility costs as the
denominator for calculating the home office contract labor cost weight
as these expenses reported on Worksheet S-3, part II reflect the entire
hospital facility. Our assumption is that the same proportion of
expenses are used among each unit of the hospital. For the 2012-based
IRF market basket, we calculated the home office cost weight using
expense data for North American Industry Classification System (NAICS)
code 55, Management of Companies and Enterprises (80 FR 47067).
(7) Capital Costs
For freestanding IRFs, we proposed that capital costs would be
equal to Medicare allowable capital costs as reported on Worksheet B,
part II, column 26, lines 30 through 35, 50 through 76 (excluding 52
and 75), 90 through 91, and 93.
For hospital-based IRFs, we proposed that capital costs would be
equal to IRF inpatient capital costs (as reported on Worksheet B, part
II, column 26, line 41) and a portion of IRF ancillary capital costs.
We calculate the portion of ancillary capital costs attributable to the
hospital-based IRF for a given cost center by multiplying total
facility ancillary capital costs for the specific ancillary cost center
(as reported on Worksheet B, part II, column 26) by the ratio of IRF
Medicare ancillary costs for the cost center (as reported on Worksheet
D-3, column 3 for hospital-based IRFs) to total Medicare ancillary
costs for the cost center (equal to the sum of Worksheet D-3, column 3
for all relevant PPS [that is, IPPS, IRF, IPF and SNF]). For example,
if hospital-based IRF Medicare physical therapy costs represent 30
percent of the total Medicare physical therapy costs for the entire
facility, then 30 percent of total facility physical therapy capital
costs (as reported in Worksheet B, part II, column 26, line 66) would
be attributable to the hospital-based IRF.
b. Final Major Cost Category Computation
After we derive costs for the major cost categories for each
provider using the MCR data as previously described, we proposed to
trim the data for outliers. For the Wages and Salaries, Employee
Benefits, Contract Labor, Pharmaceuticals, Professional Liability
Insurance, and Capital cost weights, we first divide the costs for each
of these six categories by total Medicare allowable costs calculated
for the provider to obtain cost weights for the universe of IRF
providers. We then remove those providers whose derived cost weights
fall in the top and bottom 5 percent of provider specific derived cost
weights to ensure the exclusion of outliers. After the outliers have
been excluded, we sum the costs for each category across all remaining
providers. We then divide this by the sum of total Medicare allowable
costs across all remaining providers to obtain a cost weight for the
2016-based IRF market basket for the given category.
The proposed trimming methodology for the Home Office Contract
Labor cost weight is slightly different than the proposed trimming
methodology for the other six cost categories as described above. For
the Home Office Contract Labor cost weight, since we are using total
facility data rather than Medicare-allowable costs associated with IRF
services, we proposed to trim the freestanding and hospital-based IRF
cost weights separately. For each of the providers, we first divide the
home office contract labor costs by total facility costs to obtain a
Home Office Contract Labor cost weight for the universe of IRF
providers. We then proposed to trim only the top 1 percent of providers
to exclude outliers while also allowing providers who have reported
zero home office costs to remain in the Home Office Contract Labor cost
weight calculations as not all providers will incur home office costs.
After removing these outliers, we are left with a trimmed data set for
both freestanding and hospital-based providers. We then proposed to sum
the costs for each category (freestanding and hospital-based) across
all remaining providers. We next divide this by the sum of total
facility costs across all remaining providers to obtain a freestanding
and hospital-based cost weight. Lastly, we proposed to weight these two
cost weights together using
[[Page 39075]]
the Medicare-allowable costs to derive a Home Office Contract Labor
cost weight for the 2016-based IRF market basket.
Finally, we proposed to calculate the residual ``All Other'' cost
weight that reflects all remaining costs that are not captured in the
seven cost categories listed.
We received a few comments on our proposed derivation of the Home
Office Contract Labor cost weight from the Medicare cost reports, which
are summarized below.
Comment: Commenters expressed concern with the proposed methodology
change to the Home Office Contract Labor cost weight. These commenters
stated that CMS had not provided sufficient rationale for this change
in methodology nor has CMS provided a discussion of how these data
points were reasonably validated and tested. One commenter requested
that CMS provide stakeholders with more information on the rationale
and the data validation methodologies employed in the final rule.
The commenters expressed concern with the sample of IRFs reporting
the home office cost data and found based on their analysis that
reporting was between 50 to 65 percent. These commenters suggested that
this was due to these cost report line items being an optional category
for IRFs under Medicare cost reporting requirements. One of the
commenters further expressed concern with the methodology and approach
that CMS applied in determining IRF unit Home Office Contract Labor
amounts, specifically the assumption that hospital-based IRFs utilize
the same proportion of home office expenses as the rest of the acute
care hospital in which it is located. The commenter stated that
typically IRF units are a very small part of the larger parent acute
care hospital and that the larger systems do not spend the same
proportional time and resources on these units compared to hospital
system as a whole. They stated that this assumption likely overstates
the Home Office Contract Labor cost weight.
Based on these concerns, the commenters requested that CMS not
finalize its proposed changes to the Home Office Contract Labor cost
category and instead finalize use of the previous methodology relating
to this category that was used for the 2012-based market basket. One
commenter also requested that CMS revisit this potential change with
adequate explanation and data in future rulemaking.
Response: We appreciate the commenters' concerns on the proposed
methodological change for the Home Office Contract Labor cost weight.
We proposed to revise our methodology and use the 2016 IRF MCR data to
calculate the Home Office Contract Labor costs rather than the 2012
Benchmark I-O data because it reflected more up-to-date data and we
believe it to be an improvement over the use of the BEA Benchmark I-O
data that is not specific to IRFs. The MCR data allows us to calculate
Home Office Contract Labor Costs for freestanding and IRF hospital-
based facilities.
We disagree with the commenters' concern that the MCR data
completion rates for the Home Office Contract Labor costs are
inadequate to obtain a cost weight. When developing the proposed 2016-
based IRF market basket, we conducted a thorough analysis of the MCR
data and our proposed Home Office Contract Labor cost weight
methodology. We found that approximately 90 percent of freestanding
IRFs reported having a home office, of which over 50 percent reported
home office compensation data on Worksheet S-3, part II. The
composition of the providers (by ownership-type and region) that
reported both wage index data (including those who do not have a home
office) and home office contract labor cost data were similarly
representative to all freestanding IRFs. A sensitivity analysis of
calculating a reweighted Home Office Contract Labor cost weight based
on ownership-type and region produced a Home Office Contract Labor cost
weight similar to the proposed 3.7 percent weight.
For additional sensitivity testing, recognizing that some of the
freestanding IRFs with home offices may not have completed the
applicable fields on the MCR, we calculated a weight using only
freestanding IRFs that reported having a home office (Worksheet S-2,
part I, line 140). This produced a Home Office Contract Labor cost
weight nearly identical to the freestanding IRF 2016 cost weight using
our proposed methodology. Based on this analysis, we believe that the
sample of providers included in the Home Office Contract Labor cost
weight are a technically representative sample of all IRF providers.
Regarding IRF units, we recognize the commenter's concern that they
represent a small proportion of the total facility. We believe that the
assumption that IRFs utilize the same proportion of home office
expenses as the rest of the acute care hospital is reasonable. The use
of total facility data assumes the facility Home Office Contract Labor
cost weight is equal to the Home Office Contract Labor cost weight for
the IRF unit. Further analysis of the MCR data shows IRF unit direct
patient care costs (as reported on Worksheet B, part I, column 0, line
41) account for about one percent of total facility costs (excluding
capital, Administrative and General (A&G), and Employee Benefit
department costs). Similarly, A&G costs (Worksheet B, part I, column 0,
line 5), where Home Office Contract Labor costs are likely captured,
allocated to the IRF unit account for a similar proportion of direct
patient care costs with about one percent of total A&G costs. We also
found the proportion of allocated A&G costs for other larger, more
medically-complex hospital units (such as the intensive care, surgical
care, and operating room) were consistent with direct patient care cost
proportions and the proportions for these units were higher than the
proportion of the A&G expenses allocated to the IRF unit. This supports
the commenter's claim that hospitals allocate less A&G costs to less
medically-complex services (as measured by costs). Our proposed
calculation would adhere to this assumption as well since the facility
level cost weight is applied to the IRF Medicare allowable total costs
representing these relatively less medically-complex services.
Furthermore, the Benchmark I-O methodology used in the 2012-based IRF
market basket also assumes that the IRF relative costs are the same as
those of the hospital total facility. We invite the commenters to
submit additional data that would help in this area for consideration
in future rulemaking.
We disagree with the commenters' request to use the Benchmark I-O
data to calculate the Home Office Contract Labor cost weight rather
than the proposed 2016 MCR data. We believe the proposed methodology is
a technical improvement over the prior methodology because it
represents more recent data that is representative compositionally and
geographically of IRFs. It is also is the same data used to determine
the other major cost weights in the 2016-based market basket and the
proportion of the Home Office Contract Labor cost weight that is
allocated to the Professional Fees: Labor-related and Professional
Fees: Nonlabor-related cost weights. We believe the assumptions made by
using the total facility data for the hospital-based IRFs are
reasonable and supported by the MCR data on A&G cost allocation.
Finally, we note that the methodological change accounts for only 0.2
percentage point of the 2.0 percentage points change in the labor-
related share.
[[Page 39076]]
After careful consideration of comments, we are finalizing our
methodology for deriving the major cost weights as proposed.
Table 4 presents the cost weights for these major cost categories
calculated from the Medicare cost reports for the 2016-based IRF market
basket, as well as for the 2012-based IRF market basket.
[GRAPHIC] [TIFF OMITTED] TR08AU19.007
As we did for the 2012-based IRF market basket, we proposed to
allocate the Contract Labor cost weight to the Wages and Salaries and
Employee Benefits cost weights based on their relative proportions
under the assumption that contract labor costs are comprised of both
wages and salaries and employee benefits. The Contract Labor allocation
proportion for Wages and Salaries is equal to the Wages and Salaries
cost weight as a percent of the sum of the Wages and Salaries cost
weight and the Employee Benefits cost weight. For the proposed rule,
this rounded percentage is 81 percent; therefore, we proposed to
allocate 81 percent of the Contract Labor cost weight to the Wages and
Salaries cost weight and 19 percent to the Employee Benefits cost
weight. The 2012-based IRF market basket percentage was also 81 percent
(80 FR 47056). We did not receive any specific public comments on our
proposed allocation of Contract Labor. Therefore, we are finalizing our
method of allocating Contract Labor as proposed.
Table 5 shows the Wages and Salaries and Employee Benefit cost
weights after Contract Labor cost weight allocation for both the 2016-
based IRF market basket and 2012-based IRF market basket.
[GRAPHIC] [TIFF OMITTED] TR08AU19.008
c. Derivation of the Detailed Operating Cost Weights
To further divide the ``All Other'' residual cost weight estimated
from the 2016 MCR data into more detailed cost categories, we proposed
to use the 2012 Benchmark I-O ``Use Tables/Before Redefinitions/
Purchaser Value'' for NAICS 622000, Hospitals, published by the BEA.
This data is publicly available at https://www.bea.gov/industry/io_annual.htm. For the 2012-based IRF market basket, we used the 2007
Benchmark I-O data, the most recent data available at the time (80 FR
47057).
The BEA Benchmark I-O data are scheduled for publication every 5
years with the most recent data available for 2012. The 2007 Benchmark
I-O data are derived from the 2012 Economic Census and are the building
blocks for BEA's economic accounts. Thus, they represent the most
comprehensive and complete set of data on the economic processes or
mechanisms by which output is produced and distributed.\1\ BEA also
produces Annual I-O estimates; however, while based on a similar
methodology, these estimates reflect less comprehensive and less
detailed data sources and are subject to revision when benchmark data
becomes available. Instead of using the less detailed Annual I-O data,
we proposed to inflate the 2012 Benchmark I-O data forward to 2016 by
applying the annual price changes from the respective price proxies to
the appropriate market basket cost categories that are obtained from
the 2012 Benchmark I-O data. We repeat this practice for each year. We
then proposed to calculate the cost shares that each cost category
represents of the inflated 2012 data. These resulting 2016 cost shares
are applied to the All Other residual cost weight to obtain the
detailed cost weights for the 2016-based IRF market basket. For
example, the cost for Food: Direct Purchases represents 5.0 percent of
the sum of the ``All Other'' 2012 Benchmark I-O Hospital Expenditures
inflated to 2016; therefore, the Food: Direct Purchases cost weight
represents 5.0 percent of the 2016-based IRF market basket's ``All
Other'' cost category (22.2 percent), yielding a ``final'' Food: Direct
Purchases cost weight of 1.1 percent in the 2016-based IRF market
basket (0.05 * 22.2 percent = 1.1 percent).
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\1\ https://www.bea.gov/papers/pdf/IOmanual_092906.pdf.
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Using this methodology, we proposed to derive seventeen detailed
IRF market
[[Page 39077]]
basket cost category weights from the 2016-based IRF market basket
residual cost weight (22.2 percent). These categories are: (1)
Electricity; (2) Fuel, Oil, and Gasoline; (3) Food: Direct Purchases;
(4) Food: Contract Services; (5) Chemicals; (6) Medical Instruments;
(7) Rubber & Plastics; (8) Paper and Printing Products; (9)
Miscellaneous Products; (10) Professional Fees: Labor-related; (11)
Administrative and Facilities Support Services; (12) Installation,
Maintenance, and Repair; (13) All Other Labor-related Services; (14)
Professional Fees: Nonlabor-related; (15) Financial Services; (16)
Telephone Services; and (17) All Other Nonlabor-related Services. We
note that for the 2012-based IRF market basket, we had a Water and
Sewerage cost weight. For the 2016-based IRF market basket, we proposed
to include Water and Sewerage costs in the Electricity cost weight due
to the small amount of costs in this category.
For the 2012-based IRF market basket, we used the I-O data for
NAICS 55 Management of Companies to derive the Home Office Contract
Labor cost weight, which were classified in the Professional Fees:
Labor-related and Professional Fees: Nonlabor-related cost weights. As
previously discussed, we proposed to use the MCR data to derive the
Home Office Contract Labor cost weight, which we would further classify
into the Professional Fees: Labor-related or Professional Fees:
Nonlabor-related categories.
We did not receive any specific comments on the derivation of the
detailed operating cost weights. In this final rule, we are finalizing
our methodology for deriving the detailed operating cost weights as
proposed.
d. Derivation of the Detailed Capital Cost Weights
As described in section VI.C.1.a.(6) of this final rule, we
proposed a Capital-Related cost weight of 9.0 percent as obtained from
the 2016 Medicare cost reports for freestanding and hospital-based IRF
providers. We proposed to then separate this total Capital-Related cost
weight into more detailed cost categories.
Using 2016 Medicare cost reports, we were able to group Capital-
Related costs into the following categories: Depreciation, Interest,
Lease, and Other Capital-Related costs. For each of these categories,
we proposed to determine separately for hospital-based IRFs and
freestanding IRFs what proportion of total capital-related costs the
category represents.
For freestanding IRFs, we proposed to derive the proportions for
Depreciation, Interest, Lease, and Other Capital-related costs using
the data reported by the IRF on Worksheet A-7, which is similar to the
methodology used for the 2012-based IRF market basket.
For hospital-based IRFs, data for these four categories were not
reported separately for the hospital-based IRF; therefore, we proposed
to derive these proportions using data reported on Worksheet A-7 for
the total facility. We assumed the cost shares for the overall hospital
are representative for the hospital-based IRF unit. For example, if
depreciation costs make up 60 percent of total capital costs for the
entire facility, we believe it is reasonable to assume that the
hospital-based IRF would also have a 60 percent proportion because it
is a unit contained within the total facility. This is the same
methodology used for the 2012-based IRF market basket (80 FR 47057).
To combine each detailed capital cost weight for freestanding and
hospital-based IRFs into a single capital cost weight for the 2016-
based IRF market basket, we proposed to weight together the shares for
each of the categories (Depreciation, Interest, Lease, and Other
Capital-related costs) based on the share of total capital costs each
provider type represents of the total capital costs for all IRFs for
2016. Applying this methodology results in proportions of total
capital-related costs for Depreciation, Interest, Lease and Other
Capital-related costs that are representative of the universe of IRF
providers. This is the same methodology used for the 2012-based IRF
market basket (80 FR 47057 through 47058).
Lease costs are unique in that they are not broken out as a
separate cost category in the 2016-based IRF market basket. Rather, we
proposed to proportionally distribute these costs among the cost
categories of Depreciation, Interest, and Other Capital-Related,
reflecting the assumption that the underlying cost structure of leases
is similar to that of capital-related costs in general. As was done
under the 2012-based IRF market basket, we proposed to assume that 10
percent of the lease costs as a proportion of total capital-related
costs represents overhead and assign those costs to the Other Capital-
Related cost category accordingly. We proposed to distribute the
remaining lease costs proportionally across the three cost categories
(Depreciation, Interest, and Other Capital-Related) based on the
proportion that these categories comprise of the sum of the
Depreciation, Interest, and Other Capital-related cost categories
(excluding lease expenses). This resulted in three primary capital-
related cost categories in the 2016-based IRF market basket:
Depreciation, Interest, and Other Capital-Related costs. This is the
same methodology used for the 2012-based IRF market basket (80 FR
47058). The allocation of these lease expenses are shown in Table 6.
Finally, we proposed to further divide the Depreciation and
Interest cost categories. We proposed to separate Depreciation into the
following two categories: (1) Building and Fixed Equipment; and (2)
Movable Equipment. We proposed to separate Interest into the following
two categories: (1) Government/Nonprofit; and (2) For-profit.
To disaggregate the Depreciation cost weight, we need to determine
the percent of total Depreciation costs for IRFs that are attributable
to Building and Fixed Equipment, which we hereafter refer to as the
``fixed percentage.'' For the 2016-based IRF market basket, we proposed
to use slightly different methods to obtain the fixed percentages for
hospital-based IRFs compared to freestanding IRFs.
For freestanding IRFs, we proposed to use depreciation data from
Worksheet A-7 of the 2016 Medicare cost reports. However, for hospital-
based IRFs, we determined that the fixed percentage for the entire
facility may not be representative of the hospital-based IRF unit due
to the entire facility likely employing more sophisticated movable
assets that are not utilized by the hospital-based IRF. Therefore, for
hospital-based IRFs, we proposed to calculate a fixed percentage using:
(1) Building and fixture capital costs allocated to the hospital-based
IRF unit as reported on Worksheet B, part I, line 41; and (2) building
and fixture capital costs for the top five ancillary cost centers
utilized by hospital-based IRFs. We proposed to weight these two fixed
percentages (inpatient and ancillary) using the proportion that each
capital cost type represents of total capital costs in the 2016-based
IRF market basket. We proposed to then weight the fixed percentages for
hospital-based and freestanding IRFs together using the proportion of
total capital costs each provider type represents. For both
freestanding and hospital-based IRFs, this is the same methodology used
for the 2012-based IRF market basket (80 FR 47058).
To disaggregate the Interest cost weight, we determined the percent
of total interest costs for IRFs that are attributable to government
and nonprofit facilities, which is hereafter referred to as the
``nonprofit percentage,'' as price pressures associated with these
types of interest
[[Page 39078]]
costs tend to differ from those for for-profit facilities. For the
2016-based IRF market basket, we proposed to use interest costs data
from Worksheet A-7 of the 2016 Medicare cost reports for both
freestanding and hospital-based IRFs. We proposed to determine the
percent of total interest costs that are attributed to government and
nonprofit IRFs separately for hospital-based and freestanding IRFs. We
then proposed to weight the nonprofit percentages for hospital-based
and freestanding IRFs together using the proportion of total capital
costs that each provider type represents.
We did not receive any specific public comments on the derivation
of the detailed capital cost weights. In this final rule, we are
finalizing our methodology for deriving the detailed capital cost
weights as proposed. Table 6 provides the detailed capital cost share
composition estimated from the 2016 IRF Medicare cost reports. These
detailed capital cost share composition percentages are applied to the
total Capital-Related cost weight of 9.0 percent explained in detail in
section VI.C.1.a.(6) of this final rule.
[GRAPHIC] [TIFF OMITTED] TR08AU19.009
e. 2016-Based IRF Market Basket Cost Categories and Weights
Table 7 compares the cost categories and weights for the final
2016-based IRF market basket compared to the 2012-based IRF market
basket.
[[Page 39079]]
[GRAPHIC] [TIFF OMITTED] TR08AU19.010
2. Selection of Price Proxies
After developing the cost weights for the 2016-based IRF market
basket, we selected the most appropriate wage and price proxies
currently available to represent the rate of price change for each
expenditure category. For the majority of the cost weights, we base the
price proxies on U.S. Bureau of Labor Statistics (BLS) data and group
them into one of the following BLS categories:
Employment Cost Indexes. Employment Cost Indexes (ECIs)
measure the rate of change in employment wage rates and employer costs
for employee benefits per hour worked. These indexes are fixed-weight
indexes and strictly measure the change in wage rates and employee
benefits per hour. ECIs are superior to Average Hourly Earnings (AHE)
as price proxies for input price indexes because they are not affected
by shifts in occupation or industry mix, and because they measure pure
price change and are available by both occupational group and by
industry. The industry ECIs are based on the NAICS and the occupational
ECIs are based on the Standard Occupational Classification System
(SOC).
Producer Price Indexes. Producer Price Indexes (PPIs)
measure the average change over time in the selling prices received by
domestic producers for their output. The prices included in the PPI
[[Page 39080]]
are from the first commercial transaction for many products and some
services (https://www.bls.gov/ppi/).
Consumer Price Indexes. Consumer Price Indexes (CPIs)
measure the average change over time in the prices paid by urban
consumers for a market basket of consumer goods and services (https://www.bls.gov/cpi/). CPIs are only used when the purchases are similar to
those of retail consumers rather than purchases at the producer level,
or if no appropriate PPIs are available.
We evaluate the price proxies using the criteria of reliability,
timeliness, availability, and relevance:
Reliability. Reliability indicates that the index is based
on valid statistical methods and has low sampling variability. Widely
accepted statistical methods ensure that the data were collected and
aggregated in a way that can be replicated. Low sampling variability is
desirable because it indicates that the sample reflects the typical
members of the population. (Sampling variability is variation that
occurs by chance because only a sample was surveyed rather than the
entire population.)
Timeliness. Timeliness implies that the proxy is published
regularly, preferably at least once a quarter. The market baskets are
updated quarterly, and therefore, it is important for the underlying
price proxies to be up-to-date, reflecting the most recent data
available. We believe that using proxies that are published regularly
(at least quarterly, whenever possible) helps to ensure that we are
using the most recent data available to update the market basket. We
strive to use publications that are disseminated frequently, because we
believe that this is an optimal way to stay abreast of the most current
data available.
Availability. Availability means that the proxy is
publicly available. We prefer that our proxies are publicly available
because this will help ensure that our market basket updates are as
transparent to the public as possible. In addition, this enables the
public to be able to obtain the price proxy data on a regular basis.
Relevance. Relevance means that the proxy is applicable
and representative of the cost category weight to which it is applied.
The CPIs, PPIs, and ECIs that we have selected meet these criteria.
Therefore, we believe that they continue to be the best measure of
price changes for the cost categories to which they would be applied.
Table 10 lists all price proxies that we proposed to use for the
2016-based IRF market basket. Below is a detailed explanation of the
price proxies we proposed for each cost category weight. We did not
receive any specific comments on our proposed price proxies for the
2016-based IRF market basket. Therefore, in this final rule, we are
finalizing the price proxies as proposed.
a. Price Proxies for the Operating Portion of the 2016-Based IRF Market
Basket
(1) Wages and Salaries
We proposed to continue to use the ECI for Wages and Salaries for
All Civilian workers in Hospitals (BLS series code CIU1026220000000I)
to measure the wage rate growth of this cost category. This is the same
price proxy used in the 2012-based IRF market basket (80 FR 47060).
(2) Benefits
We proposed to continue to use the ECI for Total Benefits for All
Civilian workers in Hospitals to measure price growth of this category.
This ECI is calculated using the ECI for Total Compensation for All
Civilian workers in Hospitals (BLS series code CIU1016220000000I) and
the relative importance of wages and salaries within total
compensation. This is the same price proxy used in the 2012-based IRF
market basket (80 FR 47060).
(3) Electricity
We proposed to continue to use the PPI Commodity Index for
Commercial Electric Power (BLS series code WPU0542) to measure the
price growth of this cost category. This is the same price proxy used
in the 2012-based IRF market basket (80 FR 47060).
(4) Fuel, Oil, and Gasoline
Similar to the 2012-based IRF market basket, for the 2016-based IRF
market basket, we proposed to use a blend of the PPI for Petroleum
Refineries and the PPI Commodity for Natural Gas. Our analysis of the
Bureau of Economic Analysis' 2012 Benchmark Input-Output data (use
table before redefinitions, purchaser's value for NAICS 622000
[Hospitals]), shows that Petroleum Refineries expenses account for
approximately 90 percent and Natural Gas expenses account for
approximately 10 percent of Hospitals' (NAICS 622000) total Fuel, Oil,
and Gasoline expenses. Therefore, we proposed to use a blend of 90
percent of the PPI for Petroleum Refineries (BLS series code
PCU324110324110) and 10 percent of the PPI Commodity Index for Natural
Gas (BLS series code WPU0531) as the price proxy for this cost
category. The 2012-based IRF market basket used a 70/30 blend of these
price proxies, reflecting the 2007 I-O data (80 FR 47060). We believe
that these two price proxies continue to be the most technically
appropriate indices available to measure the price growth of the Fuel,
Oil, and Gasoline cost category in the 2016-based IRF market basket.
(5) Professional Liability Insurance
We proposed to continue to use the CMS Hospital Professional
Liability Index to measure changes in PLI premiums. To generate this
index, we collect commercial insurance premiums for a fixed level of
coverage while holding non-price factors constant (such as a change in
the level of coverage). This is the same proxy used in the 2012-based
IRF market basket (80 FR 47060).
(6) Pharmaceuticals
We proposed to continue to use the PPI for Pharmaceuticals for
Human Use, Prescription (BLS series code WPUSI07003) to measure the
price growth of this cost category. This is the same proxy used in the
2012-based IRF market basket (80 FR 47060).
(7) Food: Direct Purchases
We proposed to continue to use the PPI for Processed Foods and
Feeds (BLS series code WPU02) to measure the price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47060).
(8) Food: Contract Purchases
We proposed to continue to use the CPI for Food Away From Home (BLS
series code CUUR0000SEFV) to measure the price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47060 through 47061).
(9) Chemicals
Similar to the 2012-based IRF market basket, we proposed to use a
four part blended PPI as the proxy for the chemical cost category in
the 2016-based IRF market basket. The proposed blend is composed of the
PPI for Industrial Gas Manufacturing, Primary Products (BLS series code
PCU325120325120P), the PPI for Other Basic Inorganic Chemical
Manufacturing (BLS series code PCU32518-32518-), the PPI for Other
Basic Organic Chemical Manufacturing (BLS series code PCU32519-32519-),
and the PPI for Other Miscellaneous Chemical Product Manufacturing (BLS
series code PCU325998325998). We note that the four part blended PPI
used in the 2012-based IRF market basket is composed of the PPI for
Industrial Gas Manufacturing (BLS series code
[[Page 39081]]
PCU325120325120P), the PPI for Other Basic Inorganic Chemical
Manufacturing (BLS series code PCU32518-32518-), the PPI for Other
Basic Organic Chemical Manufacturing (BLS series code PCU32519-32519-),
and the PPI for Soap and Cleaning Compound Manufacturing (BLS series
code PCU32561-32561-). For the 2016-based IRF market basket, we
proposed to derive the weights for the PPIs using the 2012 Benchmark I-
O data. The 2012-based IRF market basket used the 2007 Benchmark I-O
data to derive the weights for the four PPIs (80 FR 47061).
Table 8 shows the weights for each of the four PPIs used to create
the proposed blended Chemical proxy for the 2016 IRF market basket
compared to the 2012-based blended Chemical proxy.
[GRAPHIC] [TIFF OMITTED] TR08AU19.011
(10) Medical Instruments
We proposed to continue to use a blend of two PPIs for the Medical
Instruments cost category. The 2012 Benchmark Input-Output data shows
an approximate 57/43 split between Surgical and Medical Instruments and
Medical and Surgical Appliances and Supplies for this cost category.
Therefore, we proposed a blend composed of 57 percent of the commodity-
based PPI for Surgical and Medical Instruments (BLS series code
WPU1562) and 43 percent of the commodity-based PPI for Medical and
Surgical Appliances and Supplies (BLS series code WPU1563). The 2012-
based IRF market basket used a 50/50 blend of these PPIs based on the
2007 Benchmark I-O data (80 FR 47061).
(11) Rubber and Plastics
We proposed to continue to use the PPI for Rubber and Plastic
Products (BLS series code WPU07) to measure price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(12) Paper and Printing Products
We proposed to continue to use the PPI for Converted Paper and
Paperboard Products (BLS series code WPU0915) to measure the price
growth of this cost category. This is the same proxy used in the 2012-
based IRF market basket (80 FR 47061).
(13) Miscellaneous Products
We proposed to continue to use the PPI for Finished Goods Less Food
and Energy (BLS series code WPUFD4131) to measure the price growth of
this cost category. This is the same proxy used in the 2012-based IRF
market basket (80 FR 47061).
(14) Professional Fees: Labor-Related
We proposed to continue to use the ECI for Total Compensation for
Private Industry workers in Professional and Related (BLS series code
CIU2010000120000I) to measure the price growth of this category. This
is the same proxy used in the 2012-based IRF market basket (80 FR
47061).
(15) Administrative and Facilities Support Services
We proposed to continue to use the ECI for Total Compensation for
Private Industry workers in Office and Administrative Support (BLS
series code CIU2010000220000I) to measure the price growth of this
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(16) Installation, Maintenance, and Repair
We proposed to continue to use the ECI for Total Compensation for
Civilian workers in Installation, Maintenance, and Repair (BLS series
code CIU1010000430000I) to measure the price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(17) All Other: Labor-Related Services
We proposed to continue to use the ECI for Total Compensation for
Private Industry workers in Service Occupations (BLS series code
CIU2010000300000I) to measure the price growth of this cost category.
This is the same proxy used in the 2012-based IRF market basket (80 FR
47061).
(18) Professional Fees: Nonlabor-Related
We proposed to continue to use the ECI for Total Compensation for
Private Industry workers in Professional and Related (BLS series code
CIU2010000120000I) to measure the price growth of this category. This
is the same proxy used in the 2012-based IRF market basket (80 FR
47061).
(19) Financial Services
We proposed to continue to use the ECI for Total Compensation for
Private Industry workers in Financial Activities (BLS series code
CIU201520A000000I) to measure the price growth of this cost category.
This is the same proxy used in the 2012-based IRF market basket (80 FR
47061).
(20) Telephone Services
We proposed to continue to use the CPI for Telephone Services (BLS
series code CUUR0000SEED) to measure the price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(21) All Other: Nonlabor-Related Services
We proposed to continue to use the CPI for All Items Less Food and
Energy (BLS series code CUUR0000SA0L1E) to measure the price growth of
this cost category. This is the same proxy used in the 2012-based IRF
market basket (80 FR 47061).
b. Price Proxies for the Capital Portion of the 2016-Based IRF Market
Basket
(1) Capital Price Proxies Prior to Vintage Weighting
We proposed to continue to use the same price proxies for the
capital-related cost categories in the 2016-based
[[Page 39082]]
IRF market basket as were used in the 2012-based IRF market basket (80
FR 47062), which are provided in Table 10 and described below.
Specifically, we proposed to proxy:
Depreciation: Building and Fixed Equipment cost category
by BEA's Chained Price Index for Nonresidential Construction for
Hospitals and Special Care Facilities (BEA Table 5.4.4. Price Indexes
for Private Fixed Investment in Structures by Type).
Depreciation: Movable Equipment cost category by the PPI
for Machinery and Equipment (BLS series code WPU11).
Nonprofit Interest cost category by the average yield on
domestic municipal bonds (Bond Buyer 20-bond index).
For-profit Interest cost category by the average yield on
Moody's Aaa bonds (Federal Reserve).
Other Capital-Related cost category by the CPI-U for Rent
of Primary Residence (BLS series code CUUS0000SEHA).
We believe these are the most appropriate proxies for IRF capital-
related costs that meet our selection criteria of relevance,
timeliness, availability, and reliability. We proposed to continue to
vintage weight the capital price proxies for Depreciation and Interest
to capture the long-term consumption of capital. This vintage weighting
method is similar to the method used for the 2012-based IRF market
basket (80 FR 47062) and is described below.
(2) Vintage Weights for Price Proxies
Because capital is acquired and paid for over time, capital-related
expenses in any given year are determined by both past and present
purchases of physical and financial capital. The vintage-weighted
capital-related portion of the 2016-based IRF market basket is intended
to capture the long-term consumption of capital, using vintage weights
for depreciation (physical capital) and interest (financial capital).
These vintage weights reflect the proportion of capital-related
purchases attributable to each year of the expected life of building
and fixed equipment, movable equipment, and interest. We proposed to
use vintage weights to compute vintage-weighted price changes
associated with depreciation and interest expenses.
Capital-related costs are inherently complicated and are determined
by complex capital-related purchasing decisions, over time, based on
such factors as interest rates and debt financing. In addition, capital
is depreciated over time instead of being consumed in the same period
it is purchased. By accounting for the vintage nature of capital, we
are able to provide an accurate and stable annual measure of price
changes. Annual non-vintage price changes for capital are unstable due
to the volatility of interest rate changes, and therefore, do not
reflect the actual annual price changes for IRF capital-related costs.
The capital-related component of the 2016-based IRF market basket
reflects the underlying stability of the capital-related acquisition
process.
The methodology used to calculate the vintage weights for the 2016-
based IRF market basket is the same as that used for the 2012-based IRF
market basket (80 FR 47062 through 47063) with the only difference
being the inclusion of more recent data. To calculate the vintage
weights for depreciation and interest expenses, we first need a time
series of capital-related purchases for building and fixed equipment
and movable equipment. We found no single source that provides an
appropriate time series of capital-related purchases by hospitals for
all of the above components of capital purchases. The early Medicare
cost reports did not have sufficient capital-related data to meet this
need. Data we obtained from the American Hospital Association (AHA) do
not include annual capital-related purchases. However, we are able to
obtain data on total expenses back to 1963 from the AHA. Consequently,
we proposed to use data from the AHA Panel Survey and the AHA Annual
Survey to obtain a time series of total expenses for hospitals. We then
proposed to use data from the AHA Panel Survey supplemented with the
ratio of depreciation to total hospital expenses obtained from the
Medicare cost reports to derive a trend of annual depreciation expenses
for 1963 through 2016. We proposed to separate these depreciation
expenses into annual amounts of building and fixed equipment
depreciation and movable equipment depreciation as determined earlier.
From these annual depreciation amounts, we derive annual end-of-year
book values for building and fixed equipment and movable equipment
using the expected life for each type of asset category. While data is
not available that is specific to IRFs, we believe this information for
all hospitals serves as a reasonable alternative for the pattern of
depreciation for IRFs.
To continue to calculate the vintage weights for depreciation and
interest expenses, we also need to account for the expected lives for
Building and Fixed Equipment, Movable Equipment, and Interest for the
2016-based IRF market basket. We proposed to calculate the expected
lives using MCR data from freestanding and hospital-based IRFs. The
expected life of any asset can be determined by dividing the value of
the asset (excluding fully depreciated assets) by its current year
depreciation amount. This calculation yields the estimated expected
life of an asset if the rates of depreciation were to continue at
current year levels, assuming straight-line depreciation. We proposed
to determine the expected life of building and fixed equipment
separately for hospital-based IRFs and freestanding IRFs, and then
weight these expected lives using the percent of total capital costs
each provider type represents. We proposed to apply a similar method
for movable equipment. Using these methods, we determined the average
expected life of building and fixed equipment to be equal to 22 years,
and the average expected life of movable equipment to be equal to 11
years. For the expected life of interest, we believe vintage weights
for interest should represent the average expected life of building and
fixed equipment because, based on previous research described in the FY
1997 IPPS final rule (61 FR 46198), the expected life of hospital debt
instruments and the expected life of buildings and fixed equipment are
similar. We note that for the 2012-based IRF market basket, the
expected life of building and fixed equipment is 23 years, and the
expected life of movable equipment is 11 years (80 FR 47062).
Multiplying these expected lives by the annual depreciation amounts
results in annual year-end asset costs for building and fixed equipment
and movable equipment. We then calculate a time series, beginning in
1964, of annual capital purchases by subtracting the previous year's
asset costs from the current year's asset costs.
For the building and fixed equipment and movable equipment vintage
weights, we proposed to use the real annual capital-related purchase
amounts for each asset type to capture the actual amount of the
physical acquisition, net of the effect of price inflation. These real
annual capital-related purchase amounts are produced by deflating the
nominal annual purchase amount by the associated price proxy as
provided earlier in this final rule. For the interest vintage weights,
we proposed to use the total nominal annual capital-related purchase
amounts to capture the value of the debt instrument (including, but not
limited to, mortgages and bonds). Using these capital-related purchase
time series specific to each asset type, we proposed to calculate the
vintage weights for
[[Page 39083]]
building and fixed equipment, for movable equipment, and for interest.
The vintage weights for each asset type are deemed to represent the
average purchase pattern of the asset over its expected life (in the
case of building and fixed equipment and interest, 22 years, and in the
case of movable equipment, 11 years). For each asset type, we used the
time series of annual capital-related purchase amounts available from
2016 back to 1964. These data allow us to derive 32, 22-year periods of
capital-related purchases for building and fixed equipment and
interest, and 43, 11-year periods of capital-related purchases for
movable equipment. For each 22-year period for building and fixed
equipment and interest, or 11-year period for movable equipment, we
calculate annual vintage weights by dividing the capital-related
purchase amount in any given year by the total amount of purchases over
the entire 22-year or 11-year period. This calculation is done for each
year in the 22-year or 11-year period and for each of the periods for
which we have data. We then calculate the average vintage weight for a
given year of the expected life by taking the average of these vintage
weights across the multiple periods of data.
We did not receive any specific public comments on our proposed
calculation of the vintage weights for the 2016-based IRF market
basket. Therefore, in this final rule, we are finalizing the vintage
weights as proposed. The vintage weights for the capital-related
portion of the 2016-based IRF market basket and the 2012-based IRF
market basket are presented in Table 9.
[GRAPHIC] [TIFF OMITTED] TR08AU19.012
The process of creating vintage-weighted price proxies requires
applying the vintage weights to the price proxy index where the last
applied vintage weight in Table 8 is applied to the most recent data
point. We have provided on the CMS website an example of how the
vintage weighting price proxies are calculated, using example vintage
weights and example price indices. The example can be found at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch.html in the zip
file titled ``Weight Calculations as described in the IPPS FY 2010
Proposed Rule.''
c. Summary of Price Proxies of the 2016-Based IRF Market Basket
Table 10 shows both the operating and capital price proxies for the
2016-based IRF market basket.
BILLING CODE 4120-01-P
[[Page 39084]]
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[[Page 39085]]
BILLING CODE 4120-01-C
D. FY 2020 Market Basket Update and Productivity Adjustment
1. FY 2020 Market Basket Update
For FY 2020 (that is, beginning October 1, 2019 and ending
September 30, 2020), we proposed to use the 2016-based IRF market
basket increase factor described in section V.C. of the proposed rule
to update the IRF PPS base payment rate. Consistent with historical
practice, we proposed to estimate the market basket update for the IRF
PPS based on IHS Global Inc.'s (IGI's) forecast using the most recent
available data. IGI is a nationally-recognized economic and financial
forecasting firm with which we contract to forecast the components of
the market baskets and MFP. In the FY 2020 IRF PPS proposed rule (84 FR
17274), we proposed a market basket increase factor of 3.0 percent for
FY 2020, which was based on IGI's first quarter 2019 forecast with
historical data through fourth quarter 2018.
In the FY 2020 IRF PPS proposed rule, we also proposed that if more
recent data were subsequently available (for example, a more recent
estimate of the market basket and MFP adjustment), we would use such
data to determine the FY 2020 update in the final rule. Incorporating
more recent data, the projected 2016-based IRF market basket increase
factor for FY 2020 is 2.9 percent, which is based on IGI's second
quarter 2019 forecast with historical data through first quarter 2019.
We received several comments on our proposed market basket update
and productivity adjustment, which are summarized below.
Comment: Commenters supported the proposal to update the market
basket and MFP adjustment using the latest available data, and
encouraged CMS to update these factors using the latest available data
as part of the release of the FY 2020 IRF PPS final rule.
Response: We appreciate the commenters' support for updating the
market basket and MFP adjustments using the latest available data.
Comment: A few commenters expressed concern about the lack of
transparency of the market basket and MFP payment updates. The
commenters stated that the IGI forecast appears to be procured
specifically for the purpose of CMS updating the IRF market basket and
productivity adjustment. The commenters also noted that it is
concerning that CMS does not provide IGI's analyses or report to the
public given the key role the market basket and productivity adjustment
play in updating the payment system each year and that without such
information stakeholders are unable to evaluate the accuracy of the
update. The commenters also mentioned that the same comment was
submitted in the FY 2019 rulemaking process but they do not believe
that the response was adequate since the actual analysis or report used
to create the forecasts was not provided (83 FR 38525). The commenters
requested that CMS release an IGI report and analysis used to update
the IRF market basket and standard payment conversion factor.
Response: IGI regularly produces and publishes a wide variety of
forecasted series on a monthly or quarterly basis. These forecasts are
derived using a framework of proprietary economic models that are
created and updated regularly by IGI. IGI provides these forecasts to a
wide array of clients in addition to CMS. We use a contractor for the
price forecasts so that the forecasts are independent and reflect a
complete economic forecasting model, a capability that we do not have.
IGI has received multiple awards for their macroeconomic forecast
accuracy of major economic indicators. We use IGI's price forecasts in
all of the FFS market baskets used for payment updates and has used the
forecasts produced by this company for many years.
We select approximately 30 individual price proxies as inputs to
the IRF market basket calculation. The price series are discussed in
detail as part of the rulemaking process. In order to derive a forecast
of the IRF market basket index, we contract with IGI to procure the
forecasts of these individual price proxies on a quarterly basis. We
then combine these price proxies with the market basket base year cost
weights to derive the levels of the IRF market basket. The data sources
and methods used to derive these cost weights are discussed in detail
as part of the rulemaking process.
As provided in our previous response to this comment in the FY 2019
IRF PPS final rule (83 FR 38525), the market basket update is derived
using: (1) The market basket base year cost weights as finalized by CMS
through rulemaking; and (2) the most up-to-date forecast of the price
proxies used in the market basket as forecasted by IGI. Specifically,
for each cost category in the market basket (for example, Wages and
Salaries, Pharmaceuticals), the level of each of these price proxies
are multiplied by the cost weight for that cost category. The sum of
these products (that is, weights multiplied by proxied index levels)
for all cost categories yields the composite index level in the market
basket in a given year.
As acknowledged by the commenters, we provided a link from the CMS
website to the top-line market basket updates. We also indicated that
more detailed forecasts of the IRF market basket calculations are
readily available by request by sending an email to [email protected]
to request this information (83 FR 38525). Using these detailed data,
the commenter would be able to replicate the levels of the IRF market
basket update in the history and the forecast period. We encourage
stakeholders to utilize these data, which we believe will address the
commenters' concerns.
Incorporating more recent data, the projected 2016-based IRF market
basket update for FY 2020 is 2.9 percent. After careful consideration
of the comments, consistent with our historical practice of estimating
market basket increases based on the best available data, we are
finalizing a market basket increase factor of 2.9 percent for FY 2020.
For comparison, the current 2012-based IRF market basket is also
projected to increase by 2.9 percent in FY 2020 based on IGI's second
quarter 2019 forecast.
Table 11 compares the 2016-based IRF market basket and the 2012-
based IRF market basket percent changes.
[[Page 39086]]
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2. Productivity Adjustment
According to section 1886(j)(3)(C)(i) of the Act, the Secretary
shall establish an increase factor based on an appropriate percentage
increase in a market basket of goods and services. As described in
sections VI.C and VI.D.1. of this final rule, we are finalizing an
estimate of the IRF PPS increase factor for FY 2020 based on the 2016-
based IRF market basket. Section 1886(j)(3)(C)(ii) of the Act then
requires that, after establishing the increase factor for a FY, the
Secretary shall reduce such increase factor for FY 2012 and each
subsequent FY, by the productivity adjustment described in section
1886(b)(3)(B)(xi)(II) of the Act. Section 1886(b)(3)(B)(xi)(II) of the
Act sets forth the definition of this productivity adjustment. The
statute defines the productivity adjustment to be equal to the 10-year
moving average of changes in annual economy-wide private nonfarm
business MFP (as projected by the Secretary for the 10-year period
ending with the applicable FY, year, cost reporting period, or other
annual period) (the ``MFP adjustment''). The BLS publishes the official
measure of private nonfarm business MFP. Please see https://www.bls.gov/mfp for the BLS historical published MFP data.
MFP is derived by subtracting the contribution of labor and capital
input growth from output growth. The projections of the components of
MFP are currently produced by IGI, a nationally recognized economic
forecasting firm with which CMS contracts to forecast the components of
the market basket and MFP. For more information on the productivity
adjustment, we refer reader to the discussion in the FY 2016 IRF PPS
final rule (80 FR 47065).
Using IGI's first quarter 2019 forecast, the proposed MFP
adjustment for FY 2020 (the 10-year moving average of MFP for the
period ending FY 2020) was 0.5 percent (84 FR 17274). Thus, in
accordance with section 1886(j)(3)(C) of the Act, we proposed to base
the FY 2020 market basket update, which is used to determine the
applicable percentage increase for the IRF payments, on the most recent
estimate of the 2016-based IRF market basket. We proposed to then
reduce this percentage increase by the current estimate of the proposed
MFP adjustment for FY 2020 of 0.5 percentage point (the 10-year moving
average of MFP for the period ending FY 2020 based on IGI's first
quarter 2019 forecast). Therefore, the proposed FY 2020 IRF update was
2.5 percent (3.0 percent market basket update, less 0.5 percentage
point MFP adjustment). Furthermore, we proposed that if more recent
data are subsequently available (for example, a more recent estimate of
the market basket and MFP adjustment), we would use such data to
determine the FY 2020 market basket update and MFP adjustment in the
final rule.
We received a few comments on the application of the productivity
adjustment, which are summarized below.
Comment: Commenters continue to be concerned about the application
of the productivity adjustment to IRFs. One of the commenters stated
that they understood CMS is bound by statute to reduce the market
basket update by a productivity adjustment factor in accordance with
the PPACA, but they believe that IRFs are unable to generate additional
productivity gains at a pace matching the productivity of the economy
at large on an ongoing, consistent basis. The commenter noted that the
services provided in IRFs are labor-intensive and the services do not
lend themselves to continuous productivity improvements. The commenter
also noted that IRFs are bound by unchanging labor-intensive standards
such as the 3-hour therapy rule and other regulatory requirements that
reduce flexibility and restrict the pursuit of certain efficiencies.
The commenter noted that continued application of a productivity
adjustment to payments could results in decreased beneficiary access to
IRF services. The commenter requested that CMS continue to monitor the
impact that the multi-factor productivity adjustments have on the IRF
sector, provide feedback to Congress as appropriate, and reduce the
productivity adjustment. One commenter requested that, in addition to
monitoring its effects on overall payments, CMS should evaluate whether
IRFs are able to achieve the same level of productivity improvement as
workers across the U.S. economy.
Response: We acknowledge the commenters' concerns regarding
productivity growth at the economy-wide level and its application to
IRFs. As the commenter acknowledges, section 1886(j)(3)(C)(ii)(I) of
the Act requires the application of a productivity adjustment to the
IRF PPS market basket increase factor.
We will continue to monitor the impact of the payment updates,
including the effects of the productivity
[[Page 39087]]
adjustment, on IRF finances, as well as beneficiary access to care.
We note that each year, MedPAC makes an annual update
recommendation to Congress based on a variety of measures related to
payment adequacy, including a detailed margin analysis and analysis of
beneficiary access to care for IRF services. For FY 2020, MedPAC
recommended that Congress reduce the IRF PPS base rate by 5 percent and
found that beneficiary access to care was not a concern. The ``March
2019 Report to the Congress: Medicare Payment Policy'', chapter 10 is
publicly available at https://www.medpac.gov/-documents-/reports.
We would be very interested in better understanding IRF-specific
productivity; however, the data elements required to estimate IRF
specific multi-factor productivity are not produced at the level of
detail that would allow this analysis. We have estimated hospital-
sector multi-factor productivity and have published the findings on the
CMS website at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/ReportsTrustFunds/Downloads/ProductivityMemo2016.pdf.
After careful consideration of comments, we are incorporating more
recent data to determine the market basket update and MFP adjustment
for FY 2020. Using IGI's second quarter 2019 forecast, the current
estimate of the MFP adjustment for FY 2020 (the 10-year moving average
of MFP for the period ending FY 2020) is 0.4 percent. Thus, in
accordance with section 1886(j)(3)(C) of the Act, we are finalizing a
FY 2020 market basket update of 2.9 percent. We then reduce this
percentage increase by the most recent estimate of the MFP adjustment
for FY 2020 of 0.4 percentage point (the 10-year moving average of MFP
for the period ending FY 2020 based on IGI's second quarter 2019
forecast). Therefore, the final FY 2020 IRF productivity-adjusted
market basket update is equal to 2.5 percent (2.9 percent market basket
update, less 0.4 percentage point MFP adjustment).
For FY 2020, the Medicare Payment Advisory Commission (MedPAC)
recommends that a decrease of 5 percent be applied to IRF PPS payment
rates. As discussed, and in accordance with section 1886(j)(3)(C) of
the Act, we are finalizing an update to IRF PPS payment rates for FY
2020 by a productivity-adjusted market basket increase factor of 2.5
percent, as section 1886(j)(3)(C) of the Act does not provide the
Secretary with the authority to apply a different update factor to IRF
PPS payment rates for FY 2020.
Comment: One commenter (MedPAC) stated that they understand that
CMS is required to implement the statutory update of market basket less
productivity adjustment, but that their analysis of beneficiary access
to rehabilitative services, the supply of providers, and aggregate IRF
Medicare margins, which have been above 11 percent since 2012,
indicates that the Congress should reduce the IRF payment rate by 5
percent for FY 2020.
Response: We appreciate MedPAC's interest in the IRF increase
factor. However, we are required to update IRF PPS payments by the
market basket reduced by the productivity adjustment, as directed by
section 1886(j)(3)(C) of the Act.
E. Labor-Related Share for FY 2020
Section 1886(j)(6) of the Act specifies that the Secretary is to
adjust the proportion (as estimated by the Secretary from time to time)
of rehabilitation facilities' costs which are attributable to wages and
wage-related costs, of the prospective payment rates computed under
section 1886(j)(3) of the Act for area differences in wage levels by a
factor (established by the Secretary) reflecting the relative hospital
wage level in the geographic area of the rehabilitation facility
compared to the national average wage level for such facilities. The
labor-related share is determined by identifying the national average
proportion of total costs that are related to, influenced by, or vary
with the local labor market. We proposed to continue to classify a cost
category as labor-related if the costs are labor-intensive and vary
with the local labor market. As stated in the FY 2016 IRF PPS final
rule (80 FR 47068), the labor-related share was defined as the sum of
the relative importance of Wages and Salaries, Employee Benefits,
Professional Fees: Labor-related Services, Administrative and
Facilities Support Services, Installation, Maintenance, and Repair, All
Other: Labor-related Services, and a portion of the Capital Costs from
the 2012-based IRF market basket.
Based on our definition of the labor-related share and the cost
categories in the 2016-based IRF market basket, we proposed to include
in the labor-related share for FY 2020 the sum of the FY 2020 relative
importance of Wages and Salaries, Employee Benefits, Professional Fees:
Labor-related, Administrative and Facilities Support Services,
Installation, Maintenance, and Repair, All Other: Labor-related
Services, and a portion of the Capital-Related cost weight from the
2016-based IRF market basket.
Similar to the 2012-based IRF market basket (80 FR 47067), the
2016-based IRF market basket includes two cost categories for
nonmedical Professional Fees (including, but not limited to, expenses
for legal, accounting, and engineering services). These are
Professional Fees: Labor-related and Professional Fees: Nonlabor-
related. For the 2016-based IRF market basket, we proposed to estimate
the labor-related percentage of non-medical professional fees (and
assign these expenses to the Professional Fees: Labor-related services
cost category) based on the same method that was used to determine the
labor-related percentage of professional fees in the 2012-based IRF
market basket.
As was done in the 2012-based IRF market basket (80 FR 47067), we
proposed to determine the proportion of legal, accounting and auditing,
engineering, and management consulting services that meet our
definition of labor-related services based on a survey of hospitals
conducted by us in 2008, a discussion of which can be found in the FY
2010 IPPS/LTCH PPS final rule (74 FR 43850 through 43856). Based on the
weighted results of the survey, we determined that hospitals purchase,
on average, the following portions of contracted professional services
outside of their local labor market:
34 percent of accounting and auditing services.
30 percent of engineering services.
33 percent of legal services.
42 percent of management consulting services.
We proposed to apply each of these percentages to the respective
Benchmark I-O cost category underlying the professional fees cost
category to determine the Professional Fees: Nonlabor-related costs.
The Professional Fees: Labor-related costs were determined to be the
difference between the total costs for each Benchmark I-O category and
the Professional Fees: Nonlabor-related costs. This is the same
methodology that we used to separate the 2012-based IRF market basket
professional fees category into Professional Fees: Labor-related and
Professional Fees: Nonlabor-related cost categories (80 FR 47067).
In the 2016-based IRF market basket, nonmedical professional fees
that are subject to allocation based on these survey results represent
4.4 percent of total costs (and are limited to those fees related to
Accounting & Auditing, Legal, Engineering, and Management Consulting
services). Based on our survey results, we proposed to apportion 2.8
percentage points of the
[[Page 39088]]
4.4 percentage point figure into the Professional Fees: Labor-related
share cost category and designate the remaining 1.6 percentage point
into the Professional Fees: Nonlabor-related cost category.
In addition to the professional services listed, for the 2016-based
IRF market basket, we proposed to allocate a proportion of the Home
Office Contract Labor cost weight, calculated using the Medicare cost
reports as stated above, into the Professional Fees: Labor-related and
Professional Fees: Nonlabor-related cost categories. We proposed to
classify these expenses as labor-related and nonlabor-related as many
facilities are not located in the same geographic area as their home
office, and therefore, do not meet our definition for the labor-related
share that requires the services to be purchased in the local labor
market. For the 2012-based IRF market basket, we used the BEA I-O
expense data for NAICS 55, Management of Companies and Enterprises, to
estimate the Home Office Contract Labor cost weight (80 FR 47067). We
then allocated these expenses into the Professional Fess: Labor-related
and Professional Fees: Nonlabor-related cost categories.
Similar to the 2012-based IRF market basket, we proposed for the
2016-based IRF market basket to use the Medicare cost reports for both
freestanding IRF providers and hospital-based IRF providers to
determine the home office labor-related percentages. The MCR requires a
hospital to report information regarding their home office provider.
For the 2016-based IRF market basket, we proposed to start with the
sample of IRF providers that passed the top 1 percent trim used to
derive the Home Office Contract Labor cost weight as described in
section VI.B. of this final rule. For both freestanding and hospital-
based providers, we proposed to multiply each provider's Home Office
Contract Labor cost weight (calculated using data from the total
facility) by Medicare allowable total costs. This results in an amount
of Medicare allowable home office compensation costs for each IRF.
Using information on the Medicare cost report, we then compare the
location of the IRF with the location of the IRF's home office. We
proposed to classify an IRF with a home office located in their
respective local labor market if the IRF and its home office are
located in the same Metropolitan Statistical Area. We then calculate
the proportion of Medicare allowable home office compensation costs
that these IRFs represent of total Medicare allowable home office
compensation costs. We proposed to multiply this percentage (42
percent) by the Home Office Contract Labor cost weight (3.7 percent) to
determine the proportion of costs that should be allocated to the
labor-related share. Therefore, we allocated 1.6 percentage points of
the Home Office Contract Labor cost weight (3.7 percent times 42
percent) to the Professional Fees: Labor-related cost weight and 2.1
percentage points of the Home Office Contract Labor cost weight to the
Professional Fees: Nonlabor-related cost weight (3.7 percent times 58
percent). For the 2012-based IRF market basket, we used a similar
methodology but we relied on provider counts rather than home office/
related organization contract labor compensation costs to determine the
labor-related percentage (80 FR 47067).
In summary, we apportioned 2.8 percentage points of the non-medical
professional fees and 1.6 percentage points of the home office/related
organization contract labor cost weights into the Professional Fees:
Labor-related cost category. This amount was added to the portion of
professional fees that was identified to be labor-related using the I-O
data such as contracted advertising and marketing costs (approximately
0.6 percentage point of total costs) resulting in a Professional Fees:
Labor-related cost weight of 5.0 percent.
We received several comments on the proposed labor-related share,
which are summarized below.
Comment: A few commenters noted that the cost weight for Home
Office Contract Labor costs is 3.7 percent of all IRFs' costs and
influences changes in other payment areas, such as the total labor-
related share. The commenters stated that they believe the proposed
changes to the methodology are responsible, at least in large part, to
the notable proposed increase of approximately 2 percent of the labor-
related share. Some of the commenters also stated that the increase in
the labor-related share will adversely impact rural IRFs and IRFs with
a wage index below 1.0.
Response: The labor-related share for IRFs is derived from the
relative importance of the labor-related cost categories. The relative
importance for FY 2020 reflects the different rates of price change for
each of the individual cost categories between the base year and FY
2020. For the FY 2020 final rule, as proposed, the final labor-related
share for FY 2020 is based on a more recent forecast of the 2016-based
IRF market basket. Using the more recent forecast, the total difference
between the FY 2020 labor-related share using the 2016-based IRF market
basket and 2012-based IRF market basket is 2.0 percentage points (72.7
percent using 2016-based IRF market basket and 70.7 percent using 2012-
based IRF market basket). This difference can be separated into two
primary components: (1) Revision to the base year cost weights (1.4
percentage points); and (2) revision to starting point of calculation
of relative importance (base year) from 2012 to 2016 (0.6 percentage
point). Of the 1.4-percentage points difference in the base year cost
weights, just 0.2 percentage point is attributable to deriving the Home
Office Contract Labor cost weight using the MCR data rather than the I-
O data; the remainder is due to the increase in Compensation and
Capital cost weights (calculated using the MCR data) and the
incorporation of the 2012 Benchmark I-O data.
The impact of using the MCR data to calculate the Home Office
Contract Labor cost weight is minimal because it also lowers the
residual ``All Other'' cost weight from 25.8 percent (using the I-O
data to calculate the Home Office Contract Labor cost weight) to 22.2
percent (using the MCR data to calculate the Home Office Contract labor
cost weight). The lower residual ``All Other'' cost weight then leads
to relatively lower cost weights for Administrative and Business
Support Services, Installation, Maintenance and Repair Services, and
All Other: Labor-related Services (which are calculated using the
Benchmark I-O data), each of which is also reflected in the labor-
related share.
After careful consideration of comments, in this final rule, we are
finalizing the 2016-based IRF market basket labor-related share cost
weights as proposed.
As stated previously, we proposed to include in the labor-related
share the sum of the relative importance of Wages and Salaries,
Employee Benefits, Professional Fees: Labor-Related, Administrative and
Facilities Support Services, Installation, Maintenance, and Repair, All
Other: Labor-related Services, and a portion of the Capital-Related
cost weight from the 2016-based IRF market basket. The relative
importance reflects the different rates of price change for these cost
categories between the base year (2016) and FY 2020. Based on IGI's 2nd
quarter 2019 forecast for the 2016-based IRF market basket, the sum of
the FY 2020 relative importance for Wages and Salaries, Employee
Benefits, Professional Fees: Labor-related, Administrative and
Facilities Support Services, Installation Maintenance & Repair
Services, and All Other: Labor-related Services is 68.7 percent. The
portion of Capital costs that are influenced by the local labor market
is estimated to be 46 percent, which is the same percentage applied to
[[Page 39089]]
the 2012-based IRF market basket (80 FR 47068). Since the relative
importance for Capital is 8.6 percent of the 2016-based IRF market
basket in FY 2020, we took 46 percent of 8.6 percent to determine the
labor-related share of Capital for FY 2020 of 4.0 percent. Therefore,
we are finalizing a total labor-related share for FY 2020 of 72.7
percent (the sum of 68.7 percent for the operating costs and 4.0
percent for the labor-related share of Capital).
Table 12 shows the FY 2020 labor-related share using the final
2016-based IRF market basket relative importance and the FY 2019 labor-
related share which was based on the 2012-based IRF market basket
relative importance.
[GRAPHIC] [TIFF OMITTED] TR08AU19.015
F. Update to the IRF Wage Index To Use Concurrent IPPS Wage Index
Beginning With FY 2020
1. Background
Section 1886(j)(6) of the Act requires the Secretary to adjust the
proportion of rehabilitation facilities' costs attributable to wages
and wage-related costs (as estimated by the Secretary from time to
time) by a factor (established by the Secretary) reflecting the
relative hospital wage level in the geographic area of the
rehabilitation facility compared to the national average wage level for
those facilities. The Secretary is required to update the IRF PPS wage
index on the basis of information available to the Secretary on the
wages and wage-related costs to furnish rehabilitation services. Any
adjustment or updates made under section 1886(j)(6) of the Act for a FY
are made in a budget-neutral manner.
2. Update to the IRF Wage Index To Use Concurrent IPPS Wage Index
Beginning with FY 2020
When the IRF PPS was implemented in the FY 2002 IRF PPS final rule
(66 FR 41358), we finalized the use of the FY IPPS wage data in the
creation of an IRF wage index. We believed that a wage index based on
FY IPPS wage data was the best proxy and most appropriate wage index to
use in adjusting payments to IRFs, since both IPPS hospitals and IRFs
compete in the same labor markets. For this reason, we believed, and
continue to believe, that the wage data of IPPS hospitals accurately
captures the relationship of wages and wage-related costs of IRFs in an
area as compared with the national average. Therefore, in the FY 2002
IRF PPS final rule, we finalized use of the FY 1997 IPPS wage data to
develop the wage index for the IRF PPS, as that was the most recent
final data available.
For all subsequent years in which the IRF PPS wage index has been
updated, we have continued to use the most recent final IPPS data
available, which has led us to use the pre-floor, pre-reclassified FY
IPPS wage index values from the prior fiscal year.
In the FY 2018 IRF PPS proposed rule (82 FR 20742 through 20743),
we included a request for information (RFI) to solicit comments from
stakeholders requesting information on CMS flexibilities and
efficiencies. The purpose of the RFI was to receive feedback regarding
ways in which we could reduce burden for hospitals and physicians,
improve quality of care, decrease costs and ensure that patients
receive the best care. We received comments from IRF industry
associations, state and national hospital associations, industry
groups, representing hospitals, and individual IRF providers in
response to the solicitation. One of the responses we received to the
RFI suggested that there is concern among IRF stakeholders about the
different wage index data used in the different post-acute care (PAC)
settings. For the IRF PPS, we use a 1-year lag of the pre-floor, pre-
reclassified FY IPPS wage index, meaning that for the IRF PPS for FY
2019, we finalized use of the FY 2018 IPPS wage index (83 FR 38527).
However, we base the wage indexes for the SNF PPS and the LTCH PPS on
the concurrent IPPS wage index ((83 FR 39172 through 39178) and (83 FR
41731), respectively).
As we look towards a more unified PACpayment system, we believe
that standardizing the wage index data across PAC settings is
necessary. Therefore, we proposed to change the IRF wage index
methodology to align with other PAC settings. Specifically, we proposed
changing from our established policy of using the pre-floor, pre-
reclassified FY IPPS wage index (that is, for FY 2020 we proposed to
use the concurrent FY 2020 pre-floor, pre-reclassified IPPS wage index
under the IRF PPS). This proposed change would use the concurrent IPPS
pre-floor, pre-reclassified wage index for the IRF wage index beginning
with FY 2020 and continuing for all subsequent years. Thus, for the FY
2020 IRF wage index, we proposed to use the FY 2020 pre-floor, pre-
reclassified IPPS wage index, which is based on data submitted for
hospital cost reporting periods beginning in FY 2016. We proposed to
implement these revisions in a budget neutral manner. For more
information
[[Page 39090]]
on the distributional impacts of this proposal, we refer readers to the
FY 2020 IRF PPS proposed rule (84 FR 17278).
Using the current pre-floor, pre-reclassified FY IPPS wage index
would result in the most up-to-date wage data being the basis for the
IRF wage index. It would also result in more consistency and equity in
the wage index methodology used by Medicare.
We received 7 comments on this proposal to align the data
timeframes with that of the IPPS by using the FY 2020 pre-floor, pre-
reclassified FY IPPS wage index as the basis for the FY 2020 IRF wage
index, which are summarized below.
Comment: All of the commenters supported CMS' proposal to use the
FY 2020 pre-floor, pre-reclassified FY IPPS wage index for the FY 2020
IRF wage index. Commenters agreed that the proposed change to use the
concurrent FY IPPS wage index data would align the wage index data
across PAC settings and move in the direction of unified PAC payment. A
few commenters recommended that CMS adopt other wage index policies for
IRFs that apply to or have been proposed for IPPS hospitals, such as
geographic reclassifications, suggesting that this would increase
consistency and alignment across settings.
Response: We appreciate the commenter's support for the proposal.
We agree that finalizing this proposal is necessary as we move towards
a more unified PAC payment system. We plan to monitor the use of the
concurrent FY IPPS wage index data before we consider any other
potential wage index policy changes.
After careful consideration of the comments we received, we are
finalizing our proposal to align the data timeframes with that of the
IPPS by using the concurrent pre-floor, pre-reclassified IPPS wage
index for the IRF wage index beginning with FY 2020 and continuing for
all subsequent years. Thus, we will use the FY 2020 pre-floor, pre-
reclassified IPPS wage index as the basis for the FY 2020 IRF wage
index (that is, for all IRF discharges beginning on or after October 1,
2019). We will implement these revisions in a budget neutral manner. We
refer readers to Table 20 in section XIII.C of this final rule for more
information on the distributional effects of this change.
3. Wage Adjustment for FY 2020 Using Concurrent IPPS Wage Index Labor
Market Area Definitions and the
Due to our proposal to use the concurrent IPPS wage index beginning
with FY 2020, for FY 2020, we proposed using the policy and
methodologies described in section VI. of this final rule related to
the labor market area definitions and the wage index methodology for
areas with wage data. Thus, we proposed using the CBSA labor market
area definitions and the FY 2020 pre-reclassification and pre-floor
IPPS wage index data. In accordance with section 1886(d)(3)(E) of the
Act, the FY 2020 pre-reclassification and pre-floor IPPS wage index is
based on data submitted for hospital cost reporting periods beginning
on or after October 1, 2015 and before October 1, 2016 (that is, FY
2016 cost report data).
The labor market designations made by the OMB include some
geographic areas where there are no hospitals and, thus, no hospital
wage index data on which to base the calculation of the IRF PPS wage
index. We proposed to continue to use the same methodology discussed in
the FY 2008 IRF PPS final rule (72 FR 44299) to address those
geographic areas where there are no hospitals and, thus, no hospital
wage index data on which to base the calculation for the FY 2020 IRF
PPS wage index.
We received one comment on this proposal, which is summarized
below.
Comment: One commenter requested that, until a new wage index
system is implemented, CMS should establish a smoothing variable to be
applied to the current IRF wage index to reduce the fluctuations IRFs
experience annually.
Response: Under section 1886(j)(6) of the Act, we adjust IRF PPS
rates to account for differences in area wage levels. Any perceived
volatility in the wage index is predicated upon volatility in actual
wages in that area and reflects real differences in area wage levels.
As we believe that the application of a smoothing variable would make
the wage index values less reflective of the area wage levels, we do
not believe it would be appropriate to implement such a change to the
IRF wage index policy.
After careful consideration of the comments we received, we are
finalizing our proposal to use the policy and methodologies described
in section VI. of this final rule related to the labor market area
definitions and the wage index methodology for areas with wage data.
Thus, we are finalizing the use of the CBSA labor market area
definitions and the FY 2020 pre-reclassification and pre-floor IPPS
wage index data. We are finalizing the continued use of the same
methodology discussed in the FY 2008 IRF PPS final rule (72 FR 44299)
to address those geographic areas where there are no hospitals and,
thus, no hospital wage index data on which to base the calculation for
the FY 2020 IRF PPS wage index.
4. Core-Based Statistical Areas (CBSAs) for the FY 2020 IRF Wage Index
The wage index used for the IRF PPS is calculated using the pre-
reclassification and pre-floor IPPS wage index data and is assigned to
the IRF on the basis of the labor market area in which the IRF is
geographically located. IRF labor market areas are delineated based on
the CBSAs established by the OMB. The current CBSA delineations (which
were implemented for the IRF PPS beginning with FY 2016) are based on
revised OMB delineations issued on February 28, 2013, in OMB Bulletin
No. 13-01. OMB Bulletin No. 13-01 established revised delineations for
Metropolitan Statistical Areas, Micropolitan Statistical Areas, and
Combined Statistical Areas in the United States and Puerto Rico based
on the 2010 Census, and provided guidance on the use of the
delineations of these statistical areas using standards published in
the June 28, 2010 Federal Register (75 FR 37246 through 37252). We
refer readers to the FY 2016 IRF PPS final rule (80 FR 47068 through
47076) for a full discussion of our implementation of the OMB labor
market area delineations beginning with the FY 2016 wage index.
Generally, OMB issues major revisions to statistical areas every 10
years, based on the results of the decennial census. However, OMB
occasionally issues minor updates and revisions to statistical areas in
the years between the decennial censuses. On July 15, 2015, OMB issued
OMB Bulletin No. 15-01, which provides minor updates to and supersedes
OMB Bulletin No. 13-01 that was issued on February 28, 2013. The
attachment to OMB Bulletin No. 15-01 provides detailed information on
the update to statistical areas since February 28, 2013. The updates
provided in OMB Bulletin No. 15-01 are based on the application of the
2010 Standards for Delineating Metropolitan and Micropolitan
Statistical Areas to Census Bureau population estimates for July 1,
2012 and July 1, 2013.
In the FY 2018 IRF PPS final rule (82 FR 36250 through 36251), we
adopted the updates set forth in OMB Bulletin No. 15-01 effective
October 1, 2017, beginning with the FY 2018 IRF wage index. For a
complete discussion of the adoption of the updates set forth in OMB
Bulletin No. 15-01, we refer readers to the FY 2018 IRF PPS final rule.
In the FY 2019 IRF PPS final rule (83 FR 38527), we continued to use
the OMB delineations that were adopted
[[Page 39091]]
beginning with FY 2016 to calculate the area wage indexes, with updates
set forth in OMB Bulletin No. 15-01 that we adopted beginning with the
FY 2018 wage index.
On August 15, 2017, OMB issued OMB Bulletin No. 17-01, which
provided updates to and superseded OMB Bulletin No. 15-01 that was
issued on July 15, 2015. The attachments to OMB Bulletin No. 17-01
provide detailed information on the update to statistical areas since
July 15, 2015, and are based on the application of the 2010 Standards
for Delineating Metropolitan and Micropolitan Statistical Areas to
Census Bureau population estimates for July 1, 2014 and July 1, 2015.
In OMB Bulletin No. 17-01, OMB announced that one Micropolitan
Statistical Area now qualifies as a Metropolitan Statistical Area. The
new urban CBSA is as follows:
Twin Falls, Idaho (CBSA 46300). This CBSA is comprised of
the principal city of Twin Falls, Idaho in Jerome County, Idaho and
Twin Falls County, Idaho.
The OMB bulletin is available on the OMB website at https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/bulletins/2017/b-17-01.pdf.
As we indicated in the FY 2019 IRF PPS final rule (83 FR 38528), we
believe that it is important for the IRF PPS to use the latest labor
market area delineations available as soon as is reasonably possible to
maintain a more accurate and up-to-date payment system that reflects
the reality of population shifts and labor market conditions. As
discussed in the FY 2019 IPPS and LTCH PPS final rule (83 FR 20591),
these updated labor market area definitions were implemented under the
IPPS beginning on October 1, 2018. Therefore, we proposed to implement
these revisions for the IRF PPS beginning October 1, 2019, consistent
with our historical practice of modeling IRF PPS adoption of the labor
market area delineations after IPPS adoption of these delineations.
We received 2 comments on this proposal, which are summarized
below.
Comment: Commenters expressed concern that the IRF wage index
values published in the FY 2020 IRF PPS proposed rule were not
consistent with the values published in the FY 2020 IPPS proposed rule
wage index public use file. These commenters suggested that CMS examine
these wage index values and correct them if we find that they are in
error prior to finalizing the use of the concurrent IPPS wage index
data for the IRF PPS.
Response: We identified a slight error in the proposed rule wage
index values after the FY 2020 IRF PPS proposed rule was published. A
programming error caused the data for all providers in a single county
to be included twice, which affected the national average hourly rate,
and therefore, affected nearly all wage index values. We have corrected
the programming logic so this error cannot occur again. We also
standardized our procedures for rounding, to ensure consistency. The
correction to the proposed rule wage index data was not completed until
after the comment period closed on June 17, 2019. This final rule
reflects the corrected and updated wage index data.
We are finalizing and implementing these revisions for the IRF PPS
beginning October 1, 2019, consistent with our historical practice of
modeling IRF PPS adoption of the labor market area delineations after
IPPS adoption of these delineations.
5. Wage Adjustment
The FY 2020 wage index tables (which, as discussed in section VI.F
above, we base on the FY 2020 pre-reclassified, pre-floor FY 2020 IPPS
wage index) are available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html. Table A is for urban areas, and Table B
is for rural areas.
To calculate the wage-adjusted facility payment for the payment
rates set forth in this final rule, we would multiply the unadjusted
federal payment rate for IRFs by the FY 2020 labor-related share based
on the 2016-based IRF market basket (72.7 percent) to determine the
labor-related portion of the standard payment amount. A full discussion
of the calculation of the labor-related share is located in section
VI.E of this final rule. We would then multiply the labor-related
portion by the applicable IRF wage index from the tables in the
addendum to this final rule. These tables are available on the CMS
website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
Adjustments or updates to the IRF wage index made under section
1886(j)(6) of the Act must be made in a budget-neutral manner. We
proposed to calculate a budget-neutral wage adjustment factor as
established in the FY 2004 IRF PPS final rule (68 FR 45689), codified
at Sec. 412.624(e)(1), as described in the steps below. We proposed to
use the listed steps to ensure that the FY 2020 IRF standard payment
conversion factor reflects the updates to the IRF wage index (based on
the FY 2020 IPPS wage index) and the labor-related share in a budget-
neutral manner:
Step 1. Determine the total amount of the estimated FY 2019 IRF PPS
payments, using the FY 2019 standard payment conversion factor and the
labor-related share and the wage indexes from FY 2019 (as published in
the FY 2019 IRF PPS final rule (83 FR 38514)).
Step 2. Calculate the total amount of estimated IRF PPS payments
using the FY 2020 standard payment conversion factor and the FY 2020
labor-related share and CBSA urban and rural wage indexes.
Step 3. Divide the amount calculated in step 1 by the amount
calculated in step 2. The resulting quotient is the FY 2020 budget-
neutral wage adjustment factor of 1.0076.
Step 4. Apply the FY 2020 budget-neutral wage adjustment factor
from step 3 to the FY 2020 IRF PPS standard payment conversion factor
after the application of the increase factor to determine the FY 2020
standard payment conversion factor.
We note that we have updated our data between the FY 2020 IRF PPS
proposed and final rules to ensure that we use the most recent
available data in calculating IRF PPS payments. This updated data
includes a more complete set of claims for FY 2018 and updated wage
index data. Based on our analysis using this updated data, we now
estimate a budget-neutral wage adjustment factor of 1.0031 for FY 2020.
We discuss the calculation of the standard payment conversion
factor for FY 2020 in section VI.H. of this final rule.
We invited public comments on this proposal. However, we did not
receive any comments on the proposed methodology for calculating the
budget-neutral wage adjustment factor.
As we did not receive any comments on the proposed methodology for
calculating the budget-neutral wage adjustment factor, we are
finalizing this policy as proposed for FY 2020.
G. Wage Index Comment Solicitation
Historically, we have calculated the IRF wage index values using
unadjusted wage index values from another provider setting.
Stakeholders have frequently commented on certain aspects of the IRF
wage index values and their impact on payments. Therefore, we solicited
public comments in the FY 2020 IRF PPS proposed rule (84 FR 17280) on
concerns stakeholders may have regarding the wage index used to adjust
[[Page 39092]]
IRF payments and suggestions for possible updates and improvements to
the geographic adjustment of IRF payments.
We appreciate the commenters' responses to this solicitation and
will take them into consideration for possible future policy
development.
H. Description of the IRF Standard Payment Conversion Factor and
Payment Rates for FY 2020
To calculate the standard payment conversion factor for FY 2020, as
illustrated in Table 13, we begin by applying the increase factor for
FY 2020, as adjusted in accordance with sections 1886(j)(3)(C) of the
Act, to the standard payment conversion factor for FY 2019 ($16,021).
Applying the 2.5 percent increase factor for FY 2020 to the standard
payment conversion factor for FY 2019 of $16,021 yields a standard
payment amount of $16,422. Then, we apply the budget neutrality factor
for the FY 2020 wage index and labor-related share of 1.0031, which
results in a standard payment amount of $16,472. We next apply the
budget neutrality factor for the revised CMGs and CMG relative weights
of 1.0010, which results in the standard payment conversion factor of
$16,489 for FY 2020.
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We received one comment on the proposed FY 2020 standard payment
conversion factor, which is summarized below.
Comment: One commenter stated that the proposed rate update fails
to cover the cost of medical inflation or payment reductions due to
sequestration. As a result, this commenter expressed concern that their
hospitals' financial viability and their ability to care for their
patients will be threatened.
Response: We appreciate this commenter's concerns. However, we note
that the IRF PPS payment rates are updated annually by an increase
factor that reflects changes over time in the prices of an appropriate
mix of goods and services included in the covered IRF services, as
required by section 1886(j)(3)(C) of the Act.
After careful consideration of the comment we received, we are
finalizing the IRF standard payment conversion factor of $16,489 for FY
2020.
After the application of the CMG relative weights described in
section IV. of this final rule to the FY 2020 standard payment
conversion factor ($16,489), the resulting unadjusted IRF prospective
payment rates for FY 2020 are shown in Table 14.
BILLING CODE 4120-01-P
[[Page 39093]]
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[[Page 39094]]
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BILLING CODE 4120-01-C
H. Example of the Methodology for Adjusting the Prospective Payment
Rates
Table 15 illustrates the methodology for adjusting the prospective
payments (as described in section VI. of this final rule). The
following examples are based on two hypothetical Medicare
beneficiaries, both classified into CMG 0104 (without comorbidities).
The unadjusted prospective payment rate for
[[Page 39095]]
CMG 0104 (without comorbidities) appears in Table 14.
Example: One beneficiary is in Facility A, an IRF located in rural
Spencer County, Indiana, and another beneficiary is in Facility B, an
IRF located in urban Harrison County, Indiana. Facility A, a rural non-
teaching hospital has a Disproportionate Share Hospital (DSH)
percentage of 5 percent (which would result in a LIP adjustment of
1.0156), a wage index of 0.8319, and a rural adjustment of 14.9
percent. Facility B, an urban teaching hospital, has a DSH percentage
of 15 percent (which would result in a LIP adjustment of 1.0454
percent), a wage index of 0.8844, and a teaching status adjustment of
0.0784.
To calculate each IRF's labor and non-labor portion of the
prospective payment, we begin by taking the unadjusted prospective
payment rate for CMG 0104 (without comorbidities) from Table 14. Then,
we multiply the labor-related share for FY 2020 (72.7 percent)
described in section VI.E. of this final rule by the unadjusted
prospective payment rate. To determine the non-labor portion of the
prospective payment rate, we subtract the labor portion of the federal
payment from the unadjusted prospective payment.
To compute the wage-adjusted prospective payment, we multiply the
labor portion of the federal payment by the appropriate wage index
located in Tables A and B. These tables are available on the CMS
website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
The resulting figure is the wage-adjusted labor amount. Next, we
compute the wage-adjusted federal payment by adding the wage-adjusted
labor amount to the non-labor portion of the federal payment.
Adjusting the wage-adjusted federal payment by the facility-level
adjustments involves several steps. First, we take the wage-adjusted
prospective payment and multiply it by the appropriate rural and LIP
adjustments (if applicable). Second, to determine the appropriate
amount of additional payment for the teaching status adjustment (if
applicable), we multiply the teaching status adjustment (0.0784, in
this example) by the wage-adjusted and rural-adjusted amount (if
applicable). Finally, we add the additional teaching status payments
(if applicable) to the wage, rural, and LIP-adjusted prospective
payment rates. Table 15 illustrates the components of the adjusted
payment calculation.
[GRAPHIC] [TIFF OMITTED] TR08AU19.019
Thus, the adjusted payment for Facility A would be $28,327.82, and
the adjusted payment for Facility B would be $28,467.16.
VII. Update to Payments for High-Cost Outliers Under the IRF PPS for FY
2020
A. Update to the Outlier Threshold Amount for FY 2020
Section 1886(j)(4) of the Act provides the Secretary with the
authority to make payments in addition to the basic IRF prospective
payments for cases incurring extraordinarily high costs. A case
qualifies for an outlier payment if the estimated cost of the case
exceeds the adjusted outlier threshold. We calculate the adjusted
outlier threshold by adding the IRF PPS payment for the case (that is,
the CMG payment adjusted by all of the relevant facility-level
adjustments) and the adjusted threshold amount (also adjusted by all of
the relevant facility-level adjustments). Then, we calculate the
estimated cost of a case by multiplying the IRF's overall CCR by the
Medicare allowable covered charge. If the estimated cost of the case is
higher than the adjusted outlier threshold, we make an outlier payment
for the case equal to 80 percent of the difference between the
estimated cost of the case and the outlier threshold.
In the FY 2002 IRF PPS final rule (66 FR 41362 through 41363), we
discussed our rationale for setting the outlier threshold amount for
the IRF PPS so that estimated outlier payments would equal 3 percent of
total estimated payments. For the 2002 IRF PPS final rule, we analyzed
various outlier policies using 3, 4, and 5 percent of the total
estimated payments, and we concluded that an outlier policy set at 3
percent of total estimated payments would optimize the extent to which
we could reduce the financial risk to IRFs
[[Page 39096]]
of caring for high-cost patients, while still providing for adequate
payments for all other (non-high cost outlier) cases.
Subsequently, we updated the IRF outlier threshold amount in the
FYs 2006 through 2019 IRF PPS final rules and the FY 2011 and FY 2013
notices (70 FR 47880, 71 FR 48354, 72 FR 44284, 73 FR 46370, 74 FR
39762, 75 FR 42836, 76 FR 47836, 76 FR 59256, 77 FR 44618, 78 FR 47860,
79 FR 45872, 80 FR 47036, 81 FR 52056, 82 FR 36238, and 83 FR 38514,
respectively) to maintain estimated outlier payments at 3 percent of
total estimated payments. We also stated in the FY 2009 final rule (73
FR 46370 at 46385) that we would continue to analyze the estimated
outlier payments for subsequent years and adjust the outlier threshold
amount as appropriate to maintain the 3 percent target.
To update the IRF outlier threshold amount for FY 2020, we proposed
to use FY 2018 claims data and the same methodology that we used to set
the initial outlier threshold amount in the FY 2002 IRF PPS final rule
(66 FR 41316 and 41362 through 41363), which is also the same
methodology that we used to update the outlier threshold amounts for
FYs 2006 through 2019. The outlier threshold is calculated by
simulating aggregate payments and using an iterative process to
determine a threshold that results in outlier payments being equal to 3
percent of total payments under the simulation. To determine the
outlier threshold for FY 2020, we estimate the amount of FY 2020 IRF
PPS aggregate and outlier payments using the most recent claims
available (FY 2018) and the FY 2020 standard payment conversion factor,
labor-related share, and wage indexes, incorporating any applicable
budget-neutrality adjustment factors. The outlier threshold is adjusted
either up or down in this simulation until the estimated outlier
payments equal 3 percent of the estimated aggregate payments. Based on
an analysis of the preliminary data used for the proposed rule, we
estimated that IRF outlier payments as a percentage of total estimated
payments would be approximately 3.2 percent in FY 2019. Therefore, we
proposed to update the outlier threshold amount from $9,402 for FY 2019
to $9,935 for FY 2020 to maintain estimated outlier payments at
approximately 3 percent of total estimated aggregate IRF payments for
FY 2020.
We note that, as we typically do, we updated our data between the
FY 2020 IRF PPS proposed and final rules to ensure that we use the most
recent available data in calculating IRF PPS payments. This updated
data includes a more complete set of claims for FY 2018. Based on our
analysis using this updated data, we now estimate that IRF outlier
payments as a percentage of total estimated payments are approximately
3.0 percent in FY 2019. Although our analysis shows that we achieved
our goal to have estimated outlier payments equal 3.0 percent of total
estimated aggregate IRF payments for FY 2019, we still need to adjust
the IRF outlier threshold to reflect changes in estimated costs and
payments for IRFs in FY 2020. That is, as discussed in section VI. of
this final rule, we are finalizing our proposal to increase IRF PPS
payment rates by 2.5 percent, in accordance with section 1886(j)(3)(C)
of the Act to account for changes over time in the prices of an
appropriate mix of goods and services included in the covered IRF
services. Similarly, we estimate costs for IRFs in FY 2020 are expected
to increase to account for changes over time in the prices of goods and
services included in the covered IRF services. Therefore, we will
update the outlier threshold amount from $9,402 for FY 2019 to $9,300
for FY 2020 to account for the increases in IRF PPS payments and
estimated costs and to maintain estimated outlier payments at
approximately 3 percent of total estimated aggregate IRF payments for
FY 2020.
We received three comments on the proposed update to the FY 2020
outlier threshold, which are summarized below.
Comment: Commenters suggested that historical outlier
reconciliation dollars should be included in the calculation of the
fixed loss threshold under the IRF PPS.
Response: As we did not propose a change to the methodology used to
establish an outlier threshold for IRF PPS payments, these comments are
outside the scope of this rule. However, we will continue to monitor
our IRF outlier policies to ensure that they continue to compensate
IRFs appropriately for treating unusually high-cost patients and do not
limit access to care for patients who are likely to require unusually
high-cost care.
Comment: A few commenters suggested that CMS consider implementing
a cap on the amount of outlier payments an individual IRF can receive
under the IRF PPS. One commenter was supportive of maintaining
estimated payments for outlier payments at approximately 3 percent
while other commenters expressed concern with maintaining the 3 percent
target and suggested reducing the outlier pool below 3 percent.
Response: As we did not propose to implement a cap on the amount of
outlier payments an individual IRF can receive under the IRF PPS, these
comments are outside the scope of this rule. However, we note that any
future consideration given to imposing a limit on outlier payments
would have to carefully analyze and take into consideration the effect
on access to IRF care for certain high-cost populations.
As most recently discussed in the FY 2019 IRF PPS final rule (83 FR
38532), we analyzed various outlier policies using 3, 4, and 5 percent
of the total estimated payments for the FY 2002 IRF PPS final rule, and
we concluded that an outlier policy set at 3 percent of total estimated
payments would optimize the extent to which we could reduce the
financial risk to IRFs of caring for high-cost patients, while still
providing for adequate payments for all other (non-high cost outlier)
cases. We continue to believe that the outlier policy of 3 percent of
total estimated aggregate payments accomplishes this objective. We
refer readers to the FY 2002 IRF PPS final rule (66 FR 41316, 41362
through 41363) for more information regarding the rationale for setting
the outlier threshold amount for the IRF PPS so that estimated outlier
payments would equal 3 percent of total estimated payments.
Comment: One commenter requested that CMS update the outlier
threshold amount in the final rule using the latest available data.
Response: We agree that we should use the most recent data
available to calculate the outlier threshold. Therefore, as previously
stated, we updated the data used to calculate the outlier threshold
between the FY 2020 IRF PPS proposed and final rules.
Having carefully considered the public comments received and also
taking into account the most recent available data, we are finalizing
the outlier threshold amount of $9,300 to maintain estimated outlier
payments at approximately 3 percent of total estimated aggregate IRF
payments for FY 2020.
B. Update to the IRF Cost-to-Charge Ratio Ceiling and Urban/Rural
Averages for FY 2020
Cost-to-charge ratios are used to adjust charges from Medicare
claims to costs and are computed annually from facility-specific data
obtained from Medicare cost reports. IRF specific cost-to-charge ratios
are used in the development of the CMG relative weights and the
calculation of outlier
[[Page 39097]]
payments under the IRF prospective payment system. In accordance with
the methodology stated in the FY 2004 IRF PPS final rule (68 FR 45674,
45692 through 45694), we proposed to apply a ceiling to IRFs' CCRs.
Using the methodology described in that final rule, we proposed to
update the national urban and rural CCRs for IRFs, as well as the
national CCR ceiling for FY 2020, based on analysis of the most recent
data that is available. We apply the national urban and rural CCRs in
the following situations:
New IRFs that have not yet submitted their first Medicare
cost report.
IRFs whose overall CCR is in excess of the national CCR
ceiling for FY 2020, as discussed below in this section.
Other IRFs for which accurate data to calculate an overall
CCR are not available.
Specifically, for FY 2020, we proposed to estimate a national
average CCR of 0.500 for rural IRFs, which we calculated by taking an
average of the CCRs for all rural IRFs using their most recently
submitted cost report data. Similarly, we proposed to estimate a
national average CCR of 0.406 for urban IRFs, which we calculated by
taking an average of the CCRs for all urban IRFs using their most
recently submitted cost report data. We apply weights to both of these
averages using the IRFs' estimated costs, meaning that the CCRs of IRFs
with higher total costs factor more heavily into the averages than the
CCRs of IRFs with lower total costs. For this final rule, we have used
the most recent available cost report data (FY 2017). This includes all
IRFs whose cost reporting periods begin on or after October 1, 2016,
and before October 1, 2017. If, for any IRF, the FY 2017 cost report
was missing or had an ``as submitted'' status, we used data from a
previous fiscal year's (that is, FY 2004 through FY 2016) settled cost
report for that IRF. We do not use cost report data from before FY 2004
for any IRF because changes in IRF utilization since FY 2004 resulting
from the 60 percent rule and IRF medical review activities suggest that
these older data do not adequately reflect the current cost of care.
Using updated FY 2017 cost report data for this final rule, we estimate
a national average CCR of 0.500 for rural IRFs, and a national average
CCR of 0.405 for urban IRFs.
In accordance with past practice, we proposed to set the national
CCR ceiling at 3 standard deviations above the mean CCR. Using this
method, we proposed a national CCR ceiling of 1.31 for FY 2020. This
means that, if an individual IRF's CCR were to exceed this ceiling of
1.31 for FY 2020, we would replace the IRF's CCR with the appropriate
proposed national average CCR (either rural or urban, depending on the
geographic location of the IRF). We calculated the proposed national
CCR ceiling by:
Step 1. Taking the national average CCR (weighted by each IRF's
total costs, as previously discussed) of all IRFs for which we have
sufficient cost report data (both rural and urban IRFs combined).
Step 2. Estimating the standard deviation of the national average
CCR computed in step 1.
Step 3. Multiplying the standard deviation of the national average
CCR computed in step 2 by a factor of 3 to compute a statistically
significant reliable ceiling.
Step 4. Adding the result from step 3 to the national average CCR
of all IRFs for which we have sufficient cost report data, from step 1.
Using the updated FY 2017 cost report data for this final rule, we
estimate a national average CCR ceiling of 1.31, using the same
methodology.
We did not receive comments on the proposed update to the IRF CCR
ceiling and the urban/rural averages for FY 2020.
As we did not receive any comments on the proposed update to the
IRF CCR ceiling and the urban/rural averages for FY 2020, we are
finalizing the national average urban CCR at 0.405, the national
average rural CCR at 0.500, and the national average CCR ceiling at
1.31 for FY 2020.
VIII. Amendments to Sec. 412.622 To Clarify the Definition of a
Rehabilitation Physician
Under Sec. 412.622(a)(3)(iv), a rehabilitation physician is
defined as ``a licensed physician with specialized training and
experience in inpatient rehabilitation.'' The term rehabilitation
physician is used in several other places in Sec. 412.622, with
corresponding references to Sec. 412.622(a)(3)(iv). The definition at
Sec. 412.622(a)(3)(iv) does not specify the level or type of training
and experience required for a licensed physician to be designated as a
rehabilitation physician because we believe that the IRFs are in the
best position to make this determination for purposes of Sec. 412.622.
Therefore, we proposed to amend the definition of a rehabilitation
physician to clarify that the determination as to whether a physician
qualifies as a rehabilitation physician (that is, a licensed physician
with specialized training and experience in inpatient rehabilitation)
is made by the IRF (84 FR 17284 through 17285). For clarity, we also
proposed to remove this definition from Sec. 412.622(a)(3)(iv) and
move it to a new paragraph (Sec. 412.622(c)). We also proposed to make
corresponding technical corrections elsewhere in Sec.
412.622(a)(3)(iv), (a)(4)(i)(A), (a)(4)(iii)(A), and (a)(5)(i) to
remove the references to Sec. 412.622(a)(3)(iv) in those paragraphs,
so as to reflect the new location of the definition.
We received 1,163 comments on the proposal to clarify the
definition of a rehabilitation physician, to move the definition from
Sec. 412.622(a)(3)(iv) to Sec. 412.622(c), and to make corresponding
technical corrections elsewhere in Sec. 412.622 to remove references
to the current location of the definition in Sec. 412.622(a)(3)(iv).
The majority of these comments consisted of form letters, in which we
received multiple copies of two types of identically-worded letters
that had been signed and submitted by different individuals. The
comments we received on this are summarized below.
Comment: Many of the commenters noted appreciation and support for
the proposal to amend the definition of a rehabilitation physician to
clarify that the determination as to whether a physician qualifies as a
rehabilitation physician (that is, a licensed physician with
specialized training and experience in inpatient rehabilitation) is
made by the IRF. One commenter stated that while board-certified
physiatrists play a crucial caregiver and leadership role in
rehabilitation hospitals, they are not alone in doing so. Physicians
representing other specialties can and do also display the leadership
and caregiving skills and experience that clearly qualify them as a
rehabilitation physician. One commenter indicated that CMS' proposal is
consistent with CMS' previously stated position from 2010. Some
commenters also stated that clarifying the regulation would reduce the
number of claims denials by promoting a shared understanding of the
requirements between IRFs and Medicare contractors.
Response: We appreciate the commenters' support and agree that this
clarification in our regulations supports our longstanding position
that the responsibility is, and always has been, on the IRF to ensure
that the rehabilitation physician(s) who are making the admission
decisions and treating the patients have the necessary training and
experience.
Comment: Many commenters stated that they do not support CMS'
proposal and suggested that CMS not finalize the proposed amendments to
Sec. 412.622. These commenters requested that CMS delay any changes to
current regulations
[[Page 39098]]
until CMS and stakeholders can work together to develop a consensus
approach for protecting the quality and integrity of IRF care. These
commenters stated that they believe that allowing the IRF to determine
whether an individual physician meets the regulatory standards for a
rehabilitation physician could increase the risks that some IRFs will
hire or contract with unqualified or underqualified physicians, reduce
the quality of care that patients receive in IRFs, and reduce the value
of physiatrists. These commenters also stated that reducing the value
of physiatrists could also deter students from wanting to pursue this
specialty in the future. Some commenters also indicated that CMS'
proposal, if finalized, would undermine CMS' ability to engage in
appropriate program integrity oversight by not reviewing an IRF's
decision to hire a particular physician to fill a rehabilitation
physician role.
Response: While we appreciate and share the commenters' desire to
ensure that Medicare beneficiaries in IRFs receive the highest-quality
care from trained and qualified physicians, we do not believe that
merely clarifying our existing policy would reduce quality of care. The
regulation will continue to require a rehabilitation physician to be a
licensed physician with specialized training and experience in
inpatient rehabilitation. We are not lowering these requirements.
However, we continue to believe that we need to clarify our existing
policy that the IRF makes the determination as to whether a given
physician qualifies as a rehabilitation physician in order to eliminate
any unnecessary uncertainty on this issue. Over the past year, we have
received questions regarding how this provision can be enforced, and we
believe that this clarification will promote a shared understanding of
how we intend the enforcement to occur. We expect that IRFs will
continue to ensure that the rehabilitation physicians treating patients
in their facilities have the necessary training and experience in
inpatient rehabilitation. To this end, we will continue to work with
stakeholders to refine Medicare's IRF payment policies in the future so
that they support IRFs in providing the highest quality care to
beneficiaries.
After careful consideration of the comments we received, we are
finalizing our proposal to amend the definition of a rehabilitation
physician to clarify that the determination as to whether a physician
qualifies as a rehabilitation physician (that is, a licensed physician
with specialized training and experience in inpatient rehabilitation)
is made by the IRF. However, based on the stakeholder feedback, we will
continue to assess whether future refinements to this policy may be
needed.
For clarity, we are also removing this definition from Sec.
412.622(a)(3)(iv) and moving it to a new paragraph (Sec. 412.622(c)).
We are also making corresponding technical corrections elsewhere in
Sec. 412.622(a)(3)(iv), (a)(4)(i)(A), (a)(4)(iii)(A), and (a)(5)(i) to
remove the references to Sec. 412.622(a)(3)(iv) in those paragraphs,
so as to reflect the new location of the definition.
IX. Updates to the IRF Quality Reporting Program (QRP)
A. Background
The IRF QRP is authorized by section 1886(j)(7) of the Act, and it
applies to freestanding IRFs, as well as inpatient rehabilitation units
of hospitals or critical access hospitals (CAHs) paid by Medicare under
the IRF PPS. Under the IRF QRP, the Secretary must reduce the annual
increase factor for discharges occurring during such fiscal year by 2
percentage points for any IRF that does not submit data in accordance
with the requirements established by the Secretary. For more
information on the background and statutory authority for the IRF QRP,
we refer readers to the FY 2012 IRF PPS final rule (76 FR 47873 through
47874), the CY 2013 Hospital Outpatient Prospective Payment System/
Ambulatory Surgical Center (OPPS/ASC) Payment Systems and Quality
Reporting Programs final rule (77 FR 68500 through 68503), the FY 2014
IRF PPS final rule (78 FR 47902), the FY 2015 IRF PPS final rule (79 FR
45908), the FY 2016 IRF PPS final rule (80 FR 47080 through 47083), the
FY 2017 IRF PPS final rule (81 FR 52080 through 52081), the FY 2018 IRF
PPS final rule (82 FR 36269 through 36270), and the FY 2019 IRF PPS
final rule (83 FR 38555 through 38556).
While we did not solicit comments on previously finalized IRF QRP
policies, we received comments, which are summarized below.
Comment: A few commenters stated that the IRF QRP compliance
threshold of 95 percent for assessment-based items is too high given
the number of data elements that have been added to the IRF-PAI, and
requested that CMS lower it to 80 percent in alignment with other
programs.
Response: We did not propose any changes to the compliance
threshold, which has been codified at Sec. 412.634(f). While these
comments were out of scope for this rule, we will take these comments
under consideration.
B. General Considerations Used for the Selection of Measures for the
IRF QRP
For a detailed discussion of the considerations we use for the
selection of IRF QRP quality, resource use, and other measures, we
refer readers to the FY 2016 IRF PPS final rule (80 FR 47083 through
47084).
C. Quality Measures Currently Adopted for the FY 2021 IRF QRP
The IRF QRP currently has 15 measures for the FY 2020 program year,
which are set out in Table 16.
[[Page 39099]]
[GRAPHIC] [TIFF OMITTED] TR08AU19.020
While we did not solicit comments on currently adopted measures
(with the exception of the Discharge to Community Measure discussed in
section IX.D.3 of this rule and the policies regarding public display
of the Drug Regimen Review Conducted With Follow-Up for Identified
Issues--PAC IRF QRP in section IX.I of this rule), we received several
comments.
Comment: A few commenters had suggestions for removing measures
they believe were ``topped out'' according to the Hospital Inpatient
Quality Reporting (IQR) Program definition (83 FR 20408) and did not
demonstrate variation across facilities, including Application of
Percent of Residents Experiencing One or More Falls with Major Injury
(Long Stay) (NQF #0674) and Application of Percent of Long-Term Care
Hospital Patients with an Admission and Discharge Functional Assessment
and a Care Plan That Addresses Function (NQF #2631), and Changes in
Skin Integrity Post-Acute Care: Pressure Ulcer/Injury. One commenter
had suggestions for improving the training manual for the Drug Regimen
Review measure in terms of considered clinically significant medication
issue.
Response: We did not propose any changes to these previously
finalized measures, nor did we propose measure removals from the IRF
QRP. We wish to clarify that the IRF QRP has not adopted the Hospital
Inpatient Quality Reporting (IQR) definition of ``topped out'' in the
measure removal criteria finalized for the IRF QRP at Sec. 412.634(2).
We also note that we do not automatically remove high performing
measures, and wish to reiterate that such measures may be retained for
other specified reasons. For example, a particular measure with high
performance rates may be retained if the measure addresses a topic
related to quality that is so significant that we do not want to risk a
decline in quality that could result if we removed the measure, or if
the measure addresses a topic that is statutorily required. We will
continue to monitor and evaluate the data from all IRF QRP measures.
With regard to the commenter's suggestions about the Drug Regimen
Review measure, we interpret that the commenter is requesting
additional clarification for coding. We will take these comments into
account as we develop training materials for the IRF QRP.
D. Adoption of Two New Quality Measures and Updated Specifications for
a Third Quality Measure Beginning With the FY 2022 IRF QRP
In the FY 2020 IRF PPS proposed rule (84 FR 17286 through 17291),
we proposed to adopt two process measures for the IRF QRP that would
satisfy section 1899B(c)(1)(E)(ii) of the Act, which requires that the
quality measures specified by the Secretary include measures with
respect to the quality measure domain titled ``Accurately communicating
the existence of and providing for the transfer of health information
and care preferences of an individual to the individual, family
caregiver of the individual, and providers of services
[[Page 39100]]
furnishing items and services to the individual when the individual
transitions from a PAC provider to another applicable setting,
including a different PAC provider, a hospital, a critical access
hospital, or the home of the individual.'' Given the length of this
domain title, hereafter, we will refer to this quality measure domain
as ``Transfer of Health Information.''
The two measures we proposed to adopt are: (1) Transfer of Health
Information to the Provider--Post-Acute Care (PAC); and (2) Transfer of
Health Information to the Patient--Post-Acute Care (PAC). Both of these
measures support our Meaningful Measures priority of promoting
effective communication and coordination of care, specifically the
Meaningful Measure area of the transfer of health information and
interoperability.
In addition to the two measure proposals, we proposed to update the
specifications for the Discharge to Community--Post Acute Care (PAC)
IRF QRP measure to exclude baseline nursing facility (NF) residents
from the measure.
We sought public comment on each of these proposals. These comments
are summarized after each proposal below.
1. Transfer of Health Information to the Provider--Post-Acute Care
(PAC) Measure
The Transfer of Health Information to the Provider--Post-Acute Care
(PAC) Measure that we proposed to adopt beginning with the FY 2022 IRF
QRP is a process-based measure that assesses whether or not a current
reconciled medication list is given to the subsequent provider when a
patient is discharged or transferred from his or her current PAC
setting.
a. Background
In 2013, 22.3 percent of all acute hospital discharges were
discharged to PAC settings, including 11 percent who were discharged to
home under the care of a home health agency, and 9 percent who were
discharged to SNFs.\2\ The proportion of patients being discharged from
an acute care hospital to a PAC setting was greater among beneficiaries
enrolled in Medicare FFS. Among Medicare FFS patients discharged from
an acute hospital, 42 percent went directly to PAC settings. Of that 42
percent, 20 percent were discharged to a SNF, 18 percent were
discharged to a home health agency (HHA), 3 percent were discharged to
an IRF, and 1 percent were discharged to an LTCH.\3\ Of the Medicare
FFS beneficiaries with an IRF stay in FYs 2016 and 2017, an estimated
10 percent were discharged or transferred to an acute care hospital, 51
percent discharged home with home health services, 16 percent
discharged or transferred to a SNF, and one percent discharged or
transferred to another PAC setting (for example, another IRF, a
hospice, or an LTCH).\4\
---------------------------------------------------------------------------
\2\ Tian, W. ``An all-payer view of hospital discharge to post-
acute care,'' May 2016. Available at https://www.hcup-us.ahrq.gov/reports/statbriefs/sb205-Hospital-Discharge-Postacute-Care.jsp.
\3\ Ibid.
\4\ RTI International analysis of Medicare claims data for index
stays in IRF 2016/2017. (RTI program reference: MM150).
\5\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G.,
``Medication reconciliation during transitions of care as a patient
safety strategy: a systematic review,'' Annals of Internal Medicine,
2013, Vol. 158(5), pp. 397-403.
\6\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission
medication reconciliation on adverse drug events from admission
medication changes,'' Archives of Internal Medicine, 2011, Vol.
171(9), pp. 860-861.
\7\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman,
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital
admission with unintentional discontinuation of medications for
chronic diseases,'' JAMA, 2011, Vol. 306(8), pp. 840-847.
\8\ Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J.,
``Prescribing errors on admission to hospital and their potential
impact: a mixed-methods study,'' BMJ Quality & Safety, 2014, Vol.
23(1), pp. 17-25.
\9\ Desai, R., Williams, C.E., Greene, S.B., Pierson, S., &
Hansen, R.A., ``Medication errors during patient transitions into
nursing homes: characteristics and association with patient harm,''
The American Journal of Geriatric Pharmacotherapy, 2011, Vol. 9(6),
pp. 413-422.
\10\ Boling, P.A., ``Care transitions and home health care,''
Clinical Geriatric Medicine, 2009, Vol.25(1), pp. 135-48.
\11\ Barnsteiner, J.H., ``Medication Reconciliation: Transfer of
medication information across settings--keeping it free from
error,'' The American Journal of Nursing, 2005, Vol. 105(3), pp. 31-
36.
\12\ Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander,
H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A., ``Regardless
of age: incorporating principles from geriatric medicine to improve
care transitions for patients with complex needs,'' Journal of
General Internal Medicine, 2014, Vol. 29(6), pp. 932-939.
\13\ Jencks, S.F., Williams, M.V., & Coleman, E.A.,
``Rehospitalizations among patients in the Medicare fee-for-service
program,'' New England Journal of Medicine, 2009, Vol. 360(14), pp.
1418-1428.
\14\ Institute of Medicine. ``Preventing medication errors:
quality chasm series,'' Washington, DC: The National Academies Press
2007. Available at https://www.nap.edu/read/11623/chapter/1.
---------------------------------------------------------------------------
The transfer and/or exchange of health information from one
provider to another can be done verbally (for example, clinician-to-
clinician communication in-person or by telephone), paper-based (for
example, faxed or printed copies of records), and via electronic
communication (for example, through a health information exchange
network using an electronic health/medical record, and/or secure
messaging). Health information, such as medication information, that is
incomplete or missing increases the likelihood of a patient or resident
safety risk, and is often life-threatening.5 6 7 8 9 10 Poor
communication and coordination across health care settings contributes
to patient complications, hospital readmissions, emergency department
visits, and medication errors.11 12 13 14 15 16 17 18 19 20
Communication has been cited as the third most frequent root cause in
sentinel events, which The Joint Commission defines \21\ as a patient
safety event that results in death, permanent harm, or severe temporary
harm. Failed or ineffective patient handoffs are estimated to play a
role in 20 percent of serious preventable adverse events.\22\ When care
transitions are enhanced through care coordination activities, such as
expedited patient information flow, these activities can reduce
duplication of care services and costs of care, resolve conflicting
care plans, and prevent medical errors.23 24 25 26 27
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\15\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G.,
``Developing a medication communication framework across continuums
of care using the Circle of Care Modeling approach,'' BMC Health
Services Research, 2013, Vol. 13(1), pp. 1-10.
\16\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C., ``The
revolving door of rehospitalization from skilled nursing
facilities,'' Health Affairs, 2010, Vol. 29(1), pp. 57-64.
\17\ Institute of Medicine. ``Preventing medication errors:
quality chasm series,'' Washington, DC: The National Academies Press
2007. Available at https://www.nap.edu/read/11623/chapter/1.
\18\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G.,
``Developing a medication communication framework across continuums
of care using the Circle of Care Modeling approach,'' BMC Health
Services Research, 2013, Vol. 13(1), pp. 1-10.
\19\ Forster, A.J., Murff, H.J., Peterson, J.F., Gandhi, T.K., &
Bates, D.W., ``The incidence and severity of adverse events
affecting patients after discharge from the hospital.'' Annals of
Internal Medicine, 2003,138(3), pp. 161-167.
\20\ King, B.J., Gilmore-Bykovskyi, A.L., Roiland, R.A.,
Polnaszek, B.E., Bowers, B.J., & Kind, A.J. ``The consequences of
poor communication during transitions from hospital to skilled
nursing facility: a qualitative study,'' Journal of the American
Geriatrics Society, 2013, Vol. 61(7), 1095-1102.
\21\ The Joint Commission, ``Sentinel Event Policy'' available
at https://www.jointcommission.org/sentinel_event_policy_and_procedures/.
\22\ The Joint Commission. ``Sentinel Event Data Root Causes by
Event Type 2004-2015.'' 2016. Available at https://www.jointcommission.org/assets/1/23/jconline_Mar_2_2016.pdf.
\23\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C., ``The
revolving door of rehospitalization from skilled nursing
facilities,'' Health Affairs, 2010, Vol. 29(1), pp. 57-64.
\24\ Institute of Medicine, ``Preventing medication errors:
quality chasm series,'' Washington, DC: The National Academies
Press, 2007. Available at https://www.nap.edu/read/11623/chapter/1.
\25\ Starmer, A.J., Sectish, T.C., Simon, D.W., Keohane, C.,
McSweeney, M.E., Chung, E.Y., Yoon, C.S., Lipsitz, S.R., Wassner,
A.J., Harper, M.B., & Landrigan, C.P., ``Rates of medical errors and
preventable adverse events among hospitalized children following
implementation of a resident handoff bundle,'' JAMA, 2013, Vol.
310(21), pp. 2262-2270.
\26\ Pronovost, P., M.M.E. Johns, S. Palmer, R.C. Bono, D.B.
Fridsma, A. Gettinger, J. Goldman, W. Johnson, M. Karney, C. Samitt,
R.D. Sriram, A. Zenooz, and Y.C. Wang, Editors. Procuring
Interoperability: Achieving High-Quality, Connected, and Person-
Centered Care. Washington, DC, 2018 National Academy of Medicine.
Available at https://nam.edu/wp-content/uploads/2018/10/Procuring-Interoperability_web.pdf.
\27\ Balaban RB, Weissman JS, Samuel PA, & Woolhandler, S.,
``Redefining and redesigning hospital discharge to enhance patient
care: a randomized controlled study,'' J Gen Intern Med, 2008, Vol.
23(8), pp. 1228-33.
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[[Page 39101]]
Care transitions across health care settings have been
characterized as complex, costly, and potentially hazardous, and may
increase the risk for multiple adverse outcomes.28 29 The
rising incidence of preventable adverse events, complications, and
hospital readmissions have drawn attention to the importance of the
timely transfer of health information and care preferences at the time
of transition. Failures of care coordination, including poor
communication of information, were estimated to cost the U.S. health
care system between $25 billion and $45 billion in wasteful spending in
2011.\30\ The communication of health information and patient care
preferences is critical to ensuring safe and effective transitions from
one health care setting to another.31 32
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\28\ Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander,
H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A., ``Regardless
of age: incorporating principles from geriatric medicine to improve
care transitions for patients with complex needs,'' Journal of
General Internal Medicine, 2014, Vol 29(6), pp. 932-939.
\29\ Simmons, S., Schnelle, J., Slagle, J., Sathe, N.A.,
Stevenson, D., Carlo, M., & McPheeters, M.L., ``Resident safety
practices in nursing home settings.'' Technical Brief No. 24
(Prepared by the Vanderbilt Evidence-based Practice Center under
Contract No. 290-2015-00003-I.) AHRQ Publication No. 16-EHC022-EF.
Rockville, MD: Agency for Healthcare Research and Quality. May 2016.
Available at https://www.ncbi.nlm.nih.gov/books/NBK384624/.
\30\ Berwick, D.M. & Hackbarth, A.D. ``Eliminating Waste in US
Health Care,'' JAMA, 2012, Vol. 307(14), pp.1513-1516.
\31\ McDonald, K.M., Sundaram, V., Bravata, D.M., Lewis, R.,
Lin, N., Kraft, S.A. & Owens, D.K. Care Coordination. Vol. 7 of:
Shojania K.G., McDonald K.M., Wachter R.M., Owens D.K., editors.
``Closing the quality gap: A critical analysis of quality
improvement strategies.'' Technical Review 9 (Prepared by the
Stanford University-UCSF Evidence-based Practice Center under
contract 290-02-0017). AHRQ Publication No. 04(07)-0051-7.
Rockville, MD: Agency for Healthcare Research and Quality. June
2006. Available at https://www.ncbi.nlm.nih.gov/books/NBK44015/.
\32\ Lattimer, C., ``When it comes to transitions in patient
care, effective communication can make all the difference,''
Generations, 2011, Vol. 35(1), pp. 69-72.
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Patients in PAC settings often have complicated medication regimens
and require efficient and effective communication and coordination of
care between settings, including detailed transfer of medication
information.33 34 35 Individuals in PAC settings may be
vulnerable to adverse health outcomes due to insufficient medication
information on the part of their health care providers, and the higher
likelihood for multiple comorbid chronic conditions, polypharmacy, and
complicated transitions between care settings.36 37
Preventable adverse drug events (ADEs) may occur after hospital
discharge in a variety of settings including PAC.\38\ A 2014 Office of
Inspector General report found that 10 percent of Medicare patients in
IRFs experienced adverse events, with most of those events being
medication related. Over 45 percent of the adverse events and temporary
harm events were clearly or likely preventable.\39\ Medication errors
and one-fifth of ADEs occur during transitions between settings,
including admission to or discharge from a hospital to home or a PAC
setting, or transfer between hospitals.40 41
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\33\ Starmer A.J, Spector N.D., Srivastava R., West, D.C.,
Rosenbluth, G., Allen, A.D., Noble, E.L., & Landrigen, C.P.,
``Changes in medical errors after implementation of a handoff
program,'' N Engl J Med, 2014, Vol. 37(1), pp. 1803-1812.
\34\ Kruse, C.S. Marquez, G., Nelson, D., & Polomares, O., ``The
use of health information exchange to augment patient handoff in
long-term care: a systematic review,'' Applied Clinical Informatics,
2018, Vol. 9(4), pp. 752-771.
\35\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H.,
Thraen, I., Coarr, M.E., & Rupper, R., ``High prevalence of
medication discrepancies between home health referrals and Centers
for Medicare and Medicaid Services home health certification and
plan of care and their potential to affect safety of vulnerable
elderly adults,'' Journal of the American Geriatrics Society, 2016,
Vol. 64(11), pp. e166-e170.
\36\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E.,
Parsons, K., L., & Zuckerman, I.H., ``Medication reconciliation
during the transition to and from long-term care settings: a
systematic review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-
75.
\37\ Levinson, D.R., & General, I., ``Adverse events in skilled
nursing facilities: national incidence among Medicare
beneficiaries.'' Washington, DC: U.S. Department of Health and Human
Services, Office of the Inspector General, February 2014. Available
at https://oig.hhs.gov/oei/reports/oei-06-11-00370.pdf.
\38\ Battles J., Azam I., Grady M., & Reback K., ``Advances in
patient safety and medical liability,'' AHRQ Publication No. 17-
0017-EF. Rockville, MD: Agency for Healthcare Research and Quality,
August 2017. Available at https://www.ahrq.gov/sites/default/files/publications/files/advances-complete_3.pdf.
\39\ Health and Human Services Office of Inspector General.
Adverse Events in Rehabilitation Hospitals: National Incidence Among
Medicare Beneficiaries. (OEI-06-14-00110). 2018. Available at
https://oig.hhs.gov/oei/reports/oei-06-14-00110.asp.
\40\ Barnsteiner, J.H., ``Medication Reconciliation: Transfer of
medication information across settings--keeping it free from
error,'' The American Journal of Nursing, 2005, Vol. 105(3), pp. 31-
36.
\41\ Gleason, K.M., Groszek, J.M., Sullivan, C., Rooney, D.,
Barnard, C., Noskin, G.A., ``Reconciliation of discrepancies in
medication histories and admission orders of newly hospitalized
patients,'' American Journal of Health System Pharmacy, 2004, Vol.
61(16), pp. 1689-1694.
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Patients in PAC settings are often taking multiple medications.
Consequently, PAC providers regularly are in the position of starting
complex new medication regimens with little knowledge of the patients
or their medication history upon admission. Furthermore, inter-facility
communication barriers delay resolving medication discrepancies during
transitions of care.\42\ Medication discrepancies are common \43\ and
found to occur in 86 percent of all transitions, increasing the
likelihood of ADEs.44 45 46 Up to 90 percent of patients
experience at least one medication discrepancy in the transition from
hospital to home care, and discrepancies occur within all therapeutic
classes of medications.47 48
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\42\ Patterson M., Foust J.B., Bollinger, S., Coleman, C.,
Nguyen, D., ``Inter-facility communication barriers delay resolving
medication discrepancies during transitions of care,'' Research in
Social & Administrative Pharmacy (2018), doi: 10.1016/
j.sapharm.2018.05.124.
\43\ Manias, E., Annaikis, N., Considine, J., Weerasuriya, R., &
Kusljic, S. ``Patient-, medication- and environment-related factors
affecting medication discrepancies in older patients,'' Collegian,
2017, Vol. 24, pp. 571-577.
\44\ Tjia, J., Bonner, A., Briesacher, B.A., McGee, S., Terrill,
E., Miller, K., ``Medication discrepancies upon hospital to skilled
nursing facility transitions,'' J Gen Intern Med, 2009, Vol. 24(5),
pp. 630-635.
\45\ Sinvani, L.D., Beizer, J., Akerman, M., Pekmezaris, R.,
Nouryan, C., Lutsky, L., Cal, C., Dlugacz, Y., Masick, K., Wolf-
Klein, G.,''Medication reconciliation in continuum of care
transitions: a moving target,'' J Am Med Dir Assoc, 2013, Vol.
14(9), 668-672.
\46\ Coleman E.A., Parry C., Chalmers S., & Min, S.J., ``The
Care Transitions Intervention: results of a randomized controlled
trial,'' Arch Intern Med, 2006, Vol. 166, pp. 1822-28.
\47\ Corbett C.L., Setter S.M., Neumiller J.J., & Wood, l.D.,
``Nurse identified hospital to home medication discrepancies:
implications for improving transitional care,'' Geriatr Nurs, 2011,
Vol. 31(3), pp. 188-96.
\48\ Setter S.M., Corbett C.F., Neumiller J.J., Gates, B.J.,
Sclar, D.A., & Sonnett, T.E., ``Effectiveness of a pharmacist-nurse
intervention on resolving medication discrepancies in older patients
transitioning from hospital to home care: impact of a pharmacy/
nursing intervention,'' Am J Health Syst Pharm, 2009, Vol. 66, pp.
2027-31.
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Transfer of a medication list between providers is necessary for
medication reconciliation interventions, which have been shown to be a
cost-effective way to avoid ADEs by reducing errors 49 50 51
[[Page 39102]]
especially when medications are reviewed by a pharmacist using
electronic medical records.\52\
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\49\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission
medication reconciliation on adverse drug events from admission
medication changes,'' Archives of Internal Medicine, 2011, Vol.
171(9), pp. 860-861.
\50\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G.,
``Medication reconciliation during transitions of care as a patient
safety strategy: a systematic review,'' Annals of Internal Medicine,
2013, Vol. 158(5), pp. 397-403.
\51\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E.,
Parsons, K., L., & Zuckerman, I.H., ``Medication reconciliation
during the transition to and from long-term care settings: a
systematic review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-
75.
\52\ Agrawal A, Wu WY. ``Reducing medication errors and
improving systems reliability using an electronic medication
reconciliation system,'' The Joint Commission Journal on Quality and
Patient Safety, 2009, Vol. 35(2), pp. 106-114.
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b. Stakeholder and Technical Expert Panel (TEP) Input
The proposed measure was developed after consideration of feedback
we received from stakeholders and four TEPs convened by our
contractors. Further, the proposed measure was developed after
evaluation of data collected during two pilot tests we conducted in
accordance with the CMS Measures Management System Blueprint.
Our measure development contractors constituted a TEP which met on
September 27, 2016,\53\ January 27, 2017,\54\ and August 3, 2017 \55\
to provide input on a prior version of this measure. Based on this
input, we updated the measure concept in late 2017 to include the
transfer of a specific component of health information--medication
information. Our measure development contractors reconvened this TEP on
April 20, 2018 for the purpose of obtaining expert input on the
proposed measure, including the measure's reliability, components of
face validity, and feasibility of being implemented across PAC
settings. Overall, the TEP was supportive of the proposed measure,
affirming that the measure provides an opportunity to improve the
transfer of medication information. A summary of the April 20, 2018 TEP
proceedings titled ``Transfer of Health Information TEP Meeting 4--June
2018'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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\53\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP_Summary_Report_Final-June-2017.pdf.
\54\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP-Meetings-2-3-Summary-Report_Final_Feb2018.pdf.
\55\ Ibid.
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Our measure development contractors solicited stakeholder feedback
on the proposed measure by requesting comment on the CMS Measures
Management System Blueprint website, and accepted comments that were
submitted from March 19, 2018 to May 3, 2018. The comments received
noted overall support for the measure. Several commenters suggested
ways to improve the measure, primarily related to what types of
information should be included at transfer. We incorporated this input
into development of the proposed measure. The summary report for the
March 19 to May 3, 2018 public comment period titled ``IMPACT
Medication Profile Transferred Public Comment Summary Report'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
c. Pilot Testing
The proposed measure was tested between June and August 2018 in a
pilot test that involved 24 PAC facilities/agencies, including five
IRFs, six SNFs, six LTCHs, and seven HHAs. The 24 pilot sites submitted
a total of 801 records. Analysis of agreement between coders within
each participating facility (266 qualifying pairs) indicated a 93
percent agreement for this measure. Overall, pilot testing enabled us
to verify its reliability, components of face validity, and feasibility
of being implemented across PAC settings. Further, more than half of
the sites that participated in the pilot test stated during the
debriefing interviews that the measure could distinguish facilities or
agencies with higher quality medication information transfer from those
with lower quality medication information transfer at discharge. The
pilot test summary report titled ``Transfer of Health Information 2018
Pilot Test Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
d. Measure Applications Partnership (MAP) Review and Related Measures
We included the proposed measure in the IRF QRP section of the 2018
Measures Under Consideration (MUC) List. The MAP conditionally
supported this measure pending NQF endorsement, noting that the measure
can promote the transfer of important medication information. The MAP
also suggested that we consider a measure that can be adapted to
capture bi-directional information exchange, and recommended that the
medication information transferred include important information about
supplements and opioids. More information about the MAP's
recommendations for this measure is available at https://www.qualityforum.org/Publications/2019/02/MAP_2019_Considerations_for_Implementing_Measures_Final_Report_-_PAC-LTC.aspx.
As part of the measure development and selection process, we also
identified one NQF-endorsed quality measure similar to the proposed
measure, titled Documentation of Current Medications in the Medical
Record (NQF #0419, CMS eCQM ID: CMS68v8). This measure was adopted as
one of the recommended adult core clinical quality measures for
eligible professionals for the EHR Incentive Program beginning in 2014
and was also adopted under the Merit-based Incentive Payment System
(MIPS) quality performance category beginning in 2017. The measure is
calculated based on the percentage of visits for patients aged 18 years
and older for which the eligible professional or eligible clinician
attests to documenting a list of current medications using all
resources immediately available on the date of the encounter.
The proposed Transfer of Health Information to the Provider--Post-
Acute Care (PAC) measure addresses the transfer of information whereas
the NQF-endorsed measure #0419 assesses the documentation of
medications, but not the transfer of such information. This is
important as the proposed measure assesses for the transfer of
medication information for the proposed measure calculation. Further,
the proposed measure utilizes standardized patient assessment data
elements (SPADEs), which is a requirement for measures specified under
the Transfer of Health Information measure domain under section
1899B(c)(1)(E) of the Act, whereas NQF #0419 does not.
[[Page 39103]]
After review of the NQF-endorsed measure, we determined that the
proposed Transfer of Health Information to the Provider--Post-Acute
Care (PAC) measure better addresses the Transfer of Health Information
measure domain, which requires that at least some of the data used to
calculate the measure be collected as standardized patient assessment
data through the post-acute care assessment instruments. Section
1886(j)(7)(D)(i) of the Act requires that any measure specified by the
Secretary be endorsed by the entity with a contract under section
1890(a) of the Act, which is currently the National Quality Form (NQF).
However, when a feasible and practical measure has not been NQF
endorsed for a specified area or medical topic determined appropriate
by the Secretary, section 1886(j)(7)(D)(ii) of the Act allows the
Secretary to specify a measure that is not NQF endorsed as long as due
consideration is given to the measures that have been endorsed or
adopted by a consensus organization identified by the Secretary. For
the reasons discussed previously, we believe that there is currently no
feasible NQF-endorsed measure that we could adopt under section
1886(j)(7)(D)(ii) of the Act. However, we note that we intend to submit
the proposed measure to the NQF for consideration of endorsement when
feasible.
e. Quality Measure Calculation
The proposed Transfer of Health Information to the Provider--Post-
Acute Care (PAC) quality measure is calculated as the proportion of
patient stays with a discharge assessment indicating that a current
reconciled medication list was provided to the subsequent provider at
the time of discharge. The proposed measure denominator is the total
number of IRF patient stays ending in discharge to a subsequent
provider, which is defined as a short-term general acute-care hospital,
intermediate care (intellectual and developmental disabilities
providers), home under care of an organized home health service
organization or hospice, hospice in an institutional facility, a SNF,
an LTCH, another IRF, an IPF, or a CAH. These health care providers
were selected for inclusion in the denominator because they are
identified as subsequent providers on the discharge destination item
that is currently included on the IRF PAI. The proposed measure
numerator is the number of IRF patient stays with an IRF-PAI discharge
assessment indicating a current reconciled medication list was provided
to the subsequent provider at the time of discharge. For additional
technical information about this proposed measure, we refer readers to
the document titled, ``Final Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. The data source for the proposed
quality measure is the IRF-PAI assessment instrument for IRF patients.
For more information about the data submission requirements we
proposed for this measure, we refer readers to section VIII.G.3. of
this final rule.
Commenters submitted the following comments related to the proposed
rule's discussion of the IRF QRP Quality Measure Proposals beginning
with the FY 2022 IRF QRP. A discussion of these comments, along with
our responses, appears below. We also address comments on the proposed
Transfer of Health Information to the Patient--Post-Acute Care measure
(discussed further in a subsequent section of this final rule) in this
section because commenters frequently addressed both Transfer of Health
Information measures together.
Response: We thank the commenters for their support of the Transfer
of Health Information measures.
Comment: One commenter suggested that other providers, such as
outpatient physical therapists, should be included in the definition of
a subsequent provider for the Transfer of Health Information to the
Provider--Post-Acute Care measure.
Response: We appreciate the suggestion to expand the Transfer of
Health Information to the Provider--Post-Acute Care measure outcome to
assess the transfer of health information to other providers such as
outpatient physical therapists. We recognize that sharing medication
information with outpatient providers is important, and will take into
consideration additional providers in future measure modifications.
Through our measure development and pilot testing we learned that
outpatient providers cannot always be readily identified by the PAC
provider. For this process measure, which serves as a building block
for improving the transfer of medication information, we specified
providers who will be involved in the care of the patient and
medication management after discharge and can be readily identified
through the discharge location item on the IRF-PAI. The clear
delineation of the recipient of the medication list in the measure
specifications will improve measure reliability and validity.
Comment: A commenter recommended that the Transfer of Health
Information to the Provider--Post-Acute Care measure be expanded to
include the transfer of information that would help prevent infections
and facilitate appropriate infection prevention and control
interventions during care transitions in addition to the medication
information in the finalized measure.
Response: The Transfer of Health Information to the Provider--Post-
Acute Care measure focuses on the transfer of a reconciled medication
list. The measure was designed after input from TEPs, public comment,
and other stakeholders that suggested the quality measures focus on the
transfer of the most critical pieces of information to support patient
safety and care coordination. However, we acknowledge that the transfer
of many other forms of health information is important, and while the
focus of this measure is on a reconciled medication list, we hope to
expand our measures in the future.
Comment: Several commenters raised concerns about both of the
Transfer of Health Information measures not being endorsed by the
National Quality Forum (NQF). A few commenters requested that we
consider delaying rollout of these two new measures until endorsed by
NQF. A few commenters recommended that we only adopt measures that have
NQF approval. One commenter was opposed to the measures because they
have not been endorsed by NQF.
Response: While this measure is not currently NQF-endorsed, we
recognize that the NQF endorsement process is an important part of
measure development. As discussed in the FY 2020 IRF PPS proposed rule
(84 FR 17286 through 17291), we believe the measures better address the
Transfer of Health Information measure domain, which requires that at
least some of the data used to calculate the measure be collected as
standardized patient assessment data through the post-acute care
assessment instruments, than any endorsed measures. While section
1886(j)(7)(D)(i) of the Act requires that any measure specified by the
Secretary be endorsed by the entity with a contract under section
1890(a) of the Act, which is currently the National Quality Form (NQF),
when a feasible and practical measure has not been NQF endorsed for a
specified area or medical topic determined appropriate by the
[[Page 39104]]
Secretary, section 1886(j)(7)(D)(ii) of the Act allows the Secretary to
specify a measure that is not NQF endorsed as long as due consideration
is given to the measures that has been endorsed or adopted by a
consensus organization identified by the Secretary. We plan to submit
the measure for NQF endorsement consideration as soon as feasible.
Comment: Several commenters stated that the Transfer of Health
Information measures will add burden. Two commenters did not support
the measures for this reason. One commenter stated that achieving high
performance on the measures will add administrative burden. Another
commenter stated that the measures will add burden with no added value.
Another commenter stated that while there will be additional burden on
IRFs to collect and report data for these new measures, the benefit to
patients and the CMS program outweighs the additional burden on
providers.
Response: We agree that the benefit to patients outweighs any
additional burden on providers. We are also very mindful of burden that
may occur from the collection and reporting of our measures, as
supported by the Meaningful Measures and Patients over Paperwork
initiatives. We emphasize that both measures are comprised of one item,
and further, the activities associated with the measure align with
existing requirements related to transferring information at the time
of discharge to safeguard patients. Additionally, TEP feedback and
pilot test found that the burden of reporting will not be significant.
We believe that these measures will likely drive improvements in the
transfer of medication information between providers and with patients,
families, and caregivers.
Comment: One commenter stated that there will be no additional
burden to IRFs, because providing medication information as part of
discharge planning is a Condition of Participation requirement for
Medicaid and Medicare, and the medication list can be generated from
the electronic medical record.
Response: We believe that the Transfer of Health Information
measures will not substantially increase burden because we understand
that many hospitals already generate medication lists as a best
practice.
Comment: We received comments related to the validity and
reliability of both Transfer of Health Information measures. One
commenter suggested that CMS should ensure accuracy of these measures.
Other commenters suggested that additional testing is needed to ensure
that these measures will be able to differentiate among IRF providers.
Another commenter questioned if the measures would be topped out
shortly after adoption, since medication reconciliation is already
completed by facilities at discharge.
Response: Elements of validity and reliability were analyzed during
pilot testing of these measures, with good results, including inter-
rater reliability of at least 87 percent for all tested items. Pilot
testing also indicated that there is room for improvement for IRFs and
other settings, so we do not expect the measure to be topped out
shortly after adoption. As we monitor the outcomes of these measures,
we will ensure that reliability and validity of the measures meet
acceptable standards.
Comment: Some commenters recommended ways in which the Transfer of
Health Information measures specifications could be updated or changed.
A few commenters suggested that the ``not applicable'' (NA) answer
choice available in the home health version of the measure be made
available in all settings, including IRFs. A few commenters also
requested clarification about why patients discharged home under the
care of an organized home health service or hospice would be captured
in the denominators of both Transfer of Health information measures.
Response: We are appreciative of the measure modification
suggestions and clarify why the response option of N/A was considered
only for the HH version of this measure. The coding response N/A, or
``not applicable'' is used when the HHA was not made aware of the
transfer in a timely manner and, therefore, the HHA is not able to
provide the medication list at the time of transfer to the subsequent
provider. For example, a HHA may not be immediately aware when a
patient is taken to the emergency room. For facility settings, such as
the IRF setting, where 24-hour care is being provided, the facility
should always be aware and actively involved in the discharge of the
patient, and therefore, able to provide the current reconciled
medication list at the time of discharge. Therefore, we believed the
coding option of ``N/A'' would not be useful in the facility-based
measure as the facility is aware and involved in the discharge. We wish
to note that while the N/A option is considered for the HHA version of
the measure, the measure specifications indicate that these patients
are not removed from the denominator. In addition, discharge to home
under the care of an organized HHA or hospice is captured in the
denominator of both the Transfer of Health Information to Provider and
Transfer of Health Information to Patient measures because this type of
discharge represents two opportunities to transfer the medication list.
These measures aim to assure that each of these transfers is taking
place. We refer readers to the measure specifications where updates or
changes can be found and are available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Comment: One commenter suggested that the Transfer of Health
Information measures should include a measure of the timeliness of the
transfer. The commenter stated that, as currently specified, the
measures give equal credit for information that is sent immediately and
information sent days later.
Response: We appreciate the suggestions that CMS develop and adopt
measures that assess for the timeliness of transfer. We agree that
measure concepts of this type are important and would complement the
measures that focus for whether information was transferred at the time
the patient leaves the facility. We clarify that the measures do not
give credit for when information was sent, whether immediately or days
later. This is because there may be circumstances where information may
not be sent at the immediate time of discharge. However, the measures
do require that information be shared with the subsequent provider and/
or the patient as close to the time of discharge as this is actionable,
allows for shared decision making, and will increase coordinated care.
We are not establishing a new standard of transfer at discharge; we are
simply assessing if information was sent at the time a patient leaves
the facility. As we move through future measure development work, we
will consider a ``timeliness'' component for these measure concepts.
Comment: A commenter noted that although CMS provided guidelines
regarding what should be included in the transfer of medication
information, the data collection on this measure does not require that
these guidelines be met. The commenter questioned if CMS intends to
audit IRFs to ensure that the measure values are consistent with the
information being shared.
Response: The Transfer of Health Information measures serve as a
check to ensure that a reconciled medication list is provided as the
patient changes care settings. Defining the completeness of that
medication list is left to the discretion of the providers and patient
[[Page 39105]]
who are coordinating this care. We interpret the comment about audits
to be referring to data validation. While we do not have a data
validation program in place at this time, we are exploring such a
program akin to that of the hospital QRPs. For all measures and data
collected for the IRF QRP, we monitor and evaluate our data to assess
for coding patterns, errors, reliability, and soundness of the data.
Through data monitoring, we are able to assess if measure outcomes are
consistent with the information that is collected. We note that all
data are subject to review and audit.
Comment: A few comments included concerns that the Transfer of
Health Information measures are not indicative of provider quality and
questioned the ability of the measures to improve patient outcomes. Two
commenters did not support the measures for this reason. Commenters
noted that the measures assess whether a medication list was
transferred and not whether that medication list was accurate and
received by the subsequent provider.
Response: The Transfer of Health Information to the Provider--Post-
Acute Care and Transfer of Health Information to the Patient--Post-
Acute Care measures are process measures designed to address and
improve an important aspect of care quality. Lack of timely transfer of
medication information at transitions has been demonstrated to lead to
increased risk of adverse events, medication errors, and
hospitalizations. In addition, public commenters and our TEP members
identified many problems and gaps in the timely transfer of medication
information at transitions. Process measures, such as these, are
building blocks toward improved coordinated care and discharge
planning, providing information that will improve shared decision
making and coordination. Further, process measures hold a lot of value
as they delineate negative and/or positive aspects of the health care
process. These measures will capture the quality of the process of
medication information transfer and, we believe, help to improve those
processes. When developing future measures, we will take into
consideration suggestions about measures that assess the accuracy of
the medication list and whether it was received by the subsequent
provider.
Comment: One commenter suggested that CMS work to identify
interoperability solutions as a means of decreasing opportunities for
errors by providing clinicians and patients secure access to the most
up-to-date medication-related information. The commenter also suggests
that if CMS is required by the IMPACT Act to adopt these measures, that
we do so as an interim step, within a defined timeframe, while
interoperability solutions are explored and tested.
Response: We agree with the comments on the importance of
interoperability solutions to support health information transfer. CMS
and ONC are focused on improving interoperability and the timely
sharing of information between providers, patients, families and
caregivers. We believe that PAC provider health information exchange
supports the goals of high quality, personalized, efficient healthcare,
care coordination, person-centered care, and supports real-time, data
driven, clinical decision making. We are optimistic that this measure
will encourage the electronic transfer of current and important
medication information at transitions. These measures and related
efforts may help accelerate interoperability solutions. The Transfer of
Health Information measures assess the process of medication transfer,
which can occur through both electronic and non-electronic means. We
clarify that these measures are an interim step in improving
coordinated care, and we also believe that other interoperable
solutions should be explored. Finalizing these Transfer of Health
measures will be a first step in measuring the transfer of this
medication-related information.
After consideration of the public comments, we are finalizing our
proposal to adopt the Transfer of Health Information to the Provider--
Post Acute Care (PAC) measure, under section 1899B(c)(1)(E) of the Act,
with data collection for discharges beginning October 1, 2020.
2. Transfer of Health Information to the Patient--Post-Acute Care (PAC)
Measure
Beginning with the FY 2022 IRF QRP, we proposed to adopt the
Transfer of Health Information to the Patient--Post Acute Care (PAC)
measure, a measure that satisfies the IMPACT Act domain of Transfer of
Health Information, with data collection for discharges beginning
October 1, 2020. This process-based measure assesses whether or not a
current reconciled medication list was provided to the patient, family,
or caregiver when the patient was discharged from a PAC setting to a
private home/apartment, a board and care home, assisted living, a group
home, transitional living or home under care of an organized home
health service organization, or a hospice.
a. Background
In 2013, 22.3 percent of all acute hospital discharges were
discharged to PAC settings, including 11 percent who were discharged to
home under the care of a home health agency.\56\ Of the Medicare FFS
beneficiaries with an IRF stay in FYs 2016 and 2017, an estimated 51
percent were discharged home with home health services, 21 percent were
discharged home with self-care, and 0.5 percent were discharged with
home hospice services.\57\
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\56\ Tian, W. ``An all-payer view of hospital discharge to
postacute care,'' May 2016. Available at https://www.hcup-us.ahrq.gov/reports/statbriefs/sb205-Hospital-Discharge-Postacute-Care.jsp.
\57\ RTI International analysis of Medicare claims data for
index stays in IRF 2016/2017. (RTI program reference: MM150).
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The communication of health information, such as a reconciled
medication list, is critical to ensuring safe and effective patient
transitions from health care settings to home and/or other community
settings. Incomplete or missing health information, such as medication
information, increases the likelihood of a patient safety risk, often
life-threatening.58 59 60 61 62 Individuals who use PAC care
services are particularly vulnerable to adverse health outcomes due to
their higher likelihood of having multiple comorbid chronic conditions,
polypharmacy, and complicated transitions between care
settings.63 64 Upon discharge to home,
[[Page 39106]]
individuals in PAC settings may be faced with numerous medication
changes, new medication regimes, and follow-up
details.65 66 67 The efficient and effective communication
and coordination of medication information may be critical to prevent
potentially deadly adverse effects. When care coordination activities
enhance care transitions, these activities can reduce duplication of
care services and costs of care, resolve conflicting care plans, and
prevent medical errors.68 69
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\58\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G.,
``Medication reconciliation during transitions of care as a patient
safety strategy: A systematic review,'' Annals of Internal Medicine,
2013, Vol. 158(5), pp. 397-403.
\59\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission
medication reconciliation on adverse drug events from admission
medication changes,'' Archives of Internal Medicine, 2011, Vol.
171(9), pp. 860-861.
\60\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman,
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital
admission with unintentional discontinuation of medications for
chronic diseases,'' JAMA, 2011, Vol. 306(8), pp. 840-847.
\61\ Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J.,
``Prescribing errors on admission to hospital and their potential
impact: A mixed-methods study,'' BMJ Quality & Safety, 2014, Vol.
23(1), pp. 17-25.
\62\ Desai, R., Williams, C.E., Greene, S.B., Pierson, S., &
Hansen, R.A., ``Medication errors during patient transitions into
nursing homes: Characteristics and association with patient harm,''
The American Journal of Geriatric Pharmacotherapy, 2011, Vol. 9(6),
pp. 413-422.
\63\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H.,
Thraen, I., Coarr, M.E., & Rupper, R. ``High prevalence of
medication discrepancies between home health referrals and Centers
for Medicare and Medicaid Services home health certification and
plan of care and their potential to affect safety of vulnerable
elderly adults,'' Journal of the American Geriatrics Society, 2016,
Vol. 64(11), pp. e166-e170.
\64\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E.,
Parsons, K., L., & Zuckerman, I.H., ``Medication reconciliation
during the transition to and from long-term care settings: A
systematic review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-
75.
\65\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H.,
Thraen, I., Coarr, M.E., & Rupper, R. ``High prevalence of
medication discrepancies between home health referrals and Centers
for Medicare and Medicaid Services home health certification and
plan of care and their potential to affect safety of vulnerable
elderly adults,'' Journal of the American Geriatrics Society, 2016,
Vol. 64(11), pp. e166-e170.
\66\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman,
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital
admission with unintentional discontinuation of medications for
chronic diseases,'' JAMA, 2011, Vol. 306(8), pp. 840-847.
\67\ Sheehan, O.C., Kharrazi, H., Carl, K.J., Leff, B., Wolff,
J.L., Roth, D.L., Gabbard, J., & Boyd, C.M., ``Helping older adults
improve their medication experience (HOME) by addressing medication
regimen complexity in home healthcare,'' Home Healthcare Now. 2018,
Vol. 36(1) pp. 10-19.
\68\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D. C., ``The
revolving door of rehospitalization from skilled nursing
facilities,'' Health Affairs, 2010, Vol. 29(1), pp. 57-64.
\69\ Starmer, A.J., Sectish, T.C., Simon, D.W., Keohane, C.,
McSweeney, M.E., Chung, E.Y., Yoon, C.S., Lipsitz, S.R., Wassner,
A.J., Harper, M.B., & Landrigan, C.P., ``Rates of medical errors and
preventable adverse events among hospitalized children following
implementation of a resident handoff bundle,'' JAMA, 2013, Vol.
310(21), pp. 2262-2270.
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Finally, the transfer of a patient's discharge medication
information to the patient, family, or caregiver is common practice and
supported by discharge planning requirements for participation in
Medicare and Medicaid programs.\70\ \71\ Most PAC EHR systems generate
a discharge medication list to promote patient participation in
medication management, which has been shown to be potentially useful
for improving patient outcomes and transitional care.\72\
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\70\ CMS, ``Revision to state operations manual (SOM), Hospital
Appendix A--Interpretive Guidelines for 42 CFR 482.43, Discharge
Planning'' May 17, 2013. Available at https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Downloads/Survey-and-Cert-Letter-13-32.pdf.
\71\ The State Operations Manual Guidance to Surveyors for Long
Term Care Facilities (Guidance Sec. 483.21(c)(1) Rev. 11-22-17) for
discharge planning process. Available at https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/downloads/som107ap_pp_guidelines_ltcf.pdf.
\72\ Toles, M., Colon-Emeric, C., Naylor, M.D., Asafu-Adjei, J.,
Hanson, L.C., ``Connect-home: Transitional care of skilled nursing
facility patients and their caregivers,'' Am Geriatr Soc., 2017,
Vol. 65(10), pp. 2322-2328.
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b. Stakeholder and Technical Expert Panel (TEP) Input
The proposed measure was developed after consideration of feedback
we received from stakeholders and four TEPs convened by our
contractors. Further, the proposed measure was developed after
evaluation of data collected during two pilot tests we conducted in
accordance with the CMS Measures Management System Blueprint.
Our measure development contractors constituted a TEP which met on
September 27, 2016,\73\ January 27, 2017,\74\ and August 3, 2017 \75\
to provide input on a prior version of this measure. Based on this
input, we updated the measure concept in late 2017 to include the
transfer of a specific component of health information--medication
information. Our measure development contractors reconvened this TEP on
April 20, 2018 to seek expert input on the measure. Overall, the TEP
members supported the proposed measure, affirming that the measure
provides an opportunity to improve the transfer of medication
information. Most of the TEP members believed that the measure could
improve the transfer of medication information to patients, families,
and caregivers. Several TEP members emphasized the importance of
transferring information to patients and their caregivers in a clear
manner using plain language. A summary of the April 20, 2018 TEP
proceedings titled ``Transfer of Health Information TEP Meeting 4--June
2018'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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\73\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP_Summary_Report_Final-June-2017.pdf.
\74\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP-Meetings-2-3-Summary-Report_Final_Feb2018.pdf.
\75\ Ibid.
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Our measure development contractors solicited stakeholder feedback
on the proposed measure by requesting comment on the CMS Measures
Management System Blueprint website, and accepted comments that were
submitted from March 19, 2018 to May 3, 2018. Several commenters noted
the importance of ensuring that the instruction provided to patients
and caregivers is clear and understandable to promote transparent
access to medical record information and meet the goals of the IMPACT
Act. The summary report for the March 19 to May 3, 2018 public comment
period titled ``IMPACT--Medication Profile Transferred Public Comment
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
c. Pilot Testing
Between June and August 2018, we held a pilot test involving 24 PAC
facilities/agencies, including five IRFs, six SNFs, six LTCHs, and
seven HHAs. The 24 pilot sites submitted a total of 801 assessments.
Analysis of agreement between coders within each participating facility
(241 qualifying pairs) indicated an 87 percent agreement for this
measure. Overall, pilot testing enabled us to verify its reliability,
components of face validity, and feasibility of being implemented
across PAC settings. Further, more than half of the sites that
participated in the pilot test stated, during debriefing interviews,
that the measure could distinguish facilities or agencies with higher
quality medication information transfer from those with lower quality
medication information transfer at discharge. The pilot test summary
report titled ``Transfer of Health Information 2018 Pilot Test Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
[[Page 39107]]
d. Measure Applications Partnership (MAP) Review and Related Measures
We included the proposed measure in the IRF QRP section of the 2018
MUC list. The MAP conditionally supported this measure pending NQF
endorsement, noting that the measure can promote the transfer of
important medication information to the patient. The MAP recommended
that providers transmit medication information to patients that is easy
to understand because health literacy can impact a person's ability to
take medication as directed. More information about the MAP's
recommendations for this measure is available at https://www.qualityforum.org/Publications/2019/02/MAP_2019_Considerations_for_Implementing_Measures_Final_Report_-_PAC-LTC.aspx.
Section 1886(j)(7)(D)(i) of the Act, requires that any measure
specified by the Secretary be endorsed by the entity with a contract
under section 1890(a) of the Act, which is currently the NQF. However,
when a feasible and practical measure has not been NQF endorsed for a
specified area or medical topic determined appropriate by the
Secretary, section 1886(j)(7)(D)(ii) of the Act allows the Secretary to
specify a measure that is not NQF endorsed as long as due consideration
is given to the measures that have been endorsed or adopted by a
consensus organization identified by the Secretary. Therefore, in the
absence of any NQF-endorsed measures that address the proposed Transfer
of Health Information to the Patient--Post-Acute Care (PAC), which
requires that at least some of the data used to calculate the measure
be collected as standardized patient assessment data through PAC
assessment instruments, we believe that there is currently no feasible
NQF-endorsed measure that we could adopt under section
1886(j)(7)(D)(ii) of the Act. However, we note that we intend to submit
the proposed measure to the NQF for consideration of endorsement when
feasible.
e. Quality Measure Calculation
The calculation of the proposed Transfer of Health Information to
the Patient--Post-Acute Care (PAC) measure would be based on the
proportion of patient stays with a discharge assessment indicating that
a current reconciled medication list was provided to the patient,
family, or caregiver at the time of discharge.
The proposed measure denominator is the total number of IRF patient
stays ending in discharge to a private home/apartment, a board and care
home, assisted living, a group home, transitional living or home under
care of an organized home health service organization, or a hospice.
These locations were selected for inclusion in the denominator because
they are identified as home locations on the discharge destination item
that is currently included on the IRF-PAI. The proposed measure
numerator is the number of IRF patient stays with an IRF-PAI discharge
assessment indicating a current reconciled medication list was provided
to the patient, family, or caregiver at the time of discharge. For
technical information about this proposed measure, we refer readers to
the document titled ``Final Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Data for the proposed quality
measure would be calculated using data from the IRF-PAI assessment
instrument for IRF patients.
For more information about the data submission requirements we
proposed for this measure, we refer readers to section VIII.G.3. of
this rule.
Commenters submitted the following comments related to the proposed
rule's discussion of the IRF QRP Quality Measure Proposals Beginning
with the FY 2022 IRF QRP. A discussion of these comments, along with
our responses, appears below. We received many comments that addressed
both of the Transfer of Health Information measures. Comments that
applied to both measures are discussed above in IX.D.1 of this rule.
Comment: One commenter suggested that CMS use the field's
experience with transferring information to patients and reporting on
this measure to disseminate best practices about how to best convey the
medication list and suggested this include formats and informational
elements helpful to patients and families.
Response: We have interpreted ``the field'' to mean PAC providers.
Facilities and clinicians should use clinical judgement to guide their
practices around transferring information to patients and how to best
convey the medication list, including identifying the best formats and
informational elements. This may be determined by the patient's
individualized needs in response to their medical condition. We do not
determine clinical best practices standards and facilities are advised
to refer to other sources, such as professional guidelines.
Comment: One commenter suggested that the Transfer of Health
Information to the Patient--Post-Acute Care (PAC) Measure require
transfer of the medication list to both the patient and family or
caregiver.
Response: We agree there are times when it is appropriate for the
IRF to provide the medication list to the patient and family and this
decision should be based on clinical judgement. However, because it is
not always necessary or appropriate to provide the medication list to
both the patient and family, we are not requiring this for the measure.
After consideration of the public comments, we are finalizing our
proposal to adopt the Transfer of Health Information to the Patient--
Post Acute Care (PAC) measure, under section 1899B(c)(1)(E) of the Act,
with data collection for discharges beginning October 1, 2020.
3. Update to the Discharge to Community--Post Acute Care (PAC)
Inpatient Rehabilitation Facility (IRF) Quality Reporting Program (QRP)
Measure
In the FY 2020 IRF PPS proposed rule (84 FR 17291), we proposed to
update the specifications for the Discharge to Community--PAC IRF QRP
measure to exclude baseline nursing facility (NF) residents from the
measure. This measure reports an IRF's risk-standardized rate of
Medicare FFS patients who are discharged to the community following an
IRF stay, do not have an unplanned readmission to an acute care
hospital or LTCH in the 31 days following discharge to community, and
who remain alive during the 31 days following discharge to community.
We adopted this measure in the FY 2017 IRF PPS final rule (81 FR 52095
through 52103).
In the FY 2017 IRF PPS final rule (81 FR 52099), we addressed
public comments recommending exclusion of IRF patients who were
baseline NF residents, as these patients lived in a NF prior to their
IRF stay, as these patients may not be expected to return to the
community following their IRF stay. In the FY 2018 IRF PPS final rule
(82 FR 36285), we addressed public comments expressing support for a
potential future modification of the measure that would exclude
baseline NF residents; commenters stated that the exclusion would
result in the measure more accurately portraying quality of care
provided by IRFs, while controlling for factors outside of IRF control.
[[Page 39108]]
We assessed the impact of excluding baseline NF residents from the
measure using CY 2015 and CY 2016 data, and found that this exclusion
impacted both patient- and facility-level discharge to community rates.
We defined baseline NF residents as IRF patients who had a long-term NF
stay in the 180 days preceding their hospitalization and IRF stay, with
no intervening community discharge between the NF stay and qualifying
hospitalization for measure inclusion. Baseline NF residents
represented 0.3 percent of the measure population after all measure
exclusions were applied. Observed patient-level discharge to community
rates were significantly lower for baseline NF residents (20.82
percent) compared with non-NF residents (64.52 percent). The national
observed patient-level discharge to community rate was 64.41 percent
when baseline NF residents were included in the measure, increasing to
64.52 percent when they were excluded from the measure. After excluding
baseline NF residents, 26.9 percent of IRFs had an increase in their
risk-standardized discharge to community rate that exceeded the
increase in the national observed patient-level discharge to community
rate.
Based on public comments received and our impact analysis, we
proposed to exclude baseline NF residents from the Discharge to
Community--PAC IRF QRP measure beginning with the FY 2020 IRF QRP, with
baseline NF residents defined as IRF patients who had a long-term NF
stay in the 180 days preceding their hospitalization and IRF stay, with
no intervening community discharge between the NF stay and
hospitalization.
For additional technical information regarding the Discharge to
Community--PAC IRF QRP measure, including technical information about
the proposed exclusion, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We sought public comment on this proposal and received several
comments. A discussion of these comments, along with our responses,
appears below.
Comment: Several commenters supported the proposed exclusion of
baseline NF residents from the Discharge to Community--PAC IRF QRP
measure. Commenters referred to their recommendation of this exclusion
in prior years and appreciated CMS' willingness to consider and
implement stakeholder feedback. One commenter stated they did not
foresee any negative impacts of the exclusion. One commenter suggested
that CMS instead consider other quality measures for NF residents, such
as functional status measures, to determine whether residents receive
the appropriate standard of care they need in a long-term NF stay.
Response: We thank the commenters for their support of the proposed
exclusion of baseline nursing facility residents from this measure and
for recommending other measures for consideration for baseline NF
residents.
Comment: MedPAC did not support the proposed exclusion of baseline
nursing facility residents from the Discharge to Community--PAC IRF QRP
measure. They suggested that CMS instead expand their definition of
``return to the community'' to include baseline nursing home residents
returning to the nursing home where they live, as this represents their
home or community. MedPAC also stated that providers should be held
accountable for the quality of care they provide for as much of their
Medicare patient population as feasible.
Response: We agree that providers should be accountable for quality
of care for as much of their Medicare population as feasible; we
endeavor to do this as much as possible, only specifying exclusions we
believe are necessary for measure validity. We also believe that
monitoring quality of care and outcomes is important for all PAC
patients, including baseline NF residents who return to a NF after
their PAC stay. We publicly report several long-stay resident quality
measures on Nursing Home Compare including measures of hospitalization
and emergency department visits.
Community is traditionally understood as representing non-
institutional settings by policy makers, providers, and other
stakeholders. Including long-term care NF in the definition of
community would confuse this long-standing concept of community and
would misalign with CMS' definition of community in patient assessment
instruments. We conceptualized this measure using the traditional
definition of ``community'' and specified the measure as a discharge to
community measure, rather than a discharge to baseline residence
measure.
Baseline NF residents represent an inherently different patient
population with not only a significantly lower likelihood of discharge
to community settings, but also a higher likelihood of post-discharge
readmissions and death compared with PAC patients who did not live in a
NF at baseline. The inherent differences in patient characteristics and
PAC processes and goals of care for baseline NF residents and non-NF
residents are significant enough that we do not believe risk adjustment
using a NF flag would provide adequate control. While we acknowledge
that a return to nursing home for baseline NF residents represents a
return to their home, this outcome does not align with our measure
concept. Thus, we have chosen to exclude baseline NF residents from the
measure.
Comment: One commenter suggested the definition of ``long-term'' NF
stay in the proposed measure exclusion, requesting further
clarification in the measure specifications.
Response: We have further clarified the definition of long-term NF
stay in the final measure specifications, Final Specifications for IRF
QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. A
long-term NF stay is identified by the presence of a non-SNF PPS MDS
assessment in the 180 days preceding the qualifying prior acute care
admission and index SNF stay.
Comment: One commenter questioned whether the methodology for
calculating confidence intervals for performance categories used in
public display of the Discharge to Community--PAC measures has been
updated.
Response: On May 31, 2019, we announced an update to the
methodology used for calculating confidence intervals for provider
assignment to performance categories for public display of the
Discharge to Community--PAC measures. For more information, we refer
readers to the ``Fact Sheet for Discharge to Community Post-Acute Care
Measures'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/LTCH-Quality-Reporting/Downloads/Fact-Sheet-for-Discharge-to-Community-Post-Acute-Care-Measures.pdf and the ``FAQ for Discharge to Community Post-Acute Care
Measures'' available at https://www.cms.gov/Medicare/Quality-
Initiatives-Patient-Assessment-Instruments/LTCH-Quality-Reporting/
Downloads/FAQ-for-Discharge-to-
[[Page 39109]]
Community-Post-Acute-Care-Measures.pdf.
After consideration of the public comments, we are finalizing our
proposal to exclude baseline NF residents from the Discharge to
Community--PAC IRF QRP measure as proposed beginning with the FY 2020
IRF QRP.
E. IRF QRP Quality Measures, Measure Concepts, and Standardized Patient
Assessment Data Elements Under Consideration for Future Years: Request
for Information
We sought input on the importance, relevance, appropriateness, and
applicability of each of the measures, standardized patient assessment
data elements (SPADEs), and concepts under consideration listed in the
Table 17 for future years in the IRF QRP.
[GRAPHIC] [TIFF OMITTED] TR08AU19.021
While we will not be responding to specific comments submitted in
response to this Request for Information, we intend to use this input
to inform our future measure and SPADE development efforts.
We received several comments on this RFI, which are summarized
below.
Comment: Several commenters supported the inclusion of all of the
proposed measures and SPADEs listed in Table 17. One commenter agreed
that the SPADE categories will provide a fuller picture of the patients
in the IRF setting and could be used for creating and risk adjusting
quality measures.
Many commenters supported the dementia SPADE, since dementia can
affect a beneficiary's ability to participate in his or her care in the
PAC setting, in addition to managing chronic conditions and medications
after discharge. One commenter also agreed that regularly assessing
cognitive function and mental health status presents opportunities for
better care and quality of life.
One commenter did not support the cognitive complexity SPADEs,
since there is no singular assessment tool designed to assess executive
function and memory, and it would be overly burdensome for IRFs to
conduct testing on every patient. The commenter recommended that CMS
work with stakeholders to prioritize which patient conditions would
benefit from a cognitive complexity assessment and screen for those
cases.
Many commenters supported the caregiver status SPADE; one commenter
stated that regular assessment of caregivers will result in better care
for the beneficiary and quality of life for both individuals. Another
commenter encouraged CMS to capture caregiver status, along with the
caregiver's willingness and ability, and account for it in discharge
disposition outcomes.
With regard to an opioids-based quality measure, providers had some
concerns about unintended consequences of reporting of opioid use,
including the over- or under-prescribing of opioids or limiting
patients access to critical treatments for pain management.
Many commenters were supportive of SPADEs focused on bowel and
bladder continence. One commenter noted that this is already collected
on admission and did not support a bowel and bladder SPADE on
discharge, citing that IRFs already communicate continence needs at
discharge and this would be duplicative. A few commenters had concerns
about the burden of future measures and SPADEs. One commenter
recommended that prior to adding measures or data elements, CMS
reassess and analyze all of the measures and data elements currently
collected to limit administrative burden and create a meaningful set of
measures and data elements. Another commenter supported utilization of
data from the suggested measures and SPADEs and suggested using
existing data sources, such a Medicare claims data. One commenter did
not support any future SPADE concepts that were not required by the
IMPACT Act. Another commenter suggested that CMS should explore
beneficiary-matching methods with the Department of Veteran's Affairs
to collect veteran status without additional IRF data collection
burden.
Response: We appreciate the input provided by commenters. While we
will not be responding to specific comments submitted in response to
this Request for Information, we intend to use this input to inform our
future measure and SPADE development efforts.
F. Standardized Patient Assessment Data Reporting Beginning With the FY
2022 IRF QRP
Section 1886(j)(7)(F)(ii) of the Act requires that, for FY 2019 and
each subsequent fiscal year, IRFs must report standardized patient
assessment data required under section 1899B(b)(1) of the Act. Section
1899B(a)(1)(C) of the Act requires, in part, the Secretary to modify
the PAC assessment instruments in order for PAC providers, including
IRFs, to submit SPADEs under the Medicare program. Section
1899B(b)(1)(A) of the Act requires PAC providers to submit SPADEs under
applicable reporting provisions (which, for IRFs, is the IRF QRP) with
respect to the admission and discharge of an
[[Page 39110]]
individual (and more frequently as the Secretary deems appropriate),
and section 1899B(b)(1)(B) of the Act defines standardized patient
assessment data as data required for at least the quality measures
described in section 1899B(c)(1) of the Act and that is with respect to
the following categories: (1) Functional status, such as mobility and
self-care at admission to a PAC provider and before discharge from a
PAC provider; (2) cognitive function, such as ability to express ideas
and to understand, and mental status, such as depression and dementia;
(3) special services, treatments, and interventions, such as need for
ventilator use, dialysis, chemotherapy, central line placement, and
total parenteral nutrition; (4) medical conditions and comorbidities,
such as diabetes, congestive heart failure, and pressure ulcers; (5)
impairments, such as incontinence and an impaired ability to hear, see,
or swallow; and (6) other categories deemed necessary and appropriate
by the Secretary.
In the FY 2018 IRF PPS proposed rule (82 FR 20722 through 20739),
we proposed to adopt SPADEs that would satisfy the first five
categories. In the FY 2018 IRF PPS final rule (82 FR 36287 through
36289), we summarized comments that supported our adoption of SPADEs,
including support for our broader standardization goal and support for
the clinical usefulness of specific proposed SPADEs. However, we did
not finalize the majority of our SPADE proposals in recognition of the
concern raised by many commenters that we were moving too fast to adopt
the SPADEs and modify our assessment instruments in light of all of the
other requirements we were also adopting under the IMPACT Act at that
time (82 FR 36292 through 36294). In addition, commenters noted that we
should conduct further testing of the data elements we have proposed
(82 FR 36288).
However, we finalized the adoption of SPADEs for two of the
categories described in section 1899B(b)(1)(B) of the Act: (1)
Functional status: Data elements currently reported by IRFs to
calculate the measure Application of Percent of Long-Term Care Hospital
Patients with an Admission and Discharge Functional Assessment and a
Care Plan That Addresses Function (NQF #2631); and (2) Medical
conditions and comorbidities: The data elements used to calculate the
pressure ulcer measures, Percent of Residents or Patients with Pressure
Ulcers That Are New or Worsened (Short Stay) (NQF #0678) and the
replacement measure, Changes in Skin Integrity Post-Acute Care:
Pressure Ulcer/Injury. We stated that these data elements were
important for care planning, known to be valid and reliable, and
already being reported by IRFs for the calculation of quality measures.
Since we issued the FY 2018 IRF PPS final rule, IRFs have had an
opportunity to familiarize themselves with other new reporting
requirements that we have adopted under the IMPACT Act. We have also
conducted further testing of the SPADEs, as described more fully below,
and believe that this testing supports the use of the SPADEs in our PAC
assessment instruments. Therefore, we proposed to adopt many of the
same SPADEs that we previously proposed to adopt, along with other
SPADEs.
We proposed that IRFs would be required to report these SPADEs
beginning with the FY 2022 IRF QRP. If finalized as proposed, IRFs
would be required to report these data with respect to admission and
discharge for Medicare Part A and Medicare Advantage patients
discharged between October 1, 2020, and December 31, 2020 for the FY
2022 IRF QRP. Beginning with the FY 2023 IRF QRP, we proposed that IRFs
must report data with respect to Medicare Part A and Medicare Advantage
admissions and discharges that occur during the subsequent calendar
year (for example, CY 2021 for the FY 2023 IRF QRP, CY 2022 for the FY
2024 IRF QRP).
We also proposed that IRFs that submit the Hearing, Vision, Race,
and Ethnicity SPADEs with respect to admission will be deemed to have
submitted those SPADEs with respect to both admission and discharge,
because it is unlikely that the assessment of those SPADEs at admission
will differ from the assessment of the same SPADEs at discharge.
In selecting the proposed SPADEs below, we considered the burden of
assessment-based data collection and aimed to minimize additional
burden by evaluating whether any data that is currently collected
through one or more PAC assessment instruments could be collected as
SPADEs. In selecting the SPADEs below, we also took into consideration
the following factors with respect to each data element:
(1) Overall clinical relevance;
(2) Interoperable exchange to facilitate care coordination during
transitions in care;
(3) Ability to capture medical complexity and risk factors that can
inform both payment and quality; and
(4) Scientific reliability and validity, general consensus
agreement for its usability.
In identifying the SPADEs proposed below, we additionally drew on
input from several sources, including TEPs held by our data element
contractor, public input, and the results of a recent National Beta
Test of candidate data elements conducted by our data element
contractor (hereafter ``National Beta Test'').
The National Beta Test collected data from 3,121 patients and
residents across 143 PAC facilities (26 LTCHs, 60 SNFs, 22 IRFs, and 35
HHAs) from November 2017 to August 2018 to evaluate the feasibility,
reliability, and validity of the candidate data elements across PAC
settings. The 3,121 patients and residents with an admission assessment
included 507 in LTCHs, 1,167 in SNFs, 794 in IRFs, and 653 in HHAs. The
National Beta Test also gathered feedback on the candidate data
elements from staff who administered the test protocol in order to
understand usability and workflow of the candidate data elements. More
information on the methods, analysis plan, and results for the National
Beta Test can be found in the document titled, ``Development and
Evaluation of Candidate Standardized Patient Assessment Data Elements:
Findings from the National Beta Test (Volume 2),'' available in the
document titled, ``Development and Evaluation of Candidate Standardized
Patient Assessment Data Elements: Findings from the National Beta Test
(Volume 2),'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Further, to inform the proposed SPADEs, we took into account
feedback from stakeholders, as well as from technical and clinical
experts, including feedback on whether the candidate data elements
would support the factors described above. Where relevant, we also took
into account the results of the Post-Acute Care Payment Reform
Demonstration (PAC PRD) that took place from 2006 to 2012.
Comment: Several commenters were supportive of the SPADE proposals.
A commenter recognized that the proposed SPADEs may influence care,
impact case mix and risk adjustment scores, and drive planning for
future management. Other commenters supported the proposals to add the
proposed SPADEs to the IRF-PAI, with one noting that many of the data
elements are already collected and reported on, and the other stating
that the items are important to describing current IRF patients and are
applicable to determining patient acuity. Another
[[Page 39111]]
commenter stated that data standardization as accomplished by the
SPADEs will help facilitate appropriate payment reforms and appropriate
quality measures.
Response: We thank the commenters for their support. We selected
the proposed SPADEs in part because of the attributes that the
commenters noted, such as their ability to describe IRF patients and to
support future quality measurement.
Comment: Some commenters stated support but noted reservations. One
commenter described the SPADEs as an appropriate start, but noted that
the SPADEs cannot stand alone, and must be built upon in order to be
useful for risk adjustment and quality measurement. Similarly, another
commenter suggested CMS continue working with clinicians and
researchers to ensure that the SPADEs are collecting valid, reliable,
and useful data, and to continue to refine and explore new data
elements for standardization.
Response: We agree with the commenter's statement that the SPADEs
are an appropriate start for standardization, but we disagree that they
cannot stand alone. While we intend to evaluate the SPADEs as they are
submitted and explore additional opportunities for standardization, we
also believe that the SPADEs as proposed represent an important core
set of information about clinical status and patient characteristics
and they will be useful for quality measurement. We will continue to
explore the use of the SPADEs across our PAC setting, continuing our
efforts to explore the feasibility, reliability, validity, and
usability of the data elements in our measure models and QRPs. We would
welcome continued input, recommendations, and feedback from
stakeholders about ways to improve assessment and quality measurement
for PAC providers, including ways that the SPADEs could be used in the
IRF QRP. Input can be shared with CMS through our PAC Quality
Initiatives email address [email protected].
Comment: One commenter noted support for the goals of the IMPACT
Act, but expressed concern about the scope and timing of proposed
changes, including the SPADEs. The same commenter suggested that CMS
share with the public a data use strategy and analysis plan for the
SPADEs so that providers better understand how CMS will assess the
potential usability of the SPADEs to support changes to payment and
quality programs.
Response: We thank the commenter for the support and appreciate
their concern about the proposed changes. We intend to monitor and
evaluate SPADEs as they are submitted, and to continue to engage
stakeholders around ways the SPADEs could be best used in the PAC
quality programs. We will continue to communicate and collaborate with
stakeholders by soliciting input on use of the SPADEs in the IRF QRP
through future rulemaking.
Comment: One commenter was generally critical of the set of SPADEs
proposed, stating they fail to adequately describe a patient's clinical
situation with regard to their level of independence, including
swallowing function, communication, and cognitive function.
Response: The proposed SPADEs were selected based on their overall
clinical relevance to PAC providers, including IRFs, their ability to
facilitate care coordination during transitions, their ability to
capture medical complexity and risk factors, and their scientific
reliability and validity. We have strived to balance the scope and
level of detail of the data elements against the potential burden
placed on patients and providers. At this time, SPADEs focused on
impairments are limited to sensory impairments (that is, hearing and
vision) and do not include swallowing. The patient's ability to
communicate is also not captured with a SPADE, although we note that
the IRF-PAI includes two data elements on communication: Expression of
Ideas and Wants, and Understanding Verbal and Non-Verbal Content.
However, in combination with other sections of the IRF-PAI that have
been standardized across PAC providers, we believe the proposed SPADEs
capture key clinical information (for example, cognitive function for
patients who are able to communicate, as collected by the BIMS) and
form an important foundation of standardized assessment on which to
build.
Comment: One commenter described several concerns about the scope
and implementation of the National Beta Test, including the
representativeness of IRFs included in the sample, the share of total
IRF patients included in the National Beta Test, the reported exclusion
of patients with communication and cognitive impairments, and the
exclusion of non-English speaking patients, and described how these
concerns compromise their confidence in the findings of the National
Beta Test.
Response: In a supplementary document to the proposed rule, we
described key findings from the National Beta Test related to the
proposed SPADEs. We also referred readers to an initial volume of the
National Beta Test report that details the methodology of the field
test (``Development and Evaluation of Candidate Standardized Patient
Assessment Data Elements: Findings from the National Beta Test (Volume
2),'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html). Additional
volumes of the National Beta Test report will be available in late
2019.
To address the commenter's specific concerns, we note that the
National Beta Test was designed to generate valid and robust national
SPADE performance estimates for each of the four PAC provider types,
which required acceptable geographic diversity, sufficient sample size,
and reasonable coverage of the range of clinical characteristics. To
meet these requirements, the National Beta Test was carefully designed
so that data could be collected from a wide range of environments,
allowing for thorough evaluation of candidate SPADE performance in all
PAC settings. The approach included a stratified random sample, to
maximize generalizability, and subsequent analyses included extensive
checks on the sampling design.
The commenter further implied that the small share of overall IRF
admissions included in the Beta test is indicative of inadequate
representativeness. The objective of the National Beta Test was to
evaluate the performance of candidate SPADEs for cross-setting use. It
is true that the proportion of IRFs may not reflect actual proportion
in the United States, but our sampling design ensured that sufficient
spread of IRFs across randomly selected markets, and adequate numbers
to provide ample data with which to evaluate SPADE performance in IRFs
relative to other settings.
The National Beta Test did not exclude non-communicative patients/
residents; rather, it had two distinct samples, one of which focused on
patients/residents who were able to communicate, and one of which
focused on patient/residents who were not able to communicate. The
assessment of non-communicative patients/residents differed primarily
in that observational assessments were substituted for some interview
assessments. Non-English-speaking patients were excluded from the
National Beta Test due to feasibility constraints during the field
test. Including limited English proficiency patients/residents in the
sample would
[[Page 39112]]
have required the Beta test facilities to engage or involve translators
during the test assessments. We anticipated that this would have added
undue complexity to what facilities/agencies were being requested to
do, and would have undermined the ability of facility/agency staff to
complete the requested number of assessments during the study period.
Moreover, there is strong existing evidence for the feasibility of all
clinical patient/resident interview SPADEs included in this final rule
(BIMS [section IX.G.1 in this final rule], Pain Interference [section
IX.G.3 in this final rule], PHQ [section IX.G.1 in this final rule])
when administered in other languages, either through standard PAC
workflow, as tested and currently collected in the MDS 3.0, or through
rigorous translation and testing, such as the PHQ. For all these
reasons, we determined that the performance of translated versions of
these patient/resident interview SPADEs did not need to be further
evaluated. In addition, because their exclusion did not threaten our
ability to achieve acceptable geographic diversity, sufficient sample
size, and reasonable coverage of the range of PAC patient/resident
clinical characteristics, the exclusion of limited English proficiency
patients/residents was not considered a limitation to interpretation of
the National Beta Test results.
Comment: Two commenters wanted CMS to share more information from
the National Beta Test. One of the commenters remarked on the lack of
information about clinical characteristics that has been shared with
stakeholders, limiting their ability to draw conclusions about the
data, and requested that CMS release the data from the National Beta
Test to be analyzed by third parties. The other commenter noted that
CMS has not shared quantitative results of the National Beta Test which
has limited the ability of stakeholders to determine if these items
will yield useful information for quality and/or payment purposes, and
suggested CMS release additional information, such as response
frequencies, and analysis from the field test to provide evidence of
the validity and utility of the SPADEs for quality and payment.
Response: We shared both quantitative and qualitative findings from
the National Beta Test with stakeholders at a public meeting on
November 27, 2018. For each SPADE proposed in this rule within the
clinical categories in the IMPACT Act, we provided information in the
supplementary documents to the proposed rule (the document titled
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html) on the feasibility and reliability based on findings from
the National Beta Test.
We are in the process of writing the final report for the National
Beta Test, which includes the clinical SPADEs in this rule as well as
additional data elements. Volume 2 of that report (``Development and
Evaluation of Candidate Standardized Patient Assessment Data Elements.
Findings from the National Beta Test (Volume 2)'') was posted on CMS'
website in March 2019. The other volumes will be available in late
2019. In addition, we are committed to making data available for
researchers and the public to analyze, and to doing so in a way that
protects the privacy of patients and providers who participated in the
National Beta Test. We are in the process of creating research
identifiable files that we anticipate will be available through a data
use agreement sometime in 2019.
Comment: Many commenters expressed concerns with respect to the
standardized patient assessment data proposals. Several commenters
stated that the standardized patient assessment data reporting
requirements will impose significant burden on providers, given the
volume of new standardized patient assessment data elements, and
corresponding sub-elements, that were proposed to be added to the IRF-
PAI. One commenter noted that the addition of the proposed standardized
patient assessment data elements would require an expanded timeline to
implement to ensure necessary operational and workflow revisions.
Response: We acknowledge the additional burden that the SPADEs will
impose on providers and patients. Our development and selection process
for the SPADEs we are adopting in this final rule prioritized data
elements that are essential to comprehensive patient care. We maintain
that there will be significant benefit associated with each of the
SPADEs to providers and patients, in that they are clinically useful
(for example, for care planning), they support patient-centered care,
and they will promote interoperability and data exchange between
providers. During the SPADE development process, we were cognizant of
the changes that providers will need to make to implement these
additions to the IRF-PAI. In the last two rules (82 FR 36287 through
36289, 83 FR 38555), we provided information about goals, scope, and
timeline for implementing SPADEs, as well as updated IRFs about ongoing
development and testing of data elements through other public forums.
We believe that IRFs have had an opportunity to familiarize themselves
with other new reporting requirements that we have adopted under the
IMPACT Act and prepare for additional changes.
Comment: Some commenters expressed concern that this additional
burden was not justified because, in their view, there was limited or
no evidence for the SPADEs to describe case mix, measure quality, or
improve care. One of these commenters noted that CMS has provided
evidence of validity, reliability, and feasibility through documents
related to the National Beta Test, but stated that CMS has not provided
any evidence that the proposed SPADEs have the ``potential for
improving quality'' or ``utility for describing case mix.''
Response: The clinical SPADEs proposed in this rule were the result
of an extensive consensus vetting process in which experts and
stakeholders were engaged through Technical Expert Panels, Special Open
Door Forums, and posting of interim reports and other documents on the
CMS website. Results of these activities provide evidence that experts
and providers believe that the proposed SPADEs have the potential for
measuring quality, for describing case mix, and improving care. We
refer the commenter to the most recent TEP report: A summary of the
September 17, 2018 TEP meeting titled ``SPADE Technical Expert Panel
Summary (Third Convening)'', which is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. In this report, we summarize the TEP's discussion of
individual SPADEs in which they reflect on the clinical usefulness and
importance of the SPADEs for describing patient acuity (case mix) and
providing high-quality clinical care (improving quality). Therefore, we
have provided evidence that the SPADEs have the potential for improving
quality and utility for describing case mix.
Comment: One commenter believes that the expansion of the IRF-PAI
assessment will prove to be intrusive and prove challenging for
patients who are elderly, frail, in pain, or have cognitive deficits,
causing the patients
[[Page 39113]]
to lose focus, and thus, impact the accuracy of the data.
Response: We acknowledge that several SPADEs in this rule require
the patient to be asked questions directly. We believe that direct
patient assessment and patient-reported outcomes on these topics have
benefits for providers and patients. These data elements support
patient-centered care by soliciting the patient's perspective, and
better information on a patient's status is expected to improve the
care the patient receives.76 77 78 The burden the patient-
interview data elements place on patients is necessary for accurate
assessment of the patient's status. Regarding the validity and
performance of interview-based data elements, we note that many of
these data elements (for example, the BIMS, PHQ, and Pain Interference
data elements) are currently used in the MDS in SNFs. Evidence from
that setting, as well as from the National Beta Test, demonstrates
feasibility of these data elements for even very sick patients, such as
many patients receiving care from IRFs.
---------------------------------------------------------------------------
\76\ Boyce MB, Browne JP, Greenhalgh J The experiences of
professionals with using information from patient-reported outcome
measures to improve the quality of healthcare: A systematic review
of qualitative research BMJ Quality & Safety 2014;23:508-518.
\77\ Chen J, Ou L, Hollis SJ. A systematic review of the impact
of routine collection of patient reported outcome measures on
patients, providers and health organizations in an oncologic
setting. BMC Health Services Research 2013;13:211.
\78\ Marshall, S., Haywood, K. and Fitzpatrick, R. (2006),
Impact of patient[hyphen]reported outcome measures on routine
practice: A structured review. Journal of Evaluation in Clinical
Practice, 12: 559-568. doi:10.1111/j.1365-2753.2006.00650.x.
---------------------------------------------------------------------------
Comment: Commenters also stated that the time burden (as in,
``time-to-complete'') associated with the clinical SPADEs was
underestimated, with some commenters noting that it did not account for
clinician time to review charts and update treatment plans or that test
conditions do not represent conditions of day-to-day operation. One
commenter stated that the estimated time to complete reported in the
National Beta Test was based only on the time needed to enter a value
on a tablet and did not include the time to evaluate the patient on
each item. Another commenter stated that because testing conditions
focused on cognitively intact, English-speaking patients with no speech
or language deficits, the estimates of impact to providers' time and
resources is inadequate.
Response: We disagree with the commenters that the National Beta
Test time-to-complete estimates are underestimates. Contrary to what
one commenter noted, we wish to clarify that time-to-complete estimates
from the National Beta Test included the time spent both to collect
data, including the review of the medical record, if needed, and to
enter the data elements into a tablet. We note that time-to-complete
estimates were calculated using the data from Facility/Agency Staff
only, and not Research Nurses, who completed more training and
conducted more assessments overall than the Facility/Agency staff. This
decision to calculate time-to-complete estimates from Facility/Agency
Staff only supports our claim that the time-to-complete estimates are
accurate reflections of the time the SPADEs will require when
implemented by PAC providers in day-to-day operations. Contrary to
another commenter's statement, we also wish to clarify that National
Beta Test did exclude patients/residents who were not able to
communicate in English, but did not categorically exclude patients with
cognitive impairment or patients with speech or language deficits.
Therefore, we believe that our estimates of time-to-complete capture
the general population of IRF patients, including those with
communication impairments.
Comment: Some commenters recommended changes to when and how SPADEs
would be collected in order to reduce administrative burden. These
recommendations included collecting data only at admission when answers
are unlikely to change between admission and discharge, adopting a
staged implementation or only a subset of the proposed data elements,
and that CMS explore options for obtaining these data via claims or
voluntary reporting only, particularly as many of the proposed SPADEs
are not relevant to IRF patients.
Response: We appreciate the commenters' recommendations. To support
data exchange between settings, and to support quality measurement,
section 1899B(b)(1)(A) of the Act requires that the SPADEs be collected
with respect to both admission and discharge. In the FY 2020 IRF PPS
proposed rule (84 FR 17292), we proposed that IRFs that submit four
SPADEs with respect to admission will be deemed to have submitted those
SPADEs with respect to both admission and discharge, because we stated
that it is unlikely that the assessment of those SPADEs at admission
would differ from the assessment of the same SPADEs at discharge. We
note that a patient's ability to hear or ability to see are more likely
to change between admission and discharge than, for example, a
patient's self-report of his or her race, ethnicity, preferred
language, or need for interpreter services. The Hearing and Vision
SPADEs are also different from the other SPADEs (that is, Race,
Ethnicity, Preferred Language, and Interpreter Services) because
evaluation of sensory status is a fundamental part of the ongoing
nursing assessment conducted for IRF patients. Therefore, clinically
significant changes that occur in a patient's hearing or vision status
during the IRF stay would be captured as part of the clinical record
and communicated to the next setting of care, as well as taken into
account during discharge planning as a part of standard best practice.
After consideration of public comments discussed in sections IX.G.4
and IX.G.4.b in this final rule, we will deem IRFs that submit the
Hearing, Vision, Race, Ethnicity, Preferred Language, and Interpreter
Services SPADEs with respect to admission to have submitted with
respect to both admission and discharge. We will take into
consideration the recommendation to obtain patient data from claims
data in future work.
Comment: A commenter recommended that CMS limit the number and type
of data elements implemented in the coming year, continue ongoing
dialogue with stakeholders, and develop and implement a process to
assess the value of specific indicators for all patient types. Another
commenter recommended that CMS conduct a thorough analysis of SPADEs
currently collected to determine if any current data elements could be
eliminated. One commenter believed that CMS should not finalize the
implementation of the SPADEs until they evaluate alternative means of
data collection (such as via billing/claims data), or measures to
reduce burden (such as removal of duplicative data elements and
elimination of data collection at discharge).
Response: We note that we adopted SPADEs in the last two rule
cycles to support the adoption of the IRF Functional Outcomes Measures
(Application of Percent of Long-Term Care Hospital Patients with an
Admission and Discharge Functional Assessment and a Care Plan That
Addresses Function (80 FR 47111); Change in Self-Care for Medical
Rehabilitation Patients (80 FR 47117); Change in Mobility Score for
Medical Rehabilitation Patients (80 FR 47118); Discharge Self-Care
Score for Medical Rehabilitation Patients (80 FR 47119); Discharge
Mobility Score for Medical Rehabilitation Patients (80 FR 47120)) and
drug regimen review (Drug Regimen Review Conducted with Follow-Up for
[[Page 39114]]
Identified Issues (81 FR 52111)). We have also communicated about the
SPADE development work with stakeholders over the last 2 years through
SODFs held on June 20, 2017, September 28, 2017, December 12, 2017,
March 28, 2018, June 19, 2018, and July 25, 2018, and at a public
meeting of stakeholders on November 27, 2018. Therefore, our
implementation to date has been incremental while we have strived to
keep stakeholders apprised as to the status of ongoing SPADE
development. We have also conducted a large-scale test of feasibility
and reliability--the National Beta Test, described in the proposed rule
(84 FR 17293)--which, along with the consensus vetting activities
described in the proposals for each SPADE, provide evidence of the
value of the SPADEs for patients across PAC settings, including IRF
patients. We will monitor and conduct analysis on the SPADEs as they
are submitted in order to identify any problems and to identify any
unnecessary burden or duplication.
Comment: One commenter recommended that CMS focus on providing
funding and administrative support to allow improvements and
standardization to the electronic medical record to allow effective
interoperability across all post-acute sites.
Response: We appreciate the commenter's recommendation. At this
time, funding for electronic medical record adoption and support is not
currently authorized for PAC providers.
Final decisions on the SPADEs are given below, following more
detailed comments on each SPADE proposal.
G. Standardized Patient Assessment Data by Category
1. Cognitive Function and Mental Status Data
A number of underlying conditions, including dementia, stroke,
traumatic brain injury, side effects of medication, metabolic and/or
endocrine imbalances, delirium, and depression, can affect cognitive
function and mental status in PAC patient and resident populations.\79\
The assessment of cognitive function and mental status by PAC providers
is important because of the high percentage of patients and residents
with these conditions,\80\ and because these assessments provide
opportunity for improving quality of care.
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\79\ National Institute on Aging. (2014). Assessing Cognitive
Impairment in Older Patients. A Quick Guide for Primary Care
Physicians. Retrieved from https://www.nia.nih.gov/alzheimers/publication/assessing-cognitive-impairment-older-patients.
\80\ Gage B., Morley M., Smith L., et al. (2012). Post-Acute
Care Payment Reform Demonstration (Final report, Volume 4 of 4).
Research Triangle Park, NC: RTI International.
---------------------------------------------------------------------------
Symptoms of dementia may improve with pharmacotherapy, occupational
therapy, or physical activity,81 82 83 and promising
treatments for severe traumatic brain injury are currently being
tested.\84\ For older patients and residents diagnosed with depression,
treatment options to reduce symptoms and improve quality of life
include antidepressant medication and
psychotherapy,85 86 87 88 and targeted services, such as
therapeutic recreation, exercise, and restorative nursing, to increase
opportunities for psychosocial interaction.\89\
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\81\ Casey D.A., Antimisiaris D., O'Brien J. (2010). Drugs for
Alzheimer's Disease: Are They Effective? Pharmacology &
Therapeutics, 35, 208-11.
\82\ Graff M.J., Vernooij-Dassen M.J., Thijssen M., Dekker J.,
Hoefnagels W.H., Rikkert M.G.O. (2006). Community Based Occupational
Therapy for Patients with Dementia and their Care Givers: Randomised
Controlled Trial. BMJ, 333(7580): 1196.
\83\ Bherer L., Erickson K.I., Liu-Ambrose T. (2013). A Review
of the Effects of Physical Activity and Exercise on Cognitive and
Brain Functions in Older Adults. Journal of Aging Research, 657508.
\84\ Giacino J.T., Whyte J., Bagiella E., et al. (2012).
Placebo-controlled trial of amantadine for severe traumatic brain
injury. New England Journal of Medicine, 366(9), 819-826.
\85\ Alexopoulos G.S., Katz I.R., Reynolds C.F. 3rd, Carpenter
D., Docherty J.P., Ross R.W. (2001). Pharmacotherapy of depression
in older patients: A summary of the expert consensus guidelines.
Journal of Psychiatric Practice, 7(6), 361-376.
\86\ Arean P.A., Cook B.L. (2002). Psychotherapy and combined
psychotherapy/pharmacotherapy for late life depression. Biological
Psychiatry, 52(3), 293-303.
\87\ Hollon S.D., Jarrett R.B., Nierenberg A.A., Thase M.E.,
Trivedi M., Rush A.J. (2005). Psychotherapy and medication in the
treatment of adult and geriatric depression: Which monotherapy or
combined treatment? Journal of Clinical Psychiatry, 66(4), 455-468.
\88\ Wagenaar D, Colenda CC, Kreft M, Sawade J, Gardiner J,
Poverejan E. (2003). Treating depression in nursing homes: Practice
guidelines in the real world. J Am Osteopath Assoc. 103(10), 465-
469.
\89\ Crespy SD, Van Haitsma K, Kleban M, Hann CJ. Reducing
Depressive Symptoms in Nursing Home Residents: Evaluation of the
Pennsylvania Depression Collaborative Quality Improvement Program. J
Healthc Qual. 2016. Vol. 38, No. 6, pp. e76-e88.
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In alignment with our Meaningful Measures Initiative, accurate
assessment of cognitive function and mental status of patients and
residents in PAC is expected to make care safer by reducing harm caused
in the delivery of care; promote effective prevention and treatment of
chronic disease; strengthen person and family engagement as partners in
their care; and promote effective communication and coordination of
care. For example, standardized assessment of cognitive function and
mental status of patients and residents in PAC will support
establishing a baseline for identifying changes in cognitive function
and mental status (for example, delirium), anticipating the patient's
or resident's ability to understand and participate in treatments
during a PAC stay, ensuring patient and resident safety (for example,
risk of falls), and identifying appropriate support needs at the time
of discharge or transfer. Standardized patient assessment data elements
will enable or support clinical decision-making and early clinical
intervention; person-centered, high quality care through facilitating
better care continuity and coordination; better data exchange and
interoperability between settings; and longitudinal outcome analysis.
Therefore, reliable standardized patient assessment data elements
assessing cognitive function and mental status are needed to initiate a
management program that can optimize a patient's or resident's
prognosis and reduce the possibility of adverse events.
The data elements related to cognitive function and mental status
were first proposed as standardized patient assessment data elements in
the FY 2018 IRF PPS proposed rule (82 FR 20723 through 20726). In
response to our proposals, a few commenters noted that the proposed
data elements did not capture some dimensions of cognitive function and
mental status, such as functional cognition, communication, attention,
concentration, and agitation. One commenter also suggested that other
cognitive assessments should be considered for standardization. Another
commenter stated support for the standardized assessment of cognitive
function and mental status, because it could support appropriate use of
skilled therapy for beneficiaries with degenerative conditions, such as
dementia, and appropriate use of medications for behavioral and
psychological symptoms of dementia.
We sought comment on our proposals to collect as standardized
patient assessment data the following data with respect to cognitive
function and mental status.
Commenters submitted the following comments related to the proposed
rule's discussion of the cognitive function and mental status data
elements.
Comment: A few commenters were supportive of the proposal to adopt
the BIMS, CAM, and PHQ-2 to 9 as SPADEs on the topic of cognitive
function and mental status. One commenter agreed that standardizing
cognitive assessments will allow providers to identify changes in
status, support clinical decision-making, and improve care continuity
and interventions.
Response: We thank the commenters for their support. We selected
the
[[Page 39115]]
Cognitive Function and Mental Status data elements for proposal as
standardized data in part because of the attributes that the commenters
noted.
Comment: A few commenters noted limitations of these SPADEs to
fully assess all areas of cognition and mental status, particularly
mild to moderate cognitive impairment, and performance deficits that
may be related to cognitive impairment. Some commenters suggested CMS
continue exploring assessment tools on the topic of cognition and to
include a more comprehensive assessment of cognitive function for use
in PAC settings, noting that highly vulnerable patients with a mild
cognitive impairment cannot be readily identified through the current
SPADEs.
Response: We have strived to balance the scope and level of detail
of the data elements against the potential burden placed on patients
and providers. In our past work, we evaluated the potential of several
different cognition assessments for use as standardized data elements
in PAC settings. We ultimately decided on the BIMS, CAM, and PHQ-2 to 9
data elements in our proposal as a starting point. We would welcome
continued input, recommendations, and feedback from stakeholders about
additional data elements for standardization, which can be shared with
CMS through our PAC Quality Initiatives email address:
[email protected].
Comment: A commenter stated that cognitive assessment should be
individualized, rather than standardized, and performed as determined
by patient needs.
Response: We believe that the standardized assessment of cognitive
function is essential to achieving the goals of the IMPACT Act. We also
wish to clarify that the proposed SPADEs are not intended to replace
comprehensive clinical evaluation and in no way preclude providers from
conducting further patient evaluation or assessments in their settings
as they believe are necessary and useful.
Comment: Regarding future use of these data elements, one commenter
recommended that CMS monitor the use of the cognition and mental status
SPADEs as risk adjustors and make appropriate adjustments to
methodology as needed.
Response: We intend to monitor data submitted via the proposed
SPADEs and will consider these uses in the future. We will also
continue to review recommendation and feedback from stakeholders
regarding data elements that would both satisfy the categories listed
in the IMPACT Act and provide meaningful data.
Final decisions on the SPADEs are given below, following more
detailed comments on each SPADE proposal.
Brief Interview for Mental Status (BIMS)
In the FY 2020 IRF PPS proposed rule (84 FR 17294 through 17295),
we proposed that the data elements that comprise the BIMS meet the
definition of standardized patient assessment data with respect to
cognitive function and mental status under section 1899B(b)(1)(B)(ii)
of the Act.
As described in the FY 2018 IRF PPS Proposed Rule (82 FR 20723
through 20724), dementia and cognitive impairment are associated with
long-term functional dependence and, consequently, poor quality of life
and increased healthcare costs and mortality.\90\ This makes assessment
of mental status and early detection of cognitive decline or impairment
critical in the PAC setting. The intensity of routine nursing care is
higher for patients and residents with cognitive impairment than those
without, and dementia is a significant variable in predicting
readmission after discharge to the community from PAC providers.\91\
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\90\ Ag[uuml]ero-Torres, H., Fratiglioni, L., Guo, Z., Viitanen,
M., von Strauss, E., & Winblad, B. (1998). ``Dementia is the major
cause of functional dependence in the elderly: 3-year follow-up data
from a population-based study.'' Am J of Public Health 88(10): 1452-
1456.
\91\ RTI International. Proposed Measure Specifications for
Measures Proposed in the FY 2017 IRF QRP NPRM. Research Triangle
Park, NC. 2016.
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The BIMS is a performance-based cognitive assessment screening tool
that assesses repetition, recall with and without prompting, and
temporal orientation. The data elements that make up the BIMS are seven
questions on the repetition of three words, temporal orientation, and
recall that result in a cognitive function score. The BIMS was
developed to be a brief, objective screening tool, with a focus on
learning and memory. As a brief screener, the BIMS was not designed to
diagnose dementia or cognitive impairment, but rather to be a
relatively quick and easy to score assessment that could identify
cognitively impaired patients, as well as those who may be at risk for
cognitive decline and require further assessment. It is currently in
use in two of the PAC assessments: The MDS used by SNFs and the IRF-PAI
used by IRFs. For more information on the BIMS, we refer readers to the
document titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The data elements that comprise the BIMS were first proposed as
standardized patient assessment data elements in the FY 2018 IRF PPS
proposed rule (82 FR 20723 through 20724). In that proposed rule, we
stated that the proposal was informed by input we received through a
call for input published on the CMS Measures Management System
Blueprint website. Input submitted from August 12 to September 12,
2016, noted support for use of the BIMS, noting that it is reliable,
feasible to use across settings, and will provide useful information
about patients and residents. We also stated that the data collected
through the BIMS will provide a clearer picture of patient or resident
complexity, help with the care planning process, and be useful during
care transitions and when coordinating across providers. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the use of the BIMS,
especially in its capacity to inform care transitions, but other
commenters were critical, noting the limitations of the BIMS to assess
mild cognitive impairment and ``functional'' cognition, and that the
BIMS cannot be completed by patients and residents who are unable to
communicate. They also stated that other cognitive assessments
available in the public domain should be considered for
standardization. One commenter suggested that CMS require use of the
BIMS with respect to discharge, as well as admission.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
BIMS was included in the National Beta Test of candidate data elements
conducted by our data element contractor from November 2017 to August
2018. Results of this test found the BIMS to be feasible and reliable
for use with PAC patients and residents. More information about the
performance of the BIMS in the National Beta Test can be found in the
document titled ``Final Specifications for IRF QRP Quality Measures and
[[Page 39116]]
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements and the TEP supported the
assessment of patient or resident cognitive status with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums (SODFs) and small-group
discussions with PAC providers and other stakeholders in 2018 for the
purpose of updating the public about our ongoing SPADE development
efforts. Finally, on November 27, 2018, our data element contractor
hosted a public meeting of stakeholders to present the results of the
National Beta Test and solicit additional comments. General input on
the testing and item development process and concerns about burden were
received from stakeholders during this meeting and via email through
February 1, 2019. Some commenters also expressed concern that the BIMS,
if used alone, may not be sensitive enough to capture the range of
cognitive impairments, including mild cognitive impairment. A summary
of the public input received from the November 27, 2018 stakeholder
meeting titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We understand the concerns raised by stakeholders that BIMS, if
used alone, may not be sensitive enough to capture the range of
cognitive impairments, including functional cognition and MCI, but note
that the purpose of the BIMS data elements as SPADEs is to screen for
cognitive impairment in a broad population. We also acknowledge that
further cognitive tests may be required based on a patient's condition
and will take this feedback into consideration in the development of
future standardized patient assessment data elements. However, taking
together the importance of assessing for cognitive status, stakeholder
input, and strong test results, we proposed that the BIMS data elements
meet the definition of standardized patient assessment data with
respect to cognitive function and mental status under section
1899B(b)(1)(B)(ii) of the Act and to adopt the BIMS data elements as
standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the BIMS data elements.
Comment: One commenter supported the collection of BIMS at both
admission and discharge and believes it will result in more complete
data and better care.
Response: We thank the commenter for the support of the BIMS data
element.
Comment: One commenter stated that the BIMS fails to detect mild
cognitive impairment, differentiate cognitive impairment from a
language impairment, link impairment to functional limitation, or
identify issues with problem solving and executive function. This
commenter recommended use of the Development of Outpatient Therapy
Payment Alternatives (DOTPA) items for PAC, as well as a screener
targeting functional cognition. Another commenter also recommended CMS
identify a better cognitive assessment and not to move forward with the
proposal.
Response: We recognize that the BIMS assesses components of
cognition and does not, alone, provide a comprehensive assessment of
potential cognitive impairment. We clarify that any SPADE is intended
as a minimum assessment and does not limit the ability of providers to
conduct a more comprehensive assessment of cognition to identify the
complexities or potential impacts of cognitive impairment that the
commenter describes.
We evaluated the suitability of the DOTPA, as well as other
screening tools that targeted functional cognition, by engaging our
TEP, through ``alpha'' feasibility testing, and through soliciting
input from stakeholders. At the second meeting of TEP in March 2017,
members questioned the use of data elements that rely on assessor
observation and judgment, such as DOTPA CARE tool items, and favored
other assessments of cognition that required patient interview or
patient actions. The TEP also discussed performance-based assessment of
functional cognition. These are assessments that require patients to
respond by completing a simulated task, such as ordering from a menu,
or reading medication instructions and simulating the taking of
medications, as required by the Performance Assessment of Self-Care
Skills (PASS) items.
In Alpha 2 feasibility testing, which was conducted between April
and July 2017, we included a subset of items from the DOTPA as well as
the PASS. Findings of that test identified several limitations of the
DOTPA items for use as SPADEs, such as relatively long to administer (5
to 7 minutes), especially in the LTCH setting. Assessors also indicated
that these items had low relevance for SNF and LTCH patients. In
addition, interrater reliability was highly variable among the DOTPA
items, both overall and across settings, with some items showing very
low agreement (as low as 0.34) and others showing excellent agreement
(as high as 0.81). Similarly, findings of the Alpha 2 feasibility test
identified several limitations of the PASS for use as SPADEs. The PASS
was relatively time-intensive to administer (also 5 to 7 minutes), many
patients in HHAs and IRFs needed assistance completing the PASS tasks,
and missing data were prevalent. Unlike the DOTPA items, interrater
reliability was consistently high overall for PASS (ranging from 0.78
to 0.92), but the high reliability was not deemed to outweigh
fundamental feasibility concerns related to administration challenges.
A summary report for the Alpha 2 feasibility testing titled
``Development and Maintenance of Standardized Cross Setting Patient
Assessment Data for Post-Acute Care: Summary Report of Findings from
Alpha 2 Pilot Testing'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Alpha-2-SPADE-Pilot-Summary-Document.pdf.
Feedback was obtained on the DOTPA and other assessments of
functional cognition through a call for input that was open from April
26, 2017 to June 26, 2017. While we received support for the DOTPA,
PASS, and other assessments of functional cognition, commenters also
raised concerns about the reliability of the DOTPA, given that it is
based on staff evaluation, and the feasibility of the PASS, given that
the simulated medication task requires props, such as a medication
bottle with printed label and pill box, which may not be accessible in
all settings. A summary report for the April 26 to June 26, 2017 public
comment period titled
[[Page 39117]]
``Public Comment Summary Report 2'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Public-Comment-Summary-Report_Standardized-Patient-Assessment-Data-Element-Work_PC2_Jan-2018.pdf.
Based on the input from our TEP, results of alpha feasibility
testing, and input from stakeholders, we decided to propose the BIMS
for standardization at this time due to the body of research literature
supporting its feasibility and validity, its relative brevity, and its
existing use in the MDS and IRF-PAI.
Comment: A few commenters noted that BIMS is currently collected by
IRFs and has not been demonstrated to predict costs or differentiate
case-mix and believes that CMS has not provided any evidence that the
BIMS is capable of being utilized for quality purposes to support the
collection of these data elements at discharge. Another commenter
stated that CMS has not provided quantitative evidence that the BIMS
data elements are capable of measuring provider performance for quality
or of differentiating case-mix for payment.
Response: We reiterate that the purpose of standardizing data
elements, in accordance with the IMPACT Act, is to support care
planning, clinical decision support, inform case-mix and quality
measurement, support care transitions, and enable interoperable data
exchange and data sharing between PAC settings. Before being identified
as a SPADE, the BIMS underwent an extensive consensus vetting process
in which experts and stakeholders were engaged through TEPs, SODFs, and
posting of interim reports and other documents on the CMS.gov website.
A summary of the most recent TEP meeting (September 17, 2018) titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Results of these activities
provide evidence that experts and providers believe that the BIMS data
elements have the potential for measuring quality, describing case mix,
and improving care.
Comment: A commenter believes that assessing BIMS at discharge
would not be clinically useful and would not contribute to improved
patient care or outcomes. The commenter noted that assessing BIMS at
discharge was not evaluated during the National Beta Test, and objected
to the BIMS being proposed for use at discharge.
Response: We maintain that a standardized cognitive assessment
using the BIMS is clinically useful and has the potential to improve
patient care and outcomes. The commenter stated that the BIMS was not
administered at discharge in the National Beta Test. However, the BIMS
was in fact assessed at both admission and discharge in the National
Beta Test. Moreover, to support data exchange between settings, and to
support quality measurement, the IMPACT Act requires that the SPADEs be
collected with respect to both admission and discharge. After careful
consideration of the public comments we received, we are finalizing our
proposal to adopt the BIMS as standardized patient assessment data
beginning with the FY 2022 IRF QRP as proposed.
Confusion Assessment Method (CAM)
In the FY 2020 IRF PPS proposed rule (84 FR 17295), we proposed
that the data elements that comprise the Confusion Assessment Method
(CAM) meet the definition of standardized patient assessment data with
respect to cognitive function and mental status under section
1899B(b)(1)(B)(ii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20724),
the CAM was developed to identify the signs and symptoms of delirium.
It results in a score that suggests whether a patient or resident
should be assigned a diagnosis of delirium. Because patients and
residents with multiple comorbidities receive services from PAC
providers, it is important to assess delirium, which is associated with
a high mortality rate and prolonged duration of stay in hospitalized
older adults.\92\ Assessing these signs and symptoms of delirium is
clinically relevant for care planning by PAC providers.
---------------------------------------------------------------------------
\92\ Fick, D.M., Steis, M.R., Waller, J.L., & Inouye, S.K.
(2013). ``Delirium superimposed on dementia is associated with
prolonged length of stay and poor outcomes in hospitalized older
adults.'' J of Hospital Med 8(9): 500-505.
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The CAM is a patient assessment that screens for overall cognitive
impairment, as well as distinguishes delirium or reversible confusion
from other types of cognitive impairment. The CAM is currently in use
in two of the PAC assessments: A four-item version of the CAM is used
in the MDS in SNFs; and a six-item version of the CAM is used in the
LTCH CARE Data Set (LCDS) in LTCHs. We proposed the four-item version
of the CAM that assesses acute change in mental status, inattention,
disorganized thinking, and altered level of consciousness. For more
information on the CAM, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The data elements that comprise the CAM were first proposed as
standardized patient assessment data elements in the FY 2018 IRF PPS
proposed rule (82 FR 20724). In that proposed rule, we stated that the
proposal was informed by public input we received on the CAM through a
call for input published on the CMS Measures Management System
Blueprint website. Input submitted from August 12 to September 12, 2016
noted support for use of the CAM, noting that it would provide
important information for care planning and care coordination, and
therefore, contribute to quality improvement. We also stated that those
commenters had noted the CAM is particularly helpful in distinguishing
delirium and reversible confusion from other types of cognitive
impairment. A summary report for the August 12 to September 12, 2016
public comment period titled ``SPADE August 2016 Public Comment Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
one commenter supported use of the CAM for standardized patient
assessment data. However, some commenters expressed concerns that the
CAM data elements assess: The presence of behavioral symptoms, but not
the cause; the possibility of a false positive for delirium due to
patient cognitive or communication impairments; and the lack of
specificity of the assessment specifications. In addition, other
commenters noted that the CAM is not necessary because: Delirium is
easily diagnosed without a tool; the CAM and BIMS assessments are
redundant; and some CAM response options are not meaningful.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
CAM was included in the National Beta Test of candidate data elements
conducted by our data element contractor from November 2017 to August
2018. Results of this test found the CAM to be feasible
[[Page 39118]]
and reliable for use with PAC patients and residents. More information
about the performance of the CAM in the National Beta Test can be found
in the document titled ``Final Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although they did not
specifically discuss the CAM data elements, the TEP supported the
assessment of patient or resident cognitive status with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for delirium,
stakeholder input, and strong test results, we proposed that the CAM
data elements meet the definition of standardized patient assessment
data with respect to cognitive function and mental status under section
1899B(b)(1)(B)(ii) of the Act and to adopt the CAM data elements as
standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the proposed CAM data elements.
Comment: A few commenters stated that the CAM would be redundant
with other cognitive assessments, such as BIMS. One commenter stated
that delirium would be assessed prior to discharge from the acute care
setting, making the assessment of delirium at admission to the IRF
redundant. Another commenter stated that concerns about burden
outweighed the value that the CAM might have for some populations, and
noted that daily physician visits and daily assessments of patients by
the interdisciplinary team were sufficient to assess cognitive needs.
Response: The CAM specifically screens for change in mental status,
inattention, disorganized thinking and altered level of consciousness,
which can indicate symptoms of delirium. These symptoms are not
assessed by other cognitive assessments in the IRF-PAI. We believe the
assessment of delirium at admission and discharge is important to
informing patient care. Delirium occurs in up to half of patients/
residents receiving PAC services,\93\ and signs and symptoms of
delirium are associated with poor functional recovery,\94\ re-
hospitalization, and mortality.\95\ Because the majority of delirium
episodes are transient,\96\ we would not expect assessment of delirium
prior to discharge from the acute care setting to capture all cases of
delirium in PAC, as there may be an acute change in mental status from
the patient's baseline or fluctuations in the patient's behaviors that
are identified after PAC admission.
---------------------------------------------------------------------------
\93\ Dan K. Kiely et al., ``Characteristics Associated with
Delirium Persistence Among Newly Admitted Post-Acute Facility
Patients,'' Journals of Gerontology: Series A (Biological Sciences
and Medical Sciences), Vol. 59, No. 4, April 2004; Edward R.
Marcantonio et al., ``Delirium Symptoms in Post-Acute Care:
Prevalent, Persistent, and Associated with Poor Functional
Recovery,'' Journal of the American Geriatrics Society, Vol. 51, No.
1, January 2003.
\94\ Marcantonio, Edward R., Samuel E. Simon, Margaret A.
Bergmann, Richard N. Jones, Katharine M. Murphy, and John N. Morris,
``Delirium Symptoms in Post-Acute Care: Prevalent, Persistent, and
Associated with Poor Functional Recovery,'' Journal of the American
Geriatrics Society, Vol. 51, No. 1, January 2003, pp. 4-9.
\95\ Edward R. Marcantonio et al., Outcomes of Older People
Admitted to Postacute Facilities with Delirium,'' Journal of the
American Geratrics Society, Vol. 53, No. 6, June 2005.
\96\ Cole MG, Ciampi A, Belzile E, Zhong L. Persistent delirium
in older hospital patients: A systematic review of frequency and
prognosis. Age Ageing 2009;38:19-26.
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Comment: Several commenters noted doubts about the usefulness of
the CAM. One commenter was unsure if CAM will identify differences in
cognitive status or measure changes during the stay resulting from
therapeutic interventions. A few commenters stated that the CAM would
not provide information that would be useful clinically, that it was
not specific enough or too narrowly focused, and that it should not be
required at discharge. Another commenter suggested that CMS not include
the CAM as SPADE because they believe delirium is clinically apparent,
and therefore, doubt that a standardized assessment of delirium will
contribute to improving patient care or outcomes. Another commenter
expressed concern that the CAM data elements would not identify
cognitive needs that would impact quality in therapeutic intervention
across facilities.
Response: As with any brief screening tool, we believe that the CAM
has value as a universal assessment to identify patients in need of
further clinical evaluation. Delirium occurs in up to 50 percent of
patients/residents in PAC \97\ and is associated with poor
outcomes.98 99 Hyperactive delirium--the type of delirium
that manifests with agitation--makes up only a quarter of delirium
cases.100 101 Delirium more commonly manifests as
hypoactive, or ``quiet'' delirium,\102\ suggesting that brief,
universal screening is appropriate. Moreover, because there are
treatments for delirium that can be developed based on medication
review, physical examination, laboratory tests, and evaluation of
environmental factors,\103\
[[Page 39119]]
we believe that screening for delirium would support care planning and
care transitions for these patients.
---------------------------------------------------------------------------
\97\ Kiely DK, Jones RN, Bergmann MA, Marcantonio ER.
Association between psychomotor activity delirium subtypes and
mortality among newly admitted post-acute facility patients. J
Gerontol A Biol Sci Med Sci 2007;62:174-179.
\98\ Marcantonio, Edward R., Samuel E. Simon, Margaret A.
Bergmann, Richard N. Jones, Katharine M. Murphy, and John N. Morris,
``Delirium Symptoms in Post-Acute Care: Prevalent, Persistent, and
Associated with Poor Functional Recovery,'' Journal of the American
Geriatrics Society, Vol. 51, No. 1, January 2003, pp. 4-9.
\99\ Edward R. Marcantonio et al., Outcomes of Older People
Admitted to Postacute Facilities with Delirium,'' Journal of the
American Geratrics Society, Vol. 53, No. 6, June 2005.
\100\ Inouye SK, Westendorp RG, Saczynski JS. Delirium in
elderly people. Lancet 2014;383:911-922.
\101\ Marcantonio ER. In the clinic: Delirium. Ann Intern Med
2011;154:ITC6-1-ITC6-1.
\102\ Yang FM, Marcantonio ER, Inouye SK, et al.
Phenomenological subtypes of delirium in older persons: Patterns,
prevalence, and prognosis. Psychosomatics 2009;50:248-254.
\103\ Marcantonio ER. Delirium in Hospitalized Older Adults. N
Engl J Med. 2017 Oct 12;377(15):1456-1466.
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Comment: A few commenters believe the CAM would be difficult to
administer and raised concerns about the training that staff would
receive in order to ensure that administration is consistent and valid.
Response: We appreciate the commenters' recommendation to provide
clear training for administering the CAM, and will take it into
consideration as we revise the current training for the IRF-PAI. We
intend to reinforce assessment tips and item rationale through
training, open door forums, and future rulemaking efforts.
Comment: One commenter disagreed that delirium assesses a dimension
of cognitive function.
Response: The CAM data elements were proposed to meet the
definition of the standardized patient assessment data with respect to
cognitive function and mental status. Section 1899B(b)(1)(B)(ii) of the
Act specifies that PAC providers shall be required to submit
standardized patient assessment data for the category of cognitive
function, such as the ability to express ideas and to understand, and
mental status, such as depression and dementia. A recent deterioration
in cognitive function or present and fluctuating behaviors of
inattention, disorganized thinking, or altered level of consciousness
may indicate delirium.\104\ Delirium can also be misdiagnosed as
dementia.\105\
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\104\ Inouye SK, van Dyck CH, Alessi CA, Balkin S, Siegal AP,
Horwitz RI. Clarifying confusion: The confusion assessment method. A
new method for detection of delirium. Ann Intern Med. 1990 Dec
15;113(12):941-8.
\105\ Marcantonio ER. Delirium in Hospitalized Older Adults. N
Engl J Med. 2017 Oct 12;377(15):1456-1466.
---------------------------------------------------------------------------
Comment: A commenter stated that CMS has not provided quantitative
evidence that the CAM data elements are capable of measuring provider
performance for quality or of differentiating case-mix for payment.
Response: The clinical SPADEs proposed in this rule, including CAM,
were the result of an extensive consensus vetting process. Over the
past several years, we have engaged experts and a wide range of
stakeholders through TEPs, Special Open Door Forums, and documents made
available on the CMS.gov website. A summary of the most recent TEP
meeting (September 17, 2018) titled ``SPADE Technical Expert Panel
Summary (Third Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Results of these activities provide evidence that experts
and providers believe that the proposed SPADEs, including the CAM data
elements, have the potential for measuring quality, describing case
mix, and improving care.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the CAM as standardized patient
assessment data beginning with the FY 2022 IRF QRP as proposed.
Patient Health Questionnaire-2 to 9 (PHQ-2 to 9)
In the FY 2020 IRF PPS proposed rule (84 FR 17296 through 17297),
we proposed that the Patient Health Questionnaire-2 to 9 (PHQ-2 to 9)
data elements meet the definition of standardized patient assessment
data with respect to cognitive function and mental status under section
1899B(b)(1)(B)(ii) of the Act. The proposed data elements are based on
the PHQ-2 mood interview, which focuses on only the two cardinal
symptoms of depression, and the longer PHQ-9 mood interview, which
assesses presence and frequency of nine signs and symptoms of
depression. The name of the data element, the PHQ-2 to 9, refers to an
embedded skip pattern that transitions patients with a threshold level
of symptoms in the PHQ-2 to the longer assessment of the PHQ-9. The
skip pattern is described further below. As described in the FY 2018
IRF PPS proposed rule (82 FR 20725 through 20726), depression is a
common and under-recognized mental health condition. Assessments of
depression help PAC providers better understand the needs of their
patients and residents by: Prompting further evaluation after
establishing a diagnosis of depression; elucidating the patient's or
resident's ability to participate in therapies for conditions other
than depression during their stay; and identifying appropriate ongoing
treatment and support needs at the time of discharge.
The proposed PHQ-2 to 9 is based on the PHQ-9 mood interview. The
PHQ-2 consists of questions about only the first two symptoms addressed
in the PHQ-9: Depressed mood and anhedonia (inability to pleasure),
which are the cardinal symptoms of depression. The PHQ-2 has performed
well as both a screening tool for identifying depression, to assess
depression severity, and to monitor patient mood over
time.106 107 If a patient demonstrates signs of depressed
mood and anhedonia under the PHQ-2, then the patient is administered
the lengthier PHQ-9. This skip pattern (also referred to as a gateway)
is designed to reduce the length of the interview assessment for
patients who fail to report the cardinal symptoms of depression. The
design of the PHQ-2 to 9 reduces the burden that would be associated
with requiring the full PHQ-9, while ensuring that patients and
residents with indications of depressive symptoms based on the PHQ-2
receive the longer assessment.
---------------------------------------------------------------------------
\106\ Li, C., Friedman, B., Conwell, Y., & Fiscella, K. (2007).
``Validity of the Patient Health Questionnaire 2 (PHQ-2) in
identifying major depression in older people.'' J of the A
Geriatrics Society, 55(4): 596-602.
\107\ L[ouml]we, B., Kroenke, K., & Gr[auml]fe, K. (2005).
``Detecting and monitoring depression with a two-item questionnaire
(PHQ-2).'' J of Psychosomatic Research, 58(2): 163-171.
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Components of the proposed data elements are currently used in the
OASIS for HHAs (PHQ-2) and the MDS for SNFs (PHQ-9). For more
information on the PHQ-2 to 9, we refer readers to the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We proposed the PHQ-2 data elements as SPADEs in the FY 2018 IRF
proposed rule (82 FR 20725 through 20726). In that proposed rule, we
stated that the proposal was informed by input we received from the TEP
convened by our data element contractor on April 6 and 7, 2016. The TEP
members particularly noted that the brevity of the PHQ-2 made it
feasible to administer with low burden for both assessors and PAC
patients or residents. A summary of the April 6 and 7, 2016 TEP meeting
titled ``SPADE Technical Expert Panel Summary (First Convening)'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The rule proposal was also informed by public input that we
received through a call for input published on the CMS Measures
Management System Blueprint website. Input was submitted from August 12
to September 12, 2016 on three versions of the PHQ depression screener:
The PHQ-2; the PHQ-9; and
[[Page 39120]]
the PHQ-2 to 9 with the skip pattern design. Many commenters were
supportive of the standardized assessment of mood in PAC settings,
given the role that depression plays in well-being. Several commenters
noted support for an approach that would use PHQ-2 as a gateway to the
longer PHQ-9 while still potentially reducing burden on most patients
and residents, as well as test administrators, and ensuring the
administration of the PHQ-9, which exhibits higher specificity,\108\
for patients and residents who showed signs and symptoms of depression
on the PHQ-2. A summary report for the August 12 to September 12, 2016
public comment period titled ``SPADE August 2016 Public Comment Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------
\108\ Arroll B, Goodyear-Smith F, Crengle S, Gunn J, Kerse N,
Fishman T, et al. Validation of PHQ-2 and PHQ-9 to screen for major
depression in the primary care population. Annals of family
medicine. 2010;8(4):348-53. doi: 10.1370/afm.1139 pmid:20644190;
PubMed Central PMCID: PMC2906530.
---------------------------------------------------------------------------
In response to our proposal to use the PHQ-2 in the FY 2018 IRF PPS
proposed rule (82 FR 20725 through 20726), we received comments
agreeing to the importance of a standardized assessment of depression
in patients and residents receiving PAC services. Commenters also
raised concerns about the ability of the PHQ-2 to correctly identify
all patients and residents with signs and symptoms of depression. One
commenter supported using the PHQ-2 as a gateway assessment and
conducting a more thorough evaluation of depression symptoms with the
PHQ-9 if the PHQ-2 is positive. Another commenter expressed concern
that standardized assessment of signs and symptoms of depression via
the PHQ-2 is not appropriate in the IRF setting, as patients may have
recently experienced acute illness or injury, and routine screening may
lead to overprescribing of antidepressant medications. Another
commenter expressed concern about potential conflicts between the
results of screening assessments and documented diagnoses based on the
expertise of physicians and other clinicians. In response to these
comments, we carried out additional testing, and we provide our
findings below.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
PHQ-2 to 9 was included in the National Beta Test of candidate data
elements conducted by our data element contractor from November 2017 to
August 2018. Results of this test found the PHQ-2 to 9 to be feasible
and reliable for use with PAC patients and residents. More information
about the performance of the PHQ-2 to 9 in the National Beta Test can
be found in the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the PHQ-2 to
9. The TEP was supportive of the PHQ-2 to 9 data element set as a
screener for signs and symptoms of depression. The TEP's discussion
noted that symptoms evaluated by the full PHQ-9 (for example,
concentration, sleep, appetite) had relevance to care planning and the
overall well-being of the patient or resident, but that the gateway
approach of the PHQ-2 to 9 would be appropriate as a depression
screening assessment, as it depends on the well-validated PHQ-2 and
focuses on the cardinal symptoms of depression. A summary of the
September 17, 2018 TEP meeting titled ``SPADE Technical Expert Panel
Summary (Third Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our on-going SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for depression,
stakeholder input, and test results, we proposed that the PHQ-2 to 9
data elements meet the definition of standardized patient assessment
data with respect to cognitive function and mental status under section
1899B(b)(1)(B)(ii) of the Act and to adopt the PHQ-2 to 9 data elements
as standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the PHQ-2 to 9 data elements.
Comment: Some commenters supported the inclusion of the PHQ-2 to 9.
One of these commenters was particularly supportive of the use of the
2-item gateway in the PHQ-2 to 9 approach to improve efficiency.
Response: We thank the commenters for their support of the PHQ-2 to
9, including the gateway approach as a way to decrease burden for
providers and patients.
Comment: One commenter was unsure if PHQ-2 to 9 will identify
differences in cognitive status or measure changes during the stay
resulting from therapeutic interventions. Another commenter expressed
concern that the PHQ-2 to 9 data elements would not identify cognitive
needs that would impact quality in therapeutic intervention across
facilities.
Response: As with any brief screening tool, we believe that the
PHQ-2 to 9 has value as a universal assessment to identify patients in
need of further clinical evaluation. We believe that applying a brief,
standardized assessment of depression across PAC settings, including
IRFs, will improve detection based on the PHQ-2 to 9 interview. A
universal depression screening is expected to improve patient outcomes
by increasing the likelihood that depression will be identified and
treated in IRF patients. The proposal of the PHQ-2 to 9 was the result
of an extensive consensus vetting process in which experts and
stakeholders were engaged through TEPs, SODFs, and posting of interim
reports and other documents on CMS.gov. These experts and stakeholders
were supportive of the clinical usefulness of the PHQ-2 to 9
assessment. A summary of the most recent TEP meeting (September 17,
2018) titled ``SPADE Technical Expert Panel Summary (Third Convening)''
is available at https://www.cms.gov/Medicare/Quality-Initiatives-
Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/
IMPACT-Act-of-
[[Page 39121]]
2014/IMPACT-Act-Downloads-and-Videos.html.
Comment: A few commenters raised concerns about administration of
the PHQ-2 to 9 to IRF patients. One commenter noted that patients in
acute rehabilitation may have limited attention and working memory that
affects their ability to complete the PHQ-2 to 9. Another commenter
noted doubts that PHQ-9 is a good tool for IRFs because of the
likelihood of false positives, given patients who are adjusting to
recent injuries, surgeries, conditions, and various disabilities.
Rather, the commenter believes that assessment by rehabilitation
psychologists, who have specialty training in working with
rehabilitation populations, would provide a comprehensive evaluation
and informed treatment plan. Another commenter expressed concerns about
the use of the PHQ in short-stay IRF patients, suggesting that being
assessed for depression, especially if assessed multiple times, will
affect the patient's perception of how they should be experiencing
their situation.
Response: We recognize the challenges faced by patients receiving
care from IRF providers. We believe that the PHQ-2 to 9 is the most
accurate and appropriate depression screening for the PAC population,
including patients in IRFs, and that assessing for depression is
necessary for high-quality clinical care. As stated in our proposal
above, the PHQ-2 has performed well as a screening tool for identifying
depression, to assess depression severity, and to monitor patient mood
over time.\109\ \110\ Additionally, the PHQ-2 and PHQ-9 instruments
have been validated in primary care populations against a gold standard
diagnostic interview.\111\ We believe this prior validation research
generalizes to the IRF population. We also note that, regardless of the
LOS of patients, the timeframe over which they may have been
experiencing signs and symptoms of depression, and the types of
circumstances that have led to their IRF stay, it is the responsibility
of the IRF to deliver high quality care for all the symptoms or
conditions a patient may have. The expectation that the episode of care
will be short does not exempt an IRF from screening and treating
patients for the full range of physical and mental health problems.
Similarly, if a patient self-reports a significant number of depressive
symptoms, we do not believe that they should be considered to be a
``false positive'' because of, for example, a recent trauma or acute
care stay. As a screening tool, the PHQ-2 to 9 is intended to capture
likely depression to have those patients referred for further
evaluation, which will ascertain if their condition is consistent with
the full diagnostic criteria for a major depressive disorder. Moreover,
standardized screening for the signs and symptoms of depression with
the PHQ-2 to 9 does not preclude or provide a substitute for assessment
by rehabilitation psychologist or other clinicians, as deemed
appropriate by a patient's care team.
---------------------------------------------------------------------------
\109\ Li, C., Friedman, B., Conwell, Y., & Fiscella, K. (2007).
``Validity of the Patient Health Questionnaire 2 (PHQ-2) in
identifying major depression in older people.'' J of the A
Geriatrics Society, 55(4): 596-602.
\110\ L[ouml]we, B., Kroenke, K., & Gr[auml]fe, K. (2005).
``Detecting and monitoring depression with a two-item questionnaire
(PHQ-2).'' J of Psychosomatic Research, 58(2): 163-171.
\111\ Arroll B, Goodyear-Smith F, Crengle S, Gunn J, Kerse N,
Fishman T, et al. Validation of PHQ-2 and PHQ-9 to screen for major
depression in the primary care population. Annals of family
medicine. 2010;8(4):348-353.
---------------------------------------------------------------------------
Comment: Several commenters cited concerns related to the findings
from the National Beta Test related to the PHQ-2 to 9, namely, that
testing found it to be burdensome for staff and patients and the
wording difficult to understand.
Response: We acknowledge that some assessors in the National Beta
Test noted concerns regarding the burden of the PHQ-2 to 9 for staff
and patients and that the wording of some items was challenging for
patients to understand. In the National Beta Test, the PHQ-2 to 9 was
one of a collection of mood assessments, meaning that assessors and
patients completed additional questions about depressed mood and well-
being immediately before and after the PHQ-2 to 9. We believe that the
perception of burden of the PHQ-2 to 9 was in part due to the larger
mood assessment section included in the National Beta Test. Despite the
burden and administration challenges noted by National Beta Test
assessors, assessors generally appreciated the clinical utility and
relevance of the PHQ-2 to 9 and noted the importance of standardizing
the assessment of depressive symptoms.
Comment: Additional concerns about administration focused on the
patient interview format of the PHQ-2 to 9. Some commenters raised
concerns about administering the PHQ-2 to 9 to patients with severe
cognitive deficits, prior mental health issues, or non-communicative
conditions. One commenter suggested that CMS develop exemptions from
repeated screenings for short stay patients, and for patients whose
medical or cognitive status make it inappropriate to administer the
PHQ-2 to 9. Another commenter suggested that the PHQ-2 to 9 have an
option to be self-administered by the patient via a patient-friendly
paper and pencil layout, which would reduce time burden placed on
assessors.
Response: We appreciate commenters' concerns that administering the
PHQ-2 to 9 to patients whose medical or cognitive status make it
inappropriate to administer. The guidance for completing the data
elements will include instructions that if the patient is rarely or
never understood verbally, in writing, or using another method, the
PHQ-2 to 9 interview will not be completed and the assessor code the
responses to the first two items (Little interest or pleasure in doing
things; Feeling down, depressed, or hopeless) as 9 (no response). We
will take the suggestion to explore the possibility for patient self-
administration of the PHQ-2 to 9 into consideration in future SPADE
development work.
Comment: One commenter noted confusion about how depression relates
to cognitive function.
Response: Section 1899(b)(1)(B)(ii) of the Act specifies the
category of ``cognitive function, such as ability to express ideas and
to understand, and mental status, such as depression and dementia.'' We
proposed the PHQ-2 to 9 data elements to meet the definition of the
standardized patient assessment data with respect to cognitive function
and mental status, particularly the ``mental status'' topic within that
category.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the PHQ-2 to 9 data elements as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
2. Special Services, Treatments, and Interventions Data
Special services, treatments, and interventions performed in PAC
can have a major effect on an individual's health status, self-image,
and quality of life. The assessment of these special services,
treatments, and interventions in PAC is important to ensure the
continuing appropriateness of care for the patients and residents
receiving them, and to support care transitions from one PAC provider
to another, an acute care hospital, or discharge. In alignment with our
Meaningful Measures Initiative, accurate assessment of special
services, treatments, and interventions of patients and residents
served by PAC providers is expected to make care safer by reducing harm
caused in the delivery of care; promote effective prevention and
treatment of chronic disease; strengthen person and
[[Page 39122]]
family engagement as partners in their care; and promote effective
communication and coordination of care.
For example, standardized assessment of special services,
treatments, and interventions used in PAC can promote patient and
resident safety through appropriate care planning (for example,
mitigating risks such as infection or pulmonary embolism associated
with central intravenous access), and identifying life-sustaining
treatments that must be continued, such as mechanical ventilation,
dialysis, suctioning, and chemotherapy, at the time of discharge or
transfer. Standardized assessment of these data elements will enable or
support: Clinical decision-making and early clinical intervention;
person-centered, high quality care through, for example, facilitating
better care continuity and coordination; better data exchange and
interoperability between settings; and longitudinal outcome analysis.
Therefore, reliable data elements assessing special services,
treatments, and interventions are needed to initiate a management
program that can optimize a patient's or resident's prognosis and
reduce the possibility of adverse events.
A TEP convened by our data element contractor provided input on the
proposed data elements for special services, treatments, and
interventions. In a meeting held on January 5 and 6, 2017, this TEP
found that these data elements are appropriate for standardization
because they would provide useful clinical information to inform care
planning and care coordination. The TEP affirmed that assessment of
these services and interventions is standard clinical practice, and
that the collection of these data by means of a list and checkbox
format would conform with common workflow for PAC providers. A summary
of the January 5 and 6, 2017 TEP meeting titled ``SPADE Technical
Expert Panel Summary (Second Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Comments on the category of special services, treatments, and
interventions were also submitted by stakeholders during the FY 2018
IRF PPS proposed rule (82 FR 20726 through 20736) public comment
period. One commenter supported adding the SPADEs for special services,
treatments, and interventions. Others stated labor costs and staff
burden would increase for data collection. The Medicare Payment
Advisory Commission (MedPAC) suggested that a few other high-cost
services, such as cardiac monitoring and specialty bed/surfaces, may
warrant consideration for inclusion in future collection efforts. One
commenter believes that the low frequency of the special services,
treatments, and interventions in the IRF setting makes them not worth
assessing for patients given the cost of data collection and reporting.
A few commenters noted that many of these data elements should be
obtainable from administrative data (that is, coding and Medicare
claims), and therefore, assessing them through patient record review
would be duplicated effort.
Information on data element performance in the National Beta Test,
which collected data between November 2017 and August 2018, is reported
within each data element proposal below. Clinical staff who
participated in the National Beta Test supported these data elements
because of their importance in conveying patient or resident
significant health care needs, complexity, and progress. However,
clinical staff also noted that, despite the simple ``check box'' format
of these data element, they sometimes needed to consult multiple
information sources to determine a patient's or resident's treatments.
We sought comment on our proposals to collect as standardized
patient assessment data the following data with respect to special
services, treatments, and interventions.
Commenters submitted the following comments related to the proposed
rule's discussion of special services, treatments, and interventions
data elements.
Comment: One commenter was supportive of collecting these data
elements, noting that collection will help to better inform CMS and IRF
providers on the severity and needs of patients in this setting.
Response: We thank the commenter for the support of these items. We
selected the Special Services, Treatments, and Interventions data
elements for proposal as standardized data in part because of the
attributes noted.
Comment: Some commenters were concerned about the reliability of
the Special Services, Treatments, and Interventions data elements,
noting that the results of the National Beta Test indicated that these
data elements had a low interrater reliability kappa statistic relative
to other data elements in the test.
Response: In the category of Special Services, Treatments, and
Interventions, for SPADEs where kappas could be calculated, 1 data
element and 2 sub-elements demonstrated overall reliabilities in the
moderate range (0.41-0.60) and only 1 sub-element demonstrated an
overall reliability in the slight/poor range (0.00-0.20). These overall
reliabilities were as follows: 0.60 for the Therapeutic Diet data
element; 0.55 for the ``Continuous'' sub-element of Oxygen Therapy;
0.46 for the ``Other'' sub-element of IV Medications; and 0.13 for the
``Anticoagulant'' sub-element of IV Medications. However, the overall
reliabilities for all other data elements and sub-elements where kappas
could be calculated were substantial/good or excellent/almost perfect.
When looking at percent agreement--an alternative measure of interrater
agreement--values of overall percent agreement for all Special
Services, Treatments, and Interventions SPADEs and sub-elements ranged
from 80 to 100 percent.
Comment: Commenters also noted concern around the burden of
completing these data elements, in particular because of their low
frequency of occurrence in IRF settings. To reduce burden around
collection of this information, commenters recommended that CMS explore
obtaining this data via claims. Additionally, one commenter added that
if these data elements are finalized, they should be collected at
discharge only, to reduce administrative burden.
Response: We appreciate the commenters' concern for burden on
clinical staff due to completing assessments with respect to both
admission and discharge. We believe that assessment of various special
services, treatments, and interventions received by patients in the IRF
setting will provide important information for care planning and
resource use in IRFs. The assessments of the special services,
treatments, and interventions with multiple responses are formatted as
a ``check all that apply'' format. Therefore, when treatments do not
apply--as the commenters note, this is the case for many IRF patients--
the assessor need only check one row for ``None of the Above.'' We will
take under consideration the commenters' recommendation to explore the
feasibility of collecting information on special services, treatments,
and interventions through claims-based data. Regarding the
recommendation to collect these SPADEs at discharge only, we state that
it is clinically appropriate and important to the ultimate usefulness
of these SPADEs that they are collected with respect to both admission
and
[[Page 39123]]
discharge. For example, for patients coming from acute care or from the
community, the admission assessment establishes a baseline for the IRF
stay. For all patients, the admission assessment ensures that each
patient is systematically assessed for a broad range of health and
well-being issues, which we expect to inform care planning.
Comment: One commenter expressed concern that the Special Services,
Treatments, and Interventions data elements assess the presence or
absence of something rather than the clinical rationale or patient
outcomes. This commenter stressed the importance of bringing this
assessment to ``the next level'' in order to determine impact of these
treatments on patients' outcomes.
Response: We agree with commenter's concern that recording the
presence or absence of certain treatments is only a first step in
characterizing the complexity that is often the cause of a patient's
receipt of special services, treatments, and interventions. We clarify
that all the SPADEs we proposed were intended as a minimum assessment
and do not limit the ability of providers to conduct a more
comprehensive evaluation of a patient's situation to identify the
potential impacts on outcomes that the commenter describes.
Comment: One commenter noted that the item numbering in the Special
Services, Treatments, and Interventions data elements is extremely
confusing and needs to be reworked.
Response: Several patient assessment tools have traditionally
combined letters and numbers, along with labels, to distinguish between
data elements. The proposed data elements in the Special Services,
Treatments, and Interventions section follow the conventions
established by CMS. However, we will take this feedback into
consideration in our evaluation and refinement of patient assessment
instruments.
Final decisions on the SPADEs are given below, following more
detailed comments on each SPADE proposal.
Cancer Treatment: Chemotherapy (IV, Oral, Other)
In the FY 2020 IRF PPS proposed rule (84 FR 17297 through 17299),
we proposed that the Chemotherapy (IV, Oral, Other) data element meets
the definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20726
through 20727), chemotherapy is a type of cancer treatment that uses
drugs to destroy cancer cells. It is sometimes used when a patient has
a malignancy (cancer), which is a serious, often life-threatening or
life-limiting condition. Both intravenous (IV) and oral chemotherapy
have serious side effects, including nausea/vomiting, extreme fatigue,
risk of infection due to a suppressed immune system, anemia, and an
increased risk of bleeding due to low platelet counts. Oral
chemotherapy can be as potent as chemotherapy given by IV and can be
significantly more convenient and less resource-intensive to
administer. Because of the toxicity of these agents, special care must
be exercised in handling and transporting chemotherapy drugs. IV
chemotherapy is administered either peripherally, or more commonly,
given via an indwelling central line, which raises the risk of
bloodstream infections. Given the significant burden of malignancy, the
resource intensity of administering chemotherapy, and the side effects
and potential complications of these highly-toxic medications,
assessing the receipt of chemotherapy is important in the PAC setting
for care planning and determining resource use. The need for
chemotherapy predicts resource intensity, both because of the
complexity of administering these potent, toxic drug combinations under
specific protocols, and because of what the need for chemotherapy
signals about the patient's underlying medical condition. Furthermore,
the resource intensity of IV chemotherapy is higher than for oral
chemotherapy, as the protocols for administration and the care of the
central line (if present) for IV chemotherapy require significant
resources.
The Chemotherapy (IV, Oral, Other) data element consists of a
principal data element (Chemotherapy) and three response option sub-
elements: IV chemotherapy, which is generally resource-intensive; Oral
chemotherapy, which is less invasive and generally requires less
intensive administration protocols; and a third category, Other,
provided to enable the capture of other less common chemotherapeutic
approaches. This third category is potentially associated with higher
risks and is more resource intensive due to delivery by other routes
(for example, intraventricular or intrathecal). If the assessor
indicates that the patient is receiving chemotherapy on the principal
Chemotherapy data element, the assessor would then indicate by which
route or routes (for example, IV, Oral, Other) the chemotherapy is
administered.
A single Chemotherapy data element that does not include the
proposed three sub-elements is currently in use in the MDS in SNFs. For
more information on the Chemotherapy (IV, Oral, Other) data element, we
refer readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Chemotherapy data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20726 through 20727). In that proposed rule, we stated that the
proposal was informed by input we received through a call for input
published on the CMS Measures Management System Blueprint website.
Input submitted from August 12 to September 12, 2016 noted support for
the IV Chemotherapy data element and suggested it be included as
standardized patient assessment data. We also stated that those
commenters had noted that assessing the use of chemotherapy services is
relevant to share across the care continuum to facilitate care
coordination and care transitions and noted the validity of the data
element. Commenters also noted the importance of capturing all types of
chemotherapy, regardless of route, and stated that collecting data only
on patients and residents who received chemotherapy by IV would limit
the usefulness of this standardized data element. A summary report for
the August 12 to September 12, 2016 public comment period titled
``SPADE August 2016 Public Comment Summary Report'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Chemotherapy data
element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Chemotherapy data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results
[[Page 39124]]
of this test found the Chemotherapy data element to be feasible and
reliable for use with PAC patients and residents. More information
about the performance of the Chemotherapy data element in the National
Beta Test can be found in the document titled ``Final Specifications
for IRF QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP members
did not specifically discuss the Chemotherapy data element, the TEP
members supported the assessment of the special services, treatments,
and interventions included in the National Beta Test with respect to
both admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for chemotherapy,
stakeholder input, and strong test results, we proposed that the
Chemotherapy (IV, Oral, Other) data element with a principal data
element and three sub-elements meet the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Chemotherapy (IV, Oral, Other) data element as standardized
patient assessment data for use in the IRF QRP.
A commenter submitted the following comment related to the proposed
rule's discussion of the Chemotherapy data element.
Comment: One commenter agreed that it is important to know if a
patient is receiving chemotherapy for cancer and the method of
administration, but also expressed concern about the lack of an
association with a patient outcome. This commenter noted that
implications of chemotherapy for patients needing speech-language
pathology services include chemotherapy-related cognitive impairment,
dysphagia, and speech- and voice-related deficits.
Response: We appreciate the commenter's concern. We agree with the
commenter that chemotherapy can create related treatment needs for
patients, such as the examples noted by the commenter. However, we
believe that it is not feasible for SPADEs to capture all of a
patient's needs related to any given treatment, and we maintain that
the Special Services, Treatments, and Interventions SPADEs provide a
common foundation of clinical assessment, which can be built on by the
individual provider or a patient's care team.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Chemotherapy (IV, Oral, Other)
data element as standardized patient assessment data beginning with the
FY 2022 IRF QRP as proposed.
Cancer Treatment: Radiation
In the FY 2020 IRF PPS proposed rule (84 FR 17299), we proposed
that the Radiation data element meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20727
through 20728), radiation is a type of cancer treatment that uses high-
energy radioactivity to stop cancer by damaging cancer cell DNA, but it
can also damage normal cells. Radiation is an important therapy for
particular types of cancer, and the resource utilization is high, with
frequent radiation sessions required, often daily for a period of
several weeks. Assessing whether a patient or resident is receiving
radiation therapy is important to determine resource utilization
because PAC patients and residents will need to be transported to and
from radiation treatments, and monitored and treated for side effects
after receiving this intervention. Therefore, assessing the receipt of
radiation therapy, which would compete with other care processes given
the time burden, would be important for care planning and care
coordination by PAC providers.
The proposed data element consists of the single Radiation data
element. The Radiation data element is currently in use in the MDS in
SNFs. For more information on the Radiation data element, we refer
readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Radiation data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20727 through 20728). In that proposed rule, we stated that the
proposal was informed by input we received through a call for input
published on the CMS Measures Management System Blueprint website.
Input submitted from August 12 to September 12, 2016 noted support for
the Radiation data element, noting its importance and clinical
usefulness for patients and residents in PAC settings, due to the side
effects and consequences of radiation treatment on patients and
residents that need to be considered in care planning and care
transitions, the feasibility of the item, and the potential for it to
improve quality. A summary report for the August 12 to September 12,
2016 public comment period titled ``SPADE August 2016 Public Comment
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received
[[Page 39125]]
that were specific to the Radiation data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Radiation data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Radiation
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Radiation data
element in the National Beta Test can be found in the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP members
did not specifically discuss the Radiation data element, the TEP
members supported the assessment of the special services, treatments,
and interventions included in the National Beta Test with respect to
both admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present results of the National Beta Test and solicit
additional comments. General input on the testing and item development
process and concerns about burden were received from stakeholders
during this meeting and via email through February 1, 2019. A summary
of the public input received from the November 27, 2018 stakeholder
meeting titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for radiation,
stakeholder input, and strong test results, we proposed that the
Radiation data element meets the definition of standardized patient
assessment data with respect to special services, treatments, and
interventions under section 1899B(b)(1)(B)(iii) of the Act and to adopt
the Radiation data element as standardized patient assessment data for
use in the IRF QRP.
A commenter submitted the following comment related to the proposed
rule's discussion of the Radiation data element.
Comment: One commenter expressed concern that the Radiation data
element assesses whether a patient is receiving radiation for cancer
treatment, but does not identify the rationale for and outcomes
associated with radiation. The commenter noted that implications of
radiation for patients needing speech-language pathology services
include reduced head and neck range of motion due to radiation or
severe fibrosis, scar bands, and reconstructive surgery complications
and that these can impact both communication and swallowing abilities.
Response: We appreciate the commenter's concern. We agree with the
commenter that radiation can create related treatment needs for
patients, such as the examples noted by the commenter. However, we
believe that it is not feasible for SPADEs to capture all of a
patient's needs related to any given treatment, and we maintain that
the Special Services, Treatments, and Interventions SPADEs provide a
common foundation of clinical assessment, which can be built on by the
individual provider or a patient's care team.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Radiation data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Respiratory Treatment: Oxygen Therapy (Intermittent,
Continuous, High-concentration Oxygen Delivery System)
In the FY 2020 IRF PPS proposed rule (84 FR 17299 through 17300),
we proposed that the Oxygen Therapy (Intermittent, Continuous, High-
concentration Oxygen Delivery System) data element meets the definition
of standardized patient assessment data with respect to special
services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20728), we
proposed a similar data element related to oxygen therapy. Oxygen
therapy provides a patient or resident with extra oxygen when medical
conditions such as chronic obstructive pulmonary disease, pneumonia, or
severe asthma prevent the patient or resident from getting enough
oxygen from breathing. Oxygen administration is a resource-intensive
intervention, as it requires specialized equipment such as a source of
oxygen, delivery systems (for example, oxygen concentrator, liquid
oxygen containers, and high-pressure systems), the patient interface
(for example, nasal cannula or mask), and other accessories (for
example, regulators, filters, tubing). The data element proposed here
captures patient or resident use of three types of oxygen therapy
(intermittent, continuous, and high-concentration oxygen delivery
system), which reflects the intensity of care needed, including the
level of monitoring and bedside care required. Assessing the receipt of
this service is important for care planning and resource use for PAC
providers.
The proposed data element, Oxygen Therapy, consists of the
principal Oxygen Therapy data element and three response option sub-
elements: Continuous (whether the oxygen was delivered continuously,
typically defined as > =14 hours per day); Intermittent; or High-
concentration Oxygen Delivery System. Based on public comments and
input from expert advisors about the importance and clinical usefulness
of documenting the extent of oxygen use, we added a third sub-element,
high-concentration oxygen delivery system, to the sub-elements, which
previously included only intermittent and continuous. If the assessor
indicates that the patient is receiving oxygen therapy on the principal
oxygen therapy data element, the assessor then would indicate the type
of oxygen the patient receives (for example, Intermittent, Continuous,
High-concentration oxygen delivery system).
These three proposed sub-elements were developed based on similar
data elements that assess oxygen therapy, currently in use in the MDS
in SNFs (``Oxygen Therapy''), previously used in the OASIS (``Oxygen
(intermittent or continuous)''), and a data element tested in the PAC
PRD that focused on intensive oxygen therapy (``High O2 Concentration
Delivery System with FiO2 > 40 percent''). For more information on the
proposed Oxygen
[[Page 39126]]
Therapy (Continuous, Intermittent, High-concentration oxygen delivery
system) data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Oxygen Therapy (Intermittent, Continuous) data element was
first proposed as standardized patient assessment data in the FY 2018
IRF PPS proposed rule (82 FR 20728). In that proposed rule, we stated
that the proposal was informed by input we received on the single data
element, Oxygen (inclusive of intermittent and continuous oxygen use),
through a call for input published on the CMS Measures Management
System Blueprint website. Input submitted from August 12 to September
12, 2016, noted the importance of the Oxygen data element, noting
feasibility of this item in PAC, and the relevance of it to
facilitating care coordination and supporting care transitions, but
suggesting that the extent of oxygen use be documented. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Oxygen Therapy
(Intermittent, Continuous) data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Oxygen Therapy data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Oxygen
Therapy data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
Oxygen Therapy data element in the National Beta Test can be found in
the document titled ``Final Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Oxygen Therapy data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing oxygen therapy,
stakeholder input, and strong test results, we proposed that the Oxygen
Therapy (Intermittent, Continuous, High-concentration Oxygen Delivery
System) data element with a principal data element and three sub-
elements meets the definition of standardized patient assessment data
with respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and to adopt the Oxygen Therapy
(Intermittent, Continuous, High-concentration Oxygen Delivery System)
data element as standardized patient assessment data for use in the IRF
QRP.
We invited public comment on this proposal. While we received
support from some commenters on the Special Services, Treatments, and
Interventions section (IX.G.2 in this final rule) and its proposals as
a whole (section IX.F in this final rule), we did not receive any
specific comments on the Oxygen Therapy (Intermittent, Continuous,
High-concentration Oxygen Delivery System) data element in particular.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Oxygen Therapy (Intermittent,
Continuous, High-Concentration Oxygen Delivery System) data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Respiratory Treatment: Suctioning (Scheduled, as Needed)
In the FY 2020 IRF PPS proposed rule (84 FR 17300 through 17302),
we proposed that the Suctioning (Scheduled, As needed) data element
meets the definition of standardized patient assessment data with
respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20728
through 20729), suctioning is a process used to clear secretions from
the airway when a person cannot clear those secretions on his or her
own. It is done by aspirating secretions through a catheter connected
to a suction source. Types of suctioning include oropharyngeal and
nasopharyngeal suctioning, nasotracheal suctioning, and suctioning
through an artificial airway such as a tracheostomy tube. Oropharyngeal
and nasopharyngeal suctioning are a key part of many patients' or
residents' care plans, both to prevent the accumulation of secretions
than can lead to aspiration pneumonias (a common condition in patients
and residents with inadequate gag reflexes), and to relieve
obstructions from mucus plugging during an acute or chronic respiratory
infection, which often lead to desaturations and increased respiratory
effort. Suctioning can be done on a scheduled basis if the patient is
judged to clinically benefit from regular interventions, or can be done
as needed when secretions become so prominent that gurgling or choking
is noted, or a sudden desaturation occurs from a mucus plug. As
suctioning is generally performed by a care provider
[[Page 39127]]
rather than independently, this intervention can be quite resource
intensive if it occurs every hour, for example, rather than once a
shift. It also signifies an underlying medical condition that prevents
the patient from clearing his/her secretions effectively (such as after
a stroke, or during an acute respiratory infection). Generally,
suctioning is necessary to ensure that the airway is clear of
secretions which can inhibit successful oxygenation of the individual.
The intent of suctioning is to maintain a patent airway, the loss of
which can lead to death or complications associated with hypoxia.
The Suctioning (Scheduled, As needed) data element consists of a
principal data element, and two sub-elements: Scheduled and As needed.
These sub-elements capture two types of suctioning. Scheduled indicates
suctioning based on a specific frequency, such as every hour. As needed
means suctioning only when indicated. If the assessor indicates that
the patient is receiving suctioning on the principal Suctioning data
element, the assessor would then indicate the frequency (for example,
Scheduled, As needed). The proposed data element is based on an item
currently in use in the MDS in SNFs which does not include our proposed
two sub-elements, as well as data elements tested in the PAC PRD that
focused on the frequency of suctioning required for patients and
residents with tracheostomies (``Trach Tube with Suctioning: Specify
most intensive frequency of suctioning during stay [Every __hours]'').
For more information on the Suctioning data element, we refer readers
to the document titled ``Final Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Suctioning data element was first proposed as standardized
patient assessment data elements in the FY 2018 IRF PPS proposed rule
(82 FR 20728 through 20729). In that proposed rule, we stated that the
proposal was informed by input we received through a call for input
published on the CMS Measures Management System Blueprint website.
Input submitted from August 12 to September 12, 2016 noted support for
the Suctioning data element. The input noted the feasibility of this
item in PAC, and the relevance of this data element to facilitating
care coordination and supporting care transitions.
We also stated that those commenters had suggested that we examine
the frequency of suctioning to better understand the use of staff time,
the impact on a patient or resident's capacity to speak and swallow,
and intensity of care required. Based on these comments, we decided to
add two sub-elements (Scheduled and As needed) to the suctioning
element. The proposed Suctioning data element includes both the
principal Suctioning data element that is included on the MDS in SNFs
and two sub-elements, Scheduled and As needed. A summary report for the
August 12 to September 12, 2016 public comment period titled ``SPADE
August 2016 Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Suctioning data
element. Subsequent to receiving comments on the FY 2018 IRF PPS rule,
the Suctioning data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Suctioning
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Suctioning
data element in the National Beta Test can be found in the document
titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Suctioning data element, the TEP supported the
assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicited additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for suctioning,
stakeholder input, and strong test results, we proposed that the
Suctioning (Scheduled, As needed) data element with a principal data
element and two sub-elements meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Suctioning (Scheduled, As needed) data element as
standardized patient assessment data for use in the IRF QRP.
A commenter submitted the following comment related to the proposed
rule's discussion of the Suctioning data element.
Comment: One commenter requested that this data element also assess
the frequency of suctioning, as it can impact resource utilization and
potential medication changes in the plan of care.
Response: We appreciate the commenter's feedback that the response
options for this data element may not fully capture impacts to resource
utilization and care plans. The Suctioning data element does include
sub-elements to identify if suctioning is performed on a ``Scheduled''
or ``As Needed'' basis, but it does not directly
[[Page 39128]]
assess the frequency of suctioning by, for example, asking an assessor
to specify how often suctioning is scheduled. As finalized, this data
element differentiates between patients who only occasionally need
suctioning, and patients for whom assessment of suctioning needs is a
frequent and routine part of the care (that is, where suctioning is
performed on a schedule according to physician instructions). In our
work to identify standardized data elements, we have strived to balance
the scope and level of detail of the data elements against the
potential burden placed on patients and providers. However, we clarify
that any SPADE is intended as a minimum assessment and does not limit
the ability of providers to conduct a more comprehensive evaluation of
a patient's situation to identify the potential impacts on outcomes
that the commenter describes.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Suctioning (Scheduled, As
needed) data element as standardized patient assessment data beginning
with the FY 2022 IRF QRP as proposed.
Respiratory Treatment: Tracheostomy Care
In the FY 2020 IRF PPS proposed rule (84 FR 17302), we proposed
that the Tracheostomy Care data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20729
through 20730), a tracheostomy provides an air passage to help a
patient or resident breathe when the usual route for breathing is
obstructed or impaired. Generally, in all of these cases, suctioning is
necessary to ensure that the tracheostomy is clear of secretions, which
can inhibit successful oxygenation of the individual. Often,
individuals with tracheostomies are also receiving supplemental
oxygenation. The presence of a tracheostomy, albeit permanent or
temporary, warrants careful monitoring and immediate intervention if
the tracheostomy becomes occluded or if the device used becomes
dislodged. While in rare cases the presence of a tracheostomy is not
associated with increased care demands (and in some of those instances,
the care of the ostomy is performed by the patient) in general the
presence of such as device is associated with increased patient risk,
and clinical care services will necessarily include close monitoring to
ensure that no life-threatening events occur as a result of the
tracheostomy. In addition, tracheostomy care, which primarily consists
of cleansing, dressing changes, and replacement of the tracheostomy
cannula (tube), is a critical part of the care plan. Regular cleansing
is important to prevent infection, such as pneumonia, and to prevent
any occlusions with which there are risks for inadequate oxygenation.
The proposed data element consists of the single Tracheostomy Care
data element. The proposed data element is currently in use in the MDS
in SNFs (``Tracheostomy care''). For more information on the
Tracheostomy Care data element, we refer readers to the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Tracheostomy Care data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20729 through 20730). In that proposed rule, we
stated that the proposal was informed by input we received on the
Tracheostomy Care data element through a call for input published on
the CMS Measures Management System Blueprint website. Input submitted
from August 12 to September 12, 2016 noted support for this data
element, noting the feasibility of this item in PAC, and the relevance
of this data element to facilitating care coordination and supporting
care transitions. A summary report for the August 12 to September 12,
2016 public comment period titled ``SPADE August 2016 Public Comment
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Tracheostomy Care data
element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Tracheostomy Care data element was included in the National Beta Test
of candidate data elements conducted by our data element contractor
from November 2017 to August 2018. Results of this test found the
Tracheostomy Care data element to be feasible and reliable for use with
PAC patients and residents. More information about the performance of
the Tracheostomy Care data element in the National Beta Test can be
found in the document titled ``Final Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Tracheostomy Care data element, the TEP
supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for tracheostomy care,
stakeholder input, and strong test results, we proposed that the
[[Page 39129]]
Tracheostomy Care data element meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Tracheostomy Care data element as standardized patient
assessment data for use in the IRF QRP.
We invited public comment on this proposal. While we received
support from some commenters on Special Services, Treatments, and
Interventions as a whole (section IX.G.2 in this final rule), we did
not receive any specific comments on Tracheostomy Care data element.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Tracheostomy Care data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Respiratory Treatment: Non-Invasive Mechanical Ventilator
(BiPAP, CPAP)
In the FY 2020 IRF PPS proposed rule (84 FR 17303), we proposed
that the Non-invasive Mechanical Ventilator (Bilevel Positive Airway
Pressure [BiPAP], Continuous Positive Airway Pressure [CPAP]) data
element meets the definition of standardized patient assessment data
with respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20730),
BiPAP and CPAP are respiratory support devices that prevent the airways
from closing by delivering slightly pressurized air via electronic
cycling throughout the breathing cycle (BiPAP) or through a mask
continuously (CPAP). Assessment of non-invasive mechanical ventilation
is important in care planning, as both CPAP and BiPAP are resource-
intensive (although less so than invasive mechanical ventilation) and
signify underlying medical conditions about the patient or resident who
requires the use of this intervention. Particularly when used in
settings of acute illness or progressive respiratory decline,
additional staff (for example, respiratory therapists) are required to
monitor and adjust the CPAP and BiPAP settings and the patient or
resident may require more nursing resources.
The proposed data element, Non-invasive Mechanical Ventilator
(BiPAP, CPAP), consists of the principal Non-invasive Mechanical
Ventilator data element and two response option sub-elements: BiPAP and
CPAP. If the assessor indicates that the patient is receiving non-
invasive mechanical ventilation on the principal Non-invasive
Mechanical Ventilator data element, the assessor would then indicate
which type (for example, BiPAP, CPAP). Data elements that assess non-
invasive mechanical ventilation are currently included on LCDS for the
LTCH setting (``Non-invasive Ventilator (BiPAP, CPAP)''), and the MDS
for the SNF setting (``Non-invasive Mechanical Ventilator (BiPAP/
CPAP)''). For more information on the Non-invasive Mechanical
Ventilator (BiPAP, CPAP) data element, we refer readers to the document
titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Non-invasive Mechanical Ventilator data element was first
proposed as standardized patient assessment data elements in the FY
2018 IRF PPS proposed rule (82 FR 20730). In that proposed rule, we
stated that the proposal was informed by input we received through a
call for input published on the CMS Measures Management System
Blueprint website. Input submitted from August 12 to September 12, 2016
on a single data element, BiPAP/CPAP, that captures equivalent clinical
information but uses a different label than the data element currently
used in the MDS in SNFs and LCDS, noted support for this data element,
noting the feasibility of these items in PAC, and the relevance of this
data element for facilitating care coordination and supporting care
transitions. In addition, we also stated that some commenters supported
separating out BiPAP and CPAP as distinct sub-elements, as they are
therapies used for different types of patients and residents. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. One commenter
noted appreciation of the revisions to the Non-invasive Mechanical
Ventilator data element in response to comments submitted during a
public input period held from August 12 to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Non-invasive Mechanical Ventilator data element was included in the
National Beta Test of candidate data elements conducted by our data
element contractor from November 2017 to August 2018. Results of this
test found the Non-invasive Mechanical Ventilator data element to be
feasible and reliable for use with PAC patients and residents. More
information about the performance of the Non-invasive Mechanical
Ventilator data element in the National Beta Test can be found in the
document titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Non-invasive Mechanical Ventilator data
element, the TEP supported the assessment of the special services,
treatments, and interventions included in the National Beta Test with
respect to both admission and discharge. A summary of the September 17,
2018 TEP meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized
[[Page 39130]]
Patient Assessment Data Elements (SPADEs) Received After November 27,
2018 Stakeholder Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for non-invasive
mechanical ventilation, stakeholder input, and strong test results, we
proposed that the Non-invasive Mechanical Ventilator (BiPAP, CPAP) data
element with a principal data element and two sub-elements meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act and to adopt the Non-invasive Mechanical
Ventilator (BiPAP, CPAP) data element as standardized patient
assessment data for use in the IRF QRP.
We invited public comment on this proposal. While we received
support from some commenters on Special Services, Treatments, and
Interventions as a whole (section IX.G.2 in this final rule), we did
not receive any specific comments on the Non-invasive Mechanical
Ventilator (BiPAP, CPAP) data element.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Non-invasive Mechanical Ventilator
(BiPAP, CPAP) data element as standardized patient assessment data
beginning with the FY 2022 IRF QRP as proposed.
Respiratory Treatment: Invasive Mechanical Ventilator
In the FY 2020 IRF PPS proposed rule (84 FR 17304), we proposed
that the Invasive Mechanical Ventilator data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20730
through 20731), invasive mechanical ventilation includes ventilators
and respirators that ventilate the patient through a tube that extends
via the oral airway into the pulmonary region or through a surgical
opening directly into the trachea. Thus, assessment of invasive
mechanical ventilation is important in care planning and risk
mitigation. Ventilation in this manner is a resource-intensive therapy
associated with life-threatening conditions without which the patient
or resident would not survive. However, ventilator use has inherent
risks requiring close monitoring. Failure to adequately care for the
patient or resident who is ventilator dependent can lead to iatrogenic
events such as death, pneumonia, and sepsis. Mechanical ventilation
further signifies the complexity of the patient's underlying medical or
surgical condition. Of note, invasive mechanical ventilation is
associated with high daily and aggregate costs.\112\
---------------------------------------------------------------------------
\112\ Wunsch, H., Linde-Zwirble, W.T., Angus, D.C., Hartman,
M.E., Milbrandt, E.B., & Kahn, J.M. (2010). ``The epidemiology of
mechanical ventilation use in the United States.'' Critical Care Med
38(10): 1947-1953.
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The proposed data element, Invasive Mechanical Ventilator, consists
of a single data element. Data elements that capture invasive
mechanical ventilation are currently in use in the MDS in SNFs and LCDS
in LTCHs. For more information on the Invasive Mechanical Ventilator
data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Invasive Mechanical Ventilator data element was first proposed
as a standardized patient assessment data element in the FY 2018 IRF
PPS proposed rule (82 FR 20730 through 20731). In that proposed rule,
we stated that the proposal was informed by input we received on data
elements that assess invasive ventilator use and weaning status that
were tested in the PAC PRD (``Ventilator--Weaning'' and ``Ventilator--
Non-Weaning'') through a call for input published on the CMS Measures
Management System Blueprint website. Input submitted from August 12 to
September 12, 2016, noted support for this data element, highlighting
the importance of this information in supporting care coordination and
care transitions. We also stated that some commenters had expressed
concern about the appropriateness for standardization given: The
prevalence of ventilator weaning across PAC providers; the timing of
administration; how weaning is defined; and how weaning status in
particular relates to quality of care. These public comments guided our
decision to propose a single data element focused on current use of
invasive mechanical ventilation only, which does not attempt to capture
weaning status. A summary report for the August 12 to September 12,
2016 public comment period titled ``SPADE August 2016 Public Comment
Summary Report'' we received is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. Two commenters
noted their appreciation of the revisions to the Invasive Mechanical
Ventilator data element in response to comments submitted during a
public input period held from August 12 to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Invasive Mechanical Ventilator data element was included in the
National Beta Test of candidate data elements conducted by our data
element contractor from November 2017 to August 2018. Results of this
test found the Invasive Mechanical Ventilator data element to be
feasible and reliable for use with PAC patients and residents. More
information about the performance of the Invasive Mechanical Ventilator
data element in the National Beta Test can be found in the document
titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data element. Although the TEP did not
specifically discuss the Invasive Mechanical Ventilator data element,
the TEP supported the assessment of the special services, treatments,
and interventions included in the National Beta Test with respect to
both admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
[[Page 39131]]
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present results of the National Beta Test and solicit
additional comments. General input on the testing and item development
process and concerns about burden were received from stakeholders
during this meeting and via email through February 1, 2019. A summary
of the public input received from the November 27, 2018 stakeholder
meeting titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for invasive mechanical
ventilation, stakeholder input, and strong test results, we proposed
that the Invasive Mechanical Ventilator data element that assesses the
use of an invasive mechanical ventilator meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Invasive Mechanical Ventilator data element as
standardized patient assessment data for use in the IRF QRP.
A commenter submitted the following comment related to the proposed
rule's discussion of the Invasive Mechanical Ventilator data element.
Comment: One commenter noted disappointment over seeing that the
SPADE for invasive mechanical ventilator only assesses whether or not a
patient is on a mechanical ventilator. The commenter suggested CMS
consider collecting data to track functional outcomes related to
progress towards independence in communication and swallowing.
Response: We have attempted to balance the scope and level of
detail of the data elements against the potential burden placed on
patients and providers. We believe that assessing the use of an
invasive mechanical ventilator will be a useful point of information to
inform care planning and further assessment, such as related to
functional outcomes, as the commenter suggests.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Invasive Mechanical Ventilator
data element as standardized patient assessment data beginning with the
FY 2022 IRF QRP as proposed.
Intravenous (IV) Medications (Antibiotics, Anticoagulants,
Vasoactive Medications, Other)
In the FY 2020 IRF PPS proposed rule (84 FR 17305 through 17306),
we proposed that the IV Medications (Antibiotics, Anticoagulants,
Vasoactive Medications, Other) data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20731
through 20732), when we proposed a similar data element related to IV
medications, IV medications are solutions of a specific medication (for
example, antibiotics, anticoagulants) administered directly into the
venous circulation via a syringe or intravenous catheter. IV
medications are administered via intravenous push, single,
intermittent, or continuous infusion through a catheter placed into the
vein. Further, IV medications are more resource intensive to administer
than oral medications, and signify a higher patient complexity (and
often higher severity of illness).
The clinical indications for each of the sub-elements of the IV
Medications data element (Antibiotics, Anticoagulants, Vasoactive
Medications, and Other) are very different. IV antibiotics are used for
severe infections when the bioavailability of the oral form of the
medication would be inadequate to kill the pathogen or an oral form of
the medication does not exist. IV anticoagulants refer to anti-clotting
medications (that is, ``blood thinners''). IV anticoagulants are
commonly used for hospitalized patients who have deep venous
thrombosis, pulmonary embolism, or myocardial infarction, as well as
those undergoing interventional cardiac procedures. Vasoactive
medications refer to the IV administration of vasoactive drugs,
including vasopressors, vasodilators, and continuous medication for
pulmonary edema, which increase or decrease blood pressure or heart
rate. The indications, risks, and benefits of each of these classes of
IV medications are distinct, making it important to assess each
separately in PAC. Knowing whether or not patients and residents are
receiving IV medication and the type of medication provided by each PAC
provider will improve quality of care.
The IV Medications (Antibiotics, Anticoagulants, Vasoactive
Medications, and Other) data element we proposed consists of a
principal data element (IV Medications) and four response option sub-
elements: Antibiotics, Anticoagulants, Vasoactive Medications, and
Other. The Vasoactive Medications sub-element was not proposed in the
FY 2018 IRF PPS proposed rule (82 FR 20731 through 20732). We added the
Vasoactive Medications sub-element to our proposal in order to
harmonize the proposed IV Mediciations element with the data currently
collected in the LCDS.
If the assessor indicates that the patient is receiving IV
medications on the principal IV Medications data element, the assessor
would then indicate which types of medications (for example,
Antibiotics, Anticoagulants, Vasoactive Medications, Other). An IV
Medications data element is currently in use on the MDS in SNFs and
there is a related data element in OASIS that collects information on
Intravenous and Infusion Therapies. For more information on the IV
Medications (Antibiotics, Anticoagulants, Vasoactive Medications,
Other) data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
An IV Medications data element was first proposed as standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20731 through 20732). In that proposed rule, we stated that the
proposal was informed by input we received on Vasoactive Medications
through a call for input published on the CMS Measures Management
System Blueprint website. Input submitted from August 12 to September
12, 2016 supported this data element with one noting the importance of
this data element in supporting care transitions. We also stated that
those commenters had criticized the need for collecting specifically
Vasoactive Medications, giving feedback that the data element was too
narrowly focused. In addition, public comment received indicated that
the clinical significance of vasoactive medications administration
alone was not high enough in PAC to merit mandated assessment, noting
that related and more useful information could be captured in an item
that assessed all IV medication use. A
[[Page 39132]]
summary report for the August 12 to September 12, 2016 public comment
period titled ``SPADE August 2016 Public Comment Summary Report'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the IV Medications data
element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
IV Medications data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the IV
Medications data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
IV Medications data element in the National Beta Test can be found in
the document titled ``Final Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the IV Medications data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for IV medications,
stakeholder input, and strong test results, we proposed that the IV
Medications (Antibiotics, Anticoagulants, Vasoactive Medications,
Other) data element with a principal data element and four sub-elements
meets the definition of standardized patient assessment data with
respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and to adopt the IV Medications
(Antibiotics, Anticoagulants, Vasoactive Medications, Other) data
element as standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the IV Medications data elements.
Comment: One commenter noted that the IV Medications data elements
seem redundant of the proposed High-Risk Drug Classes: Use and
Indication data elements.
Response: We wish to clarify that the IV Medications data element
collects information on medications received by IV only, with sub-
elements specific to antibiotics, anticoagulants, and vasoactive
medications only. In contrast, the High Risk Drug Classes: Use and
Indication data element collects information on medications received by
any route, only for six specific drug classes, and collects information
on the presence of an indication. We believe the overlap between these
SPADEs is minimal, as it would only occur when a medication in a high-
risk drug class is delivered by IV. Additionally, in this case, the
High-Risk Drug Classes: Use and Indication data element would assess
the presence of an indication in the patient's medical record, which
the IV Medications data element does not do.
Comment: Commenters were concerned about the performance of the IV
Medications data element in the National Beta Test, noting that its
reliability was only fair to good and poor for the anticoagulation sub-
element.
Response: The kappa for the overarching IV Medications data element
was 0.70 across settings, which falls in the range of ``substantial/
good'' agreement. The IV Medications sub-element that had a ``slight/
poor'' reliability (in the range of 0.00-0.20) was the IV
Anticoagulants sub-element (kappa = 0.13). The Other IV Medications
sub-element had ``moderate'' reliability (kappa = 0.46). Consultation
with assessors suggested that the low kappa for the IV Anticoagulants
sub-element was likely due to inconsistent interpretation of the coding
instructions. Having identified the likely source of the relatively
lower interrater reliability, we are confident that with proper
training of IRFs on how to report the data elements, the reliability of
these sub-elements will be improved.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the IV Medications (Antibiotics,
Anticoagulants, Vasoactive Medications, Other) data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Transfusions
In the FY 2020 IRF PPS proposed rule (84 FR 17306), we proposed
that the Transfusions data element meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20732),
transfusion refers to introducing blood or blood products into the
circulatory system of a person. Blood transfusions are based on
specific protocols, with multiple safety checks and monitoring required
during and after the infusion in case of adverse events. Coordination
with the provider's blood bank is necessary, as well as documentation
by clinical staff to ensure compliance with regulatory requirements. In
addition, the need for transfusions signifies underlying patient
complexity that is likely to require care coordination and patient
monitoring, and impacts planning for transitions of care, as
transfusions are not performed by all PAC providers.
The proposed data element consists of the single Transfusions data
element. A
[[Page 39133]]
data element on transfusion is currently in use in the MDS in SNFs
(``Transfusions'') and a data element tested in the PAC PRD (``Blood
Transfusions'') was found feasible for use in each of the four PAC
settings. For more information on the Transfusions data element, we
refer readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Transfusions data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20732). In response to our proposal in the FY 2018 IRF PPS
proposed rule, we received public comments in support of the special
services, treatments, and interventions data elements in general; no
additional comments were received that were specific to the
Transfusions data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Transfusions data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the
Transfusions data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
Transfusions data element in the National Beta Test can be found in the
document titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Transfusions data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for transfusions,
stakeholder input, and strong test results, we proposed that the
Transfusions data element meets the definition of standardized patient
assessment data with respect to special services, treatments, and
interventions under section 1899B(b)(1)(B)(iii) of the Act and to adopt
the Transfusions data element as standardized patient assessment data
for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the Transfusions data element.
Comment: One commenter applauded CMS for including the Transfusions
data element, noting that it will provide information on care planning,
clinical decision making, patient safety, care transitions, and
resource use in IRFs and will contribute to higher quality and
coordinated care for patients who rely on these life-saving treatments.
Response: We thank the commenter for their support. We selected the
Transfusions data element for proposal as standardized data in part
because of the attributes that the commenter noted.
Comment: One commenter was concerned that IRFs will not have the
resources needed to provide patients with access to blood transfusions
and requested that CMS consider whether payments to IRFs are adequate
to cover the cost of this resource intensive, specialized service.
Response: We wish to clarify that this item is finalized only to
collect information on the complexity of the patient and resources the
patient requires. At this time, this item will not be used for any
payment purposes, and thus we are not able to comment on cost of this
service. We wish to clarify that this SPADE is not intended to measure
the ability of an IRF to provide in-house transfusions, only to capture
the services a given patient may be receiving. Further, for patients
who require services related to blood transfusions, information
collected by this data element is a part of common clinical workflow,
and thus, we believe that burden on resource intensity would not be
affected by the standardization of this data element.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Transfusions data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Dialysis (Hemodialysis, Peritoneal Dialysis)
In the FY 2020 IRF PPS proposed rule (84 FR 17306 through 17307),
we proposed that the Dialysis (Hemodialysis, Peritoneal dialysis) data
element meets the definition of standardized patient assessment data
with respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20732
through 20733), dialysis is a treatment primarily used to provide
replacement for lost kidney function. Both forms of dialysis
(hemodialysis and peritoneal dialysis) are resource intensive, not only
during the actual dialysis process but before, during, and following.
Patients and residents who need and undergo dialysis procedures are at
high risk for physiologic and hemodynamic instability from fluid shifts
and electrolyte disturbances, as well as infections that can lead to
sepsis. Further, patients or residents receiving hemodialysis are often
transported to a different facility, or at a minimum, to a different
location in the same facility for treatment. Close monitoring for fluid
shifts, blood pressure abnormalities, and other adverse effects is
required prior to, during, and following each dialysis session. Nursing
staff typically perform peritoneal dialysis at the bedside, and as with
hemodialysis, close monitoring is required.
[[Page 39134]]
The proposed data element, Dialysis (Hemodialysis, Peritoneal
dialysis) consists of the principal Dialysis data element and two
response option sub-elements: Hemodialysis and Peritoneal dialysis. If
the assessor indicates that the patient is receiving dialysis on the
principal Dialysis data element, the assessor would then indicate which
type (Hemodialysis or Peritoneal dialysis). The principal Dialysis data
element is currently included on the MDS in SNFs and the LCDS for LTCHs
and assesses the overall use of dialysis.
As the result public feedback described below, in the proposed
rule, we proposed a data element that includes the principal Dialysis
data element and two sub-elements (Hemodialysis and Peritoneal
dialysis). For more information on the Dialysis data element, we refer
readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Dialysis data element was first proposed as standardized
patient assessment data in the FY 2018 IRF PPS proposed rule (82 FR
20732 through 20733). In that proposed rule, we stated that the
proposal was informed by input we received on a singular Hemodialysis
data element through a call for input published on the CMS Measures
Management System Blueprint website. Input submitted from August 12 to
September 12, 2016 supported the assessment of hemodialysis and
recommended that the data element be expanded to include peritoneal
dialysis. We also stated that those commenters had supported the
singular Hemodialysis data element, noting the relevance of this
information for sharing across the care continuum to facilitate care
coordination and care transitions, the potential for this data element
to be used to improve quality, and the feasibility for use in PAC. In
addition, we received comments that the item would be useful in
improving patient and resident transitions of care. We also noted that
several commenters had stated that peritoneal dialysis should be
included in a standardized data element on dialysis and recommended
collecting information on peritoneal dialysis in addition to
hemodialysis. The rationale for including peritoneal dialysis from
commenters included the fact that patients and residents receiving
peritoneal dialysis will have different needs at post-acute discharge
compared to those receiving hemodialysis or not having any dialysis.
Based on these comments, the Hemodialysis data element was expanded to
include a principal Dialysis data element and two sub-elements,
Hemodialysis and Peritoneal dialysis. We proposed the version of the
Dialysis element that includes two types of dialysis. A summary report
for the August 12 to September 12, 2016 public comment period titled
``SPADE August 2016 Public Comment Summary Report'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received comments in support of the special services, treatments,
and interventions data elements in general. One commenter noted that
they appreciated the revisions to the Dialysis data element in response
to comments submitted during a public input period held from August 12
to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Dialysis data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Dialysis
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Dialysis data
element in the National Beta Test can be found in the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although they did not
specifically discuss the Dialysis data element, the TEP supported the
assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for dialysis,
stakeholder input, and strong test results, we proposed that the
Dialysis (Hemodialysis, Peritoneal dialysis) data element with a
principal data element and two sub-elements meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Dialysis (Hemodialysis, Peritoneal dialysis) data
element as standardized patient assessment data for use in the IRF QRP.
We invited public comment on this proposal. While we received
support from some commenters on this Special Services, Treatments, and
Interventions as a whole (section IX.G.2 in this final rule), we did
not receive any specific comments on the Dialysis (Hemodialysis,
Peritoneal dialysis) data element.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Dialysis (Hemodialysis, Peritoneal
dialysis) data element as standardized patient assessment data
beginning with the FY 2022 IRF QRP as proposed.
[[Page 39135]]
Intravenous (IV) Access (Peripheral IV, Midline, Central Line)
In the FY 2020 IRF PPS proposed rule (84 FR 17307 through 17308),
we proposed that the IV Access (Peripheral IV, Midline, Central line)
data element meets the definition of standardized patient assessment
data with respect to special services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20733
through 20734), patients or residents with central lines, including
those peripherally inserted or who have subcutaneous central line
``port'' access, always require vigilant nursing care to keep patency
of the lines and ensure that such invasive lines remain free from any
potentially life-threatening events such as infection, air embolism, or
bleeding from an open lumen. Clinically complex patients and residents
are likely to be receiving medications or nutrition intravenously. The
sub-elements included in the IV Access data elements distinguish
between peripheral access and different types of central access. The
rationale for distinguishing between a peripheral IV and central IV
access is that central lines confer higher risks associated with life-
threatening events such as pulmonary embolism, infection, and bleeding.
The proposed data element, IV Access (Peripheral IV, Midline,
Central line), consists of the principal IV Access data element and
three response option sub-elements: Peripheral IV, Midline, and Central
line. The proposed IV Access data element is not currently included on
any of the PAC assessment instruments. For more information on the IV
Access data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The IV Access data element was first proposed as standardized
patient assessment data elements in the FY 2018 IRF PPS proposed rule
(82 FR 20733 through 20734). In that proposed rule, we stated that the
proposal was informed by input we received on one of the PAC PRD data
elements, Central Line Management, through a call for input published
on the CMS Measures Management System Blueprint website. A central line
is a type of IV access. Input submitted from August 12 to September 12,
2016 supported the assessment of central line management and
recommended that the data element be broadened to also include other
types of IV access. Several commenters noted feasibility and importance
for facilitating care coordination and care transitions. However, a few
commenters recommended that the definition of this data element be
broadened to include peripherally inserted central catheters (``PICC
lines'') and midline IVs. Based on public comment feedback and in
consultation with expert input, described below, we created an
overarching IV Access data element with sub-elements for other types of
IV access in addition to central lines (that is, peripheral IV and
midline). This expanded version of IV Access is the data element being
proposed. A summary report for the August 12 to September 12, 2016
public comment period titled ``SPADE August 2016 Public Comment Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. One commenter
noted appreciation of the revisions to the IV Access data element in
response to comments submitted during a public input period held from
August 12 to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
IV Access data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the IV Access
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the IV Access data
element in the National Beta Test can be found in the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the IV Access data element, the TEP supported the
assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present results of the National Beta Test and solicit
additional comments. General input on the testing and item development
process and concerns about burden were received from stakeholders
during this meeting and via email through February 1, 2019. A summary
of the public input received from the November 27, 2018 stakeholder
meeting titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for IV access,
stakeholder input, and strong test results, we proposed that the IV
access (Peripheral IV, Midline, Central line) data element with a
principal data element and three sub-elements meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the IV Access (Peripheral IV, Midline, Central line)
data element as standardized patient assessment data for use in the IRF
QRP.
We invited public comment on this proposal. While we received
support from some commenters on this Special Services, Treatments, and
Interventions as a whole (section IX.G.2 in this final rule), we did
not receive any specific comments on the IV Access (Peripheral IV,
Midline, Central line) data element.
After careful consideration of the public comments we received on
the
[[Page 39136]]
category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the IV Access (Peripheral IV, Midline,
Central line) data element as standardized patient assessment data
beginning with the FY 2022 IRF QRP as proposed.
Nutritional Approach: Parenteral/IV Feeding
In the FY 2020 IRF PPS proposed rule (84 FR 17308 through 17309),
we proposed that the Parenteral/IV Feeding data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20734),
parenteral nutrition/IV feeding refers to a patient or resident being
fed intravenously using an infusion pump, bypassing the usual process
of eating and digestion. The need for IV/parenteral feeding indicates a
clinical complexity that prevents the patient or resident from meeting
his or her nutritional needs enterally, and is more resource intensive
than other forms of nutrition, as it often requires monitoring of blood
chemistries and the maintenance of a central line. Therefore, assessing
a patient's or resident's need for parenteral feeding is important for
care planning and resource use. In addition to the risks associated
with central and peripheral intravenous access, total parenteral
nutrition is associated with significant risks, such as air embolism
and sepsis.
The proposed data element consists of the single Parenteral/IV
Feeding data element. The proposed Parenteral/IV Feeding data element
is currently in use in the MDS in SNFs, and equivalent or related data
elements are in use in the LCDS, IRF-PAI, and OASIS. We proposed to
rename the existing Tube/Parenteral feeding item in the IRF-PAI to be
the Parenteral/IV Feeding data element. For more information on the
Parenteral/IV Feeding data element, we refer readers to the document
titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Parenteral/IV Feeding data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20734). In that proposed rule, we stated that the
proposal was informed by input we received on Total Parenteral
Nutrition (an item with nearly the same meaning as the proposed data
element, but with the label used in the PAC PRD), through a call for
input published on the CMS Measures Management System Blueprint
website. Input submitted from August 12 to September 12, 2016 supported
this data element, noting its relevance to facilitating care
coordination and supporting care transitions. After the public comment
period, the Total Parenteral Nutrition data element was renamed
Parenteral/IV Feeding, to be consistent with how this data element is
referred to in the MDS in SNFs. A summary report for the August 12 to
September 12, 2016 public comment period titled ``SPADE August 2016
Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received comments in support of the special services, treatments,
and interventions data elements in general; no additional comments were
received that were specific to the Parenteral/IV Feeding data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Parenteral/IV Feeding data element was included in the National Beta
Test of candidate data elements conducted by our data element
contractor from November 2017 to August 2018. Results of this test
found the Parenteral/IV Feeding data element to be feasible and
reliable for use with PAC patients and residents. More information
about the performance of the Parenteral/IV Feeding data element in the
National Beta Test can be found in the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Parenteral/IV Feeding data element, the TEP
supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for parenteral/IV
feeding, stakeholder input, and strong test results, we proposed that
the Parenteral/IV Feeding data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Parenteral/IV Feeding data element as standardized
patient assessment data for use in the IRF QRP.
A commenter submitted the following comment related to the proposed
rule's discussion of the Parenteral/IV Feeding data element.
Comment: One commenter was supportive of collecting this data
element, but noted that it should not be a substitute for capturing
information related to swallowing which reflects additional patient
complexity and resource use.
Response: We thank the commenter for their support and appreciate
the concerns raised. We agree that the Parenteral/IV Feeding SPADE
should not be used as a substitute for an assessment of a patient's
swallowing
[[Page 39137]]
function. The proposed SPADEs are not intended to replace comprehensive
clinical evaluation and in no way preclude providers from conducting
further patient evaluation or assessments in their settings as they
believe are necessary and useful. We agree that information related to
swallowing can capture patient complexity. However, we also note that
Parenteral/IV Feeding data element captures a different construct than
an evaluation of swallowing. That is, the Parenteral/IV Feeding data
element captures a patient's need to receive calories and nutrients
intravenously, while an assessment of swallowing would capture a
patient's functional ability to safely consume food/liquids orally for
digestion in their gastrointestinal tract.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Parenteral/IV Feeding data element
as standardized patient assessment data beginning with the FY 2022 IRF
QRP as proposed.
Nutritional Approach: Feeding Tube
In the FY 2020 IRF PPS proposed rule (84 FR 17309 through 17310),
we proposed that the Feeding Tube data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20734
through 20735), the majority of patients admitted to acute care
hospitals experience deterioration of their nutritional status during
their hospital stay, making assessment of nutritional status and method
of feeding if unable to eat orally very important in PAC. A feeding
tube can be inserted through the nose or the skin on the abdomen to
deliver liquid nutrition into the stomach or small intestine. Feeding
tubes are resource intensive, and therefore, are important to assess
for care planning and resource use. Patients with severe malnutrition
are at higher risk for a variety of complications.\113\ In PAC
settings, there are a variety of reasons that patients and residents
may not be able to eat orally (including clinical or cognitive status).
---------------------------------------------------------------------------
\113\ Dempsey, D.T., Mullen, J.L., & Buzby, G.P. (1988). ``The
link between nutritional status and clinical outcome: can
nutritional intervention modify it?'' Am J of Clinical Nutrition,
47(2): 352-356.
---------------------------------------------------------------------------
The proposed data element consists of the single Feeding Tube data
element. The Feeding Tube data element is currently included in the MDS
for SNFs, and in the OASIS for HHAs, where it is labeled Enteral
Nutrition. A related data element, collected in the IRF-PAI for IRFs
(Tube/Parenteral Feeding), assesses use of both feeding tubes and
parenteral nutrition. We proposed to rename the existing Tube/
Parenteral feeding item in the IRF-PAI to the Feeding Tube data
element. For more information on the Feeding Tube data element, we
refer readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Feeding Tube data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20734 through 20735). In that proposed rule, we stated that the
proposal was informed by input we received on an Enteral Nutrition data
element (the Enteral Nutrition data item is the same as the data
element we proposed, but is used in the OASIS under a different name)
through a call for input published on the CMS Measures Management
System Blueprint website. Input submitted from August 12 to September
12, 2016 supported the data element, noting the importance of assessing
enteral nutrition status for facilitating care coordination and care
transitions. After the public comment period, the Enteral Nutrition
data element used in public comment was renamed Feeding Tube,
indicating the presence of an assistive device. A summary report for
the August 12 to September 12, 2016 public comment period titled
``SPADE August 2016 Public Comment Summary Report'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. In addition, a
commenter recommended that the term ``enteral feeding'' be used instead
of ``feeding tube''.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Feeding Tube data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Feeding
Tube data element to be feasible and reliable for use with PAC patients
and residents. More information about the performance of the Feeding
Tube data element in the National Beta Test can be found in the
document titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Feeding Tube data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for feeding tubes,
stakeholder input, and strong test results, we
[[Page 39138]]
proposed that the Feeding Tube data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Feeding Tube data element as standardized patient
assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the Feeding Tube data element.
Comment: One commenter noted that in addition to identifying if the
patient is on a feeding tube or not, it would be important to assess
the patient's progression towards oral feeding within this data
element, as this impacts the tube feeding regimen.
Response: We agree that progression to oral feeding is important
for care planning and transfer. At this time, we are finalizing a
singular Feeding Tube SPADE, which assesses the nutritional approach
only and does not capture the patient's prognosis with regard to oral
feeding. We wish to clarify that the proposed SPADEs are not intended
to replace comprehensive clinical evaluation and in no way preclude
providers from conducting further patient evaluation or assessments in
their settings as they believe are necessary and useful. We will take
this recommendation into consideration in future work on standardized
data elements.
Comment: One commenter noted that this data element should
designate between percutaneous endoscopic gastrostomy (PEG) tube and
nasogastric (NG) tube because the different routes of access have
different levels of resource requirements.
Response: We appreciate the commenter's suggestion, but we have
decided to maintain the singular Feeding Tube SPADE. We agree that
different routes of access may have different levels of resource
requirements. However, we do not believe collecting this level of
information about nutritional therapies via a SPADE would be
significantly more clinically useful or supportive of care transitions
than the singular Feeding Tube SPADE. However, we will take this
suggestion into consideration in future refinement of the clinical
SPADEs.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Feeding Tube data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Nutritional Approach: Mechanically Altered Diet
In the FY 2020 IRF PPS proposed rule (84 FR 17310 through 17311),
we proposed that the Mechanically Altered Diet data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20735
through 20736), the Mechanically Altered Diet data element refers to
food that has been altered to make it easier for the patient or
resident to chew and swallow, and this type of diet is used for
patients and residents who have difficulty performing these functions.
Patients with severe malnutrition are at higher risk for a variety of
complications.\114\
---------------------------------------------------------------------------
\114\ Dempsey, D.T., Mullen, J.L., & Buzby, G.P. (1988). ``The
link between nutritional status and clinical outcome: can
nutritional intervention modify it?'' Am J of Clinical Nutrition,
47(2): 352-356.
---------------------------------------------------------------------------
In PAC settings, there are a variety of reasons that patients and
residents may have impairments related to oral feedings, including
clinical or cognitive status. The provision of a mechanically altered
diet may be resource intensive, and can signal difficulties associated
with swallowing/eating safety, including dysphagia. In other cases, it
signifies the type of altered food source, such as ground or puree that
will enable the safe and thorough ingestion of nutritional substances
and ensure safe and adequate delivery of nourishment to the patient.
Often, patients and residents on mechanically altered diets also
require additional nursing support, such as individual feeding or
direct observation, to ensure the safe consumption of the food product.
Therefore, assessing whether a patient or resident requires a
mechanically altered diet is important for care planning and resource
identification.
The proposed data element consists of the single Mechanically
Altered Diet data element. The proposed data element is currently
included on the MDS for SNFs. A related data element (``Modified food
consistency/supervision'') is currently included on the IRF-PAI for
IRFs. Another related data element is included in the OASIS for HHAs
that collects information about independent eating that requires ``a
liquid, pureed or ground meat diet.'' We proposed to replace the
existing Modified food consistency/supervision data element in the IRF-
PAI to the Mechanically Altered Diet data element. For more information
on the Mechanically Altered Diet data element, we refer readers to the
document titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Mechanically Altered Diet data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20735 through 20736). In response to our proposal
in the FY 2018 IRF PPS proposed rule, we received public comments in
support of the special services, treatments, and interventions data
elements in general; no additional comments were received that were
specific to the Mechanically Altered Diet data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Mechanically Altered Diet data element was included in the National
Beta Test of candidate data elements conducted by our data element
contractor from November 2017 to August 2018. Results of this test
found the Mechanically Altered Diet data element to be feasible and
reliable for use with PAC patients and residents. More information
about the performance of the Mechanically Altered Diet data element in
the National Beta Test can be found in the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Mechanically Altered Diet data element, the
TEP supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-
Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-
Initiatives/IMPACT-Act-of-2014/
[[Page 39139]]
IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for mechanically
altered diet, stakeholder input, and strong test results, we proposed
that the Mechanically Altered Diet data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Mechanically Altered Diet data element as
standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the Mechanically Altered Diet data element.
Comment: Commenters were concerned about the performance of this
data element in the National Beta Test, noting that its reliability was
only moderate in IRF settings.
Response: We provided supplementary information with the proposed
rule on the reliability of the SPADEs, described by the kappa statistic
and by the ``percent agreement'' between assessor, another measure of
reliability that is in some cases more accurate than the kappa
statistic, depending on the underlying distribution. (The document
titled ``Proposed Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html). In this document, we stated that
the interrater reliability for Mechanically Altered Diet data element,
as measured by kappa, was ``substantial/good'' across the four PAC
provider types (LTCH, SNF, HHA, and IRF) in which it was tested (kappa
= 0.65) and ``moderate'' in the IRF setting (kappa = 0.53). However,
percent agreement for the data element was 93 percent across all PAC
settings in the National Beta Test (that is, HHA, IRF, LTCH, and SNF)
and 89 percent in the IRF setting. That is, when assessing if patients
required a mechanically altered diet, the facility staff and the
external research nurse agreed 89 percent of the time for IRF patients.
Comment: One commenter was concerned that the Mechanically Altered
Diet data element does not capture clinical complexity and does not
provide any insight into resource allocation because it only measures
whether the patient needs a mechanically altered diet and not, for
example, the extent of help a patient needs in consuming his or her
meal.
Response: We believe that assessing patients' needs for
mechanically altered diets captures one piece of information about
resource intensity. That is, patients with this special nutritional
requirement may require additional nutritional planning services,
special meals, and staff to ensure that meals are prepared and served
in the way the patient needs. Additional factors that would affect
resource allocation, such as those noted by the commenter, are not
captured by this data element. We have attempted to balance the scope
and level of detail of the data elements against the potential burden
placed on providers who must complete the assessment. We will take this
suggestion into consideration in future refinement of the clinical
SPADEs.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Mechanically Altered Diet data
element as standardized patient assessment data beginning with the FY
2022 IRF QRP as proposed.
Nutritional Approach: Therapeutic Diet
In the FY 2020 IRF PPS proposed rule (84 FR 17311 through 17312),
we proposed that the Therapeutic Diet data element meets the definition
of standardized patient assessment data with respect to special
services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20736), a
therapeutic diet refers to meals planned to increase, decrease, or
eliminate specific foods or nutrients in a patient's or resident's
diet, such as a low-salt diet, for the purpose of treating a medical
condition. The use of therapeutic diets among patients and residents in
PAC provides insight on the clinical complexity of these patients and
residents and their multiple comorbidities. Therapeutic diets are less
resource intensive from the bedside nursing perspective, but do signify
one or more underlying clinical conditions that preclude the patient
from eating a regular diet. The communication among PAC providers about
whether a patient is receiving a particular therapeutic diet is
critical to ensure safe transitions of care.
The proposed data element consists of the single Therapeutic Diet
data element. This data element is currently in use in the MDS in SNFs.
For more information on the Therapeutic Diet data element, we refer
readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Therapeutic Diet data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20736). In response to our proposal in the FY 2018
IRF PPS proposed rule, we received public comments in support of the
special services, treatments, and interventions data elements in
general. One commenter recommended that the definition of Therapeutic
Diet be aligned with the Academy of Nutrition and Dietetics' definition
and that ``medically altered diet'' be added to the list of nutritional
approaches.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Therapeutic Diet data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the
Therapeutic Diet data element to be feasible and reliable for use with
PAC patients and residents. More information about the performance of
the Therapeutic Diet data element in the National Beta Test can be
found in the document titled ``Final Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-
[[Page 39140]]
Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-
of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Therapeutic Diet data element, the TEP
supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for therapeutic diet,
stakeholder input, and strong test results, we proposed that the
Therapeutic Diet data element meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Therapeutic Diet data element as standardized patient
assessment data for use in the IRF QRP.
We invited public comment on this proposal. While we received
support from some commenters on Special Services, Treatments, and
Interventions as a whole (section IX.G.2 in this final rule), we did
not receive any specific comments on the Therapeutic Diet data element.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Therapeutic Diet data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
High-Risk Drug Classes: Use and Indication
In the FY 2020 IRF PPS proposed rule (84 FR 17312 through 17314),
we proposed that the High-Risk Drug Classes: Use and Indication data
element meets the definition of standardized patient assessment data
with respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
Most patients and residents receiving PAC services depend on short-
and long-term medications to manage their medical conditions. However,
as a treatment, medications are not without risk; medications are, in
fact, a leading cause of adverse events. A study by the U.S. Department
of Health and Human Services found that 31 percent of adverse events
that occurred in 2008 among hospitalized Medicare beneficiaries were
related to medication.\115\ Moreover, changes in a patient's condition,
medications, and transitions between care settings put patients at risk
of medication errors and adverse drug events (ADEs). ADEs may be caused
by medication errors such as drug omissions, errors in dosage, and
errors in dosing frequency.\116\
---------------------------------------------------------------------------
\115\ U.S. Department of Health and Human Services. Office of
Inspector General. Daniel R. Levinson. Adverse Events in Hospitals:
National Incidence Among Medicare Beneficiaries. OEI-06-09-00090.
November 2010.
\116\ Boockvar KS, Liu S, Goldstein N, Nebeker J, Siu A, Fried
T. Prescribing discrepancies likely to cause adverse drug events
after patient transfer. Qual Saf Health Care. 2009;18(1):32-6.
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ADEs are known to occur across different types of healthcare
settings. For example, the incidence of ADEs in the outpatient setting
has been estimated at 1.15 ADEs per 100 person-months,\117\ while the
rate of ADEs in the long-term care setting is approximately 9.80 ADEs
per 100 resident-months.\118\ In the hospital setting, the incidence
has been estimated at 15 ADEs per 100 admissions.\119\ In addition,
approximately half of all hospital-related medication errors and 20
percent of ADEs occur during transitions within, admission to, transfer
to, or discharge from a hospital.\120\ \121\ \122\ ADEs are more common
among older adults, who make up most patients receiving PAC services.
The rate of emergency department visits for ADEs is three times higher
among adults 65 years of age and older compared to that among those
younger than age 65.\123\
---------------------------------------------------------------------------
\117\ Gandhi TK, Seger AC, Overhage JM, et al. Outpatient
adverse drug events identified by screening electronic health
records. J Patient Saf 2010;6:91-6.doi:10.1097/PTS.0b013e3181dcae06.
\118\ Gurwitz JH, Field TS, Judge J, Rochon P, Harrold LR,
Cadoret C, et al. The incidence of adverse drug events in two large
academic long-term care facilities. Am J Med. 2005; 118(3):2518. Epub 2005/03/05. https://doi.org/10.1016/j.amjmed.2004.09.018 PMID: 15745723.
\119\ Hug BL, Witkowski DJ, Sox CM, Keohane CA, Seger DL, Yoon
C, Matheny ME, Bates DW. Occurrence of adverse, often preventable,
events in community hospitals involving nephrotoxic drugs or those
excreted by the kidney. Kidney Int. 2009; 76:1192-1198. [PubMed:
19759525].
\120\ Barnsteiner JH. Medication reconciliation: transfer of
medication information across settings-keeping it free from error. J
Infus Nurs. 2005;28(2 Suppl):31-36.
\121\ Rozich J, Roger, R. Medication safety: one organization's
approach to the challenge. Journal of Clinical Outcomes Management.
2001(8):27-34.
\122\ Gleason KM, Groszek JM, Sullivan C, Rooney D, Barnard C,
Noskin GA. Reconciliation of discrepancies in medication histories
and admission orders of newly hospitalized patients. Am J Health
Syst Pharm. 2004;61(16):1689-1695.
\123\ Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ,
Budnitz DS. US emergency department visits for outpatient adverse
drug events, 2013-2014. JAMA. doi: 10.1001/jama.2016.16201.
---------------------------------------------------------------------------
Understanding the types of medication a patient is taking, and the
reason for its use, are key facets of a patient's treatment with
respect to medication. Some classes of drugs are associated with more
risk than others.\124\ We proposed one High-Risk Drug Class data
element with six sub-elements. The response options that correspond to
the six medication classes are: Anticoagulants, antiplatelets,
hypoglycemics (including insulin), opioids, antipsychotics, and
antibiotics. These drug classes are high-risk due to the adverse
effects that may result from use. In particular, bleeding risk is
associated with anticoagulants and antiplatelets; \125\ \126\ fluid
retention, heart failure, and lactic acidosis are associated with
hypoglycemics; \127\
[[Page 39141]]
misuse is associated with opioids; \128\ fractures and strokes are
associated with antipsychotics; \129\ \130\ and various adverse events,
such as central nervous systems effects and gastrointestinal
intolerance, are associated with antimicrobials,\131\ the larger
category of medications that include antibiotics. Moreover, some
medications in five of the six drug classes included in this data
element are included in the 2019 Updated Beers Criteria[supreg] list as
potentially inappropriate medications for use in older adults.\132\
Finally, although a complete medication list should record several
important attributes of each medication (for example, dosage, route,
stop date), recording an indication for the drug is of crucial
importance.\133\
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\124\ Ibid.
\125\ Shoeb M, Fang MC. Assessing bleeding risk in patients
taking anticoagulants. J Thromb Thrombolysis. 2013;35(3):312-319.
doi: 10.1007/s11239-013-0899-7.
\126\ Melkonian M, Jarzebowski W, Pautas E. Bleeding risk of
antiplatelet drugs compared with oral anticoagulants in older
patients with atrial fibrillation: a systematic review and
meta[hyphen]analysis. J Thromb Haemost. 2017;15:1500-1510. DOI:
10.1111/jth.13697.
\127\ Hamnvik OP, McMahon GT. Balancing Risk and Benefit with
Oral Hypoglycemic Drugs. The Mount Sinai journal of medicine, New
York. 2009; 76:234-243.
\128\ Naples JG, Gellad WF, Hanlon JT. The Role of Opioid
Analgesics in Geriatric Pain Management. Clin Geriatr Med.
2016;32(4):725-735.
\129\ Rigler SK, Shireman TI, Cook-Wiens GJ, Ellerbeck EF,
Whittle JC, Mehr DR, Mahnken JD. Fracture risk in nursing home
residents initiating antipsychotic medications. J Am Geriatr Soc.
2013; 61(5):715-722. [PubMed: 23590366].
\130\ Wang S, Linkletter C, Dore D et al. Age, antipsychotics,
and the risk of ischemic stroke in the Veterans Health
Administration. Stroke 2012;43:28-31. doi:10.1161/
STROKEAHA.111.617191.
\131\ Faulkner CM, Cox HL, Williamson JC. Unique aspects of
antimicrobial use in older adults. Clin Infect Dis. 2005;40(7):997-
1004.
\132\ American Geriatrics Society 2019 Beers Criteria Update
Expert Panel. American Geriatrics Society 2019 Updated Beers
Criteria for Potentially Inappropriate Medication Use in Older
Adults. J Am Geriatr Soc 2019; 00:1-21.
\133\ Li Y, Salmasian H, Harpaz R, Chase H, Friedman C.
Determining the reasons for medication prescriptions in the EHR
using knowledge and natural language processing. AMIA Annu Symp
Proc. 2011; 2011:768-76.
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The High-Risk Drug Classes: Use and Indication data element
requires an assessor to record whether or not a patient is taking any
medications within the six drug classes. The six response options for
this data element are high-risk drug classes with particular relevance
to PAC patients and residents, as identified by our data element
contractor. The six data element response options are Anticoagulants,
Antiplatelets, Hypoglycemics, Opioids, Antipsychotics, and Antibiotics.
For each drug class, the assessor is required to indicate if the
patient is taking any medications within the class, and, for drug
classes in which medications were being taken, whether indications for
all drugs in the class are noted in the medical record. For example,
for the response option Anticoagulants, if the assessor indicates that
the patient has received anticoagulant medication, the assessor would
then indicate if an indication is recorded in the medication record for
the anticoagulant(s).
The High-Risk Drug Classes: Use and Indication data element that is
being proposed as a SPADE was developed as part of a larger set of data
elements to assess medication reconciliation, the process of obtaining
a patient's multiple medication lists and reconciling any
discrepancies. For more information on the High-Risk Drug Classes: Use
and Indication data element, we refer readers to the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We sought public input on the relevance of conducting assessments
on medication reconciliation and specifically on the proposed High-Risk
Drug Classes: Use and Indication data element. Our data element
contractor presented data elements related to medication reconciliation
to the TEP convened on April 6 and 7, 2016. The TEP supported a focus
on high-risk drugs, because of higher potential for harm to patients
and residents, and were in favor of a data element to capture whether
or not indications for medications were recorded in the medical record.
A summary of the April 6 and 7, 2016 TEP meeting titled ``SPADE
Technical Expert Panel Summary (First Convening)'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Medication reconciliation data
elements were also discussed at a second TEP meeting on January 5 and
6, 2017, convened by our data element contractor. At this meeting, the
TEP agreed about the importance of evaluating the medication
reconciliation process, but disagreed about how this could be
accomplished through standardized assessment. The TEP also disagreed
about the usability and appropriateness of using the Beers Criteria to
identify high-risk medications.\134\ A summary of the January 5 and 6,
2017 TEP meeting titled ``SPADE Technical Expert Panel Summary (Second
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------
\134\ American Geriatrics Society 2015 Beers Criteria Update
Expert Panel. American Geriatrics Society. Updated Beers Criteria
for Potentially Inappropriate Medication Use in Older Adults. J Am
Geriatr Soc 2015; 63:2227-2246.
---------------------------------------------------------------------------
We also solicited public input on data elements related to
medication reconciliation during a public input period from April 26 to
June 26, 2017. Several commenters noted support for the medication
reconciliation data elements that were put on display, noting the
importance of medication reconciliation in preventing medication errors
and stated that the items seemed feasible and clinically useful. A few
commenters were critical of the choice of 10 drug classes posted during
that comment period, stating that ADEs are not limited to high-risk
drugs, and raised issues related to training assessors to correctly
complete a valid assessment of medication reconciliation. A summary
report for the April 26 to June 26, 2017 public comment period titled
``SPADE May-June 2017 Public Comment Summary Report'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The High-Risk Drug Classes: Use and Indication data element was
included in the National Beta Test of candidate data elements conducted
by our data element contractor from November 2017 to August 2018.
Results of this test found the High-Risk Drug Classes: Use and
Indication data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
High-Risk Drug Classes: Use and Indication data element in the National
Beta Test can be found in the document titled ``Final Specifications
for IRF QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. The TEP acknowledged the
challenges of assessing medication safety, but were supportive of some
of the data elements focused on medication reconciliation that were
tested in the National Beta Test. The TEP was especially supportive of
the focus on the six high-risk drug classes and using these classes to
assess
[[Page 39142]]
whether the indication for a drug is recorded. A summary of the
September 17, 2018 TEP meeting titled ``SPADE Technical Expert Panel
Summary (Third Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. These activities provided
updates on the field-testing work and solicited feedback on data
elements considered for standardization, including the High-Risk Drug
Classes: Use and Indication data element. One stakeholder group was
critical of the six drug classes included as response options in the
High-Risk Drug Classes: Use and Indication data element, noting that
potentially risky medications (for example, muscle relaxants) are not
included in this list; that there may be important differences between
drugs within classes (for example, more recent versus older style
antidepressants); and that drug allergy information is not captured.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. Additionally, one commenter questioned whether the time to
complete the High-Risk Drug Classes: Use and Indication data element
would differ across settings. A summary of the public input received
from the November 27, 2018 stakeholder meeting titled ``Input on
Standardized Patient Assessment Data Elements (SPADEs) Received After
November 27, 2018 Stakeholder Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing high-risk drugs and for
whether or not indications are noted for high-risk drugs, stakeholder
input, and strong test results, we proposed that the High-Risk Drug
Classes: Use and Indication data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the High-Risk Drug Classes: Use and Indication data
element as standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the High-Risk Drug Classes: Use and Indication
data element.
Comment: Some commenters noted that the proposed High-Risk Drug
Classes: Use and Indication data elements are redundant of the existing
standards in the Hospital Conditions of Participation (CoPs) and that
requiring the collection of these data elements would be duplicative,
unnecessary, and at odds with the Meaningful Measures framework.
Response: We disagree that assessing the extent to which
medications from certain drug classes are being taken and the extent to
which indications are recorded for medications in these classes is
redundant with the existing CoPs. The CoPs provide guidance on clinical
practice, while the proposed SPADEs attempt to collect information
about individual patients in order to understand clinical acuity and to
populate a core set of information that can be exchanged with the
patient across care transitions.
Comment: Commenters noted that because adverse drug events (ADEs)
are not limited to high-risk drugs, this data element has limited
utility.
Response: We acknowledge that not all ADEs are associated with
``high-risk'' drugs, and we also note that medications in the named
drug classes are mostly used in a safe manner. Prescribed high-risk
medications are defined as a ``proximate factor'' to preventable ADEs
by the Joint Commission.\135\ However, the Joint Commission's
conceptual model of preventable ADEs also includes provider, patient,
health care system, organization, and technical factors, all of which
present many opportunities for disrupting preventable ADEs. We have
decided to focus on a selection of drug classes that are commonly used
by older adults and are related to ADEs which are clinically
significant, preventable, and measurable. Anticoagulants, antibiotics,
and diabetic agents have been implicated in an estimated 46.9 percent
(95 percent CI, 44.2 percent-49.7 percent) of emergency department
visits for adverse drug events.\136\ Among older adults (aged >=65
years), three drug classes (anticoagulants, diabetic agents, and opioid
analgesics) have been implicated in an estimated 59.9 percent (95
percent CI, 56.8 percent-62.9 percent) of ED visits for adverse drug
events.\137\ Further, antipsychotic medications have been identified as
a drug class for which there is a need for increased outreach and
educational efforts to reduce use among older adults.
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\135\ Chang A, Schyve PM, Croteau RJ, O'Leary DS, Loeb JM. The
JCAHO patient safety event taxonomy: A standardized terminology and
classification schema for near misses and adverse events. Int J Qual
Health Care. 2005;17(2):95-105.
\136\ Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ,
Budnitz DS. US emergency department visits for outpatient adverse
drug events, 2013-2014. JAMA 2016;316(2):2115-2125.
\137\ Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ,
Budnitz DS. US emergency department visits for outpatient adverse
drug events, 2013-2014. JAMA 2016;316(2):2115-2125.
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Comment: One commenter was concerned with the addition of the High-
Risk Drug Classes: Use and Indication data elements, noting that
providers should be granted clinical judgment to effectively treat
patients without CMS monitoring of medications used for treatment.
Response: The proposed SPADEs attempt to collect information about
individual patients to understand clinical acuity and to populate a
core set of information that can be exchanged with the patient across
care transitions. The intent of these data elements is not to monitor
prescribing practices, but rather to assess the extent to which
indications are noted for medications in certain drug classes.
Comment: A few commenters noted that the High-Risk Drug Class: Use
and Indication data elements seemed redundant with other SPADEs (that
is, IV Medications) and measures (that is, Provision of Current
Reconciled Medication List to Subsequent Provider at Discharge), or
duplicative of existing standards in the Hospital CoPs related to
procurement, preparation, and administration of drugs, which creates
unnecessary burden.
Response: The High-Risk Drugs: Use and Indications data element
captures unique information compared to the other SPADEs and measures
to which the commenters referred. With regard to the reference to the
measure Provision of Current Reconciled Medication List to Subsequent
Provider at Discharge, we wish to clarify that the High-Risk Drug
Classes: Use and Indication data elements capture medications taken by
any route and focuses on a select set of drug classes, not the act of
communicating a complete medication list. To the extent that the
activities captured by the High-Risk Drugs: Use and Indications data
element are already being performed by providers as part of
[[Page 39143]]
the Hospital CoPs, we believe that reporting of this data elements
should be easily integrated into existing workflow.
Comment: One commenter noted that medication indications are
typically documented in narrative notes by the medical staff and would
therefore be difficult to collect.
Response: We maintain that collecting information on the presence
of indications in the medical record is clinically important
information that can inform care planning and support care transitions.
It is the responsibility of IRF providers to record patient data in a
way that is useful and appropriate to meet clinical and administrative
needs. It is possible that the adoption of this SPADE and related
reporting requirement will promote a more efficient method for
documenting the clinical indication for each medication.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the High-Risk Drug Classes: Use
and Indication data element as standardized patient assessment data
beginning with the FY 2022 IRF QRP as proposed.
3. Medical Condition and Comorbidity Data
Assessing medical conditions and comorbidities is critically
important for care planning and safety for patients and residents
receiving PAC services, and the standardized assessment of selected
medical conditions and comorbidities across PAC providers is important
for managing care transitions and understanding medical complexity.
In this section we discuss our proposals for data elements related
to the medical condition of pain as standardized patient assessment
data. Appropriate pain management begins with a standardized
assessment, and thereafter establishing and implementing an overall
plan of care that is person-centered, multi-modal, and includes the
treatment team and the patient. Assessing and documenting the effect of
pain on sleep, participation in therapy, and other activities may
provide information on undiagnosed conditions and comorbidities and the
level of care required, and do so more objectively than subjective
numerical scores. With that, we assess that taken separately and
together, these proposed data elements are essential for care planning,
consistency across transitions of care, and identifying medical
complexities including undiagnosed conditions. We also conclude that it
is the standard of care to always consider the risks and benefits
associated with a personalized care plan, including the risks of any
pharmacological therapy, especially opioids.\138\ We also conclude that
in addition to assessing and appropriately treating pain through the
optimum mix of pharmacologic, non-pharmacologic, and alternative
therapies, while being cognizant of current prescribing guidelines,
clinicians in partnership with patients are best able to mitigate
factors that contribute to the current opioid crisis.\139\ \140\ \141\
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\138\ Department of Health and Human Services: Pain Management
Best Practices Inter-Agency Task Force. Draft Report on Pain
Management Best Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
\139\ Department of Health and Human Services: Pain Management
Best Practices Inter-Agency Task Force. Draft Report on Pain
Management Best Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
\140\ Fishman SM, Carr DB, Hogans B, et al. Scope and Nature of
Pain- and Analgesia-Related Content of the United States Medical
Licensing Examination (USMLE). Pain Med Malden Mass. 2018;19(3):449-
459. doi:10.1093/pm/pnx336.
\141\ Fishman SM, Young HM, Lucas Arwood E, et al. Core
competencies for pain management: results of an interprofessional
consensus summit. Pain Med Malden Mass. 2013;14(7):971-981.
doi:10.1111/pme.12107.
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In alignment with our Meaningful Measures Initiative, accurate
assessment of medical conditions and comorbidities of patients and
residents in PAC is expected to make care safer by reducing harm caused
in the delivery of care; promote effective prevention and treatment of
chronic disease; strengthen person and family engagement as partners in
their care; and promote effective communication and coordination of
care. The SPADEs will enable or support: Clinical decision-making and
early clinical intervention; person-centered, high quality care
through: facilitating better care continuity and coordination; better
data exchange and interoperability between settings; and longitudinal
outcome analysis. Therefore, reliable data elements assessing medical
conditions and comorbidities are needed to initiate a management
program that can optimize a patient's or resident's prognosis and
reduce the possibility of adverse events.
We sought comment that applies specifically to the standardized
patient assessment data for the category of medical conditions and co-
morbidities. We did not receive any comments on the category of medical
conditions and co-morbidities.
Final decisions on the SPADEs are given below, following more
detailed comments on each SPADE proposal.
Pain Interference (Pain Effect on Sleep, Pain Interference
With Therapy Activities, and Pain Interference With Day-to-Day
Activities)
In acknowledgement of the opioid crisis, we specifically sought
comment on whether or not we should add these pain items in light of
those concerns. Commenters were asked to address to what extent the
collection of the SPADEs described below through patient queries might
encourage providers to prescribe opioids.
In the FY 2020 IRF PPS proposed rule (84 FR 17314 through 17316),
we proposed that a set of three data elements on the topic of Pain
Interference (Pain Effect on Sleep, Pain Interference with Therapy
Activities, and Pain Interference with Day-to-Day Activities) meet the
definition of standardized patient assessment data with respect to
medical condition and comorbidity data under section 1899B(b)(1)(B)(iv)
of the Act.
The practice of pain management began to undergo significant
changes in the 1990s because the inadequate, non-standardized, non-
evidence-based assessment and treatment of pain became a public health
issue.\142\ In pain management, a critical part of providing
comprehensive care is performance of a thorough initial evaluation,
including assessment of both the medical and any biopsychosocial
factors causing or contributing to the pain, with a treatment plan to
address the causes of pain and to manage pain that persists over
time.\143\ Quality pain management, based on current guidelines and
evidence-based practices, can minimize unnecessary opioid prescribing
both by offering alternatives or supplemental treatment to opioids and
by clearly stating when they may be appropriate, and how to utilize
risk-benefit analysis for opioid and non-opioid treatment
modalities.\144\
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\142\ Institute of Medicine. Relieving Pain in America: A
Blueprint for Transforming Prevention, Care, Education, and
Research. Washington (DC): National Academies Press (US); 2011.
https://www.ncbi.nlm.nih.gov/books/NBK91497/.
\143\ Department of Health and Human Services: Pain Management
Best Practices Inter-Agency Task Force. Draft Report on Pain
Management Best Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
\144\ National Academies. Pain Management and the Opioid
Epidemic: Balancing Societal and Individual Benefits and Risks of
Prescription Opioid Use. Washington DC: National Academies of
Sciences, Engineering, and Medicine.; 2017.
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[[Page 39144]]
Pain is not a surprising symptom in PAC patients and residents,
where healing, recovery, and rehabilitation often require regaining
mobility and other functions after an acute event. Standardized
assessment of pain that interferes with function is an important first
step towards appropriate pain management in PAC settings. The National
Pain Strategy called for refined assessment items on the topic of pain,
and describes the need for these improved measures to be implemented in
PAC assessments.\145\ Further, the focus on pain interference, as
opposed to pain intensity or pain frequency, was supported by the TEP
convened by our data element contractor as an appropriate and
actionable metric for assessing pain. A summary of the September 17,
2018 TEP meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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\145\ National Pain Strategy: A Comprehensive Population-Health
Level Strategy for Pain. https://iprcc.nih.gov/sites/default/files/HHSNational_Pain_Strategy_508C.pdf.
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We appreciate the important concerns related to the misuse and
overuse of opioids in the treatment of pain and to that end we note
that in the proposed rule we have also proposed a SPADE that assess for
the use of, as well as importantly the indication for the use of, high-
risk drugs, including opioids. Further, in the FY 2017 IRF PPS final
rule (81 FR 52111) we adopted the Drug Regimen Review Conducted With
Follow-Up for Identified Issues--Post Acute Care (PAC) IRF QRP measure
which assesses whether PAC providers were responsive to potential or
actual clinically significant medication issue(s), which includes
issues associated with use and misuse of opioids for pain management,
when such issues were identified.
We also note that the proposed SPADE related to pain assessment are
not associated with any particular approach to management. Since the
use of opioids is associated with serious complications, particularly
in the elderly,\146\ \147\ \148\ an array of successful non-
pharmacologic and non-opioid approaches to pain management may be
considered. PAC providers have historically used a range of pain
management strategies, including non-steroidal anti-inflammatory drugs,
ice, transcutaneous electrical nerve stimulation (TENS) therapy,
supportive devices, acupuncture, and the like. In addition, non-
pharmacological interventions for pain management include, but are not
limited to, biofeedback, application of heat/cold, massage, physical
therapy, stretching and strengthening exercises, chiropractic,
electrical stimulation, radiotherapy, and ultrasound.\149\ \150\ \151\
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\146\ Chau, D. L., Walker, V., Pai, L., & Cho, L. M. (2008).
Opiates and elderly: use and side effects. Clinical interventions in
aging, 3(2), 273-8.
\147\ Fine, P. G. (2009). Chronic Pain Management in Older
Adults: Special Considerations. Journal of Pain and Symptom
Management, 38(2): S4-S14.
\148\ Solomon, D. H., Rassen, J. A., Glynn, R. J., Garneau, K.,
Levin, R., Lee, J., & Schneeweiss, S. (2010). Archives Internal
Medicine, 170(22):1979-1986.
\149\ Byrd L. Managing chronic pain in older adults: a long-term
care perspective. Annals of Long-Term Care: Clinical Care and Aging.
2013;21(12):34-40.
\150\ Kligler, B., Bair, M.J., Banerjea, R. et al. (2018).
Clinical Policy Recommendations from the VHA State-of-the-Art
Conference on Non-Pharmacological Approaches to Chronic
Musculoskeletal Pain. Journal of General Internal Medicine, 33(Suppl
1): 16. https://doi.org/10.1007/s11606-018-4323-z.
\151\ Chou, R., Deyo, R., Friedly, J., et al. (2017).
Nonpharmacologic Therapies for Low Back Pain: A Systematic Review
for an American College of Physicians Clinical Practice Guideline.
Annals of Internal Medicine, 166(7):493-505.
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We believe that standardized assessment of pain interference will
support PAC clinicians in applying best-practices in pain management
for chronic and acute pain, consistent with current clinical
guidelines. For example, the standardized assessment of both opioids
and pain interference would support providers in successfully tapering
the dosage regimens in patients/residents who arrive in the PAC setting
with long-term opioid use off of opioids onto non-pharmacologic
treatments and non-opioid medications, as recommended by the Society
for Post-Acute and Long-Term Care Medicine,\152\ and consistent with
HHS's 5-Point Strategy To Combat the Opioid Crisis \153\ which includes
``Better Pain Management.''
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\152\ Society for Post-Acute and Long-Term Care Medicine (AMDA).
(2018). Opioids in Nursing Homes: Position Statement. https://paltc.org/opioids%20in%20nursing%20homes.
\153\ https://www.hhs.gov/opioids/about-the-epidemic/hhs-response/.
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The Pain Interference data elements consist of three data elements:
Pain Effect on Sleep, Pain Interference with Therapy Activities, and
Pain Interference with Day-to-Day Activities. Pain Effect on Sleep
assesses the frequency with which pain affects a resident's sleep. Pain
Interference with Therapy Activities assesses the frequency with which
pain interferes with a resident's ability to participate in therapies.
The Pain Interference with Day-to-Day Activities assesses the extent to
which pain interferes with a resident's ability to participate in day-
to-day activities excluding therapy.
A similar data element on the effect of pain on activities is
currently included in the OASIS. A similar data element on the effect
on sleep is currently included in the MDS instrument. For more
information on the Pain Interference data elements, we refer readers to
the document titled ``Final Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We sought public input on the relevance of conducting assessments
on pain and specifically on the larger set of Pain Interview data
elements included in the National Beta Test. The proposed data elements
were supported by comments from the TEP meeting held by our data
element contractor on April 7 to 8, 2016. The TEP affirmed the
feasibility and clinical utility of pain as a concept in a standardized
assessment. The TEP agreed that data elements on pain interference with
ability to participate in therapies versus other activities should be
addressed. Further, during a more recent convening of the same TEP on
September 17, 2018, the TEP supported the interview-based pain data
elements included in the National Beta Test. The TEP members were
particularly supportive of the items that focused on how pain
interferes with activities (that is, Pain Interference data elements),
because understanding the extent to which pain interferes with function
would enable clinicians to determine the need for appropriate pain
treatment. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We held a public input period in 2016 to solicit feedback on the
standardization of pain and several other items that were under
development in prior efforts. From the prior public comment period, we
included several pain data elements (Pain Effect on Sleep; Pain
Interference--Therapy Activities; Pain Interference--Other Activities)
in a second call for public input, open from
[[Page 39145]]
April 26 to June 26, 2017. The items we sought comment on were modified
from all stakeholder and test efforts. Commenters provided general
comments about pain assessment in general in addition to feedback on
the specific pain items. A few commenters shared their support for
assessing pain, the potential for pain assessment to improve the
quality of care, and for the validity and reliability of the data
elements. Commenters affirmed that the item of pain and the effect on
sleep would be suitable for PAC settings. Commenters' main concerns
included redundancy with existing data elements, feasibility and
utility for cross-setting use, and the applicability of interview-based
items to patients and residents with cognitive or communication
impairments, and deficits. A summary report for the April 26 to June
26, 2017 public comment period titled ``SPADE May-June 2017 Public
Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Pain Interference data elements were included in the National
Beta Test of candidate data elements conducted by our data element
contractor from November 2017 to August 2018. Results of this test
found the Pain Interference data elements to be feasible and reliable
for use with PAC patients and residents. More information about the
performance of the Pain Interference data elements in the National Beta
Test can be found in the document titled ``Final Specifications for IRF
QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018 for the purpose of soliciting input on the
standardized patient assessment data elements. The TEP supported the
interview-based pain data elements included in the National Beta Test.
The TEP members were particularly supportive of the items that focused
on how pain interferes with activities (that is, Pain Interference data
elements), because understanding the extent to which pain interferes
with function would enable clinicians to determine the need for pain
treatment. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our on-going SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. Additionally, one commenter noted strong support for the Pain
data elements and was encouraged by the fact that this portion of the
assessment goes beyond merely measuring the presence of pain. A summary
of the public input received from the November 27, 2018 stakeholder
meeting titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for the effect of pain
on function, stakeholder input, and strong test results, we proposed
that the three Pain Interference data elements (Pain Effect on Sleep,
Pain Interference with Therapy Activities, and Pain Interference with
Day-to-Day Activities) meet the definition of standardized patient
assessment data with respect to medical conditions and comorbidities
under section 1899B(b)(1)(B)(iv) of the Act and to adopt the Pain
Interference data elements (Pain Effect on Sleep; Pain Interference
with Therapy Activities; and Pain Interference with Day-to-Day
Activities) as standardized patient assessment data for use in the IRF
QRP.
Commenters submitted the following comments related to our proposal
to adopt the Pain Interference (Pain Effect on Sleep, Pain Interference
with Therapy Activities, and Pain Interference with Day-to-Day
Activities) data elements.
Comment: A few commenters noted support for the Pain Interference
data element, noting that the data element will provide a useful and
more accurate assessment of a patient's ability to function, and that
understanding the impact of pain on therapy and other activities,
including sleep, can improve the quality of care, which in turn will
support providers in their ability to provide effective pain management
services.
Response: We thank the commenters for their support of the Pain
Interference data element.
Comment: A commenter noted that the proposed Pain Interference
SPADEs document pain frequency, but stated that it is important to
identify both pain frequency and pain intensity.
Response: We wish to clarify, the Pain Interference interview data
elements question the patient on the frequency with which pain
interferes with sleep, therapy, or non-therapy activities. These data
elements therefore combine the concepts of frequency and intensity,
with the measure of intensity being interference with the named
activities. Self-reported measures of pain intensity are often
criticized for being infeasible to standardize. In these data elements,
we use interference with activities as an alternative to inquiring
about intensity.
Comment: A commenter expressed concerns about the suitability of
the Pain Interference data elements for use in patients with cognitive
and communication deficits and recommended CMS consider the use of non-
verbal means to allow patients to respond to SPADEs related to pain.
Response: We appreciate the commenter's concern surrounding pain
assessment with patients with cognitive and communication deficits. The
Pain Interference interview SPADEs require that a patient be able to
communicate, whether verbally, in writing, or using another method;
assessors may use non-verbal means to administer the questions (for
example, providing the questions and response in writing for a patient
with severe hearing impairment). Patients who are unable to communicate
by any means would not be required to complete the Pain Interference
interview SPADEs. However, evidence suggests that pain presence can be
reliably assessed in non-communicative patients through structural
observational protocols. To that end, we tested observational pain
presence elements in the National Beta Test, but have chosen not to
propose those data elements as SPADEs at this time. We will take the
commenter's concern into consideration as the SPADEs are monitored and
refined in the future.
[[Page 39146]]
Comment: A commenter expressed concerns about how CMS might use
these data elements, noting particular concern that collection of these
data elements may inappropriately translate into an assessment of
quality, and that data collection on this topic could create incentives
that directly or indirectly interfere with treatment decisions.
Response: We appreciate the commenter's concern related to wanting
to understand how we will use the SPADEs in the future. We intend to
continue to communicate and collaborate with stakeholders about how the
SPADEs will be used in the IRF QRP, as those plans are developed, by
soliciting input during the development process and establishing use of
the SPADEs in payment and quality programs through future rulemaking.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Pain Interference (Pain Effect
on Sleep, Pain Interference with Therapy Activities, and Pain
Interference with Day-to-Day Activities) data elements as standardized
patient assessment data beginning with the FY 2022 IRF QRP as proposed.
4. Impairment Data
Hearing and vision impairments are conditions that, if unaddressed,
affect activities of daily living, communication, physical functioning,
rehabilitation outcomes, and overall quality of life. Sensory
limitations can lead to confusion in new settings, increase isolation,
contribute to mood disorders, and impede accurate assessment of other
medical conditions. Failure to appropriately assess, accommodate, and
treat these conditions increases the likelihood that patients and
residents will require more intensive and prolonged treatment. Onset of
these conditions can be gradual, so individualized assessment with
accurate screening tools and follow-up evaluations are essential to
determining which patients and residents need hearing- or vision-
specific medical attention or assistive devices and accommodations,
including auxiliary aids and/or services, and to ensure that person-
directed care plans are developed to accommodate a patient's or
resident's needs. Accurate diagnosis and management of hearing or
vision impairment would likely improve rehabilitation outcomes and care
transitions, including transition from institutional-based care to the
community. Accurate assessment of hearing and vision impairment would
be expected to lead to appropriate treatment, accommodations, including
the provision of auxiliary aids and services during the stay, and
ensure that patients and residents continue to have their vision and
hearing needs met when they leave the facility.
In alignment with our Meaningful Measures Initiative, we expect
accurate and individualized assessment, treatment, and accommodation of
hearing and vision impairments of patients and residents in PAC to make
care safer by reducing harm caused in the delivery of care; promote
effective prevention and treatment of chronic disease; strengthen
person and family engagement as partners in their care; and promote
effective communication and coordination of care. For example,
standardized assessment of hearing and vision impairments used in PAC
will support ensuring patient safety (for example, risk of falls),
identifying accommodations needed during the stay, and appropriate
support needs at the time of discharge or transfer. Standardized
assessment of these data elements will: Enable or support clinical
decision-making and early clinical intervention; person-centered, high
quality care (for example, facilitating better care continuity and
coordination); better data exchange and interoperability between
settings; and longitudinal outcome analysis. Therefore, reliable data
elements assessing hearing and vision impairments are needed to
initiate a management program that can optimize a patient's or
resident's prognosis and reduce the possibility of adverse events.
Comments on the category of impairments were also submitted by
stakeholders during the FY 2018 IRF PPS proposed rule (82 FR 20737
through 20739) public comment period. A commenter stated hearing and
vision assessments should be administered at the beginning of the
assessment process to provide evidence about any sensory deficits that
may affect the patient's ability to participate in the assessment and
to allow the assessor to offer an assistive device.
We sought comment on our proposals to collect as standardized
patient assessment data the following data with respect to impairments.
We did not receive any comments on the category of impairments.
Final decisions on the SPADEs are given below, following more
detailed comments on each SPADE proposal.
Hearing
In the FY 2020 IRF PPS proposed rule (84 FR 17317 through 17318),
we proposed that the Hearing data element meets the definition of
standardized patient assessment data with respect to impairments under
section 1899B(b)(1)(B)(v) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20737
through 20738), accurate assessment of hearing impairment is important
in the PAC setting for care planning and resource use. Hearing
impairment has been associated with lower quality of life, including
poorer physical, mental, social functioning, and emotional
health.154 155 Treatment and accommodation of hearing
impairment led to improved health outcomes including, but not limited
to, quality of life.\156\ For example, hearing loss in elderly
individuals has been associated with depression and cognitive
impairment,157 158 159 higher rates of incident cognitive
impairment and cognitive decline,\160\ and less time in occupational
therapy.\161\ Accurate assessment of hearing impairment is important in
the PAC setting for care planning and defining resource use.
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\154\ Dalton DS, Cruickshanks KJ, Klein BE, Klein R, Wiley TL,
Nondahl DM. The impact of hearing loss on quality of life in older
adults. Gerontologist. 2003;43(5):661-668.
\155\ Hawkins K, Bottone FG, Jr., Ozminkowski RJ, et al. The
prevalence of hearing impairment and its burden on the quality of
life among adults with Medicare Supplement Insurance. Qual Life Res.
2012;21(7):1135-1147.
\156\ Horn KL, McMahon NB, McMahon DC, Lewis JS, Barker M,
Gherini S. Functional use of the Nucleus 22-channel cochlear implant
in the elderly. The Laryngoscope. 1991;101(3):284-288.
\157\ Sprinzl GM, Riechelmann H. Current trends in treating
hearing loss in elderly people: a review of the technology and
treatment options--a mini-review. Gerontology. 2010;56(3):351-358.
\158\ Lin FR, Thorpe R, Gordon-Salant S, Ferrucci L. Hearing
Loss Prevalence and Risk Factors Among Older Adults in the United
States. The Journals of Gerontology Series A: Biological Sciences
and Medical Sciences. 2011;66A(5):582-590.
\159\ Hawkins K, Bottone FG, Jr., Ozminkowski RJ, et al. The
prevalence of hearing impairment and its burden on the quality of
life among adults with Medicare Supplement Insurance. Qual Life Res.
2012;21(7):1135-1147.
\160\ Lin FR, Metter EJ, O'Brien RJ, Resnick SM, Zonderman AB,
Ferrucci L. Hearing Loss and Incident Dementia. Arch Neurol.
2011;68(2):214-220.
\161\ Cimarolli VR, Jung S. Intensity of Occupational Therapy
Utilization in Nursing Home Residents: The Role of Sensory
Impairments. J Am Med Dir Assoc. 2016;17(10):939-942.
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The proposed data element consists of the single Hearing data
element. This data consists of one question that assesses level of
hearing impairment. This data element is currently in use in the MDS in
SNFs. For more information on the Hearing data element, we refer
readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-
Assessment-
[[Page 39147]]
Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-and-Videos.html.
The Hearing data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20737 through 20738). In that proposed rule, we stated that the
proposal was informed by input we received on the PAC PRD form of the
data element (``Ability to Hear'') through a call for input published
on the CMS Measures Management System Blueprint website. Input
submitted from August 12 to September 12, 2016 recommended that
hearing, vision, and communication assessments be administered at the
beginning of patient assessment process. A summary report for the
August 12 to September 12, 2016 public comment period titled ``SPADE
August 2016 Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of adopting the Hearing data
element for standardized cross-setting use, noting that it would help
address the needs of patient and residents with disabilities and that
failing to identify impairments during the initial assessment can
result in inaccurate diagnoses of impaired language or cognition and
can invalidate other information obtained from patient assessment.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Hearing data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Hearing
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Hearing data
element in the National Beta Test can be found in the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on January
5 and 6, 2017 for the purpose of soliciting input on all the SPADEs,
including the Hearing data element. The TEP affirmed the importance of
standardized assessment of hearing impairment in PAC patients and
residents. A summary of the January 5 and 6, 2017 TEP meeting titled
``SPADE Technical Expert Panel Summary (Second Convening)'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. Additionally, a commenter noted support for the Hearing data
element and suggested administration at the beginning of the patient
assessment to maximize utility. A summary of the public input received
from the November 27, 2018 stakeholder meeting titled ``Input on
Standardized Patient Assessment Data Elements (SPADEs) Received After
November 27, 2018 Stakeholder Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Due to the relatively stable nature of hearing impairment, it is
unlikely that a patient's score on this assessment would change between
the start and end of the IRF stay. Therefore, we proposed that IRFs
that submit the Hearing data element with respect to admission will be
deemed to have submitted with respect to both admission and discharge.
Taking together the importance of assessing for hearing,
stakeholder input, and strong test results, we proposed that the
Hearing data element meets the definition of standardized patient
assessment data with respect to impairments under section
1899B(b)(1)(B)(v) of the Act and to adopt the Hearing data element as
standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to our proposal
for the Hearing data element.
Comment: A few commenters supported the collection of information
on hearing impairment. One of these commenters also suggested that CMS
consider how hearing impairment impacts a patient's ability to respond
to the assessment tool in general.
Response: We thank the commenters for their support of the Hearing
data element. We intend to reinforce assessment tips and item rationale
through training, open door forums, and future rulemaking efforts.
In the existing guidance manual for the IRF-PAI, we offer tips for
administration that direct assessors to take appropriate steps to
accommodate sensory and communication impairments when conducting the
assessment.
Comment: Some commenters expressed concern that severely impaired
hearing occurs infrequently in IRF patients, thereby limiting the
utility of the data collected.
Response: The Hearing SPADE consists of one data element completed
by the assessor based primarily on interacting with the patient and
reviewing the medical record. Given the low burden of reporting the
Hearing data element, and despite severe hearing impairment occurring
in a small proportion of IRF patients, we believe it is important to
systematically assess for hearing impairment in order to improve
clinical care and care transitions.
Comment: One commenter recommended adding ``unable to assess'' as a
response option, which the commenter believes would be the appropriate
choice if the patient is comatose or is unable to effectively answer
questions related to an assessment of their hearing.
Response: We appreciate the commenter's recommendation. The
assessment of hearing is completed based on observing the patient
during assessment, patient interactions with others, reviewing medical
record documentation, and consulting with patient's family and other
staff, in addition to interviewing the patient, so it can be completed
when the patient is unable to effectively answer questions related to
an assessment of their hearing.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Hearing data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Vision
In the FY 2020 IRF PPS proposed rule (84 FR 17318 through 17319),
we proposed that the Vision data element
[[Page 39148]]
meets the definition of standardized patient assessment data with
respect to impairments under section 1899B(b)(1)(B)(v) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20738
through 20739), evaluation of an individual's ability to see is
important for assessing for risks such as falls and provides
opportunities for improvement through treatment and the provision of
accommodations, including auxiliary aids and services, which can
safeguard patients and residents and improve their overall quality of
life. Further, vision impairment is often a treatable risk factor
associated with adverse events and poor quality of life. For example,
individuals with visual impairment are more likely to experience falls
and hip fracture, have less mobility, and report depressive
symptoms.162 163 164 165 166 167 168 Individualized initial
screening can lead to life-improving interventions such as
accommodations, including the provision of auxiliary aids and services,
during the stay and/or treatments that can improve vision and prevent
or slow further vision loss.
---------------------------------------------------------------------------
\162\ Colon-Emeric CS, Biggs DP, Schenck AP, Lyles KW. Risk
factors for hip fracture in skilled nursing facilities: Who should
be evaluated? Osteoporos Int. 2003;14(6):484-489.
\163\ Freeman EE, Munoz B, Rubin G, West SK. Visual field loss
increases the risk of falls in older adults: The Salisbury eye
evaluation. Invest Ophthalmol Vis Sci. 2007;48(10):4445-4450.
\164\ Keepnews D, Capitman JA, Rosati RJ. Measuring patient-
level clinical outcomes of home health care. J Nurs Scholarsh.
2004;36(1):79-85.
\165\ Nguyen HT, Black SA, Ray LA, Espino DV, Markides KS.
Predictors of decline in MMSE scores among older Mexican Americans.
J Gerontol A Biol Sci Med Sci. 2002;57(3):M181-185.
\166\ Prager AJ, Liebmann JM, Cioffi GA, Blumberg DM. Self-
reported Function, Health Resource Use, and Total Health Care Costs
Among Medicare Beneficiaries With Glaucoma. JAMA ophthalmology.
2016;134(4):357-365.
\167\ Rovner BW, Ganguli M. Depression and disability associated
with impaired vision: The MoVies Project. J Am Geriatr Soc.
1998;46(5):617-619.
\168\ Tinetti ME, Ginter SF. The nursing home life-space
diameter. A measure of extent and frequency of mobility among
nursing home residents. J Am Geriatr Soc. 1990;38(12):1311-1315.
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In addition, vision impairment is often a treatable risk factor
associated with adverse events which can be prevented and accommodated
during the stay. Accurate assessment of vision impairment is important
in the IRF setting for care planning and defining resource use.
The proposed data element consists of the single Vision data
element (Ability To See in Adequate Light) that consists of one
question with five response categories. The Vision data element that we
proposed for standardization was tested as part of the development of
the MDS and is currently in use in that assessment in SNFs. Similar
data elements, but with different wording and fewer response option
categories, are in use in the OASIS. For more information on the Vision
data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Vision data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20738 through 20739).
In that proposed rule, we stated that the proposal was informed by
input we received on the Ability to See in Adequate Light data element
(version tested in the PAC PRD with three response categories) through
a call for input published on the CMS Measures Management System
Blueprint website. Although the data element in public comment differed
from the proposed data element, input submitted from August 12 to
September 12, 2016 supported assessing vision in PAC settings and the
useful information a vision data element would provide.
We also stated that commenters had noted that the Ability to See
item would provide important information that would facilitate care
coordination and care planning, and consequently improve the quality of
care. Other commenters suggested it would be helpful as an indicator of
resource use and noted that the item would provide useful information
about the abilities of patients and residents to care for themselves.
Additional commenters noted that the item could feasibly be implemented
across PAC providers and that its kappa scores from the PAC PRD support
its validity. Some commenters noted a preference for MDS version of the
Vision data element in SNFs over the form put forward in public
comment, citing the widespread use of this data element. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received a comment supporting having a standardized patient
assessment data element for vision across PAC settings, but it stated
the proposed data element captures only basic information for risk
adjustment, and more detailed information would need to be collected to
use it as an outcome measure.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Vision data element was included in the National Beta Test of candidate
data elements conducted by our data element contractor from November
2017 to August 2018. Results of this test found the Vision data element
to be feasible and reliable for use with PAC patients and residents.
More information about the performance of the Vision data element in
the National Beta Test can be found in the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on January
5 and 6, 2017 for the purpose of soliciting input on all the SPADEs
including the Vision data element. The TEP affirmed the importance of
standardized assessment of vision impairment in PAC patients and
residents. A summary of the January 5 and 6, 2017 TEP meeting titled
``SPADE Technical Expert Panel Summary (Second Convening)'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. Additionally, a commenter noted support for the Vision data
element and suggested administration at the beginning of the patient
assessment to maximize utility. A summary of the public input received
from the November 27, 2018 stakeholder meeting
[[Page 39149]]
titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Due to the relatively stable nature of vision impairment, it is
unlikely that a patient's score on this assessment would change between
the start and end of the IRF stay. Therefore, we proposed that IRFs
that submit the Vision data element with respect to admission will be
deemed to have submitted with respect to both admissions and discharge.
Taking together the importance of assessing for vision, stakeholder
input, and strong test results, we proposed that the Vision data
element meets the definition of standardized patient assessment data
with respect to impairments under section 1899B(b)(1)(B)(v) of the Act
and to adopt the Vision data element as standardized patient assessment
data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the Vision data element.
Comment: A few commenters supported the collection of information
on vision impairment. One of the commenters noted that the collection
of information on vision impairment would support the identification
and appropriate treatment of vision problems, which they stated were
prevalent and undertreated.
Response: We thank the commenters for their support.
Comment: One commenter recommended that a doctor of optometry
should play a lead role in conducting vision assessments, and that
vision assessments done by other clinicians should also obtain the
patient's own assessment of his or her vision, such as used by the
Centers for Disease Control and Prevention (CDC) Behavioral Risk
Factors Surveillance System survey, which questions patients ``Do you
have serious difficulty seeing, even when wearing glasses?'' This
commenter expressed concerns about the proposed SPADE being subjective
and risks of mis-categorizing patients.
Response: We appreciate the commenter's recommendation about how to
assess for vision impairment. We do not require that a certain type of
clinician complete assessments; the SPADEs have been developed so that
any clinician who is trained in the administration of the assessment
will be able to administer it correctly. The proposed item relies on
the assessor's evaluation of the patient's vision, which has the
advantage of reducing burden placed on the patient. We will take the
recommendation to use patient-reported vision impairment assessment
into consideration in the development of future assessments.
Comment: Some commenters expressed concern that severely impaired
vision occurs infrequently in IRF patients, thereby limiting the
utility of the data collected.
Response: The Vision SPADE consists of one data element completed
by the assessor based primarily on interacting with the patient and
reviewing the medical record. Given the low burden of the Vision data
element, and despite severe vision impairment occurring in a small
proportion of IRF patients, we believe it is important to
systematically assess for vision impairment in order to improve
clinical care and care transitions.
Comment: A commenter recommended that CMS require a vision
assessment at discharge, noting that vision impairment could be related
to challenges in medication management and compliance with written
follow-up instructions for care.
Response: We appreciate the commenter's feedback. We agree that
adequate vision--or the accommodations and assistive technology needed
to compensate for vision impairment--is important to patient safety in
the community, in part for the reasons the commenter mentions. In the
FY 2020 IRF PPS proposed rule (84 FR 17292), we proposed that IRFs that
submitted the Vision SPADE with respect to admission will be deemed to
have submitted with respect to both admission and discharge; we stated
that it is unlikely that the assessment of this SPADEs at admission
would differ from the assessment at discharge. Vision assessment,
collected via the Vision SPADE with respect to admission, will provide
information that will support the patient's care while in the IRF. Out
of consideration for the burden of data collection, and with an
understanding that significant clinical changes to a patient's vision
will be documented in the medical record as part of routine clinical
practice, we are finalizing our proposal that IRFs that submit the
Vision SPADE with respect to admission will be deemed to have submitted
with respect to both admission and discharge. We note that during the
discharge planning process, it is incumbent on IRF providers to make
reasonable assurances that the patient's needs will be met in the next
care setting, including in the home.
Comment: One commenter recommended adding ``unable to assess'' as a
response option, which the commenter believes would be the appropriate
choice if the patient is comatose or is unable to effectively answer
questions related to an assessment of their vision.
Response: We appreciate the commenter's recommendation. However,
the assessment of vision is completed based on consulting with
patient's family and other staff, observing the patient including
requesting the patient to read text or examine pictures or numbers in
addition to interviewing the patient about their vision abilities.
These other sources/methods can be used to complete the assessment of
vision when the patient is unable to effectively answer questions
related to an assessment of their vision.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Vision data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
4. New Category: Social Determinants of Health
a. Social Determinants of Health Data Collection To Inform Measures and
Other Purposes
Section 2(d)(2)(A) of the IMPACT Act requires CMS to assess
appropriate adjustments to quality measures, resource measures and
other measures, and to assess and implement appropriate adjustments to
payment under Medicare, based on those measures, after taking into
account studies conducted by ASPE on social risk factors (described
below) and other information, and based on an individual's health
status and other factors. Paragraph (C) of section 2(d)(2) of the
IMPACT Act further requires the Secretary to carry out periodic
analyses, at least every 3 years, based on the factors referred to
paragraph (A) so as to monitor changes in possible relationships.
Paragraph (B) of section 2(d)(2) of the IMPACT Act requires CMS to
collect or otherwise obtain access to data necessary to carry out the
requirement of the paragraph (both assessing adjustments described
above in such paragraph (A) and for periodic analyses in such paragraph
(C)). Accordingly we proposed to use our authority under paragraph (B)
of section 2(d)(2) of the IMPACT Act to establish a new data source for
information to
[[Page 39150]]
meet the requirements of paragraphs (A) and (C) of section 2(d)(2) of
the IMPACT Act. In this rule, we proposed to collect and access data
about social determinants of health (SDOH) in order to perform CMS'
responsibilities under paragraphs (A) and (C) of section 2(d)(2) of the
IMPACT Act, as explained in more detail below. Social determinants of
health, also known as social risk factors, or health-related social
needs, are the socioeconomic, cultural and environmental circumstances
in which individuals live that impact their health. We proposed to
collect information on seven proposed SDOH SPADE data elements relating
to race, ethnicity, preferred language, interpreter services, health
literacy, transportation, and social isolation; a detailed discussion
of each of the proposed SDOH data elements is found in section
VII.G.5.b. of this rule.
We also proposed to use the assessment instrument for the IRF QRP,
the IRF-PAI, described as a PAC assessment instrument under section
1899B(a)(2)(B) of the Act, to collect these data via an existing data
collection mechanism. We believe this approach will provide CMS with
access to data with respect to the requirements of section 2(d)(2) of
the IMPACT Act, while minimizing the reporting burden on PAC health
care providers by relying on a data reporting mechanism already used
and an existing system to which PAC health care providers are already
accustomed.
The IMPACT Act includes several requirements applicable to the
Secretary, in addition to those imposing new data reporting obligations
on certain PAC providers as discussed in IX.G.4.b. of this final rule.
Paragraphs (A) and (B) of sections 2(d)(1) of the IMPACT Act require
the Secretary, acting through the Office of the Assistant Secretary for
Planning and Evaluation (ASPE), to conduct two studies that examine the
effect of risk factors, including individuals' socioeconomic status, on
quality, resource use and other measures under the Medicare program.
The first ASPE study was completed in December 2016 and is discussed
below, and the second study is to be completed in the fall of 2019. We
recognize that ASPE, in its studies, is considering a broader range of
social risk factors than the SDOH data elements in this proposal, and
address both PAC and non-PAC settings. We acknowledge that other data
elements may be useful to understand, and that some of those elements
may be of particular interest in non-PAC settings. For example, for
beneficiaries receiving care in the community, as opposed to an in-
patient facility, housing stability and food insecurity may be more
relevant. We will continue to take into account the findings from both
of ASPE's reports in future policy making.
One of the ASPE's first actions under the IMPACT Act was to
commission the National Academies of Sciences, Engineering, and
Medicine (NASEM) to define and conceptualize socioeconomic status for
the purposes of ASPE's two studies under section 2(d)(1) of the IMPACT
Act. The NASEM convened a panel of experts in the field and conducted
an extensive literature review. Based on the information collected, the
2016 NASEM panel report titled, ``Accounting for Social Risk Factors in
Medicare Payment: Identifying Social Risk Factors'', concluded that the
best way to assess how social processes and social relationships
influence key health-related outcomes in Medicare beneficiaries is
through a framework of social risk factors instead of socioeconomic
status. Social risk factors discussed in the NASEM report include
socioeconomic position, race, ethnicity, gender, social context, and
community context. These factors are discussed at length in chapter 2
of the NASEM report, titled ``Social Risk Factors.'' \169\ Consequently
NASEM framed the results of its report in terms of ``social risk
factors'' rather than ``socioeconomic status'' or ``sociodemographic
status.'' The full text of the ``Social Risk Factors'' NASEM report is
available for reading on the website at https://www.nap.edu/read/21858/chapter/1.
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\169\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Chapter 2. Washington, DC: The
National Academies Press.
---------------------------------------------------------------------------
Each of the data elements we proposed to collect and access under
our authority under section 2(d)(2)(B) of the IMPACT Act is identified
in the 2016 NASEM report as a social risk factor that has been shown to
impact care use, cost and outcomes for Medicare beneficiaries. CMS uses
the term social determinants of health (SDOH) to denote social risk
factors, which is consistent with the objectives of Healthy People
2020.\170\
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\170\ Social Determinants of Health. Healthy People 2020.
https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health. (February 2019).
---------------------------------------------------------------------------
ASPE issued its first Report to Congress, titled ``Social Risk
Factors and Performance Under Medicare's Value-Based Purchasing
Programs,'' under section 2(d)(1)(A) of the IMPACT Act on December 21,
2016.\171\ Using NASEM's social risk factors framework, ASPE focused on
the following social risk factors, in addition to disability: (1) Dual
enrollment in Medicare and Medicaid as a marker for low income; (2)
residence in a low-income area; (3) Black race; (4) Hispanic ethnicity;
and (5) residence in a rural area. ASPE acknowledged that the social
risk factors examined in its report were limited due to data
availability. The report also noted that the data necessary to
meaningfully attempt to reduce disparities and identify and reward
improved outcomes for beneficiaries with social risk factors have not
been collected consistently on a national level in PAC settings. Where
these data have been collected, the collection frequently involves
lengthy questionnaires. More information on the Report to Congress on
Social Risk Factors and Performance under Medicare's Value-Based
Purchasing Programs, including the full report, is available on the
website at https://aspe.hhs.gov/social-risk-factors-and-medicares-value-based-purchasing-programs-reports.
---------------------------------------------------------------------------
\171\ U.S. Department of Health and Human Services, Office of
the Assistant Secretary for Planning and Evaluation. 2016. Report to
Congress: Social Risk Factors and Performance Under Medicare's
Value-Based Payment Programs. Washington, DC.
---------------------------------------------------------------------------
Section 2(d)(2) of the IMPACT Act relates to CMS activities and
imposes several responsibilities on the Secretary relating to quality,
resource use, and other measures under Medicare. As mentioned
previously, under paragraph (A) of section 2(d)(2) of the IMPACT Act,
the Secretary is required, on an ongoing basis, taking into account the
ASPE studies and other information, and based on an individual's health
status and other factors, to assess appropriate adjustments to quality,
resource use, and other measures, and to assess and implement
appropriate adjustments to Medicare payments based on those measures.
Section 2(d)(2)(A)(i) of the IMPACT Act applies to measures adopted
under sections (c) and (d) of section 1899B of the Act and to other
measures under Medicare. However, CMS' ability to perform these
analyses, and assess and make appropriate adjustments is hindered by
limits of existing data collections on SDOH data elements for Medicare
beneficiaries. In its first study in 2016, in discussing the second
study, ASPE noted that information relating to many of the specific
factors listed in the IMPACT Act, such as health literacy, limited
English proficiency, and Medicare beneficiary activation, are not
available in Medicare data.
[[Page 39151]]
Paragraph 2(d)(2)(A) of the IMPACT Act specifically requires the
Secretary to take the studies and considerations from ASPE's reports to
Congress, as well as other information as appropriate, into account in
assessing and implementing adjustments to measures and related payments
based on measures in Medicare. The results of the ASPE's first study
demonstrated that Medicare beneficiaries with social risk factors
tended to have worse outcomes on many quality measures, and providers
who treated a disproportionate share of beneficiaries with social risk
factors tended to have worse performance on quality measures. As a
result of these findings, ASPE suggested a three-pronged strategy to
guide the development of value-based payment programs under which all
Medicare beneficiaries receive the highest quality healthcare services
possible. The three components of this strategy are to: (1) Measure and
report quality of care for beneficiaries with social risk factors; (2)
set high, fair quality standards for care provided to all
beneficiaries; and (3) reward and support better outcomes for
beneficiaries with social risk factors. In discussing how measuring and
reporting quality for beneficiaries with social risk factors can be
applied to Medicare quality payment programs, the report offered nine
considerations across the three-pronged strategy, including enhancing
data collection and developing statistical techniques to allow
measurement and reporting of performance for beneficiaries with social
risk factors on key quality and resource use measures.
Congress, in section 2(d)(2)(B) of the IMPACT Act, required the
Secretary to collect or otherwise obtain access to the data necessary
to carry out the provisions of paragraph (2) of section 2(d) of the
IMPACT Act through both new and existing data sources. Taking into
consideration NASEM's conceptual framework for social risk factors
discussed above, ASPE's study, and considerations under section
2(d)(1)(A) of the IMPACT Act, as well as the current data constraints
of ASPE's first study and its suggested considerations, we proposed to
collect and access data about SDOH under section 2(d)(2) of the IMPACT
Act. Our collection and use of the SDOH data described in section
IX.G.4.b. of this final rule, under section 2(d)(2) of the IMPACT Act
would be independent of our proposal below (in section IX.G.4.b. of
this final rule) and our authority to require submission of that data
for use as SPADE under section 1899B(a)(1)(B) of the Act.
Accessing standardized data relating to the SDOH data elements on a
national level is necessary to permit CMS to conduct periodic analyses,
to assess appropriate adjustments to quality measures, resource use
measures, and other measures, and to assess and implement appropriate
adjustments to Medicare payments based on those measures. We agree with
ASPE's observations, in the value-based purchasing context, that the
ability to measure and track quality, outcomes, and costs for
beneficiaries with social risk factors over time is critical as
policymakers and providers seek to reduce disparities and improve care
for these groups. Collecting the data as proposed will provide the
basis for our periodic analyses of the relationship between an
individual's health status and other factors and quality, resource use,
and other measures, as required by section 2(d)(2) of the IMPACT Act,
and to assess appropriate adjustments. These data will also permit us
to develop the statistical tools necessary to maximize the value of
Medicare data, reduce costs and improve the quality of care for all
beneficiaries. Collecting and accessing SDOH data in this way also
supports the three-part strategy put forth in the first ASPE report,
specifically ASPE's consideration to enhance data collection and
develop statistical techniques to allow measurement and reporting of
performance for beneficiaries with social risk factors on key quality
and resource use measures.
For the reasons discussed above, we proposed under section 2(d)(2)
of the IMPACT Act, to collect the data on the following SDOH: (1) Race,
as described in section VII.G.4.b.(1) of this rule; (2) Ethnicity, as
described in section VII.G.4.b.(1) of this rule; (3) Preferred
Language, as described in section VII.G.4.b.(2) of this rule; (4)
Interpreter Services, as described in section VII.G.4.b.(2) of this
rule; (5) Health Literacy, as described in section VII.G.4.b.(3) of
this rule; (6) Transportation, as described in section VII.G.4.b.(4) of
this rule; and (7) Social Isolation, as described in section
VII.G.4.b.(5) of this rule. These data elements are discussed in more
detail below in section VII.G.4.b of this rule. A detailed discussion
of the comments we received, along with our responses is included in
each section.
b. Standardized Patient Assessment Data
Section 1899B(b)(1)(B)(vi) of the Act authorizes the Secretary to
collect SPADEs with respect to other categories deemed necessary and
appropriate. Below we proposed to create a Social Determinants of
Health SPADE category under section 1899B(b)(1)(B)(vi) of the Act. In
addition to collecting SDOH data for the purposes outlined above under
section 2(d)(2)(B), we also proposed to collect as SPADE these same
data elements (race, ethnicity, preferred language, interpreter
services, health literacy, transportation, and social isolation) under
section 1899B(b)(1)(B)(vi) of the Act. We believe that this proposed
new category of Social Determinants of Health will inform provider
understanding of individual patient risk factors and treatment
preferences, facilitate coordinated care and care planning, and improve
patient outcomes. We proposed to deem this category necessary and
appropriate, for the purposes of SPADE, because using common standards
and definitions for PAC data elements is important in ensuring
interoperable exchange of longitudinal information between PAC
providers and other providers to facilitate coordinated care,
continuity in care planning, and the discharge planning process from
PAC settings.
All of the Social Determinants of Health data elements we proposed
under section 1899B(b)(1)(B)(vi) of the Act have the capacity to take
into account treatment preferences and care goals of patients, and to
inform our understanding of patient complexity and risk factors that
may affect care outcomes. While acknowledging the existence and
importance of additional social determinants of health, we proposed to
assess some of the factors relevant for patients receiving PAC that PAC
settings are in a position to impact through the provision of services
and supports, such as connecting patients with identified needs with
transportation programs, certified interpreters, or social support
programs.
We proposed to adopt the following seven data elements as SPADE
under the proposed Social Determinants of Health category: Race,
ethnicity, preferred language, interpreter services, health literacy,
transportation, and social isolation. To select these data elements, we
reviewed the research literature, a number of validated assessment
tools and frameworks for addressing SDOH currently in use (for example,
Health Leads,\172\ NASEM, Protocol for Responding to and Assessing
Patients' Assets, Risks, and Experiences (PRAPARE), and ICD-10), and we
engaged in discussions with stakeholders. We also prioritized balancing
the reporting burden for PAC providers with our policy objective to
collect SPADEs that will inform care
[[Page 39152]]
planning and coordination and quality improvement across care settings.
Furthermore, incorporating SDOH data elements into care planning has
the potential to reduce readmissions and help beneficiaries achieve and
maintain their health goals.
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\172\ Health Leads. Available at https://healthleadsusa.org/.
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We also considered feedback received during a listening session
that we held on December 13, 2018. The purpose of the listening session
was to solicit feedback from health systems, research organizations,
advocacy organizations and state agencies and other members of the
public on collecting patient-level data on SDOH across care settings,
including consideration of race, ethnicity, spoken language, health
literacy, social isolation, transportation, sex, gender identity, and
sexual orientation. We also gave participants an option to submit
written comments. A full summary of the listening session, titled
``Listening Session on Social Determinants of Health Data Elements:
Summary of Findings,'' includes a list of participating stakeholders
and their affiliations, and is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We solicited comment on these proposals.
Commenters submitted the following comments related to the proposed
rule's discussion of SDOH SPADEs. A discussion of these comments, along
with our responses, appears below.
Comment: One commenter supported the incorporation of SDOH in the
IRF QRP, in the interest of promoting access and assuring high-quality
care for all beneficiaries. The commenter also encouraged CMS to be
mindful of meaningful data collection and the potential impact for data
overload. Since SDOH have impacts far beyond the post[hyphen]acute care
setting, the commenter cautioned data collection that cannot be readily
gathered, shared, or replicated beyond the PAC setting.
The commenter also encouraged CMS to consider leveraging data
points collected during primary care visits by using social risk factor
data captured during those encounters. They pointed out that the
ability to have a hospital's or physician's EHR also collect, capture,
and exchange segments of this information is powerful. The commenter
recommended that CMS take a holistic view of SDOH across the care
continuum so that all care settings may gather, collect or leverage
this data efficiently and in way that maximizes its impact.
Response: We agree that collecting SDOH data elements can be useful
in identifying and addressing health disparities. We also agree that
CMS should be mindful that data elements selected are useful. The
proposed SDOH SPADEs are aligned with SDOH identified in the 2016 NASEM
report, which was commissioned by ASPE. Regarding the commenter's
suggestion that CMS consider how it can align existing and future SDOH
data collection to minimize burden on providers, we agree that it is
important to minimize duplication of effort and will take this under
advisement for future policy development.
Comment: One commenter recommended that CMS consider admission
assessment for certain SPADEs as also fulfilling the discharge
assessment requirement. The commenter supported the inclusion of the
SDOH SPADEs and recommended that CMS require these items be assessed at
some point during the patient's stay instead of during the admission
assessment time window. The commenter recommended that any SDOH SPADES
finalized should be assessed at any point during the stay.
Response: We disagree with the commenters regarding SDOH SPADES
should be assessed at any point during the stay. Each of the SDOH SPADE
data elements will assist with care planning when the patient is
admitted. It is important for providers to identify a patient's needs
in order to better inform the patient's care decisions made during and
after the stay, including a patient's unique risk factors and treatment
preferences.
Comment: Commenters were generally in favor of the concept of
collecting SDOH data elements and provided that, if implemented
appropriately, the data could be useful in identifying and addressing
health care disparities, as well as refining the risk adjustment of
outcome measures. However, some of the commenters suggested CMS not to
finalize the proposed policy until CMS can address important issues
around the potential future uses of these elements and the requirements
around data collection for certain elements. The commenters provided
that CMS did not state explicitly in the rule whether it anticipates
the SDOH SPADEs will be used in adjusting measures and believe that the
IMPACT Act's requirements make it likely the SPADEs will be considered
for use in future adjustments. The commenters recommended CMS to be
circumspect and transparent in its approaches to incorporating the data
elements proposed in payment and quality adjustments, such as by
collecting stakeholder feedback before implementing any adjustments.
Response: We appreciate the commenters for recognizing that
collecting SDOH data elements can be useful in identifying and address
health disparities. We intend to use this data to assess the impact
that the social determinants of health have on health outcomes. We will
continue to work with stakeholders to promote transparency and support
providers who serve vulnerable populations, promote high quality care,
and refine and further implement SDOH SPADE. We appreciate the comment
on collecting stakeholder feedback before implementing any adjustments
to measures based on the SDOH SPADE. Collection of this data will help
us in identifying potential disparities, conducting analyses, and
assessing whether any adjustments are needed. Any future policy
development based on this data would be done transparently, and involve
solicitation of stakeholder feedback through the notice and comment
rulemaking process as appropriate.
Comment: Several commenters recommended that CMS include disability
status as a SDOH that contributes to overall patient access to care,
health status, outcomes, and many other determinants of health since it
is already included in some Medicare risk adjustment. The commenters
stated that ASPE's report to Congress entitled ``Social Risk Factors
and Performance Under Medicare's Value-Based Purchasing Programs''
reported that disability is an independent predictor of poor mental and
physical health outcomes and that individuals with disabilities may
receive lower-quality preventive care.
Response: We appreciate the comments and suggestions provided by
the commenters. We agree that it is important to understand and meet
the needs of patients with disabilities. While disability is not being
currently assessed through the SPADE, it is comprehensively assessed as
part of existing protocols around care plans and health goals. However,
as we continue to evaluate SDOH SPADEs, we will keep commenters'
feedback in mind and may consider these suggestions in future
rulemaking.
Comment: One commenter supported CMS's proposal to collect SDOH
data within SPADEs but was concerned that all of these new elements may
be burdensome. The commenter recommended that CMS require data
collection on race, ethnicity, preferred
[[Page 39153]]
language, and interpreter services, and make data collection on health
literacy, transportation, and social isolation voluntary for now and
have the requirement phased into future rulemaking. The commenter noted
that this would give IRFs an opportunity to adjust to the new data
collection methods, while signaling their importance as entities that
are currently collecting information on SDOH are experiencing various
workflow, privacy, and other challenges. The commenter recommended that
CMS consider including the collection of housing status in the future
as individuals with unmet housing needs, such as homelessness or
substandard housing, have higher health care costs and can be at risk
for readmissions.
Response: We thank the commenter for their comment. As discussed
above, section 2(d)(2)(B) of the IMPACT Act requires the Secretary to
collect or otherwise obtain access to the data necessary to carry out
the provisions of paragraph (2) of section 2(d) of the IMPACT Act
through both new and existing data sources. Accessing standardized data
relating to the SDOH data elements on a national level is necessary to
permit CMS to conduct periodic analyses, to assess appropriate
adjustments to quality measures, resource use measures, and other
measures, and to assess and implement appropriate adjustments to
Medicare payments based on those measures. Collecting the data as
proposed will provide the basis for our periodic analyses of the
relationship between an individual's health status and other factors
and quality, resource use, and other measures, as required by section
2(d)(2) of the IMPACT Act, and to assess appropriate adjustments.
Regarding the suggestion that CMS consider a housing status SPADE data
element in future rulemaking efforts, we appreciate this feedback and
will consider this suggestion in future rulemaking efforts on SPADE
SDOH data elements.
(1) Race and Ethnicity
The persistence of racial and ethnic disparities in health and
health care is widely documented, including in PAC
settings.173 174 175 176 177 Despite the trend toward
overall improvements in quality of care and health outcomes, the Agency
for Healthcare Research and Quality, in its National Healthcare Quality
and Disparities Reports, consistently indicates that racial and ethnic
disparities persist, even after controlling for factors such as income,
geography, and insurance.\178\ For example, racial and ethnic
minorities tend to have higher rates of infant mortality, diabetes and
other chronic conditions, and visits to the emergency department, and
lower rates of having a usual source of care and receiving
immunizations such as the flu vaccine.\179\ Studies have also shown
that African Americans are significantly more likely than white
Americans to die prematurely from heart disease and stroke.\180\
However, our ability to identify and address racial and ethnic health
disparities has historically been constrained by data limitations,
particularly for smaller populations groups such as Asians, American
Indians and Alaska Natives, and Native Hawaiians and other Pacific
Islanders.\181\
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\173\ 2017 National Healthcare Quality and Disparities Report.
Rockville, MD: Agency for Healthcare Research and Quality; September
2018. AHRQ Pub. No. 18-0033-EF.
\174\ Fiscella, K. and Sanders, M.R. Racial and Ethnic
Disparities in the Quality of Health Care. (2016). Annual Review of
Public Health. 37:375-394.
\175\ 2018 National Impact Assessment of the Centers for
Medicare & Medicaid Services (CMS) Quality Measures Reports.
Baltimore, MD: U.S. Department of Health and Human Services, Centers
for Medicare and Medicaid Services; February 28, 2018.
\176\ Smedley, B.D., Stith, A.Y., & Nelson, A.R. (2003). Unequal
treatment: Confronting racial and ethnic disparities in health care.
Washington, DC, National Academy Press.
\177\ Chase, J., Huang, L. and Russell, D. (2017). Racial/ethnic
disparities in disability outcomes among post-acute home care
patients. J of Aging and Health. 30(9):1406-1426.
\178\ National Healthcare Quality and Disparities Reports.
(December 2018). Agency for Healthcare Research and Quality,
Rockville, MD. https://www.ahrq.gov/research/findings/nhqrdr/.
\179\ National Center for Health Statistics. Health, United
States, 2017: With special feature on mortality. Hyattsville,
Maryland. 2018.
\180\ HHS. Heart disease and African Americans. 2016b. (October
24, 2016). https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=19.
\181\ National Academies of Sciences, Engineering, and Medicine;
Health and Medicine Division; Board on Population Health and Public
Health Practice; Committee on Community-Based Solutions to Promote
Health Equity in the United States; Baciu A, Negussie Y, Geller A,
et al., editors. Communities in Action: Pathways to Health Equity.
Washington (DC): National Academies Press (US); 2017 Jan 11. 2, The
State of Health Disparities in the United States. Available at
https://www.ncbi.nlm.nih.gov/books/NBK425844/.
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The ability to improve understanding of and address racial and
ethnic disparities in PAC outcomes requires the availability of better
data. There is currently a Race and Ethnicity data element, collected
in the MDS, LCDS, IRF-PAI, and OASIS, that consists of a single
question, which aligns with the 1997 Office of Management and Budget
(OMB) minimum data standards for federal data collection efforts.\182\
The 1997 OMB Standard lists five minimum categories of race: (1)
American Indian or Alaska Native; (2) Asian; (3) Black or African
American; (4) Native Hawaiian or Other Pacific Islander; (5) and White.
The 1997 OMB Standard also lists two minimum categories of ethnicity:
(1) Hispanic or Latino; and (2) Not Hispanic or Latino. The 2011 HHS
Data Standards requires a two-question format when self-identification
is used to collect data on race and ethnicity. Large federal surveys
such as the National Health Interview Survey, Behavioral Risk Factor
Surveillance System, and the National Survey on Drug Use and Health,
have implemented the 2011 HHS race and ethnicity data standards. CMS
has similarly updated the Medicare Current Beneficiary Survey, Medicare
Health Outcomes Survey, and the Health Insurance Marketplace
Application for Health Coverage with the 2011 HHS data standards. More
information about the HHS Race and Ethnicity Data Standards are
available on the website at https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=54.
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\182\ ``Revisions to the Standards for the Classification of
Federal Data on Race and Ethnicity (Notice of Decision)''. Federal
Register 62:210 (October 30, 1997) pp. 58782-58790. Available at
https://www.govinfo.gov/content/pkg/FR-1997-10-30/pdf/97-28653.pdf.
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We proposed to revise the current Race and Ethnicity data element
for purposes of this proposal to conform to the 2011 HHS Data Standards
for person-level data collection, while also meeting the 1997 OMB
minimum data standards for race and ethnicity. Rather than one data
element that assesses both race and ethnicity, we proposed two separate
data elements: One for Race and one for Ethnicity, that would conform
with the 2011 HHS Data Standards and the 1997 OMB Standard. In
accordance with the 2011 HHS Data Standards a two-question format would
be used for the proposed race and ethnicity data elements.
The proposed Race data element asks, ``What is your race? We
proposed to include fourteen response options under the race data
element: (1) White; (2) Black or African American; (3) American Indian
or Alaska Native; (4) Asian Indian; (5) Chinese; (6) Filipino; (7)
Japanese; (8) Korean; (9) Vietnamese; (10) Other Asian; (11) Native
Hawaiian; (12) Guamanian or Chamorro; (13) Samoan; and (14) Other
Pacific Islander.
The proposed Ethnicity data element asks, ``Are you Hispanic,
Latino/a, or Spanish origin?'' We proposed to include five response
options under the ethnicity data element: (1) Not of Hispanic, Latino/
a, or Spanish origin; (2) Mexican, Mexican American,
[[Page 39154]]
Chicano/a; (3) Puerto Rican; (4) Cuban; and (5) Another Hispanic,
Latino, or Spanish Origin. We are including the addition of ``of'' to
the Ethnicity data element to read, ``Are you of Hispanic, Latino/a, or
Spanish origin?''
We believe that the two proposed data elements for race and
ethnicity conform to the 2011 HHS Data Standards for person-level data
collection, while also meeting the 1997 OMB minimum data standards for
race and ethnicity, because under those standards, more detailed
information on population groups can be collected if those additional
categories can be aggregated into the OMB minimum standard set of
categories.
In addition, we received stakeholder feedback during the December
13, 2018 SDOH listening session on the importance of improving response
options for race and ethnicity as a component of health care
assessments and for monitoring disparities. Some stakeholders
emphasized the importance of allowing for self-identification of race
and ethnicity for more categories than are included in the 2011 HHS
Standard to better reflect state and local diversity, while
acknowledging the burden of coding an open-ended health care assessment
question across different settings.
We believe that the proposed modified race and ethnicity data
elements more accurately reflect the diversity of the U.S. population
than the current race/ethnicity data element included in MDS, LCDS,
IRF-PAI, and OASIS.183 184 185 186 We believe, and research
consistently shows, that improving how race and ethnicity data are
collected is an important first step in improving quality of care and
health outcomes. Addressing disparities in access to care, quality of
care, and health outcomes for Medicare beneficiaries begins with
identifying and analyzing how SDOH, such as race and ethnicity, align
with disparities in these areas.\187\ Standardizing self-reported data
collection for race and ethnicity allows for the equal comparison of
data across multiple healthcare entities.\188\ By collecting and
analyzing these data, CMS and other healthcare entities will be able to
identify challenges and monitor progress. The growing diversity of the
U.S. population and knowledge of racial and ethnic disparities within
and across population groups supports the collection of more granular
data beyond the 1997 OMB minimum standard for reporting categories. The
2011 HHS race and ethnicity data standard includes additional detail
that may be used by PAC providers to target quality improvement efforts
for racial and ethnic groups experiencing disparate outcomes. For more
information on the Race and Ethnicity data elements, we refer readers
to the document titled ``Final Specifications for IRF QRP Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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\183\ Penman-Aguilar, A., Talih, M., Huang, D., Moonesinghe, R.,
Bouye, K., Beckles, G. (2016). Measurement of Health Disparities,
Health Inequities, and Social Determinants of Health to Support the
Advancement of Health Equity. J Public Health Manag Pract. 22 Suppl
1: S33-42.
\184\ Ramos, R., Davis, J.L., Ross, T., Grant, C.G., Green, B.L.
(2012). Measuring health disparities and health inequities: Do you
have REGAL data? Qual Manag Health Care. 21(3):176-87.
\185\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and
Language Data: Standardization for Health Care Quality Improvement.
Washington, DC: The National Academies Press.
\186\ ``Revision of Standards for Maintaining, Collecting, and
Presenting Federal Data on Race and Ethnicity: Proposals From
Federal Interagency Working Group (Notice and Request for
Comments).'' Federal Register 82: 39 (March 1, 2017) p. 12242.
\187\ National Academies of Sciences, Engineering, and Medicine;
Health and Medicine Division; Board on Population Health and Public
Health Practice; Committee on Community-Based Solutions to Promote
Health Equity in the United States; Baciu A, Negussie Y, Geller A,
et al., editors. Communities in Action: Pathways to Health Equity.
Washington (DC): National Academies Press (US); 2017 Jan 11. 2, The
State of Health Disparities in the United States. Available at
https://www.ncbi.nlm.nih.gov/books/NBK425844/.
\188\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and
Language Data: Standardization for Health Care Quality Improvement.
Washington, DC: The National Academies Press.
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In an effort to standardize the submission of race and ethnicity
data among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in
section 1899B(a)(1)(B) of the Act, while minimizing the reporting
burden, we proposed to adopt the Race and Ethnicity data elements
described above as SPADEs with respect to the proposed Social
Determinants of Health category.
Specifically, we proposed to replace the current Race/Ethnicity
data element with the proposed Race and Ethnicity data elements on the
IRF-PAI. We also proposed that IRFs that submit the Race and Ethnicity
data elements with respect to admission will be considered to have
submitted with respect to discharge as well, because it is unlikely
that the results of these assessment findings will change between the
start and end of the IRF stay, making the information submitted with
respect to a patient's admission the same with respect to a patient's
discharge.
We solicited comment on these proposals.
Commenters submitted the following comments related to the proposed
rule's discussion of the Race and Ethnicity SPADEs. A discussion of
these comments, along with our responses, appears below.
Comment: Some commenters noted that the response options for race
do not align with those used in other government data, such as the U.S.
Census or the Office of Management and Budget (OMB). The commenters
also stated these responses are not consistent with the recommendations
made in the 2009 Institute of Medicine report. The commenters pointed
out that IOM report recommended using broader OMB race categories and
granular ethnicities chosen from a national standard set that can be
``rolled up'' into the broader categories. The commenters stated that
it is unclear how CMS chose the 14 response options under the race data
element and the five options under the ethnicity element and worried
that these response options would add to the confusion that already may
exist for patients about what terms like ``race'' and ``ethnicity''
mean for the purposes of health care data collection. The commenters
also noted that CMS should confer directly with experts on the issue to
ensure patient assessments are collecting the right data in the right
way before these SDOH SPADEs are finalized.
Response: The proposed Race and Ethnicity categories align with and
are rolled up into the 1997 OMB minimum data standards and conforming
with the 2011 HHS Data Standards as described in the implementation
guidance titled ``U.S. Department of Health and Human Services
Implementation Guidance on Data Collection Standards for Race,
Ethnicity, Sex, Primary Language, and Disability Status'' at https://aspe.hhs.gov/basic-report/hhs-implementation-guidance-data-collection-standards-race-ethnicity-sex-primary-language-and-disability-status. As
stated in the proposed rule, the 14 race categories and the 5 ethnicity
categories conform with the 2011 HHS Data Standards for person-level
data collection, which were developed in fulfillment of section 4302 of
the Affordable Care Act that required the Secretary of HHS to establish
data collection standards for race, ethnicity, sex, primary language,
and disability status. Through the HHS Data Council, which is the
principal, senior internal Departmental forum and advisory body to the
Secretary on health and human
[[Page 39155]]
services data policy and coordinates HHS data collection and analysis
activities, the Section 4302 Standards Workgroup was formed. The
Workgroup included representatives from HHS, the OMB, and the Census
Bureau. The Workgroup examined current federal data collection
standards, adequacy of prior testing, and quality of the data produced
in prior surveys; consulted with statistical agencies and programs;
reviewed OMB data collection standards and the Institute of Medicine
(IOM) Report Race, Ethnicity, and Language Data Collection:
Standardization for Health Care Quality Improvement; sought input from
national experts; and built on its members' experience with collecting
and analyzing demographic data. As a result of this Workgroup, a set of
data collection standards were developed, and then published for public
comment. This set of data collection standards is referred to as the
2011 HHS Data Standards.\189\ As described in the implementation
guidance provided above, the categories of race and ethnicity under the
2011 HHS Data Standards allow for more detailed information to be
collected and the additional categories under the 2011 HHS Data
Standards can be aggregated into the OMB minimum standards set of
categories.
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\189\ HHS Data Standards. Available at https://aspe.hhs.gov/basic-report/hhs-implementation-guidance-data-collection-standards-race-ethnicity-sex-primary-language-and-disability-status.
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As noted in the FY 2020 IRF PPS proposed rule (84 FR 17321 through
17323), we conferred with experts by conducting a listening session
regarding the proposed SDOH data elements regarding the importance of
improving response options for race and ethnicity as a component of
health care assessments and for monitoring disparities. Some
stakeholders emphasized the importance of allowing for self-
identification of race and ethnicity for more categories than are
included in the 2011 HHS Data Standards to better reflect state and
local diversity.
Comment: A commenter recommended that CMS consider the implications
of having PAC providers collect Race and Ethnicity codes that vary from
the Race and Ethnicity codes collected by other healthcare providers,
specifically acute-care hospitals. The commenter noted that unless all
care providers are expected to utilize the uniform 2011 HHS Data
Standards, the consistency and accuracy of race and ethnicity data
across settings will likely be unreliable and problematic. Another
commenter provided that the proposed list of response options for Race
may not include all races that should be reflected, for example, Native
African and Middle Eastern. In addition, the item should include
``check all that apply'' to ensure accurate and complete data
collection. The commenter encouraged CMS to refine the list of response
options for Race and provide a rationale for the final list of response
options.
Response: We thank the commenter and agree that it is important to
collect race and ethnicity data in a consistent way. The race and
ethnicity categories that were proposed align with the 2011 HHS Data
Standards and are rolled up into the 1997 OMB minimum data standards,
which can be found at https://aspe.hhs.gov/basic-report/hhs-implementation-guidance-data-collection-standards-race-ethnicity-sex-primary-language-and-disability-status. For example, the 1997 OMB
minimum data standard for Hispanic is the roll up category for the
following response options on the 2011 HHS Data Standards: Mexican,
Mexican American, Chicano/a; Puerto Rican; Cuban; another Hispanic,
Latino, or Spanish origin. However, we will take the comment under
advisement for future consideration. We also note that the option for
``check all that apply'' is available for providers to choose from the
list of response options.
Comment: A commenter supported the opportunities to better account
for SDOH in the diagnosis and treatment of patients but is concerned by
the specificity of several of the seven proposed element for data
collection for example, collection of race by Japanese, Chinese,
Korean, etc. The commenter's concern is with the added burden in
collecting the level of specificity outlined, and the commenter
requested that CMS provide more detailed guidance in the final rule
regarding how this information should be collected and shared in
compliance with HIPAA. Further, the commenter asked that the agency
outlines its expectations for how this newly collected information will
be used by Medicare for payment and public reporting.
Response: For the Race and Ethnicity SPADE, this data should be
completed based on the response of the patient. It is important to ask
the patient to select the category or categories that most closely
correspond to their race and ethnicity. Respondents should be offered
the option of selecting one or more race and ethnicity categories.
Observer identification or medical record documentation may not be
used.
The SDOH data elements that will be collected will assist with care
coordination and with evaluating the impact of disparities. With
respect to how the data will be used for payment and public reporting,
any potential future use of the data for these purposes would be done
through future rulemaking.
SDOH data elements should be treated the same as other data
collected on the assessment tool. As to any specific HIPAA questions,
we appreciate the commenter's commitment to compliance with the HIPAA
requirements, but note that the Office for Civil Rights (OCR) is tasked
with implementing and enforcing HIPAA, not CMS. Commenters should
consult appropriate counsel in instances in which they are unsure of
their HIPAA status, or the permissibility of a disclosure under the
HIPAA Privacy Rule. In doing so, commenters may wish to consult 45 CFR
164.103 (definition of ``required by law'') and Sec. 164.512(a)
(allowing ``required by law'' disclosures).
(2) Preferred Language and Interpreter Services
More than 64 million Americans speak a language other than English
at home, and nearly 40 million of those individuals have limited
English proficiency (LEP).\190\ Individuals with LEP have been shown to
receive worse care and have poorer health outcomes, including higher
readmission rates.191 192 193 Communication with individuals
with LEP is an important component of high quality health care, which
starts by understanding the population in need of language services.
Unaddressed language barriers between a patient and provider care team
negatively affects the ability to identify and address individual
medical and non-medical care needs, to convey and understand clinical
information, as well as discharge and follow up instructions, all of
which are necessary for providing high quality care. Understanding the
communication assistance needs of patients with LEP, including
individuals who are Deaf or hard of
[[Page 39156]]
hearing, is critical for ensuring good outcomes.
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\190\ U.S. Census Bureau, 2013-2017 American Community Survey 5-
Year Estimates.
\191\ Karliner LS, Kim SE, Meltzer DO, Auerbach AD. Influence of
language barriers on outcomes of hospital care for general medicine
inpatients. J Hosp Med. 2010 May-Jun;5(5):276-82. doi: 10.1002/
jhm.658.
\192\ Kim EJ, Kim T, Paasche-Orlow MK, et al. Disparities in
Hypertension Associated with Limited English Proficiency. J Gen
Intern Med. 2017 Jun;32(6):632-639. doi: 10.1007/s11606-017-3999-9.
\193\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Washington, DC: The National
Academies Press.
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Presently, the preferred language of patients and residents and
need for interpreter services are assessed in two PAC assessment tools.
The LCDS and the MDS use the same two data elements to assess preferred
language and whether a patient or resident needs or wants an
interpreter to communicate with health care staff. The MDS initially
implemented preferred language and interpreter services data elements
to assess the needs of SNF residents and patients and inform care
planning. For alignment purposes, the LCDS later adopted the same data
elements for LTCHs. The 2009 NASEM (formerly Institute of Medicine)
report on standardizing data for health care quality improvement
emphasizes that language and communication needs should be assessed as
a standard part of health care delivery and quality improvement
strategies.\194\
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\194\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and
Language Data: Standardization for Health Care Quality Improvement.
Washington, DC: The National Academies Press.
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In developing our proposal for a standardized language data element
across PAC settings, we considered the current preferred language and
interpreter services data elements that are in LCDS and MDS. We also
considered the 2011 HHS Primary Language Data Standard and peer-
reviewed research. The current preferred language data element in LCDS
and MDS asks, ``What is your preferred language?'' Because the
preferred language data element is open-ended, the patient or resident
is able to identify their preferred language, including American Sign
Language (ASL). Finally, we considered the recommendations from the
2009 NASEM (formerly Institute of Medicine) report, ``Race, Ethnicity,
and Language Data: Standardization for Health Care Quality
Improvement.'' In it, the committee recommended that organizations
evaluating a patient's language and communication needs for health care
purposes, should collect data on the preferred spoken language and on
an individual's assessment of his/her level of English proficiency.
A second language data element in LCDS and MDS asks, ``Do you want
or need an interpreter to communicate with a doctor or health care
staff?'' and includes yes or no response options. In contrast, the 2011
HHS Primary Language Data Standard recommends either a single question
to assess how well someone speaks English or, if more granular
information is needed, a two-part question to assess whether a language
other than English is spoken at home and if so, identify that language.
However, neither option allows for a direct assessment of a patient's
or resident's preferred spoken or written language nor whether they
want or need interpreter services for communication with a doctor or
care team, both of which are an important part of assessing patient/
resident needs and the care planning process. More information about
the HHS Data Standard for Primary Language is available on the website
at https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=54.
Research consistently recommends collecting information about an
individual's preferred spoken language and evaluating those responses
for purposes of determining language access needs in health care.\195\
However, using ``preferred spoken language'' as the metric does not
adequately account for people whose preferred language is ASL, which
would necessitate adopting an additional data element to identify
visual language. The need to improve the assessment of language
preferences and communication needs across PAC settings should be
balanced with the burden associated with data collection on the
provider and patient. Therefore we proposed to retain the Preferred
Language and Interpreter Services data elements currently in use on the
MDS and LCDS on the IRF-PAI.
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\195\ Guerino, P. and James, C. Race, Ethnicity, and Language
Preference in the Health Insurance Marketplaces 2017 Open Enrollment
Period. Centers for Medicare & Medicaid Services, Office of Minority
Health. Data Highlight: Volume 7--April 2017. Available at https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Highlight-Race-Ethnicity-and-Language-Preference-Marketplace.pdf.
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In addition, we received feedback during the December 13, 2018
listening session on the importance of evaluating and acting on
language preferences early to facilitate communication and allowing for
patient self-identification of preferred language. Although the
discussion about language was focused on preferred spoken language,
there was general consensus among participants that stated language
preferences may or may not accurately indicate the need for interpreter
services, which supports collecting and evaluating data to determine
language preference, as well as the need for interpreter services. An
alternate suggestion was made to inquire about preferred language
specifically for discussing health or health care needs. While this
suggestion does allow for ASL as a response option, we do not have data
indicating how useful this question might be for assessing the desired
information and thus we are not including this question in our
proposal.
Improving how preferred language and need for interpreter services
data are collected is an important component of improving quality by
helping PAC providers and other providers understand patient needs and
develop plans to address them. For more information on the Preferred
Language and Interpreter Services data elements, we refer readers to
the document titled ``Final Specifications for IRF QRP Measures and
Standardized Patient Assessment Data Elements,'' available on the
website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In an effort to standardize the submission of language data among
IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
proposed to adopt the Preferred Language and Interpreter Services data
elements currently used on the MDS and LCDS, and described above, as
SPADEs with respect to the Social Determinants of Health category. We
proposed to add the current Preferred Language and Interpreter Services
data elements from the MDS and LCDS to the IRF-PAI.
We solicited comment on these proposals.
Commenters submitted the following comments related to the proposed
rule's discussion of Preferred Language and Interpreter Services
SPADEs. A discussion of these comments, along with our responses,
appears below.
Comment: Some commenters noted that, if finalized, IRFs should only
need to submit data on the race and ethnicity SPADEs with respect to
admission and would not need to collect and report again at discharge,
as it is unlikely that patient status for these elements will change.
The commenters believe that a patient's preferred language and need for
an interpreter also are unlikely to change between admission and
discharge; thus, the commenter urged CMS to require collection of these
SDOH SPADEs with respect to admission only.
Response: We thank the commenters for the comment. With regard to
the submission of the Preferred Language SPADE and the Interpreter
Services SPADE, we agree with the commenters that it is unlikely that
the assessment of Preferred Language and Interpreter
[[Page 39157]]
Services at admission would differ from assessment at discharge. As
discussed in previous response for Vision and Hearing, we believe that
the submission of preferred language and the need for an interpreter is
similar to the submission of Race, Ethnicity, Hearing, and Vision
SPADES.
We account for this change to the Collection of Information
requirements for the IRF QRP in XIV.C of this final rule. Based on the
comments received, and for the reasons discussed, we are finalizing
that the Preferred Language and Interpreter Services SPADEs be
collected as proposed with the modification that we will deem IRFs that
submit these two SPADEs with respect to admission to have submitted
with respect to both admission and discharge.
(3) Health Literacy
The Department of Health and Human Services defines health literacy
as ``the degree to which individuals have the capacity to obtain,
process, and understand basic health information and services needed to
make appropriate health decisions.'' \196\ Similar to language
barriers, low health literacy can interfere with communication between
the provider and patient and the ability for patients or their
caregivers to understand and follow treatment plans, including
medication management. Poor health literacy is linked to lower levels
of knowledge about health, worse health outcomes, and the receipt of
fewer preventive services, but higher medical costs and rates of
emergency department use.\197\
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\196\ U.S. Department of Health and Human Services, Office of
Disease Prevention and Health Promotion. National action plan to
improve health literacy. Washington (DC): Author; 2010.
\197\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Washington, DC: The National
Academies Press.
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Health literacy is prioritized by Healthy People 2020 as an
SDOH.\198\ Healthy People 2020 is a long-term, evidence-based effort
led by the Department of Health and Human Services that aims to
identify nationwide health improvement priorities and improve the
health of all Americans. Although not designated as a social risk
factor in NASEM's 2016 report on accounting for social risk factors in
Medicare payment, the NASEM noted that health literacy is impacted by
other social risk factors and can affect access to care, as well as
quality of care and health outcomes.\199\ Assessing for health literacy
across PAC settings would facilitate better care coordination and
discharge planning. A significant challenge in assessing the health
literacy of individuals is avoiding excessive burden on patients and
health care providers. The majority of existing, validated health
literacy assessment tools use multiple screening items, generally with
no fewer than four, which would make them burdensome if adopted in MDS,
LCDS, IRF-PAI, and OASIS. The Single Item Literacy Screener (SILS)
question questions, ``How often do you need to have someone help you
when you read instructions, pamphlets, or other written material from
your doctor or pharmacy?'' Possible response options are: (1) Never;
(2) Rarely; (3) Sometimes; (4) Often; and (5) Always. The SILS
question, which assesses reading ability, (a primary component of
health literacy), tested reasonably well against the 36 item Short Test
of Functional Health Literacy in Adults (S-TOFHLA), a thoroughly vetted
and widely adopted health literacy test, in assessing the likelihood of
low health literacy in an adult sample from primary care practices
participating in the Vermont Diabetes Information
System.200 201 The S-TOFHLA is a more complex assessment
instrument developed using actual hospital related materials such as
prescription bottle labels and appointment slips, and often considered
the instrument of choice for a detailed evaluation of health
literacy.\202\ Furthermore, the S-TOFHLA instrument is proprietary and
subject to purchase for individual entities or users.\203\ Given that
SILS is publicly available, shorter and easier to administer than the
full health literacy screen, and research found that a positive result
on the SILS demonstrates an increased likelihood that an individual has
low health literacy, we proposed to use the single-item reading
question for health literacy in the standardized data collection across
PAC settings. We believe that use of this data element will provide
sufficient information about the health literacy of IRF patients to
facilitate appropriate care planning, care coordination, and
interoperable data exchange across PAC settings.
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\198\ Social Determinants of Health. Healthy People 2020.
https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health. (February 2019).
\199\ U.S. Department of Health & Human Services, Office of the
Assistant Secretary for Planning and Evaluation. Report to Congress:
Social Risk Factors and Performance Under Medicare's Value-Based
Purchasing Programs. Available at https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs. Washington, DC: 2016.
\200\ Morris, N.S., MacLean, C.D., Chew, L.D., & Littenberg, B.
(2006). The Single Item Literacy Screener: evaluation of a brief
instrument to identify limited reading ability. BMC family practice,
7, 21. doi:10.1186/1471-2296-7-21.
\201\ Brice, J.H., Foster, M.B., Principe, S., Moss, C., Shofer,
F.S., Falk, R.J., Ferris, M.E., DeWalt, D.A. (2013). Single-item or
two-item literacy screener to predict the S-TOFHLA among adult
hemodialysis patients. Patient Educ Couns. 94(1):71-5.
\202\ University of Miami, School of Nursing & Health Studies,
Center of Excellence for Health Disparities Research. Test of
Functional Health Literacy in Adults (TOFHLA). (March 2019).
Available at https://elcentro.sonhs.miami.edu/research/measures-library/tofhla/.
\203\ Nurss, J.R., Parker, R.M., Williams, M.V., &Baker, D.W.
David W. (2001). TOFHLA. Peppercorn Books & Press. Available at
https://www.peppercornbooks.com/catalog/information.php?info_id=5.
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In addition, we received feedback during the December 13, 2018 SDOH
listening session on the importance of recognizing health literacy as
more than understanding written materials and filling out forms, as it
is also important to evaluate whether patients understand their
conditions. However, the NASEM recently recommended that health care
providers implement health literacy universal precautions instead of
taking steps to ensure care is provided at an appropriate literacy
level based on individualized assessment of health literacy.\204\ Given
the dearth of Medicare data on health literacy and gaps in addressing
health literacy in practice, we recommend the addition of a health
literacy data element.
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\204\ Hudson, S., Rikard, R.V., Staiculescu, I. & Edison, K.
(2017). Improving health and the bottom line: The case for health
literacy. In Building the case for health literacy: Proceedings of a
workshop. Washington, DC: The National Academies Press.
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The proposed Health Literacy data element is consistent with
considerations raised by NASEM and other stakeholders and research on
health literacy, which demonstrates an impact on health care use, cost,
and outcomes.\205\ For more information on the proposed Health Literacy
data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Measures and Standardized Patient Assessment
Data Elements,'' available on the website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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\205\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for Social Risk Factors in Medicare Payment:
Identifying Social Risk Factors. Washington, DC: The National
Academies Press.
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In an effort to standardize the submission of health literacy data
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
[[Page 39158]]
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
proposed to adopt SILS question described above for the Health Literacy
data element as SPADE under the Social Determinants of Health Category.
We proposed to add the Health Literacy data element to the IRF-PAI.
We solicited comment on this proposals. A discussion of these
comments, along with our responses, appears below.
Comment: Some commenters noted that, if finalized, IRFs should only
need to submit data on the race and ethnicity SPADEs with respect to
admission and would not need to collect and report again at discharge,
as it is unlikely that patient status for these elements will change.
The commenters believe that a patient's health literacy is unlikely to
change between admission and discharge; thus, the commenter urged CMS
to require collection of all SDOH SPADEs with respect to admission
only.
Response: We disagree with the commenters that it is unlikely
patient status for health literacy will change from admission to
discharge. Unlike the Vision, Hearing, Race, Ethnicity, Preferred
Language, and Interpreter Services SPADEs, we believe that the response
to this data element may change from admission to discharge for some
patients. Health literacy can impact a patient's ability to manage
their conditions, and it something that should be taken into account
when developing care plans. The collection of the Health Literacy SPADE
at discharge is to support patients, whose circumstances may have
changed over the duration of their admission, in having the appropriate
supports post-discharge. Therefore, the health literacy data element
should be collected at both admission and discharge given the impact
this could have on health outcomes and care planning.
Comment: One commenter stated that the health literacy question
could be improved to capture whether the patient can read, understand,
and implement/respond to the information. In addition, the commenter
stated that the question does not take into account whether a patient's
need for help is due to limited vision, which is different from the
purpose of the separate Vision Impairment data element. Another
possible question the commenter suggested was ``How often do you have
difficulty?'' The commenter suggested that a single construct may not
be sufficient for this area, depending on the aspect of health literacy
that CMS intends to identify.
Response: We thank the commenters for the comment on the health
literacy data element. We agree that knowing whether a patient has a
reading or comprehension challenge, or limited vision would be helpful.
However, we specifically proposed data elements that have been tested.
We were also mindful to try and limit the potential burden of asking
additional questions related to health literacy. The SILS Health
Literacy data element that we proposed performed well when tested, and
it minimizes concerns related to burden by requiring one instead of
multiple questions on health literacy.206 207 If commenters
have examples of SDOH questions that have been cognitively tested, we
would welcome that feedback as we seek to refine SDOH SPADE data
elements in future rulemaking.
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\206\ Morris, N.S., MacLean, C.D., Chew, L.D., & Littenberg, B.
(2006). The Single Item Literacy Screener: Evaluation of a brief
instrument to identify limited reading ability. BMC family practice,
7, 21. doi:10.1186/1471-2296-7-21.
\207\ Brice, J.H., Foster, M.B., Principe, S., Moss, C., Shofer,
F.S., Falk, R.J., Ferris, M.E., DeWalt, D.A. (2013). Single-item or
two-item literacy screener to predict the S-TOFHLA among adult
hemodialysis patients. Patient Educ Couns. 94(1):71-5.
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(4) Transportation
Transportation barriers commonly affect access to necessary health
care, causing missed appointments, delayed care, and unfilled
prescriptions, all of which can have a negative impact on health
outcomes.\208\ Access to transportation for ongoing health care and
medication access needs, particularly for those with chronic diseases,
is essential to successful chronic disease management. Adopting a data
element to collect and analyze information regarding transportation
needs across PAC settings would facilitate the connection to programs
that can address identified needs. We therefore proposed to adopt as
SPADE a single transportation data element that is from the Protocol
for Responding to and Assessing Patients' Assets, Risks, and
Experiences (PRAPARE) assessment tool and currently part of the
Accountable Health Communities (AHC) Screening Tool.
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\208\ Syed, S.T., Gerber, B.S., and Sharp, L.K. (2013).
Traveling Towards Disease: Transportation Barriers to Health Care
Access. J Community Health. 38(5): 976-993.
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The proposed Transportation data element from the PRAPARE tool
questions, ``Has lack of transportation kept you from medical
appointments, meetings, work, or from getting things needed for daily
living?'' The three response options are: (1) Yes, it has kept me from
medical appointments or from getting my medications; (2) Yes, it has
kept me from non-medical meetings, appointments, work, or from getting
things that I need; and (3) No. The patient would be given the option
to select all responses that apply. We proposed to use the
transportation data element from the PRAPARE Tool, with permission from
National Association of Community Health Centers (NACHC), after
considering research on the importance of addressing transportation
needs as a critical SDOH.\209\
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\209\ Health Research & Educational Trust. (2017, November).
Social determinants of health series: Transportation and the role of
hospitals. Chicago, IL. Available at www.aha.org/transportation.www.aha.org/transportation.
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The proposed data element is responsive to research on the
importance of addressing transportation needs as a critical SDOH and
would adopt the Transportation item from the PRAPARE tool.\210\ This
data element comes from the national PRAPARE social determinants of
health assessment protocol, developed and owned by NACHC, in
partnership with the Association of Asian Pacific Community Health
Organization, the Oregon Primary Care Association, and the Institute
for Alternative Futures. Similarly the Transportation data element used
in the AHC Screening Tool was adapted from the PRAPARE tool. The AHC
screening tool was implemented by the Center for Medicare and Medicaid
Innovation's AHC Model and developed by a panel of interdisciplinary
experts that looked at evidence-based ways to measure SDOH, including
transportation. While the transportation access data element in the AHC
screening tool serves the same purposes as our proposed SPADE
collection about transportation barriers, the AHC tool has binary yes
or no response options that do not differentiate between challenges for
medical versus non-medical appointments and activities. We believe that
this is an important nuance for informing PAC discharge planning to a
community setting, as transportation needs for non-medical activities
may differ than for medical activities and should be taken into
account.\211\ We believe that use of this data element will provide
sufficient information about transportation barriers to medical and
non-medical care for IRF patients to facilitate appropriate discharge
planning and care coordination across PAC settings. As such, we
proposed to adopt the Transportation data element from PRAPARE. More
information about
[[Page 39159]]
development of the PRAPARE tool is available on the website at https://protect2.fireeye.com/url?k=7cb6eb44-20e2f238-7cb6da7b-0cc47adc5fa2-1751cb986c8c2f8c&u=https://www.nachc.org/prapare.
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\210\ Health Research & Educational Trust. (2017, November).
Social determinants of health series: Transportation and the role of
hospitals. Chicago, IL. Available at www.aha.org/transportation.
\211\ Northwestern University. (2017). PROMIS Item Bank v. 1.0--
Emotional Distress--Anger--Short Form 1.
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In addition, we received stakeholder feedback during the December
13, 2018 SDOH listening session on the impact of transportation
barriers on unmet care needs. While recognizing that there is no
consensus in the field about whether providers should have
responsibility for resolving patient transportation needs, discussion
focused on the importance of assessing transportation barriers to
facilitate connections with available community resources.
Adding a Transportation data element to the collection of SPADE
would be an important step to identifying and addressing SDOH that
impact health outcomes and patient experience for Medicare
beneficiaries. For more information on the Transportation data element,
we refer readers to the document titled ``Final Specifications for IRF
QRP Measures and Standardized Patient Assessment Data Elements,''
available on the website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In an effort to standardize the submission of transportation data
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
proposed to adopt the Transportation data element described above as
SPADE with respect to the proposed Social Determinants of Health
category. If finalized as proposed, we would add the Transportation
data element to the IRF-PAI.
We solicited comment on these proposals. A discussion of these
comments, along with our responses, appears below.
Comment: One commenter supported the collection of data to capture
the reason(s) transportation affects a patient's access to health care.
The commenter appreciated the inclusion of these items on the IRF-PAI
and encouraged exploration of quality measures in this area as
transportation is an extremely important instrumental activity of daily
living to effectively transition to the community.
Response: We thank the commenter and we will consider this feedback
as we continue to improve and refine the SPADEs.
Comment: Some commenters noted that, if finalized, IRFs should only
need to submit data on the race and ethnicity SPADEs with respect to
admission and would not need to collect and report again at discharge,
as it is unlikely that patient status for these elements will change.
The commenters believe that a patient's access to transportation is
unlikely to change between admission and discharge; thus, the commenter
suggested CMS to require collection of all SDOH SPADEs with respect to
admission only.
Response: We disagree with the commenters that stated that access
to transportation will always be the same from admission to discharge.
Unlike the Vision, Hearing, Race, Ethnicity, Preferred Language, and
Interpreter Services SPADEs, we believe that the response to this data
element is likely to change from admission to discharge for some
patients. For example, a patient could lose a family member or
caregiver between admission and discharge, which could impact his or
her access to transportation and impact how the patient responds to the
access to transportation SPADE data element. Therefore, we believe that
the response to this SDOH data element is likely to change from
admission to discharge for some patients and we proposed to collect
this SPADE data element with respect to both admission and discharge.
As outlined in the FY 2020 IRF PPS proposed rule, multiple studies
have demonstrated that access to transportation has an impact on the
health of patients (84 FR 17325). Therefore, it is important for
providers to be able to identify a patient's needs when the patient is
admitted and when the patient is discharged in order to better inform
the patient's care decisions made during and after the stay, including
understanding the patient's unique risk factors and treatment
preferences. Because of this, we are requiring that the Access to
Transportation data element be assessed with respect to both admission
and discharge.
(5) Social Isolation
Distinct from loneliness, social isolation refers to an actual or
perceived lack of contact with other people, such as living alone or
residing in a remote area.212 213 Social isolation tends to
increase with age, is a risk factor for physical and mental illness,
and a predictor of mortality.214 215 216 PAC providers are
well-suited to design and implement programs to increase social
engagement of patients, while also taking into account individual needs
and preferences. Adopting a data element to collect and analyze
information about social isolation in IRFs and across PAC settings
would facilitate the identification of patients who are socially
isolated and who may benefit from engagement efforts.
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\212\ Tomaka, J., Thompson, S., and Palacios, R. (2006). The
Relation of Social Isolation, Loneliness, and Social Support to
Disease Outcomes Among the Elderly. J of Aging and Health. 18(3):
359-384.
\213\ Social Connectedness and Engagement Technology for Long-
Term and Post-Acute Care: A Primer and Provider Selection Guide.
(2019). Leading Age. Available at https://www.leadingage.org/white-papers/social-connectedness-and-engagement-technology-long-term-and-post-acute-care-primer-and#1.1.
\214\ Landeiro, F., Barrows, P., Nuttall Musson, E., Gray, A.M.,
and Leal, J. (2017). Reducing Social Loneliness in Older People: A
Systematic Review Protocol. BMJ Open. 7(5): e013778.
\215\ Ong, A.D., Uchino, B.N., and Wethington, E. (2016).
Loneliness and Health in Older Adults: A Mini-Review and Synthesis.
Gerontology. 62:443-449.
\216\ Leigh-Hunt, N., Bagguley, D., Bash, K., Turner, V.,
Turnbull, S., Valtorta, N., and Caan, W. (2017). An overview of
systematic reviews on the public health consequences of social
isolation and loneliness. Public Health. 152:157-171.
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We proposed to adopt as SPADE a single social isolation data
element that is currently part of the AHC Screening Tool. The AHC item
was selected from the Patient-Reported Outcomes Measurement Information
System (PROMIS[supreg]) Item Bank on Emotional Distress and questions,
``How often do you feel lonely or isolated from those around you?'' The
five response options are: (1) Never; (2) Rarely; (3) Sometimes; (4)
Often; and (5) Always.\217\ The AHC Screening Tool was developed by a
panel of interdisciplinary experts that looked at evidence-based ways
to measure SDOH, including social isolation. More information about the
AHC Screening Tool is available on the website at https://innovation.cms.gov/Files/worksheets/ahcm-screeningtool.pdf.
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\217\ Northwestern University. (2017). PROMIS Item Bank v. 1.0--
Emotional Distress--Anger--Short Form 1.
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In addition, we received stakeholder feedback during the December
13, 2018 SDOH listening session on the value of receiving information
on social isolation for purposes of care planning. Some stakeholders
also recommended assessing social isolation as an SDOH as opposed to
social support.
The proposed Social Isolation data element is consistent with NASEM
considerations about social isolation as a function of social
relationships that impacts health outcomes and increases mortality
risk, as well as the current work of a NASEM committee examining how
social isolation and loneliness
[[Page 39160]]
impact health outcomes in adults 50 years and older. We believe that
adding a Social Isolation data element would be an important component
of better understanding patient complexity and the care goals of
patients, thereby facilitating care coordination and continuity in care
planning across PAC settings. For more information on the Social
Isolation data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Measures and Standardized Patient Assessment
Data Elements,'' available on the website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In an effort to standardize the submission of social isolation data
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
proposed to adopt the Social Isolation data element described above as
SPADE with respect to the proposed Social Determinants of Health
category. We proposed to add the Social Isolation data element to the
IRF-PAI.
We sought public comment on this proposal. A discussion of these
comments, along with our responses, appears below.
Comment: Commenters agreed with CMS that SDOH data could provide
Medicare with valuable information about the role that non-clinical
factors play in PAC patient outcomes and that the addition of the SDOH
SPADEs will facilitate communication between PAC settings and other
health care providers. A commenter noted that common standards and
definitions are important for interoperability and communication across
providers and encouraged CMS to ensure that the SDOH elements collected
in IRF settings are aligned with future proposed SDOH data collection
requirements in other settings. One commenter stated that there is
increasing attention on the critical role that social factors play in
individual and population health and that addressing health-related
social needs through enhanced clinical-community linkages can improve
health outcomes and reduce costs. Another commenter was also pleased
that CMS is looking at SDOH and believes it is a positive step toward
identifying disparities in health care.
Response: We thank the commenters for the comments.
Comment: Some commenters noted that, if finalized, IRFs should only
need to submit data on the race and ethnicity SPADEs with respect to
admission and would not need to collect and report again at discharge,
as it is unlikely that patient status for these elements will change.
The commenters believe that a patient's response to social isolation is
unlikely to change between admission and discharge; thus, the commenter
suggested CMS to require collection of all SDOH SPADEs with respect to
admission only.
Response: We disagree with the commenters that stated that the
response to the Social Isolation data element will be the same from
admission to discharge. Unlike the Vision, Hearing, Race, Ethnicity,
Preferred Language, and Interpreter Services SPADEs, we believe that
the response to this data element is likely to change from admission to
discharge for some patients. For example, a patient could lose a family
member or caregiver between admission and discharge, which could impact
their response to the Social Isolation data element. Therefore, we
proposed to collect this SPADE data element with respect to both
admission and discharge. As outlined in the FY 2020 IRF PPS proposed
rule, multiple studies have demonstrated that social isolation has an
impact on the health of patients (84 FR 17325 through 17326).
Therefore, it is important for providers to be able to identify a
patient's needs when the patient is admitted and when the patient is
discharged in order to better inform the patient's care decisions made
during and after the stay, including understanding the patient's unique
risk factors and treatment preferences. Because of this, we are
requiring that the Social Isolation data element be assessed at both
admission and discharge.
Comment: One commenter stated that the proposed question on social
isolation may have a very different answer based on the time horizon
considered by the beneficiary as beneficiaries who are newly admitted
to an IRF may have experienced differing levels of social isolation
over the preceding week due to interactions with health care providers,
emergency providers, and friends or family visiting due to
hospitalization. The commenter believes this question could be improved
by adding a timeframe to the question. For example, ``How often have
you felt lonely or isolated from those around you in the past 6
months?''
Response: We thank the commenter for this comment. The Social
Isolation data element assesses whether a patient has experienced
social isolation in the past 6 months to a year. The social isolation
question proposed is currently part of the Accountable Health
Communities (AHC) Screening Tool. The AHC item was selected from the
Patient-Reported Outcomes Measurement Information System
(PROMIS[supreg]) Item Bank on Emotional Distress.
Comment: A commenter suggested that collecting SDOH SPADEs that
have no clinical value, such as transportation and social isolation
during an assigned period of either admission or discharge, is a
significant concern. The commenter stated that at admission, the focus
should be on assessing the patient's medical needs and plan of care,
and at discharge, the focus shifts to patient's transition plan and
caregiver education. As there are already multiple required assessments
on the IRF-PAI, the SDOH SPADEs would add burden and recommended that
any SDOH SPADEs finalized should be assessed at any point during the
stay.
Response: We disagree with the commenters that the Social Isolation
and Transportation data elements have no value. As proposed in the
transportation and social isolation section, multiple studies have
demonstrated that access to transportation and social isolation have an
impact on the health of patients.218 219 For example, access
to transportation is important to medication access. Similarly, social
isolation is a predictor of mortality. Therefore, it is important for
providers to identify a patient's needs both at admission and discharge
in order to better inform the patient's care decisions made during and
after the stay, including a patient's unique risk factors and treatment
preferences. To minimize burden, we proposed to collect this data
element with respect to admission and discharge, rather than more
frequently.
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\218\ Syed, S.T., Gerber, B.S., and Sharp, L.K. (2013).
Traveling Towards Disease: Transportation Barriers to Health Care
Access. J Community Health. 38(5): 976-993.
\219\ Leigh-Hunt, N., Bagguley, D., Bash, K., Turner, V.,
Turnbull, S., Valtorta, N., and Caan, W. (2017). An overview of
systematic reviews on the public health consequences of social
isolation and loneliness. Public Health. 152:157-171.
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After consideration of the public comments, we are finalizing our
proposals to collect SDOH data for the purposes of section 2(d)(2)(B)
of the IMPACT Act and section 1899B(b)(1)(B)(vi) of the Act as follows.
With regard to Race, Ethnicity, Health Literacy, Transportation, and
Social Isolation, we are finalizing our proposals as proposed. In
response to stakeholder comments, we are revising our proposed policies
and finalizing
[[Page 39161]]
that IRFs that submit the Preferred Language and Interpreter Services
SPADEs with respect to admission will be deemed to have submitted with
respect to both admission and discharge.
H. Form, Manner, and Timing of Data Submission Under the IRF QRP
1. Background
We refer readers to Sec. 412.634(b) for information regarding the
current policies for reporting IRF QRP data.
2. Update to the CMS System for Reporting Quality Measures and
Standardized Patient Assessment Data and Associated Procedural
Proposals
IRFs are currently required to submit IRF-PAI data to CMS using the
Quality Improvement and Evaluation System (QIES) Assessment and
Submission Processing (ASAP) system. We will be migrating to a new
internet Quality Improvement and Evaluation System (iQIES) that will
enable real-time upgrades, and we proposed to designate that system as
the data submission system for the IRF QRP beginning October 1, 2019.
We proposed to revise Sec. 412.634(a)(1) by replacing ``Certification
and Survey Provider Enhanced Reports (CASPER)'' with ``CMS designated
data submission''. We proposed to revise Sec. 412.634(d)(1) by
replacing the reference to ``Quality Improvement and Evaluation System
Assessment Submission and Processing (QIES ASAP) system'' with ``CMS
designated data submission system''. We proposed to revise Sec.
412.634(d)(5) by replacing reference to the ``QIES ASAP'' with ``CMS
designated data submission''. We proposed to revise Sec. 412.634(f)(1)
by replacing ``QIES'' with ``CMS designated data submission system''.
In addition, we proposed to notify the public of any future changes to
the CMS designated system using subregulatory mechanisms, such as
website postings, listserv messaging, and webinars.
We invited public comment on our proposals.
Comment: One commenter supported this proposal and recommended that
CMS begin educating and preparing IRFs for the transition as soon as
possible.
Response: We thank the commenter for their support and appreciate
the importance of educating for this transition. Information regarding
the transition to iQIES and instructions for onboarding has been
provided to IRFs and will be ongoing. Training resources are currently
available on You-Tube at https://go.cms.gov/iQIES_Training and
additional help content for users is available within iQIES. Ongoing
technical support via email is also available at [email protected].
After consideration of the public comments, we are finalizing our
proposal to revise Sec. 412.634(a)(1), Sec. 412.634(d)(1), Sec.
412.634(d)(5), and Sec. 412.634(f)(1) as proposed. We are also
finalizing our proposal to notify the public of any future changes to
the CMS designated system using subregulatory mechanisms, such as
website postings, listserv messaging, and webinars.
3. Schedule for Reporting the Transfer of Health Information Quality
Measures Beginning With the FY 2022 IRF QRP
As discussed in section VIII.D. of this final rule, we proposed to
adopt the Transfer of Health Information to the Provider--Post-Acute
Care (PAC) and Transfer of Health Information to the Patient--Post-
Acute Care (PAC) quality measures beginning with the FY 2022 IRF QRP.
We also proposed that IRFs would report the data on those measures
using the IRF-PAI. IRFs would be required to collect data on both
measures for Medicare Part A and Medicare Advantage patients beginning
with patients discharged on or after October 1, 2020. We refer readers
to the FY 2018 IRF PPS final rule (82 FR 36291 through 36292) for the
data collection and submission timeframes that we finalized for the IRF
QRP.
We sought public comment on this proposal and did not receive any
comments.
We are finalizing our proposal that IRFs report the data on
Transfer of Health Information to the Provider--Post-Acute Care (PAC)
and Transfer of Health Information to the Patient--Post-Acute Care
(PAC) quality measures using the IRF-PAI as proposed. IRFs will be
required to collect data on both measures for Medicare Part A and
Medicare Advantage patients beginning with patients discharged on or
after October 1, 2020.
4. Schedule for Reporting Standardized Patient Assessment Data Elements
Beginning With the FY 2022 IRF QRP
As discussed in section IV.F. of the proposed rule, we proposed to
adopt SPADEs beginning with the FY 2022 IRF QRP. We proposed that IRFs
would report the data using the IRF-PAI. Similar to the proposed
schedule for reporting the Transfer of Health Information to the
Provider--Post-Acute Care (PAC) and Transfer of Health Information to
the Patient--Post-Acute Care (PAC) quality measures, IRFs would be
required to collect the SPADEs for all Medicare Part A and Medicare
Advantage patients discharged on or after October 1, 2020, at both
admission and discharge. IRFs that submit data with respect to
admission for the Hearing, Vision, Race, and Ethnicity SPADEs would be
considered to have submitted data with respect to discharges. We refer
readers to the FY 2018 IRF PPS final rule (82 FR 36291 through 36292)
for the data collection and submission timeframes that we finalized for
the IRF QRP.
We sought public comment on this proposal and did not receive any
comments.
We are finalizing our proposal that IRFs must submit the SPADEs for
all Medicare Part A and Medicare Advantage patients discharged on or
after October 1, 2020, with respect to both admission and discharge,
using the IRF-PAI. IRFs that submit data with respect to admission for
the Hearing, Vision, Preferred Language, Interpreter Services, Race,
and Ethnicity SPADEs will be considered to have submitted data with
respect to discharges.
5. Data Reporting on Patients for the IRF Quality Reporting Program
Beginning With the FY 2022 IRF QRP
We received public input suggesting that the quality measures used
in the IRF QRP should be calculated using data collected from all IRF
patients, regardless of the patients' payer. This input was provided to
us via comments requested about quality measure development on the CMS
Measures Management System Blueprint website,\220\ as well as through
comments we received from stakeholders via our IRF QRP mailbox, and
feedback received from the NQF-convened MAP as part of their
recommendations on Coordination Strategy for Post-Acute Care and Long-
Term Care Performance Measurement.\221\ Further, in the FY 2018 IRF PPS
proposed rule (82 FR 20740), we sought input on expanding the reporting
of quality measures to include all patients, regardless of payer, so as
to ensure that the IRF QRP makes publicly available information
regarding the quality of the services furnished to the IRF population
as a whole, rather
[[Page 39162]]
than just those patients who have Medicare.
---------------------------------------------------------------------------
\220\ Public Comment Summary Report Posting for Transfer of
Health Information and Care Preferences. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Development-of-Cross-Setting-Transfer-of-Health-Information-Quality-Meas.pdf.
\221\ MAP Coordination Strategy for Post-Acute Care and Long-
Term Care Performance Measurement. Feb 2012. https://www.qualityforum.org/Publications/2012/02/MAP_Coordination_Strategy_for_Post-Acute_Care_and_Long-Term_Care_Performance_Measurement.aspx.
---------------------------------------------------------------------------
In response to that request for public input, several commenters,
including MedPAC, submitted comments stating that they would be
supportive of an effort to collect data specified under the IRF QRP
from all IRF patients regardless of their payer. Many commenters noted
that this would not be overly burdensome, as most of their
organizations' members currently complete the IRF-PAI on all patients,
regardless of their payer. A few commenters had concerns, including
recommending that CMS continue to align the patient assessment
instruments across PAC settings and whether the use of the data would
outweigh any additional reporting burden. For a more detailed
discussion, we refer readers to the FY 2018 IRF final rule (82 FR
36292). We have taken these concerns under consideration in proposing
this policy.
Further, given that we do not have access to other payer claims, we
believe that the most accurate representation of the quality provided
in IRFs would be best conveyed using data collected via the IRF-PAI on
all IRF patients, regardless of payer, for the purposes of the IRF QRP.
Medicare is the primary payer for approximately 60 percent of IRF
patients.\222\
---------------------------------------------------------------------------
\222\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Washington, DC: The National
Acadiemies Press.
---------------------------------------------------------------------------
We also believe that data reporting on standardized patient
assessment data elements using IRF-PAI should include all IRF patients
for the same reasons for collecting data on all residents for the IRF
QRP's quality measures: To promote higher quality and more efficient
health care for Medicare beneficiaries and all patients receiving IRF
services, for example through the exchange of information and
longitudinal analysis of the data. With that, we believe that
collecting quality measure and standardized patient assessment data via
the IRF-PAI on all IRF patients ensures that quality care is provided
for Medicare beneficiaries, and patients receiving IRF services as a
whole. While we appreciate that collecting quality data on all patients
regardless of payer may create additional burden, we also note that the
effort to separate out Medicare beneficiaries from other patients is
also burdensome.
Collecting data on all IRF patients will provide us with the most
robust, accurate reflection of the quality of care delivered to
Medicare beneficiaries as compared with non-Medicare patients and
residents, and we intend to display the calculation of this data on IRF
Compare in the future. Accordingly, we proposed that IRFs collect data
on all IRF patients to ensure that all patients, regardless of their
payer, are receiving the same care and that provider metrics measure
performance across the spectrum of patients.
Therefore, to meet the quality reporting requirements for IRFs for
the FY 2022 payment determination and each subsequent year, we proposed
to expand the reporting of IRF-PAI data used for the IRF QRP to include
data on all patients, regardless of their payer, beginning with
patients discharged on or after October 1, 2020 for the FY 2022 IRF QRP
and the IRF-PAI V4.0, effective October 1, 2020.
We sought public comment on this proposal and received several
comments, which are discussed below.
Comment: Many commenters, including MedPAC, supported the proposal
to expand the reporting of quality measures to all patients regardless
of payer, agreeing that quality care should be a goal for all patients.
Several commenters agreed that most providers already complete an IRF-
PAI for all patients. MedPAC also cautioned that any future Medicare
payment adjustments related to performance should be based only on
outcomes for Medicare beneficiaries. One commenter stated that this
approach is consistent with other quality programs and offers consumers
a fuller picture of quality of care. One commenter recommended
including quality data about all payers on IRF Compare, and another
commenter supported the proposal but suggested CMS to allow adequate
time to review and validate data before it is made public and allow
data on IRF Compare to be analyzed by payer.
Response: We thank commenters for their support and appreciate
suggestions for implementing this policy.
Comment: A few commenters requested additional details about how
this proposal would be implemented. One commenter suggested that CMS
verify comprehensive data submission on all patients to avoid ``cherry-
picking'' patients. A few commenters recommended that CMS delay this
proposal and study how this additional data affects quality measure
performance.
Response: We appreciate the commenters' request for more details
regarding the implementation of this proposal, how data submission will
be verified to avoid cherry-picking, and how this data will affect
quality measure performance. We acknowledge the commenters' concerns
about the proposal's implementation timeline and the request to delay
the proposal; however instead of delaying, we plan to use the comments
received during this rulemaking cycle to bring a new all-payer policy
proposal in the future. Therefore, after consideration of the public
comments we received on these issues, we have decided that at this
time, we will not finalize this proposal. We agree that it would be
useful to assess further how to best implement the collection of data
for all payers for the IRF QRP.
Comment: Many commenters had concerns about the burden of
collecting quality data on all patients regardless of payer, citing
that it contradicted the Patients over Paperwork initiative. One
commenter suggested that CMS make this requirement voluntary and to
conduct an analysis on the administrative burden on IRFs. Another
commenter suggested that the Collection of Information section should
contain an estimate of burden required for this reporting.
Response: We do not believe that that the intent of this policy
contradicts the Patients over Paperwork initiative, which aims to
simplify the documentation required for our programs. However, the all
payer proposal would have imposed a new reporting burden on IRFs. We
are sensitive to the issue of burden associated with data collection
and acknowledge the commenters' concerns about the additional burden
required to collect quality data on all patients. Although we believe
that the reporting of all-payer data under the IRF QRP would add value
to the program and provide a more accurate representation of the
quality provided by IRFs, we believe we need to better quantify the new
reporting burden on IRFs from this proposal for stakeholders to submit
comments. Therefore, after consideration of the public comments, we
received on these issues, we have decided that at this time, we will
not finalize this proposal. We agree that this burden should be
accounted for and we will estimate this burden in future rulemaking.
Comment: One commenter questioned whether IRFs support this
proposal. Another commenter was concerned that this proposal would add
complexity to CMS' administration of the IRF QRP compliance
determination process. One commenter was concerned that quality data
would be skewed because younger, non-Medicare patients have more room
for improvement compared to older patients.
Response: We do not believe this will add complexity to the IRF QRP
[[Page 39163]]
compliance determination process, since adding more patients will not
change the overall process that we follow with regard to determining
compliance. With regard to IRF support for this proposal, we sought
input on this topic in the FY 2018 IRF PPS proposed rule (82 FR 20740)
and we received several supportive comments. With regard to the
commenter's concerns that quality data would be skewed because younger
non-Medicare patients have more room for improvement, we note that risk
adjustment is currently used for many quality measures, including
measures that focus on improvement, such as the functional outcome
measures. We take patient characteristics, such as age, into
consideration when developing measures, and these are included as risk
adjustors for the functional outcome measures.
Comment: Several commenters did not support the proposal, citing
concerns about patient privacy. Some commenters suggested that
collecting quality data from non-Medicare beneficiaries would be a
violation of the Health Insurance Portability and Accountability Act of
1996 (HIPAA) since it is not required for reimbursement purposes.
Another commenter was concerned that CMS' collection of, and possible
disclosing of, sensitive health information from non-Medicare patients
without consent may violate the Privacy Act of 1974, the E-Government
Act of 2002, and other state level privacy acts. The commenter suggests
amending Sec. 412.608(a) to require the clinician at the IRF to
provide the Privacy Act Statement and other information to non-Medicare
patients.
Other commenters questioned how CMS would keep this non-Medicare
data secure and were concerned that CMS could work with other payers to
de-identify this data. A few commenters recommended informing non-
Medicare beneficiaries of this reporting and to use only de-identified
data. A few commenters requested more details from CMS about the scope
of data collection, including non-quality information on the IRF-PAI.
Response: We appreciate the commenters' concerns but disagree that
this proposal is a violation of HIPAA, Privacy Act of 1974, and e-
Government Act of 2002. IRF-PAI data is collected under an existing
system of records notice (66 FR 56682). Any disclosure of the data will
be made in accordance with the Privacy Act and those routine uses
outlined in the SORN. Medicare patients are currently given a Privacy
Act Statement and would be given to every patient under the IRF QRP.
Section 208 of the e-Government Act of 2002 requires federal agencies
to perform Privacy Impact Assessments when acquiring or developing new
information technology or making substantial changes to existing
information technology that involves the collection maintenance, or
dissemination of information in identifiable form. Because we are not
acquiring or developing new information technology, or making
substantial changes to existing information technology under this
proposal, we disagree that this policy violates the e-Government Act.
With regard to questions about how CMS would keep data non-Medicare
data secure, we safeguard the IRF-PAI data in a secure data system. The
system limits data access to authorized users and monitors such users
to ensure against unauthorized data access or disclosures. This system
conforms to all applicable federal laws and regulations as well as
federal government, Department of Health & Human Services (HHS), and
CMS policies and standards as they relate to information security and
data privacy. The applicable laws and regulations include, but are not
limited to: The Privacy Act of 1974; the Federal Information Security
Management Act of 2002; the Computer Fraud and Abuse Act of 1986; the
Health Insurance Portability and Accountability Act of 1996; the E-
Government Act of 2002; the Clinger-Cohen Act of 1996; the Medicare
Modernization Act of 2003; and the corresponding implementing
regulations. With regard to the scope of data collection, IRFs would be
required to submit quality measure and standardized patient assessment
data elements required by the IRF QRP. After consideration of the
public comments we received on these issues, we have decided that at
this time, we will not finalize this proposal. We appreciate concerns
raised by providers and will take them into consideration for future
rulemaking.
Comment: One commenter questioned whether CMS has the statutory
authority to require IRFs to submit IRF-PAI data for the IRF QRP for
all patients, regardless of payer, citing that it is inconsistent with
section 1886(j)(2)(D) of the Act because data from non-Medicare IRF
patients are not ``necessary'' for administering the IRF PPS. The
commenter further noted that Sec. 412.604(c) currently requires IRFs
to complete an IRF-PAI for all Medicare Part A and Part C patients that
an IRF admits or discharges and does not address reporting for non-
Medicare patients.
Response: We believe that we generally have authority to collect
all payer data for the IRF QRP under section 1886(j)(7) of the Act. We
also note that with respect to the data submitted in accordance with
section 1886(j)(7)(F) of the Act, the statute expressly requires that
data on quality measures specified under section 1899B(c)(1) of the Act
be submitted using the IRF PAI, to the extent possible, and that SPADE
required under section 1899B(b)(1) of the Act be submitted using the
IRF PAI. No all payer data collected for the IRF QRP would be used for
purposes of administering the IRF PPS.
We appreciate the support offered by some commenters for our
proposal to collect data on all IRF patients regardless of payer so as
to ensure that the IRF QRP makes publicly available information
regarding the quality of the services furnished to Medicare
beneficiaries, as well as to the IRF population as a whole. However, we
also acknowledge the concerns raised by some commenters with respect to
the administrative challenges of implementing all payer data
collection, the need to account for the burden related to this policy,
as well as the need for us to provide further detail and training to
IRFs. We continue to believe that the collection of quality data to
include all patients would help to ensure that Medicare patients
receive the same quality of care as other patients who are treated by
IRFs.
Therefore, after careful consideration of the public comments we
received, we will not finalize the proposal to expand the reporting of
IRF quality data to include all patients, regardless of payer, at this
time. We plan to use the comments we received on this proposal to help
inform a future all payer proposal.
I. Policies Regarding Public Display of Measure Data for the IRF QRP
Section 1886(j)(7)(E) of the Act requires the Secretary to
establish procedures for making the IRF QRP data available to the
public after ensuring that IRFs have the opportunity to review their
data prior to public display. Measure data are currently displayed on
the Inpatient Rehabilitation Facility Compare website, an interactive
web tool that assists individuals by providing information on IRF
quality of care. For more information on IRF Compare, we refer readers
to the website at https://www.medicare.gov/inpatientrehabilitationfacilitycompare/. For a more detailed discussion
about our
[[Page 39164]]
policies regarding public display of IRF QRP measure data and
procedures for the opportunity to review and correct data and
information, we refer readers to the FY 2017 IRF PPS final rule (81 FR
52125 through 52131).
In the proposed rule, we proposed to begin publicly displaying data
for the Drug Regimen Review Conducted With Follow-Up for Identified
Issues--PAC IRF QRP measure beginning CY 2020 or as soon as technically
feasible. We finalized the Drug Regimen Review Conducted With Follow-Up
for Identified Issues--PAC IRF QRP measure in the FY 2017 IRF PPS final
rule (81 FR 52111 through 52116).
Data collection for this assessment-based measure began with
patients discharged on or after October 1, 2018. We proposed to display
data based on four rolling quarters, initially using discharges from
January 1, 2019 through December 31, 2019 (Quarter 1 2019 through
Quarter 4 2019). To ensure the statistical reliability of the data, we
proposed that we would not publicly report an IRF's performance on the
measure if the IRF had fewer than 20 eligible cases in any four
consecutive rolling quarters. IRFs that have fewer than 20 eligible
cases would be distinguished with a footnote that states, ``The number
of cases/patient stays is too small to publicly report.''
We sought public comment on these proposals and received several,
which are summarized below.
Comment: Several commenters supported the proposal to begin
publicly displaying data for the Drug Regimen Review Conducted With
Follow-Up for Identified Issues--PAC IRF QRP measure in CY 2020 or as
soon as technically feasible, including the exception for IRFs with
fewer than 20 eligible cases. One commenter clarified that its support
is contingent on the measure not utilizing performance categories.
Response: We appreciate the commenter's support.
After consideration of the public comments, we are finalizing our
proposal to begin publicly displaying data for the Drug Regimen Review
Conducted With Follow-Up for Identified Issues--PAC IRF QRP measure
beginning CY 2020 or as soon as technically feasible.
J. Removal of the List of Compliant IRFs
In the FY 2016 IRF PPS final rule (80 FR 47125 through 47127), we
finalized that we would publish a list of IRFs that successfully met
the reporting requirements for the applicable payment determination on
the IRF QRP website and update the list on an annual basis. We have
received feedback from stakeholders that this list offers minimal
benefit. Although the posting of successful providers was the final
step in the applicable payment determination process, it does not
provide new information or clarification to the providers regarding
their annual payment update status. Therefore, we proposed that we will
no longer publish a list of compliant IRFs on the IRF QRP website,
effective beginning with the FY 2020 payment determination.
We sought public comment on this proposal and received several
comments.
Comment: One commenter supported this proposal, but suggested that
CMS make this information available to stakeholders upon request in the
interest of transparency.
Response: We thank commenters for their support. At this time, we
do not plan to make the list of compliant IRFs available upon request,
in alignment with other QRPs that do not provide this list. We believe
stakeholders can find sufficient quality information about IRFs on the
IRF compare website.
Comment: Several commenters did not support the proposal removal of
the list of compliant IRFs. One commenter agreed that the list was not
relevant to IRF providers in reviewing their own compliance status, but
stated that it could be of interest to patients and other IRFs. Other
commenters recommended posting the list because it is helpful for large
health systems to quickly determine which hospitals are compliant. One
commenter further suggested that the list continue to be posted in a
standardized manner across the various QRPs to improve transparency.
Response: We acknowledge commenters' concerns about removing the
requirement to post the list of compliant IRFs. Patients and consumers
can still find information about IRF quality on the IRF Compare
website. We do not believe that removing this list will have a negative
impact for IRFs, since the list does not give any new information to
IRF providers or health providers about their own compliance status. We
also note that other QRPs do not require posting of a list of compliant
facilities.
After consideration of the comments, we are finalizing our proposal
and will no longer publish a list of compliant IRFs on the IRF QRP
website, beginning with the FY 2020 payment determination.
K. Method for Applying the Reduction to the FY 2020 IRF Increase Factor
for IRFs That Fail To Meet the Quality Reporting Requirements
As previously noted, section 1886(j)(7)(A)(i) of the Act requires
the application of a 2-percentage point reduction of the applicable
market basket increase factor for payments for discharges occurring
during such fiscal year for IRFs that fail to comply with the quality
data submission requirements.
We proposed to apply a 2-percentage point reduction to the
applicable FY 2020 proposed market basket increase factor in
calculating an adjusted FY 2020 proposed standard payment conversion
factor to apply to payments for only those IRFs that failed to comply
with the data submission requirements. As previously noted, application
of the 2-percentage point reduction may result in an update that is
less than 0.0 for a fiscal year and in payment rates for a fiscal year
being less than such payment rates for the preceding fiscal year. Also,
reporting-based reductions to the market basket increase factor will
not be cumulative; they will only apply for the FY involved.
We invited public comment on the proposed method for applying the
reduction to the FY 2020 IRF increase factor for IRFs that fail to meet
the quality reporting requirements, which are summarized below.
Comment: Some commenters suggested that CMS provide flexibility in
its application of the IRF QRP payment penalty for IRFs who make a
good-faith effort to comply and submit quality reporting data.
Response: We interpret the commenter's suggestion that we take into
consideration case by case exceptions and apply leniency for providers
have attempted but failed to submit their quality reporting data for
the IRF QRP. We are unable to provide flexibility with respect to the 2
percent payment penalty; as noted previously, section 1886(j)(7) of the
Act requires the Secretary to reduce the annual increase factor for
IRFs that fail to comply with the quality data submission requirements.
While we did not seek comment on flexibilities on which the penalty is
applied, we note that we have provided flexibility where the failure of
the IRF to comply with the requirements of the IRF QRP stemmed from
circumstances beyond its control. For example, we have finalized
policies that grant exceptions or extensions for IRFs if we determine
that a systemic problem with one of our data collection systems
affected the ability of IRFs to submit data (79 FR 45920). We have also
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adopted policies (78 FR 47920) that allow us to grant exemptions or
extensions to an IRF if it has experienced an extraordinary
circumstance beyond its control. In addition, we set the reporting
compliance threshold at 95 percent rather than at 100 percent to data
to for account for the rare instances when assessment data collection
and submission maybe impossible, such as when patients have been
discharged emergently, or against medical advice.
Table 18 shows the calculation of the adjusted FY 2020 standard
payment conversion factor that will be used to compute IRF PPS payment
rates for any IRF that failed to meet the quality reporting
requirements for the applicable reporting period.
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After consideration of the comments, we are finalizing our proposal
to apply a 2-percentage point reduction to the applicable FY 2020
proposed market basket increase factor in calculating an adjusted FY
2020 proposed standard payment conversion factor to apply to payments
for only those IRFs that failed to comply with the data submission
requirements.
X. Miscellaneous Comments
We received several comments that were outside the scope of the FY
2020 IRF PPS proposed rule. Specifically, we received comments
regarding the processes for updating the IRF facility-level adjustment
factors and the transparency of these updates, the application of a
cost-of-living adjustment for IRFs located in Alaska and Hawaii, the
need for CMS education and instruction on the appropriate IGC/ICD
coding on the IRF-PAI, re-evaluating and phasing out the 60 percent
rule as criteria for IRF admission, and federal funding for universal
health care. We thank commenters for bringing these issues to our
attention, and we will take these comments into consideration for
potential policy refinements.
XI. Provisions of the Final Regulations
In this final rule, we are adopting the provisions set forth in the
FY 2020 IRF PPS proposed rule (84 FR 17244).
Specifically:
We will adopt an unweighted motor score to assign patients
to CMGs, the removal of one item from the score, and revisions to the
CMGs beginning on October 1, 2019, based on analysis of 2 years of data
(FYs 2017 and 2018) using the Quality Indicator items in the IRF-PAI.
This includes revisions to the CMG relative weights and average LOS
values for FY 2020, in a budget neutral manner, as discussed in section
IV. of this final rule.
We will rebase and revise the IRF market basket to reflect
a 2016 base year rather than the current 2012 base year as discussed in
section VI. of this FY 2020 IRF PPS final rule.
We will update the IRF PPS payment rates for FY 2020 by
the market basket increase factor, based upon the most current data
available, with a productivity adjustment required by section
1886(j)(3)(C)(ii)(I) of the Act, as described in section VI. of this
final rule.
We will update to the IRF wage index to use the concurrent
FY IPPS wage index and the FY 2020 labor-related share in a budget-
neutral manner, as described in section VI. of this final rule.
The facility-level adjustments will remain frozen at the
FY 2014 levels for FY 2015 and all subsequent years, as discussed in
section V. of this final rule.
We will calculate the final IRF standard payment
conversion factor for FY 2020, as discussed in section VI. of this
final rule.
We will update the outlier threshold amount for FY 2020,
as discussed in section VII. of this final rule.
We will update the CCR ceiling and urban/rural average
CCRs for FY 2020, as discussed in section VII. of this final rule.
We will amend the regulations at Sec. 412.622 to clarify
that the determination as to whether a physician qualifies as a
rehabilitation physician (that is, a licensed physician with
specialized training and experience in inpatient rehabilitation) is
made by the IRF, as discussed in section VIII. of this final rule.
We will adopt updates requirements to the IRF QRP, as
discussed in section IX. of this final rule.
XII. Collection of Information Requirements
A. Statutory Requirement for Solicitation of Comments
Under the Paperwork Reduction Act of 1995 (PRA), we are required to
provide 60-day notice in the Federal Register and solicit public
comment before a collection of information requirement is submitted to
the OMB for review and approval. To fairly evaluate whether an
information collection should be approved by OMB, section 3506(c)(2)(A)
of the PRA requires that we solicit comment on the following issues:
The need for the information collection and its usefulness
in carrying out the proper functions of our agency.
The accuracy of our estimate of the information collection
burden.
The quality, utility, and clarity of the information to be
collected.
Recommendations to minimize the information collection
burden on the affected public, including automated collection
techniques.
This final rule makes reference to associated information
collections that are not discussed in the regulation text contained in
this document.
B. Collection of Information Requirements for Updates Related to the
IRF QRP
An IRF that does not meet the requirements of the IRF QRP for a
fiscal year will receive a 2 percentage point reduction to its
otherwise applicable
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annual increase factor for that fiscal year. Information is not
currently available to determine the precise number of IRFs that will
receive less than the full annual increase factor for FY 2020 due to
non-compliance with the requirements of the IRF QRP.
We believe that the burden associated with the IRF QRP is the time
and effort associated with complying with the requirements of the IRF
QRP. As of July 15, 2019, there are approximately 1,122 IRFs reporting
quality data to CMS. For the purposes of calculating the costs
associated with the collection of information requirements, we obtained
mean hourly wages for these staff from the U.S. Bureau of Labor
Statistics' May 2018 National Occupational Employment and Wage
Estimates (https://www.bls.gov/oes/current/oes_nat.htm). To account for
overhead and fringe benefits, we have doubled the hourly wage. These
amounts are detailed in Table 19.
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As discussed in section VIII.D. of this final rule, we are adopting
two new measures, (1) Transfer of Health Information to the Provider--
Post-Acute Care (PAC); and (2) Transfer of Health Information to the
Patient--Post-Acute Care (PAC), beginning with the FY 2022 IRF QRP. As
a result, the estimated burden and cost for IRFs for complying with
requirements of the FY 2022 IRF QRP will increase. Specifically, we
believe that there will be a 1.2 minute addition in clinical staff time
to report data per patient stay. We estimate 411,622 discharges from
1,122 IRFs annually. This equates to an increase of 8,232 hours in
burden for all IRFs (0.02 hours per assessment x 411,622 discharges).
Given 0.7 minutes of RN time at $70.72 per hour and 0.5 minutes of LVN
time at $43.96 per hour, we estimate that the total cost will be
increased by $437 per IRF annually, or $490,314 for all IRFs annually.
This increase in burden will be accounted for in the information
collection under OMB control number (0938-0842), which expires December
31, 2021.
In addition, we are finalizing our proposal to add the standardized
patient assessment data elements described in section VIII.F of this
final rule beginning with the FY 2022 IRF QRP. As a result, the
estimated burden and cost for IRFs for complying with requirements of
the FY 2022 IRF QRP will be increased. Specifically, we believe that
there will be an addition of 7.8 minutes on admission, and 10.95
minutes on discharge, for a total of 18.8 minutes of additional
clinical staff time to report data per patient stay. Note that this is
a decrease from the proposed 11.1 minutes at discharge because of the
changes in section XIII.G.4.2 of this final rule. We estimate 411,622
discharges from 1,122 IRFs annually. This equates to an increase of
122,995 hours in burden for all IRFs (0.3 hours per assessment x
409,982 discharges). Given 11.3 minutes of RN time at $70.72 per hour
and 7.5 minutes of LVN time at $43.96 per hour, we estimate that the
total cost will be increased by $6,902 per IRF annually, or $7,744,044
for all IRFs. This increase in burden will be accounted for in the
information collection under OMB control number (0938-0842), which
expires December 31, 2021.
In summary, the newly adopted IRF QRP quality measures and
standardized patient assessment data elements will result in a burden
addition of $7,339 per IRF annually, and $8,234,450 for all IRFs
annually.
XIII. Regulatory Impact Analysis
A. Statement of Need
This final rule updates the IRF prospective payment rates for FY
2020 as required under section 1886(j)(3)(C) of the Act. It responds to
section 1886(j)(5) of the Act, which requires the Secretary to publish
in the Federal Register on or before the August 1 that precedes the
start of each fiscal year, the classification and weighting factors for
the IRF PPS's CMGs, and a description of the methodology and data used
in computing the prospective payment rates for that fiscal year.
This final rule also implements sections 1886(j)(3)(C) of the Act.
Section 1886(j)(3)(C)(ii)(I) of the Act requires the Secretary to apply
a MFP adjustment to the market basket increase factor. The productivity
adjustment applies to FYs from 2012 forward.
Furthermore, this final rule also adopts policy changes under the
statutory discretion afforded to the Secretary under section 1886(j)(7)
of the Act. Specifically, we are rebasing and revising the IRF market
basket to reflect a 2016 base year rather than the current 2012 base
year, revising the CMGs, making a technical correction to the
regulatory language to indicate that the determination of whether a
treating physician has specialized training and experience in inpatient
rehabilitation is made by the IRF and updating regulatory language
related to IRF QRP data collection.
B. Overall Impact
We have examined the impacts of this rule as required by Executive
Order 12866 on Regulatory Planning and Review (September 30, 1993),
Executive Order 13563 on Improving Regulation and Regulatory Review
(January 18, 2011), the Regulatory Flexibility Act (RFA) (September 19,
1980, Pub. L. 96-354), section 1102(b) of the Act, section 202 of the
Unfunded Mandates Reform Act of 1995 (March 22, 1995; Pub. L. 104-4),
Executive Order 13132 on Federalism (August 4, 1999), the Congressional
Review Act (5 U.S.C. 804(2) and Executive Order 13771 on Reducing
Regulation and Controlling Regulatory Costs (January 30, 2017).
Executive Orders 12866 and 13563 direct agencies to assess all
costs and benefits of available regulatory alternatives and, if
regulation is necessary, to select regulatory approaches that maximize
net benefits (including potential economic, environmental, public
health and safety effects, distributive impacts, and equity). Section
3(f) of Executive Order 12866 defines a ``significant regulatory
action'' as an action that is likely to result in a rule: (1) Having an
annual effect on the economy of $100 million or more in any 1 year, or
adversely and materially affecting a sector of the economy,
productivity, competition, jobs, the environment, public health or
safety, or state, local or tribal governments or communities (also
[[Page 39167]]
referred to as ``economically significant''); (2) creating a serious
inconsistency or otherwise interfering with an action taken or planned
by another agency; (3) materially altering the budgetary impacts of
entitlement grants, user fees, or loan programs or the rights and
obligations of recipients thereof; or (4) raising novel legal or policy
issues arising out of legal mandates, the President's priorities, or
the principles set forth in the Executive Order.
A regulatory impact analysis (RIA) must be prepared for major rules
with economically significant effects ($100 million or more in any 1
year). We estimate the total impact of the policy updates described in
this final rule by comparing the estimated payments in FY 2020 with
those in FY 2019. This analysis results in an estimated $210 million
increase for FY 2020 IRF PPS payments. Additionally we estimate that
costs associated with the proposals to update the reporting
requirements under the IRF QRP result in an estimated $8.2 million
addition in costs in FY 2020 for IRFs. We estimate that this rulemaking
is ``economically significant'' as measured by the $100 million
threshold, and hence also a major rule under the Congressional Review
Act. Also, the rule has been reviewed by OMB. Accordingly, we have
prepared a Regulatory Impact Analysis that, to the best of our ability,
presents the costs and benefits of the rulemaking.
C. Anticipated Effects
1. Effects on IRFs
The RFA requires agencies to analyze options for regulatory relief
of small entities, if a rule has a significant impact on a substantial
number of small entities. For purposes of the RFA, small entities
include small businesses, nonprofit organizations, and small
governmental jurisdictions. Most IRFs and most other providers and
suppliers are small entities, either by having revenues of $7.5 million
to $38.5 million or less in any 1 year depending on industry
classification, or by being nonprofit organizations that are not
dominant in their markets. (For details, see the Small Business
Administration's final rule that set forth size standards for health
care industries, at 65 FR 69432 at https://www.sba.gov/sites/default/files/files/Size_Standards_Table.pdf, effective March 26, 2012 and
updated on February 26, 2016.) Because we lack data on individual
hospital receipts, we cannot determine the number of small proprietary
IRFs or the proportion of IRFs' revenue that is derived from Medicare
payments. Therefore, we assume that all IRFs (an approximate total of
1,120 IRFs, of which approximately 55 percent are nonprofit facilities)
are considered small entities and that Medicare payment constitutes the
majority of their revenues. The HHS generally uses a revenue impact of
3 to 5 percent as a significance threshold under the RFA. As shown in
Table 20, we estimate that the net revenue impact of this final rule on
all IRFs is to increase estimated payments by approximately 2.5
percent. The rates and policies set forth in this final rule will not
have a significant impact (not greater than 3 percent) on a substantial
number of small entities. Medicare Administrative Contractors are not
considered to be small entities. Individuals and states are not
included in the definition of a small entity.
In addition, section 1102(b) of the Act requires us to prepare a
regulatory impact analysis if a rule may have a significant impact on
the operations of a substantial number of small rural hospitals. This
analysis must conform to the provisions of section 604 of the RFA. For
purposes of section 1102(b) of the Act, we define a small rural
hospital as a hospital that is located outside of a Metropolitan
Statistical Area and has fewer than 100 beds. As discussed in detail
below in this section, the rates and policies set forth in this final
rule will not have a significant impact (not greater than 3 percent) on
a substantial number of rural hospitals based on the data of the 136
rural units and 11 rural hospitals in our database of 1,122 IRFs for
which data were available.
Section 202 of the Unfunded Mandates Reform Act of 1995 (Pub. L.
104-04, enacted March 22, 1995) (UMRA) also requires that agencies
assess anticipated costs and benefits before issuing any rule whose
mandates require spending in any 1 year of $100 million in 1995
dollars, updated annually for inflation. In 2019, that threshold is
approximately $154 million. This final rule does not mandate any
requirements for State, local, or tribal governments, or for the
private sector.
Executive Order 13132 establishes certain requirements that an
agency must meet when it issues a proposed rule (and subsequent final
rule) that imposes substantial direct requirement costs on state and
local governments, preempts state law, or otherwise has federalism
implications. As stated, this final rule will not have a substantial
effect on state and local governments, preempt state law, or otherwise
have a federalism implication.
Executive Order 13771, titled Reducing Regulation and Controlling
Regulatory Costs, was issued on January 30, 2017 and requires that the
costs associated with significant new regulations ``shall, to the
extent permitted by law, be offset by the elimination of existing costs
associated with at least two prior regulations.'' This final rule is
considered an E.O. 13771 regulatory action. We estimate that this rule
would generate $6.18 million in annualized cost, discounted at 7
percent relative to year 2016, over a perpetual time horizon. Details
on the estimated costs of this rule can be found in the preceding
analyses.
2. Detailed Economic Analysis
This final rule updates to the IRF PPS rates contained in the FY
2019 IRF PPS final rule (83 FR 38514). Specifically, this final rule
updates the CMG relative weights and average LOS values, the wage
index, and the outlier threshold for high-cost cases. This final rule
applies a MFP adjustment to the FY 2020 IRF market basket increase
factor in accordance with section 1886(j)(3)(C)(ii)(I) of the Act.
Further, this final rule rebases and revises the IRF market basket to
reflect a 2016 base year rather than the current 2012 base year,
revises the CMGs based on FYs 2017 and 2018 data and amends the
regulatory language to clarify that the determination of whether a
treating physician has specialized training and experience in inpatient
rehabilitation is made by the IRF.
We estimate that the impact of the changes and updates described in
this final rule will be a net estimated increase of $210 million in
payments to IRF providers. This estimate does not include the
implementation of the required 2 percentage point reduction of the
market basket increase factor for any IRF that fails to meet the IRF
quality reporting requirements (as discussed in section IX.K. of this
final rule). The impact analysis in Table 20 of this final rule
represents the projected effects of the updates to IRF PPS payments for
FY 2020 compared with the estimated IRF PPS payments in FY 2019. We
determine the effects by estimating payments while holding all other
payment variables constant. We use the best data available, but we do
not attempt to predict behavioral responses to these changes, and we do
not make adjustments for future changes in such variables as number of
discharges or case-mix.
We note that certain events may combine to limit the scope or
accuracy of our impact analysis, because such an analysis is future-
oriented and, thus,
[[Page 39168]]
susceptible to forecasting errors because of other changes in the
forecasted impact time period. Some examples could be legislative
changes made by the Congress to the Medicare program that would impact
program funding, or changes specifically related to IRFs. Although some
of these changes may not necessarily be specific to the IRF PPS, the
nature of the Medicare program is such that the changes may interact,
and the complexity of the interaction of these changes could make it
difficult to predict accurately the full scope of the impact upon IRFs.
In updating the rates for FY 2020, we are adopting standard annual
revisions described in this final rule (for example, the update to the
wage and market basket indexes used to adjust the federal rates). We
are also implementing a productivity adjustment to the FY 2020 IRF
market basket increase factor in accordance with section
1886(j)(3)(C)(ii)(I) of the Act. We estimate the total increase in
payments to IRFs in FY 2020, relative to FY 2019, will be approximately
$210 million.
This estimate is derived from the application of the FY 2020 IRF
market basket increase factor, as reduced by a productivity adjustment
in accordance with section 1886(j)(3)(C)(ii)(I) of the Act, which
yields an estimated increase in aggregate payments to IRFs of $210
million. Outlier payments are estimated to remain at 3 percent in FY
2020. Therefore, we estimate that these updates will result in a net
increase in estimated payments of $210 million from FY 2019 to FY 2020.
The effects of the updates that impact IRF PPS payment rates are
shown in Table 20. The following updates that affect the IRF PPS
payment rates are discussed separately below:
The effects of the update to the outlier threshold amount,
from approximately 3.0 percent to 3.0 percent of total estimated
payments for FY 2020, consistent with section 1886(j)(4) of the Act.
The effects of the annual market basket update (using the
IRF market basket) to IRF PPS payment rates, as required by sections
1886(j)(3)(A)(i) and (j)(3)(C) of the Act, including a productivity
adjustment in accordance with section 1886(j)(3)(C)(i)(I) of the Act.
The effects of applying the budget-neutral labor-related
share and wage index adjustment, as required under section 1886(j)(6)
of the Act.
The effects of the budget-neutral changes to the CMGs,
relative weights and average LOS values, under the authority of section
1886(j)(2)(C)(i) of the Act.
The total change in estimated payments based on the FY
2020 payment changes relative to the estimated FY 2019 payments.
3. Description of Table 20
Table 20 shows the overall impact on the 1,122 IRFs included in the
analysis.
The next 12 rows of Table 20 contain IRFs categorized according to
their geographic location, designation as either a freestanding
hospital or a unit of a hospital, and by type of ownership; all urban,
which is further divided into urban units of a hospital, urban
freestanding hospitals, and by type of ownership; and all rural, which
is further divided into rural units of a hospital, rural freestanding
hospitals, and by type of ownership. There are 975 IRFs located in
urban areas included in our analysis. Among these, there are 697 IRF
units of hospitals located in urban areas and 278 freestanding IRF
hospitals located in urban areas. There are 147 IRFs located in rural
areas included in our analysis. Among these, there are 136 IRF units of
hospitals located in rural areas and 11 freestanding IRF hospitals
located in rural areas. There are 393 for-profit IRFs. Among these,
there are 357 IRFs in urban areas and 36 IRFs in rural areas. There are
616 non-profit IRFs. Among these, there are 526 urban IRFs and 90 rural
IRFs. There are 113 government-owned IRFs. Among these, there are 92
urban IRFs and 21 rural IRFs.
The remaining four parts of Table 20 show IRFs grouped by their
geographic location within a region, by teaching status, and by DSH PP.
First, IRFs located in urban areas are categorized for their location
within a particular one of the nine Census geographic regions. Second,
IRFs located in rural areas are categorized for their location within a
particular one of the nine Census geographic regions. In some cases,
especially for rural IRFs located in the New England, Mountain, and
Pacific regions, the number of IRFs represented is small. IRFs are then
grouped by teaching status, including non-teaching IRFs, IRFs with an
intern and resident to average daily census (ADC) ratio less than 10
percent, IRFs with an intern and resident to ADC ratio greater than or
equal to 10 percent and less than or equal to 19 percent, and IRFs with
an intern and resident to ADC ratio greater than 19 percent. Finally,
IRFs are grouped by DSH PP, including IRFs with zero DSH PP, IRFs with
a DSH PP less than 5 percent, IRFs with a DSH PP between 5 and less
than 10 percent, IRFs with a DSH PP between 10 and 20 percent, and IRFs
with a DSH PP greater than 20 percent.
The estimated impacts of each policy described in this rule to the
facility categories listed are shown in the columns of Table 20. The
description of each column is as follows:
Column (1) shows the facility classification categories.
Column (2) shows the number of IRFs in each category in
our FY 2020 analysis file.
Column (3) shows the number of cases in each category in
our FY 2020 analysis file.
Column (4) shows the estimated effect of the adjustment to
the outlier threshold amount.
Column (5) shows the estimated effect of the update to the
IRF labor-related share and wage index, in a budget-neutral manner.
Column (6) shows the estimated effect of the update to the
CMGs, relative weights, and average LOS values, in a budget-neutral
manner.
Column (7) compares our estimates of the payments per
discharge, incorporating all of the policies reflected in this final
rule for FY 2020 to our estimates of payments per discharge in FY 2019.
The average estimated increase for all IRFs is approximately 2.5
percent. This estimated net increase includes the effects of the IRF
market basket increase factor for FY 2020 of 2.9 percent, reduced by a
productivity adjustment of 0.4 percentage point in accordance with
section 1886(j)(3)(C)(ii)(I) of the Act. There is no change in
estimated IRF outlier payments from the update to the outlier threshold
amount. Since we are making the updates to the IRF wage index and the
CMG relative weights in a budget-neutral manner, they will not be
expected to affect total estimated IRF payments in the aggregate.
However, as described in more detail in each section, they will be
expected to affect the estimated distribution of payments among
providers.
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4. Impact of the Update to the Outlier Threshold Amount
The estimated effects of the update to the outlier threshold
adjustment are presented in column 4 of Table 20. In the FY 2019 IRF
PPS final rule (83 FR 38531 through 38532), we used FY 2017 IRF claims
data (the best, most complete data available at that time) to set the
outlier threshold amount for FY 2019 so that estimated outlier payments
would equal 3 percent of total estimated payments for FY 2019.
For the FY 2020 IRF PPS proposed rule (84 FR 17244), we used
preliminary FY 2018 IRF claims data, and, based on that preliminary
analysis, we estimated that IRF outlier payments as a percentage of
total estimated IRF payments would be 3.2 percent in FY 2019. As we
typically do between the proposed and final rules each year, we updated
our FY 2018 IRF claims data to ensure that we are using the most recent
available data in setting IRF payments. Therefore, based on updated
analysis of the most recent IRF claims data for this final rule, we now
estimate that IRF outlier payments as a percentage of total IRF
payments as 3.0 in FY 2019. Thus, we are adjusting the outlier
threshold amount in this final rule to maintain total estimated outlier
payments equal to 3 percent of total estimated payments in FY 2020.
The impact of this outlier adjustment update (as shown in column 4
of Table 20) is to maintain estimated overall payments to IRFs at 3
percent.
5. Impact of the CBSA Wage Index and Labor-Related Share
In column 5 of Table 20, we present the effects of the budget-
neutral update of the wage index and labor-related share. The changes
to the wage index and the labor-related share are discussed together
because the wage index is applied to the labor-related share portion of
payments, so the changes in the two have a combined effect on payments
to providers. As discussed in section VI.E. of this final rule, we are
updating the labor-related share from 70.5 percent in FY 2019 to 72.7
percent in FY 2020.
6. Impact of the Update to the CMG Relative Weights and Average LOS
Values
In column 6 of Table 20, we present the effects of the budget-
neutral update of the CMGs, relative weights and average LOS values. In
the aggregate, we do not estimate that these updates will affect
overall estimated payments of IRFs. However, we do expect these updates
to have small distributional effects.
7. Effects of the Requirements for the IRF QRP for FY 2020
In accordance with section 1886(j)(7)(A) of the Act, the Secretary
must reduce by 2 percentage points the market basket increase factor
otherwise applicable to an IRF for a fiscal year if the IRF does not
comply with the requirements of the IRF QRP for that fiscal year. In
section VIII.J of this final rule, we discuss the method for applying
the 2 percentage point reduction to IRFs that fail to meet the IRF QRP
requirements.
As discussed in section VIII.D. of this final rule, we are
finalizing our proposal to add two measures to the IRF QRP: (1)
Transfer of Health Information to the Provider--Post-Acute Care (PAC);
and (2) Transfer of Health Information to the Patient--Post-Acute Care
(PAC), beginning with the FY 2022 IRF QRP. We are also finalizing our
proposal to add standardized patient assessment data elements, as
discussed in section IV.G of this final rule. We describe the estimated
burden and cost reductions for both of these measures in section VIII.C
of this final rule. In summary, the changes to the IRF QRP will result
in a burden addition of $7,339 per IRF annually, and $8,234,450 for all
IRFs annually.
We intend to continue to closely monitor the effects of the IRF QRP
on IRFs and to help perpetuate successful reporting outcomes through
ongoing stakeholder education, national trainings, IRF announcements,
website postings, CMS Open Door Forums, and general and technical help
desks.
8. Effects of the Amending Sec. 412.622(a)(3)(iv) To Clarify the
Definition of a Rehabilitation Physician
As discussed in section VIII. of this final rule, we are amending
Sec. 412.622(a)(3)(iv) to clarify that the determination as to whether
a physician qualifies as a rehabilitation physician (that is, a
licensed physician with specialized training and experience in
inpatient rehabilitation) is made by the IRF. We do not expect this to
have any effect on the quality of care that beneficiaries receive in
IRFs because we continue to require that the rehabilitation physicians
caring for patients in IRFs be licensed physicians with specialized
training and experience in inpatient rehabilitation. We expect IRFs to
continue ensuring that the rehabilitation physicians meet these
requirements. Although we do not currently collect data from IRFs on
the physicians specialties that are providing care to patients in IRFs,
we do not expect this to change as a result of the amendments we are
making to Sec. 412.622(a)(3)(iv). However, we will continue to monitor
the quality of care beneficiaries receive in IRFs, and will initiate
appropriate actions through future rulemaking if we observe any
declines in quality of care in IRFs.
As this is merely clarifying our existing policy regarding the
definition of a rehabilitation physician in Sec. 412.622(a)(3)(iv), we
do not expect this to result in any financial impacts for the Medicare
contractors, IRFs, other providers, or for the Medicare program.
However, we expect that this clarification may ease some administrative
burden for IRFs and for Medicare contractors by making it easier for
IRF providers to document their decisions regarding the licensed
physicians in their facilities that meet the regulatory definition of a
rehabilitation physician and for the Medicare contractors to continue
to accept the IRFs' decisions in this regard. We are unable at this
time to quantify how much administrative burden may have existed
because of the previous ambiguity surrounding the definition of a
rehabilitation physician, but we are hopeful that this clarification
will alleviate any administrative burden that might have existed
before.
We expect this clarification to enhance Medicare's program
integrity efforts in this area by eliminating uncertainty surrounding
the definition of a rehabilitation physician.
D. Alternatives Considered
The following is a discussion of the alternatives considered for
the IRF PPS updates contained in this final rule.
Section 1886(j)(3)(C) of the Act requires the Secretary to update
the IRF PPS payment rates by an increase factor that reflects changes
over time in the prices of an appropriate mix of goods and services
included in the covered IRF services.
We are adopting a market basket increase factor for FY 2020 that is
based on a rebased and revised market basket reflecting a 2016 base
year. We considered the alternative of continuing to use the IRF market
basket without rebasing to determine the market basket increase factor
for FY 2020. However, we typically rebase and revise the market baskets
for the various PPS every 4 to 5 years so that the cost weights and
price proxies reflect more recent data. Therefore, we believe it is
more technically appropriate to use a 2016-based IRF market basket
since it allows for the FY 2020 market basket increase
[[Page 39171]]
factor to reflect a more up-to-date cost structure experienced by IRFs.
As noted previously in this final rule, section
1886(j)(3)(C)(ii)(I) of the Act requires the Secretary to apply a
productivity adjustment to the market basket increase factor for FY
2020. Thus, in accordance with section 1886(j)(3)(C) of the Act, we are
updating the IRF prospective payments in this final rule by 2.5 percent
(which equals the 2.9 percent estimated IRF market basket increase
factor for FY 2020 reduced by a 0.4 percentage point productivity
adjustment as determined under section 1886(b)(3)(B)(xi)(II) of the Act
(as required by section 1886(j)(3)(C)(ii)(I) of the Act)).
As we finalized in the FY 2019 IRF PPS final rule (83 FR 38514) use
of the Quality Indicators items in determining payment and the
associated CMG and CMG relative weight revisions using 2 years of data
(FYs 2017 and 2018) beginning with FY 2020, we did not consider any
alternative to proposing these changes.
However, we did consider whether or not to apply a weighting
methodology to the IRF motor score that was finalized in the FY 2019
IRF PPS final rule (83 FR 38514) to assign patients to CMGs beginning
in FY 2020. As described in the FY 2020 IRF PPS proposed rule (84 FR
17244, 17249 through 17260), we explored the use of a weighted motor
score, as requested by stakeholders. Our analysis showed that weighting
the motor score would improve the accuracy of payments under the IRF
PPS. The improved accuracy combined with the requests from stakeholders
to explore a weighted methodology led us to propose to use a weighted
motor score to assign patients to CMGs beginning on October 1, 2019.
However, in light of the many concerned stakeholder comments on the FY
2020 IRF PPS proposed rule that requested that we go back to an
unweighted motor score methodology until we can more fully analyze a
weighted motor score, the fact that the improvement in accuracy using
the weighted motor score is small, and the greater simplicity achieved
through the use of an unweighted motor score, we are finalizing an
unweighted motor score, in which each of the 18 items have a weight of
1, beginning October 1, 2019. We will continue to analyze weighted
motor score approaches and will consider possible revisions to the
motor score for future rulemaking.
We considered not removing the item GG0170A1 Roll left and right
from the composition of the motor score. However, this item was found
to be very collinear with other items in the motor score and did not
behave as expected in the models. Therefore, we believe it is
appropriate to remove this item from the construction of the motor
score.
We considered updating facility-level adjustment factors for FY
2020. However, as discussed in more detail in the FY 2015 final rule
(79 FR 45872), we believe that freezing the facility-level adjustments
at FY 2014 levels for FY 2015 and all subsequent years (unless and
until the data indicate that they need to be further updated) will
allow us an opportunity to monitor the effects of the substantial
changes to the adjustment factors for FY 2014, and will allow IRFs time
to adjust to the previous changes.
We considered not updating the IRF wage index to use the concurrent
fiscal year's IPPS wage index and instead continuing to use a 1-year
lag of the IPPS wage index. However, we believe that updating the IRF
wage index based on the concurrent fiscal year's IPPS wage index will
better align the data across acute and PAC settings in support of our
efforts to move toward more unified Medicare payments across PAC
settings.
We considered maintaining the existing outlier threshold amount for
FY 2020. However, the outlier threshold must be adjusted to reflect
changes in estimated costs and payments for IRFs in FY 2020.
Consequently, we are adjusting the outlier threshold amount in this
final rule to maintain total outlier payments equal to 3 percent of
aggregate estimated payments in FY 2020.
We considered not amending Sec. 412.622(a)(3)(iv) to clarify that
the determination as to whether a physician qualifies as a
rehabilitation physician (that is, a licensed physician with
specialized training and experience in inpatient rehabilitation) is
made by the IRF. Instead, we considered addressing this issue through
subregulatory means, such as issuing guidance to the Medicare
contractors. However, we believe that it is important to clarify this
definition in regulation to ensure that IRF providers and Medicare
contractors have a shared understanding of these regulatory
requirements and to make certain that there is no room for further
ambiguity on this point.
In addition, we considered addressing this issue by amending Sec.
412.622(a)(3)(iv) to add further specificity to the definition of a
rehabilitation physician. However, we did not take this approach
because we continue to believe that the IRFs are in the best position
to make the determination as to which licensed physicians meet the
requirements for purposes of Sec. 412.622, and we did not want to
inadvertently affect access to IRF care for beneficiaries. However, we
will continue to monitor this policy and engage with stakeholders to
determine if further specificity of these requirements may be warranted
in the future.
E. Regulatory Review Costs
If regulations impose administrative costs on private entities,
such as the time needed to read and interpret this final rule, we
should estimate the cost associated with regulatory review. Due to the
uncertainty involved with accurately quantifying the number of entities
that will review the rule, we assume that the total number of unique
commenters on the FY 2020 IRF PPS proposed rule will be the number of
reviewers of this final rule. We acknowledge that this assumption may
understate or overstate the costs of reviewing this final rule. It is
possible that not all commenters reviewed the FY 2020 IRF PPS proposed
rule in detail, and it is also possible that some reviewers chose not
to comment on the proposed rule. For these reasons we thought that the
number of past commenters would be a fair estimate of the number of
reviewers of this final rule.
We also recognize that different types of entities are in many
cases affected by mutually exclusive sections of this final rule, and
therefore, for the purposes of our estimate we assume that each
reviewer reads approximately 50 percent of the rule. We sought comments
on this assumption.
Using the wage information from the BLS for medical and health
service managers (Code 11-9111), we estimate that the cost of reviewing
this rule is $107.38 per hour, including overhead and fringe benefits
(https://www.bls.gov/oes/current/oes_nat.htm). Assuming an average
reading speed, we estimate that it would take approximately 2 hours for
the staff to review half of this final rule. For each IRF that reviews
the rule, the estimated cost is $218.72 (2 hours x $109.36). Therefore,
we estimate that the total cost of reviewing this regulation is
$274,931.04 ($218.72 x 1,257 reviewers).
We received one comment on the proposed methodology for estimating
the total cost of reviewing this regulation which is summarized below.
Comment: One commenter suggested that CMS should take into
consideration the number of times the proposed rule has been downloaded
in estimating the cost of reviewing this regulation.
Response: The regulatory review cost is an estimate that makes
several assumptions such as average reading speed and number of the
people who
[[Page 39172]]
read the document, etc. For more than 2 years, we have used the number
of comments received as a proxy for the number of staff members who
review the document. This assumption is well accepted by the general
public. The number of comments received is a more reasonable proxy than
the number of downloads since those who provide comments must actually
read the rule, as those that download the rule may not read the rule.
F. Accounting Statement and Table
As required by OMB Circular A-4 (available at https://www.whitehouse.gov/sites/default/files/omb/assets/omb/circulars/a004/a-4.pdf), in Table 21, we have prepared an accounting statement showing
the classification of the expenditures associated with the provisions
of this final rule. Table 21 provides our best estimate of the increase
in Medicare payments under the IRF PPS as a result of the updates
presented in this final rule based on the data for 1,122 IRFs in our
database. In addition, Table 21 presents the costs associated with the
new IRF QRP requirements for FY 2020.
[GRAPHIC] [TIFF OMITTED] TR08AU19.025
G. Conclusion
Overall, the estimated payments per discharge for IRFs in FY 2020
are projected to increase by 2.5 percent, compared with the estimated
payments in FY 2019, as reflected in column 7 of Table 20.
IRF payments per discharge are estimated to increase by 2.4 percent
in urban areas and 4.4 percent in rural areas, compared with estimated
FY 2019 payments. Payments per discharge to rehabilitation units are
estimated to increase 5.0 percent in urban areas and 5.7 percent in
rural areas. Payments per discharge to freestanding rehabilitation
hospitals are estimated to increase 0.2 percent in urban areas and
decrease 2.1 percent in rural areas.
Overall, IRFs are estimated to experience a net increase in
payments as a result of the policies in this final rule. The largest
payment increase is estimated to be a 6.8 percent increase for rural
government IRFs and rural IRFs located in the West South Central
region. The analysis above, together with the remainder of this
preamble, provides a Regulatory Impact Analysis.
In accordance with the provisions of Executive Order 12866, this
regulation was reviewed by the Office of Management and Budget.
List of Subjects in 42 CFR Part 412
Administrative practice and procedure, Health facilities, Medicare,
Puerto Rico, Reporting and recordkeeping requirements.
For the reasons set forth in the preamble, the Centers for Medicare
& Medicaid Services amends 42 CFR chapter IV as set forth below:
PART 412--PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL
SERVICES
0
1. The authority citation for part 412 is revised to read as follows:
Authority: 42 U.S.C. 1302 and 1395hh.
0
2. Section 412.622 is amended by revising paragraphs (a)(3)(iv),
(a)(4)(i)(A), (a)(4)(iii)(A), and (a)(5)(i) and adding paragraph (c) to
read as follows:
Sec. 412.622 Basis of payment.
(a) * * *
(3) * * *
(iv) Requires physician supervision by a rehabilitation physician.
The requirement for medical supervision means that the rehabilitation
physician must conduct face-to-face visits with the patient at least 3
days per week throughout the patient's stay in the IRF to assess the
patient both medically and functionally, as well as to modify the
course of treatment as needed to maximize the patient's capacity to
benefit from the rehabilitation process. The post-admission physician
evaluation described in paragraph (a)(4)(ii) of this section may count
as one of the face-to-face visits.
(4) * * *
(i) * * *
(A) It is conducted by a licensed or certified clinician(s)
designated by a rehabilitation physician within the 48 hours
immediately preceding the IRF admission. A preadmission screening that
includes all of the required elements, but that is conducted more than
48 hours immediately preceding the IRF admission, will be accepted as
long as an update is conducted in person or by telephone to update the
patient's medical and functional status within the 48 hours immediately
preceding the IRF admission and is documented in the patient's medical
record.
* * * * *
(iii) * * *
(A) It is developed by a rehabilitation physician with input from
the interdisciplinary team within 4 days of the patient's admission to
the IRF.
* * * * *
(5) * * *
(i) The team meetings are led by a rehabilitation physician and
further consist of a registered nurse with specialized training or
experience in rehabilitation; a social worker or case manager (or
both); and a licensed or certified therapist from each therapy
discipline involved in treating the patient. All team members must have
current knowledge of the patient's medical and functional status. The
rehabilitation physician may lead the interdisciplinary team meeting
remotely via a mode of communication such as video or telephone
conferencing.
* * * * *
(c) Definitions. As used in this section--
Rehabilitation physician means a licensed physician who is
determined by the IRF to have specialized training and experience in
inpatient rehabilitation.
0
3. Section 412.634 is amended by revising paragraphs (a)(1), (d)(1) and
(5), and (f)(1) to read as follows:
[[Page 39173]]
Sec. 412.634 Requirements under the Inpatient Rehabilitation
Facility (IRF) Quality Reporting Program (QRP).
(a) * * *
(1) For the FY 2018 payment determination and subsequent years, an
IRF must begin reporting data under the IRF QRP requirements no later
than the first day of the calendar quarter subsequent to 30 days after
the date on its CMS Certification Number (CCN) notification letter,
which designates the IRF as operating in the CMS designated data
submission system.
* * * * *
(d) * * *
(1) IRFs that do not meet the requirement in paragraph (b) of this
section for a program year will receive a written notification of non-
compliance through at least one of the following methods: The CMS
designated data submission system, the United States Postal Service, or
via an email from the Medicare Administrative Contractor (MAC).
* * * * *
(5) CMS will notify IRFs, in writing, of its final decision
regarding any reconsideration request through at least one of the
following methods: CMS designated data submission system, the United
States Postal Service, or via an email from the Medicare Administrative
Contractor (MAC).
* * * * *
(f) * * *
(1) IRFs must meet or exceed two separate data completeness
thresholds: One threshold set at 95 percent for completion of required
quality measures data and standardized patient assessment data
collected using the IRF-PAI submitted through the CMS designated data
submission system; and a second threshold set at 100 percent for
measures data collected and submitted using the CDC NHSN.
* * * * *
Dated: July 23, 2019.
Seema Verma,
Administrator, Centers for Medicare & Medicaid Services.
Dated: July 25, 2019.
Alex M. Azar II,
Secretary, Department of Health and Human Services.
[FR Doc. 2019-16603 Filed 7-31-19; 4:15 pm]
BILLING CODE 4120-01-P