Medicare Program; Inpatient Rehabilitation Facility Prospective Payment System for Federal Fiscal Year 2017, 52055-52141 [2016-18196]
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Vol. 81
Friday,
No. 151
August 5, 2016
Part III
Department of Health and Human Services
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Centers for Medicare & Medicaid Services
42 CFR Part 412
Medicare Program; Inpatient Rehabilitation Facility Prospective Payment
System for Federal Fiscal Year 2017; Final Rule
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DEPARTMENT OF HEALTH AND
HUMAN SERVICES
under the IRF quality reporting program
(QRP).
Executive Summary
Centers for Medicare & Medicaid
Services
DATES:
This final rule updates the
prospective payment rates for IRFs for
FY 2017 (that is, for discharges
occurring on or after October 1, 2016,
and on or before September 30, 2017) 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 rule includes the classification and
weighting factors for the IRF PPS’s casemix groups and a description of the
methodologies and data used in
computing the prospective payment
rates for FY 2017. This final rule also
finalizes revisions and updates to the
quality measures and reporting
requirements under the IRF QRP.
Effective Dates: These regulations are
effective on October 1, 2016.
Applicability Dates: The updated IRF
prospective payment rates are
applicable for IRF discharges occurring
on or after October 1, 2016, and on or
before September 30, 2017 (FY 2017).
The updated quality measures and
reporting requirements under the IRF
QRP are effective for IRF discharges
occurring on or after October 1, 2016.
42 CFR Part 412
[CMS–1647–F]
RIN 0938–AS78
Medicare Program; Inpatient
Rehabilitation Facility Prospective
Payment System for Federal Fiscal
Year 2017
FOR FURTHER INFORMATION CONTACT:
Centers for Medicare &
Medicaid Services (CMS), HHS.
ACTION: Final rule.
AGENCY:
This final rule will update the
prospective payment rates for inpatient
rehabilitation facilities (IRFs) for federal
fiscal year (FY) 2017 as required by the
statute. As required by section 1886(j)(5)
of the Act, this rule includes the
classification and weighting factors for
the IRF prospective payment system’s
(IRF PPS’s) case-mix groups and a
description of the methodologies and
data used in computing the prospective
payment rates for FY 2017. This final
rule also revises and updates quality
measures and reporting requirements
SUMMARY:
Gwendolyn Johnson, (410) 786–6954,
for general information. Catie Kraemer,
(410) 786–0179, for information about
the wage index. Christine Grose, (410)
786-1362, for information about the
quality reporting program. Kadie Derby,
(410) 786–0468, or Susanne Seagrave,
(410) 786–0044, for information about
the payment policies and payment rates.
The IRF
PPS Addenda along with other
supporting documents and tables
referenced in this final rule are available
through the Internet on the CMS Web
site at https://www.cms.hhs.gov/
Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/.
SUPPLEMENTARY INFORMATION:
A. Purpose
B. Summary of Major Provisions
In this final rule, we use the methods
described in the FY 2016 IRF PPS final
rule (80 FR 47036) to update the federal
prospective payment rates for FY 2017
using updated FY 2015 IRF claims and
the most recent available IRF cost report
data, which is FY 2014 IRF cost report
data. We are also finalizing revisions
and updates to the quality measures and
reporting requirements under the IRF
QRP.
C. Summary of Impacts
Provision description
Transfers
FY 2017 IRF PPS payment rate update ............
The overall economic impact of this final rule is an estimated $145 million in increased payments from the Federal government to IRFs during FY 2017.
Provision description
Costs
New quality reporting program requirements .....
The total costs in FY 2017 for IRFs as a result of the new quality reporting requirements are
estimated to be $5,231,398.17.
To assist readers in referencing
sections contained in this document, we
are providing the following Table of
Contents.
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Table of Contents
I. Background
A. Historical Overview of the IRF PPS
B. Provisions of the Affordable Care Act
Affecting the IRF PPS in FY 2012 and
Beyond
C. Operational Overview of the Current IRF
PPS
D. Advancing Health Information Exchange
II. Summary of Provisions of the Proposed
Rule
III. Analysis and Responses to Public
Comments
IV. Update to the Case-Mix Group (CMG)
Relative Weights and Average Length of
Stay Values for FY 2017
V. Facility-Level Adjustment Factors
VI. FY 2017 IRF PPS Payment Update
A. Background
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B. FY 2017 Market Basket Update and
Productivity Adjustment
C. Labor-Related Share for FY 2017
D. Wage Adjustment
E. Description of the IRF Standard Payment
Conversion Factor and Payment Rates for
FY 2017
F. Example of the Methodology for
Adjusting the Federal Prospective
Payment Rates
VII. Update to Payments for High-Cost
Outliers Under the IRF PPS
A. Update to the Outlier Threshold
Amount for FY 2017
B. Update to the IRF Cost-to-Charge Ratio
Ceiling and Urban/Rural Averages
VIII. Revisions and Updates to the IRF
Quality Reporting Program (QRP)
A. Background and Statutory Authority
B. General Considerations Used for
Selection of Quality, Resource Use, and
Other Measures for the IRF QRP
C. Policy for Retention of IRF QRP
Measures Adopted for Previous Payment
Determinations
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D. Policy for Adopting Changes to IRF QRP
Measures
E. Quality Measures Previously Finalized
for and Currently Used in the IRF QRP
F. IRF QRP Quality, Resource Use and
Other Measures Finalized for the FY
2018 Payment Determination and
Subsequent Years
G. IRF QRP Quality Measure Finalized for
the FY 2020 Payment Determination and
Subsequent Years
H. IRF QRP Quality Measures and Measure
Concepts Under Consideration for Future
Years
I. Form, Manner, and Timing of Quality
Data Submission for the FY 2018
Payment Determination and Subsequent
Years
J. IRF QRP Data Completion Thresholds for
the FY 2016 Payment Determination and
Subsequent Years
K. IRF QRP Data Validation Process for the
FY 2016 Payment Determination and
Subsequent Years
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L. Previously Adopted and Codified IRF
QRP Submission Exception and
Extension Policies
M. Previously Adopted and Finalized IRF
QRP Reconsideration and Appeals
Procedures
N. Public Display of Measure Data for the
IRF QRP & Procedures for the
Opportunity to Review and Correct Data
and Information
O. Mechanism for Providing Feedback
Reports to IRFs
P. Method for Applying the Reduction to
the FY 2017 IRF Increase Factor for IRFs
That Fail To Meet the Quality Reporting
Requirements
IX. Miscellaneous Comments
X. Provisions of the Final Regulations
XI. Collection of Information Requirements
A. Statutory Requirement for Solicitation
of Comments
B. Collection of Information Requirements
for Updates Related to the IRF QRP
XII. Regulatory Impact Analysis
A. Statement of Need
B. Overall Impacts
C. Detailed Economic Analysis
D. Alternatives Considered
E. Accounting Statement
F. Conclusion
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Acronyms, Abbreviations, and Short
Forms
Because of the many terms to which
we refer by acronym, abbreviation, or
short form in this final rule, we are
listing the acronyms, abbreviation, and
short forms used and their
corresponding terms in alphabetical
order.
The Act The Social Security Act
ADC Average Daily Census
ADE Adverse Drug Events
The Affordable Care Act Patient Protection
and Affordable Care Act (Pub. L. 111–148,
enacted on March 23, 2010)
AHRQ Agency for Healthcare Research and
Quality
APU Annual Payment Update
ASAP Assessment Submission and
Processing
ASCA The Administrative Simplification
Compliance Act of 2002 (Pub. L. 107–105,
enacted on December 27, 2002)
ASPE Office of the Assistant Secretary for
Planning and Evaluation
BLS U.S. Bureau of Labor Statistics
BMI Body Mass Index
CAH Critical Access Hospitals
CASPER Certification and Survey Provider
Enhanced Reports
CAUTI Catheter-Associated Urinary Tract
Infection
CBSA Core-Based Statistical Area
CCR Cost-to-Charge Ratio
CDC The Centers for Disease Control and
Prevention
CDI Clostridium difficile Infection
CFR Code of Federal Regulations
CMG Case-Mix Group
CMS Centers for Medicare & Medicaid
Services
COA Care for Older Adults
CY Calendar year
DSH Disproportionate Share Hospital
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DSH PP Disproportionate Share Patient
Percentage
DRG Diagnosis-Related Group
eCQMs Electronically Specified Clinical
Quality Measures
ESRD End-Stage Renal Disease
FFS Fee-for-Service
FR Federal Register
FY Federal Fiscal Year
GEMS General Equivalence Mapping
GPCI Geographic Practice Cost Index
HAI Healthcare Associated Infection
HCC Hierarchical Condition Category
HHA Home Health Agencies
HCP Home Care Personnel
HHS U.S. Department of Health & Human
Services
HIPAA Health Insurance Portability and
Accountability Act of 1996 (Pub. L. 104–
191, enacted on August 21, 1996)
Hospital VBP Hospital Value-Based
Purchasing Program (also HVBP)
ICD–9–CM International Classification of
Diseases, 9th Revision, Clinical
Modification
ICD–10–CM International Classification of
Diseases, 10th Revision, Clinical
Modification
IGC Impairment Group Code
IGI IHS Global Insight
IMPACT Act Improving Medicare PostAcute Care Transformation Act of 2014
(Pub. L. 113–185, enacted on October 6,
2014)
IME Indirect Medical Education
IPF Inpatient Psychiatric Facility
IPPS Inpatient prospective payment system
IQR Inpatient Quality Reporting Program
IRF Inpatient Rehabilitation Facility
IRF–PAI Inpatient Rehabilitation FacilityPatient Assessment Instrument
IRF PPS Inpatient Rehabilitation Facility
Prospective Payment System
IRF QRP Inpatient Rehabilitation Facility
Quality Reporting Program
IRVEN Inpatient Rehabilitation Validation
and Entry
LIP Low-Income Percentage
IVS Influenza Vaccination Season
LTCH Long-Term Care Hospital
MA (Medicare Part C) Medicare Advantage
MAC Medicare Administrative Contractor
MAP Measures Application Partnership
MedPAC Medicare Payment Advisory
Commission
MFP Multifactor Productivity
MMSEA Medicare, Medicaid, and SCHIP
Extension Act of 2007 (Pub. L. 110–173,
enacted on December 29, 2007)
MRSA Methicillin-Resistant
Staphylococcus aureus
MSPB Medicare Spending per Beneficiary
MUC Measures under Consideration
NHSN National Healthcare Safety Network
NQF National Quality Forum
OMB Office of Management and Budget
ONC Office of the National Coordinator for
Health Information Technology
OPPS/ASC Outpatient Prospective Payment
System/Ambulatory Surgical Center
PAC Post-Acute Care
PAC/LTC Post-Acute Care/Long-Term Care
PAI Patient Assessment Instrument
PPR Potentially Preventable Readmissions
PPS Prospective Payment System
PRA Paperwork Reduction Act of 1995
(Pub. L. 104–13, enacted on May 22, 1995)
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QIES Quality Improvement Evaluation
System
QM Quality Measure
QRP Quality Reporting Program
RIA Regulatory Impact Analysis
RIC Rehabilitation Impairment Category
RFA Regulatory Flexibility Act (Pub. L. 96–
354, enacted on September 19, 1980)
RN Registered Nurse
RPL Rehabilitation, Psychiatric, and LongTerm Care market basket
RSRR Risk-standardized readmission rate
SIR Standardized Infection Ratio
SNF Skilled Nursing Facilities
SRR Standardized Risk Ratio
SSI Supplemental Security Income
TEP Technical Expert Panel
I. Background
A. Historical Overview of the IRF PPS
Section 1886(j) of the Act provides for
the implementation of a per-discharge
prospective payment system (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 below a
general description of the IRF PPS for
FYs 2002 through 2016.
Under the IRF PPS from FY 2002
through FY 2005 the federal prospective
payment rates were computed across
100 distinct case-mix groups (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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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 federal 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 federal 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.
We established a CMS Web site as a
primary information resource for the
IRF PPS which is available at https://
www.cms.gov/Medicare/Medicare-Feefor-Service-Payment/
InpatientRehabFacPPS/. The
Web site may be accessed to download
or view publications, software, data
specifications, educational materials,
and other information pertinent to the
IRF PPS.
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) that we
published on September 30, 2005, we
finalized a number of refinements to the
IRF PPS case-mix classification system
(the CMGs and the corresponding
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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,
revision and rebasing of the market
basket index used to update IRF
payments, and updates to the rural, lowincome 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 (70 FR
47880 and 70 FR 57166).
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 (71 FR
48354).
In the FY 2008 IRF PPS final rule (72
FR 44284), we updated the federal
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 implemented for
FY 2008, please refer to the FY 2008 IRF
PPS final rule (72 FR 44284), in which
we published the final FY 2008 IRF
federal prospective payment rates. 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 on 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 federal
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prospective payment rates for each FY.
Based on the legislative change to the
increase factor, we revised the FY 2008
federal prospective payment rates for
IRF discharges occurring on or after
April 1, 2008. Thus, the final FY 2008
IRF federal 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 federal 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 federal prospective
payment rates are available on the CMS
Web site 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 length of stay
values, and the outlier threshold;
clarified IRF wage index policies
regarding the treatment of ‘‘New
England deemed’’ counties and multicampus 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 (73 FR 46370), in which
we published the final FY 2009 IRF
federal prospective payment rates.
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) that we
published on October 1, 2009, we
updated the federal prospective
payment rates, the CMG relative
weights, the average length of stay
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
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to the FY 2010 IRF PPS final rule (74 FR
39762 and 74 FR 50712), in which we
published the final FY 2010 IRF federal
prospective payment rates.
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 on 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 on
March 30, 2010) (collectively,
hereinafter referred to as ‘‘The
Affordable Care Act’’), 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 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 Affordable Care Act, the
adjusted FY 2010 rate was only to be
applied to discharges occurring on or
after April 1, 2010. Based on the selfimplementing legislative changes to
section 1886(j)(3) of the Act, we
adjusted the FY 2010 federal
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
federal 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
federal prospective payment rates
applied to discharges occurring on or
after April 1, 2010, and on or before
September 30, 2010. The adjusted FY
2010 federal prospective payment rates
are available on the CMS Web site at
https://www.cms.gov/Medicare/
Medicare-Fee-for-Service-Payment/
InpatientRehabFacPPS/Data-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
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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 are 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 2011 and FY 2010 IRF PPS
federal 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
federal prospective payment rates, the
CMG relative weights, and the average
length of stay 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 (75 FR 42836 and 75 FR
70013).
In the FY 2012 IRF PPS final rule (76
FR 47836), we updated the IRF federal
prospective payment rates, rebased and
revised the RPL market basket, and
established a new quality reporting
program for IRFs in accordance with
section 1886(j)(7) of the Act. We also
revised regulation text for the purpose
of updating and providing greater
clarity. For more information on the
policy changes implemented for FY
2012, please refer to the FY 2012 IRF
PPS final rule (76 FR 47836), in which
we published the final FY 2012 IRF
federal prospective payment rates.
The FY 2013 IRF PPS notice (77 FR
44618) described the required
adjustments to the FY 2013 federal
prospective payment rates and outlier
threshold amount for IRF discharges
occurring on or after October 1, 2012,
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and on or before September 30, 2013. It
also updated the FY 2013 federal
prospective payment rates, the CMG
relative weights, and the average length
of stay values. For more information on
the updates for FY 2013, please refer to
the FY 2013 IRF PPS notice (77 FR
44618).
In the FY 2014 IRF PPS final rule (78
FR 47860), we updated the federal
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 Inpatient Rehabilitation
Facility-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
revised and updated quality measures
and reporting requirements under the
IRF quality reporting program. For more
information on the policy changes
implemented for FY 2014, please refer
to the FY 2014 IRF PPS final rule (78 FR
47860), in which we published the final
FY 2014 IRF federal prospective
payment rates.
In the FY 2015 IRF PPS final rule (79
FR 45872), we updated the federal
prospective payment rates, the CMG
relative weights, and the outlier
threshold amount. We also further
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 revised and
updated quality measures and reporting
requirements under the IRF quality
reporting program. For more
information on the policy changes
implemented for FY 2015, please refer
to the FY 2015 IRF PPS final rule (79 FR
45872) and the FY 2015 IRF PPS
correction notice (79 FR 59121).
In the FY 2016 IRF PPS final rule (80
FR 47036), we updated the federal
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 one-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 revisions and updates
to the IRF QRP. For more information
on the policy changes implemented for
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FY 2016, please refer to the FY 2016 IRF
PPS final rule (80 FR 47036).
B. Provisions of the Affordable Care Act
Affecting the IRF PPS in FY 2012 and
Beyond
The Affordable Care Act 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
Affordable Care Act also added section
1886(j)(3)(C)(ii)(I) (providing for a
‘‘productivity adjustment’’ for fiscal
year 2012 and each subsequent fiscal
year). The productivity adjustment for
FY 2017 is discussed in section VI.B. of
this final rule. Section 3401(d) of the
Affordable Care Act requires an
additional 0.75 percentage point
adjustment to the IRF increase factor for
each of FYs 2017, 2018, and 2019. The
applicable adjustment for FY 2017 is
discussed in section VI.B. of this final
rule. Section 1886(j)(3)(C)(ii)(II) of the
Act notes that the application of these
adjustments 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. Section 3004(b) of the Affordable
Care Act also addressed the IRF PPS
program. It reassigned the previously
designated section 1886(j)(7) of the Act
to section 1886(j)(8) and inserted a new
section 1886(j)(7), which contains
requirements for the Secretary to
establish a quality reporting program 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 of the applicable market
basket increase factor for IRFs that fail
to comply with the quality data
submission requirements. 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 will not be
cumulative; they will only apply for the
FY involved.
Under section 1886(j)(7)(D)(i) and (ii)
of the Act, the Secretary is generally
required to select quality measures for
the IRF quality reporting program from
those that have been endorsed by the
consensus-based entity which holds a
performance measurement contract
under section 1890(a) of the Act. This
contract is currently held by the
National Quality Forum (NQF). So long
as due consideration is given to
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measures that have been endorsed or
adopted by a consensus-based
organization, section 1886(j)(7)(D)(ii) of
the Act authorizes the Secretary to
select non-endorsed measures for
specified areas or medical topics when
there are no feasible or practical
endorsed measure(s).
Section 1886(j)(7)(E) of the Act
requires the Secretary to establish
procedures for making the IRF PPS
quality reporting data available to the
public. In so doing, the Secretary must
ensure that IRFs have the opportunity to
review any such data prior to its release
to the public.
C. Operational Overview of the Current
IRF PPS
As described in the FY 2002 IRF PPS
final rule, upon the admission and
discharge of a Medicare Part A Fee-forService (FFS) patient, the IRF is
required to complete the appropriate
sections of a patient assessment
instrument (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) (formerly called Medicare Part C)
patient, as described in the FY 2010 IRF
PPS final rule. 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 5character CMG number. The first
character is an alphabetic character that
indicates the comorbidity tier. The last
4 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 Web site at https://www.cms.gov/
Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/
Software.html.
Once a Medicare FFS Part A patient
is discharged, the IRF submits a
Medicare claim as a Health Insurance
Portability and Accountability Act of
1996 (Pub. L. 104–191, enacted on
August 21, 1996) (HIPAA) compliant
electronic claim or, if the
Administrative Simplification
Compliance Act of 2002 (Pub. L. 107–
105, enacted on December 27, 2002)
(ASCA) permits, a paper claim (a UB–
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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 Medicare Advantage
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 Medicare Advantage
days are included in the hospital’s
Supplemental Security Income (SSI)
ratio (used in calculating the IRF lowincome percentage adjustment) for fiscal
year 2007 and beyond. Claims
submitted to Medicare must comply
with both ASCA and HIPAA.
Section 3 of the ASCA amends 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, parts
160 and 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
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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).
D. Advancing Health Information
Exchange
The Department of Health & Human
Services (HHS) has a number of
initiatives designed to encourage and
support the adoption of health
information technology and to promote
nationwide health information exchange
to improve health care. As discussed in
the August 2013 Statement ‘‘Principles
and Strategies for Accelerating Health
Information Exchange’’ (available at
https://www.healthit.gov/sites/default/
files/acceleratinghieprinciples_
strategy.pdf). HHS believes that all
individuals, their families, their
healthcare and social service providers,
and payers should have consistent and
timely access to health information in a
standardized format that can be securely
exchanged between the patient,
providers, and others involved in the
individual’s care. Health IT that
facilitates the secure, efficient, and
effective sharing and use of healthrelated information when and where it
is needed is an important tool for
settings across the continuum of care,
including inpatient rehabilitation
facilities. The effective adoption and use
of health information exchange and
health IT tools will be essential as IRFs
seek to improve quality and lower costs
through value-based care.
The Office of the National
Coordinator for Health Information
Technology (ONC) has released a
document entitled ‘‘Connecting Health
and Care for the Nation: A Shared
Nationwide Interoperability Roadmap’’
(available at https://https://
www.healthit.gov/sites/default/files/hieinteroperability/nationwideinteroperability-roadmap-final-version1.0.pdf). In the near term, the Roadmap
focuses on actions that will enable
individuals and providers across the
care continuum to send, receive, find,
and use a common set of electronic
clinical information at the nationwide
level by the end of 2017. The Roadmap’s
goals also align with the Improving
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Medicare Post-Acute Care
Transformation Act of 2014 (Pub. L.
113–185, enacted on October 6, 2014)
(IMPACT Act), which requires
assessment data to be standardized and
interoperable to allow for exchange of
the data.
The Roadmap identifies four critical
pathways that health IT stakeholders
should focus on now in order to create
a foundation for long-term success: (1)
Improve technical standards and
implementation guidance for priority
data domains and associated elements;
(2) rapidly shift and align federal, state,
and commercial payment policies from
FFS to value-based models to stimulate
the demand for interoperability; (3)
clarify and align federal and state
privacy and security requirements that
enable interoperability; and (4) align
and promote the use of consistent
policies and business practices that
support interoperability, in coordination
with stakeholders. In addition, ONC has
released the final version of the 2016
Interoperability Standards Advisory
(available at https://www.healthit.gov/
standards-advisory/2016), which
provides a list of the best available
standards and implementation
specifications to enable priority health
information exchange functions.
Providers, payers, and vendors are
encouraged to take these ‘‘best available
standards’’ into account as they
implement interoperable health
information exchange across the
continuum of care, including care
settings such as inpatient rehabilitation
facilities.
We encourage stakeholders to utilize
health information exchange and
certified health IT to effectively and
efficiently help providers improve
internal care delivery practices, engage
patients in their care, support
management of care across the
continuum, enable the reporting of
electronically specified clinical quality
measures (eCQMs), and improve
efficiencies and reduce unnecessary
costs. As adoption of certified health IT
increases and interoperability standards
continue to mature, HHS will seek to
reinforce standards through relevant
policies and programs. We received one
comment on health information
exchange, which is summarized below.
Comment: A commenter stated that
the rule focuses only on providers,
vendors, and institutions, not
individuals and that sharing
information requires standardized data
exchange. The commenter suggested
that CMS add a system-wide measure to
assess whether robust data standards,
policies, and governance infrastructure
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52061
exists to support widespread industry
and individual participation.
Response: We agree with the
commenter that all individuals,
families, and healthcare providers
should have consistent and timely
access to health information, in
accordance with applicable law, in a
standardized format that can be securely
exchanged to support the health and
wellness of individuals and shared
decision-making. We agree nationwide
interoperability across the care
continuum will require stakeholders to
agree to and follow a common set of
standards, services, policies and
practices that facilitates the exchange
and use of interoperable health
information. ONC recently requested
comment on system-wide measures of
interoperability required under the
Medicare Access and CHIP
Reauthorization Act of 2015 (81 FR
20651, https://federalregister.gov/a/
2016–08134).
II. Summary of Provisions of the
Proposed Rule
In the FY 2017 IRF PPS proposed rule
(81 FR 24178), we proposed to update
the IRF federal prospective payment
rates for FY 2017 and to revise and
update quality measures and reporting
requirements under the IRF QRP.
The proposed updates to the IRF
federal prospective payment rates for FY
2017 were as follows:
• Update the FY 2017 IRF PPS
relative weights and average length of
stay values using the most current and
complete Medicare claims and cost
report data in a budget-neutral manner,
as discussed in section III of the FY
2017 IRF PPS proposed rule (81 FR
24178, 24184 through 24187).
• Describe the continued use of FY
2014 facility-level adjustment factors as
discussed in section IV of the FY 2017
IRF PPS proposed rule (81 FR 24178 at
24187).
• Update the FY 2017 IRF PPS
payment rates by the proposed market
basket increase factor, based upon the
most current data available, with a 0.75
percentage point reduction as required
by sections 1886(j)(3)(C)(ii)(II) and
1886(j)(3)(D)(v) of the Act and a
proposed productivity adjustment
required by section 1886(j)(3)(C)(ii)(I) of
the Act, as described in section V of the
FY 2017 IRF PPS proposed rule (81 FR
24178, 24187 through 24189).
• Update the FY 2017 IRF PPS
payment rates by the FY 2017 wage
index and the labor-related share in a
budget-neutral manner, as discussed in
section V of the FY 2017 IRF PPS
proposed rule (81 FR 24178, 24189
through 24190).
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• Describe the calculation of the IRF
standard payment conversion factor for
FY 2017, as discussed in section V of
the FY 2017 IRF PPS proposed rule (81
FR 24178, 24190 through 24192).
• Update the outlier threshold
amount for FY 2017, as discussed in
section VI of the FY 2017 IRF PPS
proposed rule (81 FR 24178, at 24193).
• Update the cost-to-charge ratio
(CCR) ceiling and urban/rural average
CCRs for FY 2017, as discussed in
section VI of the FY 2017 IRF PPS
proposed rule (81 FR 24178, 24193
through 24194).
• Describe proposed revisions and
updates to quality measures and
reporting requirements under the
quality reporting program for IRFs in
accordance with section 1886(j)(7) of the
Act, as discussed in section VII of the
FY 2017 IRF PPS proposed rule (81 FR
24194 through 24220).
III. Analysis and Responses to Public
Comments
We received 61 timely responses from
the public, many of which contained
multiple comments on the FY 2017 IRF
PPS proposed rule (81 FR 24178). We
received comments from various trade
associations, inpatient rehabilitation
facilities, 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. Update to the Case-Mix Group
(CMG) Relative Weights and Average
Length of Stay Values for FY 2017
As specified in § 412.620(b)(1), we
calculate a relative weight for each CMG
that is proportional to the resources
needed by an average inpatient
rehabilitation case in that CMG. For
example, cases in a CMG with a relative
weight of 2, on average, will cost twice
as much as cases in a CMG with a
relative weight of 1. Relative weights
account for the variance in cost per
discharge due to the variance in
resource utilization among the payment
groups, and their use helps to ensure
that IRF PPS payments support
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beneficiary access to care, as well as
provider efficiency.
In the FY 2017 IRF PPS proposed rule
(81 FR 24178, 24184 through 24187), we
proposed to update the CMG relative
weights and average length of stay
values for FY 2017. As required by
statute, we always use the most recent
available data to update the CMG
relative weights and average lengths of
stay. For FY 2017, we proposed to use
the FY 2015 IRF claims and FY 2014
IRF cost report data. These data are the
most current and complete data
available at this time.
We note that, as we typically do, we
updated our data between the FY 2017
IRF PPS proposed and final rules to
ensure that we use the most recent
available data in calculating IRF PPS
payments. This updated data reflects a
more complete set of claims for FY 2015
and additional cost report data for FY
2014.
In the FY 2017 IRF PPS proposed
rule, we proposed to apply these data
using the same methodologies that we
have used to update the CMG relative
weights and average length of stay
values each fiscal year since we
implemented an update to the
methodology to use the more detailed
CCR data from the cost reports of IRF
subprovider units of primary acute care
hospitals, instead of CCR data from the
associated primary care hospitals, to
calculate IRFs’ average costs per case, as
discussed in the FY 2009 IRF PPS final
rule (73 FR 46372). 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. The process used to calculate the
CMG relative weights for this final rule
is as follows:
Step 1. We estimate the effects that
comorbidities have on costs.
Step 2. We adjust the cost of each
Medicare discharge (case) to reflect the
effects found in the first step.
Step 3. We use the adjusted costs from
the second step to calculate CMG
relative weights, using the hospitalspecific relative value method.
Step 4. We normalize the FY 2017
CMG relative weights to the same
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average CMG relative weight from the
CMG relative weights implemented in
the FY 2016 IRF PPS final rule (80 FR
47036).
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
CMG relative weights for FY 2017 in
such a way that total estimated
aggregate payments to IRFs for FY 2017
are the same with or without the
changes (that is, 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 2017 CMG relative weights, we use
the following steps:
Step 1. Calculate the estimated total
amount of IRF PPS payments for FY
2017 (with no changes to the CMG
relative weights).
Step 2. Calculate the estimated total
amount of IRF PPS payments for FY
2017 by applying the changes to the
CMG relative weights (as discussed 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 (0.9992) that would
maintain the same total estimated
aggregate payments in FY 2017 with and
without the changes to the CMG relative
weights.
Step 4. Apply the budget neutrality
factor (0.9992) to the FY 2016 IRF PPS
standard payment amount after the
application of the budget-neutral wage
adjustment factor.
In section VI.E. of this final rule, we
discuss the proposed use of the existing
methodology to calculate the standard
payment conversion factor for FY 2017.
In Table 1, ‘‘Relative Weights and
Average Length of Stay Values for CaseMix Groups,’’ we present the CMGs, the
comorbidity tiers, the corresponding
relative weights, and the average length
of stay values for each CMG and tier for
FY 2017. The average length of stay 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.
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TABLE 1: Relative Weights and Average Length of Stay Values for Case-Mix Groups
CMG
Description
(M=motor,
C=cognitive,
A=age)
Relative Weight
Average Length of Stay
Tier 1
0101
0102
0103
0104
0105
0106
0107
0108
0109
0110
0201
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0202
0203
VerDate Sep<11>2014
Stroke
M>51.05
Stroke
M>44.45 and
M<51.05 and
C>18.5
Stroke
M>44.45 and
M<51.05 and
C<18.5
Stroke
M>38.85 and
M<44.45
Stroke
M>34.25 and
M<38.85
Stroke
M>30.05 and
M<34.25
Stroke
M>26.15 and
M<30.05
Stroke
M<26.15 and
A>84.5
Stroke
M>22.35 and
M<26.15 and
A<84.5
Stroke
M<22.35 and
A<84.5
Traumatic
brain injury
M>53.35 and
C>23.5
Traumatic
brain injury
M>44.25 and
M<53.35 and
C>23.5
Traumatic
brain injury
M>44.25 and
C<23.5
18:14 Aug 04, 2016
Tier2
Tier3
0.7992
0.7117
0.6511
0.6215
8
9
9
8
1.0130
0.9020
0.8252
0.7877
11
12
10
10
1.1836
1.0540
0.9642
0.9204
11
13
12
12
1.2598
1.1218
1.0263
0.9796
12
12
12
12
1.4572
1.2976
1.1871
1.1331
14
15
14
14
1.6296
1.4511
1.3275
1.2671
16
16
15
15
1.8187
1.6195
1.4815
1.4142
17
19
17
17
2.2893
2.0386
1.8649
1.7801
21
22
21
20
2.0584
1.8329
1.6768
1.6005
19
20
18
19
2.7320
2.4327
2.2255
2.1243
29
27
24
24
0.7753
0.6341
0.5715
0.5343
8
8
8
7
1.0945
0.8951
0.8067
0.7542
12
10
9
10
1.2173
0.9955
0.8973
0.8388
11
12
11
11
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Sfmt 4725
Tier 1
Tier2
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Tier3
05AUR3
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ER05AU16.000
CMG
CMG
0204
0205
0206
0207
0301
0302
0303
0304
0401
0402
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CMG
Description
(M=motor,
C=cognitive,
A=age)
Traumatic
brain injury
M>40.65 and
M<44.25
Traumatic
brain injury
M>28.75 and
M<40.65
Traumatic
brain injury
M>22.05 and
M<28.75
Traumatic
brain injury
M<22.05
Non-traumatic
brain injury
M>41.05
Non-traumatic
brain injury
M>35.05 and
M<41.05
Non-traumatic
brain injury
M>26.15 and
M<35.05
Non-traumatic
brain injury
M<26.15
Traumatic
spinal cord
injury
M>48.45
Traumatic
spinal cord
injury
M>30.35 and
M<48.45
Traumatic
spinal cord
injury
M>16.05 and
M<30.35
Traumatic
spinal cord
injury
M<16.05 and
A>63.5
18:14 Aug 04, 2016
Relative Weight
Average Length of Stay
1.3455
1.1003
0.9918
0.9272
16
13
12
11
1.6224
1.3269
1.1959
1.1181
14
15
14
13
1.9239
1.5734
1.4182
1.3258
19
18
16
15
2.5284
2.0678
1.8637
1.7424
31
23
20
19
1.1424
0.9432
0.8571
0.8002
10
11
10
10
1.4063
1.1610
1.0551
0.9850
13
13
12
12
1.6490
1.3614
1.2372
1.1550
15
15
14
14
2.1336
1.7614
1.6007
1.4944
21
20
17
16
0.9799
0.8616
0.7947
0.7213
11
11
10
9
1.4052
1.2357
1.1396
1.0344
14
14
14
13
2.2165
1.9492
1.7976
1.6316
20
21
20
19
3.8702
3.4033
3.1387
2.8489
46
37
34
31
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0405
0501
0502
0503
0504
0505
0506
0601
0602
0603
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0604
0701
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CMG
Description
(M=motor,
C=cognitive,
A=age)
Traumatic
spinal cord
injury
M<16.05 and
A<63.5
Non-traumatic
spinal cord
injury
M>51.35
Non-traumatic
spinal cord
injury
M>40.15 and
M<51.35
Non-traumatic
spinal cord
injury
M>31.25 and
M<40.15
Non-traumatic
spinal cord
injury
M>29.25 and
M<31.25
Non-traumatic
spinal cord
injury
M>23.75 and
M<29.25
Non-traumatic
spinal cord
injury
M<23.75
Neurological
M>47.75
Neurological
M>37.35 and
M<47.75
Neurological
M>25.85 and
M<37.35
Neurological
M<25.85
Fracture of
lower
extremity
M>42.15
18:14 Aug 04, 2016
Relative Weight
Average Length of Stay
3.4395
3.0246
2.7894
2.5319
49
33
28
28
0.8524
0.6715
0.6395
0.5751
9
8
7
8
1.1600
0.9139
0.8703
0.7827
11
11
10
10
1.4557
1.1469
1.0921
0.9822
14
13
13
12
1.7087
1.3462
1.2819
1.1529
19
16
14
14
1.9607
1.5447
1.4709
1.3229
20
17
17
16
2.7151
2.1391
2.0369
1.8320
28
24
22
21
1.0352
0.8205
0.7577
0.6939
10
9
9
9
1.3322
1.0560
0.9751
0.8930
12
12
11
11
1.6411
1.3008
1.2012
1.1001
14
14
13
13
2.1752
1.7241
1.5922
1.4581
20
18
17
16
0.9991
0.8136
0.7767
0.7052
10
9
9
9
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05AUR3
ER05AU16.002
CMG
52065
CMG
0702
0703
0704
0801
0802
0803
0804
0805
0806
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CMG
Description
(M=motor,
C=cognitive,
A=age)
Fracture of
lower
extremity
M>34.15 and
M<42.15
Fracture of
lower
extremity
M>28.15 and
M<34.15
Fracture of
lower
extremity
M<28.15
Replacement
of lower
extremity joint
M>49.55
Replacement
of lower
extremity joint
M>37.05 and
M<49.55
Replacement
of lower
extremity joint
M>28.65 and
M<37.05 and
A>83.5
Replacement
of lower
extremity joint
M>28.65 and
M<37.05 and
A<83.5
Replacement
of lower
extremity joint
M>22.05 and
M<28.65
Replacement
of lower
extremity joint
M<22.05
Other
orthopedic
M>44.75
18:14 Aug 04, 2016
Relative Weight
Average Length of Stay
1.2759
1.0390
0.9919
0.9006
12
12
12
11
1.5383
1.2527
1.1958
1.0858
15
14
14
13
1.9943
1.6240
1.5503
1.4076
18
18
17
16
0.7983
0.6443
0.5958
0.5476
8
8
7
7
1.0333
0.8340
0.7713
0.7089
11
10
9
9
1.3823
1.1156
1.0317
0.9482
13
13
12
12
1.2445
1.0044
0.9289
0.8537
12
12
11
10
1.4806
1.1949
1.1051
1.0157
15
13
12
12
1.7987
1.4517
1.3425
1.2339
16
16
15
14
0.9839
0.7940
0.7356
0.6693
11
10
9
8
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ER05AU16.003
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0902
0903
0904
1001
1002
1003
1101
1102
1201
1202
1203
1301
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1303
VerDate Sep<11>2014
CMG
Description
(M=motor,
C=cognitive,
A=age)
Other
orthopedic
M>34.35 and
M<44.75
Other
orthopedic
M>24.15 and
M<34.35
Other
orthopedic
M<24.15
Amputation,
lower
extremity
M>47.65
Amputation,
lower
extremity
M>36.25 and
M<47.65
Amputation,
lower
extremity
M<36.25
Amputation,
non-lower
extremity
M>36.35
Amputation,
non-lower
extremity
M<36.35
Osteoarthritis
M>37.65
Osteoarthritis
M>30.75 and
M<37.65
Osteoarthritis
M<30.75
Rheumatoid,
other arthritis
M>36.35
Rheumatoid,
other arthritis
M>26.15 and
M<36.35
Rheumatoid,
other arthritis
M<26.15
18:14 Aug 04, 2016
Relative Weight
Average Length of Stay
1.2583
1.0155
0.9408
0.8560
12
12
11
10
1.5810
1.2760
1.1821
1.0755
15
15
13
13
2.0014
1.6153
1.4965
1.3615
18
18
16
16
1.0715
0.9448
0.8199
0.7400
11
11
10
9
1.3906
1.2261
1.0641
0.9604
14
15
12
12
1.9639
1.7317
1.5029
1.3564
18
19
17
16
1.3222
1.1985
0.9739
0.8842
12
12
10
11
1.8953
1.7181
1.3961
1.2676
17
16
16
14
1.0379
1.0241
0.9306
0.8231
10
11
11
10
1.2061
1.1900
1.0813
0.9564
12
13
12
11
1.5370
1.5165
1.3780
1.2188
14
17
15
14
1.1939
0.9393
0.8690
0.8007
13
10
10
10
1.6397
1.2900
1.1935
1.0997
14
15
13
13
2.0215
1.5904
1.4715
1.3558
16
20
15
15
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05AUR3
ER05AU16.004
CMG
52067
CMG
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CMG
Description
(M=motor,
C=cognitive,
A=age)
Cardiac
M>48.85
1404
1403
1502
1503
1504
1601
1602
1603
1701
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1702
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0.6025
9
7
8
8
0.9981
0.9047
0.8211
11
11
11
10
1.1899
1.0785
0.9788
13
13
12
11
1.7805
1.5048
1.3640
1.2379
17
16
15
14
1.0089
0.8543
0.7888
0.7436
10
9
9
8
1.2746
1.0793
0.9966
0.9394
11
11
11
10
1.5543
1.3162
1.2153
1.1456
15
14
12
12
1.9370
Pain
syndrome
M>37.15
Pain
syndrome
M>26.75 and
M<37.15
Pain
syndrome
M<26.75
Major multiple
trauma
without brain
or spinal cord
injury
M>39.25
Major multiple
trauma
without brain
or spinal cord
injury
M>31.05 and
M<39.25
0.6639
1.4079
Pulmonary
M>39.05 and
M<49.25
Pulmonary
M>29.15 and
M<39.05
Pulmonary
M<29.15
0.7324
1.1810
Pulmonary
M>49.25
1402
Average Length of Stay
0.8666
Cardiac
M>38.55 and
M<48.85
Cardiac
M>31.15 and
M<38.55
Cardiac
M<31.15
1501
Relative Weight
1.6402
1.5145
1.4276
19
17
15
14
0.9889
0.8933
0.8321
0.7677
9
9
10
9
1.2901
1.1654
1.0855
1.0015
12
13
12
12
1.6155
1.4592
1.3592
1.2540
13
17
15
14
1.1345
0.9258
0.8520
0.7671
16
10
10
10
1.4253
1.1631
1.0704
0.9637
13
14
13
12
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ER05AU16.005
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1703
1704
1801
1802
1803
1901
CMG
Description
(M=motor,
C=cognitive,
A=age)
Major multiple
trauma
without brain
or spinal cord
injury
M>25.55 and
M<31.05
Major multiple
trauma
without brain
or spinal cord
injury
M<25.55
Major multiple
trauma with
brain or spinal
cord injury
M>40.85
Major multiple
trauma with
brain or spinal
cord injury
M>23.05 and
M<40.85
Major multiple
trauma with
brain or spinal
cord injury
M<23.05
Guillian Barre
M>35.95
2004
Miscellaneous
M>38.75 and
M<49.15
Miscellaneous
M>27.85 and
M<38.75
Miscellaneous
M<27.85
2101
Burns
M>O
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2003
VerDate Sep<11>2014
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1.3862
1.2758
1.1486
16
15
15
14
2.1821
1.7806
1.6387
1.4753
22
19
18
17
1.2932
1.0595
0.9203
0.8254
14
13
12
10
1.8234
1.4939
1.2976
1.1639
17
17
15
14
2.8692
2.3507
2.0419
1.8314
31
27
21
20
1.2267
1.0516
0.9270
0.9134
14
13
11
11
1.9106
1.6843
1.6595
20
22
19
19
3.1447
2.7722
2.7315
52
31
32
30
0.9225
0.7562
0.6942
0.6285
9
9
8
8
1.2097
0.9916
0.9104
0.8241
12
11
11
10
1.5124
1.2397
1.1381
1.0303
14
14
13
12
1.9412
1.5912
1.4608
1.3224
19
17
16
15
1.6899
2001
Miscellaneous
M>49.15
1.6987
3.6684
1903
2002
Average Length of Stay
2.2288
Guillian Barre
M>18.05 and
M<35.95
Guillian Barre
M<18.05
1902
Relative Weight
1.6899
1.5061
1.3813
24
18
16
17
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CMG
52069
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Generally, updates to the CMG
relative weights result in some increases
and some decreases to the CMG relative
weight values. Table 2 shows how we
estimate that the application of the
revisions for FY 2017 would affect
particular CMG relative weight values,
which would affect the overall
distribution of payments within CMGs
and tiers. Note that, because we
proposed to implement the CMG
relative weight revisions in a budgetneutral manner (as previously
described), total estimated aggregate
payments to IRFs for FY 2017 would not
be affected as a result of the proposed
CMG relative weight revisions.
However, the proposed revisions would
affect the distribution of payments
within CMGs and tiers.
TABLE 2—DISTRIBUTIONAL EFFECTS OF THE CHANGES TO THE CMG RELATIVE WEIGHTS
[FY 2016 values compared with FY 2017 values]
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Increased by 15% or more ..........................................................................................................................
Increased by between 5% and 15% ...........................................................................................................
Changed by less than 5% ...........................................................................................................................
Decreased by between 5% and 15% ..........................................................................................................
Decreased by 15% or more ........................................................................................................................
As Table 2 shows, 99.7 percent of all
IRF cases are in CMGs and tiers that
would experience less than a 5 percent
change (either increase or decrease) in
the CMG relative weight value as a
result of the revisions for FY 2017. The
largest estimated increase in the CMG
relative weight values that affects the
largest number of IRF discharges would
be a 0.7 percent change in the CMG
relative weight value for CMG 0604—
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18:14 Aug 04, 2016
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Neurological, with a motor score less
than 25.85—in the ‘‘no comorbidity’’
tier. In the FY 2015 claims data, 8,572
IRF discharges (2.2 percent of all IRF
discharges) were classified into this
CMG and tier.
The largest decrease in a CMG relative
weight value affecting the largest
number of IRF cases would be a 1.4
percent decrease in the CMG relative
weight for CMG 0110—Stroke, with a
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0
540
395,897
761
41
Percentage of
cases affected
(percent)
0.0
0.1
99.7
0.2
0.0
motor score less than 22.35 and age less
than 84.5—in the ‘‘no comorbidity’’ tier.
In the FY 2015 IRF claims data, this
change would have affected 13,739
cases (3.5 percent of all IRF cases).
The proposed changes in the average
length of stay values for FY 2017,
compared with the FY 2016 average
length of stay values, are small and do
not show any particular trends in IRF
length of stay patterns.
E:\FR\FM\05AUR3.SGM
05AUR3
ER05AU16.007
Number of cases
affected
Percentage change
Federal Register / Vol. 81, No. 151 / Friday, August 5, 2016 / Rules and Regulations
We received 3 comments on the
proposed update to the CMG relative
weights and average length of stay
values for FY 2017, which are
summarized below.
Comment: Commenters, while
supportive of the methodology used to
calculate the weights, requested that we
provide more detail about the use of the
CCR data in the CMG relative weight
calculations. Additionally, the
commenters requested that we outline
the methodology used to calculate the
average length of stay values in the FY
2017 IRF PPS proposed rule.
Response: As we discussed, most
recently, in the FY 2016 IRF PPS final
rule (80 FR 47036, 47045), a key
variable used to calculate the CMG
relative weights is a facility’s average
cost per case, which is obtained by
averaging the estimated cost per case for
every patient discharged from the
facility in a given fiscal year. To obtain
the estimated cost per case for a given
IRF patient, we start by pulling the
appropriate charges from the Medicare
claim for that patient. Then, we
calculate the appropriate CCRs from the
Medicare cost report submitted by the
facility. The CCRs are then multiplied
by the charges from the Medicare claim
to obtain the estimated IRF cost for the
case. This variable is used as the
dependent variable in the regression
analysis to estimate the CMG relative
weights.
As we also discussed in the FY 2016
IRF PPS final rule (80 FR 47036, 47045),
the methodology for calculating the
average length of stay values is available
for download from the IRF PPS Web site
at https://www.cms.gov/Medicare/
Medicare-Fee-for-Service-Payment/
InpatientRehabFacPPS/Research.html.
Final Decision: After careful
consideration of the public comments,
we are finalizing our proposal to update
the CMG relative weight and average
length of stay values for FY 2017, as
shown in Table 1 of this final rule.
These updates are effective October 1,
2016.
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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 federal 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
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18:14 Aug 04, 2016
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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 final rule
(78 FR 47860, 47868 through 47872), in
the FY 2015 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 2017, 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 2017 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, which is referred
to as a market basket index. According
to section 1886(j)(3)(A)(i) of the Act, the
increase factor shall be used to update
the IRF federal prospective payment
rates for each FY. Section
1886(j)(3)(C)(ii)(I) of the Act requires the
application of a productivity
adjustment, as described below. In
addition, sections 1886(j)(3)(C)(ii)(II)
and 1886(j)(3)(D)(v) of the Act require
the application of a 0.75 percentage
point reduction to the market basket
increase factor for FY 2017. Thus, in the
FY 2017 IRF PPS proposed rule (81 FR
24178, 24187 through 24188), we
proposed to update the IRF PPS
payments for FY 2017 by a market
basket increase factor as required by
section 1886(j)(3)(C) of the Act, with a
productivity adjustment as required by
section 1886(j)(3)(C)(ii)(I) of the Act, and
a 0.75 percentage point reduction as
required by sections 1886(j)(3)(C)(ii)(II)
and 1886(j)(3)(D)(v) of the Act.
For FY 2015, IRF PPS payments were
updated using the 2008-based RPL
market basket. Beginning with the FY
2016 IRF PPS, we created and adopted
a stand-alone IRF market basket, which
was referred to as the 2012-based IRF
market basket, reflecting the operating
and capital cost structures for
freestanding IRFs and hospital-based
IRFs. The general structure of the 2012based IRF market basket is similar to the
2008-based RPL market basket;
however, we made several notable
changes. In developing the 2012-based
IRF market basket, we derived cost
weights from Medicare cost report data
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52071
for both freestanding and hospital-based
IRFs (the 2008-based RPL market basket
was based on freestanding data only),
incorporated the 2007 Input-Output
data from the Bureau of Economic
Analysis (the 2008-based RPL market
basket was based on the 2002 InputOutput data); used new price proxy
blends for two cost categories (Fuel, Oil,
and Gasoline and Medical Instruments);
added one additional cost category
(Installation, Maintenance, and Repair),
which was previously included in the
residual All Other Services: LaborRelated cost category of the 2008-based
RPL market basket; and eliminated three
cost categories (Apparel, Machinery &
Equipment, and Postage). The FY 2016
IRF PPS final rule (80 FR 47046 through
47068) contains a complete discussion
of the development of the 2012-based
IRF market basket.
B. FY 2017 Market Basket Update and
Productivity Adjustment
For FY 2017, we proposed to use the
same methodology described in the FY
2016 IRF PPS final rule (80 FR 47066)
to compute the FY 2017 market basket
increase factor 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
Insight’s forecast using the most recent
available data. IHS Global Insight (IGI),
Inc. is a nationally recognized economic
and financial forecasting firm with
which CMS contracts to forecast the
components of the market baskets and
multifactor productivity (MFP).
Based on IGI’s first quarter 2016
forecast with historical data through the
fourth quarter of 2015, we proposed that
the projected 2012-based IRF market
basket increase factor for FY 2017
would be 2.7 percent. We also proposed
that if more recent data were
subsequently available (for example, a
more recent estimate of the market
basket update), we would use such data
to determine the FY 2017 update in the
final rule. Incorporating the most recent
data available, based on IGI’s second
quarter 2016 forecast with historical
data through the first quarter of 2016,
the projected 2012-based IRF market
basket increase factor for FY 2017 is 2.7
percent.
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. 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
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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 10year 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. A
complete description of the MFP
projection methodology is available on
the CMS Web site at https://
www.cms.gov/Research-Statistics-Dataand-Systems/Statistics-Trends-andReports/MedicareProgramRatesStats/
MarketBasketResearch.html.
Using IGI’s first quarter 2016 forecast,
the proposed MFP adjustment for FY
2017 (the 10-year moving average of
MFP for the period ending FY 2017) was
0.5 percent. We proposed that if more
recent data were subsequently available,
we would use such data to determine
the FY 2017 MFP adjustment in the
final rule. Incorporating the most recent
data available, based on IGI’s second
quarter 2016 forecast with historical
data through the first quarter of 2016,
the projected MFP adjustment for FY
2017 is 0.3 percent.
Thus, in accordance with section
1886(j)(3)(C) of the Act, we proposed to
base the FY 2017 market basket update,
which is used to determine the
applicable percentage increase for the
IRF payments, on the most recent
estimate of the 2012-based IRF market
basket. We proposed to then reduce this
percentage increase by the most up-todate estimate of the MFP adjustment for
FY 2017. Following application of the
MFP, we proposed to further reduce the
applicable percentage increase by 0.75
percentage point, as required by
sections 1886(j)(3)(C)(ii)(II) and
1886(j)(3)(D)(v) of the Act. Therefore,
the estimate of the FY 2017 IRF update
for the proposed rule was 1.45 percent
(2.7 percent market basket update, less
0.5 percentage point MFP adjustment,
less 0.75 percentage point legislative
adjustment). Incorporating the most
recent data, the current estimate of the
FY 2017 IRF update is 1.65 percent (2.7
percent market basket update, less 0.3
percentage point MFP adjustment, less
0.75 percentage point legislative
adjustment).
For FY 2017, the Medicare Payment
Advisory Commission (MedPAC)
recommends that a 0-percent update be
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applied to IRF PPS payment rates. As
discussed, and in accordance with
sections 1886(j)(3)(C) and 1886(j)(3)(D)
of the Act, the Secretary proposed to
update the IRF PPS payment rates for
FY 2017 by an adjusted market basket
increase factor of 1.45 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 2017. As
noted above, incorporating the most
recent data, the current estimate of the
FY 2017 IRF update is 1.65 percent.
We received 10 comments on the
proposed market basket increase update
and productivity adjustment, which are
summarized below.
Comment: One commenter (MedPAC)
stated that it understood that CMS is
required to implement this statutory
payment update; however, MedPAC
noted that after reviewing many
factors—including indicators of
beneficiary access to rehabilitative
services, the supply of providers, and
Medicare margins—it determined that
Medicare’s current payment rates for
IRFs appear to be adequate and
therefore recommended no update to
IRF payment rates for FY 2017. MedPAC
appreciated that CMS cited its
recommendation, even while noting that
the Secretary does not have the
authority to deviate from statutorily
mandated updates.
Response: As discussed, and in
accordance with sections 1886(j)(3)(C)
and 1886(j)(3)(D) of the Act, the
Secretary is updating IRF PPS payment
rates for FY 2017 by an adjusted market
basket increase factor of 1.65 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 2017.
Comment: Several commenters
requested that, with respect to the
productivity adjustment, CMS remain
cognizant of the intensive labor, time
and costs required by state and/or
federal regulations to which IRFs are
bound. These commenters stated that
these requirements may be barriers to
IRFs achieving further gains in
productivity efficiencies. Further, some
commenters stated that successful
rehabilitation outcomes require an
intense labor component, including the
interaction of the full multidisciplinary
treatment team, which includes
physicians, nurses, physical and
occupational therapists, speech
language pathologists as well as social
workers, psychologists and others. In
addition, these commenters indicated
that some states have regulations
mandating increased professional
staffing ratios between health care
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providers and patients. A few
commenters claimed that, since CMS
has stated its policy is that the majority
of patient therapy should be one-on-one,
which is highly labor-intensive, then
CMS should not mandate further
efficiencies such as productivity
adjustments while simultaneously
implementing new regulations or
interpreting existing regulations in ways
that preclude IRFs from adopting
clinically appropriate innovations that
would allow for greater efficiencies.
These commenters requested that the
0.5 percentage point productivity
adjustment be ‘‘reversed.’’ In addition,
several commenters requested that CMS
be mindful of the additional labor costs
and quality improvement activities that
IRFs will incur as a result of the
additional items required in version 1.4
of the IRF PAI beginning on October 1,
2016 as well as the IRF PAI proposed
changes relating to the drug regimen
measure for which data would start to
be collected on October 1, 2018.
Response: Section 1886(j)(3)(C)(ii)(I)
of the Act requires the application of a
productivity adjustment that must be
applied to the IRF PPS market basket
update. The statute does not provide the
Secretary with the authority to
‘‘reverse’’ the productivity adjustment
or apply a different adjustment. We will
continue to monitor the impact of the
payment updates, including the effects
of the productivity adjustment, on IRF
provider margins as well as beneficiary
access to care.
Comment: One commenter
recommended that CMS use the latest
data available in estimating the market
basket in the final rule.
Response: We agree with the
commenter’s recommendation, and it is
consistent with the proposed rule
language stating that the final IRF PPS
payment update will be based on the
most recent forecast of the market basket
update and productivity adjustment. As
noted above, the most recent estimate of
the 2012-based IRF market basket is
based on IGI’s second quarter 2016
forecast with historical data through the
first quarter of 2016.
Final Decision: Based on careful
consideration of the comments, we are
finalizing the FY 2017 market basket
update for IRF payments of 1.65 percent
(2.7 percent market basket update, less
0.3 percentage point MFP adjustment,
less 0.75 percentage point legislative
adjustment), which is based on the most
recent forecasts of the 2012-based IRF
market basket update and the MFP
adjustment.
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C. Labor-Related Share for FY 2017
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) 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 continue to
classify a cost category as labor-related
if the costs are labor-intensive and vary
with the local labor market.
Based on our definition of the laborrelated share and the cost categories in
the 2012-based IRF market basket, we
proposed to include in the labor-related
share for FY 2017 the sum of the FY
2017 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 2012-based
IRF market basket. For more details
regarding the methodology for
determining specific cost categories for
inclusion in the 2012-based IRF laborrelated share, see the FY 2016 IRF final
rule (80 FR 47066 through 47068).
Using this method and the IHS Global
Insight, Inc. first quarter 2016 forecast
for the 2012-based IRF market basket,
the proposed IRF labor-related share for
FY 2017 was 71.0 percent. We proposed
that if more recent data were
subsequently available, we would use
such data to determine the FY 2017 IRF
labor-related share in the final rule.
Incorporating the most recent estimate
of the 2012-based IRF market basket
based on IGI’s second quarter 2016
forecast with historical data through the
first quarter of 2016, the sum of the
52073
relative importance for FY 2017
operating costs (Wages and Salaries,
Employee Benefits, Professional Fees:
Labor-related, Administrative and
Facilities Support Services, Installation
Maintenance & Repair Services, and All
Other: Labor-related Services) using the
2012-based IRF market basket is 67.0
percent. We proposed that the portion of
Capital-Related Costs that is influenced
by the local labor market is estimated to
be 46 percent. Incorporating the most
recent estimate of the FY 2017 relative
importance of Capital-Related costs
from the 2012-based IRF market basket
based on IGI’s second quarter 2016
forecast with historical data through the
first quarter of 2016, which is 8.4
percent, we take 46 percent of 8.4
percent to determine the labor-related
share of Capital for FY 2017. As we
proposed, we then add this amount (3.9
percent) to the sum of the relative
importance for FY 2017 operating costs
(67.0 percent) to determine the total
labor-related share for FY 2017 of 70.9
percent.
TABLE 3—IRF LABOR-RELATED SHARE
FY 2017
Final labor-related
share 1
FY 2016
Final labor-related
share 2
Wages and Salaries ....................................................................................................................................
Employee Benefits .......................................................................................................................................
Professional Fees: Labor-related ................................................................................................................
Administrative and Facilities Support Services ...........................................................................................
Installation, Maintenance, and Repair .........................................................................................................
All Other: Labor-related Services ................................................................................................................
Subtotal ........................................................................................................................................................
Labor-related portion of capital (46%) .........................................................................................................
47.7
11.3
3.5
0.8
1.9
1.8
67.0
3.9
47.6
11.4
3.5
0.8
2.0
1.8
67.1
3.9
Total Labor-Related Share ...................................................................................................................
70.9
71.0
1 Based
on the 2012-based IRF Market Basket, IHS Global Insight, Inc. 2nd quarter 2016 forecast.
Register 80 FR 47068.
2 Federal
Final Decision: As we did not receive
any comments on the proposed laborrelated share for FY 2017, we are
finalizing the FY 2017 labor-related
share of 70.9 percent.
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D. Wage Adjustment
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
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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.
For FY 2017, we proposed to maintain
the policies and methodologies
described in the FY 2016 IRF PPS final
rule (80 FR 47036, 47068 through
47075) related to the labor market area
definitions and the wage index
methodology for areas with wage data.
Thus, we proposed to use the CBSA
labor market area definitions and the FY
2016 pre-reclassification and pre-floor
hospital wage index data. The current
statistical areas which were
implemented in FY 2016 are based on
OMB standards published on February
28, 2013, in OMB Bulletin No. 13–01.
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For FY 2017, we are continuing to use
the new OMB delineations that we
adopted beginning with FY 2016. In
accordance with section 1886(d)(3)(E) of
the Act, the FY 2016 pre-reclassification
and pre-floor hospital wage index is
based on data submitted for hospital
cost reporting periods beginning on or
after October 1, 2011, and before
October 1, 2012 (that is, FY 2012 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
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hospitals and, thus, no hospital wage
index data on which to base the
calculation for the FY 2017 IRF PPS
wage index.
We did not receive any comments on
these proposals. Therefore, we are
finalizing our proposal to use the CBSA
labor market area definitions and the FY
2016 pre-reclassification and pre-floor
hospital wage index data for areas with
wage data. We are also finalizing our
proposal 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.
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2. Update
The wage index used for the IRF PPS
is calculated using the prereclassification and pre-floor acute care
hospital 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. In the FY 2016
IRF PPS final rule (80 FR 47036, 47068),
we established an IRF wage index based
on FY 2011 acute care hospital wage
data to adjust the FY 2016 IRF payment
rates. We also adopted the revised
CBSAs set forth by 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, and provided guidance
on the use of the delineations of these
statistical areas based on new standards
published on June 28, 2010, in the
Federal Register (75 FR 37246 through
37252). A copy of this bulletin may be
obtained at https://www.whitehouse.gov/
sites/default/files/omb/bulletins/2013/b13-01.pdf. For FY 2017, we are
continuing to use the new OMB
delineations that we adopted beginning
with FY 2016 to calculate the area wage
indexes and the transition periods,
which we discuss below.
3. Transition Period
In FY 2016, we applied a 1-year
blended wage index for all IRF
providers to mitigate the impact of the
wage index change due to the
implementation of the revised CBSA
delineations. Under that policy, all IRF
providers are receiving a blended wage
index in FY 2016 using 50 percent of
their FY 2016 wage index based on the
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revised OMB CBSA delineations and 50
percent of their FY 2016 wage index
based on the OMB delineations used in
FY 2015. For FY 2017, we proposed to
maintain the policy established in FY
2016 IRF PPS final rule related to the
blended one-year transition wage index
(see 80 FR 47036, 47073 through 47074).
Thus, the 1-year blended wage index
that became effective on October 1,
2015, will expire on September 30,
2016.
We did not receive any comments on
the proposal to maintain the policy
established in FY 2016 IRF PPS final
rule related to the blended one-year
transition wage index.
Final decision: As we did not receive
any comments on our proposal to
maintain the 1-year blended wage index
for all IRF providers, we are finalizing
the expiration of this policy on
September 30, 2016.
For FY 2016, in addition to the
blended wage index, we also adopted a
3-year budget neutral phase out of the
rural adjustment for IRFs that were rural
in FY 2015 and became urban in FY
2016 under the revised CBSA
delineations. In FY 2016, IRFs that were
designated as rural in FY 2015 and
became designated as urban in FY 2016
received two-thirds of the 2015 rural
adjustment of 14.9 percent. FY 2017
represents the second year of the 3-year
phase out of the rural adjustment, in
which these same IRFs will receive onethird of the 2015 rural adjustment of
14.9 percent, as finalized in the FY 2016
IRF PPS final rule (80 FR 47036, 47073
through 47074).
For FY 2017, the wage index will be
based solely on the previously adopted
revised CBSA delineations and their
respective wage index (rather than on a
blended wage index). Furthermore, we
will continue the 3-year phase out of the
rural adjustments for IRF providers that
changed from rural to urban status that
was finalized in the FY 2016 IFR PPS
final rule (80 FR 47036, 47073 through
47074).
We received one comment on our
proposal to continue the 3-year phase
out of the rural adjustments for IRF
providers that changed from rural to
urban status and that was finalized in
the FY 2016 IFR PPS final rule.
Comment: One commenter suggested
that we implement a 5-year phase-out of
the rural adjustment or allow IRFs that
are losing the FY 2015 rural adjustment
due to the changes in the CBSA
delineations to apply for reclassification
back to rural status for a period of 5
years.
Response: The intent of the 3-year
phase-out of the rural adjustment is to
mitigate potential negative payment
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effects on rural facilities that are
redesignated as urban facilitates,
effective FY 2016. As described in more
detail in the FY 2006 IRF PPS final rule
(70 FR 47880), our analysis determined
that a 3-year budget-neutral transition
policy would best accomplish the goals
of mitigating the loss of the rural
adjustment for existing IRFs that were
rural in FY 2005 and became urban in
FY 2006 under the new CBSA
designations. For a complete discussion
of this policy, we refer readers to the FY
2006 IRF PPS final rule (70 FR 47880,
47921 through 47925). As discussed in
the FY 2016 IRF PPS final rule (80 FR
47036, 47074), we continue to believe
that a 3-year budget-neutral phase-out of
the rural adjustment appropriately
mitigates the adverse payment impacts
for these IRFs while also ensuring that
payment rates for all IRFs are set
accurately and appropriately.
Final Decision: After careful
consideration, we are finalizing the
continuation of the 3-year phase-out of
the rural adjustment for IRFs that were
designated as rural in FY 2015 but
changed to urban in FY 2016 under the
new OMB market area delineations. For
FY 2017, these IRFs will receive the full
FY 2017 wage index and one-third of
the FY 2015 rural adjustment. For FY
2018, these IRFs will receive the full FY
2018 wage index with no rural
adjustment.
For a full discussion of our
implementation of the new OMB labor
market area delineations for the FY 2016
wage index, please refer to the FY 2016
IRF PPS final rule (80 FR 47036, 47068
through 47076). While conducting
analysis for the FY 2017 IRF PPS final
rule, an additional IRF provider was
identified as being eligible for the 3-year
phase out of the rural adjustments for
IRF providers that changed from rural to
urban status. The original 19 providers
were identified in FY 2014 claims data
for the FY 2016 IRF PPS proposed and
final rules. This newly eligible provider
was new in FY 2015 and thus had no
claims data in FY 2014. An analysis of
the FY 2015 claims determined that this
provider should have received twothirds of the rural adjustment in FY
2016. This provider will be added to the
group of providers receiving two-thirds
of the rural adjustment in FY 2016 and
one-third of the rural adjustment in FY
2017. For FY 2017, 20 IRFs that were
designated as rural in FY 2015 and
became designated as urban in FY 2016
will receive the FY 2017 wage index
(based solely on the revised CBSA
delineations) and one-third of the FY
2015 rural adjustment of 14.9 percent
(80 FR 47036, 47073 through 47076).
The wage index applicable to FY 2017
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is available on the CMS Web site at
https://www.cms.gov/Medicare/
Medicare-Fee-for-Service-Payment/
InpatientRehabFacPPS/Data-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 multiply the
unadjusted federal payment rate for
IRFs by the FY 2017 labor-related share
based on the 2012-based IRF market
basket (70.9 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.C of this final
rule. We 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 through the Internet on the
CMS Web site at https://www.cms.gov/
Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/DataFiles.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 2017 IRF standard
payment conversion factor reflects the
update to the wage indexes (based on
the FY 2012 hospital cost report data)
and the labor-related share in a budgetneutral manner:
Step 1. Determine the total amount of
the estimated FY 2016 IRF PPS
payments, using the FY 2016 standard
payment conversion factor and the
labor-related share and the wage
indexes from FY 2016 (as published in
the FY 2016 IRF PPS final rule (80 FR
47036)).
Step 2. Calculate the total amount of
estimated IRF PPS payments using the
FY 2017 standard payment conversion
factor and the FY 2017 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
2017 budget-neutral wage adjustment
factor of 0.9992.
Step 4. Apply the FY 2017 budgetneutral wage adjustment factor from
step 3 to the FY 2016 IRF PPS standard
payment conversion factor after the
application of the adjusted market
basket update to determine the FY 2017
standard payment conversion factor.
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We discuss the calculation of the
standard payment conversion factor for
FY 2017 in section VI.E of this final
rule.
We did not receive any specific
comments on the proposal to calculate
a budget-neutral wage adjustment factor.
Final Decision: As we did not receive
any comments on the proposal to
calculate a budget-natural wage
adjustment factor, we are finalizing our
calculation of the budget-neutral wage
adjustment factor of 0.9992 for FY 2017.
We received 11 public comments on
the proposed IRF wage adjustment for
FY 2017, which are summarized below.
Comment: Commenters again
recommended that we develop a new
methodology for the area wage
adjustment that eliminates hospital
wage index reclassifications for all
hospitals and reduces the problems
associated with annual fluctuations in
wage indices and across geographic
boundaries. Until such time as the new
methodology may be developed,
commenters also recommended that we
consider adopting certain wage index
policies currently employed under the
IPPS, because IRFs compete in a similar
labor pool as acute care hospitals. Such
comments included requests that CMS
grant IRFs the ability to request
reclassification and/or establish a rural
floor policy. One commenter further
recommended that, until a new wage
index system is implemented, we
institute a ‘‘smoothing’’ variable to the
current process to reduce the
fluctuations IRFs annually experience.
Response: Consistent with our
previous responses to these comments
(most recently published in our FY 2016
IRF PPS final rule (80 FR 47036,
47076)), we note that the IRF PPS does
not account for geographic
reclassification under sections
1886(d)(8) and (d)(10) of the Act, and
does not apply the ‘‘rural floor’’ under
section 4410 of the BBA. Furthermore,
as we do not have an IRF-specific wage
index, we are unable to determine at
this time the degree, if any, to which a
geographic reclassification adjustment
or a rural floor policy under the IRF PPS
would be appropriate. The rationale for
our current wage index policies is fully
described in the FY 2006 IRF PPS final
rule (70 FR 47880, 47926 through
47928).
Additionally, while some commenters
recommended that we adopt IPPS
reclassification and/or floor policies, we
note the MedPAC’s June 2007 report to
the Congress, titled ‘‘Report to Congress:
Promoting Greater Efficiency in
Medicare’’ (available at https://
www.medpac.gov/-documents-/reports),
recommends that Congress ‘‘repeal the
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52075
existing hospital wage index statute,
including reclassification and
exceptions, and give the Secretary
authority to establish new wage index
systems.’’ We continue to believe it
would not be appropriate at this time to
adopt the IPPS wage index policies,
such as reclassification and/or floor
policies. Therefore, we will continue to
use the CBSA labor market area
definitions and the pre-reclassification
and pre-floor hospital wage index data
based on 2012 cost report data as this is
the most recent final data available.
With regard to issues mentioned
about ensuring that the wage index
minimizes fluctuations, matches the
costs of labor in the market, and
provides for a single wage index policy,
we note that section 3137(b) of the
Affordable Care Act required us to
submit a report to the Congress by
December 31, 2011 that includes a plan
to reform the hospital wage index
system. This report describes the
concept of a Commuting Based Wage
Index as a potential replacement to the
current Medicare wage index
methodology. While this report
addresses the goals of broad based
Medicare wage index reform, no
consensus has been achieved regarding
how best to implement a replacement
system. These concerns will be taken
into consideration while CMS continues
to explore potential wage index reforms.
The report that we submitted is
available online at https://www.cms.gov/
Medicare/Medicare-Fee-for-ServicePayment/AcuteInpatientPPS/WageIndex-Reform.html.
Comment: Several commenters
suggested that CMS use the most current
wage data that is available and align the
timeframe for the IRF wage index with
other post-acute and acute care settings.
These commenters indicated that this
would position the IRF PPS to be more
in line with alternative payment models
that are currently being developed and
tested.
Response: As we did not propose any
changes to the methodology for
determining the wage index for IRF
providers, these comments are outside
the scope of the proposed rule. We
appreciate the commenters’ suggestions
and agree that this issue needs to be
considered within the broader context
of Medicare post-acute care payment
reform efforts. We will consider these
suggestions for future analyses.
Final Decision: After careful
consideration of the comments, we are
finalizing use of the FY 2016 pre-floor,
pre-reclassified hospital wage index
data to derive the applicable IRF PPS
wage index for FY 2017. We are also
continuing to implement the 3-year
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phase-out of the rural adjustment for
IRFs that were designated as rural in FY
2015 but changed to urban in FY 2016
under the new OMB market area
delineations. For FY 2017, these IRFs
will receive the full FY 2017 wage index
and one-third of the FY 2015 rural
adjustment. For FY 2018, these IRFs
will receive the full FY 2018 wage index
with no rural adjustment.
E. Description of the IRF Standard
Payment Conversion Factor and
Payment Rates for FY 2017
To calculate the standard payment
conversion factor for FY 2017, as
illustrated in Table 4, we begin by
applying the adjusted market basket
increase factor for FY 2017 that was
adjusted in accordance with sections
1886(j)(3)(C) and (D) of the Act, to the
standard payment conversion factor for
FY 2016 ($15,478). Applying the 1.65
percent adjusted market basket increase
for FY 2017 to the standard payment
conversion factor for FY 2016 of $15,478
yields a standard payment amount of
$15,733. Then, we apply the budget
neutrality factor for the FY 2017 wage
index and labor-related share of 0.9992,
which results in a standard payment
amount of $15,721. We next apply the
budget neutrality factor for the revised
CMG relative weights of 0.9992, which
results in the standard payment
conversion factor of $15,708 for FY
2017.
TABLE 4—CALCULATIONS TO DETERMINE THE FY 2017 STANDARD PAYMENT CONVERSION FACTOR
Explanation for adjustment
Calculations
Standard Payment Conversion Factor for FY 2016 ....................................................................................................................
Market Basket Increase Factor for FY 2017 (2.7 percent), reduced by 0.3 percentage point for the productivity adjustment
as required by section 1886(j)(3)(C)(ii)(I) of the Act, and reduced by 0.75 percentage point in accordance with paragraphs 1886(j)(3)(C) and (D) of the Act ..................................................................................................................................
Budget Neutrality Factor for the Wage Index and Labor-Related Share ....................................................................................
Budget Neutrality Factor for the Revisions to the CMG Relative Weights .................................................................................
FY 2017 Standard Payment Conversion Factor .........................................................................................................................
We did not receive comments
specifically on the proposed FY 2017
standard payment conversion factor. We
received comments on how the FY 2016
IRF QRP relates to the proposed FY
2017 standard payment conversion
factor, which we have summarized in
section IX. of this final rule.
Final Decision: As we did not receive
comments specifically on the proposed
FY 2017 standard payment conversion
factor, we are finalizing the IRF
standard payment conversion factor of
$15,708 for FY 2017.
$15,478
×
×
×
=
1.0165
0.9992
0.9992
15,708
After the application of the proposed
CMG relative weights described in
section IV of this final rule to the FY
2017 standard payment conversion
factor ($15,708), the resulting
unadjusted IRF prospective payment
rates for FY 2017 are shown in Table 5.
TABLE 5—FY 2017 PAYMENT RATES
Payment rate
Tier 1
mstockstill on DSK3G9T082PROD with RULES3
CMG
0101
0102
0103
0104
0105
0106
0107
0108
0109
0110
0201
0202
0203
0204
0205
0206
0207
0301
0302
0303
0304
0401
0402
0403
0404
0405
0501
0502
0503
0504
0505
0506
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
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$12,553.83
15,912.20
18,591.99
19,788.94
22,889.70
25,597.76
28,568.14
35,960.32
32,333.35
42,914.26
12,178.41
17,192.41
19,121.35
21,135.11
25,484.66
30,220.62
39,716.11
17,944.82
22,090.16
25,902.49
33,514.59
15,392.27
22,072.88
34,816.78
60,793.10
54,027.67
13,389.50
18,221.28
22,866.14
26,840.26
30,798.68
42,648.79
Sfmt 4700
Payment rate
Tier 2
$11,179.38
14,168.62
16,556.23
17,621.23
20,382.70
22,793.88
25,439.11
32,022.33
28,791.19
38,212.85
9,960.44
14,060.23
15,637.31
17,283.51
20,842.95
24,714.97
32,481.00
14,815.79
18,236.99
21,384.87
27,668.07
13,534.01
19,410.38
30,618.03
53,459.04
47,510.42
10,547.92
14,355.54
18,015.51
21,146.11
24,264.15
33,600.98
E:\FR\FM\05AUR3.SGM
05AUR3
Payment rate
Tier 3
$10,227.48
12,962.24
15,145.65
16,121.12
18,646.97
20,852.37
23,271.40
29,293.85
26,339.17
34,958.15
8,977.12
12,671.64
14,094.79
15,579.19
18,785.20
22,277.09
29,275.00
13,463.33
16,573.51
19,433.94
25,143.80
12,483.15
17,900.84
28,236.70
49,302.70
43,815.90
10,045.27
13,670.67
17,154.71
20,136.09
23,104.90
31,995.63
Payment rate
no comorbidity
$9,762.52
12,373.19
14,457.64
15,387.56
17,798.73
19,903.61
22,214.25
27,961.81
25,140.65
33,368.50
8,392.78
11,846.97
13,175.87
14,564.46
17,563.11
20,825.67
27,369.62
12,569.54
15,472.38
18,142.74
23,474.04
11,330.18
16,248.36
25,629.17
44,750.52
39,771.09
9,033.67
12,294.65
15,428.40
18,109.75
20,780.11
28,777.06
Federal Register / Vol. 81, No. 151 / Friday, August 5, 2016 / Rules and Regulations
52077
TABLE 5—FY 2017 PAYMENT RATES—Continued
Payment rate
Tier 1
CMG
mstockstill on DSK3G9T082PROD with RULES3
0601
0602
0603
0604
0701
0702
0703
0704
0801
0802
0803
0804
0805
0806
0901
0902
0903
0904
1001
1002
1003
1101
1102
1201
1202
1203
1301
1302
1303
1401
1402
1403
1404
1501
1502
1503
1504
1601
1602
1603
1701
1702
1703
1704
1801
1802
1803
1901
1902
1903
2001
2002
2003
2004
2101
5001
5101
5102
5103
5104
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
.................................................................................................................
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.................................................................................................................
.................................................................................................................
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.................................................................................................................
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F. Example of the Methodology for
Adjusting the Federal Prospective
Payment Rates
Table 6 illustrates the methodology
for adjusting the federal prospective
payments (as described in sections VI.A.
through VI.F. of this final rule). The
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Payment rate
Tier 2
Payment rate
Tier 3
16,260.92
20,926.20
25,778.40
34,168.04
15,693.86
20,041.84
24,163.62
31,326.46
12,539.70
16,231.08
21,713.17
19,548.61
23,257.26
28,253.98
15,455.10
19,765.38
24,834.35
31,437.99
16,831.12
21,843.54
30,848.94
20,769.12
29,771.37
16,303.33
18,945.42
24,143.20
18,753.78
25,756.41
31,753.72
13,612.55
18,551.15
22,115.29
27,968.09
15,847.80
20,021.42
24,414.94
30,426.40
15,533.64
20,264.89
25,376.27
17,820.73
22,388.61
26,683.18
34,276.43
20,313.59
28,641.97
45,069.39
19,269.00
35,009.99
57,623.23
14,490.63
19,001.97
23,756.78
30,492.37
26,544.95
........................
........................
........................
........................
........................
12,888.41
16,587.65
20,432.97
27,082.16
12,780.03
16,320.61
19,677.41
25,509.79
10,120.66
13,100.47
17,523.84
15,777.12
18,769.49
22,803.30
12,472.15
15,951.47
20,043.41
25,373.13
14,840.92
19,259.58
27,201.54
18,826.04
26,987.91
16,086.56
18,692.52
23,821.18
14,754.52
20,263.32
24,982.00
11,504.54
15,678.15
18,690.95
23,637.40
13,419.34
16,953.64
20,674.87
25,764.26
14,031.96
18,306.10
22,921.11
14,542.47
18,269.97
21,774.43
27,969.66
16,642.63
23,466.18
36,924.80
16,518.53
30,011.70
49,396.95
11,878.39
15,576.05
19,473.21
24,994.57
26,544.95
........................
........................
........................
........................
........................
11,901.95
15,316.87
18,868.45
25,010.28
12,200.40
15,580.77
18,783.63
24,352.11
9,358.83
12,115.58
16,205.94
14,591.16
17,358.91
21,087.99
11,554.80
14,778.09
18,568.43
23,507.02
12,878.99
16,714.88
23,607.55
15,298.02
21,929.94
14,617.86
16,985.06
21,645.62
13,650.25
18,747.50
23,114.32
10,428.54
14,211.03
16,941.08
21,425.71
12,390.47
15,654.59
19,089.93
23,789.77
13,070.63
17,051.03
21,350.31
13,383.22
16,813.84
20,040.27
25,740.70
14,456.07
20,382.70
32,074.17
14,561.32
26,456.98
43,545.72
10,904.49
14,300.56
17,877.27
22,946.25
23,657.82
........................
........................
........................
........................
........................
following examples are based on two
hypothetical Medicare beneficiaries,
both classified into CMG 0110 (without
comorbidities). The unadjusted federal
prospective payment rate for CMG 0110
(without comorbidities) appears in
Table 5.
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Payment rate
no comorbidity
10,899.78
14,027.24
17,280.37
22,903.83
11,077.28
14,146.62
17,055.75
22,110.58
8,601.70
11,135.40
14,894.33
13,409.92
15,954.62
19,382.10
10,513.36
13,446.05
16,893.95
21,386.44
11,623.92
15,085.96
21,306.33
13,889.01
19,911.46
12,929.25
15,023.13
19,144.91
12,577.40
17,274.09
21,296.91
9,464.07
12,897.84
15,374.99
19,444.93
11,680.47
14,756.10
17,995.08
22,424.74
12,059.03
15,731.56
19,697.83
12,049.61
15,137.80
18,042.21
23,174.01
12,965.38
18,282.54
28,767.63
14,347.69
26,067.43
42,906.40
9,872.48
12,944.96
16,183.95
20,772.26
21,697.46
2,489.72
10,657.88
26,084.70
12,569.54
33,300.96
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
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mstockstill on DSK3G9T082PROD with RULES3
Hospital (DSH) percentage of 5 percent
(which would result in a LIP adjustment
of 1.0156), a wage index of 0.8297, 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
0.8756, and a teaching status adjustment
of 0.0784.
To calculate each IRF’s labor and nonlabor portion of the federal prospective
payment, we begin by taking the
unadjusted federal prospective payment
rate for CMG 0110 (without
comorbidities) from Table 5. Then, we
multiply the labor-related share for FY
2017 (70.9 percent) described in section
VI.C. of this final rule by the unadjusted
federal prospective payment rate. To
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Jkt 238001
determine the non-labor portion of the
federal prospective payment rate, we
subtract the labor portion of the federal
payment from the unadjusted federal
prospective payment.
To compute the wage-adjusted federal
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 CMS Web site at https://
www.cms.hhs.gov/Medicare/MedicareFee-for-Service-Payment/
InpatientRehabFacPPS/. The resulting
figure is the wage-adjusted labor
amount. Next, we compute the wageadjusted federal payment by adding the
wage-adjusted labor amount to the nonlabor portion.
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Adjusting the wage-adjusted federal
payment by the facility-level
adjustments involves several steps.
First, we take the wage-adjusted federal
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 federal prospective payment
rates. Table 6 illustrates the components
of the adjusted payment calculation.
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ER05AU16.008
52078
Federal Register / Vol. 81, No. 151 / Friday, August 5, 2016 / Rules and Regulations
Thus, the adjusted payment for
Facility A would be $34,236.98 and the
adjusted payment for Facility B would
be $34,192.08.
VII. Update to Payments for High-Cost
Outliers Under the IRF PPS
mstockstill on DSK3G9T082PROD with RULES3
A. Update to the Outlier Threshold
Amount for FY 2017
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
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 2016 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, and
77 FR 44618, 78 FR 47860, 79 FR 45872,
80 FR 47036, 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
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appropriate to maintain the 3 percent
target.
To update the IRF outlier threshold
amount for FY 2017, we proposed to use
FY 2015 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 2016. 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 2.8 percent in FY 2016.
Therefore, we proposed to update the
outlier threshold amount from $8,658
for FY 2016 to $8,301 for FY 2017 to
maintain estimated outlier payments at
approximately 3 percent of total
estimated aggregate IRF payments for
FY 2017.
We note that, as we typically do, we
updated our data between the FY 2017
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
2015. Based on our analysis using this
updated data, we now estimate that IRF
outlier payments as a percentage of total
estimated payments are approximately
2.7 percent in FY 2016. Therefore, we
will update the outlier threshold
amount from $8,658 for FY 2016 to
$7,984 for FY 2017 to maintain
estimated outlier payments at
approximately 3 percent of total
estimated aggregate IRF payments for
FY 2017.
We received 7 public comments on
the proposed update to the FY 2017
outlier threshold amount to maintain
estimated outlier payments at
approximately 3 percent of total
estimated IRF payments, which are
summarized below.
Comment: Commenters, while
supportive of maintaining estimated
payments for outlier payments at
approximately 3 percent, suggested that
CMS review its methodology for setting
the outlier threshold amount and
modify as needed so that the full 3
percent is paid as outlier payments.
Some commenters suggested
implementing a forecast error correction
if the full amount of the outlier pool is
not paid out.
Response: 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,
thereby, promote access to care for
patients who are likely to require
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52079
unusually high-cost care. As we have
indicated in previous IRF PPS final
rules, we do not make adjustments to
IRF PPS payment rates for the sole
purpose of accounting for differences
between projected and actual outlier
payments. We use the best available
data at the time to establish an outlier
threshold for IRF PPS payments prior to
the beginning of each fiscal year to help
ensure that estimated outlier payments
for that fiscal year will equal 3 percent
of total estimated IRF PPS payments.
We analyze expenditures annually, and
if there is a difference from our
projection, that information is used to
make a prospective adjustment to lower
or raise the outlier threshold for the
upcoming fiscal year. We believe a
retrospective adjustment would not be
appropriate, given that we do not
recoup or make excess payments to
hospitals.
If outlier payments for a given year
turn out to be greater than projected, we
do not recoup money from hospitals; if
outlier payments for a given year are
lower than projected, we do not make
an adjustment to account for the
difference. Payments for a given
discharge in a given fiscal year are
generally intended to reflect or address
the prospective average costs of that
discharge in that year; that goal would
be undermined if we adjusted IRF PPS
payments to account for
‘‘underpayments’’ or ‘‘overpayments’’ in
IRF outliers in previous years.
Comment: One commenter
recommended that we expand the
outlier pool from 3 percent to 5 percent
in order to ensure that payments are
more equitably distributed within the
IRF payment system. However, this
same commenter noted that such an
expansion in the outlier pool could
inappropriately reward facilities for
inefficiencies. Several other commenters
stated that expanding the outlier pool
would be inappropriate for this same
reason.
Response: We refer readers to the
2002 IRF PPS final rule (66 FR 41316,
41362 through 41363), for a discussion
of 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. 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
of caring for high-cost patients, while
still providing for adequate payments
for all other (non-high cost outlier)
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mstockstill on DSK3G9T082PROD with RULES3
cases. We believe that the outlier policy
of 3 percent of total estimated payments
optimizes the extent to which we can
encourage facilities to continue to take
patients that are likely to have
unusually high costs, while still
providing adequate payment for all
other cases. Increasing the outlier pool
would leave less money available to
cover the costs of non-outlier cases, due
to the fact that we would implement
such a change in a budget-neutral
manner. We believe that our current
outlier policy, to set outlier payments at
3 percent of total payments, is
consistent with the statute and the goals
of the prospective payment system.
Comment: Several commenters
recommended that CMS impose a cap
on the amount of outlier payments an
individual IRF can receive under the
IRF PPS.
Response: Comments regarding the
amount of outlier payments an
individual IRF can receive are outside
the scope of this rule. However, any
future consideration given to imposing
a limit on outlier payments would have
to be carefully analyzed and would need
to take into account any effect on access
to IRF care it would have for certain
high-cost populations.
Final Decision: 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 $7,984 to maintain estimated outlier
payments at approximately 3 percent of
total estimated aggregate IRF payments
for FY 2017. This update is effective
October 1, 2016.
B. Update to the IRF Cost-to-Charge
Ratio Ceiling and Urban/Rural Averages
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 2017, 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 2017,
as discussed below.
• Other IRFs for which accurate data
to calculate an overall CCR are not
available.
Specifically, for FY 2017, we
proposed to estimate a national average
CCR of 0.562 for rural IRFs, which we
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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 0.435 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. We used
FY 2013 IRF cost report data for the
proposed rule. (Please note that we
erroneously stated in the proposed rule
that we used FY 2014 cost report data.)
For this final rule, we have used the
most recent available cost report data
(FY 2014). This includes all IRFs whose
cost reporting periods begin on or after
October 1, 2013, and before October 1,
2014. If, for any IRF, the FY 2014 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 2013) 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 the
updated FY 2014 cost report data for
this final rule, we estimate a national
average CCR of 0.522 for rural IRFs, and
a national average CCR of 0.421 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 1.36 for FY
2017. This means that, if an individual
IRF’s CCR were to exceed this proposed
ceiling of 1.36 for FY 2017, 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.
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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 2014 cost
report data for this final rule, we
estimate a national average CCR ceiling
of 1.29, using this same methodology.
We did not receive any comments on
the proposed update to the IRF CCR
ceiling and the urban/rural averages for
FY 2017.
Final Decision: As we did not receive
any comments on the proposed updates
to the IRF CCR ceiling and the urban/
rural averages for FY 2017, we are
finalizing the national average urban
CCR at 0.421, the national average rural
CCR at 0.522, and the national CCR
ceiling at 1.29 for FY 2017. These
updates are effective October 1, 2016.
VIII. Revisions and Updates to the IRF
Quality Reporting Program (QRP)
A. Background and Statutory Authority
We seek to promote higher quality
and more efficient health care for
Medicare beneficiaries, and our efforts
are furthered by QRPs coupled with
public reporting of that information.
Section 3004(b) of the Affordable Care
Act amended section 1886(j)(7) of the
Act, requiring the Secretary to establish
the IRF QRP. This program applies to
freestanding IRFs, as well as IRF units
affiliated with either acute care facilities
or critical access hospitals (CAHs).
Beginning with the FY 2014 payment
determination and subsequent years, the
Secretary is required to reduce any
annual update to the standard federal
rate for discharges occurring during
such fiscal year by 2 percentage points
for any IRF that does not comply with
the requirements established by the
Secretary. Section 1886(j)(7) of the Act
requires that for the FY 2014 payment
determination and subsequent years,
each IRF submit data on quality
measures specified by the Secretary in
a form and manner, and at a time,
specified by the Secretary. For more
information on the statutory history of
the IRF QRP, please refer to the FY 2015
IRF PPS final rule (79 FR 45908).
The Improving Medicare Post-Acute
Care Transformation Act of 2014
(IMPACT Act) imposed new data
reporting requirements for certain PAC
providers, including IRFs. For
information on the statutory background
of the IMPACT Act, please refer to the
FY 2016 IRF PPS final rule (80 FR 47080
through 47083).
In the FY 2016 IRF PPS final rule, we
reviewed general activities and finalized
the general timeline and sequencing of
such activities that will occur under the
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IRF QRP. For further information, please
refer to the FY 2016 IRF PPS final rule
(80 FR 40708 through 47128). In
addition, we established our approach
for identifying cross-cutting measures
and process for the adoption of
measures, including the application and
purpose of the Measures Application
Partnership (MAP) and the notice-andcomment rulemaking process (80 FR
47080 through 47084). For information
on these topics, please refer to the FY
2016 IRF PPS final rule (80 FR 47080).
B. General Considerations Used for
Selection of Quality, Resource Use, and
Other Measures for the IRF QRP
For a detailed discussion of the
considerations we use for the selection
of IRF QRP quality measures, such as
alignment with the CMS Quality
Strategy,1 which incorporates the 3
broad aims of the National Quality
Strategy,2 please refer to the FY 2015
IRF PPS final rule (79 FR 45911) and the
FY 2016 IRF PPS final rule (80 FR 47083
through 47084). Overall, we strive to
promote high quality and efficiency in
the delivery of health care to the
beneficiaries we serve. Performance
improvement leading to the highestquality health care requires continuous
evaluation to identify and address
performance gaps and reduce the
unintended consequences that may arise
in treating a large, vulnerable, and aging
population. QRPs, coupled with public
reporting of quality information, are
critical to the advancement of health
care quality improvement efforts. Valid,
reliable, relevant quality measures are
fundamental to the effectiveness of our
QRPs. Therefore, selection of quality
measures is a priority for us in all of our
QRPs.
In the IRF PPS FY 2017 proposed rule
(81 FR 24178), we proposed to adopt for
the IRF QRP one measure that we are
specifying under section 1899B(c)(1) of
the Act to meet the Medication
Reconciliation domain, that is, Drug
Regimen Review Conducted with
Follow-Up for Identified Issues-Post
Acute Care Inpatient Rehabilitation
Facility Quality Reporting Program.
Further, we proposed to adopt for the
IRF QRP three measures to meet the
resource use and other measure
domains identified in section
1899B(d)(1) of the Act. These measures
include: (1) Total Estimated Medicare
Spending per Beneficiary: Medicare
Spending per Beneficiary-Post Acute
1 https://www.cms.gov/Medicare/QualityInitiatives-Patient-Assessment-Instruments/
QualityInitiativesGenInfo/CMS-QualityStrategy.html.
2 https://www.ahrq.gov/workingforquality/nqs/
nqs2011annlrpt.htm.
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Care Inpatient Rehabilitation Facility
Quality Reporting Program; (2)
Discharge to Community: Discharge to
Community-Post Acute Care Inpatient
Rehabilitation Facility Quality
Reporting Program, and (3) Potentially
Preventable 30-Day Post-Discharge
Readmission Measure for Inpatient
Rehabilitation Facility Quality
Reporting Program. We also proposed
an additional measure, which is not
required under the IMPACT Act: (4)
Potentially Preventable Within Stay
Readmission Measure for Inpatient
Rehabilitation Facilities.
In our development and specification
of measures, we employed a transparent
process in which we seek input from
stakeholders and national experts and
engage in a process that allows for prerulemaking input on each measure, as
required by section 1890A of the Act. To
meet this requirement, we provided the
following opportunities for stakeholder
input: Our measure development
contractor convened technical expert
panels (TEPs) that included stakeholder
experts and patient representatives on
July 29, 2015, for the Drug Regimen
Review Conducted with Follow-Up for
Identified Issues measures; on August
25, 2015, September 25, 2015, and
October 5, 2015, for the Discharge to
Community measures; on August 12 and
13, 2015, and October 14, 2015, for the
Potentially Preventable 30-Day PostDischarge Readmission Measures and
Potentially Preventable Within Stay
Readmission Measure for IRFs; and on
October 29 and 30, 2015, for the
Medicare Spending per Beneficiary
(MSPB) measures. In addition, we
released draft quality measure
specifications for public comment for
the Drug Regimen Review Conducted
with Follow-Up for Identified Issues
measures from September 18, 2015, to
October 6, 2015; for the Discharge to
Community measures from November 9,
2015, to December 8, 2015; for the
Potentially Preventable 30-Day PostDischarge Readmission Measure for
IRFs and Potentially Preventable Within
Stay Readmission Measure for IRFs from
November 2, 2015 to December 1, 2015;
and for the MSPB measures from
January 13, 2016 to February 5, 2016.
We implemented a public mailbox,
PACQualityInitiative@cms.hhs.gov, for
the submission of public comments.
This PAC mailbox is accessible on our
post-acute care quality initiatives Web
site at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-of-2014-Data-
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Standardization-and-Cross-SettingMeasuresMeasures.html.
Additionally, we sought public input
from the NQF-convened MAP PostAcute Care, Long-Term Care Workgroup
during the annual in-person meeting
held December 14 and 15, 2015. The
MAP, composed of multi-stakeholder
groups, is tasked to provide input on the
selection of quality and efficiency
measures described in section
1890(b)(7)(B) of the Act.
The MAP reviewed each IMPACT
Act-related measure, as well as other
quality measures proposed in this rule
for use in the IRF QRP. For more
information on the MAP’s
recommendations, please refer to the
MAP 2016 Final Recommendations to
HHS and CMS public report at https://
www.qualityforum.org/Publications/
2016/02/MAP_2016_Considerations_
for_Implementing_Measures_in_
Federal_Programs_-_PAC-LTC.aspx.
For measures that do not have NQF
endorsement, or which are not fully
supported by the MAP for use in the IRF
QRP, we proposed for the IRF QRP for
the purposes of satisfying the measure
domains required under the IMPACT
Act, measures that closely align with the
national priorities identified in the
National Quality Strategy (https://
www.ahrq.gov/workingforquality/) and
for which the MAP supports the
measure concept. Further discussion as
to the importance and high-priority
status of these proposed measures in the
IRF setting is included under each
quality measure in this final rule.
Although we did not solicit feedback
on General Considerations Used for
Selection of Quality, Resource Use, and
Other Measures for the IRF QRP, we
received a number of comments, which
are summarized with our responses
below.
Comment: One commenter supported
CMS’s intention to select measures that
are already incorporated in various
quality reporting programs to minimize
burden. One commenter commented
that CMS should recognize burden of
data collection and focus on measures
that are the most clinically relevant and
actionable to the facility and patients.
Additionally, the commenter
recommended that CMS use minimum
standards in the development of new
measures so that they are as clear and
consistent across facilities as possible.
Response: We appreciate the
commenters’ support of CMS’s intention
to select measures that are already
incorporated in the various quality
reporting programs to minimize burden.
In addition, we note that we strive to
strike a balance between minimizing
burden and addressing gaps in quality
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of care as we continue to expand the IRF
QRP. We interpret the commenter’s
suggestion that CMS apply minimum
standards in its measure development to
suggest that we simplify our approach to
quality measure development itself. We
will take these recommendations into
consideration in our future measure
development.
We also received several comments
related to the proposed measures, the
IMPACT Act, NQF endorsement, the
NQF MAP review process, and the use
of TEPs, which are addressed below.
Comment: We received several
comments supporting the goals of the
IMPACT Act and the implementation of
cross-setting measures across PAC
settings as required by the IMPACT Act.
One commenter appreciated the use of
TEPs and input of stakeholders. These
commenters noted the importance of
functional status measures and
recommended that CMS include
additional functional status measures in
future iterations. Also, one of the
commenters indicated that achieving
standardized and interoperable patient
assessment data will allow for better
cross-setting comparisons of quality and
will support the development of better
quality measures with uniform risk
standardization.
Response: We believe that
standardizing patient assessment data
will allow for the exchange of data
among PAC providers in order to
facilitate care coordination and improve
patient outcomes. We appreciate the
importance of functional status
measures and will consider inclusion of
additional measures. As with our
measure development process, we will
continue to use TEPs, public comments,
open door forums, and the prerulemaking process in order to gather
stakeholder input on all measures under
development.
Comment: One commenter
recommended that CMS seek an
increased level of patient engagement in
order to discern what quality measures
are of greatest value to patients.
Response: We value the patient
perspective in the measure development
process. We have employed a
transparent process in which we seek
input from stakeholders, as described
earlier. We have also have taken several
steps to engage stakeholders, including
patients, in all TEPs, public comments,
and special open door forums. In
addition, a summary of the IMPACT Act
measure TEP proceedings, public
comments, and special open door
forums is available on the PAC Quality
Initiatives Downloads and Videos Web
site 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 Patient engagement is a
priority for CMS, and we will continue
to take steps to include the patient
perspective, especially with regard to
assembling TEP, which review and
comment on our measure development
activities.
Comment: Several commenters
recommended that CMS delay
implementation of proposed measures
until NQF has completed its review and
has endorsed measures that are
appropriate for the specific
characteristics of the IRF patient
population. A few commenters
suggested that CMS seek NQF’s formal
consensus development process instead
of a time-limited endorsement, as it was
perceived that the time-limited
endorsement was not sufficient.
Response: We received several
comments regarding the NQF
endorsement status for the proposed
measures, and acknowledge the
commenters’ recommendation to submit
the measures to the NQF prior to
implementation. We consider and
propose appropriate measures that have
been endorsed by the NQF whenever
possible. However, when this is not
feasible because there is no NQFendorsed measure, we utilize our
statutory authority that allows the
Secretary to specify a measure for the
IRF QRP that is not NQF-endorsed
where, as in the case for the proposed
measures, we have not been able to
identify other measures that are
endorsed or adopted by a consensus
organization. While we appreciate the
importance of consensus endorsement
and intend to seek such endorsement,
we must balance the need to address
gaps in quality and adhere to statutorily
required timelines as in the case of the
quality and resource use measures that
we proposed to address the IMPACT
Act. In regard to the comments
surrounding time-limited endorsement,
NQF uses time-limited endorsement for
measures that meet all of the NQF’s
endorsement criteria with the exception
of field testing and are critical to
advancing quality improvement. When
measures are granted this two-year
endorsement rather than the traditional
three-year period, measure developers
must test the measure and return results
to NQF within the two-year window to
maintain the endorsement. We wish to
clarify that we have not yet sought
endorsement of the proposed measures,
time-limited or otherwise.
Comment: Several commenters stated
the NQF MAP committee did not
endorse the proposed measures; instead,
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the commenters recommended that
CMS delay measure implementation
until the measures are fully developed
and tested and brought back to the NQF
for further consideration. One
commenter further stated that TEP
members and other stakeholders who
provided feedback in the measure
development process did not support
measures moving forward without
further testing.
Response: We interpret this comment
to address the activities of the Measures
Application Partnership, a multistakeholder partnership convened by
NQF that provides input to the U.S.
Department of Health and Human
Services (HHS) on its selection of
measures for certain Medicare programs.
We would like to clarify that the MAP
‘‘encouraged continued development’’
for the proposed measures. According to
the MAP, the term ‘‘encourage
continued development’’ is applied
when a measure addresses a critical
program objective or promotes
alignment, but is in an earlier stage of
development. In contrast, the MAP uses
the phrase ‘‘do not support’’ when it
does not support the measure at all.
Since the MAP recommendation of
‘‘encourage continued development’’ for
the proposed measures during the
December 2015 NQF-convened PAC
LTC MAP meeting, further refinement of
measure specifications and testing of
measure validity and reliability have
been performed. These efforts have
included: A pilot test in 12 post-acute
care settings, including IRFs, to
determine the feasibility of assessment
items for use in calculation of the Drug
Regimen Review Conducted with
Follow-Up for Identified Issues
measure, and further development of
the risk-adjusted models for the
Discharge to Community, Medicare
Spending per Beneficiary, Potentially
Preventable Readmissions, and
Potentially Preventable Within Stay
Readmissions Measure for Inpatient
Rehabilitation Facilities measures.
Additional information regarding testing
is further described in the specific
measure sections. Additional
information regarding testing that was
performed since the MAP Meeting, TEP
meetings, and public comment periods
is further described below in our
responses to comments on individual
proposed measures.
For these reasons, we believe that the
measures have been fully and robustly
developed, and believe they are
appropriate for implementation and
should not be delayed.
Comment: Several commenters,
including MedPAC, expressed concern
regarding the standardization and
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interoperability of the proposed
measures as they perceived the
measures to have different inclusion/
exclusion criteria, episode constructions
and risk factors, and therefore do not
meet the mandate of the IMPACT Act.
The commenters expressed further
concern about future implications of
such variations and recommend
delaying implementation until measures
are standardized and interoperable
across PAC settings. One commenter
further indicated that the measure
names were different for each setting,
pointing out the words ‘‘IRF QRP’’ or
‘‘Inpatient Rehabilitation Facility’’ were
included in the measures’ titles to
designate a difference in the measure in
each setting. One commenter stated
implementing the quality measures in
an unstandardized fashion would result
in additional costs in the future for
aligning measures between PAC
providers.
MedPAC suggested that the measures
use uniform definitions, specifications,
and risk-adjustment methods, conveying
that findings from their work on a
unified PAC payment system suggest
overlap or similar care provided for
Medicare beneficiaries with similar
needs across PAC settings. As a result of
this work, MedPAC recommended that
the IMPACT Act measures be
standardized to facilitate quality
comparison across PAC settings to
inform Medicare beneficiary choice and
provide an opportunity for CMS to
evaluate the value of PAC services,
noting that differences in rates should
reflect differences in quality of care
rather than differences in the way rates
are constructed.
Response: We wish to clarify that the
IMPACT Act requires that the patient
assessment instruments be modified to
enable the submission of standardized
data, for purposes such as
interoperability. However, measures
themselves are not ‘‘interoperable.’’
CMS, in collaboration with our
measure contractors, developed the
proposed measures with the intent to
standardize the measure methodology
so that we are able to detect variation
among PAC providers in order to be able
to assess differences in quality of care.
For example, the proposed patient
assessment-based quality measure, Drug
Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF
QRP, was developed across PAC settings
with uniform definitions and
specifications. This measure is not risk
adjusted. The standardized
development of this assessment-based
measure follows the mandate of the
IMPACT Act to develop standardized
patient assessment-based measures for
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the four PAC settings (section
1899B(c)(1) of the Act). The resource
use and other measures, Discharge to
the Community-PAC IRF QRP and AllCondition Risk-Adjusted Potentially
Preventable Hospital Readmissions
Rates—PAC IRF QRP were developed to
be uniform across the PAC settings in
terms of their definitions, measure
calculations, and risk-adjustment
approach where applicable. However,
there is variation in each measure
primarily due to the data sources for
each PAC setting. Further, the riskadjustment approach for the resource
use and other IMPACT Act measures is
aligned, but is tailored to each measure
based on measure testing results.
Adjusting for relevant case-mix
characteristics in each setting improves
the validity and explanatory power of
risk adjustment models, and helps
ensure that any differences in measure
performance reflect differences in the
care provided rather than differences in
patient case-mix. We employ this
approach to measure development to
enable appropriate cross-setting
comparisons in PAC settings and to
maximize measure reliability and
validity. It should be noted that sections
1899B(c)(3)(B) and 1899B(d)(3)(B) of the
Act require that quality measures and
resource use and other measures be risk
adjusted, as determined appropriate by
the Secretary.
Comment: Several commenters
expressed concerns regarding the
validity and reliability of IMPACT Act
measures and encouraged CMS to
conduct further analysis of data to
ensure comparability across post-acute
care settings, prior to implementation
and public reporting of data.
Response: We have tested for validity
and reliability all of the IMPACT Act
measures, and the results of that testing
is available at: https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/IRF-QualityReporting/IRF-Quality-ReportingProgram-Measures-Information-.html.
We intend to continue to monitor the
reliability and validity of the IRF QRP
measures, including whether the
measures are reliable and valid for
cross-setting purposes.
Comment: A few commenters voiced
concern regarding the burden of
implementing the proposed measures in
the IRF setting. One commenter
requested that CMS proceed cautiously
to ensure new measures are associated
with minimal administrative and data
collection burden. One commenter
expressed concern that the new
measures increase provider burden by
increasing the time providers are
ensuring data accuracy and move the
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focus away from patient-centered care
towards a more metric-based focus.
Response: We appreciate the
importance of avoiding undue burden
on providers and will continue to
evaluate and consider any unnecessary
burden associated with the
implementation of the IRF QRP. We
wish to note that the three proposed
resource measures are claims-based, and
will require no additional data
collection by providers and thus result
in minimal increases in burden. The
measure, Drug Regimen Review
Conducted with Follow-Up for
Identified Issues, is calculated using
assessment data and requires the
addition of three items to the IRF–PAI,
also requiring minimal additional
burden. We address the issue of burden
further under section XI.B. of this final
rule.
Comment: Several commenters
recommended that CMS engage in
several activities which would afford
greater transparency with stakeholders
regarding proposed measure
development. These commenters also
requested that measures undergo field
testing with providers prior to
implementation. Commenters also
requested that more detailed measure
specifications be posted in order to
enable providers to evaluate measure
design decisions. Commenters requested
that IRF providers be provided with
confidential preview reports as a part of
a ‘‘dry run’’ process as this would
enable providers to review data and
provide CMS with feedback on potential
technical issues with proposed measure.
Finally, the commenters requested that
measure data be provided to IRFs on a
patient level on a quarterly basis,
similar to other quality reporting
programs, in order to make effective use
of the data and improve performance.
Response: With regard to the testing
and analytic results provided for this
measure, since the December 2015 MAP
meeting, further refinement of measure
specifications and testing of measure
validity and reliability have been
performed.
We direct readers to the Measure
Specifications for Measures Adopted in
the FY 2017 IRF QRP final rule are
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/IRF-QualityReporting/IRF-Quality-ReportingProgram-Measures-Information-.html,
which include detailed information
regarding measure specifications,
including results of the final risk
adjustment models for the resource use
measures. For resource use measures,
our testing results are within range for
similar outcome measures finalized in
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public reporting and value-based
purchasing programs, including the AllCause Unplanned Readmission Measure
for 30 Days Post Discharge from IRFs
(NQF #2502), previously adopted into
the IRF QRP.
We appreciate the comment
requesting that we provide performance
data on IRF QRP measures on a more
frequent, such as quarterly, basis in
order to promote quality improvement.
We wish to note that the proposed
claims-based measures are based on 2
consecutive years of data in order to
ensure a sufficient sample size to
reliably assess IRFs’ performance.
However, we will investigate the
feasibility and usability of providing
IRFs with information more frequently,
such as unadjusted counts of PPRs and
discharge data. We also appreciate the
commenters’ suggestions related to the
implementation of dry run activities,
such as confidential reports, for the
purposes of identifying any technical
issues prior to public reporting, as was
successfully provided in the fall of 2015
for the All Cause Unplanned
Readmission Measure for 30 Days Post
Discharge from IRFs (NQF#2502). We
wish to note that we intend to provide
confidential feedback reports beginning
in October, 2017, as described in section
VIII.O of this final rule, and we believe
that the reports could serve as an
opportunity for providers to extend to
us any technical issues they may
discover. We note that, as described in
section VIII.P of this final rule, we are
unable at this time to provide patientlevel information for the claims-based
measure, for example, the readmission
measures, because such data comes
from a separate entity. Finally, we wish
to note that we intend to continue
refining specifications, and we will
consider pilot testing in addition to the
performance testing that we currently
conduct.
C. Policy for Retention of IRF QRP
Measures Adopted for Previous Payment
Determinations
In 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 68507), we adopted a
policy that allows any quality measure
adopted for use in the IRF QRP to
remain in effect until the measure was
actively removed, suspended, or
replaced, when we initially adopt a
measure for the IRF QRP for a payment
determination. For the purpose of
streamlining the rulemaking process,
when we initially adopt a measure for
the IRF QRP for a payment
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determination, this measure will also be
adopted for all subsequent years or until
we remove, suspend, or replace the
measure. For further information on
how measures are considered for
removal, suspension, or replacement,
please refer to the CY 2013 OPPS/ASC
final rule (77 FR 68500). We did not
propose any changes to the policy for
retaining IRF QRP measures adopted for
previous payment determinations.
D. Policy for Adopting Changes to IRF
QRP Measures
In the CY 2013 OPPS/ASC final rule
(77 FR 68500 through 68507), we
adopted a subregulatory process to
incorporate NQF updates to IRF quality
measure specifications that do not
substantively change the nature of the
measure. Substantive changes will be
proposed and finalized through
rulemaking. For further information on
what constitutes a substantive versus a
nonsubstantive change and the
subregulatory process for
nonsubstantive changes, please refer to
the CY 2013 OPPS/ASC final rule (77
FR 68500). We did not propose any
changes to the policy for adopting
changes to IRF QRP measures.
E. Quality Measures Previously
Finalized for and Currently Used in the
IRF QRP
A history of the IRF QRP quality
measures adopted for the FY 2014
payment determinations and subsequent
years is presented in Table 7. The year
in which each quality measure was first
adopted and implemented, and then
subsequently re-proposed or revised, if
applicable, is displayed. The initial and
subsequent annual payment
determination years are also shown in
Table 7. For more information on a
particular measure, please refer to the
IRF PPS final rule and associated page
numbers referenced in Table 7.
Although we did not solicit feedback,
we received a number of comments
about previously finalized measures for
and currently used in the IRF QRP.
These comments are summarized and
addressed below.
Comment: One commenter was
generally supportive of implementing
additional quality measures in postacute care, especially those that are
cross-setting, but recommended that
CMS take steps to validate data and
assess provider experience during the
first several months of reporting. One
commenter supported the retention of
the NHSN measures.
With regard to the measure, Pressure
Ulcers that are New or Worsened (ShortStay) (NQF #0678), several commenters
recommended that future updates to the
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measure include clinical guidance that
is consistent with the most current
evidence-based processes.
We received several comments about
the NHSN Facility-Wide Inpatient
Hospital-Onset Clostridium difficile
Infection (CDI) Outcome Measure (NQF
#1717). Several commenters
recommended that CMS revise the
measure so that it is only reported at the
first site of discovery, to avoid
penalizing IRFs for the presence of the
infection that started in a previous care
setting.
With regard to the measure,
Application of Percent of Residents
Experiencing One or More Falls with
Major Injury (NQF #0674), one
commenter had concerns that the nature
of IRF treatment could lead to a
frequency of falls higher than other
settings. The commenter was concerned
that including assisted falls in the
definition of falls for this quality
measure was inappropriate and
confusing and recommended that CMS
revisit the definition and include only
falls with major injury.
Response: With regard to the measure
Pressure Ulcers that are New or
Worsened (Short-Stay) (NQF #0678), we
intend to continue our ongoing measure
development and refinement activities
to inform the ongoing evaluation of this
measure, to ensure that the measure
remains valid and reliable to inform
quality improvement within and across
each PAC setting, and to fulfill the
public reporting goals of quality
reporting programs, including the IRF
QRP. Reviewing the most current
evidence-based clinical guidance is part
of that process. With regard to the
comments about the NHSN FacilityWide Inpatient Hospital-Onset CDI
Outcome Measure (NQF #1717), the
scope of NQF#1717 extends to acute
care hospitals, long-term care hospitals,
inpatient rehabilitation facilities, and
cancer hospitals. The same measure
specifications are used by all these
facility types to report Clostridium
difficile Laboratory Identified events to
NHSN, and these measure specifications
differentiate between community-onset
events, which include events that had
their onset at another healthcare facility,
from healthcare-associated events,
which are attributed to the facility
reporting the event. CDC reports only
incident healthcare-associated events on
behalf of healthcare facilities to CMS.
To limit Clostridium difficile Laboratory
Identified event reporting to the first site
of discovery offers opportunity for
missed ‘‘true’’ healthcare-associated
events (those recognized on or after
hospital day 4) and would require
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additional data collection and
investigation burden to users.
The measure specifications for
NQF#1717, by design, align with the
NHSN LabID Event protocol, which was
developed to require minimal
investigation on the part of facilities and
to provide a proxy measure of infection.
Dates of admission and specimen
collection are required and can easily be
collected via electronic methods and
identified as healthcare-associated (HO)
or community-onset (CO). To require a
facility to determine if a CDI LabID
Event had been identified in another
facility would call for manual review of
medical records and potential
communication with transferring
facilities. In accordance with protocol
guidelines, IRF-based events are
categorized as ‘‘incident’’ (first nonduplicate event for the IRF) in addition
to a CO/HO categorization. IRF facilities
are analyzed independently of any other
reporting facility, that is, are viewed as
separate reporting facilities.
With regard to the measure, An
Application of Percent of Residents
Experiencing One or More Falls with
Major Injury (Long Stay) (NQF #0674),
we would like to clarify that the quality
measure adopted for the IRF QRP
includes only falls with a major injury,
satisfying the IMPACT Act domain,
Incidence of Major Falls. Thus, falls
with no injury, such as those that may
be considered near-falls, are not
included in the measure.
Additionally, we received a number
of comments specifically regarding
quality measures that were finalized
into the IRF QRP in the FY 2016 IRF
PPS final rule.
Comment: Many commenters
indicated they had concerns about the
use of CARE items or the use of the
CARE Tool. Several commenters were
concerned that the CARE items added to
the IRF–PAI would be duplicative and
confusing to clinicians because they are
similar to the FIM® items. One
commenter suggested the FIM® items be
removed from the IRF–PAI. Other
commenters supported continued use of
the FIM® instrument, and recommended
a delay in implementing the CARE
items. The commenters also had
concerns about the precision of the
CARE items and the patient types with
which it was tested, the timeframe and
six-point scale, as well as NQFendorsement of CARE items in all
settings. Commenters noted that the
FIM® instrument has demonstrated
increased efficiency and decreased
length of stay, and allows for
comparison of functional gains across
patients with similar debility levels.
Commenters had concerns about lack of
credentialing of staff for CARE items, as
this is currently required for the FIM®
instrument to ensure consistent scoring.
Several commenters were concerned
about the training, data submission
specifications, and support CMS has
provided for items being required on the
IRF–PAI Version 1.4, effective October
1, 2016. Several commenters were
concerned that the data were collected
for research purposes. One commenter
indicated there was a discrepancy
between the IRF–PAI Training Manual
and the data submission specifications.
Many commenters had concerns about
the need for further clarification about
the patient’s usual status, and another
commenter requested clarification about
the use of a dash to indicate that an item
was not assessed.
Response: As we did not propose any
changes to the quality measures
finalized in the FY 2016 IRF PPS final
rule, these comments are outside the
scope of the proposed rule. However,
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we would like to clarify that we are not
implementing the CARE Tool for the
IRF QRP to meet the mandate of the
IMPACT Act. To meet the mandate, and
to standardize quality measures and
data items, we retained the use of the
IRF–PAI as the collection instrument for
all IRF settings. We incorporated items
from the CARE Tool into new section
GG: Functional Abilities and Goals of
the IRF–PAI Version 1.4 in order to
calculate the 5 function quality
measures that were adopted into the IRF
QRP in the IRF PPS FY 2016 Final Rule
(80 FR 47100 through 47120). The items
were not added to the IRF–PAI for
research purposes.
We refer the readers to the FY 2016
final rule (80 FR 47100 through 47120)
for discussion about the testing,
including the rating scale, reliability,
validity and sensitivity of the function
items that were added to the IRF–PAI,
as well as plans for ongoing evaluation
of these items, and concerns related to
FIM® item duplication. With regard to
training and provider support, we agree
with the importance of thorough and
comprehensive training. Information
about and materials from each IRF QRP
training are posted on the IRF–QRP
Training Web site at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/
Training.html. With regard to the
comments related to the data
specifications, we post data
specifications and errata on the CMS
Web site as soon as we are able so that
vendors and providers are able to
review and understand the valid data
codes for all items and the associated
requirements: https://www.cms.gov/
Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/
Software.html.
TABLE 7—QUALITY MEASURES PREVIOUSLY FINALIZED FOR AND CURRENTLY USED IN THE IRF QUALITY REPORTING
PROGRAM
Annual payment
determination: Initial and
subsequent APU years
Final rule
Data collection
start date
National Healthcare Safety Network (NHSN)
Catheter-Associated Urinary Tract Infection
(CAUTI) Outcome Measure (NQF #0138).
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Measure title
Adopted an application of the measure in FY
2012 IRF PPS Final Rule (76 FR 47874
through 47886).
Adopted the NQF-endorsed version and expanded measure (with standardized infection ratio) in CY 2013 OPPS/ASC Final
Rule (77 FR 68504 through 68505).
Adopted application of measure in FY 2012
IRF PPS final rule (76 FR 47876 through
47878).
Adopted a non-risk-adjusted application of
the NQF-endorsed version in CY 2013
OPPS/ASC Final Rule (77 FR 68500
through 68507).
October 1, 2012
FY 2014 and subsequent
years.
January 1, 2013
FY 2015 and subsequent
years.
October 1, 2012
FY 2014 and subsequent
years.
January 1, 2013
FY 2015 and subsequent
years.
Percent of Residents or Patients with Pressure Ulcers That Are New or Worsened
(Short Stay) (NQF #0678).
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TABLE 7—QUALITY MEASURES PREVIOUSLY FINALIZED FOR AND CURRENTLY USED IN THE IRF QUALITY REPORTING
PROGRAM—Continued
Annual payment
determination: Initial and
subsequent APU years
Final rule
Data collection
start date
Adopted the risk adjusted, NQF-endorsed
version in FY 2014 IRF PPS Final Rule
(78 FR 47911 through 47912).
Adopted in the FY 2016 IRF PPS final rule
(80 FR 47089 through 47096) to fulfill IMPACT Act requirements.
Adopted in FY 2014 IRF PPS final rule (78
FR 47906 through 47911).
October 1, 2014
FY 2017 and subsequent
years.
October 1, 2015
FY 2018 and subsequent
years.
October 1, 2014
FY 2017 and subsequent
years.
Adopted in FY 2014 IRF PPS final rule (78
FR 47905 through 47906).
Adopted in FY 2014 IRF PPS final rule (78
FR 47906 through 47910).
October 1, 2014
FY 2016 and subsequent
years.
FY 2017 and subsequent
years.
Adopted the NQF-endorsed version in FY
2016 IRF PPS final rule (80 FR 47087
through 47089).
Adopted in the FY 2015 IRF PPS final rule
(79 FR 45911 through 45913).
N/A ...................
FY 2018 and subsequent
years.
January 1, 2015
FY 2017 and subsequent
years.
Adopted in the FY 2015 IRF PPS final rule
(79 FR 45913 through 45914).
January 1, 2015
FY 2017 and subsequent
years.
Adopted an application of the measure in FY
2016 IRF PPS Final Rule (80 FR 47096
through 47100).
Adopted an application of the measure in the
FY 2016 IRF PPS final rule (80 FR 47100
through 47111).
October 1, 2016
FY 2018 and subsequent
years.
October 1, 2016
FY 2018 and subsequent
years.
Adopted in the FY 2016 IRF PPS final rule
(80 FR 47111 through 47117).
October 1, 2016
FY 2018 and subsequent
years.
Adopted in the FY 2016 IRF PPS final rule
(80 FR 47117 through 47118).
October 1, 2016
FY 2018 and subsequent
years.
Adopted in the FY 2016 IRF PPS final rule
(80 FR 47118 through 47119).
October 1, 2016
FY 2018 and subsequent
years.
Adopted in the FY 2016 IRF PPS final rule
(80 FR 47119 through 47120).
October 1, 2016
FY 2018 and subsequent
years.
Measure title
Percent of Residents or Patients Who Were
Assessed and Appropriately Given the
Seasonal Influenza Vaccine (Short Stay)
(NQF #0680).
Influenza Vaccination Coverage among
Healthcare Personnel (NQF #0431).
All-Cause Unplanned Readmission Measure
for 30 Days Post Discharge from Inpatient
Rehabilitation Facilities (NQF #2502).
National Healthcare Safety Network (NHSN)
Facility-Wide
Inpatient
Hospital-Onset
Methicillin-Resistant
Staphylococcus
aureus (MRSA) Bacteremia Outcome
Measure (NQF #1716).
National Healthcare Safety Network (NHSN)
Facility-Wide
Inpatient
Hospital-Onset
Clostridium difficile Infection (CDI) Outcome Measure (NQF #1717).
Application of Percent of Residents Experiencing One or More Falls with Major Injury
(Long Stay) (NQF #0674).
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).
IRF Functional Outcome Measure: Change in
Self-Care for Medical Rehabilitation Patients (NQF #2633).*
IRF Functional outcome Measure: Change in
Mobility Score for Medical Rehabilitation
(NQF #2634).*
IRF Functional Outcome Measure: Discharge
Self-Care Score for Medical Rehabilitation
Patients (NQF #2635).
IRF Functional Outcome Measure: Discharge
Mobility Score for Medical Rehabilitation
Patients (NQF #2636).
N/A ...................
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* These measures were under review at NQF when they were finalized for use in the IRF QRP. These measures are now NQF-endorsed.
F. IRF QRP Quality, Resource Use and
Other Measures Finalized for the FY
2018 Payment Determination and
Subsequent Years
For the FY 2018 payment
determinations and subsequent years, in
addition to the quality measures we are
retaining under our policy described in
section VIII.C. of this final rule, we
proposed four new measures. Three of
these measures were developed to meet
the requirements of IMPACT Act. They
are:
(1) MSPB–PAC IRF QRP,
(2) Discharge to Community–PAC IRF
QRP, and
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(3) Potentially Preventable 30-Day
Post-Discharge Readmission Measure for
IRF QRP.
The fourth measure is: (4) Potentially
Preventable Within Stay Readmission
Measure for IRFs. The measures are
described in more detail below.
For the risk-adjustment of the
resource use and other measures, we
understand the important role that
sociodemographic status plays in the
care of patients. However, we continue
to have concerns about holding
providers to different standards for the
outcomes of their patients of diverse
sociodemographic status because we do
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not want to mask potential disparities or
minimize incentives to improve the
outcomes of disadvantaged populations.
We routinely monitor the impact of
sociodemographic status on providers’
results for our measures.
The NQF is currently undertaking a
two-year trial period in which new
measures and measures undergoing
maintenance review will be assessed to
determine if risk-adjusting for
sociodemographic factors is appropriate.
For 2 years, NQF will conduct a trial of
temporarily allowing inclusion of
sociodemographic factors in the riskadjustment approach for some
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performance measures. At the
conclusion of the trial, NQF will issue
recommendations on future permanent
inclusion of sociodemographic factors.
During the trial, measure developers are
expected to submit information such as
analyses and interpretations as well as
performance scores with and without
sociodemographic factors in the risk
adjustment model.
Furthermore, the Office of the
Assistant Secretary for Planning and
Evaluation (ASPE) is conducting
research to examine the impact of
sociodemographic status on quality
measures, resource use, and other
measures under the Medicare program
as directed by the IMPACT Act. We will
closely examine the findings of the
ASPE reports and related Secretarial
recommendations and consider how
they apply to our quality programs at
such time as they are available.
We received several comments on the
impact of sociodemographic status on
quality measures, resource use, and
other measures, which are summarized
with our responses below.
Comment: Several commenters
indicated their support for the inclusion
of sociodemographic status adjustment
in quality measures, resource use, and
other measures. Commenters suggested
that failure to account for patient
characteristics could penalize IRFs for
providing care to a more medicallycomplex and socioeconomically
disadvantaged patient population and
affect provider performance. Some
commenters expressed concerns about
standardization and interoperability of
the measures as it pertain to riskadjusting, particularly for SDS
characteristics. Many commenters
recommended incorporating
socioeconomic factors as risk-adjustors
for the measures, and several
commenters suggested conducting
additional testing and NQFendorsement prior to implementation of
these measures. In addition, many
commenters recommended including
functionality as an additional riskadjustment factor, and several
commenters suggested risk-adjustment
for cognitive impairment.
A few commenters, including
MedPAC, did not support riskadjustment of measures by
socioeconomic status (SES) or SDS
status. One commenter did not support
risk-adjustment, stating that it can hide
disparities and create different
standards of care for IRFs based on the
demographics in the facility. MedPAC
reiterated that risk adjustment can hide
disparities in care and suggested that
risk-adjustment reduces pressure on
providers to improve quality of care for
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low-income Medicare beneficiaries.
Instead, MedPAC supported peer
provider group comparisons with
providers of similar low-income
beneficiary populations. Another
commenter stated that SDS factors
should not be included in measures that
examine the patient during an IRF stay,
but should only be considered for
measures evaluating care after the IRF
discharge.
Response: We appreciate the
considerations and suggestions
conveyed in relation to the measures
and the importance in balancing
appropriate risk adjustment along with
ensuring access to high-quality care. We
note that in the measures that are risk
adjusted, we do take into account
characteristics associated with medical
complexity, as well as factors such as
age where appropriate to do so. For
those cross-setting post-acute measures,
such as those intended to satisfy the
IMPACT Act domains that use the
patient assessment-based data elements
for risk adjustment, we have either
made such items standardized, or
intend to do so as feasible. With regard
to the incorporation of additional
factors, such as function, we have and
will continue to take such factors into
account, which would include further
testing as part of our ongoing measure
development monitoring activities. As
discussed previously, we intend to seek
NQF endorsement for our measures.
We also received suggestions
pertaining to the incorporation of
socioeconomic factors as risk-adjustors
for the measures, including in those
measures that pertain to after the patient
was discharged from the IRF, additional
testing and/or NQF endorsement prior
to implementation of these measures,
and comments that pertain to potential
consequences associated with such risk
adjustors and alternative approaches to
grouping comparative data. We wish to
reiterate that as previously discussed,
NQF is currently undertaking a 2-year
trial period in which new measures and
measures undergoing maintenance
review will be assessed to determine if
risk-adjusting for sociodemographic
factors is appropriate. This trial entails
temporarily allowing inclusion of
sociodemographic factors in the riskadjustment approach for some
performance measures. At the
conclusion of the trial, NQF will issue
recommendations on future permanent
inclusion of sociodemographic factors.
During the trial, measure developers are
encouraged to submit information such
as analyses and interpretations as well
as performance scores with and without
sociodemographic factors in the risk
adjustment model. Several measures
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52087
developed by CMS have been brought to
NQF since the beginning of the trial.
CMS, in compliance with NQF’s
guidance, has tested sociodemographic
factors in the measures’ risk models and
made recommendations about whether
or not to include these factors in the
endorsed measure. We intend to
continue engaging in the NQF process
as we consider the appropriateness of
adjusting for sociodemographic factors
in our outcome measures.
Furthermore, the Office of the ASPE
is conducting research to examine the
impact of sociodemographic status on
quality measures, resource use, and
other measures under the Medicare
program as directed by the IMPACT Act.
We will closely examine the findings of
the ASPE reports and related Secretarial
recommendations and consider how
they apply to our quality programs at
such time as they are available.
1. Measure to Address the IMPACT Act
Domain of Resource Use and Other
Measures: Total Estimated MSPB–PAC
IRF QRP
We proposed an MSPB–PAC IRF QRP
measure for inclusion in the IRF QRP
for the FY 2018 payment determination
and subsequent years. Section
1899B(d)(1)(A) of the Act requires the
Secretary to specify resource use
measures, including total estimated
MSPB, on which PAC providers
consisting of Skilled Nursing Facilities
(SNFs), IRFs, Long-Term Care Hospitals
(LTCHs), and Home Health Agencies
(HHAs) are required to submit necessary
data specified by the Secretary.
Rising Medicare expenditures for
post-acute care as well as wide variation
in spending for these services
underlines the importance of measuring
resource use for providers rendering
these services. Between 2001 and 2013,
Medicare PAC spending grew at an
annual rate of 6.1 percent and doubled
to $59.4 billion, while payments to
inpatient hospitals grew at an annual
rate of 1.7 percent over this same
period.3 A study commissioned by the
Institute of Medicine discovered that
variation in PAC spending explains 73
percent of variation in total Medicare
spending across the United States.4
We reviewed the NQF’s consensusendorsed measures and were unable to
identify any NQF-endorsed resource use
measures for PAC settings. As such, we
proposed this MSPB–PAC IRF QRP
measure under the Secretary’s authority
3 MedPAC, ‘‘A Data Book: Health Care Spending
and the Medicare Program,’’ (2015). 114.
4 Institute of Medicine, ‘‘Variation in Health Care
Spending: Target Decision Making, Not
Geography,’’ (Washington, DC: National Academies
2013). 2.
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to specify non-NQF-endorsed measures
under section 1899B(e)(2)(B) of the Act.
Given the current lack of resource use
measures for PAC settings, our MSPB–
PAC IRF QRP measure will provide
valuable information to IRF providers
on their relative Medicare spending in
delivering services to approximately
338,000 Medicare beneficiaries.5
The MSPB–PAC IRF QRP episodebased measure will provide actionable
and transparent information to support
IRF providers’ efforts to promote care
coordination and deliver high quality
care at a lower cost to Medicare. The
MSPB–PAC IRF QRP measure holds IRF
providers accountable for the Medicare
payments within an ‘‘episode of care’’
(episode), which includes the period
during which a patient is directly under
the IRF’s care, as well as a defined
period after the end of the IRF
treatment, which may be reflective of
and influenced by the services
furnished by the IRF. MSPB–PAC IRF
QRP episodes, constructed according to
the methodology described below, have
high levels of Medicare spending with
substantial variation. In FY 2013 and FY
2014, Medicare FFS beneficiaries
experienced 613,089 MSPB–PAC IRF
QRP episodes triggered by admission to
an IRF. The mean paymentstandardized, risk-adjusted episode
spending for these episodes is $30,370.
There is substantial variation in the
Medicare payments for these MSPB–
PAC IRF QRP episodes—ranging from
approximately $15,059 at the 5th
percentile to approximately $55,912 at
the 95th percentile. This variation is
partially driven by variation in
payments occurring following IRF
treatment.
Evaluating Medicare payments during
an episode creates a continuum of
accountability between providers that
should improve post-treatment care
planning and coordination. While some
stakeholders throughout the measure
development process supported the
MSPB–PAC measures and believed that
measuring Medicare spending was
critical for improving efficiency, others
believed that resource use measures did
not reflect quality of care in that they do
not take into account patient outcomes
or experience beyond those observable
in claims data. However, IRFs involved
in the provision of high quality PAC
care as well as appropriate discharge
planning and post-discharge care
coordination would be expected to
perform well on this measure since
beneficiaries would likely experience
fewer costly adverse events (for
5 Figures for 2013. MedPAC, ‘‘Medicare Payment
Policy,’’ Report to the Congress (2015). xvii–xviii.
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example, avoidable hospitalizations,
infections, and emergency room usage).
Further, it is important that the cost of
care be explicitly measured so that, in
conjunction with other quality
measures, we can publicly report which
IRFs provide high quality care at lower
cost.
We developed an MSPB–PAC
measure for each of the four PAC
settings. We proposed an LTCH-specific
MSPB–PAC measure in the FY 2017
IPPS/LTCH proposed rule (81 FR 25216
through 25220), an IRF-specific MSBP–
PAC measure in the FY 2017 IRF PPS
proposed rule (81 FR 24197 through
24201), a SNF-specific MSPB–PAC
measure in the FY 2017 SNF proposed
rule (81 FR 24258 through 24262), and
a HHA-specific MSBP–PAC measure in
the CY 2017 HH proposed rule (81 FR
43760 through 43764). The four settingspecific MSPB–PAC measures are
closely aligned in terms of episode
construction and measure calculation.
Each of the MSPB–PAC measures assess
Medicare Part A and Part B spending
during an episode, and the numerator
and denominator are defined similarly
for each of the MSPB–PAC measures.
However, setting-specific measures
allow us to account for differences
between settings in payment policy, the
types of data available, and the
underlying health characteristics of
beneficiaries. For example, we use the
IRF setting-specific rehabilitation
impairment categories (RICs) in the
MSPB–PAC IRF QRP risk adjustment
model, as detailed below.
The MSPB–PAC measures mirror the
general construction of the inpatient
prospective payment system (IPPS)
hospital MSPB measure, which was
adopted for the Hospital IQR Program
beginning with the FY 2014 program,
and was implemented in the Hospital
VBP Program beginning with the FY
2015 program. The measure was
endorsed by the NQF on December 6,
2013 (NQF #2158).6 The hospital MSPB
measure evaluates hospitals’ Medicare
spending relative to the Medicare
spending for the national median
hospital during a hospital MSPB
episode. It assesses Medicare Part A and
Part B payments for services performed
by hospitals and other healthcare
providers during a hospital MSPB
episode, which is comprised of the
periods immediately prior to, during,
and following a patient’s hospital
6 QualityNet, ‘‘Measure Methodology Reports:
Medicare Spending per Beneficiary (MSPB)
Measure,’’ (2015). https://www.qualitynet.org/dcs/
ContentServer?pagename=QnetPublic%2F
Page%2FQnetTier3&cid=1228772053996.
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stay.7 8 Similarly, the MSPB–PAC
measures assess all Medicare Part A and
Part B payments for FFS claims with a
start date during the episode window
(which, as discussed in this section, is
the time period during which Medicare
FFS Part A and Part B services are
counted towards the MSPB–PAC IRF
QRP episode). There are differences
between the MSPB–PAC measures and
the hospital MSPB measure to reflect
differences in payment policies and the
nature of care provided in each PAC
setting. For example, the MSPB–PAC
measures exclude a limited set of
services (for example, clinically
unrelated services) provided to a
beneficiary during the episode window,
while the hospital MSPB measure does
not exclude any services.9
MSPB–PAC episodes may begin
within 30 days of discharge from an
inpatient hospital as part of a patient’s
trajectory from an acute to a PAC
setting. An IRF stay beginning within 30
days of discharge from an inpatient
hospital would therefore be included
once in the hospital’s MSPB measure,
and once in the IRF provider’s MSPB–
PAC measure. Aligning the hospital
MSPB and MSPB–PAC measures in this
way creates continuous accountability
and aligns incentives to improve care
planning and coordination across
inpatient and PAC settings.
We sought and considered the input
of stakeholders throughout the measure
development process for the MSPB–
PAC measures. We convened a TEP
consisting of 12 panelists with
combined expertise in all of the PAC
settings on October 29 and 30, 2015 in
Baltimore, Maryland. A follow-up email
survey was sent to TEP members on
November 18, 2015 to which seven
responses were received by December 8,
2015. The MSPB–PAC TEP Summary
Report is available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/Downloads/TechnicalExpert-Panel-on-Medicare-SpendingPer-Beneficiary.pdf. The measures were
also presented to the MAP Post-Acute
Care/Long-Term Care (PAC/LTC)
Workgroup on December 15, 2015. As
the MSPB–PAC measures were under
7 QualityNet, ‘‘Measure Methodology Reports:
Medicare Spending per Beneficiary (MSPB)
Measure,’’ (2015). https://www.qualitynet.org/dcs/
ContentServer?pagename=QnetPublic%2F
Page%2FQnetTier3&cid=1228772053996.
8 FY 2012 IPPS/LTCH PPS final rule (76 FR
51619).
9 National Quality Forum, Applications
Partnership, ‘‘Process and Approach for MAP PreRulemaking Deliberations, 2015-2016’’ (February
2016) https://www.qualityforum.org/WorkArea/
linkit.aspx?LinkIdentifier=id&Ote,OD=81693.
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development, there were three voting
options for members: Encourage
continued development, do not
encourage further consideration, and
insufficient information.10 The MAP
PAC/LTC workgroup voted to
‘‘encourage continued development’’ for
each of the MSPB–PAC measures.11 The
MAP PAC/LTC workgroup’s vote of
‘‘encourage continued development’’
was affirmed by the MAP Coordinating
Committee on January 26, 2016.12 The
MAP’s concerns about the MSPB–PAC
measures, as outlined in their final
report ‘‘MAP 2016 Considerations for
Implementing Measures in Federal
Programs: Post-Acute Care and LongTerm Care’’ and Spreadsheet of Final
Recommendations, were taken into
consideration during the measure
development process and are discussed
as part of our responses to public
comments, described below.13 14
Since the MAP’s review and
recommendation of continued
development, CMS continued to refine
risk adjustment models and conduct
measure testing for the IMPACT Act
measures in compliance with the MAP’s
recommendations. The IMPACT Act
measures are consistent with the
information submitted to the MAP and
support the scientific acceptability of
these measures for use in quality
reporting programs.
In addition, a public comment period,
accompanied by draft measures
specifications, was open from January
13 to 27, 2016 and extended to February
5. A total of 45 comments on the MSPB–
PAC measures were received during this
3.5 week period. The comments
received also covered each of the MAP’s
concerns as outlined in their Final
10 National Quality Forum, Measure Applications
Partnership, ‘‘Process and Approach for MAP PreRulemaking Deliberations, 2015–2016’’ (February
2016) https://www.qualityforum.org/WorkArea/
linkit.aspx?LinkIdentifier=id&ItemID=81693.
11 National Quality Forum, Measure Applications
Partnership Post-Acute Care/Long-Term Care
Workgroup, ‘‘Meeting Transcript—Day 2 of 2’’
(December 15, 2015) 104–106. https://
www.qualityforum.org/WorkArea/
linkit.aspx?LinkIdentifier=id&ItemID=81470.
12 National Quality Forum, Measure Applications
Partnership, ‘‘Meeting Transcript—Day 1 of 2’’
(January 26, 2016) 231–232 https://
www.qualityforum.org/WorkArea/
linkit.aspx?LinkIdentifier=id&ItemID=81637.
13 National Quality Forum, Measure Applications
Partnership, ‘‘MAP 2016. Considerations for
Implementing Measures in Federal Programs: PostAcute Care and Long-Term Care’’ Final Report,
(February 2016) https://www.qualityforum.org/
Publications/2016/02/MAP_2016_Considerations_
for_Implementing_Measures_in_Federal_Programs_
-_PAC-LTC.aspx.
14 National Quality Forum, Measure Applications
Partnership, ‘‘Spreadsheet of MAP 2016 Final
Recommendations’’ (February 1, 2016) https://
www.qualityforum.org/WorkArea/
linkit.aspx?LinkIdentifier=id&ItemID=81593.
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Recommendations.15 The MSPB–PAC
Public Comment Summary Report is
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/Downloads/
2016_03_24_mspb_pac_public_
comment_summary_report.pdf and the
MSPB–PAC Public Comment
Supplementary Materials are available
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/Downloads/2016_03_24_
mspb_pac_public_comment_summary_
report_supplementary_materials.pdf:
These documents contain the public
comments, along with our responses
including statistical analyses. The
MSPB–PAC IRF QRP measure, along
with the other MSPB–PAC measures, as
applicable, will be submitted for NQF
endorsement when feasible.
To calculate the MSPB–PAC IRF QRP
measure for each IRF provider, we first
defined the construction of the MSPB–
PAC IRF QRP episode, including the
length of the episode window as well as
the services included in the episode.
Next, we apply the methodology for the
measure calculation. The specifications
are discussed further in this section.
More detailed specifications for the
MSPB–PAC measures, including the
MSPB–PAC IRF QRP measure, are
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/IRF-QualityReporting/IRF-Quality-ReportingProgram-Measures-Information-.html.
a. Episode Construction
An MSPB–PAC IRF QRP episode
begins at the episode trigger, which is
defined as the patient’s admission to an
IRF. The admitting facility is the
attributed provider, for whom the
MSPB–PAC IRF QRP measure is
calculated. The episode window is the
time period during which Medicare FFS
Part A and Part B services are counted
towards the MSPB–PAC IRF QRP
episode. Because Medicare FFS claims
are already reported to the Medicare
program for payment purposes, IRF
providers would not be required to
report any additional data to CMS for
calculation of this measure. Thus, there
would be no additional data collection
burden from the implementation of this
measure.
The episode window is comprised of
a treatment period and an associated
services period. The treatment period
15 National Quality Forum, Measure Applications
Partnership, ‘‘Spreadsheet of MAP 2016 Final
Recommendations’’ (February 1, 2016) https://
www.qualityforum.org/WorkArea/
linkit.aspx?LinkIdentifier=id&ItemID=81593.
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begins at the trigger (that is, on the day
of admission to the IRF) and ends on the
day of discharge from that IRF.
Readmissions to the same facility
occurring within 7 or fewer days do not
trigger a new episode, and instead are
included in the treatment period of the
original episode. When two sequential
stays at the same IRF occur within 7 or
fewer days of one another, the treatment
period ends on the day of discharge for
the latest IRF stay. The treatment period
includes those services that are
provided directly or reasonably
managed by the IRF provider that are
directly related to the beneficiary’s care
plan. The associated services period is
the time during which Medicare Part A
and Part B services (with certain
exclusions) are counted towards the
episode. The associated services period
begins at the episode trigger and ends 30
days after the end of the treatment
period. The distinction between the
treatment period and the associated
services period is important because
clinical exclusions of services may
differ for each period. Certain services
are excluded from the MSPB–PAC IRF
QRP episodes because they are
clinically unrelated to IRF care, and/or
because IRF providers may have limited
influence over certain Medicare services
delivered by other providers during the
episode window. These limited servicelevel exclusions are not counted
towards a given IRF provider’s Medicare
spending to ensure that beneficiaries
with certain conditions and complex
care needs receive the necessary care.
Certain services that are determined to
be outside of the control of an IRF
provider include planned hospital
admissions, management of certain
preexisting chronic conditions (for
example, dialysis for end-stage renal
disease (ESRD), and enzyme treatments
for genetic conditions), treatment for
preexisting cancers, organ transplants,
and preventive screenings (for example,
colonoscopy and mammograms).
Exclusion of such services from the
MSPB–PAC IRF QRP episode ensures
that facilities do not have disincentives
to treat patients with certain conditions
or complex care needs.
An MSPB–PAC episode may begin
during the associated services period of
an MSPB–PAC IRF QRP episode in the
30 days post-treatment. One possible
scenario occurs where an IRF provider
discharges a beneficiary who is then
admitted to an LTCH within 30 days.
The LTCH claim will be included once
as an associated service for the
attributed provider of the first MSPB–
PAC IRF QRP episode and once as a
treatment service for the attributed
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provider of the second MSPB–PAC
LTCH QRP episode. As in the case of
overlap between hospital and PAC
episodes discussed earlier, this overlap
is necessary to ensure continuous
accountability between providers
throughout a beneficiary’s trajectory of
care, as both providers share incentives
to deliver high quality care at a lower
cost to Medicare. Even within the IRF
setting, one MSPB–PAC IRF QRP
episode may begin in the associated
services period of another MSPB–PAC
IRF QRP episode in the 30 days posttreatment. The second IRF claim would
be included once as an associated
service for the attributed IRF provider of
the first MSPB–PAC IRF QRP episode
and once as a treatment service for the
attributed IRF provider of the second
MSPB–PAC IRF QRP episode. Again,
this ensures that IRF providers have the
same incentives throughout both
MSPB–PAC IRF QRP episodes to deliver
quality care and engage in patientfocused care planning and coordination.
If the second MSPB–PAC IRF QRP
episode were excluded from the second
IRF provider’s MSPB–PAC IRF QRP
measure, that provider would not share
the same incentives as the first IRF
provider of the first MSPB–PAC IRF
QRP episode. The MSPB–PAC IRF QRP
measure was designed to benchmark the
resource use of each attributed provider
against what their spending is expected
to be as predicted through risk
adjustment. As discussed further in this
section, the measure takes the ratio of
observed spending to expected spending
for each episode and then takes the
average of those ratios across all of the
attributed provider’s episodes. The
measure is not a simple sum of all costs
across a provider’s episodes, thus
mitigating concerns about double
counting.
b. Measure Calculation
Medicare payments for Part A and
Part B claims for services included in
MSPB–PAC IRF QRP episodes, defined
according to the methodology
previously discussed, are used to
calculate the MSPB–PAC IRF QRP
measure. Measure calculation involves
determination of the episode exclusions,
the approach for standardizing
payments for geographic payment
differences, the methodology for risk
adjustment of episode spending to
account for differences in patient case
mix, and the specifications for the
measure numerator and denominator.
(1) Exclusion Criteria
In addition to service-level exclusions
that remove some payments from
individual episodes, we exclude certain
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episodes in their entirety from the
MSPB–PAC IRF QRP measure to ensure
that the MSPB–PAC IRF QRP measure
accurately reflects resource use and
facilitates fair and meaningful
comparisons between IRF providers.
The episode-level exclusions are as
follows:
• Any episode that is triggered by an
IRF claim outside the 50 states, DC,
Puerto Rico, and U.S. Territories.
• Any episode where the claim(s)
constituting the attributed IRF
provider’s treatment have a standard
allowed amount of zero or where the
standard allowed amount cannot be
calculated.
• Any episode in which a beneficiary
is not enrolled in Medicare FFS for the
entirety of a 90-day lookback period
(that is, a 90-day period prior to the
episode trigger) plus episode window
(including where a beneficiary dies), or
is enrolled in Part C for any part of the
lookback period plus episode window.
• Any episode in which a beneficiary
has a primary payer other than Medicare
for any part of the 90-day lookback
period plus episode window.
• Any episode where the claim(s)
constituting the attributed IRF
provider’s treatment include at least one
related condition code indicating that it
is not a prospective payment system
bill.
(2) Standardization and Risk
Adjustment
Section 1899B(d)(2)(C) of the Act
requires that the MSPB–PAC measures
are adjusted for the factors described
under section 1886(o)(2)(B)(ii) of the
Act, which include adjustment for
factors such as age, sex, race, severity of
illness, and other factors that the
Secretary determines appropriate.
Medicare payments included in the
MSPB–PAC IRF QRP measure are
payment-standardized and riskadjusted. Payment standardization
removes sources of payment variation
not directly related to clinical decisions
and facilitates comparisons of resource
use across geographic areas. We
proposed to use the same payment
standardization methodology that was
used in the NQF-endorsed hospital
MSPB measure. This methodology
removes geographic payment
differences, such as wage index and
geographic practice cost index (GPCI),
incentive payment adjustments, and
other add-on payments that support
broader Medicare program goals
including indirect graduate medical
education (IME) and hospitals serving a
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disproportionate share of uninsured
patients (DSH).16
Risk adjustment uses patient claims
history to account for case-mix variation
and other factors that affect resource use
but are beyond the influence of the
attributed IRF provider. To assist with
risk adjustment, we created mutually
exclusive and exhaustive clinical case
mix categories using the most recent
institutional claim in the 60 days prior
to the start of the MSPB–PAC IRF QRP
episode. The beneficiaries in these
clinical case mix categories have a
greater degree of clinical similarity than
the overall IRF patient population, and
allow us to more accurately estimate
Medicare spending. Our MSPB–PAC
IRF QRP measure, adapted for the IRF
setting from the NQF-endorsed hospital
MSPB measure, uses a regression
framework with a 90-day hierarchical
condition category (HCC) lookback
period and covariates including the
clinical case mix categories, HCC
indicators, age brackets, indicators for
originally disabled, ESRD enrollment,
and long-term care status, and selected
interactions of these covariates where
sample size and predictive ability make
them appropriate. We sought and
considered public comment regarding
the treatment of hospice services
occurring within the MSPB–PAC IRF
QRP episode window. Given the
comments received, we proposed to
include the Medicare spending for
hospice services but risk adjust for
them, such that MSPB–PAC IRF QRP
episodes with hospice services are
compared to a benchmark reflecting
other MSPB–PAC IRF QRP episodes
with hospice services. We believe this
strikes a balance between the measure’s
intent of evaluating Medicare spending
and ensuring that providers do not have
incentives against the appropriate use of
hospice services in a patient-centered
continuum of care.
We proposed to use RICs in response
to commenters’ concerns about the risk
adjustment approach for the MSPB–PAC
IRF QRP measure. Commenters
suggested the use of case mix groups
(CMGs); however, we believed that the
use of RICs may be more appropriate
given that the other covariates
incorporated in the model partially
account for factors in CMGs (for
example, age and certain HCC
indicators). RICs do not account for
functional status as CMGs do, as the
functional status information in CMGs
is based on the IRF–PAI. Given the
16 QualityNet, ‘‘CMS Price (Payment)
Standardization—Detailed Methods’’ (Revised May
2015) https://qualitynet.org/dcs/ContentServer?c=
Page&pagename=QnetPublic%2FPage%2FQnetTier
4&cid=1228772057350.
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analyses and interpretations as well as
performance scores with and without
sociodemographic factors in the risk
adjustment model.
Furthermore, ASPE is conducting
research to examine the impact of
sociodemographic status on quality
measures, resource use, and other
measures under the Medicare program
as required under the IMPACT Act. We
will closely examine the findings of the
ASPE reports and related Secretarial
recommendations and consider how
they apply to our quality programs at
such time as they are available.
While we conducted analyses on the
impact of age by sex on the performance
of the MSPB–PAC IRF QRP riskadjustment model, we did not propose
to adjust the MSPB–PAC IRF QRP
measure for socioeconomic factors. As
this MSPB–PAC IRF QRP measure
would be submitted for NQF
endorsement, we prefer to await the
results of this trial and study before
deciding whether to risk adjust for
socioeconomic factors. We will monitor
the results of the trial, studies, and
recommendations. We invited public
comment on how socioeconomic and
demographic factors should be used in
risk adjustment for the MSPB–PAC IRF
QRP measure.
where
• Yij = attributed standardized spending for
episode i and provider j
ˆ
• Yij = expected standardized spending for
episode i and provider j, as predicted
from risk adjustment
• nj = number of episodes for provider j
• n = total number of episodes nationally
• i ∈ {Ij} = all episodes i in the set of
episodes attributed to provider j.
d. Cohort
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c. Data Sources
The MSPB–PAC IRF QRP resource
use measure is an administrative claimsbased measure. It uses Medicare Part A
and Part B claims from FFS
beneficiaries and Medicare eligibility
files.
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(3) Measure Numerator and
Denominator
The MPSB–PAC IRF QRP measure is
a payment-standardized, risk-adjusted
The measure cohort includes
Medicare FFS beneficiaries with an IRF
treatment period ending during the data
collection period.
e. Reporting
We intend to provide initial
confidential feedback to providers, prior
to public reporting of this measure,
based on Medicare FFS claims data from
discharges in CY 2015 and 2016. We
intend to publicly report this measure
using claims data from discharges in CY
2016 and 2017.
We proposed to use a minimum of 20
episodes for reporting and inclusion in
the IRF QRP. For the reliability
calculation, as described in the measure
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ratio that compares a given IRF
provider’s Medicare spending against
the Medicare spending of other IRF
providers within a performance period.
Similar to the hospital MSPB measure,
the ratio allows for ease of comparison
over time as it obviates the need to
adjust for inflation or policy changes.
The MSPB–PAC IRF QRP measure is
calculated as the ratio of the MSPB–PAC
Amount for each IRF provider divided
by the episode-weighted median MSPB–
PAC Amount across all IRF providers.
To calculate the MSPB–PAC Amount for
each IRF provider, one calculates the
average of the ratio of the standardized
episode spending over the expected
episode spending (as predicted in risk
adjustment), and then multiplies this
quantity by the average episode
spending level across all IRF providers
nationally. The denominator for an IRF
provider’s MSPB–PAC IRF QRP
measure is the episode-weighted
national median of the MSPB–PAC
Amounts across all IRF providers. An
MSPB–PAC IRF QRP measure of less
than 1 indicates that a given IRF
provider’s Medicare spending is less
than that of the national median IRF
provider during a performance period.
Mathematically, this is represented in
equation (A) below:
specifications for which a link has been
provided above, we used 2 years of data
(FY 2013 and FY 2014) to increase the
statistical reliability of this measure.
The reliability results support the 20
episode case minimum, and 99.74
percent of IRF providers had moderate
or high reliability (above 0.4).
We invited public comment on our
proposal to adopt the MSPB–PAC IRF
QRP measure for the IRF QRP. The
comments we received, with our
responses, appear below.
Comment: Several commenters
expressed concern about the lack of
NQF endorsement for proposed
measures; some believed that the
measure should not be finalized until
NQF endorsement is obtained.
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ER05AU16.009
move toward standardized data that was
mandated by the IMPACT Act, we have
chosen to defer risk adjustment for
functional status until standardized data
become available. We sought comments
on whether the use of CMGs would be
appropriate to include in the MSPB–
PAC IRF QRP risk adjustment model.
We understand the important role that
sociodemographic factors, beyond age,
play in the care of patients. However,
we continue to have concerns about
holding providers to different standards
for the outcomes of their patients of
diverse sociodemographic status
because we do not want to mask
potential disparities or minimize
incentives to improve the outcomes of
disadvantaged populations. We will
monitor the impact of sociodemographic
status on providers’ results on our
measures.
The NQF is currently undertaking a 2year trial period in which new measures
and measures undergoing maintenance
review will be assessed to determine if
risk-adjusting for sociodemographic
factors is appropriate. For 2 years, NQF
will conduct a trial of temporarily
allowing inclusion of sociodemographic
factors in the risk-adjustment approach
for some performance measures. At the
conclusion of the trial, NQF will issue
recommendations on future permanent
inclusion of sociodemographic factors.
During the trial, measure developers are
expected to submit information such as
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Response: Regarding the lack of NQF
endorsement, refer to section VIII.B. of
this final rule where we also discuss
this topic.
Comment: Some commenters
recommended the use of uniform single
MSPB–PAC measure that could be used
to compare providers’ resource use
across settings, but the commenters also
recognized that we do not have a
uniform PPS for all the PAC settings
currently. In the absence of a single PAC
PPS, the commenters recommended a
single MSPB–PAC measure for each
setting that could be used to compare
providers within a setting. Under a
single measure, the episode definitions,
service inclusions/exclusions, and risk
adjustment methods would be the same
across all PAC settings.
Response: The four separate MSPB–
PAC measures reflect the unique
characteristics of each PAC setting and
the population it serves. The four
setting-specific MSPB–PAC measures
are defined as consistently as possible
across settings given the differences in
the payment systems for each setting,
and types of patients served in each
setting. We have taken into
consideration these differences and
aligned the specifications, such as
episode definitions, service inclusions/
exclusions and risk adjustment methods
for each setting, to the extent possible
while ensuring the accuracy of the
measures in each PAC setting.
Each of the measures assess Medicare
Part A and Part B spending during the
episode window which begins upon
admission to the provider’s care and
ends 30 days after the end of the
treatment period. The service-level
exclusions are harmonized across
settings. The definition of the numerator
and denominator is the same across
settings. However, specifications differ
between settings when necessary to
ensure that the measures accurately
reflect patient care and align with each
setting’s payment system. For example,
Medicare pays LTCHs and IRFs a staylevel payment based on the assigned
MS–LTC–DRG and CMG, respectively,
while SNFs are paid a daily rate based
on the RUG level, and HHA providers
are reimbursed based on a fixed 60-day
period for standard home health claims.
While the definition of the episode
window is consistent across settings and
is based on the period of time that a
beneficiary is under a given provider’s
care, the duration of the treatment
period varies to reflect how providers
are reimbursed under the PPS that
applies to each setting. The length of the
post-treatment period is consistent
between settings. There are also
differences in services covered under
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the PPS that applies to each setting: For
example, durable medical equipment,
prosthetics, orthotics, and supplies
(DMEPOS) claims are covered LTCH,
IRF, and SNF services but are not
covered HHA services. This affects the
way certain first-day service exclusions
are defined for each measure.
We recognize that beneficiaries may
receive similar services as part of their
overall treatment plan in different PAC
settings, but believe that there are some
important differences in beneficiaries’
care profiles that are difficult to capture
in a single measure that compares
resource use across settings.
Also, the risk adjustment models for
the MSPB–PAC measures share the
same covariates to the greatest extent
possible to account for patient case mix.
However, the measures also incorporate
additional setting-specific information
where available to increase the
predictive power of the risk adjustment
models. For example, the MSPB–PAC
LTCH QRP risk adjustment model uses
MS–LTC–DRGs and Major Diagnostic
Categories (MDCs) and the MSPB–PAC
IRF QRP model includes Rehabilitation
Impairment Categories (RICs). The HH
and SNF settings do not have analogous
variables that directly reflect a patient’s
clinical profile.
We will continue to work towards a
more uniform measure across settings as
we gain experience with these
measures, and we plan to conduct
further research and analyses about
comparability of resource use measures
across settings for clinically similar
patients, different treatment periods and
windows, risk adjustment, service
exclusions, and other factors.
Comment: A few commenters noted
that the MSPB–PAC measures are
resource use measures that are not a
standalone indicator of quality.
Response: We appreciate the
comment regarding the proposed
MSPB–PAC measures as resource use
measures. The MSPB–PAC IRF QRP
measure is one of five QRP measures
that were proposed in the FY 2017 IRF
PPS proposed rule for inclusion in the
IRF QRP: In addition to the MSPB–PAC
IRF QRP measure, these proposed
measures were the Discharge to
Community—PAC IRF QRP measure (81
FR 24201 through 24204), the
Potentially Preventable 30-day PostDischarge Readmission Measure for IRF
QRP (81 FR 24204 through 24206), the
Potentially Preventable Within Stay
Readmission Measure for IRFs (81 FR
242096 through 24207), and the Drug
Regimen Review Conducted with
Follow-Up for Identified Issues—PAC
IRF QRP measure (81 FR 24207 through
24209). As part of the IRF QRP, the
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MSPB–PAC IRF QRP measure will be
paired with quality measures; we direct
readers to section VIII.E. of this final for
a discussion of quality measures
previously finalized for use in the IRF
QRP. We believe it is important that the
cost of care be explicitly measured so
that, in conjunction with other quality
measures, we can publicly report which
IRF providers are involved in the
provision of high quality care at lower
cost.
Comment: One commenter
recommended that proposed quality
measures obtain the support of a TEP
including IRF representatives to ensure
the applicability of the measures to the
IRF setting.
Response: We thank the commenter
for their recommendation. As discussed
in the proposed rule (81 FR 24198), we
convened a TEP consisting of 12
panelists with combined expertise in
PAC settings, including IRFs, on
October 29 and 30, 2015, in Baltimore,
Maryland. TEPs do not formally support
or endorse measures. However, their
feedback on risk adjustment, episode
windows, exclusions, and other key
elements of measure construction were
incorporated into measure development.
The MSPB–PAC TEP Summary Report
Web site is listed above in this section.
Comment: Several commenters
recommended that the risk adjustment
model for the MSPB–PAC IRF QRP
measure include variables for SES/SDS
factors. A commenter recommended
that a ‘‘fairer’’ approach than using SES/
SDS factors as risk adjustment variables
would be to compare resource use levels
that have not been adjusted for SES/SDS
factors across peer providers (that is,
providers with similar shares of
beneficiaries with similar SES
characteristics).
Response: With regard to the
suggestions that the model include
sociodemographic factors and the
suggestion pertaining to an approach
with which to convey data comparisons,
we refer readers to section VIII.F of this
final rule where we also discuss these
topics.
Comment: Some commenters
recommended that additional variables
be included in risk adjustment to better
capture clinical complexity. A few
commenters suggested the inclusion of
functional and cognitive status, other
patient assessment data and patientreported data. Commenters
recommended that additional variables
should include obesity, amputations,
CVAs (hemiplegia/paresis), ventilator
status, and discharged against medical
advice.
Response: We thank the commenters
for their suggestions. HCC indicators
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that are already included in the risk
adjustment model account for
amputations, hemiplegia, and paresis.
We believe that the other risk
adjustment variables adequately adjust
for ventilator dependency and obesity
by accounting for HCCs, clinical case
mix categories, and prior inpatient and
ICU length of stay. Excluding patients
who are discharged against medical
advice may create incentives for
providers to use this discharge status
code to remove high-cost patients from
their MSPB–PAC measure calculation.
Patient-reported data is not currently
available on Medicare FFS claims. The
addition of such data would likely be
burdensome on IRF providers and the
reliability of the data would need to be
thoroughly tested before use in
Medicare programs.
We recognize the importance of
accounting for beneficiaries’ functional
and cognitive status in the calculation of
predicted episode spending. We
considered the potential use of
functional status information in the risk
adjustment models for the MSPB–PAC
measures. However, we decided not to
include this information derived from
current setting-specific assessment
instruments given the move towards
standardized data as mandated by the
IMPACT Act. We will revisit the
inclusion of functional status in these
measures’ risk adjustment models in the
future when the standardized functional
status data mandated by the IMPACT
Act become available. Once they are
available, we will take a gradual and
systematic approach in evaluating how
they might be incorporated. We intend
to implement any changes if appropriate
based on testing.
Comment: A few commenters
expressed concern that the measures
will give incentive to IRFs to avoid
admitting medically complex patients,
which would result in unintended
consequences.
Response: To mitigate the risk of
creating incentives for IRFs to avoid
admitting medically complex patients,
who may be at higher risk for poor
outcomes and higher costs, we have
included factors related to medical
complexity in the risk adjustment
methodology for the MSPB–PAC IRF
QRP measure. We also intend to
conduct ongoing monitoring to assess
for potential unintended consequences
associated with the implementation of
these measures.
Comment: Several commenters
recommended that IRF interrupted stays
be excluded as those patients would
appear more expensive for receiving
necessary care outside of the control of
the IRF (that is, during the interruption).
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Response: We believe that IRFs are in
a position to influence a patient’s
experience and outcomes after the
initial discharge from the IRF, including
the likelihood and intensity of IRF
readmissions. As noted in the proposed
rule (81 FR 24197), the proposed
MSPB–PAC IRF QRP measure will
support IRF providers’ efforts to
promote care coordination.
Comment: Several commenters
expressed concerns over the inclusion
of spending that occurs within the thirty
day post-discharge timeframe in the
measure, believing that providers do not
have sufficient control over the patient
in the post-treatment period.
Response: We believe that the posttreatment period may be reflective of
and influenced by the services
furnished by the PAC provider,
therefore, including the 30-day posttreatment period in the MSPB–PAC IRF
QRP measure creates a continuum of
accountability between providers and
may incentivize improvements in posttreatment care planning and
coordination. The MSPB–PAC measures
complement the NQF-endorsed hospital
MSPB measure: As they all include a
period during which post-treatment
spending is attributed to the provider,
this accountability incentivizes acute
and PAC providers to engage in
appropriate discharge planning and
post-treatment care coordination to
minimize the likelihood of costly
adverse events, such as avoidable
hospitalizations.
Comment: Several commenters
recommended first day service
exclusions for IRFs that are the same as
other PAC settings, such as SNFs.
Response: As discussed in the MSPB–
PAC Measure Specifications, the Web
site that is listed above in this section,
treatment services occurring on the first
day of MSPB–PAC episodes are subject
to exclusions related to prior
institutional care such as discharge care
services. IRFs provide more intense
hospital-level care and have physicians
or midlevel practitioners evaluate
patients upon admission, which enables
the facility to influence many services
delivered on the first day of the PAC
stay. As such, only a limited number of
discharge care services are excluded.
Moreover, the NQF-endorsed hospital
MSPB measure includes a period during
which post-treatment spending is
attributed to the provider; this
accountability incentivizes acute and
PAC providers to engage in appropriate
discharge planning and post-treatment
care coordination.
Comment: Several commenters
recommended that short stays be
excluded from the MSPB–PAC IRF QRP
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52093
measure as these patients are identified
as not being suitable for IRF care.
Response: We believe that including
short stay discharges in the measure
promotes timely and accurate preadmission screening, as well as
discharge planning and post-discharge
care coordination. Including IRF short
stays maintains consistency across the
MSPB–PAC measures to the greatest
extent possible. Short stays constitute a
very small share of IRF stays nationally;
in FY 2014, approximately 1.8 percent
of IRF stays were short stay discharges.
Moreover, the MSPB–PAC IRF QRP
measure’s methodology excludes outlier
episodes. Therefore, we do not believe
that inclusion of short stays in the
MSPB–PAC IRF QRP measure will
unfairly disadvantage or advantage an
IRF provider in their performance on
the measure. Moreover, including short
stay discharges incentivizes providers to
maintain beneficiaries under their care
for the appropriate length of time, and
will not incentivize IRFs to prematurely
discharge their beneficiaries. We are
finalizing the MSPB–PAC IRF QRP
measure to include short stay discharges
after careful consideration of the
commenter’s input.
Comment: Several commenters
recommended the use of CMGs for risk
adjustment instead of RICs to more fully
and accurately account for and explain
variances in resource utilization and
case mix in the IRF setting. Commenters
noted that CMGs incorporate functional
status and are weighted to account for
patients’ predicted resource
requirements, while RICs only indicate
patients’ overall medical condition; as
such there can be wide variation of
reimbursement within a single RIC.
Response: We have carefully
considered the commenters feedback
and are proceeding to finalize the
measure as proposed. We believe the
beneficiary’s principal diagnosis or
impairment as provided by the RIC
currently supports the accurate
estimation of Medicare spending while
also reflecting clinical information that
is accurately and consistently coded on
IRF claims. The inclusion of RICs as
variables in the MSPB–PAC IRF QRP
risk adjustment model maintains
consistency between MSPB–PAC
resource use measures for each setting
to the greatest extent possible, in that
the other settings’ MSPB–PAC measures
do not incorporate variables reflecting
the beneficiaries’ functional status
information. We may reconsider how to
consistently incorporate functional
status into the risk adjustment models
for the MSPB–PAC measures once
standardized data mandated by the
IMPACT Act become available in the
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future. Furthermore, the covariates
incorporated in the MSPB–PAC IRF
QRP risk adjustment model partially
account for two factors in CMGs—age
and co-morbidities. For co-morbidities,
the risk adjustment specifications use
flags for Hierarchical Condition
Categories (HCCs) defined by scanning
inpatient, Part B physician/carrier, and
outpatient claims during a 90-day
lookback period. We appreciate
commenters’ thoughtful input and thank
them for their engagement with this
measure through the rulemaking
process.
Comment: A few commenters
suggested that descriptive statistics on
the measure score by provider-level
characteristics (for example, urban/rural
status and bed size) would be useful to
evaluate measure design decisions.
Response: Table 8 shows the MSPB–
PAC IRF provider scores by provider
characteristics, calculated using FY
2013 and FY 2014 data.
TABLE 8—MSPB–PAC IRF SCORES BY PROVIDER CHARACTERISTICS
Number
of
providers
Provider characteristic
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All Providers .................................
Urban/Rural:
Urban ....................................
Rural .....................................
Ownership Type:
For profit ...............................
Non-profit ..............................
Government ..........................
Unknown ...............................
Census Division:
New England ........................
Middle Atlantic ......................
East North Central ................
West North Central ...............
South Atlantic ........................
East South Central ...............
West South Central ..............
Mountain ...............................
Pacific ...................................
Other .....................................
Bed Count:
0–49 ......................................
50–99 ....................................
100–199 ................................
200–299 ................................
300 + .....................................
Number of Episodes:
0–99 ......................................
100–249 ................................
250–499 ................................
500–1000 ..............................
1000 + ...................................
Teaching:
Non-teaching .........................
Patient to ADC less than
10% ...................................
Patient to ADC 10%–20% ....
Patient to ADC greater than
20% ...................................
18:14 Aug 04, 2016
Score percentile
1st
10th
25th
50th
75th
90th
99th
1,169
0.99
0.78
0.88
0.93
0.98
1.04
1.09
1.24
979
190
0.99
0.98
0.77
0.79
0.88
0.88
0.93
0.91
0.98
0.97
1.04
1.04
1.08
1.10
1.24
1.25
345
569
142
113
1.01
0.97
0.98
0.97
0.82
0.76
0.81
0.77
0.91
0.87
0.88
0.88
0.97
0.91
0.93
0.91
1.01
0.97
0.98
0.96
1.06
1.02
1.02
1.02
1.10
1.07
1.08
1.06
1.24
1.28
1.23
1.31
36
153
210
103
162
78
226
91
106
4
1.03
0.99
0.96
0.94
1.00
1.00
1.01
1.00
0.96
0.88
0.86
0.79
0.79
0.76
0.80
0.87
0.85
0.79
0.74
0.74
0.92
0.89
0.87
0.83
0.90
0.92
0.91
0.88
0.83
0.74
0.97
0.93
0.91
0.90
0.95
0.96
0.95
0.93
0.89
0.79
1.03
0.98
0.97
0.94
1.00
0.99
1.02
0.98
0.95
0.90
1.08
1.05
1.01
0.99
1.05
1.04
1.05
1.05
1.02
0.97
1.12
1.09
1.04
1.03
1.09
1.08
1.12
1.12
1.08
0.98
1.16
1.30
1.10
1.14
1.24
1.11
1.24
1.99
1.32
0.98
114
188
231
184
452
1.01
1.01
0.98
0.97
0.98
0.79
0.80
0.79
0.77
0.77
0.91
0.91
0.87
0.87
0.88
0.96
0.96
0.92
0.91
0.92
1.01
1.00
0.98
0.97
0.97
1.04
1.06
1.04
1.01
1.03
1.12
1.09
1.10
1.07
1.08
1.25
1.30
1.24
1.44
1.24
108
344
327
216
174
1.00
0.97
0.98
0.99
1.01
0.74
0.76
0.82
0.83
0.89
0.81
0.86
0.88
0.92
0.94
0.89
0.90
0.92
0.95
0.97
0.97
0.96
0.97
0.99
1.02
1.07
1.03
1.03
1.03
1.06
1.16
1.08
1.08
1.07
1.08
1.83
1.31
1.23
1.17
1.15
1,059
0.98
0.77
0.88
0.93
0.98
1.03
1.08
1.24
63
36
0.99
1.02
0.83
0.83
0.90
0.89
0.93
0.95
0.98
1.00
1.04
1.06
1.08
1.11
1.30
1.83
11
1.00
0.88
0.90
0.91
1.03
1.06
1.07
1.08
Comment: One commenter
recommended that a geographic-specific
(for example, state or regional) median
should be used instead of the national
median, citing differences in cost,
patient population, and regulation.
Response: As noted in the proposed
rule (81 FR 24199), we proposed to use
the same payment standardization
methodology that used in the NQFendorsed hospital MSPB measure to
account for variation in Medicare
spending. This methodology removes
geographic payment differences, such as
wage index and geographic practice cost
index (GPCI), incentive payment
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score
Jkt 238001
adjustments, and other add-on
payments that support broader Medicare
program goals including indirect
graduate medical education (IME) and
hospitals serving a disproportionate
share of uninsured patients (DSH). We
believe that this approach accounts for
the differences that the commenter
raises while also maintaining
consistency with the NQF-endorsed
hospital MSPB measure’s methodology
for addressing regional variation
through payment standardization.
Comment: Some commenters
recommended that the measure be
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tested for reliability and validity prior to
finalization.
Response: The MSPB–PAC IRF QRP
measure has been tested for reliability
using 2 years of data (FY 2013 and FY
2014). The reliability results support the
20 episode case minimum, and 99.74
percent of IRF providers had moderate
or high reliability (above 0.4). Further
details on the reliability calculation are
provided in the MSPB–PAC Measure
Specifications Web site that is listed
above in this section.
Comment: Some commenters
recommended an initial confidential
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data preview period for providers, prior
to public reporting.
Response: Providers will receive a
confidential preview report with 30
days for review in advance of their data
and information being publically
displayed.
Comment: A few commenters
believed that the measure is a burden
for providers.
Response: We appreciate the
importance of avoiding undue burden
on providers. The MSPB–PAC IRF QRP
measure relies on Medicare FFS claims,
which are already reported to the
Medicare program for payment
purposes. PAC providers will not be
required to report additional data to
CMS for calculation of this measure
Comment: One commenter requested
that if the measures are finalized after a
trial, that the same FIM Rating system
be used to eliminate confusion and
ensure that providers are submitting
accurate information.
Response: The MSPB–PAC IRF QRP
Measure focuses on comparing resource
use among providers within a given
PAC setting and does not measure
clinical outcomes such as severity of
disability.
In summary, after consideration of the
public comments we received, we are
finalizing the specifications of the
MSPB–PAC IRF QRP resource use
measure, as proposed. A Web site for
the measure specifications has been
provided above in this section.
Specifically, we are finalizing the
definition of an MSPB–PAC IRF QRP
episode, beginning from episode trigger.
An episode window comprises a
treatment period beginning at the trigger
and ended upon discharge, and
associated services period beginning at
the trigger and ending 30 days after the
end of the treatment period.
Readmissions to the same IRF within 7
or fewer days do not trigger a new
episode and are instead included in the
treatment period of the first episode.
We exclude certain services that are
clinically unrelated to IRF care and/or
because IRF providers may have limited
influence over certain Medicare services
delivered by other providers during the
episode window. We also exclude
certain episodes in their entirety from
the MSPB–PAC IRF QRP measure, such
as where a beneficiary is not enrolled in
Medicare FFS for the entirety of the
lookback period plus episode window.
We finalize the inclusion of Medicare
payments for Part A and Part B claims
for services included in the MSPB–PAC
IRF QRP episodes to calculate the
MSPB–PAC IRF QRP measure.
We are finalizing our proposal to risk
adjust using covariates including age
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brackets, HCC indicators, prior inpatient
stay length, ICU stay length, clinical
case mix categories, and indicators for
originally disabled, ESRD enrollment,
long-term care status, and hospice claim
in episode window. The measure also
adjusts for geographic payment
differences such as wage index and
GPCI, and adjust for Medicare payment
differences resulting from IME and DSH.
We calculate the individual providers’
MSPB–PAC Amount which is inclusive
of MSPB–PAC IRF QRP observed
episode spending over the expected
episode spending as predicted through
risk adjustment. Individual IRF
providers’ scores are calculated as their
individual MSPB–PAC Amount divided
by the median MSPB–PAC amount
across all IRFs.
2. Measure To Address the IMPACT Act
Domain of Resource Use and Other
Measures: Discharge to Community-Post
Acute Care (PAC) Inpatient
Rehabilitation Facility (IRF) Quality
Reporting Program (QRP)
Sections 1899B(d)(1)(B) and
1899B(a)(2)(E)(ii) of the Act require the
Secretary to specify a measure to
address the domain of discharge to
community by SNFs, LTCHs, and IRFs
by October 1, 2016, and HHAs by
January 1, 2017. We proposed to adopt
the measure, Discharge to CommunityPAC IRF QRP, for the IRF QRP for the
FY 2018 payment determination and
subsequent years as a Medicare FFS
claims-based measure to meet this
requirement.
This measure assesses successful
discharge to the community from an IRF
setting, with successful discharge to the
community including no unplanned
rehospitalizations and no death in the
31 days following discharge from the
IRF. Specifically, this measure reports
an IRF’s risk-standardized rate of
Medicare FFS patients who are
discharged to the community following
an IRF stay, and 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. The
term ‘‘community’’, for this measure, is
defined as home or self care, with or
without home health services, based on
Patient Discharge Status Codes 01, 06,
81, and 86 on the Medicare FFS
claim.17 18 This measure is
17 National Uniform Billing Committee Official
UB–04 Data Specifications Manual 2017, Version
11, July 2016, Copyright 2016, American Hospital
Association.
18 This definition is not intended to suggest that
board and care homes, assisted living facilities, or
other settings included in the definition of
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52095
conceptualized uniformly across the
PAC settings, in terms of the definition
of the discharge to community outcome,
the approach to risk adjustment, and the
measure calculation.
Discharge to a community setting is
an important health care outcome for
many patients for whom the overall
goals of post-acute care include
optimizing functional improvement,
returning to a previous level of
independence, and avoiding
institutionalization. Returning to the
community is also an important
outcome for many patients who are not
expected to make functional
improvement during their IRF stay, and
for patients who may be expected to
decline functionally due to their
medical condition. The discharge to
community outcome offers a multidimensional view of preparation for
community life, including the cognitive,
physical, and psychosocial elements
involved in a discharge to the
community.19 20
In addition to being an important
outcome from a patient and family
perspective, patients discharged to
community settings, on average, incur
lower costs over the recovery episode,
compared with those discharged to
institutional settings.21 22 Given the high
costs of care in institutional settings,
encouraging IRFs to prepare patients for
discharge to community, when
clinically appropriate, may have costsaving implications for the Medicare
program.23 Also, providers have
discovered that successful discharge to
community was a major driver of their
ability to achieve savings, where
capitated payments for post-acute care
‘‘community’’ for the purpose of this measure are
the most integrated setting for any particular
individual or group of individuals under the
Americans with Disabilities Act (ADA) and Section
504.
19 El-Solh AA, Saltzman SK, Ramadan FH,
Naughton BJ. Validity of an artificial neural
network in predicting discharge destination from a
postacute geriatric rehabilitation unit. Archives of
physical medicine and rehabilitation.
2000;81(10):1388–1393.
20 Tanwir S, Montgomery K, Chari V, Nesathurai
S. Stroke rehabilitation: Availability of a family
member as caregiver and discharge destination.
European journal of physical and rehabilitation
medicine. 2014;50(3):355–362.
21 Dobrez D, Heinemann AW, Deutsch A,
Manheim L, Mallinson T. Impact of Medicare’s
prospective payment system for inpatient
rehabilitation facilities on stroke patient outcomes.
American journal of physical medicine &
rehabilitation/Association of Academic Physiatrists.
2010;89(3):198–204.
22 Gage B, Morley M, Spain P, Ingber M.
Examining Post Acute Care Relationships in an
Integrated Hospital System. Final Report. RTI
International;2009.
23 Ibid.
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were in place.24 For patients who
require long-term care due to persistent
disability, discharge to community
could result in lower long-term care
costs for Medicaid and for patients’ outof-pocket expenditures.25
Analyses conducted for ASPE on PAC
episodes, using a 5 percent sample of
2006 Medicare claims, revealed that
relatively high average, unadjusted
Medicare payments are associated with
discharge to institutional settings from
IRFs, SNFs, LTCHs or HHAs, as
compared with payments associated
with discharge to community settings.26
Average, unadjusted Medicare payments
associated with discharge to community
settings ranged from $0 to $4,017 for IRF
discharges, $0 to $3,544 for SNF
discharges, $0 to $4,706 for LTCH
discharges, and $0 to $992 for HHA
discharges. In contrast, payments
associated with discharge to noncommunity settings were considerably
higher, ranging from $11,847 to $25,364
for IRF discharges, $9,305 to $29,118 for
SNF discharges, $12,465 to $18,205 for
LTCH discharges, and $7,981 to $35,192
for HHA discharges.27
Measuring and comparing facilitylevel discharge to community rates is
expected to help differentiate among
facilities with varying performance in
this important domain, and to help
avoid disparities in care across patient
groups. Variation in discharge to
community rates has been reported
within and across post-acute settings;
across a variety of facility-level
characteristics, such as geographic
location (for example, regional location,
urban or rural location), ownership (for
example, for-profit or nonprofit), and
freestanding or hospital-based units;
and across patient-level characteristics,
such as race and gender.28 29 30 31 32 33
24 Doran JP, Zabinski SJ. Bundled payment
initiatives for Medicare and non-Medicare total
joint arthroplasty patients at a community hospital:
Bundles in the real world. The journal of
arthroplasty. 2015;30(3):353–355.
25 Newcomer RJ, Ko M, Kang T, Harrington C,
Hulett D, Bindman AB. Health Care Expenditures
After Initiating Long-term Services and Supports in
the Community Versus in a Nursing Facility.
Medical Care. 2016;54(3):221–228.
26 Gage B, Morley M, Spain P, Ingber M.
Examining Post Acute Care Relationships in an
Integrated Hospital System. Final Report. RTI
International;2009.
27 Ibid.
28 Reistetter TA, Karmarkar AM, Graham JE, et al.
Regional variation in stroke rehabilitation
outcomes. Archives of physical medicine and
rehabilitation. 2014;95(1):29–38.
29 El-Solh AA, Saltzman SK, Ramadan FH,
Naughton BJ. Validity of an artificial neural
network in predicting discharge destination from a
postacute geriatric rehabilitation unit. Archives of
physical medicine and rehabilitation.
2000;81(10):1388–1393.
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Discharge to community rates in the IRF
setting have been reported to range from
about 60 to 80 percent.34 35 36 37 38 39
Longer-term studies show that rates of
discharge to community from IRFs have
decreased over time as IRF length of
stay has decreased.40 41 In the IRF
Medicare FFS population, using CY
2013 national claims data, we
discovered that approximately 69
percent of patients were discharged to
the community. Greater variation in
discharge to community rates is seen in
the SNF setting, with rates ranging from
30 March 2015 Report to the Congress: Medicare
Payment Policy. Medicare Payment Advisory
Commission; 2015.
31 Bhandari VK, Kushel M, Price L, Schillinger D.
Racial disparities in outcomes of inpatient stroke
rehabilitation. Archives of physical medicine and
rehabilitation. 2005;86(11):2081–2086.
32 Chang PF, Ostir GV, Kuo YF, Granger CV,
Ottenbacher KJ. Ethnic differences in discharge
destination among older patients with traumatic
brain injury. Archives of physical medicine and
rehabilitation. 2008;89(2):231–236.
33 Berges IM, Kuo YF, Ostir GV, Granger CV,
Graham JE, Ottenbacher KJ. Gender and ethnic
differences in rehabilitation outcomes after hipreplacement surgery. American journal of physical
medicine & rehabilitation/Association of Academic
Physiatrists. 2008;87(7):567–572.
34 Galloway RV, Granger CV, Karmarkar AM, et al.
The Uniform Data System for Medical
Rehabilitation: Report of patients with debility
discharged from inpatient rehabilitation programs
in 2000–2010. American journal of physical
medicine & rehabilitation/Association of Academic
Physiatrists. 2013;92(1):14–27.
35 Morley MA, Coots LA, Forgues AL, Gage BJ.
Inpatient rehabilitation utilization for Medicare
beneficiaries with multiple sclerosis. Archives of
physical medicine and rehabilitation.
2012;93(8):1377–1383.
36 Reistetter TA, Graham JE, Deutsch A, Granger
CV, Markello S, Ottenbacher KJ. Utility of
functional status for classifying community versus
institutional discharges after inpatient
rehabilitation for stroke. Archives of physical
medicine and rehabilitation. 2010;91(3):345–350.
37 Gagnon D, Nadeau S, Tam V. Clinical and
administrative outcomes during publicly-funded
inpatient stroke rehabilitation based on a case-mix
group classification model. Journal of rehabilitation
medicine. 2005;37(1):45–52.
38 DaVanzo J, El-Gamil A, Li J, Shimer M,
Manolov N, Dobson A. Assessment of patient
outcomes of rehabilitative care provided in
inpatient rehabilitation facilities (IRFs) and after
discharge. Vienna, VA: Dobson DaVanzo &
Associates, LLC;2014.
39 Kushner DS, Peters KM, Johnson-Greene D.
Evaluating Siebens Domain Management Model for
Inpatient Rehabilitation to Increase Functional
Independence and Discharge Rate to Home in
Geriatric Patients. Archives of physical medicine
and rehabilitation. 2015;96(7):1310–1318.
40 Galloway RV, Granger CV, Karmarkar AM, et al.
The Uniform Data System for Medical
Rehabilitation: Report of patients with debility
discharged from inpatient rehabilitation programs
in 2000–2010. American journal of physical
medicine & rehabilitation/Association of Academic
Physiatrists. 2013;92(1):14–27.
41 Mallinson T, Deutsch A, Bateman J, et al.
Comparison of discharge functional status after
rehabilitation in skilled nursing, home health, and
medical rehabilitation settings for patients after hip
fracture repair. Archives of physical medicine and
rehabilitation. 2014;95(2):209–217.
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31 to 65 percent.42 43 44 45 A multi-center
study of 23 LTCHs demonstrated that
28.8 percent of 1,061 patients who were
ventilator-dependent on admission were
discharged to home.46 A single-center
study revealed that 31 percent of LTCH
hemodialysis patients were discharged
to home.47 One study noted that 64
percent of beneficiaries who were
discharged from the home health
episode did not use any other acute or
post-acute services paid by Medicare in
the 30 days after discharge.48 However,
significant numbers of patients were
admitted to hospitals (29 percent) and
lesser numbers to SNFs (7.6 percent),
IRFs (1.5 percent), home health (7.2
percent) or hospice (3.3 percent).49
Discharge to community is an
actionable health care outcome, as
targeted interventions have been shown
to successfully increase discharge to
community rates in a variety of postacute settings.50 51 52 53 Many of these
42 El-Solh AA, Saltzman SK, Ramadan FH,
Naughton BJ. Validity of an artificial neural
network in predicting discharge destination from a
postacute geriatric rehabilitation unit. Archives of
physical medicine and rehabilitation.
2000;81(10):1388–1393.
43 Hall RK, Toles M, Massing M, et al. Utilization
of acute care among patients with ESRD discharged
home from skilled nursing facilities. Clinical
journal of the American Society of Nephrology:
CJASN. 2015;10(3):428–434.
44 Stearns SC, Dalton K, Holmes GM, Seagrave
SM. Using propensity stratification to compare
patient outcomes in hospital-based versus
freestanding skilled-nursing facilities. Medical care
research and review: MCRR. 2006;63(5):599–622.
45 Wodchis WP, Teare GF, Naglie G, et al. Skilled
nursing facility rehabilitation and discharge to
home after stroke. Archives of physical medicine
and rehabilitation. 2005;86(3):442–448.
46 Scheinhorn DJ, Hassenpflug MS, Votto JJ, et al.
Post-ICU mechanical ventilation at 23 long-term
care hospitals: A multicenter outcomes study.
Chest. 2007;131(1):85–93.
47 Thakar CV, Quate-Operacz M, Leonard AC,
Eckman MH. Outcomes of hemodialysis patients in
a long-term care hospital setting: A single-center
study. American journal of kidney diseases: The
official journal of the National Kidney Foundation.
2010;55(2):300–306.
48 Wolff JL, Meadow A, Weiss CO, Boyd CM, Leff
B. Medicare home health patients’ transitions
through acute and post-acute care settings. Medical
care. 2008;46(11):1188–1193.
49 Ibid.
50 Kushner DS, Peters KM, Johnson-Greene D.
Evaluating Siebens Domain Management Model for
Inpatient Rehabilitation to Increase Functional
Independence and Discharge Rate to Home in
Geriatric Patients. Archives of physical medicine
and rehabilitation. 2015;96(7):1310–1318.
51 Wodchis WP, Teare GF, Naglie G, et al. Skilled
nursing facility rehabilitation and discharge to
home after stroke. Archives of physical medicine
and rehabilitation. 2005;86(3):442–448.
52 Berkowitz RE, Jones RN, Rieder R, et al.
Improving disposition outcomes for patients in a
geriatric skilled nursing facility. Journal of the
American Geriatrics Society. 2011;59(6):1130–1136.
53 Kushner DS, Peters KM, Johnson-Greene D.
Evaluating use of the Siebens Domain Management
Model during inpatient rehabilitation to increase
functional independence and discharge rate to
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interventions involve discharge
planning or specific rehabilitation
strategies, such as addressing discharge
barriers and improving medical and
functional status.54 55 56 57 The
effectiveness of these interventions
suggests that improvement in discharge
to community rates among post-acute
care patients is possible through
modifying provider-led processes and
interventions.
A TEP convened by our measure
development contractor was strongly
supportive of the importance of
measuring discharge to community
outcomes, and implementing the
measure, Discharge to Community-PAC
IRF QRP in the IRF QRP. The panel
provided input on the technical
specifications of this measure, including
the feasibility of implementing the
measure, as well as the overall measure
reliability and validity. A summary of
the TEP proceedings is available on the
PAC Quality Initiatives Downloads and
Videos Web site at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
We also solicited stakeholder
feedback on the development of this
measure through a public comment
period held from November 9, 2015,
through December 8, 2015. Several
stakeholders and organizations,
including the MedPAC, among others,
supported this measure for
implementation. The public comment
summary report for the measure is
available on our Web site at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
home in stroke patients. PM & R: The journal of
injury, function, and rehabilitation. 2015;7(4):354–
364.
54 Kushner DS, Peters KM, Johnson-Greene D.
Evaluating Siebens Domain Management Model for
Inpatient Rehabilitation to Increase Functional
Independence and Discharge Rate to Home in
Geriatric Patients. Archives of physical medicine
and rehabilitation. 2015;96(7):1310–1318.
55 Wodchis WP, Teare GF, Naglie G, et al. Skilled
nursing facility rehabilitation and discharge to
home after stroke. Archives of physical medicine
and rehabilitation. 2005;86(3):442–448.
56 Berkowitz RE, Jones RN, Rieder R, et al.
Improving disposition outcomes for patients in a
geriatric skilled nursing facility. Journal of the
American Geriatrics Society. 2011;59(6):1130–1136.
57 Kushner DS, Peters KM, Johnson-Greene D.
Evaluating use of the Siebens Domain Management
Model during inpatient rehabilitation to increase
functional independence and discharge rate to
home in stroke patients. PM & R: The journal of
injury, function, and rehabilitation. 2015;7(4):354–
364.
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IMPACT-Act-Downloads-andVideos.html.
The NQF-convened MAP met on
December 14 and 15, 2015, and
provided input on the use of this
Discharge to Community-PAC IRF QRP
measure in the IRF QRP. The MAP
encouraged continued development of
the measure to meet the mandate of the
IMPACT Act. The MAP supported the
alignment of this measure across PAC
settings, using standardized claims data.
More information about the MAP’s
recommendations for this measure is
available at https://
www.qualityforum.org/Publications/
2016/02/MAP_2016_Considerations_
for_Implementing_Measures_in_
Federal_Programs_-_PAC-LTC.aspx.
Since the MAP’s review and
recommendation of continued
development, we have continued to
refine risk-adjustment models and
conduct measure testing for this
measure, as recommended by the MAP.
This measure is consistent with the
information submitted to the MAP, and
the original MAP submission and our
continued refinements support its
scientific acceptability for use in quality
reporting programs. As discussed with
the MAP, we fully anticipate that
additional analyses will continue as we
submit this measure to the ongoing
measure maintenance process.
We reviewed the NQF’s consensusendorsed measures and were unable to
identify any NQF-endorsed resource use
or other measures for post-acute care
focused on discharge to community. In
addition, we are unaware of any other
post-acute care measures for discharge
to community that have been endorsed
or adopted by other consensus
organizations. Therefore, we proposed
the measure, Discharge to CommunityPAC IRF QRP, under the Secretary’s
authority to specify non-NQF-endorsed
measures under section 1899B(e)(2)(B)
of the Act.
We proposed to use data from the
Medicare FFS claims and Medicare
eligibility files to calculate this measure.
We proposed to use data from the
‘‘Patient Discharge Status Code’’ on
Medicare FFS claims to determine
whether a patient was discharged to a
community setting for calculation of
this measure. In all PAC settings, we
tested the accuracy of determining
discharge to a community setting using
the ‘‘Patient Discharge Status Code’’ on
the PAC claim by examining whether
discharge to community coding based
on PAC claim data agreed with
discharge to community coding based
on PAC assessment data. We found
excellent agreement between the two
data sources in all PAC settings, ranging
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from 94.6 percent to 98.8 percent.
Specifically, in the IRF setting, using
2013 data, we found 98.8 percent
agreement in coding of community and
non-community discharges when
comparing discharge status codes on
claims and the Discharge to Living
Setting (item 44A) codes on the IRF–
PAI. We further examined the accuracy
of the ‘‘Patient Discharge Status Code’’
on the PAC claim by assessing how
frequently discharges to an acute care
hospital were confirmed by follow-up
acute care claims. We discovered that 88
percent to 91 percent of IRF, LTCH, and
SNF claims with acute care discharge
status codes were followed by an acute
care claim on the day of, or day after,
PAC discharge. We believed these data
support the use of the claims ‘‘Patient
Discharge Status Code’’ for determining
discharge to a community setting for
this measure. In addition, this measure
can feasibly be implemented in the IRF
QRP because all data used for measure
calculation are derived from Medicare
FFS claims and eligibility files, which
are already available to CMS.
Based on the evidence discussed
above, we proposed to adopt the
measure, Discharge to Community-PAC
IRF QRP, for the IRF QRP for FY 2018
payment determination and subsequent
years. This measure is calculated using
2 years of data. We proposed a
minimum of 25 eligible stays in a given
IRF for public reporting of the measure
for that IRF. Since Medicare FFS claims
data are already reported to the
Medicare program for payment
purposes, and Medicare eligibility files
are also available, IRFs will not be
required to report any additional data to
us for calculation of this measure. The
measure denominator is the riskadjusted expected number of discharges
to community. The measure numerator
is the risk-adjusted estimate of the
number of patients who are discharged
to the community, do not have an
unplanned readmission to an acute care
hospital or LTCH in the 31-day postdischarge observation window, and who
remain alive during the post-discharge
observation window. The measure is
risk-adjusted for variables such as age
and sex, principal diagnosis,
comorbidities, ESRD status, and
dialysis, among other variables. For
technical information about the
proposed measure, including
information about the measure
calculation, risk adjustment, and
denominator exclusions, we referred
readers to the document titled, Proposed
Measure Specifications for Measures
Proposed in the FY 2017 IRF QRP
proposed rule, available at https://
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www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html.
We stated in the proposed rule that
we intend to provide initial confidential
feedback to IRFs, prior to public
reporting of this measure, based on
Medicare FFS claims data from
discharges in CY 2015 and 2016. We
intend to publicly report this measure
using claims data from discharges in CY
2016 and 2017. We will submit this
measure to the NQF for consideration
for endorsement.
In the CY 2013 OPPS/ASC final rule
(77 FR 68500), we finalized our policy
to use a subregulatory approach to
incorporate non-substantive changes to
measures adopted in the IRF QRP,
including changes to exclusions. In that
rule, we noted that we expect to make
this determination on a measure-bymeasure basis and that examples of nonsubstantive changes to measures might
include exclusions for a measure. For
the proposed Discharge to CommunityIRF QRP measure, we have added an
exclusion of patients/residents with a
hospice benefit in the post-discharge
observation window, in response to
comments received during measure
development and our ongoing analysis
and testing. The rationale for the
exclusion of patients/residents with a
hospice benefit in the post-discharge
observation window aligns with the
rationale for exclusion of discharges to
hospice. Based on testing, we found that
patients/residents with a post-discharge
hospice benefit have a much higher
death rate in the post-discharge
observation window compared with
patients/residents without a hospice
benefit. We determined that the
addition of this hospice exclusion
enhances the measure by excluding
patients/residents with a high
likelihood of post-discharge death and
improves the national observed
discharge to community rate for IRFs by
approximately 0.8 percent. With the
addition of this hospice exclusion, we
do not believe burden is added, nor that
the addition of this exclusion is a
substantive change to the overall
measure. Failure to include this hospice
exclusion could lead to unintended
consequences and access issues for
terminally-ill patients/residents in our
PAC populations.
We invited public comment on our
proposal to adopt the measure,
Discharge to Community-PAC IRF QRP,
for the IRF QRP. The comments we
received on this topic, with our
responses, appear below.
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Comment: Multiple commenters,
including MedPAC, supported the
Discharge to Community-PAC IRF QRP
measure, noting that it is a critical
measure assessing the ability of PAC
providers to avoid patient
institutionalization. One commenter
noted that measuring the rate that the
various PAC settings discharge patients
to the community, without an
admission (or readmission) to an acute
care hospital within 30 days, is one of
the most relevant patient-centered
measures that exists in the post-acute
care area. One commenter conveyed that
successful transitions to the community
are expected to decrease potentially
preventable readmissions, while another
was appreciative that the measure did
not place additional data collection
burden on facilities. One commenter
stated that achieving a standardized and
interoperable patient assessment data
set and stable quality measures as
quickly as possible will allow for better
cross-setting comparisons and the
evolution of better quality measures
with uniform risk standardization.
Response: We thank the commenters
for their support of the Discharge to
Community-PAC IRF QRP measure, and
their recognition of the patientcenteredness of this measure, its
potential to decrease post-discharge
readmissions, and its lack of data
collection burden. We also thank the
commenter for their support of
standardized and interoperable patient
assessment data and quality measures.
As mandated by the IMPACT Act, we
are moving toward the goal of
standardized patient assessment data
and quality measures across PAC
settings.
Comment: One commenter
interpreted our measure proposal
language as suggesting that functional
improvement is not a requirement, and
encouraged that Medicare coverage for
maintenance nursing and therapy be
ensured and reflected by the measure.
Response: Our intent in the measure
proposal was to acknowledge that
discharge to community can be an
important goal even for patients who
may not be able to make functional
improvement. This measure does not
impact Medicare coverage rules for
maintenance nursing and therapy.
Comment: Several commenters
expressed concerns regarding the use of
the Patient Discharge Status Code
variable to define community
discharges. Commenters emphasized
that it was important to ensure that only
home and community based settings
were included in the definition of
community, and were concerned that
Code 01 (Discharge to home or self-care)
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included institutional settings such as
jail or law enforcement. One commenter
expressed that many settings included
under Code 01 do not satisfy the home
and community based settings rule, and
may be inconsistent with the integration
mandate of the Americans with
Disabilities Act. Commenters strongly
recommended that CMS either revise
Patient Discharge Status Code 01 to
exclude non community-based settings,
or use alternative variables to capture
discharge to community.
Response: We agree with the
commenters that the discharge to
community measure should only
capture discharges to home and
community based settings. We believe
that the comment referring to the ‘‘home
and community based settings rule’’
refers to Medicaid regulations
applicable to services authorized under
sections 1915(c), 1915(i) and 1915(k) of
the Social Security Act (the Act), which
are provided through waivers or state
plans amendments approved by CMS.
We would like to clarify that this
measure only captures discharges to
home and community based settings,
not to institutional settings, and is
consistent with both Medicaid
regulations requiring home and
community based settings to support
integration, and also with the
Americans with Disabilities Act (ADA),
based on Patient Discharge Status Codes
01, 06, 81, and 86 on the Medicare FFS
PAC claim.58 Discharges to court or law
enforcement are not included under
Code 01 of the Patient Discharge Status
Code; rather these are included under
Code 21 (Discharged/transferred to
Court/Law Enforcement).
We also note that Title II of the ADA
requires public entities to administer
services, programs, and activities in the
most integrated setting appropriate to
the needs of qualified individuals with
disabilities (28 CFR 35.130(d)). The
preamble discussion of the ‘‘integration
regulation’’ explains that ‘‘the most
integrated setting’’ is one that enables
individuals with disabilities to interact
with nondisabled persons to the fullest
extent possible. Integrated settings are
those that provide individuals with
disabilities opportunities to live, work,
and receive services in the greater
community, like individuals without
disabilities (28 CFR part 35, app. A
(2010) (addressing § 35.130)).
Comment: Several commenters stated
that PAC patients/residents discharged
to a nursing facility as long-term care
58 National Uniform Billing Committee Official
UB–04 Data Specifications Manual 2017, Version
11, July 2016, Copyright 2016, American Hospital
Association.
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residents should not be considered
discharges to community, particularly if
they were discharged to the nursing
facility from the Medicare-certified
skilled nursing part of the same nursing
home, and even if they resided in a
long-term nursing facility at baseline.
Commenters emphasized that a nursing
home does not represent an individual’s
own home in their own community.
These commenters interpreted the
measure specifications as allowing these
discharges to nursing facility to be
coded as ‘‘group home’’, ‘‘foster care’’,
or ‘‘other residential care arrangement’’
under discharge status code 01.
Commenters expressed concern that
coding discharges from the SNF to
residential/long-term care facility
within the same nursing home as
discharges to community would
unfairly advantage SNFs and artificially
inflate their discharge to community
rates, would disadvantage other PAC
providers, and would miscommunicate
a facility’s actual discharge to
community performance to the average
Medicare beneficiary. One commenter
suggested exclusion of patients
discharged to a non-Medicare certified
residence, such as a ‘‘group home’’ or
‘‘foster care’’ or other arrangement.
Response: We agree with the
commenters that discharges to long-term
care nursing facilities, or any other
institutional settings, should not be
coded as discharges to community. We
also recognize the differences in
required discharge planning processes
and resources for discharging a patient/
resident to the community compared
with discharging to a long-term nursing
facility. The discharge to community
measure only captures discharges to
home and community based settings as
discharges to community, based on
Patient Discharge Status Codes 01, 06,
81, and 86 on the Medicare FFS PAC
claim.59 These codes do not include
discharges to long-term care nursing
facilities or any other institutional
setting that may violate the integration
mandate of Title II of the ADA. Instead,
depending on the nature of the facility
to which patients/residents are
discharged, such discharges may be
coded on the Medicare FFS claim as 04,
64, 84, 92, or another appropriate code
for an institutional discharge.
In response to the commenters’
concerns that SNFs may be unfairly
advantaged by this measure as
compared with other PAC providers, we
would like to note that, in our measure
development samples, the national
discharge to community rate for SNFs
was 47.26 percent, while this rate for
59 Ibid.
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IRFs was considerably higher (69.51
percent). Further, using an MDS-claims
linked longitudinal file, we found that
of the SNF stays that had a prehospitalization non-PPS MDS
assessment suggesting prior nursing
facility residence, two-thirds had a
discharge status code of 30 (still
patient), and approximately 18 percent
had a discharge status code of 02 (acute
hospital). Less than 5 percent of these
patients had a Discharge Status Code of
01 (discharge to home or self care).
Thus, the commenters’ concerns that
discharges from SNF to nursing facility
are largely coded as Patient Discharge
Status Code 01 are not reflected in our
data.
Comment: Some commenters
expressed concern that the discharge to
community measure fails to distinguish
patients/residents who lived in a longterm care nursing facility at baseline
and returned to the nursing facility after
their PAC stay. Commenters
recommended that baseline long-stay
nursing facility residents be excluded
from the discharge to community
measure, as they could not be
reasonably expected to discharge back
to the community. One commenter
noted that these residents have a very
different discharge process back to the
nursing facility compared with patients
discharged to the community. The
commenter recommended that different
measures be developed for the baseline
nursing facility resident population,
such as return to prior level of function,
improvement in function, prevention of
further functional decline, development
of pressure ulcers, or accidental falls.
The commenter also recognized CMS’s
current efforts in monitoring transitions
of care and quality requirements in
long-term care facilities. Commenters
suggested that CMS could use
longitudinal Minimum Data Set-linkage
to identify and exclude baseline nursing
facility residents.
Response: We appreciate the
commenters’ concerns and their
recommendation to exclude baseline
nursing facility residents from the
discharge to community measure, and to
distinguish baseline custodial nursing
facility residents who are discharged
back to the nursing facility after their
PAC stay. We recognize that patients/
residents who permanently lived in a
nursing facility at baseline may not be
expected to discharge back to a home
and community based setting after their
PAC stay. We also recognize that, for
baseline nursing facility residents, a
discharge back to their nursing facility
represents a discharge to their baseline
residence. We agree with the commenter
about the differences in discharge
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planning processes when discharging a
patient/resident to the community
compared with discharging to a longterm nursing facility. However, using
Medicare FFS claims alone, we are
unable to accurately identify baseline
nursing facility residents. Potential
future modifications of the measure
could include the assessment of the
feasibility and impact of excluding
baseline nursing facility residents from
the measure through the addition of
patient assessment-based data.
However, we note that, currently, the
IRF–PAI is the only PAC assessment
that contains an item related to prehospital baseline living setting.
Comment: A few commenters
questioned the inclusion of only
Medicare FFS patients/residents in the
measure, and stated whether the
measure would be expanded to include
patients/residents with other payers or
plan types. One commenter
recommended that the patient
populations be consistent across IRF
measures, and not vary by payer or plan
type, stating that consistency in measure
populations across IRF measures was
important for facilities to understand
their quality metrics. Other commenters
recommended that the discharge to
community measure include other payer
populations, and particularly
emphasized the importance of including
Medicare Advantage patients in the
measure, highlighting that Medicare
Advantage patients were included in the
IRF Drug Regimen Review measure. The
commenters noted that the Medicare
Advantage population was a rapidly
growing Medicare population,
warranting their inclusion in quality
measures.
Response: We agree that is it
important to monitor quality and
resource use outcomes of all post-acute
care patients/residents, not just
Medicare FFS patients/residents. The
discharge to community measure is
limited to the Medicare FFS population
through the use of a Medicare FFS
claim, but we will consider the
appropriateness and feasibility of
including Managed Care patients/
residents in future modifications of the
measure. We would like to note that
further expansion of the measure to
include Medicare Managed Care or
other payer populations would require
standardized data collection across all
settings and payer populations.
Comment: MedPAC recommended
that CMS confirm discharge to a
community setting with the absence of
a subsequent claim to a hospital, IRF,
SNF, or LTCH, to ensure that discharge
to community rates reflect actual facility
performance. Other commenters also
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recommended that CMS assess the
reliability and validity of the Patient
Discharge Status Code on PAC claims.
Commenters cited MedPAC and other
studies, noting that Patient Discharge
Status Codes often have low reliability,
and that this could impact accurate
portrayal of measure performance.
Response: We are committed to
developing measures based on reliable
and valid data. This measure does
confirm the absence of hospital or LTCH
claims following discharge to a
community setting. Unplanned hospital
and LTCH readmissions following the
discharge to community, including
those on the day of IRF discharge, are
considered an unfavorable outcome. We
will consider verifying the absence of
IRF and SNF claims following discharge
to a community setting, as we continue
to refine this measure. Nonetheless, we
would like to note that an ASPE report
on post-acute care relationships found
that, following discharge to community
settings from IRFs, LTCHs, or SNFs in
a 5 percent Medicare sample, IRFs or
SNFs were very infrequently reported as
the next site of post-acute care.60
Because the discharge to community
measure is a measure of discharge
destination from the PAC setting, we
have chosen to use the PAC-reported
discharge destination (from the
Medicare FFS claims) to determine
whether a patient/resident was
discharged to the community (based on
discharge status codes 01, 06, 81, 86).
We assessed the reliability of the claims
discharge status code(s) by examining
agreement between discharge status on
claims and assessment instruments in
all four PAC settings. We found between
94 and 99 percent agreement in coding
of community discharges on matched
claims and assessments in each of the
PAC settings. We also assessed how
frequently discharges to acute care, as
indicated on the PAC claim, were
confirmed by follow-up acute care
claims, and found that 88 percent to 91
percent of IRF, LTCH, and SNF claims
indicating acute care discharge were
followed by an acute care claim on the
day of, or day after, PAC discharge. We
believe that these data support the use
of the ‘‘Patient Discharge Status Code’’
from the PAC claim for determining
discharge to a community setting for
this measure.
The use of the claims discharge status
code to identify discharges to the
community was discussed at length
with the TEP convened by our measure
development contractor. TEP members
did not express significant concerns
regarding the accuracy of the claims
discharge status code in coding
community discharges, nor about our
use of the discharge status code for
defining this quality measure. A
summary of the TEP proceedings is
available on the PAC Quality Initiatives
Downloads and Videos Web site at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Comment: A few commenters
conveyed the importance of ensuring
consistency in coding of discharge
status codes across PAC settings, and
requested a clear definition of
community discharge for purposes of
this measure.
Response: This measure captures
discharges to home and community
based settings, with or without home
health services. Community, for this
measure, is defined as Patient Discharge
Status codes 01, 06, 81, and 86 on the
PAC claim. Code 01 refers to discharge
to home or self care; Code 06 refers to
discharge with home health services;
Code 81 refers to discharge to home or
self care with a planned acute care
readmission; and Code 86 refers to
discharge with home health services
with a planned acute care readmission.
We refer readers to the National
Uniform Billing Committee Data
Specifications Manual for coding
instructions.61 For further details on
measure specifications, including the
definition of community, we refer
readers to the Measure Specifications
for Measures Adopted in the FY 2017
IRF QRP final rule, posted on the CMS
IRF QRP Web site at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html.
Comment: Some commenters were
concerned about overlap between the
discharge to community and
readmissions measures, specifically
expressing concern that a single postdischarge readmission would affect a
facility’s performance on two measures.
One commenter expressed that the
discharge to community measure
essentially functioned as a readmission
measure, and that different definitions
of readmissions could be confusing for
60 Gage B, Morley M, Spain P, Ingber M.
Examining Post Acute Care Relationships in an
Integrated Hospital System Final Report. RTI
International; 2009.
61 National Uniform Billing Committee Official
UB–04 Data Specifications Manual 2017, Version
11, July 2016, Copyright 2016, American Hospital
Association.
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providers and patients, lead to
unintended differences in the data CMS
receives, and skew the data. One
commenter indicated that the IMPACT
Act measures overemphasized reducing
readmissions and did not adequately
address the domains they are meant to
measure. This commenter suggested that
quality measures should exclude
aspects measured by other domains
and/or quality measures, and instead
should measure unique domains. This
commenter further recommended that
the Secretary suspend this measure
until CMS can evaluate whether the
inclusion of readmissions within each
quality measure is necessary, and
whether it produces duplicative results
within the various quality reporting
programs.
Response: There are distinct
differences between the discharge to
community and readmission measures
under the IRF QRP. Although there may
be some overlap in the outcomes
captured across the two measures (for
example, patients who have a postdischarge readmission also have an
unsuccessful discharge to community),
the discharge to community and
readmission measures each have a
distinct purpose, outcome definition,
and measure population. For example,
the discharge to community measure
assesses the rate of successful
discharges to the community, defined as
discharge to a community setting
without post-discharge unplanned
readmissions or death, while the
readmission measures assess the rate of
readmissions for patients discharged to
lower levels of care from the IRF.
Our goal is to develop measures that
are meaningful to patients and
consumers, and assist them in making
informed choices when selecting postacute providers. Since the goal of PAC
for most patients and family members is
to be discharged to the community and
remain in the community, from a
patient/consumer perspective, it is
important to assess whether a patient
remained in the community after
discharge and to separately report
discharge to community rates. In
addition to assessing the success of
community discharges, the inclusion of
post-discharge readmission and death
outcomes in this measure is intended to
avoid the potential unintended
consequence of inappropriate
discharges to the community.
Comment: Several commenters
expressed concern that the discharge to
community measure holds IRFs
accountable for post-discharge adverse
outcomes, including unplanned
readmissions and death. Commenters
expressed that IRFs have little control
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over patient behavior or adherence once
the patient is discharged from the
facility, and should not be penalized for
post-discharge events. We received
recommendations to exclude patients
who have been discharged to the
community and then expire within the
post-discharge window; this
recommendation was based on the
explanation that the types of patients
treated in IRFs greatly varied and that
including post-discharge death in the
measure could lead to an inaccurate
reflection of the quality of care
furnished by the IRF.
Response: We monitor 31-day postdischarge unplanned readmissions and
death in the measure to more accurately
capture successful discharge to
community outcomes, and to avoid the
potential unintended consequence of
inappropriate discharges to the
community. We expect that improved
care transitions and care coordination
across providers will reduce these postdischarge adverse outcomes. Members
of our TEP unanimously believed that
the definition of discharge to
community should be broader than
discharge destination alone, and should
incorporate indicators of post-discharge
patient outcomes. TEP members agreed
with the inclusion of both postdischarge readmissions and death in the
discharge to community measure.
We found, through our analyses in
our measure development sample, that
death in the 31 days following discharge
to community is an infrequent event,
with only 0.9 percent of IRF Medicare
FFS beneficiaries dying during that
period. By risk adjusting for prior
service use (that is, number of
hospitalizations in the past year), our
intent is to adjust for patient
characteristics, such as access, patient
compliance, or sociodemographic and
socioeconomic factors that may
influence the likelihood of postdischarge readmissions. Additionally,
by excluding patients discharged against
medical advice from the measure, we
are excluding patients who demonstrate
non-compliance or non-adherence
during the PAC stay.
We would like to note that we do not
expect facilities to achieve a 0 percent
readmission or death rate in the
measure’s post-discharge observation
window; the focus is to identify
facilities with unexpectedly high rates
of unplanned readmissions and death
for quality monitoring purposes.
Comment: Multiple commenters
suggested that the measure include risk
adjustment for sociodemographic factors
such as home and community caregivers
and supports, and socioeconomic
factors of patients and communities.
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Response: We understand the
importance of home and community
supports, sociodemographic factors, and
socioeconomic factors in ensuring a
successful discharge to community
outcome. The discharge to community
measure is a claims-based measure in its
first phase of development. Currently,
there are no standardized data on
variables such as living status or family
and caregiver supports across the four
PAC settings. As we refine the measure
in the future, we will consider testing
and adding additional relevant data
sources and standardized items for risk
adjustment of this measure. We refer
readers to section VIII.F of this final rule
for a more detailed discussion of the
role of SES/SDS factors in risk
adjustment of our measures.
Comment: A few commenters
emphasized the relationship between
functional gains during the IRF stay and
the ability to discharge to the
community, stating that functional
status measures are important indicators
of recovery and achievement of
rehabilitation goals and should be more
intimately embedded in the proposed
discharge to community measure. One
commenter stated that return to one’s
previous home represents part of the
goal of care. The commenter noted that,
additionally, it is also important that the
patient is able to function to the greatest
possible extent in the home and
community setting and achieve the
highest quality of life possible. The
commenter recommended that CMS
delay adopting this measure until it
incorporated metrics that assess
whether patients achieved their
functional and independence goals
based on their plan of care and their
specific condition.
Multiple commenters suggested that
the measure include risk adjustment for
functional status in all settings, as it is
closely associated with patients’
discharge destination. One commenter
noted that functional status is associated
with increased risk of 30-day all-cause
hospital readmissions, and since
readmissions and discharge to
community are closely related,
functional status risk adjustment is also
important for this measure. One
commenter suggested that the SNF and
LTCH measures include risk adjustment
that is similar to the risk adjustment for
CMGs in the IRF setting and Activities
of Daily Living in the HHA setting. One
commenter interpreted the measure
proposal as stating that CMS will not
adjust the quality measures, including
the discharge to community measure, to
account for functional status of
beneficiaries until such data are
collected under the IMPACT Act.
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Response: We agree that it is
important to assess various aspects of
patient outcomes that are indicative of
successful discharge from the IRF
setting. We also agree that functional
status may be related to discharge to
community outcomes, and that it is
important to test admission functional
status risk adjustment when assessing
discharge to community outcomes. The
discharge to community measure does
include functional status risk
adjustment in the IRF setting using
CMGs from claims, and in the home
health setting using Activities of Daily
Living from claims.
As mandated by the IMPACT Act, we
are moving toward the goal of collecting
standardized patient assessment data for
functional status across PAC settings.
The IRF QRP includes five NQFendorsed functional status quality
measures, with a data collection start
date of October 1, 2016. Two measures
are related to mobility functional
outcomes, two are related to self-care
functional outcomes, and one is a
process measure. Once standardized
functional status data become available
across settings, it is our intent to use
these data to assess patients’ functional
gains during their PAC stay, and to
examine the relationship between
functional status, discharge destination,
and patients’ ability to discharge to the
community. As we examine these
relationships between functional
outcomes and discharge to community
outcomes in the future, we will assess
the feasibility of leveraging these
standardized patient assessment data to
incorporate functional outcomes into
the discharge to community measure.
Standardized cross-setting patient
assessment data will also allow us to
examine interrelationships between the
quality and resource use measures in
each PAC setting, and to understand
how these measures are correlated.
Comment: One commenter questioned
the appropriateness of using HCCs for
risk adjustment in the new quality
measures proposed for the IRF QRP. The
commenters noted that HCCs were
initially developed for setting payment
benchmarks for the Medicare Advantage
program, and broad application of HCCs
across quality measures may be beyond
the scope of their appropriate use. The
commenter cited reports suggesting that
the HCC risk model was inaccurate at
cost-estimation, and recommended that
CMS reconsider the validity and
reliability of the HCC risk-adjustment
model. The commenter suggested that
CMS instead develop a refined model
that encompasses the diversity and
complexity of PAC patients to a greater
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extent, and is more sensitive to their
levels of resource use.
Response: We agree that
comorbidities are important risk
adjusters when examining quality and
resource use measures. The HCCs were
developed to separate clinically-related
codes by Medicare utilization
implications; they represent diagnosisbased, clinically meaningful clusters of
ICD codes that have also been grouped
by cost implications. When we apply
HCCs for risk adjustment of quality or
resources use measures, we do not use
the HCC models applied to payment. In
our measure development, we typically
test individual HCCs that are relevant to
the outcome of interest; we estimate the
effects of the individual HCCs or
clusters on the dependent variable in
the particular model and retain those
that are significant or meaningful
predictors of outcomes. We believe that
risk adjusting for individual HCCs or
small clusters provides greater
sensitivity than using a single
comorbidity index, which is based on
selected diagnoses. Our approach
accounts for an average effect for each
comorbidity or comorbidity group,
rather than an overall burden of
comorbidities.
The HCCs are more comprehensive
than the simpler diagnosis-based
systems, such as the Elixhauser
Comorbidity Index or Charlson
Comorbidity Index, which were targeted
for predicting specific outcomes (for
example, hospital mortality). We believe
that HCCs provide a good representation
of health risk, and their use to examine
outcomes other than costs is supported
in the literature.62 63 A study comparing
the ability of five comorbidity indices to
predict discharge functional status of
IRF patients found that HCCs slightly
outperformed other comorbidity
indices.64 The superior performance of
HCCs was hypothesized to be related to
the inclusion of more medical
conditions, and the inclusion of more
ICD codes per condition in HCCs,
making them a slightly more sensitive
index for predicting clinical outcomes
compared with other comorbidity
indices.65
62 Li P, Kim MM, Doshi JA. Comparison of the
performance of the CMS Hierarchical Condition
Category (CMS–HCC) risk adjuster with the
Charlson and Elixhauser comorbidity measures in
predicting mortality. BMC Health Serv Res. 2010
Aug 20;10:245. doi: 10.1186/1472–6963–10–245.
63 Kumar A, Graham JE, Resnik L, Karmarkar AM,
Tan A, Deutsch A, Ottenbacher KJ. Comparing
Comorbidity Indices to Predict Post-Acute
Rehabilitation Outcomes in Older Adults. Am J
Phys Med Rehabil. 2016 May 4. [Epub ahead of
print]
64 Ibid.
65 Ibid.
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We have successfully used HCCs as
risk adjusters in several other quality
measures, such as the readmissions and
functional status measures for postacute care. We have found HCCs to be
significant and important predictors of
outcomes across these quality measures.
Comment: One commenter stated that
ventilator use is included as a risk
adjuster in the LTCH setting only, but
should be used across all settings. This
commenter also requested information
on the hierarchical logistic regression
modeling and variables that will be used
for risk adjustment.
Response: We would like to clarify
that risk adjustment for ventilator use is
included in both LTCH and SNF
settings. We investigated the need for
risk adjustment for ventilator use in
IRFs, but found that less than 0.01
percent of the IRF population (19
patient stays in 2012, and 9 patient stays
in 2013) had ventilator use in the IRF.
Given the low frequency of ventilator
use in IRFs, any associated estimates
would not be reliable, and therefore,
ventilator use is not included as a risk
adjuster in the IRF setting measure.
However, we will continue to assess this
risk adjuster for inclusion in the IRF
model for this measure.
For details on measure specifications,
modeling, and calculations, we refer
readers to the Measure Specifications
for Measures Adopted in the FY 2017
IRF QRP final rule, posted on the CMS
IRF QRP Web page at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html.
Comment: Two commenters requested
clarification on the dual status of IRFs
as qualifying hospitals for the purposes
of the SNF ‘‘3-Day Stay’’ rule, and PAC
providers for purposes of the discharge
to community measure. Specifically, the
commenters questioned whether a
discharge from a SNF back to an IRF
would count as a readmission under
this measure (and thus result in a
‘‘failed’’ community discharge for the
SNF), or whether it would only count as
a non-community discharge.
Response: For the discharge to
community measure, a PAC stay must
be preceded by an acute care stay in the
past 30 days to be included in the
measure. IRF stays are not considered
qualifying stays for the purposes of
inclusion in the discharge to community
measure. When examining discharge
destination from PAC, a discharge to an
IRF would be considered a noncommunity discharge. Additionally, in
the current measure specification, if a
patient is discharged from PAC to the
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community and has a subsequent IRF
admission in the post-discharge
observation window, this IRF admission
does not translate into a failed
community discharge. In future measure
work, we will assess the impact of
flagging IRF admissions in the postdischarge window as failed discharges
to community.
Comment: One commenter
encouraged CMS to provide PAC
settings with access to measure
performance data as early as possible so
providers have time to adequately
review these data, and implement
strategies to decrease readmissions
where necessary.
Response: We intend to provide
initial confidential feedback to PAC
providers, prior to public reporting of
this measure, based on Medicare FFS
claims data from discharges in CY 2015
and 2016.
Comment: A few commenters were
concerned about potential unintended
consequences associated with perceived
conflicting incentives of measures
within the IRF QRP. One commenter
noted that while the discharge to
community measure may incentivize
IRFs to discharge patients with home
health services in order to continue
their recovery and function in a safe,
lower cost setting, the MSPB measure
may create an opposite incentive for
IRFs to avoid the use of home health to
reduce post-discharge resource
utilization. Another commenter
conveyed that IRFs may not be
incentivized to discharge patients to the
community as there is a risk of postdischarge readmissions affecting their
measure performance. The commenter
expressed that decreased discharge to
community rates may result in
increased costs.
Response: We expect that, on average,
discharges to community settings rather
than institutional settings, will result in
lower healthcare costs. We choose
measures for our quality reporting
programs that reflect patientcenteredness, and assess healthcare
outcomes and utilization that may be
indicators of poor quality of care or
inefficient resource use. As with all our
measures, we will monitor for
unintended consequences as part of
measure monitoring and evaluation to
ensure that measures do not reduce
quality of care or access for patients.
Comment: Several commenters
expressed concern that the discharge to
community measure had not been
endorsed by the NQF, and had not been
fully developed and tested when
presented to the NQF MAP. Some
commenters recommended that CMS
delay measure implementation and seek
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NQF endorsement before measure
adoption, while others recommended
that CMS submit the measures for NQF
endorsement as soon as feasible after
measure adoption. A few commenters
suggested that CMS obtain the support
of a TEP before deciding whether to
implement new quality measures, and
that the TEP include IRF setting
representatives.
Response: We would like to clarify
that the discharge to community
measure has been fully developed and
tested. We plan to submit the Discharge
to Community-PAC IRF QRP measure to
the NQF for consideration for
endorsement.
As with all measure development, our
measure development contractor held
three TEP meetings to seek input to
guide development of the Discharge to
Community measure. The TEP
represented members of IRF, LTCH,
SNF and home health agency settings. A
summary of the TEP proceedings is
available on the PAC Quality Initiatives
Downloads and Videos Web site at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html. TEP members were very
supportive of the discharge to
community measure concept across all
PAC settings. We incorporated various
TEP member recommendations into the
measure specifications.
Final Decision: After careful
consideration of the public comments,
we are finalizing our proposal to adopt
the measure, Discharge to CommunityPAC IRF QRP as a Medicare FFS claimsbased measure for the FY 2018 payment
determination and subsequent years,
with the added exclusion of patients
with a hospice benefit in the 31-day
post-discharge observation window.
For measure specifications, we refer
readers to the Measure Specifications
for Measures Adopted in the FY 2017
IRF QRP final rule, posted on the CMS
IRF QRP Web site at: https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html.
3. Measure To Address the IMPACT Act
Domain of Resource Use and Other
Measures: Potentially Preventable 30Day Post-Discharge Readmission
Measure for Inpatient Rehabilitation
Facility Quality Reporting Program
Sections 1899B(a)(2)(E)(ii) and
1899B(d)(1)(C) of the Act require the
Secretary to specify measures to address
the domain of all-condition risk-
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adjusted potentially preventable
hospital readmission rates by SNFs,
LTCHs, and IRFs by October 1, 2016,
and HHAs by January 1, 2017. We
proposed the measure Potentially
Preventable 30-Day Post-Discharge
Readmission Measure for IRF QRP as a
Medicare FFS claims-based measure to
meet this requirement for the FY 2018
payment determination and subsequent
years.
The measure assesses the facility-level
risk-standardized rate of unplanned,
potentially preventable hospital
readmissions for Medicare FFS
beneficiaries in the 30 days post IRF
discharge. The IRF admission must have
occurred within up to 30 days of
discharge from a prior proximal hospital
stay which is defined as an inpatient
admission to an acute care hospital
(including IPPS, CAH, or a psychiatric
hospital). Hospital readmissions include
readmissions to a short-stay acute-care
hospital or an LTCH, with a diagnosis
considered to be unplanned and
potentially preventable. This measure is
claims-based, requiring no additional
data collection or submission burden for
IRFs. Because the measure denominator
is based on IRF admissions, each
Medicare beneficiary may be included
in the measure multiple times within
the measurement period. Readmissions
counted in this measure are identified
by examining Medicare FFS claims data
for readmissions to either acute care
hospitals (IPPS or CAH) or LTCHs that
occur during a 30-day window
beginning 2 days after IRF discharge.
This measure is conceptualized
uniformly across the PAC settings, in
terms of the measure definition, the
approach to risk adjustment, and the
measure calculation. Our approach for
defining potentially preventable
hospital readmissions is described in
more detail below.
Hospital readmissions among the
Medicare population, including
beneficiaries that utilize PAC, are
common, costly, and often
preventable.66 67 MedPAC and a study
by Jencks et al. estimated that 17 to 20
percent of Medicare beneficiaries
discharged from the hospital were
readmitted within 30 days. MedPAC
found that more than 75 percent of 30day and 15-day readmissions and 84
percent of 7-day readmissions were
66 Friedman, B., and Basu, J.: The rate and cost
of hospital readmissions for preventable conditions.
Med. Care Res. Rev. 61(2):225–240, 2004.
doi:10.1177/1077558704263799.
67 Jencks, S.F., Williams, M.V., and Coleman,
E.A.: Rehospitalizations among patients in the
Medicare Fee-for-Service Program. N. Engl. J. Med.
360(14):1418–1428, 2009. doi:10.1016/
j.jvs.2009.05.045.
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considered ‘‘potentially preventable.’’ 68
In addition, MedPAC calculated that
annual Medicare spending on
potentially preventable readmissions
were $12 billion for 30-day, $8 billion
for 15-day, and $5 billion for 7-day
readmissions in 2005.69 For hospital
readmissions from one post-acute care
setting, SNFs, MedPAC deemed 76
percent of these readmissions as
‘‘potentially avoidable’’–associated with
$12 billion in Medicare expenditures.70
Mor et al. analyzed 2006 Medicare
claims and SNF assessment data
(Minimum Data Set), and reported a
23.5 percent readmission rate from
SNFs, associated with $4.3 billion in
expenditures.71 Fewer studies have
investigated potentially preventable
readmission rates from the remaining
post-acute care settings.
We have addressed the high rates of
hospital readmissions in the acute care
setting as well as in PAC. For example,
we developed the following measure:
All-Cause Unplanned Readmission
Measure for 30 Days Post-Discharge
from IRFs (NQF #2502), as well as
similar measures for other PAC
providers (NQF #2512 for LTCHs and
NQF #2510 for SNFs).72 These measures
are endorsed by the NQF, and the NQFendorsed IRF measure (NQF #2502) was
adopted into the IRF QRP in the FY
2016 IRF PPS final rule (80 FR 47087
through 47089). Note that these NQFendorsed measures assess all-cause
unplanned readmissions.
Several general methods and
algorithms have been developed to
assess potentially avoidable or
preventable hospitalizations and
readmissions for the Medicare
population. These include the Agency
for Healthcare Research and Quality’s
(AHRQ’s) Prevention Quality Indicators,
approaches developed by MedPAC, and
proprietary approaches, such as the
3MTM algorithm for Potentially
Preventable Readmissions.73 74 75 Recent
68 MedPAC: Payment policy for inpatient
readmissions, in Report to the Congress: Promoting
Greater Efficiency in Medicare. Washington, DC, pp.
103–120, 2007. Available from https://
www.medpac.gov/documents/reports/Jun07_
EntireReport.pdf.
69 ibid.
70 ibid.
71 Mor, V., Intrator, O., Feng, Z., et al.: The
revolving door of rehospitalization from skilled
nursing facilities. Health Aff. 29(1):57–64, 2010.
doi:10.1377/hlthaff.2009.0629.
72 National Quality Forum: All-Cause Admissions
and Readmissions Measures. pp. 1–319, April 2015.
Available from https://www.qualityforum.org/
Publications/2015/04/All-Cause_Admissions_and_
Readmissions_Measures_-_Final_Report.aspx.
73 Goldfield, N.I., McCullough, E.C., Hughes, J.S.,
et al.: Identifying potentially preventable
readmissions. Health Care Finan. Rev. 30(1):75–91,
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work led by Kramer et al. for MedPAC
identified 13 conditions for which
readmissions were deemed as
potentially preventable among SNF and
IRF populations.76 77 Although much of
the existing literature addresses hospital
readmissions more broadly and
potentially avoidable hospitalizations
for specific settings like long-term care,
these findings are relevant to the
development of potentially preventable
readmission measures for PAC.78 79 80
Potentially Preventable Readmission
Measure Definition: We conducted a
comprehensive environmental scan,
analyzed claims data, and obtained
input from a TEP to develop a definition
and list of conditions for which hospital
readmissions are potentially
preventable. The Ambulatory Care
Sensitive Conditions and Prevention
Quality Indicators, developed by AHRQ,
served as the starting point in this work.
For patients in the 30-day post-PAC
discharge period, a potentially
preventable readmission refers to a
readmission for which the probability of
occurrence could be minimized with
adequately planned, explained, and
implemented post-discharge
instructions, including the
establishment of appropriate follow-up
ambulatory care. Our list of PPR
2008. Available from https://www.ncbi.nlm.nih.gov/
pmc/articles/PMC4195042/.
74 National Quality Forum: Prevention Quality
Indicators Overview. 2008.
75 MedPAC: Online Appendix C: Medicare
Ambulatory Care Indicators for the Elderly. pp. 1–
12, prepared for Chapter 4, 2011. Available from
https://www.medpac.gov/documents/reports/Mar11_
Ch04_APPENDIX.pdf?sfvrsn=0.
76 Kramer, A., Lin, M., Fish, R., et al.:
Development of Inpatient Rehabilitation Facility
Quality Measures: Potentially Avoidable
Readmissions, Community Discharge, and
Functional Improvement. pp. 1–42, 2015. Available
from https://www.medpac.gov/documents/
contractor-reports/development-of-inpatientrehabilitation-facility-quality-measures-potentiallyavoidable-readmissions-community-discharge-andfunctional-improvement.pdf?sfvrsn=0.
77 Kramer, A., Lin, M., Fish, R., et al.:
Development of Potentially Avoidable Readmission
and Functional Outcome SNF Quality Measures.
pp. 1–75, 2014. Available from https://
www.medpac.gov/documents/contractor-reports/
mar14_snfqualitymeasures_
contractor.pdf?sfvrsn=0.
78 Allaudeen, N., Vidyarthi, A., Maselli, J., et al.:
Redefining readmission risk factors for general
medicine patients. J. Hosp. Med. 6(2):54–60, 2011.
doi:10.1002/jhm.805.
79 4 Gao, J., Moran, E., Li, Y.-F., et al.: Predicting
potentially avoidable hospitalizations. Med. Care
52(2):164–171, 2014. doi:10.1097/
MLR.0000000000000041.
80 Walsh, E.G., Wiener, J.M., Haber, S., et al.:
Potentially avoidable hospitalizations of dually
eligible Medicare and Medicaid beneficiaries from
nursing facility and home-and community-based
services waiver programs. J. Am. Geriatr. Soc.
60(5):821–829, 2012. doi:10.1111/j.1532–
5415.2012.03920.x.
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conditions is categorized by 3 clinical
rationale groupings:
• Inadequate management of chronic
conditions;
• Inadequate management of
infections; and
• Inadequate management of other
unplanned events.
Additional details regarding the
definition for potentially preventable
readmissions are available in the
document titled, Proposed Measure
Specifications for Measures Proposed in
the FY 2017 IRF QRP proposed rule,
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/IRF-QualityReporting/IRF-Quality-ReportingProgram-Measures-Information-.html.
This measure focuses on readmissions
that are potentially preventable and also
unplanned. Similar to the All-Cause
Unplanned Readmission Measure for 30
Days Post-Discharge from IRFs (NQF
#2502), this measure uses the current
version of the CMS Planned
Readmission Algorithm as the main
component for identifying planned
readmissions. A complete description of
the CMS Planned Readmission
Algorithm, which includes lists of
planned diagnoses and procedures, can
be found on the CMS Web site https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/HospitalQualityInits/
Measure-Methodology.html. In addition
to the CMS Planned Readmission
Algorithm, this measure incorporates
procedures that are considered planned
in post-acute care settings, as identified
in consultation with TEPs. Full details
on the planned readmissions criteria
used, including the CMS Planned
Readmission Algorithm and additional
procedures considered planned for postacute care, can be found in the
document titled, Proposed Measure
Specifications for Measures Proposed in
the FY 2017 IRF QRP proposed rule,
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/IRF-QualityReporting/IRF-Quality-ReportingProgram-Measures-Information-.html.
The measure, Potentially Preventable
30-Day Post-Discharge Readmission
Measure for IRF QRP, assesses
potentially preventable readmission
rates while accounting for patient
demographics, principal diagnosis in
the prior hospital stay, comorbidities,
and other patient factors. While
estimating the predictive power of
patient characteristics, the model also
estimates a facility-specific effect,
common to patients treated in each
facility. This measure is calculated for
each IRF based on the ratio of the
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predicted number of risk-adjusted,
unplanned, potentially preventable
hospital readmissions that occur within
30 days after an IRF discharge,
including the estimated facility effect, to
the estimated predicted number of riskadjusted, unplanned inpatient hospital
readmissions for the same patients
treated at the average IRF. A ratio above
1.0 indicates a higher than expected
readmission rate (worse) while a ratio
below 1.0 indicates a lower than
expected readmission rate (better). This
ratio is referred to as the standardized
risk ratio (SRR). The SRR is then
multiplied by the overall national raw
rate of potentially preventable
readmissions for all IRF stays. The
resulting rate is the risk-standardized
readmission rate (RSRR) of potentially
preventable readmissions.
An eligible IRF stay is followed until:
(1) The 30-day post-discharge period
ends; or (2) the patient is readmitted to
an acute care hospital (IPPS or CAH) or
LTCH. If the readmission is unplanned
and potentially preventable, it is
counted as a readmission in the measure
calculation. If the readmission is
planned, the readmission is not counted
in the measure rate. This measure is risk
adjusted. The risk adjustment modeling
estimates the effects of patient
characteristics, comorbidities, and select
health care variables on the probability
of readmission. More specifically, the
risk-adjustment model for IRFs accounts
for demographic characteristics (age,
sex, original reason for Medicare
entitlement), principal diagnosis during
the prior proximal hospital stay, body
system specific surgical indicators, IRF
case-mix groups which capture motor
function, comorbidities, and number of
acute care hospitalizations in the
preceding 365 days.
The measure is calculated using 2
consecutive calendar years of FFS
claims data, to ensure the statistical
reliability of this measure for facilities.
In addition, we proposed a minimum of
25 eligible stays for public reporting of
the measure.
A TEP convened by our measure
contractor provided recommendations
on the technical specifications of this
measure, including the development of
an approach to define potentially
preventable hospital readmission for
PAC. Details from the TEP meetings,
including TEP members’ ratings of
conditions proposed as being
potentially preventable, are available in
the TEP summary report available on
the CMS Web site 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 solicited
stakeholder feedback on the
development of this measure through a
public comment period held from
November 2 through December 1, 2015.
Comments on the measure varied, with
some commenters supportive of the
measure, while others either were not in
favor of the measure, or suggested
potential modifications to the measure
specifications, such as including
standardized function data. A summary
of the public comments is also available
on our Web site at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
The MAP encouraged continued
development of the proposed measure.
Specifically, the MAP stressed the need
to promote shared accountability and
ensure effective care transitions. More
information about the MAP’s
recommendations for this measure is
available at https://
www.qualityforum.org/Publications/
2016/02/MAP_2016_Considerations_
for_Implementing_Measures_in_
Federal_Programs_-_PAC-LTC.aspx. At
the time, the risk-adjustment model was
still under development. Following
completion of that development work,
we were able to test for measure validity
and reliability as identified in the
measure specifications document
provided above. Testing results are
within range for similar outcome
measures finalized in public reporting
and value-based purchasing programs,
including the All-Cause Unplanned
Readmission Measure for 30 Days Post
Discharge from IRFs (NQF #2502)
adopted into the IRF QRP.
We reviewed the NQF’s consensus
endorsed measures and were unable to
identify any NQF-endorsed measures
focused on potentially preventable
hospital readmissions. We are unaware
of any other measures for this IMPACT
Act domain that have been endorsed or
adopted by other consensus
organizations. Therefore, we proposed
the Potentially Preventable 30-Day PostDischarge Readmission Measure for IRF
QRP, under the Secretary’s authority to
specify non-NQF-endorsed measures
under section 1899B(e)(2)(B) of the Act,
for the IRF QRP for the FY 2018
payment determination and subsequent
years, given the evidence previously
discussed above.
We plan to submit the measure to the
NQF for consideration of endorsement.
We stated in the proposed rule that we
intended to provide initial confidential
feedback to providers, prior to public
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reporting of this measure, based on 2
calendar years of data from discharges
in CY 2015 and 2016. We also stated
that we intended to publicly report this
measure using data from CY 2016 and
2017.
We invited public comment on our
proposal to adopt the measure,
Potentially Preventable 30-Day PostDischarge Readmission Measure for IRF
QRP. We received several comments,
which are summarized with our
responses below.
Comment: We received several
comments in support of the proposed
Potentially Preventable 30-Day PostDischarge Readmission Measure for IRF
QRP. In particular, MedPAC supported
this measure and believes that IRFs
should be held accountable for
readmissions in the post-discharge
readmission window. Some commenters
preferred a potentially preventable
readmission measure over an all-cause
readmission measure.
Response: We thank commenters for
their support of this measure.
Comment: One commenter
specifically supported the inclusion of
infectious conditions in the inadequate
management of infections and
inadequate management of other
unplanned events categories in the
measure’s definition of potentially
preventable hospital readmissions.
Another commenter expressed concern
over being ‘‘penalized’’ for readmissions
that are clinically unrelated to a
patient’s original reason for IRF
admission. One commenter
recommended that CMS continue
evaluating and testing the measure to
ensure that the codes used for the PPR
definition are clinically relevant.
Another commenter expressed concern
over using DRGs as the basis for
defining the reasons for receiving
inpatient rehabilitation or the reason for
a subsequent hospital readmission given
variation in coding practices in acute
care hospitals.
Response: As described in the
proposed rule, the definition for
potentially preventable readmissions for
this measure was developed based on
existing evidence and was vetted by a
TEP, which included clinicians and
post-acute care experts. We also
conducted a comprehensive
environmental scan to identify
conditions for which readmissions may
be considered potentially preventable.
Results of this environmental scan and
details of the TEP input received were
made available in the PPR TEP
summary report available on the CMS
Web site at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-Acute-
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Care-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html. We also solicited
stakeholder feedback on the
development of this measure through a
public comment period held from
November 2 through December 1, 2015.
Comments on the measure varied, with
some commenters supportive of the
proposed measure, while others either
were not in favor of the measure, or
suggested potential modifications to the
measure specifications, such as
including standardized function data. A
summary of the public comments is also
available on the CMS Web site at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
Though readmissions may be
considered potentially preventable even
if they may not appear to be clinically
related to the patient’s original reason
for IRF admission, there is substantial
evidence that the conditions included in
the definition may be preventable with
adequately planned, explained, and
implemented post-discharge
instructions, including the
establishment of appropriate follow-up
ambulatory care. Furthermore, this
measure is based on Medicare FFS
claims data and it may not always be
feasible to determine whether a
subsequent readmission is or is not
clinically related to the reason why the
patient was receiving inpatient
rehabilitation. We intend to conduct
ongoing evaluation and monitoring of
this measure, and will take these
comments into consideration.
With regard to the comment related to
DRGs, we wish to clarify that this
measure does not use hospital DRGs to
define PPRs or in the risk adjustment.
Potentially preventable hospital
readmissions are defined by the
principal diagnosis on the readmission
claim. Our risk-adjustment model uses
diagnoses (not DRGs) from the prior
hospital claim as risk adjusters. Though
there may be variation in coding
practices, claims data are the most
reliable source to identify potentially
preventable hospital readmissions postIRF discharge. We would also like to
clarify that the reason for receiving
inpatient rehabilitation is captured as a
risk adjuster by the use of the IRF PPS
CMGs which also incorporate the RICs
as well as function.
Comment: Several commenters
expressed support for the cross-setting
standardization of the inclusion and
exclusion criteria for the PPR measures.
MedPAC and another commenter
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commented that the measure definition
and risk adjustment should be identical
across PAC settings so that potentially
preventable readmission rates can be
compared across settings. One
commenter expressed concern over the
‘‘nonalignment’’ specifically between
the IRF and SNF versions of the
measure, adding that this may lead to
confusion. Another commenter
suggested a single or harmonized
measure to better inform patients,
caregivers, and payers. One comment
encouraged CMS to assess readmission
measures across the agency’s programs
to ensure that they promote
collaboration and support readmission
reduction efforts.
Response: The PPR definition (that is,
list of conditions for which
readmissions would be considered
potentially preventable) is aligned for
measures with the same readmission
window, regardless of PAC setting.
Specifically, the post-PAC discharge
PPR measures that were developed for
each of the PAC settings contain the
same list of PPR conditions. Although
there are some minor differences in the
specifications across these potentially
preventable readmissions measures (for
example, years of data used to calculate
the measures to ensure reliability and
some of the measure exclusions
necessary to attribute responsibility to
the individual settings), the IMPACT
Act PPR measures are standardized. As
described for all IMPACT Act measures
in section VIII.B in this final rule, the
statistical approach for risk adjustment
is also aligned across the measures;
however, there is variation in the exact
risk adjusters. The risk-adjustment
models are empirically driven and differ
between measures as a consequence of
case mix differences, which is necessary
to ensure that the estimates are valid.
We appreciate the comment that the
readmission measures across our
programs be assessed to ensure they
promote collaboration and support
readmission reduction efforts. As we
continually evaluate and monitor the
PAC quality reporting and other CMS
programs, we will take the commenter’s
suggestion into consideration.
Comment: Several commenters
expressed concern that this measure
would capture outcomes that are
outside of PAC providers’ control,
specifically with respect to chronically
ill patients, instances of poor patient
compliance, unhealthy choices, and
various SDS factors, such as lack of
resources or limited access to follow up
or primary care. One commenter also
expressed concern over the added risk
of caring for a high volume of transplant
patients that other IRFs may choose not
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to admit. Another commenter noted that
even though the risk adjustment will
account for some of these
circumstances, it is difficult for
providers to fully evaluate the riskadjustment model because the testing
and risk-adjustment coefficients have
not been finalized. A few commenters
recommend these measures be
suspended until CMS explains how the
measures will treat each of these
scenarios.
Response: As noted by one
commenter, the measure’s
comprehensive risk-adjustment
approach and exclusion criteria are
intended to capture many of these
factors. As described above, there is
substantial evidence that the conditions
included in the definition may be
preventable with adequately planned,
explained, and implemented postdischarge instructions, including the
establishment of appropriate follow-up
ambulatory care. We would like to
clarify that the focus of the PPR measure
is to identify excess PPR rates for the
purposes of quality improvement.
We would also like to clarify that the
finalized risk-adjustment models and
coefficients are included in the measure
specifications available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html.
Comment: Several commenters
expressed concern over the overlap
between the proposed PPR measure and
other IRF QRP measures, including the
existing all-cause readmission measure.
Commenters expressed concern that
public reporting of more than one
hospital readmission measure for IRFs
may result in confusion among the
public; the commenters also noted
providers could face confusion over two
distinct but similar measures, which
could potentially pose challenges for
quality improvement efforts. One
commenter noted that the proposed PPR
measures and the existing all-cause
measure are distinct yet overlapping,
adding that the PPR measure is a subset
of the all-cause readmission measure.
Given this overlap, one commenter was
concerned that providers who perform
poorly on the all-cause readmission
measure are likely to do so for the
proposed PPR measure as well, and
suggested CMS suspend the measure
until it could evaluate the necessity of
each measure. Some commenters
requested that CMS clarify the overlap
and intent of these measures, and
provide more education to providers
and the public on the multiple IRF QRP
readmission measures. Another
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commenter suggested that CMS conduct
dry runs of the readmission measures,
similar to those conducted for the allcause measure.
One commenter supported the use of
Medicare claims data to calculate these
measures because it does not require the
submission of additional data by IRFs.
Another commenter noted that despite
the lack of a data collection burden for
providers, multiple readmission
measures in the program will create
burden on the part of providers to track
and improve performance. Another
commenter expressed concern that the
measures are ‘‘extensive’’ and will place
additional financial burden on
providers.
Response: The All-Cause Unplanned
Hospital Readmission Measure for 30
Days Post-IRF Discharge (NQF #2502)
was adopted for the IRF QRP prior to
the IMPACT Act. The measure of
potentially preventable hospital
readmissions was developed in
response to the statutory mandate of the
IMPACT Act. We would like to clarify
that providers are not held financially
accountable for their performance on
these measures, but only whether they
report the necessary data for the IRF
QRP.
With regard to overlap with the
existing IRF QRP readmission measure,
retaining the all-cause measure will
allow us to monitor trends in both allcause and PPR rates in order to assess
the extent to which changes in facility
performance for one measure are
reflected in the other. We are committed
to ensuring that measures in the IRF
QRP are useful in assessing quality and
will continue to evaluate all
readmission measures over time.
We thank commenters for their
feedback related to provider burden on
the measure. We would like to note that
the PPR measure uses Medicare claims
data and is not collected by means of an
assessment instrument. Therefore, the
measure does not increase data
collection burden on the provider as
this data is currently collected by
providers. Despite the lack of data
collection burden, we appreciate the
comments that more education will be
required for the public and providers to
understand the differences between the
readmission measures in the IRF QRP.
Comment: Several commenters raised
concerns over the risk-adjustment
approach for the PPR measure. One
commenter expressed concern that the
HCC risk-adjustment method is
insufficient at predicting costs for
certain patient populations. The
commenter suggested CMS research and
develop a refined risk-adjustment model
that encompasses more of the diversity
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and complexity of PAC patients and is
more sensitive to their levels of resource
use. Several commenters expressed
concern that the proposed measure is
not adjusted for socio-economic factors,
and a couple commenters, including
MedPAC, suggested the use of peer
group comparisons of performance rates
to address this issue.
Another commenter supported the
proposed risk-adjustment methodology
commenting it will provide a valid
assessment of quality of care in
preventing unplanned, preventable
hospital readmissions. One commenter
also suggested that, in addition to the
measure exclusion for non-surgical
treatment of cancer, that other
conditions with similar disease
trajectories be excluded from the
measure, citing end-stage Multiple
Sclerosis (MS), motor neuron disease,
and Alzheimer’s disease.
Response: We would like to note that
the measure is fully developed and the
finalized risk-adjustment model and
coefficients are included in the measure
specifications available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html.
The HCCs were developed to separate
clinically-related codes by Medicare
utilization implications; they represent
diagnosis-based, clinically meaningful
clusters of ICD codes that have also been
grouped by cost implications. When we
apply HCCs for risk adjustment of
quality or resources use measures, we
do not use the HCC models applied to
payment. In our measure development,
we typically test individual HCCs that
are relevant to the outcome of interest;
we estimate the effects of the individual
HCCs or clusters on the dependent
variable in the particular model and
retain those that are significant or
meaningful predictors of outcomes. We
believe that risk adjusting for individual
HCCs or small clusters provides greater
sensitivity than using a single
comorbidity index, which is based on
selected diagnoses. Our approach
accounts for an average effect for each
comorbidity or comorbidity group,
rather than an overall burden of
comorbidities.
The HCCs are more comprehensive
than the simpler diagnosis-based
systems, such as the Elixhauser
Comorbidity Index or Charlson
Comorbidity Index, which were targeted
for predicting specific outcomes (for
example, hospital mortality). We believe
that HCCs provide a good representation
of health risk, and their use to examine
outcomes other than costs is supported
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in the literature.81 82 A study comparing
the ability of five comorbidity indices to
predict discharge functional status of
IRF patients found that HCCs slightly
outperformed other comorbidity
indices.83 The superior performance of
HCCs was hypothesized to be related to
the inclusion of more medical
conditions in HCCs, and the inclusion
of more ICD codes per condition in
HCCs, making them a slightly more
sensitive index for predicting clinical
outcomes compared with other
comorbidity indices.84
We wish to clarify that the model
included in the specifications using
HCCs as risk adjusters for comorbidities
posted for the proposed rule
demonstrated sufficient discrimination
power. The model had a c-statistic of
0.74 which is within range, if not higher
than, similar readmission measures
finalized in public reporting programs,
including the All-Cause Unplanned
Readmission Measure for 30 Days PostDischarge from IRFs (NQF #2502)
previously adopted for the IRF QRP.
With regard to the suggestions that the
model include sociodemographic factors
and the suggestion pertaining to an
approach with which to convey data
comparisons, we refer the readers to
section VIII.F of this final rule where we
also discuss these topics. In response to
the suggestion to include additional
conditions from the measure, such as
end-stage MS, motor neuron disease,
and Alzheimer’s disease, we wish to
clarify that we risk adjust for these
clinical characteristics in our riskadjustment model. These are low
prevalence conditions and the claims
data are limited in their ability to
identify disease progression. However,
we will take this suggestion into
consideration.
Comment: Several commenters
expressed concern that the measures are
81 Kumar A, Graham JE, Resnik L, Karmarkar AM,
Tan A, Deutsch A, Ottenbacher KJ. Comparing
Comorbidity Indices to Predict Post-Acute
Rehabilitation Outcomes in Older Adults. Am J
Phys Med Rehabil. 2016 May 4. [Epub ahead of
print]
82 Li P, Kim MM, Doshi JA. Comparison of the
performance of the CMS Hierarchical Condition
Category (CMS–HCC) risk adjuster with the
Charlson and Elixhauser comorbidity measures in
predicting mortality. BMC Health Serv Res. 2010
Aug 20;10:245. doi: 10.1186/1472–6963–10–245.
83 Kumar A, Graham JE, Resnik L, Karmarkar AM,
Tan A, Deutsch A, Ottenbacher KJ. Comparing
Comorbidity Indices to Predict Post-Acute
Rehabilitation Outcomes in Older Adults. Am J
Phys Med Rehabil. 2016 May 4. [Epub ahead of
print]
84 Kumar A, Graham JE, Resnik L, Karmarkar AM,
Tan A, Deutsch A, Ottenbacher KJ. Comparing
Comorbidity Indices to Predict Post-Acute
Rehabilitation Outcomes in Older Adults. Am J
Phys Med Rehabil. 2016 May 4. [Epub ahead of
print]
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not NQF-endorsed, and some had
additional concerns over measure
testing and development. Some of these
commenters recommended that CMS
should adopt measures endorsed by the
NQF in quality reporting programs or
recommended that CMS submit the
measures through the NQF endorsement
process as soon as feasible.
Response: With regard to NQF
endorsement, as noted in the proposed
rule, we intend to submit this measure
to NQF for consideration of
endorsement. In addition, we noted that
we reviewed the NQF’s consensus
endorsed measures and were unable to
identify any NQF endorsed measures
focused on potentially preventable
hospital readmissions. We are unaware
of any other measures for this IMPACT
Act domain that have been endorsed or
adopted by other consensus
organizations. Therefore, we proposed
the Potentially Preventable 30-Day PostDischarge Readmission Measure for IRF
QRP, under the Secretary’s authority to
specify non-NQF endorsed measures
under section 1899B(e)(2)(B) of the Act,
for the IRF QRP.
We would also like to clarify that the
finalized risk-adjustment models and
coefficients are included in the measure
specifications available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html. We will make
additional testing results available in
the future.
We would like to clarify that the MAP
encouraged continued development of
the proposed measure. More
information about the MAP’s
recommendations for this measure is
available at https://
www.qualityforum.org/Publications/
2016/02/MAP_2016_Considerations_
for_Implementing_Measures_in_
Federal_Programs_-_PAC-LTC.aspx.
Comment: Some commenters raised
concerns over unintended consequences
of the measure. One commenter was
concerned that the measure could create
an incentive for IRFs to be selective
about the types of patients they admit
(that is, ‘‘cherry pick’’ their patients) in
order to reduce the risk of PPRs.
Another comment suggested that IRFs
should not be held accountable for IRF
patients with planned procedures that
are not admitted and treated as
observation stays and requested that
CMS provide clarification on how these
types of patients will be assessed by the
measure.
Response: We intend to conduct
ongoing monitoring to assess for
potential unintended consequences
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associated with the implementation of
this measure and will take these
suggestions into account.
In response to the concern regarding
holding an IRF accountable for planned
procedures that are treated as
observation stays instead of planned
hospital readmissions, we appreciate
the commenter’s concern and expect
that this is a relatively infrequent
occurrence given that most of the
planned procedures are invasive
surgical procedures. The measure is of
hospital readmissions and does not
count planned procedures that are
treated as observation stays. We will
take this issue into consideration for
future measure development.
Comment: One commenter expressed
concern over using claims data for
hospital readmissions, noting that these
data may not be accurate.
Response: We appreciate the
commenter’s concern over the accuracy
of claims data. However, we wish to
clarify that claims data have been
validated for the purposes of assessing
hospital readmissions and are used for
several NQF-endorsed measures
adopted for CMS programs, including
the IRF QRP. Multiple studies have been
conducted to examine the validity of
using Medicare hospital claims to
calculate several NQF-endorsed quality
measures for public
reporting.85 86 87Additionally, although
assessment and other data sources may
be valuable for risk adjustment, we are
not aware of any other data source aside
from Medicare claims data that could be
used to reliably assess potentially
preventable hospital readmissions for
this measure.
Final Decision: After careful
consideration of the public comments,
we are finalizing our proposal to adopt
the measure, Potentially Preventable 30Day Post-Discharge Readmission
Measure for IRF QRP. Measure
Specifications for Measures Adopted in
the FY 2017 IRF QRP final rule are
available at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/IRF-QualityReporting/IRF-Quality-ReportingProgram-Measures-Information-.html.
85 Bratzler DW, Normand SL, Wang Y, et al. An
administrative claims model for profiling hospital
30-day mortality rates for pneumonia patients. PLoS
One 2011;6(4):e17401.
86 Keenan PS, Normand SL, Lin Z, et al. An
administrative claims measure suitable for profiling
hospital performance on the basis of 30-day allcause readmission rates among patients with heart
failure. Circulation 2008;1(1):29–37.
87 Krumholz HM, Wang Y, Mattera JA, et al. An
administrative claims model suitable for profiling
hospital performance based on 30-day mortality
rates among patients with heart failure. Circulation
2006;113:1693–1701.
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4. Potentially Preventable Within Stay
Readmission Measure for Inpatient
Rehabilitation Facilities
In addition to the measure finalized in
section VIII.F.3. of this final rule,
Potentially Preventable 30-Day PostDischarge Readmission Measure for IRF
QRP, we proposed the Potentially
Preventable Within Stay Readmission
Measure for IRFs for the FY 2018
payment determination and subsequent
years. This measure is similar to the
Potentially Preventable 30-Day PostDischarge Readmission Measure for IRF
QRP; however, the readmission window
for this measure focuses on potentially
preventable hospital readmissions that
take place during the IRF stay as
opposed to during the 30-day postdischarge period. The two PPR
measures are intended to function in
tandem, covering readmissions during
the IRF stay and for 30 days following
discharge from the IRF. Utilizing two
PPR measures in the IRF QRP will
enable us to assess different aspects of
care and care coordination. The within
stay measure focuses on the care
transition into inpatient rehabilitation
as well as the care provided during the
IRF stay, whereas the 30-day post-IRF
discharge measure focuses on
transitions from the IRF into lessintensive levels of care or the
community.
Similar to the Potentially Preventable
30-Day Post-Discharge Readmission
Measure for IRF QRP measure for IRFs,
this measure assesses the facility-level
risk-standardized rate of unplanned,
potentially preventable hospital
readmissions during the IRF stay.
Hospital readmissions include
readmissions to a short-stay acute-care
hospital or an LTCH, with a diagnosis
considered to be unplanned and
potentially preventable. This Medicare
FFS measure is claims-based, requiring
no additional data collection or
submission burden for IRFs. As
described in section VIII.F.3. of this
final rule, we developed the approach
for defining PPR measure based on a
comprehensive environmental scan,
analysis of claims data, and TEP input.
Also, we obtained public comment.
The definition for PPRs differs by
readmission window. For the withinIRF stay window, PPRs should be
avoidable with sufficient medical
monitoring and appropriate patient
treatment. The list of PPR conditions for
the Potentially Preventable Within Stay
Readmission Measure for IRFs are
categorized by 4 clinical rationale
groupings:
• Inadequate management of chronic
conditions;
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• Inadequate management of
infections;
• Inadequate management of other
unplanned events; and
• Inadequate injury prevention.
Additional details regarding the
definition for PPRs are available in our
document titled, Proposed Measure
Specifications for Measures Proposed in
the FY 2017 IRF QRP proposed rule
available on our Web site at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html.
Section VIII.F of this final rule
discusses the relevant background and
details that are also relevant for this
measure. This measure defines planned
readmissions in the same manner as
described in section VIII.F.3 of this final
rule, for the Potentially Preventable 30Day Post-Discharge Readmission
Measure for IRF QRP. In addition,
similar to the Potentially Preventable
30-Day Post-Discharge Readmission
Measure for IRF QRP measure, this
measure uses the same risk-adjustment
and statistical approach as described in
section VIII.F.3 of this final rule. Note
the full methodology is detailed in the
document titled, Proposed Measure
Specifications for Measures Proposed in
the FY 2017 IRF QRP proposed rule, at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html. This measure is also
based on 2 consecutive calendar years of
Medicare FFS claims data.
A TEP convened by our measure
contractor provided recommendations
on the technical specifications of this
measure, including the development of
an approach to define potentially
preventable hospital readmission for
PAC. Details from the TEP meetings,
including TEP members’ ratings of
conditions proposed as being
potentially preventable, are available in
the TEP Summary Report available on
our Web site at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html. We also solicited
stakeholder feedback on the
development of this measure through a
public comment period held from
November 2 through December 1, 2015.
Comments on this and other PAC
measures of PPR measures varied, with
some commenters supportive of the
proposed measure, while others either
were not in favor of the measure, or
suggested potential modifications to the
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measure specifications, such as
including standardized function data. A
summary of our public comment period
is also available on our Web site at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The MAP encouraged continued
development of the proposed measure.
Specifically, the MAP stressed the need
to promote shared accountability and
ensure effective care transitions. More
information about the MAP’s
recommendations for this measure is
available at https://
www.qualityforum.org/Publications/
2016/02/MAP_2016_Considerations_
for_Implementing_Measures_in_
Federal_Programs_-_PAC-LTC.aspx. At
the time, the risk-adjustment model was
still under development. Following
completion of that development work,
we were able to test for measure validity
and reliability as described in the
measure specifications document
provided above. Testing results are
within range for similar outcome
measures finalized in public reporting
and value-based purchasing programs,
including the All-Cause Unplanned
Readmission Measure for 30 Days PostDischarge from IRFs (NQF #2502) that
we previously adopted into the IRF
QRP.
We plan to submit the measure to the
NQF for consideration of endorsement.
We stated in the proposed rule that we
intended to provide initial confidential
feedback to providers, prior to public
reporting of this measure, based on 2
calendar years of claims data from
discharges in 2015 and 2016. We
proposed a minimum of 25 eligible stays
in a given IRF for public reporting of the
measure for that IRF. We also stated that
we intended to publicly report this
measure using claims data from
calendar years 2016 and 2017.
We invited public comment on our
proposal to adopt this measure,
Potentially Preventable Within Stay
Readmission Measure for IRFs. We
received several comments, which are
summarized with our responses below.
Comment: CMS received comments in
support of this measure. In particular,
MedPAC supported this measure, and
further suggested that it should be
applied identically across the four PAC
settings so that post-discharge rates can
be meaningfully compared.
Response: We wish to clarify that this
particular measure, developed and
proposed for use in the IRF QRP, is
unique in that it is a within stay
readmission measure. Analogous
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measures applicable to other PAC
settings may be considered in future
rulemaking.
Comment: Several commenters
expressed concern over cross-setting
alignment of measures, some urging
CMS to delay implementation of this
measure until there are equivalent
within stay PPR measures for each PAC
setting. Commenters noted this measure
is not required by the IMPACT Act and
that incongruences between measures in
the different PAC settings present
concerns for cross-setting comparisons
and potential confusion for IRFs about
their quality performance. One
commenter was particularly concerned
about the differences between the IRF
within stay measure and the SNF PPR
measure proposed for the SNF VBP
Program that assess PPRs 30 days after
discharge from the prior hospital.
Response: We are clarifying that
though this within-stay PPR measure is
not required by the IMPACT Act,
capturing potentially preventable
readmission measures during an IRF
stay assesses important aspects of
inpatient post-acute care. The measure
is a starting point for this work, which
is being conducted in phases, and
additional measures that calculate PPRs
using different readmission windows in
other PAC settings will be considered in
the future. We will take this comment
into consideration.
Comment: Some commenters
expressed that IRFs may not be able to
control or prevent hospital readmissions
that take place during an IRF stay,
especially within the first few days of
admission, if patients are admitted to
IRFs prior to the availability of
diagnostic testing results, or if they did
not receive adequate acute care. One
commenter cited the example of
patients with leukemia, who are often
readmitted to the hospital for treatment.
Another commenter noted that even
though the risk adjustment will account
for some of these circumstances, it is
difficult for providers to fully evaluate
the risk-adjustment model because the
testing and risk-adjustment coefficients
have not been finalized. The commenter
recommended these measures be
suspended until CMS explains how the
measures will treat each of these
scenarios. Commenters suggested that
the IRF within-stay PPR measure should
account for the three-day, short-stay and
transfer care policies that exist in the
IRF PPS. One commenter expressed
concern that the proposed measure’s
readmission window and IRF payment
rules would cause a ‘‘double penalty’’
for short-stay episodes that end in a
readmission. Commenters noted that the
home health measures account for short-
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stay payment policies and that the IRF
measure should be designed in a similar
manner.
Response: We recognize the concerns
raised related to potential delays in
receiving diagnostic information and/or
inadequate care provided in the prior
acute setting for some patients.
However, we wish to clarify that this
measure is intended to address
potentially preventable hospital
readmissions and does not count all
hospital readmissions that take place
during the IRF stay. The goal of this
measure is to improve care transitions
and coordination of care, which is
important for all patients. Furthermore,
providers assume the responsibility for
this outcome for all patients that they
admit into their facility, including those
with shorter lengths of stay.
We would like to clarify that for the
commenter’s example regarding patients
with leukemia, these patients would
most likely be excluded from the
measure because non-surgical treatment
of cancer is a measure exclusion. Based
on analysis of data from 2013, 0.5
percent of the IRF sample was excluded
because the prior short-term acute-care
stay was for nonsurgical treatment of
cancer which includes leukemia. In
addition, leukemia and other cancer
patients that are not excluded from the
measure are more likely being
readmitted for planned procedures and
treatments; however, this is a measure
of potentially preventable hospital
readmissions that are also unplanned.
With regard to excluding
readmissions during the first three days
of an IRF stay, we would like to clarify
that the policy cited is for IRF payment
determination and is not related to
measurement of quality of care. This
measure focuses on care transitions and
coordination which is relevant to all
patients, including those with shorter
lengths of stay. Furthermore, excluding
readmissions during the first three days
of an IRF stay may result in transferring
patients back sooner in order to exclude
patients from the measure.
We would also like to clarify that the
finalized risk-adjustment models and
coefficients are included in the measure
specifications available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html.
Comment: Some commenters
expressed concern over the
‘‘multiplicity’’ of the IRF QRP’s
readmission measures, adding that this
may lead to confusion and make it
difficult for IRFs to track and improve
performance. There was also concern
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that this IRF within stay PPR measure
was not required by the IMPACT Act,
nor did it align with a domain in CMS’s
National Quality Strategy. Several
commenters expressed concern over the
overlap between the PPR measure and
the existing all-cause readmission
measures adopted for the IRF QRP. A
few commenters recommended CMS not
to adopt this measure, or to postpone
implementation, commenting that the
purpose and implications of the
measure were ambiguous and its
introduction was premature. The
commenters respectfully recommended
CMS not to adopt this measure, and
some commenters suggested postponing
the implementation of this measure
pending further development or use in
a cross-setting and standardized
manner.
Response: We appreciate the
comment related to the potential
challenges that may be associated with
proposing multiple readmission
measures for the program. However,
given that each measure focuses on a
different aspect of care, we believe that
each measure provides value in the
program. We are committed to ensuring
that measures in the IRF QRP are useful
in assessing quality and will evaluate
the readmission measures in the future.
In addition, we wish to clarify that
though this measure is not required by
the IMPACT Act, capturing potentially
preventable readmission measures
during an IRF stay assesses important
aspects of inpatient post-acute care,
including care coordination. Like other
hospital readmission measures for postacute care, the measure fits within the
National Quality Strategy
communication and care coordination
priority area. We also wish to clarify
that this measure does not overlap
readmission captured in other
readmission measures proposed or
adopted for the IRF QRP.
We would also like to clarify that the
full measure specifications including
preliminary results were made available
at the time of the proposed rule’s
display. The measure is fully developed
and the final measure specifications,
including the finalized risk-adjustment
models and descriptive statistics on
IRFs’ risk-standardized within-stay PPR
rates, are available are included in the
measure specifications available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html.
Comment: One commenter
specifically supported the inclusion of
infectious conditions in the inadequate
management of infections and
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inadequate management of other
unplanned events categories in the
measure’s definition of potentially
preventable hospital readmissions.
Another commenter expressed support
for the inclusion of chronic conditions
and infections as conditions for which
readmissions would be considered
potentially preventable, citing infection
prevention and other interventions that
are effective in preventing such
readmissions. Another commenter
expressed appreciation for the focus on
preventable readmissions, but
recommended that CMS continue
evaluating and testing the measure to
ensure that the codes used for the PPR
definition are clinically relevant. One
commenter expressed concern over
being ‘‘penalized’’ for readmissions that
are clinically unrelated to a patient’s
original reason for IRF admission.
Response: As described in the
proposed rule, the definition for
potentially preventable readmissions for
this measure was developed based on
existing evidence and was vetted by a
TEP, which included clinicians and
post-acute care experts. We also
conducted a comprehensive
environmental scan to identify
conditions for which readmissions may
be considered potentially preventable.
Results of this environmental scan and
details of the TEP input received were
made available in the PPR TEP
summary report available on the CMS
Web site at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
Though readmissions may be
considered potentially preventable even
if they may not appear to be clinically
related to the patient’s original reason
for IRF admission, there is substantial
evidence that the conditions included in
the definition may be preventable with
sufficient medical monitoring and
appropriate patient treatment.
Furthermore, this measure is based on
Medicare claims data and it may not
always be feasible to determine whether
a subsequent readmission is or is not
clinically related to the reason why the
patient was receiving inpatient
rehabilitation. We intend to conduct
ongoing evaluation and monitoring of
this measure, and will take these
comments into consideration.
Comment: One commenter expressed
concern that the measure could create
an incentive for IRFs to be selective
about the types of patients they admit in
order to reduce the risk of PPRs (that is,
‘‘cherry pick’’ less complex patients for
IRF admission). Another commenter
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noted this measure could incentivize
longer acute hospital stays and delay
admission to IRFs, expressing concern
over being penalized for brief
readmissions for follow-up procedures.
Response: We wish to clarify that this
measure does not count planned
procedures as these types of
readmissions do not reflect quality of
care or care transitions. We intend to
conduct ongoing monitoring to assess
for potential unintended consequences
associated with the implementation of
this measure, and will take these
suggestions into account.
Comment: One commenter raised
concerns over the risk-adjustment
approach for the within-stay PPR
measure. The commenter expressed
concern that the HCC risk-adjustment
method is insufficient at predicting
costs for certain patient populations.
The commenters suggested CMS
reconsider the validity and reliability of
the HCC risk-adjustment model, and
research and develop a refined riskadjustment model that encompasses
more of the diversity and complexity of
PAC patients and is more sensitive to
their levels of resource use. The
commenter also expressed concern that
the proposed measure is not adjusted for
socio-economic factors.
Response: We appreciate the
comment received regarding the riskadjustment model and will take this
comment into consideration. We refer
readers to our response on the use of
HCCs as described in section VIII.F.3. of
this final rule. We wish to clarify that
the model included in the specifications
using HCCs as risk adjusters for
comorbidities posted for the proposed
rule demonstrated more than adequate
discrimination power. The model had a
c-statistic of 0.74 which is within range
if not higher for similar readmission
measures finalized in public reporting
programs, including the All-Cause
Unplanned Readmission Measure for 30
Days Post-Discharge from IRFs (NQF
#2502) previously adopted for the IRF
QRP. We would also like to clarify that
the finalized risk-adjustment models
and coefficients are included in the
measure specifications available at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html.
With regard to the suggestions that the
model include sociodemographic factors
and the suggestion pertaining to an
approach with which to convey data
comparisons, we refer the readers to
section VIII.F of this final rule where we
also discuss these topics.
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Comment: Some commenters
expressed concern over provider burden
and questioned CMS’s intention of
applying both all-cause and potentially
preventable readmission measures. The
commenters also noted that with the
finalization of all required measures by
the IMPACT Act, the industry would be
subject to significant changes and an
increased data reporting burden with
regard to the quality reporting program.
Some commenters noted that there
would not be an additional reporting or
data collection burden given the
measure is claim-based; however,
providers would take on additional
burdens, including understanding the
measure design, evaluating its
implications, and reconciling the
CASPER Quality Measure feedback data.
Response: We would like to note that
the within-stay PPR measures use a data
source of claims data and are not
collected by means of an assessment
instrument. Therefore, the measure does
not increase data collection burden on
the provider as this data is currently
collected by providers. Despite the lack
of data collection burden, we appreciate
the comments that more education will
be required for the public and providers
to understand the differences between
the readmission measures in the IRF
QRP. We also wish to clarify that the
within-stay readmission measure does
not overlap any existing readmission
measures.
Comment: Several commenters
expressed concern that the measures are
not NQF-endorsed, some with
additional concerns over measure
testing and development. Some of these
commenters recommended that CMS
should adopt measures endorsed by the
NQF in quality reporting programs or
recommended that CMS submit the
measures through the NQF endorsement
process as soon as feasible.
Response: With regard to NQF
endorsement, as noted in the proposed
rule, we intend to submit this measure
to NQF for consideration of
endorsement. We are unaware of any
other measures that assess potentially
preventable readmissions during an IRF
stay. We appreciate the comments
related to the measure’s testing. We
would also like to clarify that the
finalized risk-adjustment models and
coefficients are included in the measure
specifications available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html. We will make results
of additional testing and evaluation of
the measure beyond those provided in
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the final measure specifications
available in the future.
Final Decision: After careful
consideration of the public comments,
we are finalizing our proposal to adopt
this measure, Potentially Preventable
Within Stay Readmission Measure for
IRFs. Measure Specifications for
Measures Adopted in the FY 2017 IRF
QRP Final Rule are available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html.
G. IRF QRP Quality Measure Finalized
for the FY 2020 Payment Determination
and Subsequent Years
We proposed to adopt one new
quality measure to meet the
requirements of the IMPACT Act
beginning with the FY 2020 payment
determination and subsequent years.
The measure, Drug Regimen Review
Conducted with Follow-Up for
Identified Issues—PAC IRF QRP,
addresses the IMPACT Act quality
domain of Medication Reconciliation.
1. Quality Measure Addressing the
IMPACT Act Domain of Medication
Reconciliation: Drug Regimen Review
Conducted With Follow-Up for
Identified Issues—Post Acute Care
Inpatient Rehabilitation Facility Quality
Reporting Program
Sections 1899B(a)(2)(E)(i)(III) and
1899B(c)(1)(C) of the Act, as added by
the IMPACT Act, require the Secretary
to specify a quality measure to address
the quality domain of medication
reconciliation by October 1, 2018 for
IRFs, LTCHs and SNFs by January 1,
2017 for HHAs. We proposed to adopt
the quality measure, Drug Regimen
Review Conducted with Follow-Up for
Identified Issues—PAC IRF QRP, for the
IRF QRP as a patient-assessment based,
cross-setting quality measure to meet
the IMPACT Act requirements with data
collection beginning October 1, 2018 for
the FY 2020 payment determinations
and subsequent years.
This measure assesses whether PAC
providers were responsive to potential
or actual clinically significant
medication issue(s) when such issues
were identified. Specifically, the quality
measure reports the percentage of
patient stays in which a drug regimen
review was conducted at the time of
admission and timely follow-up with a
physician occurred each time potential
clinically significant medication issues
were identified throughout that stay.
For this quality measure, drug
regimen review is defined as the review
of all medications or drugs the patient
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52111
is taking to identify any potential
clinically significant medication issues.
The quality measure utilizes both the
processes of medication reconciliation
and a drug regimen review, in the event
an actual or potential medication issue
occurred. The measure informs whether
the PAC facility identified and
addressed each clinically significant
medication issue and if the facility
responded or addressed the medication
issue in a timely manner. Of note, drug
regimen review in PAC settings is
generally considered to include
medication reconciliation and review of
the patient’s drug regimen to identify
potential clinically significant
medication issues.88 This measure is
applied uniformly across the PAC
settings.
Medication reconciliation is a process
of reviewing an individual’s complete
and current medication list. Medication
reconciliation is a recognized process
for reducing the occurrence of
medication discrepancies that may lead
to Adverse Drug Events (ADEs).89
Medication discrepancies occur when
there is conflicting information
documented in the medical records. The
World Health Organization regards
medication reconciliation as a standard
operating protocol necessary to reduce
the potential for ADEs that cause harm
to patients. Medication reconciliation is
an important patient safety process that
addresses medication accuracy during
transitions in patient care and in
identifying preventable ADEs.90 The
Joint Commission added medication
reconciliation to its list of National
Patient Safety Goals (2005), suggesting
that medication reconciliation is an
integral component of medication
safety.91 The Society of Hospital
Medicine published a statement in
agreement of the Joint Commission’s
emphasis and value of medication
reconciliation as a patient safety goal.92
There is universal agreement that
medication reconciliation directly
addresses patient safety issues that can
result from medication
88 Institute of Medicine. Preventing Medication
Errors. Washington DC: National Academies Press;
2006.
89 Ibid.
90 Leotsakos A., et al. Standardization in patient
safety: The WHO High 5s project. Int J Qual Health
Care. 2014:26(2):109–116.
91 The Joint Commission. 2016 Long Term Care:
National Patient Safety Goals Medicare/Medicaid
Certification-based Option. (NPSG.03.06.01).
92 Greenwald, J.L., Halasyamani, L., Greene, J.,
LaCivita, C., et al. (2010). Making inpatient
medication reconciliation patient centered,
clinically relevant and implementable: A consensus
statement on key principles and necessary first
steps. Journal of Hospital Medicine, 5(8), 477–485.
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miscommunication and unavailable or
incorrect information.93 94 95
The performance of timely medication
reconciliation is valuable to the process
of drug regimen review. Preventing and
responding to ADEs is of critical
importance as ADEs account for
significant increases in health services
utilization and costs 96 97 98 including
subsequent emergency room visits and
re-hospitalizations.99 Annual health
care costs in the United States from
ADEs are estimated at $3.5 billion,
resulting in 7,000 deaths annually.100 101
Medication errors include the
duplication of medications, delivery of
an incorrect drug, inappropriate drug
omissions, or errors in the dosage, route,
frequency, and duration of medications.
Medication errors are one of the most
common types of medical error and can
occur at any point in the process of
ordering and delivering a medication.
Medication errors have the potential to
result in an ADE.102 103 104 105 106 107
93 Leotsakos A., et al. Standardization in patient
safety: The WHO High 5s project. Int J Qual Health
Care. 2014:26(2):109–116.
94 The Joint Commission. 2016 Long Term Care:
National Patient Safety Goals Medicare/Medicaid
Certification-based Option. (NPSG.03.06.01).
95 IHI. Medication Reconciliation to Prevent
Adverse Drug Events [Internet]. Cambridge, MA:
Institute for Healthcare Improvement; [cited 2016
Jan 11]. Available from: https://www.ihi.org/topics/
adesmedicationreconciliation/Pages/default.aspx.
96 Institute of Medicine. Preventing Medication
Errors. Washington DC: National Academies Press;
2006.
97 Jha AK, Kuperman GJ, Rittenberg E, et al.
Identifying hospital admissions due to adverse drug
events using a computer-based monitor.
Pharmacoepidemiol Drug Saf. 2001;10(2):113–119.
98 Hohl CM, Nosyk B, Kuramoto L, et al.
Outcomes of emergency department patients
presenting with adverse drug events. Ann Emerg
Med. 2011;58:270–279.
99 Kohn LT, Corrigan JM, Donaldson MS. To Err
Is Human: Building a Safer Health System
Washington, DC: National Academies Press; 1999.
100 Greenwald, J.L., Halasyamani, L., Greene, J.,
LaCivita, C., et al. (2010). Making inpatient
medication reconciliation patient centered,
clinically relevant and implementable: A consensus
statement on key principles and necessary first
steps. Journal of Hospital Medicine, 5(8), 477–485.
101 Phillips, David P.; Christenfeld, Nicholas; and
Glynn, Laura M. Increase in US Medication-Error
Deaths between 1983 and 1993. The Lancet.
351:643–644, 1998.
102 Institute of Medicine. To err is human:
Building a safer health system. Washington, DC:
National Academies Press; 2000.
103 Lesar TS, Briceland L, Stein DS. Factors
related to errors in medication prescribing. JAMA.
1997:277(4): 312–317.
104 Bond CA, Raehl CL, & Franke T. Clinical
pharmacy services, hospital pharmacy staffing, and
medication errors in United States hospitals.
Pharmacotherapy. 2002:22(2): 134–147.
105 Bates DW, Cullen DJ, Laird N, Petersen LA,
Small SD, et al. Incidence of adverse drug events
and potential adverse drug events. Implications for
prevention. JAMA. 1995:274(1): 29–34.
106 Barker KN, Flynn EA, Pepper GA, Bates DW,
& Mikeal RL. Medication errors observed in 36
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Inappropriately prescribed medications
are also considered a major healthcare
concern in the United States for the
elderly population, with costs of
roughly $7.2 billion annually.108
There is strong evidence that
medication discrepancies occur during
transfers from acute care facilities to
post-acute care facilities. Discrepancies
occur when there is conflicting
information documented in the medial
records. Almost one-third of medication
discrepancies have the potential to
cause patient harm.109 An estimated 50
percent of patients experienced a
clinically important medication error
after hospital discharge in an analysis of
two tertiary care academic hospitals.110
Medication reconciliation has been
identified as an area for improvement
during transfer from the acute care
facility to the receiving post-acute care
facility. PAC facilities report gaps in
medication information between the
acute care hospital and the receiving
post-acute-care setting when performing
medication reconciliation.111 112
Hospital discharge has been identified
as a particularly high risk time point,
with evidence that medication
reconciliation identifies high levels of
discrepancy.113 114 115 116 117 118 Also,
health care facilities. JAMA. 2002: 162(16):1897–
1903.
107 Bates DW, Boyle DL, Vander Vliet MB,
Schneider J, & Leape L. Relationship between
medication errors and adverse drug events. J Gen
Intern Med. 1995:10(4): 199–205.
108 Fu, Alex Z., et al. ‘‘Potentially inappropriate
medication use and healthcare expenditures in the
US community-dwelling elderly.’’ Medical care
45.5 (2007): 472–476.
109 Wong, Jacqueline D., et al. ‘‘Medication
reconciliation at hospital discharge: Evaluating
discrepancies.’’ Annals of Pharmacotherapy 42.10
(2008): 1373–1379.
110 Kripalani S, Roumie CL, Dalal AK, et al. Effect
of a pharmacist intervention on clinically important
medication errors after hospital discharge: A
randomized controlled trial. Ann Intern Med.
2012:157(1):1–10.
111 Gandara, Esteban, et al. ‘‘Communication and
information deficits in patients discharged to
rehabilitation facilities: An evaluation of five acute
care hospitals.’’ Journal of Hospital Medicine 4.8
(2009): E28–E33.
112 Gandara, Esteban, et al. ‘‘Deficits in discharge
documentation in patients transferred to
rehabilitation facilities on anticoagulation: Results
of a system wide evaluation.’’ Joint Commission
Journal on Quality and Patient Safety 34.8 (2008):
460–463.
113 Coleman EA, Smith JD, Raha D, Min SJ. Post
hospital medication discrepancies: Prevalence and
contributing factors. Arch Intern Med. 2005
165(16):1842–1847.
114 Wong JD, Bajcar JM, Wong GG, et al.
Medication reconciliation at hospital discharge:
Evaluating discrepancies. Ann Pharmacother. 2008
42(10):1373–1379.
115 Hawes EM, Maxwell WD, White SF, Mangun
J, Lin FC. Impact of an outpatient pharmacist
intervention on medication discrepancies and
health care resource utilization in post
hospitalization care transitions. Journal of Primary
Care & Community Health. 2014; 5(1):14–18.
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there is evidence that medication
reconciliation discrepancies occur
throughout the patient stay.119 120 For
older patients, who may have multiple
comorbid conditions and thus multiple
medications, transitions between acute
and post-acute care settings can be
further complicated,121 and medication
reconciliation and patient knowledge
(medication literacy) can be inadequate
post-discharge.122 The quality measure,
Drug Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF
QRP, evaluates an important component
of care coordination for PAC settings
and will affect a large proportion of the
Medicare population who transfer from
hospitals into PAC services each year.
For example, in 2013, 1.7 million
Medicare FFS beneficiaries had SNF
stays, 338,000 beneficiaries had IRF
stays, and 122,000 beneficiaries had
LTCH stays.123
A TEP convened by our measure
development contractor provided input
on the technical specifications of this
quality measure, Drug Regimen Review
Conducted with Follow-Up for
Identified Issues-PAC IRF QRP,
including components of reliability,
validity, and the feasibility of
implementing the measure across PAC
settings. The TEP supported the
measure’s implementation across PAC
settings and was supportive of our plans
to standardize this measure for crosssetting development. A summary of the
TEP proceedings is available on the PAC
Quality Initiatives Downloads and
116 Foust JB, Naylor MD, Bixby MB, Ratcliffe SJ.
Medication problems occurring at hospital
discharge among older adults with heart failure.
Research in Gerontological Nursing. 2012, 5(1): 25–
33.
117 Pherson EC, Shermock KM, Efird LE, et al.
Development and implementation of a post
discharge home-based medication management
service. Am J Health Syst Pharm. 2014; 71(18):
1576–1583.
118 Pronovosta P, Weasta B, Scwarza M, et al.
Medication reconciliation: A practical tool to
reduce the risk of medication errors. J Crit Care.
2003; 18(4): 201–205.
119 Bates DW, Cullen DJ, Laird N, Petersen LA,
Small SD, et al. Incidence of adverse drug events
and potential adverse drug events. Implications for
prevention. JAMA. 1995:274(1): 29–34.
120 Himmel, W., M. Tabache, and M. M. Kochen.
‘‘What happens to long-term medication when
general practice patients are referred to
hospital?.’’European journal of clinical
pharmacology 50.4 (1996): 253–257.
121 Chhabra, P.T., et al. (2012). ‘‘Medication
reconciliation during the transition to and from
long-term care settings: A systematic review.’’ Res
Social Adm Pharm 8(1): 60–75.
122 Kripalani S, Roumie CL, Dalal AK, et al. Effect
of a pharmacist intervention on clinically important
medication errors after hospital discharge: A
randomized controlled trial. Ann Intern Med.
2012:157(1):1–10.
123 March 2015 Report to the Congress: Medicare
Payment Policy. Medicare Payment Advisory
Commission; 2015.
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Federal Register / Vol. 81, No. 151 / Friday, August 5, 2016 / Rules and Regulations
Video Web site at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/Post-AcuteCare-Quality-Initiatives/IMPACT-Act-of2014/IMPACT-Act-Downloads-andVideos.html.
We solicited stakeholder feedback on
the development of this measure by
means of a public comment period held
from September 18 through October 6,
2015. Through public comments
submitted by several stakeholders and
organizations, we received support for
implementation of this measure. The
public comment summary report for the
measure is available on the CMS Web
site at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
The NQF-convened MAP met on
December 14 and 15, 2015, and
provided input on the use of this
measure, Drug Regimen Review
Conducted with Follow-Up for
Identified Issues-PAC IRF QRP. The
MAP encouraged continued
development of the quality measure to
meet the mandate added by the IMPACT
Act. The MAP agreed with the measure
gaps identified by CMS, including
medication reconciliation, and stressed
that medication reconciliation be
present as an ongoing process. More
information about the MAPs
recommendations for this measure is
available at https://www.quality
forum.org/Publications/2016/02/MAP_
2016_Considerations_for_Implementing
_Measures_in_Federal_Programs_-_PAC
-LTC.aspx.
Since the MAP’s review and
recommendation of continued
development, we have continued to
refine this measure in compliance with
the MAP’s recommendations. The
measure is consistent with the
information submitted to the MAP and
supports its scientific acceptability for
use in quality reporting programs.
Therefore, we proposed this measure for
implementation in the IRF QRP as
required by the IMPACT Act.
We reviewed the NQF’s endorsed
measures and identified one NQFendorsed cross-setting and quality
measure related to medication
reconciliation, which applies to the
SNF, LTCH, IRF, and HHA settings of
care: Care for Older Adults (COA), (NQF
#0553). The quality measure, Care for
Older Adults (COA), (NQF #0553)
assesses the percentage of adults 66
years and older who had a medication
review. The Care for Older Adults
(COA), (NQF #0553) measure requires at
least one medication review conducted
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by a prescribing practitioner or clinical
pharmacist during the measurement
year and the presence of a medication
list in the medical record. This is in
contrast to the quality measure, Drug
Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF
QRP, which reports the percentage of
patient stays in which a drug regimen
review was conducted at the time of
admission and that timely follow-up
with a physician occurred each time one
or more potential clinically significant
medication issues were identified
throughout that stay.
After careful review of both quality
measures, we decided to propose the
quality measure, Drug Regimen Review
Conducted with Follow-Up for
Identified Issues-PAC IRF QRP for the
following reasons:
• The IMPACT Act requires the
implementation of quality measures,
using patient assessment data that are
standardized and interoperable across
PAC settings. The quality measure, Drug
Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF
QRP, employs three standardized
patient-assessment data elements for
each of the four PAC settings so that
data are standardized, interoperable,
and comparable; whereas, the Care for
Older Adults (COA), (NQF #0553)
quality measure does not contain data
elements that are standardized across all
four PAC settings.
• The quality measure, Drug Regimen
Review Conducted with Follow-Up for
Identified Issues-PAC IRF QRP, requires
the identification of potential clinically
significant medication issues at the
beginning, during, and at the end of the
patient’s stay to capture data on each
patient’s complete PAC stay; whereas,
the Care for Older Adults (COA), (NQF
#0553) quality measure only requires
annual documentation in the form of a
medication list in the medical record of
the target population.
• The quality measure, Drug Regimen
Review Conducted with Follow-Up for
Identified Issues-PAC IRF QRP, includes
identification of the potential clinically
significant medication issues and
communication with the physician (or
physician designee) as well as
resolution of the issue(s) within a rapid
timeframe (by midnight of the next
calendar day); whereas, the Care for
Older Adults (COA), (NQF #0553)
quality measure does not include any
follow-up or timeframe in which the
follow-up would need to occur.
• The quality measure, Drug Regimen
Review Conducted with Follow-Up for
Identified Issues-PAC IRF QRP, does not
have age exclusions; whereas, the Care
for Older Adults (COA), (NQF #0553)
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52113
quality measure limits the measure’s
population to patients aged 66 and
older.
• The quality measure, Drug Regimen
Review Conducted with Follow-Up for
Identified Issues-PAC IRF QRP, will be
reported to IRFs quarterly to facilitate
internal quality monitoring and quality
improvement in areas such as patient
safety, care coordination, and patient
satisfaction; whereas, the Care for Older
Adults (COA), (NQF #0553) quality
measure would not enable quarterly
quality updates, and thus data
comparisons within and across PAC
providers would be difficult due to the
limited data and scope of the data
collected.
Therefore, based on the evidence
discussed above, we proposed to adopt
the quality measure entitled, Drug
Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF
QRP, for the IRF QRP for FY 2020
payment determination and subsequent
years. We plan to submit the quality
measure to the NQF for consideration
for endorsement.
The calculation of the quality measure
is based on the data collection of three
standardized items to be included in the
IRF–PAI. The collection of data by
means of the standardized items will be
obtained at admission and discharge.
For more information about the data
submission required for this measure,
we refer readers to section VIII.I.c of this
final rule.
The standardized items used to
calculate this quality measure do not
duplicate existing items currently used
for data collection within the IRF–PAI.
The measure denominator is the number
of patient stays with a discharge
assessment during the reporting period.
The measure numerator is the number
of stays in the denominator where the
medical record contains documentation
of a drug regimen review conducted at:
(1) Admission and (2) discharge with a
lookback through the entire patient stay
with all potential clinically significant
medication issues identified during the
course of care and followed up with a
physician or physician designee by
midnight of the next calendar day. This
measure is not risk adjusted. For
technical information about this
measure, including information about
the measure calculation and discussion
pertaining to the standardized items
used to calculate this measure, we refer
readers to the document titled, Proposed
Measure Specifications for Measures
Proposed in the FY 2017 IRF QRP
proposed rule available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRF-
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Quality-Reporting-Program-MeasuresInformation-.html.
Data for the quality measure, Drug
Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF
QRP, will be collected using the IRF–
PAI with submission through the
Quality Improvement Evaluation
System (QIES) Assessment Submission
and Processing (ASAP) system.
We invited public comment on our
proposal to adopt the quality measure,
Drug Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF
QRP for the IRF QRP. We received
several comments, which are
summarized with our responses below.
Comment: Several commenters,
including MedPAC, expressed support
for the quality measure. Commenters
supported the medication reconciliation
concept, and one commenter conveyed
that preventing and responding to ADEs
that account for increases in health
services utilization and cost is critically
important. MedPAC further noted that
the medication reconciliation and
follow-up process can help reduce
medication errors that are especially
common among patients who have
multiple health care providers and
multiple comorbidities.
Response: We agree that medication
reconciliation is an important patient
safety process for addressing medication
accuracy during transitions in patient
care and identifying preventable ADEs,
which may lead to reduced health
services utilization and associated costs.
Comment: Several commenters
recommended that CMS add an
additional response option, to indicate
that the item N2003 Medication Followup (completed at admission) is not
applicable if a patient does not take any
medication. Alternatively, commenters
suggested that CMS clarify whether this
item would be mandatory in the event
that a patient is not taking any
medications.
Response: We wish to point out that
Measure item N2003 has a skip pattern
that allows the user to skip over this
item if the patient does not take
medication. Additional guidance will be
included in the IRF–PAI training
manual.
Comment: We received several
comments regarding concerns about
whether the measure has been fully
developed and tested. Many
commenters noted that the NQFconvened MAP recommended
continued development for the measure
and requested testing of the measure to
ensure that it is appropriate for the IRF
setting. Several commenters expressed
concern that the measure was not NQFendorsed.
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Response: Since the time of the NQFconvened MAP, with our measure
contractor, we tested this measure in a
pilot test involving twelve post-acute
care facilities (IRF, SNF, LTCH),
representing variation across geographic
location, size, profit status, and clinical
records system. Two clinicians in each
facility collected data on a sample of 10
to 20 patients for a total of 298 records
(147 qualifying pairs). Analysis of
agreement between coders within each
participating facility indicated a 71
percent agreement for item DRR–01 124
Drug Regimen Review (admission); 69
percent agreement for item DRR–02 125
Medication Follow-up (admission); and
61 percent agreement for DRR–03 126
Medication Intervention (during stay
and discharge). Overall, pilot testing
enabled us to verify feasibility of the
measure. Furthermore, measure
development included convening a TEP
to provide input on the technical
specifications of this quality measure,
including components of reliability,
validity and the feasibility of
implementing the measure across PAC
settings. The TEP included stakeholders
from the IRF setting and was supportive
of our plans to standardize this measure
for cross-setting development. A
summary of the TEP proceedings is
available on the PAC Quality Initiatives
Downloads and Videos Web site at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-andVideos.html.
As noted above, we plan to conduct
further testing on this measure once we
have started collecting data from the
PAC settings. Once we have completed
this additional measure performance
testing, we plan to submit the measure
to NQF for endorsement.
Comment: We received several
comments about guidance and training.
One commenter requested clear and
consistent information for training staff
and resources to meet the requirements
of the measure. We received several
comments requesting guidance
regarding the definition of ‘‘clinically
significant medication issues.’’ Several
commenters were concerned that the
phrase could be interpreted differently
by the many providers involved in a
124 DRR pilot items DRR–01, DRR–02 and DRR–
03 are equivalent to the proposed rule DRR PAC
instrument items N. 2001, N. 2003 and N. 2005
125 DRR pilot items DRR–01, DRR–02 and DRR–
03 are equivalent to the proposed rule DRR PAC
instrument items N. 2001, N. 2003 and N. 2005
126 DRR pilot items DRR–01, DRR–02 and DRR–
03 are equivalent to the proposed rule DRR PAC
instrument items N. 2001, N. 2003 and N. 2005
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patient’s treatment and that this could
result in a challenge to collect reliable
and accurate data for this quality
measure. One commenter further
conveyed that there are likely to be
variations in measure performance that
are not based on differences in care, but
rather on differences in data collection.
In addition, one commenter requested a
specific definition in the measure
specifications for the word ‘‘potential,’’
and another commenter requested
further guidance on what would be
considered an ‘‘adequate response’’ to a
clinically significant medication issue.
Response: For this measure, potential
clinically significant medication issues
are defined as those issues that, in the
clinician’s professional judgment,
warrant interventions, such as alerting
the physician and/or others, and the
timely completion of any recommended
actions (by midnight of the next
calendar day) so as to avoid and
mitigate any untoward or adverse
outcomes. The definition of ‘‘clinically
significant’’ in this measure was
conceptualized during the measure
development process. For purposes of
the measure, the decision regarding
whether or not a medication issue is
‘‘clinically significant’’ will need to be
made on a case-by-case basis, but we
also intend to provide additional
guidance and training on this issue.
Comment: We received several
comments regarding the patient
populations for the measure,
specifically conveying concern that the
populations are not standardized across
PAC settings. For example, many
commenters noted that IRF QRP
measure includes data collection for
Medicare Fee for Service and Medicare
Advantage patients, while the SNF QRP
measure only includes Medicare Part A
patients, and the LTCH QRP includes all
patients. Commenters were concerned
that this could result in selective
sampling of the patient population that
would skew the collected data and
distort or otherwise invalidate
meaningful comparisons across
measures and across settings, thereby
falling short of the PAC standardization
goals of the IMPACT Act. Several
commenters suggested that CMS
exclude Medicare Advantage patients,
while others recommended that they be
included for all measures across all PAC
settings.
Response: We are working to
standardize all measures as mandated
by the IMPACT Act to increase data
comparability and interoperability. We
will take the commenter’s comments
and concerns into consideration as we
work to standardize the proposed
measure.
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Comment: We received several
comments regarding the time period for
the proposed measure. One commenter
disagreed with the measure’s
requirement that a facility must respond
to urgent medication issues within one
calendar day, noting that some
medication issues may need to be
resolved much more quickly for the
patient’s well-being. Another
commenter was concerned that the
measure tracks medication issues during
any point of the patient’s stay, citing
that medication reconciliation occurs
only during transitions of care such as
admission, transfer and discharge.
Therefore, this commenter had concerns
that this drug regimen review process
was fundamentally different than a
medication reconciliation measure that
focused only on care transitions.
Response: We appreciate the
challenges in coordinating patient care
in IRF settings. However, we chose to
set the intervention timeline as
midnight of the next calendar day
because we believe this timeline is
consistent with current standard clinical
practice where a clinically significant
medication issue arises. The measure
evaluates responsiveness to potential or
actual clinically significant medication
issues when such issues are identified.
The measure evaluates responsiveness
to potential or actual clinically
significant medication issues when such
issues are identified. We would like to
note that the measure is simply
assessing responsiveness to issues and
does not prevent clinicians from acting
more quickly when an issue is
identified.
We agree that medication
discrepancies can occur during patient
admissions, transfers, and discharges.
We wish to clarify that the quality
measure requires the identification of
potential clinically significant
medication issues for each patient’s
complete IRF stay, from admission to
discharge. Medication reconciliation
and drug regimen review are
interrelated activities; while medication
reconciliation is a process that identifies
the most accurate and current list of
medications, particularly during
transitions of care, it also includes the
evaluation of the name, dosage,
frequency, and route. Drug regimen
review is a process that necessitates and
includes the review of all medications
for additional purposes such as the
identification of potential adverse
effects. The process of drug regimen
review includes medication
reconciliation at the time of patient
transitions and throughout the patient’s
stay.
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Comment: We received several
comments pertaining to the scope of the
measure. Several commenters
commented that medication
reconciliation and drug regimen review
are distinct processes. Several
commenters were concerned that the
measure does not meet the medication
reconciliation domain of the IMPACT
Act. Commenters maintained that the
services provided as part of drug
regimen review are distinctly different
from the services provided as part of
medication reconciliation, and that they
are completed by different members of
the care team. These commenters
believe that the measure goes beyond
the statutory mandate of the medication
reconciliation domain of the IMPACT
Act. One commenter was also
concerned that, according to the
definition provided in the Home Health
Conditions of Participation, drug
regimen review includes taking into
consideration a patient’s noncompliance
with drug therapy, significant side
effects, and ineffective drug therapy,
which are not feasible for a facility to
assess during admission. The
commenter conveyed that this was
distinct from medication reconciliation.
Many commenters were concerned that
the measure only evaluates whether the
patient’s current medications are being
reviewed and does not determine
whether this review affects the patient’s
quality of care.
Response: We disagree with the
commenters’ suggestion that the
measure does not meet the requirements
of the IMPACT Act. Medication
reconciliation and drug regimen review
are interrelated activities; while
medication reconciliation is a process
that identifies the most accurate and
current list of medications, particularly
during transitions in care, it also
includes the evaluation of the name,
dosage, frequency, and route. Drug
regimen review is a process that
necessitates, and includes the review of
all medications for additional purposes,
such as the identification of potential
adverse effects. The process of drug
regimen review includes medication
reconciliation at the time of patient
transitions and throughout the patient’s
stay. Therefore, we believe that
medication reconciliation and drug
regimen review are processes that are
appropriate to combine into a single
measure for purposes of the IRF QRP.
We would also like to note that during
the development of the measure, the
definitions of medication reconciliation
and drug regimen review, as detailed in
the State Operations Manual (SOM),
which includes the Conditions of
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Participation, were taken into
consideration. We do not believe that
the measure’s use of the term ‘‘clinically
significant’’ overrides or conflicts with
the guidance as outlined in the SOM.
Further, we wish to clarify that the
specification of the measure does not
preclude the activities of drug regimen
reviews that are consistent with the
SOM. The measure encompasses the
IMPACT Act’s medication
reconciliation domain.
Comment: Several commenters were
concerned that the measure does not
specify which healthcare provider is
required to perform the drug regimen
review, or the level of clinical training
required to do so. The commenters were
concerned that this lack of
standardization could lead to
differences across the PAC settings.
Many commenters conveyed that in the
IRF setting, medication reconciliation is
complicated and time consuming, as
IRF patients with multiple clinical
needs often arrive from an acute
hospital where many physicians,
including specialists, have made
changes to patients’ prescriptions. One
commenter noted that patient
medications may be adjusted more
frequently in an IRF due to the high
level of physician supervision and was
concerned that the measure would not
count the extensive drug regimen
review being done if a clinically
significant medication issue was not
identified during the stay. However,
commenters note that other PAC
settings may lack the clinical expertise
required to perform such thorough
medication reviews. Commenters were
concerned that the assessment items
proposed do not capture the intense
involvement of a pharmacist, physician,
and nurse that occurs in complex cases.
Response: We wish to clarify that the
measure does not override, supersede or
conflict with current CMS guidance or
regulations related to drug regimen
review. The measure also does not
specify what clinical professional is
required to perform these activities. We
do not prescribe guidance on which
clinician may complete patient
assessments. We also appreciate
concerns about standardization across
the PAC settings and acknowledge the
complexity of drug regimen review in
the IRF settings. While we agree that
this measure does not capture every
aspect of the drug regimen review
process undertaken for each IRF patient,
we emphasize that it is intended to
assess whether PAC providers were
responsive to potential or actual
clinically significant medication issue(s)
when such issues were identified. As
noted in the measure specifications, the
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measure’s assessment items are
standardized.
Comment: Many commenters,
including MedPAC, encouraged CMS to
develop a measure to evaluate
medication reconciliation throughout
the care continuum. Commenters,
including MedPAC, suggested CMS
focus on discharge from the PAC setting
and evaluate whether the PAC sends a
medication list to the patient’s primary
care physician or to the next PAC
provider. One commenter recommended
that CMS not proceed with the measure
and instead focus on medication
reconciliation at discharge.
Response: PAC facilities are expected
to document information pertaining to
the process of a drug regimen review,
which includes medication
reconciliation, in the patient’s discharge
medical record. Further, it is standard
practice for patient discharge records to
include a medication list to be
transferred to the admitting PAC
facility. We appreciate MedPAC and
other commenters’ recommendation for
a quality measure that assesses postdischarge medication communication
with primary care providers for patients
discharged to home. We will take the
recommendation into consideration for
future measure development in
accordance with the IMPACT Act,
which emphasizes the transfer of
interoperable patient information across
the continuum of care.
Comment: We received a number of
comments related to unintended
consequences of the measure. One
commenter expressed concern that the
measure would discourage PAC
clinicians from reporting and correcting
medication errors. Another commenter
was concerned that the measure does
not require an IRF to take steps to
identify clinically significant
medication issues, but instead measures
whether steps were taken once an issue
was identified, which could be abused
by PAC providers who limit the
identification of clinically significant
medication issues in order to artificially
increase their score.
Response: Since it is a professional
standard of practice for all providers to
address potential clinically significant
medication issues before they lead to
avoidable harm to the patient, we do not
believe that the measure will discourage
a clinician from reporting a significant
medication issue. We reiterate that the
quality measure encourages PAC
providers to conduct thorough drug
regimen review to identify, address, and
follow up for all clinically significant
medication errors. The measure was
informed by current evidence
surrounding medication reconciliation
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and drug regimen review, as well as a
review of best practice and professional
standards of care.
Comment: We received multiple
comments related to burden and
expenses related to this measure. One
commenter expressed concern that the
requirements required increased
resources without clear benefit or
increase in pay to providers for
additional expenses. One commenter
conveyed concern that providers’
existing electronic medical record
systems (EMRs) likely do not include
data collection and reporting
capabilities required by the measure.
The commenter conveyed the challenge
of collecting the data for this measure
manually and had concerns about the
cost of doing so, and resulting data
inaccuracy.
Response: We are very sensitive to the
issue of burden associated with data
collection and have proposed only the
minimal number of items needed to
calculate the quality measures. We
emphasize that this measure follows
standard clinical practice requirements
of ongoing review, documentation, and
timely reconciliation of all patient
medications, with appropriate follow-up
to address all clinically significant
medication concerns. While we support
the use of EMRs, we do not require that
providers use EMRs to populate
assessment data.
Comment: One commenter suggested
that CMS exclude patients from the
measure who were unexpectedly
discharged before the medication
reconciliation process is completed.
Response: We would like to clarify
that this IRF measure includes all
Medicare Part A and Medicare
Advantage patient stays, including stays
where a patient has an unexpected
discharge. Data for coding N2005
Medicare Interventions can be obtained
from the patient’s medical records, so it
is feasible to code the measure item
when a patient has an unexpected
discharge.
Comment: One commenter conveyed
concern that drug regimen review
occurs differently across the care
settings. The commenter specifically
expressed that inpatient settings may
handle clinically significant medication
issues more immediately than home
health agencies.
Response: We believe that this
comment is immaterial to the intent of
the measure. It should be noted that we
strive for consistency in the collection
and application of the measure across
all PAC settings.
Comment: One commenter requested
for clarification about whether the
measure is intended to include
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instances where a drug was reviewed for
potential adverse effects and drug
reactions prior to being ordered. The
commenter conveyed that the measure
only included medications that have
been ordered for the patient but not
those that were prevented from being
ordered by a drug regimen process.
Response: We appreciate the
commenter’s concern regarding
medications that were prevented from
being ordered by the drug regimen
review process. If finalized, we would
provide guidance on these and other
clinical examples as part of the training
efforts.
Final Decision: After careful
consideration of the public comments,
we are finalizing our proposal to adopt
the quality measure, Drug Regimen
Review Conducted with Follow-Up for
Identified Issues-PAC IRF QRP measure
for the IRF QRP for FY 2020 payment
determination and subsequent years, as
described in the Measure Specifications
for Measures Adopted in the FY 2017
IRF QRP final rule, available at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/
Technical-Information.html.
H. IRF QRP Quality Measures and
Measure Concepts under Consideration
for Future Years
We invited comment on the
importance, relevance, appropriateness,
and applicability of each of the quality
measures listed in Table 8 for future
years in the IRF QRP. We are developing
a measure related to the IMPACT Act
domain, ‘‘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
furnishing items and services to the
individual, when the individual
transitions.’’ We considered the
possibility of adding quality measures
that rely on the patient’s perspective;
that is, measures that include patientreported experience of care and health
status data. We recently posted a
‘‘Request for Information to Aid in the
Design and Development of a Survey
Regarding Patient and Family Member
Experiences with Care Received in
Inpatient Rehabilitation Facilities’’ (80
FR 72725). Also, we are considering a
measure focused on pain that relies on
the collection of patient-reported pain
data. Finally, we are considering a
measure related to patient safety,
Venous Thromboembolism Prophylaxis.
We received several comments about
IRF QRP quality measures under
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consideration for future years which are
summarized with our responses below.
Comment: Commenters had concerns
about the current process for seeking
stakeholder feedback, noting that sevenand fourteen-day public comment
periods are unreasonable for
stakeholders. Other commenters did not
support the addition of process
measures, citing administrative burden
and expense, and recommended that
CMS focus on outcome measures and
postpone any measures outside the
requirements of the IMPACT Act.
Many commenters remarked on the
limited number of items in the IRF–PAI
related to communication, cognition,
and swallowing and noted that these
domains are important in treating
individuals with neurological disorders.
One commenter encouraged CMS to
adopt a specific screening instrument
(Montreal Cognitive Assessment
(MoCA)) or similar screening tools and
assessment tools (such as the Continuity
Assessment Record and EvaluationCommunity, or CARE–C) to best meet
the needs of Medicare beneficiaries and
the intent of the IMPACT Act. Another
commenter requested that CMS add a
functional cognition assessment item to
the IRF discharge assessment and that
this information be provided to the next
provider when a patient is transferred.
The commenters offered to collaborate
with CMS to develop future measures in
the area of cognitive function.
Response: We wish to note that
several of the measures currently
adopted in the IRF QRP are outcome
measures, including: Percent of
Residents or Patients with Pressure
Ulcers that are New or Worsened (ShortStay) (NQF #0678), NHSN CAUTI
Outcome Measure (NQF #0138), AllCause Unplanned Readmission Measure
for 30 Days Post Discharge from an IRF
(NQF #2502), NHSN Facility-wide
Inpatient Hospital-onset MRSA
Bacteremia Outcome Measure (NQF
#1716), and NHSN Facility-wide
Inpatient Hospital-onset CDI Outcome
Measure (NQF #1717). Measures that
have been finalized for implementation
October 1, 2016 also include outcome
measures: Application of Percent of
Residents Experiencing One or More
Falls with Major Injury (NQF #0674),
IRF Functional Outcome Measure:
Change in Self-Care Score for Medical
Rehabilitation Patients (NQF #2633),
IRF Functional Outcome Measure:
Change in Mobility Score for Medical
Rehabilitation Patients (NQF #2634),
Discharge Self-Care Score for Medical
Rehabilitation Patients (NQF #2635),
Discharge Mobility Score for Medical
Rehabilitation Patients (NQF #2636) We
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agree that future development of
outcome measures should include other
areas of function, such as
communication, cognition and
swallowing, and are important
components of functional assessment
and improvement for patients who
receive care in PAC settings, including
IRFs. We appreciate comments related
to the public comment periods during
the measure development and
stakeholder feedback process, and will
continue to engage stakeholders as we
develop and implement quality
measures to meet the requirements of
the IMPACT Act.
Comment: Several commenters
supported a Venous Thromboembolism
(VTE) Prophylaxis measure but
suggested that the measure take into
account that not all VTEs can be
prevented due to its complexity. Some
commenters did not support a process
measure, since VTE prophylaxis is
already a standard of practice and the
measure would add burden, but have no
clinical significance. These commenters
do support the development of a VTE
outcome measure.
Response: We thank the commenters
for their comments on the VTE
Prophylaxis measure under
consideration for future implementation
in the IRF QRP and will take into
consideration the commenters’
recommendations.
Comment: Several commenters
recommended that a pain measure take
into consideration pain that might be
experienced as the result of intense
therapy. One commenter suggested that
pain management was a more
meaningful measure for IRF patients
and requested guidance on the
definitions of moderate and severe pain.
Response: We will take these
suggested quality measure concepts and
recommendations regarding measure
specifications into consideration in our
ongoing measure development and
testing efforts.
Comment: We received several
comments regarding the patient
experience of care measure. Several
commenters had concerns about survey
fatigue across the continuum of care.
Many commenters were concerned that
for one episode of care, a patient could
receive a survey from each setting
which could result in confusion in
responses and inaccurate results. Many
commenters were concerned that since
many IRFs are small units, their data
may not be statistically representative or
may show high variability. The
commenters recommended that CMS
take a systems-based approach with
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patient experience surveys to avoid
these problems.
Many commenters supported a
patient experience of care measure, and
supported accepting proxy response
from family members and caregivers to
support accurate and reliable results at
the facility level. Other commenters
supported a measure of patient
experience, instead of only patient
satisfaction, and recommended that it
include several aspects unique to IRF
care, including goal setting and
discharge planning. Commenters
recommended that CMS implement the
survey as a voluntary tool prior to
requiring it, which would allow IRFs to
transition operationally and find a
vendor, if needed. Commenters also
recommended that the quality measure
adjust for factors already in place for
existing CAHPS® surveys, including
adjusting for mode of survey
administration, as well as IRF-specific
patient-mix adjustment. The commenter
also suggested converting responses to a
0 to 100 linear-scaled score. Several
commenters recommended that CMS
seek stakeholder input on the
development of a patient experience of
care measure.
Response: We will take these
recommendations regarding measure
specifications and survey fatigue across
the care continuum into consideration
in our ongoing measure development
and testing efforts, and will continue to
engage stakeholders in the development
process.
Comment: We received several
comments regarding the transfer of
health information and care preferences
measure. Many commenters
recommended that development efforts
for this measure should recognize that
there is a large amount of variation in
the different health information systems
used by different IRFs to record, store,
retrieve, and share patient information.
The commenter noted that hospitals are
already required to transfer health
information and care preferences as part
of their Medicare Conditions of
Participation, and posited that adding
such a measure to the IRF QRP would
rely on receiving accurate and complete
discharge information from a prior level
of care, which may be out of the IRF’s
control.
Response: As we move through the
development of this measure concept,
we will consider the variation in health
information systems used by different
IRFs, as well as the concerns about
receiving complete discharge
information from a prior level of care for
these measure concepts.
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TABLE 9—IRF QRP QUALITY MEASURES UNDER CONSIDERATION FOR FUTURE YEARS
IMPACT Act Domain .......................
IMPACT Act Measure ..............
NQS Priority ....................................
Measures .................................
NQS Priority ....................................
Measure ...................................
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
furnishing items and services to the individual, when the individual transitions.
• Transfer of health information and care preferences when an individual transitions.
Patient- and Caregiver-Centered Care.
• Patient Experience of Care.
• Percent of Patients with Moderate to Severe Pain.
Patient Safety.
• Venous Thromboembolism Prophylaxis.
I. Form, Manner, and Timing of Quality
Data Submission for the FY 2018
Payment Determination and Subsequent
Years
1. Background
Section 1886(j)(7)(C) of the Act
requires that, for the FY 2014 payment
determination and subsequent years,
each IRF submit to the Secretary data on
quality measures specified by the
Secretary. In addition, section
1886(j)(7)(F) of the Act requires that, for
the fiscal year beginning on the
specified application date, as defined in
section 1899B(a)(2)(E) of the Act, and
each subsequent year, each IRF submit
to the Secretary data on measures
specified by the Secretary under section
1899B of the Act. The data required
under section 1886(j)(7)(C) and (F) of
the Act must be submitted in a form and
manner, and at a time, specified by the
Secretary. As required by section
1886(j)(7)(A)(i) of the Act, for any IRF
that does not submit data in accordance
with section 1886(j)(7)(C) and (F) of the
Act for a given fiscal year, the annual
increase factor for payments for
discharges occurring during the fiscal
year must be reduced by 2 percentage
points.
a. Timeline for Data Submission Under
the IRF QRP for the FY 2018, FY 2019
and Subsequent Year Payment
Determinations
Tables 10 through 18 represent our
finalized data collection and data
submission quarterly reporting periods,
as well as the quarterly review and
correction periods and submission
deadlines for the quality measure data
submitted via the IRF–PAI and the CDC/
NHSN affecting the FY 2018 and
subsequent year payment
determinations. We also provide in
Table 10 our previously finalized
claims-based measures for FY 2018 and
subsequent years, although we note that,
for claims-based measures, there is no
corresponding quarterly-based data
collection or submission reporting
periods with quarterly-based review and
correction deadline periods.
Further, in the FY 2016 IRF PPS final
rule (80 FR 47122 through 47123), we
established that the IRF–PAI-based
measures finalized for adoption into the
IRF QRP will transition from reporting
based on the fiscal year to an annual
schedule consistent with the calendar
year, with quarterly reporting periods
followed by quarterly review and
correction periods and submission
deadlines, unless there is a clinical
reason for an alternative data collection
time frame. The pattern for annual,
calendar year-based data reporting, in
which we use 4 quarters of data, is
illustrated in Table 10 and is in place
for all Annual Payment Update (APU)
years except for the measure in Table 10
for which the FY 2018 APU
determination will be based on 5
calendar year quarters in order to
transition this measure from FY to CY
reporting. We also wish to clarify that
payment determinations for the
measures finalized for use in the IRF
QRP that use the IRF–PAI or CDC NHSN
data sources will subsequently use the
quarterly data collection/submission
and review, correction and submission
deadlines described in Table 10 unless
otherwise specified, as is with the
measure NQF #0680: Percent of
Residents or Patients Who Were
Assessed and Appropriately Given the
Seasonal Influenza Vaccine. For this
measure, we clarify in a subsequent
discussion that the data collection and
reporting periods, which are based on
the Influenza Season, span 2
consecutive years from July 1 through
June 30th and we therefore separately
illustrate those collection/submission
quarterly reporting periods, review and
correction periods, and submission
deadlines for FY 2019 and subsequent
years in Table 10. We also separately
distinguish the reporting periods and
data submission timeframes for the
finalized measure Influenza Vaccination
Coverage among Healthcare Personnel
which spans 2 consecutive years, as this
measure is based on the Influenza
vaccination season, in Table 10.
TABLE 10—ANNUAL QRP CY IRF–PAI & CDC/NHSN DATA COLLECTION/SUBMISSION REPORTING PERIODS AND DATA
SUBMISSION/CORRECTION DEADLINES ** PAYMENT DETERMINATIONS ∧
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Proposed CY data collection
quarter
Quarter
Quarter
Quarter
Quarter
1
2
3
4
Data Collection/submission
quarterly reporting period
........................................
........................................
........................................
........................................
January 1–March 31 * ...................
April 1–June 30 ............................
July 1–September 30 ...................
October 1–December 31 * ............
QRP Quarterly review and correction periods data submission deadlines for payment determination **
April 1–August 15 * .......................
July 1–November 15 ....................
October 1–February 15 ................
January 1–May 15 * ......................
Deadline:
Deadline:
Deadline:
Deadline:
August 15.*
November 15.
February 15.
May 15.*
* We refer readers to Table 10 for the annual data collection time frame for the measure, Influenza Vaccination Coverage among Healthcare
Personnel
** We note that the submission of IRF–PAI data must also adhere to the IRF PPS deadlines
∧ We refer readers to Table 10 for the 12 month (July–June) data collection/submission quarterly reporting periods, review and correction periods and submission deadlines for APU determinations for the measure NQF #0680: Percent of Residents or Patients Who Were Assessed and
Appropriately Given the Seasonal Influenza Vaccine
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52119
TABLE 11—SUMMARY DETAILS ON DATA COLLECTION PERIOD AND DATA SUBMISSION TIMELINE FOR PREVIOUSLY ADOPTED QUALITY MEASURE AFFECTING THE FY 2018 PAYMENT DETERMINATION THAT WILL USE 5 CY QUARTERS IN
ORDER TO TRANSITION FROM A FY TO A CY REPORTING CYCLE
Finalized Measure:
• NQF # 0678 Percent of Residents or Patients with Pressure Ulcers That Are New or Worsened (Short Stay) (80 FR 47122)
Submission method
Data collection/submission quarterly
reporting period(s)
IRF–PAI/QIES ASAP System ..................
CY
CY
CY
CY
CY
15
16
16
16
16
Q4,
Q1,
Q2,
Q3,
Q4,
Quarterly review and correction periods
data submission deadlines for payment
determination */**
10/1/15–12/31/15 ...................
1/1/16–3/31/16 .......................
4/1/16–6/30/16 .......................
7/1/16–9/30/16 .......................
10/01/16–12/31/16 .................
1/1/2016–5/15/16 deadline ......................
4/1/2016–8/15/16 deadline.
7/1/16–11/15/16 deadline.
10/1/16–2/15/17 deadline.
1/1/17–5/15/17 deadline.
APU
Determination
affected
FY 2018.
* We refer readers to the Table 11 for an illustration of the data collection/submission quarterly reporting periods and correction and submission
deadlines
** We note that the submission of IRF–PAI data must also adhere to the IRF PPS deadlines
TABLE 12—SUMMARY DETAILS ON DATA COLLECTION PERIOD AND DATA SUBMISSION TIMELINE FOR PREVIOUSLY ADOPTED IRF–PAI QUALITY MEASURE, NQF #0680 PERCENT OF RESIDENTS OR PATIENTS WHO WERE ASSESSED AND APPROPRIATELY GIVEN THE SEASONAL INFLUENZA VACCINE, AFFECTING THE FY 2018 PAYMENT DETERMINATION
Finalized Measure:
• NQF #0680 Percent of Residents or Patients Who Were Assessed and Appropriately Given the Seasonal Influenza Vaccine (80 FR 47122)
Submission method
Data collection/submission quarterly
reporting period(s)
Quarterly review and correction periods
data submission deadlines for payment
determination *
IRF–PAI/QIES ASAP System ..................
CY 15 Q4, 10/1/15–12/31/15 ...................
CY 16 Q1, 1/1/16–3/31/16 .......................
CY 16 Q2, 4/1/16–6/30/16 .......................
1/1/2016–5/15/16 deadline ......................
4/1/2016–8/15/16 deadline.
7/1/16–11/15/16 deadline.
APU
Determination
affected
FY 2018.
* We note that the submission of IRF–PAI data must also adhere to the IRF PPS deadlines
TABLE 13—SUMMARY DETAILS ON DATA COLLECTION PERIOD AND DATA SUBMISSION TIMELINE FOR PREVIOUSLY ADOPTED QUALITY MEASURES AFFECTING THE FY 2018 PAYMENT DETERMINATION THAT WILL USE ONLY 1 CY QUARTER
OF DATA INITIALLY FOR THE PURPOSE OF DETERMINING PROVIDER COMPLIANCE
Finalized Measure:
• NQF #0674 Application of Percent of Residents Experiencing One or More Falls with Major Injury (Long Stay) (80 FR 47122)
• NQF #2631 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 47122)
• NQF #2633 IRF Functional Outcome Measure: Change in Self-Care Score for Medical Rehabilitation Patients (80 FR 47122)
• NQF #2634 IRF Functional Outcome Measure: Change in Mobility Score for Medical Rehabilitation Patients (80 FR 47122)
• NQF #2635 IRF Functional Outcome Measure: Discharge Self-Care Score for Medical Rehabilitation Patients (80 FR 47122)
• NQF #2636 IRF Functional Outcome Measure: Discharge Mobility Score for Medical Rehabilitation Patients (80 FR 47122)
Submission method
Data collection/submission quarterly
reporting period(s)
Quarterly review and correction periods
data submission deadlines for payment
determination */**
IRF–PAI/QIES ASAP System ..................
CY 16 Q4, 10/1/16–12/31/16 ...................
1/1/2017–5/15/17 .....................................
APU
Determination
affected
FY 2018.
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* We refer readers to the Table 12 for an illustration of the data collection/submission quarterly reporting periods and correction and submission
deadlines, which will be followed for the above measures, for all payment determinations subsequent to that of FY 2018.
** We note that the submission of IRF–PAI data must also adhere to the IRF PPS deadlines.
TABLE 14—SUMMARY DETAILS ON DATA COLLECTION PERIOD AND DATA SUBMISSION TIMELINE FOR PREVIOUSLY ADOPTED CDC/NHSN QUALITY MEASURES AFFECTING THE FY 2018 PAYMENT DETERMINATION AND SUBSEQUENT YEARS
THAT WILL USE 4 CY QUARTERS *
Finalized Measures:
• NQF #0138 NHSN Catheter-Associated Urinary Tract Infection (CAUTI) Outcome Measure (80 FR 47122 through 47123)
• NQF #1716 NHSN Facility-wide Inpatient Hospital-onset Methicillin-resistant Staphylococcus aureus (MRSA) Bacteremia Outcome Measure
(80 FR 47122 through 47123)
• NQF #1717 NHSN Facility-wide Inpatient Hospital-onset Clostridium difficile Infection (CDI) Outcome Measure (79 FR 45917)
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TABLE 14—SUMMARY DETAILS ON DATA COLLECTION PERIOD AND DATA SUBMISSION TIMELINE FOR PREVIOUSLY ADOPTED CDC/NHSN QUALITY MEASURES AFFECTING THE FY 2018 PAYMENT DETERMINATION AND SUBSEQUENT YEARS
THAT WILL USE 4 CY QUARTERS *—Continued
Submission method
Data Collection/submission
Quarterly Reporting Period(s)
Quarterly Review and Correction
Periods Data Submission
Deadlines for Payment
Determination
APU determination affected
CDC/NHSN ....................................
CY 16 Q1, 1/1/16–3/31/16 and
Q1 of subsequent Calendar
Years.
CY 16 Q2, 4/1/16–6/30/16 and
Q2 of subsequent Calendar
Years.
CY 16 Q3, 7/1/16–9/30/16 and
Q3 of subsequent Calendar
Years.
CY 16 Q4, 10/1/16–12/31/16 and
Q4 of subsequent Calendar
Years.
4/1/2016–8/15/16 ** and 4/1–8/15
of subsequent years.
FY 2018 and subsequent years.**
7/1/16–11/15/16 **and 7/1–11/15
of subsequent years.
10/1/16–2/15/17 ** and 10/1–2/15
of subsequent years.
1/1/17–5/15/17 ** and 1/1–5/15 of
subsequent years.
* We refer readers to the Table 14 for an illustration of the data collection/submission quarterly reporting periods and correction and submission
deadlines.
** As is illustrated in Table 14: Subsequent years follow the same CY Quarterly Data Collection/submission Quarterly Reporting Periods and
Quarterly Review and Correction Periods Deadlines for Payment Determination in which every CY quarter is followed by approximately 135 days
for IRFs to review and correct their data until midnight on the final submission deadline dates.
TABLE 15—SUMMARY DETAILS ON DATA COLLECTION PERIOD AND DATA SUBMISSION TIMELINE FOR PREVIOUSLY ADOPTED IRF–PAI QUALITY MEASURES AFFECTING THE FY 2019 PAYMENT DETERMINATION AND SUBSEQUENT YEARS
THAT WILL USE 4 CY QUARTERS
Finalized Measures:
• NQF #0678 Percent of Residents or Patients with Pressure Ulcers That Are New or Worsened (Short Stay) (80 FR 47122)
• NQF #0674 Application of Percent of Residents Experiencing One or More Falls with Major Injury (Long Stay) (80 FR 47122)
• NQF #2631 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 47122)
• NQF #2633 IRF Functional Outcome Measure: Change in Self-Care Score for Medical Rehabilitation Patients (80 FR 47122)
• NQF #2634 IRF Functional Outcome Measure: Change in Mobility Score for Medical Rehabilitation Patients (80 FR 47122)
• NQF #2635 IRF Functional Outcome Measure: Discharge Self-Care Score for Medical Rehabilitation Patients (80 FR 47122)
• NQF #2636 IRF Functional Outcome Measure: Discharge Mobility Score for Medical Rehabilitation Patients (80 FR 47122)
Submission method
Data Collection/submission
Quarterly Reporting Period(s)
Quarterly Review and Correction
Periods Data Submission
Deadlines for Payment
Determination */**
IRF–PAI/QIES ASAP System ........
CY 17 Q1, 1/1/17–3/31/17 and
Q1 of subsequent Calendar
Years.
CY 17 Q2, 4/1/17–6/30/17 and
Q2 of subsequent Calendar
Years.
CY 17 Q3, 7/1/17–9/30/17 and
Q3 of subsequent Calendar
Years.
CY 17 Q4, 10/1/17–12/31/17 and
Q4 of subsequent Calendar
Years.
4/1/2017–8/15/17 *** and 4/1–8/15
of subsequent years.
APU determination affected
FY
2019
years.***
and
subsequent
7/1/17–11/15/17 *** and 7/1–11/15
of subsequent years.
10/1/17–2/15/18 *** and 10/1–1/15
of subsequent years.
1/1/18–5/15/18 *** and 1/1–5/15 of
subsequent years.
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We refer readers to the Table 15 for an illustration of the data collection/submission quarterly reporting periods and correction and submission
deadlines.
** We note that the submission of IRF–PAI data must also adhere to the IRF PPS deadlines.
*** As is illustrated in Table 15: Subsequent years follow the same CY Quarterly Data Collection/submission Quarterly Reporting Periods and
Quarterly Review and Correction Periods) and Data Submission Deadlines for Payment Determination in which every CY quarter is followed by
approximately 135 days for IRFs to review and correct their data until midnight on the final submission deadline dates.
In the FY 2014 IRF PPS final rule, we
adopted the Percent of Residents or
Patients Who Were Assessed and
Appropriately Given the Seasonal
Influenza Vaccine (Short Stay) (NQF
#0680) measure for the FY 2017
payment determination and subsequent
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years (78 FR 47910 through 47911). In
the FY 2014 IRF PPS final rule (78 FR
47917 through 47919), we finalized the
data submission timelines and
submission deadlines for the measures
for FY 2017 payment determination.
Refer to the FY 2014 final rule (78 FR
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Fmt 4701
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47917 through 47919) for a more
detailed discussion of these timelines
and deadlines.
We want to clarify that this measure
includes all patients in the IRF one or
more days during the influenza
vaccination season (IVS) (October 1 of
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any given CY through March 31 of the
subsequent CY). This includes, for
example, a patient is admitted
September 15, 2015, and discharged
April 1, 2016 (thus, the patient was in
the IRF during the 2015–2016 influenza
vaccination season). If a patient’s stay
did not include one or more days in the
IRF during the IVS, IRFs must also
complete the influenza items. For
example, if a patient was admitted after
April 1, 2016, and discharged
September 30, 2016, and the patient did
not receive the influenza vaccine during
the IVS, IRFs should code the reason the
patient did not receive the influenza
vaccination as ‘‘patient was not in the
facility during this year’s influenza
vaccination season.’’
Further, we wish to clarify that the
data submission timeline for this
measure includes 4 calendar quarters
and is based on the influenza season
(July 1 through June 30 of the
subsequent year), rather than on the
calendar year. For the purposes of APU
determination and for public reporting,
data calculation and analysis uses data
from an influenza vaccination season
that is within the influenza season
itself. While the influenza vaccination
season is October 1 of a given year (or
when the vaccine becomes available)
through March 31 of the subsequent
year, this timeframe rests within a
greater time period of the influenza
season which spans 12 months—that is
July 1 of a given year through June 30
of the subsequent year. Thus for this
measure, we utilize data from a
timeframe of 12 months that mirrors the
influenza season which is July 1 of a
given year through June 30th of the
subsequent year. Additionally, for the
APU determination, we review data that
has been submitted beginning on July 1
of the calendar year 2 years prior to the
calendar year of the APU effective date
and ending June 30 of the subsequent
calendar year, one year prior to the
calendar year of the APU effective date.
For example, and as provided in Table
14 for the FY 2019 (October 1, 2018)
APU determination, we review data
submission beginning July 1 of 2016
through June 30th of June 2017 for the
2016/2017 influenza vaccination season,
so as to capture all data that an IRF will
have submitted with regard to the 2016/
2017 Influenza season itself. We will
use assessment data for that time period
as well for public reporting. Further,
because we enable the opportunity to
review and correct data for all
assessment based IRF–PAI measures
within the IRF QRP, we continue to
follow quarterly calendar data
collection/submission quarterly
reporting period(s) and their subsequent
quarterly review and correction periods
with data submission deadlines for
public reporting and payment
52121
determinations. However, rather than
using CY timeframe, these quarterly
data collection/submission periods and
their subsequent quarterly review and
correction periods and submission
deadlines begin with CY quarter 3, July
1, of a given year and end June 30th, CY
quarter 2, of the following year. For
further information on data collection
for this measure, please refer to section
4 of the IRF–PAI training manual, which
is available on the CMS IRF QRP
Measures Information Web site at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Program-MeasuresInformation-.html, under the downloads
section. For further information on data
submission of the IRF–PAI, please refer
to the IRF–PAI Data Specifications
Version 1.12.1 (FINAL)—in effect on
October 1, 2015, available for download
at https://www.cms.gov/Medicare/
Medicare-Fee-for-Service-Payment/
InpatientRehabFacPPS/Software.html.
Refer to Table 16 for details about the
quarterly data collection/submission
and the review and correction deadlines
for FY 2019 and subsequent years for
NQF #0680 Percent of Residents or
Patients Who Were Assessed and
Appropriately Given the Seasonal
Influenza Vaccine.
TABLE 16—SUMMARY DETAILS ON DATA COLLECTION PERIOD AND DATA SUBMISSION TIMELINE FOR PREVIOUSLY ADOPTED IRF–PAI QUALITY MEASURE, NQF #0680 PERCENT OF RESIDENTS OR PATIENTS WHO WERE ASSESSED AND APPROPRIATELY GIVEN THE SEASONAL INFLUENZA VACCINE, AFFECTING THE FY 2019 PAYMENT DETERMINATION AND
SUBSEQUENT YEARS *
Finalized Measure:
• NQF #0680 Percent of Residents or Patients Who Were Assessed and Appropriately Given the Seasonal Influenza Vaccine (80 FR 47122)
Data collection/submission
Quarterly Reporting Period(s)
Quarterly review and correction
periods data submission
deadlines for payment
determination **
APU determination affected
IRF–PAI/QIES ASAP System ........
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Submission method
CY 16 Q3, 7/1/16–9/30/16 and
Q3 of subsequent Calendar
Years.
CY 16 Q4, 10/1/16–12/31/16 and
Q4 of subsequent Calendar
Years.
CY 17 Q1, 1/1/17–3/31/17 and
Q1 of subsequent Calendar
Years.
CY 17 Q2, 4/1/17–6/30/17 and
Q2 of subsequent Calendar
Years.
10/1/16–2/15/17 ** and 10/1–2/15
of subsequent years.
FY 2019 and subsequent years.**
1/1/17–5/15/17 ** and 1/1–5/15 of
subsequent years.
4/1/17–8/15/17 ** and 4/1–8/15 of
subsequent years.
7/1/17–11/15/17 ** and 7/1–11/15
of subsequent years.
* We note that the submission of IRF–PAI data must also adhere to the IRF PPS deadlines.
** As is illustrated in Table 16: Subsequent years follow the same CY Quarterly Data Collection/submission Quarterly Reporting Periods and
Quarterly Review and Correction Periods (IRF–PAI) and Data Submission (CDC/NHSN) Deadlines for Payment Determination in which every CY
quarter is followed by approximately 135 days for IRFs to review and correct their data until midnight on the final submission deadline dates.
We finalized in the FY 2014 IRF PPS
final rule (78 FR 47905 through 47906)
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that for FY 2016 and subsequent years
IRFs will submit data on the quality
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measure Influenza Vaccination Coverage
among Healthcare Personnel (NQF
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Federal Register / Vol. 81, No. 151 / Friday, August 5, 2016 / Rules and Regulations
#0431) beginning with data submission
starting October 1, 2014 (or when the
influenza vaccine becomes available).
To clarify that while the data collected
by IRFs for this measure includes
vaccination information for a flu
vaccination season that begins October
1 (or when the vaccine becomes
available) of a given year through March
31 of the subsequent year, the CDC/
NHSN system only allows for the
submission of the corresponding data
any time between October 1 of a given
year until March 31 of the subsequent
year; however, corrections can be made
to such data until May 15th of that year.
Quality data for this measure are only
required to be submitted once per IVS
(Oct 1 through March 31), but must be
submitted prior to the May 15 deadline
for the year in which the IVS ends;
quarterly reporting is not required. For
example, for FY 2018 payment
determinations, while IRFs can begin
immunizing their staff when the vaccine
is available throughout the influenza
vaccination season which ends on
March 31, 2016, IRFs can only begin
submitting the data for this measure via
the CDC/NHSN system starting on
October 1, 2015, and may do so up until
May 15 of 2016.
TABLE 17—SUMMARY DETAILS ON THE DATA SUBMISSION TIMELINE AND CORRECTION DEADLINE TIMELINE FOR THE PREVIOUSLY ADOPTED INFLUENZA VACCINATION COVERAGE AMONG HEALTHCARE PERSONNEL AFFECTING CY 2018 AND
SUBSEQUENT YEARS
Influenza Vaccination Coverage
Among Healthcare Personnel
Data submission Quarters +
Data submission Period
CY QTR 4 through Subsequent
CY QTR 1.
10/1/15–3/31/16 and 10/1–3/31 of
subsequent years.
Review and Correction Periods Data Submission (CDC/NHSN) Deadlines for payment determination ++
4/1/16–5/15/16 and 4/1–5/15 of
subsequent years.
Deadline: May 15, 2016 and May
15 of subsequent years.
+ Data on this measure may be submitted via the CDC/NHSN system from October 1 of a given year through May 15 of the subsequent year.
++ A time period of April 1–May 15th is also allotted for the submission, review, and corrections.
TABLE 18—FINALIZED IRF QRP CLAIMS-BASED MEASURE AFFECTING FY 2018 AND SUBSEQUENT YEARS
Data submission method
Performance period
NQF #2502 All-Cause Unplanned Readmission Measure for 30 Days
Post-Discharge from Inpatient Rehabilitation Facilities (80 FR 47087
through 47089).
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Quality measure
Medicare FFS Claims ....................
CY 2013 and 2014 for public reporting in 2016.
CY 2014 and 2015 for public reporting in 2017.
Although we did not solicit feedback,
we received several comments about the
previously finalized policy to adopt
calendar year data collection time
frames, unless there is a clinical reason
for an alternative data collection time
frame, which are summarized with our
responses below.
Comment: Several commenters
supported these data collection
timelines to simplify the data collection
and reporting process, as summarized in
the FY 2016 IRF PPS Final Rule (80 FR
47122 through 47123).
Response: We thank these
commenters for their support.
Comment: One commenter generally
supported the change to calendar year,
but was concerned that the IRF–PAI
versions aligned with the fiscal year.
Several others also commented that
since updates are made to the IRF–PAI
on a FY basis, this change would create
a discrepancy within a single calendar
year’s data set. Many commenters were
concerned that variations in FY 2018
APU data collection periods placed an
increased burden on IRFs to maintain
compliance and requested that CMS
grant some leniency to an IRF the first
time it is not compliant with quality
reporting due to the new CY-based
deadlines.
Response: When we finalized this
change in the FY 2016 IRF PPS final
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rule (80 FR 47122 through 47123), we
posited this change would simplify the
data collection and submission time
frame under the IRF QRP for IRF
providers. It would also eliminate the
situation in which data collection
during a quarter in the same calendar
year can affect 2 different years of
annual payment update determination
(that is, October 1 to December 31 is the
first quarter of data collection for quality
measures with a FY-based data
collection time frame and the last
quarter of data collection for quality
measures with a CY-based data
collection time frame). This change
means that when additional quality
measures that use IRF–PAI as the data
collection mechanism, such as the
measure Drug Regimen Review
Conducted with Follow-Up for
Identified Issues, are adopted for future
use in the IRF QRP, the first data
collection time frame for those newlyadopted measures will be 3 months
(October to December) and subsequent
data collection time frames would
follow a calendar year data collection
time frame. This policy only affects IRFs
insofar as for these newly adopted
measures, compliance determinations
for the applicable FY APU will only
reflect data collection and submission
for Q4 of the CY in which data
collection begins. This does not create a
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discrepancy in the data set, as stated by
the commenter, as we would use the
following CY of data for APU analysis
and public reporting purposes, should
state measures be proposed and
finalized for public display in the
future.
With regard to concerns about
increased burden with the change in
data collection periods and requests for
leniency regarding submission
deadlines, we disagree that leniency is
warranted, given that there is no
discrepancy in the data set and the
policy only affects the first quarter of
data collection for newly adopted
measures. We have ongoing education
regarding data submission deadlines,
including quarterly email reminders of
upcoming deadlines. We also remind
the reader of the availability of the
reconsideration process, in which IRFs
may file for reconsideration if they
believe the finding of non-compliance is
in error, or they have evidence of the
impact of extraordinary circumstances
which prevented timely submission of
data.
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Federal Register / Vol. 81, No. 151 / Friday, August 5, 2016 / Rules and Regulations
b. Timeline and Data Submission
Mechanisms for the FY 2018 Payment
Determination and Subsequent Years for
the IRF QRP Resource Use and Other
Measures Claims-Based Measures
The MSPB PAC IRF QRP measure;
Discharge to Community PAC IRF QRP
measure; Potentially Preventable 30-Day
Post-Discharge Readmission Measure for
IRF QRP; and Potentially Preventable
Within Stay Readmission Measure for
IRFs, which we are finalizing in this
final rule, are Medicare FFS claimsbased measures. Because claims-based
measures can be calculated based on
data that are already reported to the
Medicare program for payment
purposes, no additional information
collection will be required from IRFs.
As discussed in section VIII.F of this
final rule, these measures will use 2
years of claims-based data beginning
with CY 2015 and CY 2016 claims to
inform confidential feedback reports for
IRFs, and CYs 2016 and 2017 claims
data for public reporting.
We invited public comments on this
proposal. We did not receive comments
related to data submission mechanisms
for these measures. For comments
related to the measures, please see
section VIII.F of this final rule. For
comments related to the future public
display of these measures, please see
section VIII.N of this final rule.
We finalize the timeline and data
submission mechanisms for FY 2018
payment determination and subsequent
years as proposed.
c. Timeline and Data Submission
Mechanisms for the IRF QRP Quality
Measure for the FY 2020 Payment
Determination and Subsequent Years
As discussed in section VIII.F of this
final rule, we proposed that the data for
the quality measure, Drug Regimen
Review Conducted with Follow-Up for
Identified Issues—PAC IRF QRP,
affecting FY 2020 payment
determination and subsequent years, be
collected by completing data elements
that will be added to the IRF–PAI with
submission through the QIES–ASAP
system. Data collection will begin on
October 1, 2018. More information on
IRF reporting using the QIES–ASAP
system is located at the Web site at
https://www.cms.gov/Medicare/
Medicare-Fee-for-Service-Payment/
InpatientRehabFacPPS/IRFPAI.html.
52123
For the FY 2020 payment
determinations, we proposed to use CY
2018 4th quarter data, that is, beginning
with discharges on October 1, 2018,
through discharges on December 31,
2018, to remain consistent with the
usual October release schedule for the
IRF–PAI, to give IRFs sufficient time to
update their systems so that they can
comply with the new data reporting
requirements, and to give us sufficient
time to determine compliance for the FY
2020 program. The proposed use of 1
quarter of data for the initial year of
assessment data reporting in the IRF
QRP, to make compliance
determinations related to the applicable
FY APU, is consistent with the
approach we used previously for the
SNF, LTCH, and Hospice QRPs.
Table 18 presents the proposed data
collection period and data submission
timelines for the new IRF QRP quality
measure, Drug Regimen Review
Conducted with Follow-Up for
Identified Issues—PAC IRF QRP, for the
FY 2020 Payment Determination. We
invited public comments on this
proposal.
TABLE 19—DETAILS ON THE PROPOSED DATA COLLECTION PERIOD AND DATA SUBMISSION TIMELINE FOR RESOURCE USE
AND OTHER MEASURES AFFECTING THE FY 2020 PAYMENT DETERMINATION
Quality measure
Submission method
Drug Regimen Review Conducted with Follow-Up for Identified Issues PAC IRF QRP.
IRF–PAI/QIES ASAP
Data collection period
Data correction deadlines *
CY 2018 Q4, 10/1/18–12/31/18;
Quarterly for each subsequent
calendar year.
5/15/19 Quarterly approximately
135 days after the end of
each quarter for subsequent
years..
APU
determination
affected
FY 2020.
* We note that the submission of IRF–PAI data must also adhere to the IRF PPS deadlines.
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Following the close of the reporting
quarter, October 1, 2018, through
December 31, 2018, for the FY 2020
payment determination, IRFs will have
the already established additional 4.5
months to correct their quality data and
that the final deadline for correcting
data for the FY 2020 payment
determination will be May 15, 2019 for
these measures. We further proposed
that for the FY 2021 payment
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determination and subsequent years, we
will collect data using the calendar year
reporting cycle as described in section
VIII.I.c of this final rule, and illustrated
in Table 20. We invited public
comments on this proposal.
We did not receive any comments on
the proposed data collection periods
and data submission timelines for the
new proposed IRF QRP quality measure
for the FY 2020 and FY 2021 payment
determination and subsequent years.
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Final Decision: We finalize the
timeline and data submission
mechanisms for FY 2020 and FY2021
payment determinations and subsequent
years as proposed, as described in Table
19. For comments related to the
measure, Drug Regimen Review
Conducted with Follow-Up for
Identified Issues PAC IRF QRP, please
see section VIII.G of final rule.
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Federal Register / Vol. 81, No. 151 / Friday, August 5, 2016 / Rules and Regulations
TABLE 20—PROPOSED DATA COLLECTION PERIOD AND DATA CORRECTION DEADLINES * AFFECTING THE FY 2021
PAYMENT DETERMINATION AND SUBSEQUENT YEARS
Quality measure
Proposed
CY data
collection
quarter
Submission method
Drug Regimen Review Conducted with Follow-Up for Identified Issues PAC IRF QRP.
IRF–PAI/QIES ASAP
Quarter
Quarter
Quarter
Quarter
1
2
3
4
Proposed quarterly review and
data correction periods *
deadlines for
payment determination
Proposed data collection period
......
......
......
......
January 1–March 31 ..................
April 1–June 30 ..........................
July 1–September 30 .................
October 1–December 31 ............
April 1– August 15.
July 1–November 15.
October 1–February 15.
January 1–May 15.
* We note that the submission of IRF–PAI data must also adhere to the IRF PPS deadlines.
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J. IRF QRP Data Completion Thresholds
for the FY 2016 Payment Determination
and Subsequent Years
In the FY 2015 IRF PPS final rule (79
FR 45921 through 45923), we finalized
IRF QRP thresholds for completeness of
IRF data submissions. To ensure that
IRFs are meeting an acceptable standard
for completeness of submitted data, we
finalized the policy that, beginning with
the FY 2016 payment determination and
for each subsequent year, IRFs must
meet or exceed two separate data
completeness thresholds: One threshold
set at 95 percent for completion of
quality measures data collected using
the IRF–PAI submitted through the
QIES and a second threshold set at 100
percent for quality measures data
collected and submitted using the CDC
NHSN.
Additionally, we stated that we will
apply the same thresholds to all
measures adopted as the IRF QRP
expands and IRFs begin reporting data
on previously finalized measure sets.
That is, as we finalize new measures
through the regulatory process, IRFs
will be held accountable for meeting the
previously finalized data completion
threshold requirements for each
measure until such time that updated
threshold requirements are proposed
and finalized through a subsequent
regulatory cycle.
Further, we finalized the requirement
that an IRF must meet or exceed both
thresholds to avoid receiving a 2
percentage point reduction to their
annual payment update for a given
fiscal year, beginning with FY 2016 and
for all subsequent payment updates. For
a detailed discussion of the finalized
IRF QRP data completion requirements,
please refer to the FY 2015 IRF PPS final
rule (79 FR 45921 through 45923). We
proposed to codify the IRF QRP Data
Completion Thresholds at § 412.634. We
invited public comments on this
proposal.
We received several comments with
concerns about the proposal to codify
the IRF QRP Data Completion
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18:14 Aug 04, 2016
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Thresholds at § 412.634, which are
summarized below.
Comment: One commenter supported
the 100 percent standard, but had
concerns regarding technical errors with
the NHSN that IRFs have experienced in
the past year. Several commenters
expressed concern about the threshold
set at 100 percent for quality measures
data collected and submitted using the
CDC NHSN, citing significant burden on
infection preventionists to review and
complete reports in NHSN. One
commenter expressed concern that the
data completion threshold would be
applied to data collected in FY 2014,
having a retroactive impact on payment.
One commenter recommended changes
to the NHSN that could alleviate the
reporting requirement, including
minimize the reporting of elements
outside of CMS regulatory requirements,
as well as altering the system to remove
monthly reporting plans or allowing
them to be submitted electronically.
Response: We wish to clarify that the
IRF QRP thresholds for completeness of
IRF data submissions were finalized in
the FY 2015 IRF PPS final rule (79 FR
45921 through 45923), beginning with
FY 2016, which considered quality data
submitted during CY 2014. We have
continually maintained that providers
should be submitting complete and
accurate data, and the adoption of the
data completion thresholds in the FY
2015 IRF PPS final rule did not change
this policy. We believe that both data
completion thresholds are achievable, as
evidenced by the 91 percent of IRFs that
were able to achieve these thresholds for
purposes of the FY 2016 payment
determination. We have also taken
strides to assist providers achieve
compliance, including regular
notification of upcoming deadlines,
updated guidance documents, increased
outreach to providers with incomplete
data submissions, and the development
of several reports which will help
providers better determine where they
stand with respect to compliance
throughout the year. We appreciate the
commenters’ concerns related to burden
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and have taken this into consideration
when issuing data completion
thresholds.
Final Decision: We are finalizing our
proposal to codify the IRF QRP data
completion thresholds at § 412.634.
K. IRF QRP Data Validation Process for
the FY 2016 Payment Determination
and Subsequent Years
Validation is intended to provide
added assurance of the accuracy of the
data that will be reported to the public
as required by sections 1886(j)(7)(E) and
1899B(g) of the Act. In the FY 2015 IRF
PPS rule (79 FR 45923), we finalized, for
the FY 2016 adjustments to the IRF PPS
annual increase factor and subsequent
years, a process to validate the data
submitted for quality purposes.
However, in the FY 2016 IRF PPS final
rule (80 FR 47124), we finalized our
decision to temporarily suspend the
implementation of this policy. We did
not propose a data validation policy in
the FY 2017 IRF PPS proposed rule, as
we are developing a policy that could be
applied to several PAC QRPs. We intend
to propose a data validation policy
through future rulemaking.
L. Previously Adopted and Codified IRF
QRP Submission Exception and
Extension Policies
Refer to § 412.634 for requirements
pertaining to submission exception and
extension for the FY 2017 payment
determination and subsequent years. We
proposed to revise § 412.634 to change
the timing for submission of these
exception and extension requests from
30 days to 90 days from the date of the
qualifying event which is preventing an
IRF from submitting their quality data
for the IRF QRP. We proposed the
increased time allotted for the
submission of the requests from 30 to 90
days to be consistent with other quality
reporting programs; for example, the
Hospital Inpatient Quality Reporting
(IQR) Program also proposed to extend
the deadline to 90 days in the FY 2017
IPPS/LTCH PPS proposed rule (81 FR
25205). We believe that this increased
time will assist providers experiencing
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an event in having the time needed to
submit such a request. We believe that
allowing only 30 days was insufficient.
With the exception of this one change,
we did not propose any additional
changes to the exception and extension
policies for the IRF QRP at this time.
We invited public comments on the
proposal to revise § 412.634 to change
the timing for submission of these
exception and extension requests from
30 days to 90 days from the date of the
qualifying event which is preventing an
IRF from submitting their quality data
for the IRF QRP. We received one
comment on this proposal, which is
summarized and addressed below in
this section.
Comment: One commenter supported
changing the timing for submission of
exception and extension requests from
30 days to 90 days from the date of the
qualifying event preventing an IRF from
submitting their IRF QRP data.
Response: We thank the commenter
for their support.
Final Decision: After careful
consideration of the public comments,
we are finalizing our proposal to revise
§ 412.634 to change the timing for
submission of these exception and
extension requests from 30 days to 90
days from the date of the qualifying
event which is preventing an IRF from
submitting their quality data for the IRF
QRP.
M. Previously Adopted and Finalized
IRF QRP Reconsideration and Appeals
Procedures
Refer to § 412.634 for a summary of
our finalized reconsideration and
appeals procedures for the IRF QRP for
FY 2017 payment determination and
subsequent years. We did not propose
any changes to this policy. However, we
wish to clarify that in order to notify
IRFs found to be non-compliant with
the reporting requirements set forth for
a given payment determination, we may
include the QIES mechanism in
addition to U.S. Mail, and we may elect
to utilize the MACs to administer such
notifications.
We received several comments about
the previously adopted and finalized
IRF QRP reconsideration and appeals
procedures, which are summarized
below.
Comment: One commenter requested
that the notification also include the
reason for non-compliance. Multiple
commenters appreciated that CMS is
using both U.S. Mail and the QIES
system to notify IRFs found to be noncompliant. Another commenter
recommended that CMS continue using
the U.S. Mail method, noting that QIES
may not be a reliable way to distribute
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time-sensitive information. Several
commenters were concerned about the
possibility of using MACs to administer
notifications, citing their lack of
expertise in quality reporting, and
requested that CMS clarify the authority
that MACs would have to consider IRF
QRP compliance and levy corrective
action.
Response: We intend to retain this
method of notification in addition to the
use of QIES. We wish to clarify that the
role of the MACs is for notification
purposes only. They do not have a role
in determining provider compliance in
meeting the IRF QRP reporting
requirements. We intend to include the
reason for non-compliance in the
notifications distributed via the
CASPER folders; however, we wish to
remind facilities that there are reports
available in QIES (more information at:
https://www.qtso.com/irfpai.html) and
NHSN (more information at: https://
www.cdc.gov/nhsn/cms/) that can be
utilized to confirm quality measure data
submissions. Additional information
regarding non-compliance is also
available on the IRF QRP
Reconsiderations Web site at https://
www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/IRFQuality-Reporting-Reconsideration-andException-and-Extension.html.
N. Public Display of Measure Data for
the IRF QRP & Procedures for the
Opportunity to Review and Correct Data
and Information
1. Public Display of Measures
Section 1886(j)(7)(E) of the Act
requires the Secretary to establish
procedures for making the IRF QRP data
available to the public. In the FY 2016
IRF PPS final rule (80 FR 47126 through
47127), we finalized our proposals to
display performance data for the IRF
QRP quality measures by Fall 2016 on
a CMS Web site, such as the Hospital
Compare, after a 30-day preview period,
and to give providers an opportunity to
review and correct data submitted to the
QIES–ASAP system or to the CDC
NHSN. The procedures for the
opportunity to review and correct data
are provided in section VIII.N.2 of this
final rule. In addition, we finalized the
proposal to publish a list of IRFs that
successfully meet the reporting
requirements for the applicable payment
determination on the IRF QRP Web site
at https://www.cms.gov/Medicare/
Quality-Initiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/
Spotlights-Announcements.html. In the
FY 2016 IRF PPS final rule, we finalized
that we will update the list after the
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reconsideration requests are processed
on an annual basis.
Also, in the FY 2016 IRF PPS final
rule (80 FR 47126 through 47127), we
also finalized that the display of
information for fall 2016 contains
performance data on three quality
measures:
• Percent of Residents or Patients
with Pressure Ulcers That Are New or
Worsened (Short Stay) (NQF #0678);
• NHSN CAUTI Outcome Measure
(NQF #0138); and
• All-Cause Unplanned Readmission
Measure for 30 Days Post-Discharge
from IRFs (NQF #2502).
The measures Percent of Residents or
Patients with Pressure Ulcers That Are
New or Worsened (Short Stay) (NQF
#0678) and NHSN CAUTI Outcome
Measure (NQF #0138) are based on data
collected beginning with the first
quarter of 2015 or discharges beginning
on January 1, 2015. With the exception
of the All-Cause Unplanned
Readmission Measure for 30 Days PostDischarge from IRFs (NQF #2502), rates
are displayed based on 4 rolling quarters
of data and will initially use discharges
from January 1, 2015, through December
31, 2015 (CY 2015) for Percent of
Residents or Patients with Pressure
Ulcers That Are New or Worsened
(Short Stay) (NQF #0678) and data
collected from January 1, 2015, through
December 31, 2015 (CY 2015) for NHSN
CAUTI Outcome Measure (NQF #0138).
For the readmissions measure, data will
be publicly report beginning with data
collected for discharges beginning
January 1, 2013, and rates will be
displayed based on 2 consecutive years
of data. For IRFs with fewer than 25
eligible cases, we proposed to assign the
IRF to a separate category: ‘‘The number
of cases is too small (fewer than 25) to
reliably tell how well the IRF is
performing.’’ If an IRF has fewer than 25
eligible cases, the IRF’s readmission
rates and interval estimates will not be
publicly reported for the measure.
Calculations for all three measures are
discussed in detail in the FY 2016 IRF
PPS final rule (80 FR 47126 through
47127).
Pending the availability of data, we
proposed to publicly report data in CY
2017 on 4 additional measures
beginning with data collected on these
measures for the first quarter of 2015, or
discharges beginning on January 1,
2015: (1) Facility-wide Inpatient
Hospital-onset Methicillin-resistant
Staphylococcus aureus (MRSA)
Bacteremia Outcome Measure (NQF
#1716) ; (2) Facility-wide Inpatient
Hospital-onset Clostridium difficile
Infection (CDI) Outcome Measure (NQF
#1717) and, beginning with the 2015–16
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influenza vaccination season, these two
measures; (3) Influenza Vaccination
Coverage Among Healthcare Personnel
(NQF #0431); and (4) Percent of
Residents or Patients Who Were
Assessed and Appropriately Given the
Seasonal Influenza Vaccine (NQF
#0680).
Standardized infection ratios (SIRs)
for the Facility-wide Inpatient Hospitalonset Methicillin-resistant
Staphylococcus aureus (MRSA)
Bacteremia Outcome Measure (NQF
#1716) and Facility-wide Inpatient
Hospital-onset Clostridium difficile
Infection (CDI) Outcome Measure (NQF
#1717) will be displayed based on 4
rolling quarters of data and will initially
use MRSA bacteremia and CDI events
that occurred from January 1, 2015
through December 31, 2015 (CY 2015),
for calculations. We proposed that the
display of these ratios will be updated
quarterly. Rates for the Influenza
Vaccination Coverage Among
Healthcare Personnel (NQF #0431) will
initially be displayed for personnel
working in the reporting facility October
1, 2015 through March 31, 2016. Rates
for the Percent of Residents or Patients
Who Were Assessed and Appropriately
Given the Seasonal Influenza Vaccine
(NQF #0680) will also initially be
displayed for patients in the IRF during
the influenza vaccination season, from
October 1, 2015, through March 31,
2016. We proposed that the display of
these rates will be updated annually for
subsequent influenza vaccination
seasons.
Calculations for the MRSA and CDI
Healthcare Associated Infection (HAI)
measures adjust for differences in the
characteristics of hospitals and patients
using a SIR. The SIR is a summary
measure that takes into account
differences in the types of patients that
a hospital treats. For a more detailed
discussion of the SIR, please refer to the
FY 2016 IRF PPS final rule (80 FR 47126
through 47127). The MRSA and CDI
SIRs may take into account the
laboratory methods, bed size of the
hospital, and other facility-level factors.
It compares the actual number of HAIs
in a facility or state to a national
benchmark based on previous years of
reported data and adjusts the data based
on several factors. A confidence interval
with a lower and upper limit is
displayed around each SIR to indicate
that there is a high degree of confidence
that the true value of the SIR lies within
that interval. A SIR with a lower limit
that is greater than 1.0 means that there
were more HAIs in a facility or state
than were predicted, and the facility is
classified as ‘‘Worse than the U.S.
National Benchmark.’’ If the SIR has an
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upper limit that is less than 1, the
facility had fewer HAIs than were
predicted and is classified as ‘‘Better
than the U.S. National Benchmark.’’ If
the confidence interval includes the
value of 1, there is no statistical
difference between the actual number of
HAIs and the number predicted, and the
facility is classified as ‘‘No Different
than U.S. National Benchmark.’’ If the
number of predicted infections is less
than 1.0, the SIR and confidence
interval are not calculated by CDC.
Calculations for the Influenza
Vaccination Coverage Among
Healthcare Personnel (NQF #0431) are
based on reported numbers of personnel
who received an influenza vaccine at
the reporting facility or who provided
written documentation of influenza
vaccination outside the reporting
facility. The sum of these two numbers
is divided by the total number of
personnel working at the facility for at
least 1 day from October 1 through
March 31 of the following year, and the
result is multiplied by 100 to produce
a compliance percentage (vaccination
coverage). No risk adjustment is
applicable to these calculations. More
information on these calculations and
measure specifications is available at
https://www.cdc.gov/nhsn/pdfs/hpsmanual/vaccination/4-hcp-vaccinationmodule.pdf. We proposed that this data
will be displayed on an annual basis
and will include data submitted by IRFs
for a specific, annual influenza
vaccination season. A single compliance
(vaccination coverage) percentage for all
eligible healthcare personnel will be
displayed for each facility.
We invited public comment on our
proposal to begin publicly reporting in
CY 2017, pending the availability of
data, on Facility-wide Inpatient
Hospital-onset MRSA Bacteremia
Outcome Measure (NQF #1716);
Facility-wide Inpatient Hospital-onset
CDI Outcome Measure (NQF #1717);
and Influenza Vaccination Coverage
Among Healthcare Personnel (NQF
#0431). These comments are
summarized and addressed below.
Comment: Several commenters,
including MedPAC, supported public
reporting of quality measures. MedPAC
encouraged ongoing development and
public reporting of cross-cutting
measures for all provider settings.
Response: We will continue to move
forward with cross-setting measure
development and public reporting of
these measures to meet the mandate of
the IMPACT Act.
Comment: Several commenters stated
CMS should risk-adjust IRFs’ publicly
displayed data for Percent of Residents
or Patients with Pressure Ulcers That
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Are New or Worsened (Short Stay) (NQF
#0678) for the number of patients that
have pressure ulcers.
Response: We refer commenters to the
FY 2016 IRF PPS final rule (80 FR 47126
through 47127) that finalized public
display of the risk-adjusted quality
measure, the Percent of Residents or
Patients with Pressure Ulcers That Are
New or Worsened (Short Stay) (NQF
#0678)
Comment: One commenter expressed
concerns that CMS will utilize data from
the CARE Tool or IRF–PAI for public
reporting of the quality measures and
that such data is subjective and nonevidence based and there is a lack of
ability to access the competency of staff
completing the tool either within or
across PAC settings. Therefore, the
commenter is concerned that the
publicly reported data will not represent
the quality of care provided in IRFs and
comparing across IRFs.
Response: We appreciate the
comment expressing concern regarding
the CARE Tool and IRF–PAI data for
public reporting. We would like to
clarify that quality measures set for
public display have already been
finalized, and the Secretary has a
statutory obligation under sections
1886(j)(7)(E) and 1899B(g) of the Act to
establish procedures to make the data
publicly available.
Comment: Several commenters
expressed concern that the public
display of quality measure information
is based on measures that do not
exemplify the IRF experience, target
very small populations of cases, and are
not a good indicator of the overall
quality of IRFs. Many commenters
conveyed that the goals of IRFs are to
provide medically necessary
rehabilitation therapies to bring about
recovery and improved function and the
measures fail to assess IRFs success at
achieving these goals.
Response: Section 3004 of the
Affordable Care Act and the IMPACT
Act require the Secretary of Health and
Human Services to publish the data on
the quality measures implemented in
the IRF QRP through rulemaking. The
public reporting of the three measures
finalized for public reporting in the FY
2016 IRF PPS final rule and the four
measures proposed for public reporting
in the FY 2017 IRF PPS proposed rule
supports the goals of the National
Quality Strategy, the CMS Quality
Strategy, the HHS HAI Action Plan, and
the Hospital Acquired Condition
Reduction Program. It is both a CMS
and an HHS priority to ensure the
delivery of high quality, patientcentered, and safe care across all care
settings. While the main focus of care in
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an IRF may be centered on restoration
of a patient’s functional status, we
believe that this cannot be achieved
without attention to the basic tenants of
patient care, which speak to prevention
and patient safety, and believe that our
quality measures reflect these aspects of
quality. The IMPACT Act requires us to
address the domain of functional status
and requires the public reporting of this
data within 2 years of a finalized
quality, resource use, and other
measure’s specified application date.
We believe that the addition of these
measures to the public display of IRF
quality data will help to address any
concerns relayed by the commenter.
Comment: One commenter expressed
concerns that the NHSN Facility-Wide
Inpatient Hospital-Onset MRSA
Bacteremia Outcome Measure (NQF
#1716) does not reflect care provided in
an IRF, specifically, rehabilitation
provided to promote functional recovery
and achievement of goals. The
commenter also noted that the
incidence of MRSA is rare, and
generally, if a patient in rehabilitation
has MRSA, the infection is present upon
admission to the rehabilitation facility
following transfer from the acute care
facility. Finally, the commenter noted
that the inclusion of the NHSN FacilityWide Inpatient Hospital-Onset MRSA
Bacteremia Outcome Measure (NQF
#1716) within the IRF QRP may cause
rehabilitation facilities to
inappropriately screen for this
condition, resulting in unnecessary
costs to the Medicare program.
Response: Section 3004 of the
Affordable Care Act and the IMPACT
Act requires the Secretary of Health and
Human Services to publish the data on
the quality measures implemented in
the IRF QRP through rulemaking. The
public reporting of the NHSN FacilityWide Inpatient Hospital-Onset MRSA
Bacteremia Outcome Measure (NQF
#1716) support the goals of the National
Quality Strategy, the CMS Quality
Strategy, the HHS HAI Action Plan, and
the Hospital Acquired Condition
Reduction Program. It is both a CMS
and an HHS priority to ensure the
delivery of high quality, patientcentered, and safe care across all care
settings.
According to the CDC, the steward of
this quality measure, cases defined by
NHSN as Community-onset MRSA
Bacteremia are excluded from the data
that is provided by NHSN to CMS. Only
those cases that meet the NHSN
definition of Incident and Healthcare
Facility-onset are reported as a part of
the CMS IRF QRP. For IRF units within
a hospital that participate in the CMS
IRF QRP will be given a single MRSA
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bacteremia LabID SIR for each type of
CMS-certified IRF unit (adult and
pediatric) mapped within the hospital
according to CMS Certification Number
(CCN). The MRSA Bacteremia LabID SIR
is calculated as: Number of all incident
blood source MRSA LabID events
identified >3 days after admission to an
IRF unit and where the patient had no
positive MRSA bacteremia LabID events
in the prior 14 days in any CMScertified IRF unit of that type divided by
the total number of predicted incident
healthcare facility-onset blood source
MRSA LabID events. Clinicians should
base decisions about diagnostic testing
on the needs and clinical picture of the
patient. Patients with MRSA bacteremia
would be expected to be symptomatic.
Routine collection of blood cultures on
patients not suspected of being
bacteremic would be outside of the
standards of medical care. For
additional information on the
specifications for this measure, please
refer to the CDC reference: https://
www.cdc.gov/nhsn/pdfs/cms/irfs/
linelists_irfunits_indicators.pdf.
Comment: Several commenters
recommended that CMS revise the
Facility-wide Inpatient Hospital-onset
CDI Outcome Measure (NQF #1717)
because there are multiple C. difficile
quality measures for Medicare providers
across the continuum of care (acute care
hospitals, IRFs, etc.) and one incident of
C. difficile onset may be reported by
three providers and effectively, and
unreasonably, be a ‘‘triple hit’’ for
multiple providers so that it is only
reported at the first site of discovery.
Response: The Facility-wide Inpatient
Hospital-onset CDI Outcome Measure
(NQF #1717) was adopted in the IRF
QRP and finalized in the FY 2015 IRF
PPS final rule (79 FR 45913 through
45914). The CDC, the steward of this
measure, noted that the measure
specifications for NQF #1717, by design,
align with the NHSN LabID Event
protocol, which was developed to
require minimal investigation on the
part of healthcare facilities and to
provide a proxy measure of infection.
Dates of admission and specimen
collection are required and can easily be
collected via electronic methods. These
dates enable differentiation of
healthcare-associated and communityonset events. To require a facility to
determine if a CDI LabID Event had
been identified in another facility would
call for manual review of medical
records and potential communication
with transferring facilities. The design
of LabID event reporting via NHSN is by
single facility, which means that events
are reported for the facility where they
occur. Analysis is by single facility
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identifier (NHSN organizational ID) and
does not cross admissions to a different
NHSN facility (or a different type
reporting facility such as nursing home
to acute care facility) or transfer from
facility A to facility B. Cases defined by
NHSN as community-onset Clostridium
difficile are excluded from the data that
is provided by NHSN to CMS. Only
those cases that meet the NHSN
definitions of an Incident (nonduplicate) Healthcare Facility-onset are
reported as a part of the CMS IRF QRP.
Therefore, cases that are identified
during the first 3 days of admission to
a facility, and which may be related to
a discharge from another hospital, will
not be included in the Clostridium
difficile LabID Event data reported for
the admitting facility.
Comment: The commenter was
concerned that the public display of
these measures will provide misleading
interpretations of quality, as almost all
the measures will be based on different
time frames and will use different
minimum patient thresholds and
potentially varying patient populations.
The commenter recommends that CMS
suspend public display of IRF QRP data
until (1) all IMPACT Act domains are
implemented and (2) the patient
populations for each measure are
standardized.
Response: The Secretary has a
statutory obligation under section
1899B(g) and 1886(j)(7)(E) of the Act to
make the data available to the public.
We are transitioning towards aligning
the data collection periods to follow the
calendar year. Once this is achieved, the
only measure that will not be in
alignment is the influenza measure
since these measures require taking into
account the influenza season and
vaccination season for the data
collection period.
Minimum patient thresholds and
populations are dependent on the
specific measure. Each measure is
specifically applied in public reporting
so that there is enough volume of cases
reported to protect anonymity and
provide meaningful results with
representative sample size. Public
reporting must comply with applicable
privacy laws and provide minimum
sample sizes in order for facilities to
compare their performance with other
IRFs. If the sample size is too small, the
results will not reflect their facility
performance for comparison purposes.
Final Decision: After careful
consideration of the public comments,
we are finalizing our proposal to begin
publicly reporting in CY 2017, pending
the availability of data, on Facility-wide
Inpatient Hospital-onset MRSA
Bacteremia Outcome Measure (NQF
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#1716); Facility-wide Inpatient
Hospital-onset CDI Outcome Measure
(NQF #1717); and Influenza Vaccination
Coverage Among Healthcare Personnel
(NQF #0431).
For the Percent of Residents or
Patients Who Were Assessed and
Appropriately Given the Seasonal
Influenza Vaccine (Short Stay) (NQF
#0680), we proposed to display rates
annually based on the influenza season
to avoid reporting for more than one
influenza vaccination within a CY. For
example, in 2017 we will display rates
for the patient vaccination measure
based on discharges starting on July 1,
2015, to June 30, 2016. This is proposed
because it includes the entire influenza
vaccination season (October 1, 2015, to
March 31, 2016).
Calculations for Percent of Residents
or Patients Who Were Assessed and
Appropriately Given the Seasonal
Influenza Vaccine (Short Stay) (NQF
#0680) will be based on patients
meeting any one of the following
criteria: Patients who received the
influenza vaccine during the influenza
season, patients who were offered and
declined the influenza vaccine, and
patients who were ineligible for the
influenza vaccine due to
contraindication(s). The facility’s
summary observed score will be
calculated by combining the observed
counts of all the criteria. This is
consistent with the publicly reported
patient influenza vaccination measure
for Nursing Home Compare.
Additionally, for the patient influenza
measure, we will exclude IRFs with
fewer than 20 stays in the measure
denominator. For additional
information on the specifications for
this measure, please refer to the IRF
Quality Reporting Measures Information
Web page at https://www.cms.gov/
Medicare/Quality-Initiatives-PatientAssessment-Instruments/IRF-QualityReporting/IRF-Quality-ReportingProgram-Measures-Information-.html.
We invited public comments on our
proposal to begin publicly reporting the
Percent of Residents or Patients Who
Were Assessed and Appropriately Given
the Seasonal Influenza Vaccine (Short
Stay) (NQF #0680) measure on
discharges from July 1st of the previous
calendar year to June 30th of the current
calendar year. We invited comments on
the public display of the measure
Percent of Residents or Patients Who
Were Assessed and Appropriately Given
the Seasonal Influenza Vaccine (NQF
#0680) in 2017 pending the availability
of data.
We received several comments, which
are summarized below.
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Comment: Several commenters
expressed concern that the Percent of
Residents or Patients Who Were
Assessed and Appropriately Given the
Seasonal Influenza Vaccine (Short-Stay)
(NQF #0680) is not a true indicator of
the quality of care provided in IRFs,
which focuses on functional recovery so
that patients are able to function to their
maximum potential in the least
restrictive environment. Commenters
expressed concern that the influenza
vaccination rates do not adequately
assess whether quality care was
provided and that CMS has not
provided any evidence in the IRF QRP
that differences in influenza vaccination
rates between facilities affect the quality
of outcomes or the patient experience.
Response: We appreciate the concerns
by several commenters in regard to the
Percent of Residents or Patients Who
Were Assessed and Appropriately Given
the Seasonal Influenza Vaccine (ShortStay) (NQF #0680). However, this
quality measure was adopted in the IRF
QRP and finalized in the FY 2014 IRF
PPS final rule (78 FR 47906 through
47911).
Final Decision: After careful
consideration of the public comments,
we are finalizing our proposal to begin
publicly reporting the Percent of
Residents or Patients Who Were
Assessed and Appropriately Given the
Seasonal Influenza Vaccine (Short Stay)
(NQF #0680) measure, pending the
availability of data, on discharges from
July 1st of the previous calendar year to
June 30th of the current calendar year.
Additionally, we requested public
comments on whether to include, in the
future, public display comparison rates
based on CMS regions or US census
regions for Percent of Residents or
Patients with Pressure Ulcers That Are
New or Worsened (Short Stay) (NQF
#0678); All-Cause Unplanned
Readmission Measure for 30 Days PostDischarge from IRFs (NQF #2502); and
Percent of Residents or Patients Who
Were Assessed and Appropriately Given
the Seasonal Influenza Vaccine (Short
Stay) (NQF #0680) for CY 2017 public
display.
We did not receive any comments
about whether to include, in the future,
public display comparison rates based
on CMS regions or US census regions
for CY 2017 public display.
2. Procedures for the Opportunity To
Review and Correct Data and
Information
Section 1899B(g) of the Act requires
the Secretary to establish procedures for
public reporting of IRFs’ performance,
including the performance of individual
IRFs, on quality measures specified
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under section 1899B(c)(1) of the Act and
resource use and other measures
specified under section 1899B(d)(1) of
the Act (collectively, IMPACT Act
measures) beginning not later than 2
years after the applicable specified
application date under section
1899B(a)(2)(E) of the Act. Under section
1899B(g)(2) of the Act, the procedures
must ensure, including through a
process consistent with the process
applied under section
1886(b)(3)(B)(viii)(VII) of the Act, which
refers to public display and review
requirements in the Hospital IQR
Program, that each IRF has the
opportunity to review and submit
corrections to its data and information
that are to be made public prior to the
information being made public.
In the FY 2016 IRF PPS final rule (80
FR 47126 through 47128), and as
illustrated in Table 10 in section VIII.I.a
of this final rule, we finalized that once
the provider has an opportunity to
review and correct quarterly data related
to measures submitted via the QIES–
ASAP system or CDC NHSN, we will
consider the provider to have been
given the opportunity to review and
correct this data. We wish to clarify that
although the correction of data
(including claims) can occur after the
submission deadline, if such corrections
are made after a particular quarter’s
submission and correction deadline,
such corrections will not be captured in
the file that contains data for calculation
of measures for public reporting
purposes. To have publicly displayed
performance data that is based on
accurate underlying data, it will be
necessary for IRFs to review and correct
this data before the quarterly
submission and correction deadline.
We restated and proposed additional
details surrounding procedures that will
allow individual IRFs to review and
correct their data and information on
measures that are to be made public
before those measure data are made
public.
For assessment-based measures, we
proposed a process by which we will
provide each IRF with a confidential
feedback report that will allow the IRF
to review its performance on such
measures and, during a review and
correction period, to review and correct
the data the IRF submitted to CMS via
the CMS QIES–ASAP system for each
such measure. In addition, during the
review and correction period, the IRF
will be able to request correction of any
errors in the assessment-based measure
rate calculations.
We proposed that these confidential
feedback reports will be available to
each IRF using the CASPER system. We
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refer to these reports as the IRF Quality
Measure (QM) Reports. We proposed to
provide monthly updates to the data
contained in these reports as data
become available. We proposed to
provide the reports so that providers
will be able to view their data and
information at both the facility and
patient level for its quality measures.
The CASPER facility level QM Reports
may contain information such as the
numerator, denominator, facility rate,
and national rate. The CASPER patientlevel QM Reports may contain
individual patient information which
will provide information related to
which patients were included in the
quality measures to identify any
potential errors for those measures in
which we receive patient-level data.
Currently, we do not receive patientlevel data on the CDC measure data
received via the NHSN system. In
addition, we will make other reports
available in the CASPER system, such as
IRF–PAI assessment data submission
reports and provider validation reports,
which will disclose the IRFs data
submission status providing details on
all items submitted for a selected
assessment and the status of records
submitted. We refer providers to the
CDC/NHSN system Web site for
information on obtaining reports
specific to NHSN submitted data at
https://www.cdc.gov/nhsn/inpatientrehab/. Additional
information regarding the content and
availability of these confidential
feedback reports will be provided on an
ongoing basis on our Web site(s) at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/
index.html.
As previously finalized in the FY
2016 IRF PPS final rule and illustrated
in Table 18 in section VIII.I.c of this
final rule, IRFs will have approximately
4.5 months after the reporting quarter to
correct any errors of their assessmentbased data (that appear on the CASPER
generated QM reports) and NHSN data
used to calculate the measures. During
the time of data submission for a given
quarterly reporting period and up until
the quarterly submission deadline, IRFs
could review and perform corrections to
errors in the assessment data used to
calculate the measures and could
request correction of measure
calculations. However, as already
established, once the quarterly
submission deadline occurs, the data is
‘‘frozen’’ and calculated for public
reporting and providers can no longer
submit any corrections. We will
encourage IRFs to submit timely
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assessment data during a given quarterly
reporting period and review their data
and information early during the review
and correction period so that they can
identify errors and resubmit data before
the data submission deadline.
As noted above, the assessment data
will be populated into the confidential
feedback reports, and we intend to
update the reports monthly with all data
that have been submitted and are
available. We believe that the data
collection/submission quarterly
reporting periods plus 4.5 months to
review correct and review the data is
sufficient time for IRFs to submit,
review and, where necessary, correct
their data and information. These time
frames and deadlines for review and
correction of such measures and data
satisfy the statutory requirement that
IRFs be provided the opportunity to
review and correct their data and
information and are consistent with the
informal process hospitals follow in the
Hospital IQR Program.
In FY 2016 IRF PPS final rule (80 FR
47126 through 47128), we finalized the
data submission/correction and review
period. Also, we afford IRFs a 30-day
preview period prior to public display
during which IRFs may preview the
performance information on their
measures that will be made public. We
want to clarify that we will provide the
preview report using the CASPER
system, with which IRFs are familiar.
The CASPER preview reports inform
providers of their performance on each
measure which will be publicly
reported. Please note that the CASPER
preview reports for the reporting quarter
will be available after the 4.5 month
correction period and the applicable
data submission/correction deadline
have passed and are refreshed on a
quarterly basis for those measures
publicly reported quarterly, and
annually for those measure publicly
reported annually. We proposed to give
IRFs 30 days to review the preview
report beginning from the date on which
they can access the report. As already
finalized, corrections to the underlying
data will not be permitted during this
time; however, IRFs may ask for a
correction to their measure calculations
during the 30-day preview period,
should they believe the calculation is
inaccurate. We proposed that if we agree
that the measure, as it is displayed in
the preview report, contains a
calculation error, we could suppress the
data on the public reporting Web site,
recalculate the measure and publish it at
the time of the next scheduled public
display date. This process will be
consistent with informal processes used
in the Hospital IQR Program. If
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finalized, we intend to utilize a
subregulatory mechanism, such as our
IRF QRP Web site, to provide more
information about the preview reports,
such as when they will be made
available and explain the process for
how and when providers may ask for a
correction to their measure calculations.
We invited public comment on these
proposals to provide preview reports
using the CASPER system, giving IRFs
30 days review the preview report and
ask for a correction, and to use a
subregulatory mechanism to explain the
process for how and when providers
may ask for a correction.
In addition to assessment-based
measures and CDC measure data
received via the NHSN system, we have
also proposed claims-based measures
for the IRF QRP. The claims-based
measures include those proposed to
meet the requirements of the IMPACT
Act as well as the All-Cause Unplanned
Readmission Measure for 30 Days PostDischarge from IRFs (NQF #2502) which
was finalized for public display in the
FY 2016 IRF PPS final rule (80 FR 47126
through 47127). As noted in section
VII.N.2. of this final rule, section
1899B(g)(2) of the Act requires
prepublication provider review and
correction procedures that are
consistent with those followed in the
Hospital IQR Program. Under the
Hospital IQR Program’s informal
procedures, for claims-based measures,
we provide hospitals 30 days to preview
their claims-based measures and data in
a preview report containing aggregate
hospital-level data. We proposed to
adopt a similar process for the IRF QRP.
Prior to the public display of our
claims-based measures, in alignment
with the Hospital IQR, HAC and
Hospital VBP Programs, we proposed to
make available through the CASPER
system, a confidential preview report
that will contain information pertaining
to claims-based measure rate
calculations, for example, facility and
national rates. The data and information
will be for feedback purposes only and
could not be corrected. This information
will be accompanied by additional
confidential information based on the
most recent administrative data
available at the time we extract the
claims data for purposes of calculating
the measures. Because the claims-based
measures are recalculated on an annual
basis, these confidential CASPER QM
reports for claims-based measures will
be refreshed annually. As previously
finalized in the FY 2016 IRF PPS final
rule (80 FR 47126 through 47128), IRFs
will have 30 days from the date the
preview report is made available in
which to review this information. The
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30-day preview period is the only time
when IRFs will be able to see claimsbased measures before they are publicly
displayed. IRFs will not be able to make
corrections to underlying claims data
during this preview period, nor will
they be able to add new claims to the
data extract. However, IRFs may request
that we correct our measure calculation
if the IRF believes it is incorrect during
the 30 day preview period. We proposed
that if we agree that the measure, as it
is displayed in the preview report,
contains a calculation error, we could
suppress the data on the public
reporting Web site, recalculate the
measure, and publish it at the time of
the next scheduled public display date.
This process will be consistent with
informal policies followed in the
Hospital IQR Program. If finalized, we
intend to utilize a subregulatory
mechanism, such as our IRF QRP Web
site, to explain the process for how and
when providers may contest their
measure calculations
The proposed claims-based
measures—The MSPB–PAC IRF QRP
measure; Discharge to Community—
PAC, Potentially Preventable 30-Day
Post-Discharge Readmission Measure for
IRF QRP, and Potentially Preventable
Within Stay Readmission Measure for
IRFs—use Medicare administrative data
from hospitalizations for Medicare FFS
beneficiaries. Public reporting of data
will be based on 2 consecutive calendar
years of data, which is consistent with
the specifications of the proposed
measures. We proposed to create data
extracts using claims data for the
proposed claims-based measures–The
MSPB–PAC IRF QRP measure;
Discharge to Community—PAC,
Potentially Preventable 30-Day PostDischarge Readmission Measure for IRF
QRP, and Potentially Preventable
Within Stay Readmission Measure for
IRFs—at least 90 days after the last
discharge date in the applicable period,
which we will use for the calculations.
For example, if the last discharge date
in the applicable period for a measure
is December 31, 2017, for data collection
January 1, 2016, through December 31,
2017, we will create the data extract on
approximately March 31, 2018, at the
earliest, and use that data to calculate
the claims-based measures for that
applicable period. Since IRFs will not
be able to submit corrections to the
underlying claims snapshot nor add
claims (for measures that use IRF
claims) to this data set at the conclusion
of the at least the 90-day period
following the last date of discharge used
in the applicable period, at that time we
will consider IRF claims data to be
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complete for purposes of calculating the
claims-based measures.
We proposed that beginning with data
that will be publicly displayed in 2018,
claims-based measures will be
calculated using claims data at least 90
days after the last discharge date in the
applicable period, at which time we will
create a data extract or snapshot of the
available claims data to use for the
measures calculation. This timeframe
allows us to balance the need to provide
timely program information to IRFs with
the need to calculate the claims-based
measures using as complete a data set as
possible. As noted, under this
procedure, during the 30-day preview
period, IRFs will not be able to submit
corrections to the underlying claims
data or to add new claims to the data
extract. This is for two reasons: First, for
certain measures, the claims data used
to calculate the measure is derived not
from the IRF’s claims, but from the
claims of another provider. For
example, the proposed measure
Potentially Preventable 30-Day PostDischarge Readmission Measure for IRF
QRP uses claims data submitted by the
hospital to which the patient was
readmitted. The claims are not those of
the IRF and, therefore, the IRF could not
make corrections to them. Second, even
where the claims used to calculate the
measures are those of the IRF, it will not
be not possible to correct the data after
it is extracted for the measures
calculation. This is because it is
necessary to take a static ‘‘snapshot’’ of
the claims in order to perform the
necessary measure calculations.
We seek to have as complete a data set
as possible. We recognize that the at
least 90-day ‘‘run-out’’ period, when we
will take the data extract to calculate the
claims-based measures, is less than the
Medicare program’s current timely
claims filing policy under which
providers have up to 1 year from the
date of discharge to submit claims. We
considered a number of factors in
determining that the proposed at least
90-day run-out period is appropriate to
calculate the claims-based measures.
After the data extract is created, it takes
several months to incorporate other data
needed for the calculations (particularly
in the case of risk-adjusted or episodebased measures). We then need to
generate and check the calculations.
Because several months lead time is
necessary after acquiring the data to
generate the claims-based calculations,
if we were to delay our data extraction
point to 12 months after the last date of
the last discharge in the applicable
period, we will not be able to deliver the
calculations to IRFs sooner than 18 to 24
months after the last discharge. We
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believe this will create an unacceptably
long delay both for IRFs and for us to
deliver timely calculations to IRFs for
quality improvement.
We invited public comment on these
proposals. We received a number of
comments, which are summarized
below.
Comment: Several commenters
expressed concern that for claims-based
measures, CMS proposes to calculate
claims-based measures on an annual
basis and the CASPER QM provider
reports for these measures would only
be available annually. Commenters also
expressed concern that CMS does not
propose to allow providers to correct
their metrics on claims-based measures;
reports would be for feedback purposes
only. Several commenters requested
CMS provide claims-based feedback
reports at least twice a year as well as
providing patient-level data.
Response: We appreciate the
commenters’ concerns and suggestions
to provide feedback reports at least
twice a year as well as providing
patient-level data for claims-based
measures. As discussed previously, the
All-Cause Unplanned Readmission
Measure for 30 Days Post-Discharge
from IRFs (NQF #2502) is based on 2
consecutive years of data in order to
ensure a sufficient sample size to
reliably assess IRFs’ performance. The
decision to update claims-based
measures on an annual basis was to
ensure that the amount of data received
during the reporting period was
sufficient to generate reliable measure
rates. However, we will explore the
feasibility of providing IRFs with
information more frequently. We believe
that we are limited in our ability to
provide patient level information that
stems from claims submitted by
providers other than IRF, but we will
explore the feasibility of providing
patient-level data. With regard to the
concern for the correction of claimsbased measures and the IRF’s ability to
correct their metrics, and that the
reports we provide will be for feedback
purposes only, we interpret the
commenter to be referring to both the
preview reports and the QM reports we
discussed. The limitation on claimsbased data and corrections is that the
measures are calculated after the claims
file has been obtained. If the IRF
determines there are errors in the claims
data they submitted, then they can
correct such data. The corrections to the
claims data will be reflected in the
subsequent measure calculation. We
urge IRFs to submit timely and accurate
claims-based data.
Comment: One commenter expressed
concern that 30 days is inadequate to
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preview and assess the QM reports and
recommends 60 days and that CMS
should establish a process to discuss
and reconcile issues or incongruities
between CMS’s and the provider’s data.
Response: We interpret the
commenter to be referring to the
preview reports we will provide prior to
public reporting and appreciate their
concern for the 30-day timeframe for
which IRFs have to review and assess
the preview reports. The 30-day preview
period, previously finalized, is
consistent with other public reporting
programmatic procedures. As described,
this timeframe is for providers to
evaluate their data that will be
published and alert us to any
discrepancies they may find. In
addition, as described, IRFs will have an
opportunity to review their information
and data using various reports, which
are provided through the CASPER
system and can be used to inform data
correction needs on behalf of the IRF.
For example, as discussed, we intend to
provide IRF QM Reports that will
provide monthly reporting on both
facility-level and patient-level CMS
assessment-based data. Further, we refer
the commenter to the discussion we
provide in which IRFs will have 4.5
months to review and correct data prior
to the quarterly freeze dates and posting
of the final preview reports in QIES.
Final Decision: After careful
consideration of the public comments,
we are finalizing our proposals related
to procedures for the opportunity to
review and correct data and
information. We are finalizing as
proposed, our policies and procedures
pertaining to public reporting and the
opportunity to review and correct data
and information. We are also finalizing
as proposed, our policies and
procedures for claims-based measures
for public reporting.
O. Mechanism for Providing Feedback
Reports to IRFs
Section 1899B(f) of the Act requires
the Secretary to provide confidential
feedback reports to post-acute care
providers on their performance to the
measures specified under section
1899B(c)(1) and (d)(1) of the Act,
beginning 1 year after the specified
application date that applies to such
measures and PAC providers. As
discussed earlier, the reports we
proposed to provide for use by IRFs to
review their data and information will
be confidential feedback reports that
will enable IRFs to review their
performance on the measures required
under the IRF QRP. We proposed that
these confidential feedback reports will
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be available to each IRF using the
CASPER system. Data contained within
these CASPER reports will be updated
as previously described, on a monthly
basis as the data become available
except for our claims-based measures,
which are only updated on an annual
basis.
We intend to provide detailed
procedures to IRFs on how to obtain
their confidential feedback CASPER
reports on the IRF QRP Web site at
https://www.cms.gov/Medicare/QualityInitiatives-Patient-AssessmentInstruments/IRF-Quality-Reporting/
index.html. We proposed to use the
CMS QIES–ASAP system to provide
quality measure reports in a manner
consistent with how providers obtain
various reports to date. The QIES–ASAP
system is a confidential and secure
system with access granted to providers,
or their designees.
We sought public comment on this
proposal to satisfy the requirement to
provide confidential feedback reports to
IRFs. We received several comments,
which are summarized are below.
Comment: Several commenters
recommended CMS provide more
frequent feedback, such as quarterly, for
assessment-based measures and every
six months reporting for claims-based
measures.
Response: We appreciate commenters’
suggestion for CMS to provide more
frequent feedback, such as quarterly, for
assessment-based measures and every 6
months for claims-based measures.
As previously discussed, IRFs will
have an opportunity to review and
utilize their data using confidential
reports provided through the CASPER
system. The decision to update claimsbased measures on an annual basis was
basis was explained previously in
response to the comment concerning
providing feedback reports at least twice
a year.
Comment: One commenter
recommended CMS conduct a ‘‘dry run’’
in which providers receive confidential
preview reports prior to publicly
reporting measures so that providers can
become familiar with the methodology,
understand the measure results, know
how well they are performing, and have
an opportunity to give CMS feedback on
potential technical issues with the
measures.
Response: We intend to offer
providers information related to their
measures so that they become familiar
with the measure’s methodology and
can utilize their confidential preview
reports which they will receive prior to
the public reporting of new IRF QRP
measures. IRFs will also receive other
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confidential reports such as the IRF
facility and patient level QM Reports as
well as an additional confidential
facility-level report to incorporate the
quarterly freeze dates, for example, the
Review and Correct Report. We believe
that these various reports will provide
an indication on how well the IRF is
performing as well as opportunities to
provide CMS feedback on technical
issues with the measures. Therefore, no
additional dry run period is warranted.
Final Decision: After careful
consideration of the public comments,
we are finalizing our proposal to
provide confidential feedback reports to
IRFs, as proposed.
P. Method for Applying the Reduction to
the FY 2017 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 IRFs that fail
to comply with the quality data
submission requirements. In compliance
with section 1886(j)(7)(A)(i) of the Act,
we proposed to apply a 2-percentage
point reduction to the applicable FY
2017 market basket increase factor in
calculating a proposed adjusted FY 2017
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, reportingbased 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 2017 IRF increase
factor for IRFs that fail to meet the
quality reporting requirements. We did
not receive any comments on this
proposal.
Final Decision: We are finalizing our
proposed method for applying the
reduction to the FY 2017 IRF increase
factor for IRFs that fail to meet the
quality reporting requirements.
Table 21 shows the calculation of the
adjusted FY 2017 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(s).
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TABLE 21—CALCULATIONS TO DETERMINE THE ADJUSTED FY 2017 STANDARD PAYMENT CONVERSION FACTOR FOR
IRFS THAT FAILED TO MEET THE QUALITY REPORTING REQUIREMENT
Explanation for adjustment
Calculations
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Standard Payment Conversion Factor for FY 2016 ........................................................................................................................
Market Basket Increase Factor for FY 2017 (2.7 percent), reduced by 0.3 percentage point for the productivity adjustment as
required by section 1886(j)(3)(C)(ii)(I) of the Act, reduced by 0.75 percentage point in accordance with sections
1886(j)(3)(C) and (D) of the Act and further reduced by 2 percentage points for IRFs that failed to meet the quality reporting requirement.
Budget Neutrality Factor for the Wage Index and Labor-Related Share ........................................................................................
Budget Neutrality Factor for the Revisions to the CMG Relative Weights .....................................................................................
Adjusted FY 2017 Standard Payment Conversion Factor ..............................................................................................................
IX. Miscellaneous Comments
Comment: Several commenters were
supportive of our continued use of the
FY 2014 facility-level adjustments and
recommended that CMS continue
monitoring the adjustments. Other
commenters suggested that CMS be
more transparent about the methodology
and the factors it utilizes for calculating
facility adjustment payments to IRFs.
Several commenters suggested that CMS
should establish a three-year minimum
interval for any change in the IRF
provider-level adjustment factors and
recommended that if any factor varies
by a minimum amount, the factor
should be adjusted. Some commenters
also recommended that CMS monitor
the facility-level adjustment factors
annually and adjust them if there is a
change in excess of 5 to 10 percent.
Response: As we did not propose any
changes to the facility-level
adjustments, these comments are
outside the scope of the proposed rule.
In the FY 2017 IRF PPS proposed rule
(81 FR 24177), we noted that, in the FY
2015 IRF PPS final rule (79 FR 45872 at
45882), we froze the facility-level
adjustments at FY 2014 levels for FY
2015 and all subsequent years (unless
and until we propose to update them
again through future notice-andcomment rulemaking). We will continue
to monitor the facility-level adjustments
and update them as necessary through
rulemaking to ensure the continued
accuracy of IRF PPS payments.
Comment: Several commenters
expressed concerns about the impact of
the changes to the 60 percent rule
compliance methodology that we
finalized in the FY 2014 and FY 2015
IRF PPS final rules on beneficiary access
to IRF services, and suggested that we
revisit them. These commenters further
stated that the translation of
International Classification of Diseases,
9th Revision, Clinical Modification
(ICD–9–CM) codes to International
Classification of Diseases, 10th
Revision, Clinical Modification (ICD–
10–CM) codes using the General
Equivalence Mapping (GEMS) tool may
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have unintentionally caused some
diagnoses to now be excluded from
counting under the presumptive
compliance methodology. In particular,
the commenters suggested that we
review the codes excluded under the
IGCs for traumatic brain injury, hip
fracture, and major multiple trauma,
and add these cases back in as
presumptively compliant cases under
the 60 percent rule. Some commenters
suggested that we issue clarifications to
MACs and CMS Regional Offices that
these codes are considered
presumptively compliant. Further, one
commenter suggested that we revisit our
decision on no longer considering
presumptively compliant diagnoses
codes for rheumatoid myopathy and
polyneuropathy, unilateral amputations,
and amputation status/aftercare.
Response: As we did not propose any
changes to the methodology for
determining IRFs’ compliance with the
60 percent rule in the FY 2017 IRF PPS
proposed rule, these comments are
outside the scope of the proposed rule.
We appreciate the commenter’s
suggestions, and will continue to
monitor and assess the implications of
the changes to the presumptive
methodology that we finalized in the FY
2014 and FY 2015 IRF PPS final rules
to determine if any further refinements
to the methodology are needed. We
intend to take a comprehensive look at
the ICD–10–CM codes to identify any
diagnosis codes that may need to be
added to the presumptive compliance
methodology, as well as any codes that
may need to be removed.
Comment: Several commenters
suggested that, as height and weight are
now required information on the IRF–
PAI (beginning October 1, 2014), CMS
should now use this information to
identify patients with unilateral joint
replacements and body mass indexes
(BMI) greater than 50 for presumptive
compliance with the 60 percent rule
requirements.
Response: As we did not propose any
changes to the methodology for
determining IRFs’ compliance with the
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$15,478.
× 0.9965.
× 0.9992.
× 0.9992.
= 15,399.
60 percent rule, these comments are
outside the scope of the proposed rule.
However, we will take these suggestions
into consideration.
Comment: One commenter stated that
the translation to ICD–10–CM has
created a problem with the grouping of
rehabilitation diagnosis-related groups
(DRGs) in rehabilitation units due to the
loss of the ‘‘V code’’ under ICD–10–CM.
The commenter expressed concern that
rehabilitation patients may not be
reimbursed appropriately and in many
instances would be paid under the
Hospital IPPS MS–DRGs.
Response: As payment under the IRF
PPS is not based on diagnosis-related
groups, this comment is outside the
scope of the proposed rule. This final
rule only applies to rehabilitation units
that are paid under the IRF PPS, not to
other types of rehabilitation units which
may be present in an acute care hospital
but that are paid under other Medicare
payment systems.
Comment: One commenter stated that
CMS should review its policy regarding
the use of ‘‘D-subsequent encounter’’ as
an eligible 7th character for traumatic
injury diagnosis codes as advised by the
AHA Coding Clinic for ICD–10–CM and
ICD–10–PCS Editorial Advisory Board
(reference material for this can be found
at https://www.ahacentraloffice.org/
codes/Resources.shtml). The commenter
stated that ‘‘subsequent encounter’’ is an
appropriate option for rehabilitation
services and that CMS should allow the
‘‘D’’ as an eligible 7th character for
traumatic injury diagnosis codes.
Response: IRFs are permitted to use
‘‘D’’ as an eligible 7th character for
traumatic injury diagnosis codes on
both the IRF claim and the IRF–PAI.
However, for the reasons indicated in
the FY 2015 IRF PPS final rule (79 FR
45872, 45907), effective with discharges
occurring on or after October 1, 2015,
ICD–10–CM codes with the seventh
character extension of ‘‘D’’ are not
included in the ICD–10–CM versions of
the ‘‘List of Comorbidities,’’ ‘‘ICD–10–
CM Codes That Meet Presumptive
Compliance Criteria,’’ or ‘‘Impairment
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Group Codes That Meet Presumptive
Compliance Criteria.’’ Whereas the AHA
Coding Clinic for ICD–10–CM and ICD–
10 PCS (Vol. 2, No. 1) guidelines
instruct providers to use the 7th
character ‘‘D’’ for traumatic injury
diagnosis codes used in an IRF setting,
the guidelines specifically say that the
AHA Coding Clinic guidelines only
apply to the IRF claim and that
providers should refer to the
instructions provided in the IRF–PAI
training manual, which is available for
download from the IRF PPS Web site at
https://www.cms.gov/Medicare/
Medicare-Fee-for-Service-Payment/
InpatientRehabFacPPS/IRFPAI.html, for
instructions on how to code the IRF–
PAI. Thus, ICD–10–CM diagnosis codes
with the 7th character ‘‘D,’’ if used for
traumatic injury diagnosis codes on the
IRF–PAI, will not result in a tier
payment or result in a case being
presumptively compliant with the IRF
60 percent rule for the reasons stated in
the FY 2015 IRF PPS final rule (79 FR
45872, 45907).
Comment: Several commenters stated
that the FY 2017 update to the standard
payment conversion factor does not
include additional payment to IRFs for
the time and resources needed to
complete assessments for quality
reporting. These commenters further
stated that the additional quality
reporting elements in the FY 2016 IRF
PPS final rule will add time spent
collecting information while decreasing
the time available for direct patient care.
Several commenters stated that the
proposed increase does not cover the
costs of medical inflation, or of the
technical implementation, training, and
data collection related to the quality
reporting measures even though costs
will be significant. Several commenters
stated that the ‘‘minimal increase’’ does
not adequately take into account the
estimated costs of implementing the
quality reporting measures and request
that CMS add the estimated costs of
these measures to the FY 2017 payment
update.
Response: We refer readers to the FY
2016 IRF PPS final rule (80 FR 47129
through 47137) for details regarding the
Collection of Information Requirements
and Regulatory Impact Analysis for the
finalized measures. We would also like
to clarify that quality program reporting
requirements are not included in the
standard payment conversion factor.
However, in accordance with section
1886(j)(7)(A) of the Act, the applicable
annual increase factor for any IRF that
does not submit the required data to
CMS must be reduced by two
percentage points.
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Comment: One commenter reiterated
MedPAC’s March 2016 recommendation
that we should analyze patterns of
coding across IRFs and reassess the
inter-rater reliability of the IRF–PAI.
Response: This comment involves
data monitoring activities that are not
discussed in the proposed rule, and are
therefore outside the scope of the rule.
However, we will share this
recommendation with the appropriate
components within CMS for their
consideration of these issues.
X. Provisions of the Final Regulations
In this final rule, we are adopting the
provisions set forth in the FY 2017 IRF
PPS proposed rule (81 FR 24178).
Specifically:
• We will update the FY 2017 IRF
PPS relative weights and average length
of stay values using the most current
and complete Medicare claims and cost
report data in a budget-neutral manner,
as discussed in section IV of this final
rule.
• As established in the FY 2015 IRF
PPS final rule (79 FR 45872 at 45882),
the facility-level adjustments will
remain frozen at FY 2014 levels for FY
2015 and all subsequent years (unless
and until we propose to update them
again through future notice-andcomment rulemaking), as discussed in
section V of this final rule.
• We will update the FY 2017 IRF
PPS payment rates by the market basket
increase factor, based upon the most
current data available, with a 0.75
percentage point reduction as required
by sections 1886(j)(3)(C)(ii)(II) and
1886(j)(3)(D)(v) of the Act and the
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 the FY 2017 IRF
PPS payment rates by the FY 2017 wage
index and the labor-related share in a
budget-neutral manner and continue the
phase-out of the rural adjustment as
discussed in section VI of this final rule.
• We will calculate the final IRF
standard payment conversion factor for
FY 2017, as discussed in section VI of
this final rule.
• We will update the outlier
threshold amount for FY 2017, as
discussed in section VII of this final
rule.
• We will update the cost-to-charge
ratio (CCR) ceiling and urban/rural
average CCRs for FY 2017, as discussed
in section VII of this final rule.
• We will adopt revisions and
updates to quality measures and
reporting requirements under the
quality reporting program for IRFs in
accordance with section 1886(j)(7) of the
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Act, as discussed in section VIII of this
final rule.
XI. 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
Failure to submit data required under
section 1886(j)(7)(C) and (F) of the Act
will result in the reduction of the
annual update to the standard federal
rate for discharges occurring during
such fiscal year by 2 percentage points
for any IRF that does not comply with
the requirements established by the
Secretary. At the time that this analysis
was prepared, 91, or approximately 8
percent, of the 1166 active Medicarecertified IRFs did not receive the full
annual percentage increase for the FY
2016 annual payment update
determination. Information is not
available to determine the precise
number of IRFs that will not meet the
requirements to receive the full annual
percentage increase for the FY 2017
payment determination.
We believe that the burden associated
with the IRF QRP is the time and effort
associated with data collection and
reporting. As of February 1, 2016 there
are approximately 1131 IRFs currently
reporting quality data to CMS. In this
final rule, we are adopting 5 measures.
For the FY 2018 payment
determinations and subsequent years,
we proposed four new measures: (1)
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MSPB–PAC IRF QRP; (2) Discharge to
Community-PAC IRF QRP, and (3)
Potentially Preventable 30-Day PostDischarge Readmission Measure for IRF
QRP; (4) Potentially Preventable 30-Day
Within Stay Readmission Measure for
IRF QRP. These four measures are
Medicare claims-based measures.
Because claims-based measures can be
calculated based on data that are already
reported to the Medicare program for
payment purposes, we believe there will
be no additional impact.
For the FY 2020 payment
determination and subsequent years, we
proposed one measure: Drug Regimen
Review Conducted with Follow-Up for
Identified Issues-PAC IRF QRP.
Additionally, we proposed that data for
this new measure will be collected and
reported using the IRF–PAI (version
effective October 1, 2018).
Our burden calculations take into
account all ‘‘new’’ items required on the
IRF–PAI (version effective October 1,
2018) to support data collection and
reporting for this measure. The addition
of the new items required to collect the
newly proposed measure is for the
purpose of achieving standardization of
data elements.
We estimate the additional elements
for the newly proposed Drug Regimen
Review Conducted with Follow-Up for
Identified Issues-PAC IRF QRP measure
will take 6 minutes of nursing/clinical
staff time to report data on admission
and 4 minutes of nursing/clinical staff
time to report data on discharge, for a
total of 10 minutes. We estimate that the
additional IRF–PAI items we proposed
will be completed by Registered Nurses
(RN) for approximately 75 percent of the
time required, and Pharmacists for
approximately 25 percent of the time
required. Individual providers
determine the staffing resources
necessary. In accordance with OMB
control number 0938–0842, we estimate
398,254 discharges from all IRFs
annually, with an additional burden of
10 minutes. This will equate to
66,375.67 total hours or 58.69 hours per
IRF. We believe this work will be
completed by RNs (75 percent) and
Pharmacists (25 percent). We obtained
mean hourly wages for these staff from
the U.S. Bureau of Labor Statistics’ May
2014 National Occupational
Employment and Wage Estimates
(https://www.bls.gov/oes/current/oes_
nat.htm), and to account for overhead
and fringe benefits, we have doubled the
mean hourly wage. Per the U.S. Bureau
of Labor and Statistics, the mean hourly
wage for a RN is $33.55. However, to
account for overhead and fringe
benefits, we have doubled the mean
hourly wage, making it $67.10 for an
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RN. Per the U.S. Bureau of Labor and
Statistics, the mean hourly wage for a
pharmacist is $56.98. However, to
account for overhead and fringe
benefits, we have doubled the mean
hourly wage, making it $113.96 for a
pharmacist. Given these wages and time
estimates, the total cost related to the
newly proposed measures is estimated
at $4,625.46 per IRF annually, or
$5,231,398.17 for all IRFs annually.
For the quality reporting during
extraordinary circumstances, in section
VIII.L of this final rule, we add a
previously finalized process that IRFs
may request an exception or extension
from the FY 2019 payment
determination and that of subsequent
payment determinations. The request
must be submitted by email within 90
days from the date that the
extraordinary circumstances occurred.
While the preparation and submission
of the request is an information
collection, unlike the aforementioned
temporary exemption of the data
collection requirements for the new
drug regimen review measure, the
request is not expected to be submitted
to OMB for formal review and approval
since we estimate less than two requests
(total) per year. Since we estimate fewer
than 10 respondents annually, the
information collection requirement and
associated burden is not subject as
stated in 5 CFR 1320.3(c) of the
implementing regulations of the
Paperwork Reduction Act of 1995.
As discussed in section VIII.M of this
final rule, we add a previously finalized
process that will enable IRFs to request
reconsiderations of our initial noncompliance decision in the event that it
believes that it was incorrectly
identified as being subject to the 2percentage point reduction to its annual
increase factor due to non-compliance
with the IRF QRP reporting
requirements. While there is burden
associated with filing a reconsideration
request, 5 CFR 1320.4 of OMB’s
implementing regulations for PRA
excludes activities during the conduct
of administrative actions such as
reconsiderations.
We received comments about the
collection of information requirements
associated with measures being
proposed for the IRF QRP, which are
summarized and addressed below.
Comment: One commenter
appreciated that the claims-based
measures being proposed do not place
additional burden on the facilities and
their staff. Other commenters had
concerns about the claims-based
measures, noting that while they had no
data collection burden, they were
associated with time and resources
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needed to compile and verify data for
submission. One commenter expressed
concerns that the burden estimate
doubles the resources required to collect
data but doesn’t take into consideration
limited resources smaller organizations
have.
Response: We recognize the
commenter’s concern pertaining to
burden due to the requirements being
added to the IRF Quality Reporting
Program. We are very sensitive to the
issue of burden associated with data
collection and have proposed only the
minimal number of additional items (3)
needed to calculate the proposed quality
measure. Though we recognize that new
IRF–PAI items will require additional
activities and efforts by providers, we
would like to clarify that burden
estimates are intended to reflect only
the time needed to complete IRF–PAI
items, independent of clinical time
spent assessing the patient. Similarly,
burden estimates are not indented to
reflect costs of training and operational
processes; these are considered part of
the operating costs for an IRF. Time
estimates for coding required items
being added for the Drug Regimen
Review measure were based on a Drug
Regimen Review pilot testing conducted
in November and December 2015. It
should be noted that with each
assessment release, we provide free
software to our providers that allows for
the completion and submission of any
required assessment data. Free
downloads of the Inpatient
Rehabilitation Validation and Entry
(IRVEN) software product are available
on the CMS Web site at https://
www.cms.gov/Medicare/Medicare-Feefor-Service-Payment/
InpatientRehabFacPPS/Software.html.
We also wish to note that, as pointed
out by one commenter, four of the five
measures proposed are claims-based
and have no additional data collection
burden to providers. Since the data
source for these measures is claims data,
and is not collected by means of an
assessment instrument, the measure
does not increase data collection burden
on the provider as this data is currently
collected by providers. We also note
that providers will be given a chance to
review their claims-based measure data
via feedback provided in the CASPER
system. Despite the lack of data
collection burden, we appreciate the
comments that more education will be
required for the public and providers to
understand the claims-based measures
and the feedback mechanism. We will
be providing additional training for the
reports that are, and will be, available
for providers for reviewing their data.
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Although we did not solicit feedback
on the burden associated with the
measures finalized in the FY 2016 IRF
PPS final rule (80 FR 47100 through
47120), including functional status
measures, which will be collected via
the IRF–PAI Version 1.4 effective
October 1, 2016, we received several
comments, which are summarized
below.
Comment: Several commenters were
concerned that the additional 41.5
minutes required to collect new
required data elements finalized in the
FY 2016 IRF PPS final rule, including
training staff and updating medical
records, led to increased costs to IRFs
that are not covered in the update to the
standard payment conversion factor
proposed for IRFs. One commenter also
noted that delays in training led to
additional expenses for preparing staff
and electronic health records.
Response: We refer the reader to our
discussion of burden due to data set
revisions, data collection, or training of
staff due to the revisions in the IRF–PAI
Version 1.4 in the FY 2016 IRF PPS final
rule (80 FR 47086 through 47120).
Feedback relating to provider burden
will be taken into account as we
consider future updates to the IRF QRP.
With regards to comments about the
updated SPCF, we refer readers to the
IRF PPS FY 2016 final rule (80 FR 47129
through 47137) for details regarding the
Collection of Information Requirements
and Regulatory Impact Analysis for the
measures finalized in FY 2016. We
would also like to clarify that QRP
requirements are not included in the
SPCF, however, per statutory
requirements, the applicable annual
increase factor for any IRF that does not
submit the required data to CMS is
reduced by 2 percentage points.
Additional responses to these comments
are included in sections VI.E and IX. of
this final rule.
XII. Regulatory Impact Analysis
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A. Statement of Need
This final rule updates the IRF
prospective payment rates for FY 2017
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 case-mix groups 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) and (D) of the Act.
Section 1886(j)(3)(C)(ii)(I) of the Act
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requires the Secretary to apply a multifactor productivity 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
through 2019.
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 will revise and
update the quality measures and
reporting requirements under the IRF
quality reporting program.
B. Overall Impacts
We have examined the impacts of this
final rule as required by Executive
Order 12866 (September 30, 1993,
Regulatory Planning and Review),
Executive Order 13563 on Improving
Regulation and Regulatory Review
(January 18, 2011), the Regulatory
Flexibility Act (September 19, 1980,
Pub. L. 96–354) (RFA), section 1102(b)
of the Act, section 202 of the Unfunded
Mandates Reform Act of 1995 (Pub. L.
104–4), Executive Order 13132 on
Federalism (August 4, 1999), and the
Congressional Review Act (5 U.S.C.
804(2)).
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). Executive Order 13563
emphasizes the importance of
quantifying both costs and benefits, of
reducing costs, of harmonizing rules,
and of promoting flexibility. A
regulatory impact analysis (RIA) must
be prepared for a major final rule 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 2017 with those in FY 2016. This
analysis results in an estimated $145
million increase for FY 2017 IRF PPS
payments. As a result, this final rule is
designated as economically
‘‘significant’’ under section 3(f)(1) of
Executive Order 12866, and hence a
major rule under the Congressional
Review Act. Also, the rule has been
reviewed by OMB.
The Regulatory Flexibility Act (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
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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 1,100 IRFs, of which
approximately 60 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 22,
we estimate that the net revenue impact
of this final rule on all IRFs is to
increase estimated payments by
approximately 1.9 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 140 rural units and 11 rural
hospitals in our database of 1,133 IRFs
for which data were available.
Section 202 of the Unfunded
Mandates Reform Act of 1995 (Pub. L.
104–04, enacted on March 22, 1995)
also requires that agencies assess
anticipated costs and benefits before
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issuing any rule whose mandates
require spending in any 1 year of $100
million in 1995 dollars, updated
annually for inflation. In 2016, that
threshold level is approximately $146
million. This final rule will not mandate
spending costs on state, local, or tribal
governments, in the aggregate, or by the
private sector, of greater than $146
million.
Executive Order 13132 establishes
certain requirements that an agency
must meet when it promulgates a 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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C. Detailed Economic Analysis
1. Basis and Methodology of Estimates
This final rule updates to the IRF PPS
rates contained in the FY 2016 IRF PPS
final rule (80 FR 47036). Specifically,
this final rule updates the CMG relative
weights and average length of stay
values, the wage index, and the outlier
threshold for high-cost cases. This final
rule applies a MFP adjustment to the FY
2017 IRF market basket increase factor
in accordance with section
1886(j)(3)(C)(ii)(I) of the Act, and a 0.75
percentage point reduction to the FY
2017 IRF market basket increase factor
in accordance with sections
1886(j)(3)(C)(ii)(II) and (D)(v) of the Act.
Further, this final rule contains
revisions to the IRF quality reporting
requirements that are expected to result
in some additional financial effects on
IRFs. In addition, section VIII of this
final rule discusses 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, in
accordance with section 1886(j)(7) of the
Act.
We estimate that the impact of the
changes and updates described in this
final rule will be a net estimated
increase of $145 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 XII.C.6. of this final rule). The
impact analysis in Table 22 of this final
rule represents the projected effects of
the updates to IRF PPS payments for FY
2017 compared with the estimated IRF
PPS payments in FY 2016. We
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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,
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 2017, 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 2017
IRF market basket increase factor in
accordance with section
1886(j)(3)(C)(ii)(I) of the Act, and a 0.75
percentage point reduction to the FY
2017 IRF market basket increase factor
in accordance with sections
1886(j)(3)(C)(ii)(II) and (D)(v) of the Act.
We estimate the total increase in
payments to IRFs in FY 2017, relative to
FY 2016, will be approximately $145
million.
This estimate is derived from the
application of the FY 2017 IRF market
basket increase factor, as reduced by a
productivity adjustment in accordance
with section 1886(j)(3)(C)(ii)(I) of the
Act, and a 0.75 percentage point
reduction in accordance with sections
1886(j)(3)(C)(ii)(II) and (D)(v) of the Act,
which yields an estimated increase in
aggregate payments to IRFs of $125
million. Furthermore, there is an
additional estimated $20 million
increase in aggregate payments to IRFs
due to the update of the outlier
threshold amount. Outlier payments are
estimated to increase from
approximately 2.7 percent in FY 2016 to
3.0 percent in FY 2017. Therefore,
summed together, we estimate that these
updates will result in a net increase in
estimated payments of $145 million
from FY 2016 to FY 2017.
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The effects of the updates that impact
IRF PPS payment rates are shown in
Table 22. 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 2.7 percent to 3.0 percent
of total estimated payments for FY 2017,
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 section 1886(j)(3)(A)(i) and
sections 1886(j)(3)(C) and (D) of the Act,
including a productivity adjustment in
accordance with section
1886(j)(3)(C)(i)(I) of the Act, and a 0.75
percentage point reduction in
accordance with sections
1886(j)(3)(C)(ii)(II) and (D)(v) 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.
• The effects of the budget-neutral
changes to the CMG relative weights
and average length of stay values, under
the authority of section 1886(j)(2)(C)(i)
of the Act.
• The total change in estimated
payments based on the FY 2017
payment changes relative to the
estimated FY 2016 payments.
2. Description of Table 22
Table 22 categorizes IRFs by
geographic location, including urban or
rural location, and location for CMS’s 9
Census divisions (as defined on the cost
report) of the country. In addition, the
table divides IRFs into those that are
separate rehabilitation hospitals
(otherwise called freestanding hospitals
in this section), those that are
rehabilitation units of a hospital
(otherwise called hospital units in this
section), rural or urban facilities,
ownership (otherwise called for-profit,
non-profit, and government), by
teaching status, and by disproportionate
share patient percentage (DSH PP). The
top row of Table 22 shows the overall
impact on the 1,133 IRFs included in
the analysis.
The next 12 rows of Table 22 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 982
IRFs located in urban areas included in
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our analysis. Among these, there are 730
IRF units of hospitals located in urban
areas and 252 freestanding IRF hospitals
located in urban areas. There are 151
IRFs located in rural areas included in
our analysis. Among these, there are 140
IRF units of hospitals located in rural
areas and 11 freestanding IRF hospitals
located in rural areas. There are 409 forprofit IRFs. Among these, there are 356
IRFs in urban areas and 53 IRFs in rural
areas. There are 653 non-profit IRFs.
Among these, there are 564 urban IRFs
and 89 rural IRFs. There are 71
government-owned IRFs. Among these,
there are 62 urban IRFs and 9 rural IRFs.
The remaining four parts of Table 22
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
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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 final rule to the facility
categories listed are shown in the
columns of Table 22. 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 2016
analysis file.
• Column (3) shows the number of
cases in each category in our FY 2016
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.
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52137
• Column (6) shows the estimated
effect of the update to the CMG relative
weights and average length of stay
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 2017
to our estimates of payments per
discharge in FY 2016.
The average estimated increase for all
IRFs is approximately 1.9 percent. This
estimated net increase includes the
effects of the IRF market basket increase
factor for FY 2017 of 2.7 percent,
reduced by a productivity adjustment of
0.3 percentage point in accordance with
section 1886(j)(3)(C)(ii)(I) of the Act, and
further reduced by 0.75 percentage
point in accordance with sections
1886(j)(3)(C)(ii)(II) and (D)(v) of the Act.
It also includes the approximate 0.3
percent overall increase 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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TABLE 22: IRF Impact Table for FY 2017 (Columns 4 through 7 in percentage)
Facility Classification
(1)
Total
Urban unit
Rural unit
Urban hospital
Rural hospital
Urban For-Profit
Rural For-Profit
Urban Non-Profit
Rural Non-Profit
Urban Govemment
Rural Govemment
Urban
Rural
Urban by region
Urban New England
Urban Middle Atlantic
Urban South Atlantic
Urban East North Central
Urban East South Central
Urban West North Central
Urban West South Central
Urban Mountain
Urban Pacific
Rural by ree;ion
Rural New England
Rural Middle Atlantic
Rural South Atlantic
Rural East North Central
Rural East South Central
Rural West North Central
Rural West South Central
Rural Mountain
Rural Pacific
Teachine; status
Non-teaching
Resident to ADC less than 10%
Resident to ADC 10%-19%
Resident to ADC greater than 1
Number of Number of
IRFs
Cases
(2)
(3)
1,133
400,781
180,021
730
140
23,192
252
193,104
11
4,464
181,789
356
10,255
53
564
172,204
15,724
89
62
19,132
1,677
9
982
373,125
151
27,656
Total
Percent
CMG
Weights
Change 1
(6)
(7)
0.0
1.9
2.2
0.0
0.0
1.5
0.0
1.8
0.0
0.0
0.0
1.7
0.0
1.1
2.3
0.0
1.4
0.0
0.0
1.8
0.1
0.7
0.0
2.0
0.0
1.2
31
144
146
170
57
74
183
77
100
16,762
57,765
73,307
50,459
26,179
20,139
77,887
26,367
24,260
0.2
0.2
0.2
0.3
0.2
0.3
0.2
0.2
0.6
0.2
0.8
-0.1
-0.1
-0.5
-0.7
-0.1
0.0
0.3
0.1
0.0
0.0
0.1
-0.1
0.0
0.0
0.0
0.0
2.1
2.7
1.8
2.0
1.4
1.3
1.7
1.9
2.6
5
12
17
28
18
21
40
7
3
1,321
1,717
4,536
4,906
3,515
3,106
7,742
601
212
0.4
0.3
0.2
0.3
0.3
0.5
0.3
1.0
1.4
-1.6
-2.0
-0.4
0.1
-0.5
-0.5
-1.4
-0.6
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.4
0.0
1.4
2.0
1.4
1.7
0.6
2.1
3.1
1,025
64
31
13
357,005
31,283
10,703
1,790
0.3
0.3
0.4
0.2
0.0
0.1
0.2
-0.4
0.0
0.1
0.0
-0.1
1.9
2.1
2.3
1.4
34
157
316
371
255
7,345
60,158
129,305
137,759
66,214
0.4
0.2
0.2
0.3
0.4
-0.1
0.4
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
2.0
2.3
1.8
1.8
2.1
Disproportionate share patient
percentage (DSHPP)
DSHPP=O%
DSHPP<5%
DSH PP 5%-10%
DSHPP 10%-20%
DSH PP greater than 20%
1This column includes the impact of the updates in columns (4), (5), and (6) above, and of the IRF market basket
increase factor for FY 2017 (2. 7 percent), reduced by 0.3 percentage point for the productivity adjustment as
required by section 1886G)(3)(C)(ii)(I) of the Act, and reduced by 0.75 percentage point in accordance with sections
1886G)(3)(C)(ii)(II) and -(D)(v) of the Act.
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Outlier
(4)
0.3
0.5
0.4
0.1
0.0
0.2
0.3
0.4
0.4
0.4
0.3
0.3
0.3
F¥2017
CBSA
wage index
and laborshare
(5)
0.0
0.0
-0.6
0.1
-1.6
-0.1
-0.9
0.2
-0.7
-0.4
-1.3
0.1
-0.8
Federal Register / Vol. 81, No. 151 / Friday, August 5, 2016 / Rules and Regulations
3. 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 22.
For the FY 2017 IRF PPS proposed
rule, we used preliminary FY 2015 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
2.8 percent in FY 2016 (81 FR 24178,
24193). As we typically do between the
proposed and final rules each year, we
updated our FY 2015 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
estimated IRF payments are 2.7 percent
in FY 2016. Thus, we are adjusting the
outlier threshold amount in this final
rule to set total estimated outlier
payments equal to 3 percent of total
estimated payments in FY 2017. The
estimated change in total IRF payments
for FY 2017, therefore, includes an
approximate 0.3 percent increase in
payments because the estimated outlier
portion of total payments is estimated to
increase from approximately 2.7 percent
to 3 percent.
The impact of this outlier adjustment
update (as shown in column 4 of Table
22) is to increase estimated overall
payments to IRFs by about 0.3 percent.
We estimate the largest increase in
payments from the update to the outlier
threshold amount to be 1.4 percent for
rural IRFs in the Pacific region.
4. Impact of the CBSA Wage Index and
Labor-Related Share
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In column 5 of Table 22, 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.C. of this final
rule, we will decrease the labor-related
share from 71.0 percent in FY 2016 to
70.9 percent in FY 2017.
5. Impact of the Update to the CMG
Relative Weights and Average Length of
Stay Values
In column 6 of Table 22, we present
the effects of the budget-neutral update
of the CMG relative weights and average
length of stay values. In the aggregate,
we do not estimate that these updates
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will affect overall estimated payments of
IRFs. However, we do expect these
updates to have small distributional
effects. The largest estimated increase in
payments is a 0.1 percent increase for
rural IRFs in the Middle Atlantic region,
and urban IRFs in the New England and
East North Central regions. Rural IRFs in
the Pacific region and urban IRFs in the
East south Central regions are estimated
to experience a 0.1 percent decrease in
payments due to the CMG relative
weights change.
6. Effects of Requirements for the IRF
QRP for FY 2018
In accordance with section 1886(j)(7)
of the Act, we will implement a 2
percentage point reduction in the FY
2018 increase factor for IRFs that have
failed to report the required quality
reporting data to us during the most
recent IRF quality reporting period. In
section VIII.P of this final rule, we
discuss the proposed method for
applying the 2 percentage point
reduction to IRFs that fail to meet the
IRF QRP requirements. At the time that
this analysis was prepared, 91, or
approximately 8 percent, of the 1166
active Medicare-certified IRFs did not
receive the full annual percentage
increase for the FY 2016 annual
payment update determination.
Information is not available to
determine the precise number of IRFs
that will not meet the requirements to
receive the full annual percentage
increase for the FY 2017 payment
determination.
In section VIII.L of this final rule, we
discuss our proposal to suspend the
previously finalized data accuracy
validation policy for IRFs. While we
cannot estimate the change in the
number of IRFs that will meet IRF QRP
compliance standards at this time, we
believe that this number will increase
due to the temporary suspension of this
policy. Thus, we estimate that the
suspension of this policy will decrease
impact on overall IRF payments, by
increasing the rate of compliance, in
addition to decreasing the cost of the
IRF QRP to each IRF provider by
approximately $47,320 per IRF, which
was the estimated cost to each IRF
provider to the implement the
previously finalized policy.
In section VIII.F of this final rule, we
are finalizing four measures for the FY
2018 payment determinations and
subsequent years: (1) MSPB–PAC IRF
QRP; (2) Discharge to Community-PAC
IRF QRP, and (3) Potentially Preventable
30-Day Post-Discharge Readmission
Measure for IRF QRP; (4) Potentially
Preventable Within Stay Readmission
Measure IRFs. These four measures are
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52139
Medicare claims-based measures;
because claims-based measures can be
calculated based on data that are already
reported to the Medicare program for
payment purposes, we believe there will
be no additional impact.
In section VIII.G of this final rule, we
are also finalizing one measure for the
FY 2020 payment determination and
subsequent years: Drug Regimen Review
Conducted with Follow-Up for
Identified Issues-PAC IRF QRP.
Additionally, data for this measure will
be collected and reported using the IRF–
PAI (version effective October 1, 2018).
While the reporting of data on quality
measures is an information collection,
we believe that the burden associated
with modifications to the IRF–PAI
discussed in this final rule fall under
the PRA exceptions provided in
1899B(m) of the Act because they are
required to achieve the standardization
of patient assessment data. Section
1899B(m) of the Act provides that the
PRA does not apply to section 1899B
and the sections referenced in section
1899B(a)(2)(B) of the Act that require
modification to achieve the
standardization of patient assessment
data. The requirement and burden will,
however, be submitted to OMB for
review and approval when the
modifications to the IRF–PAI or other
applicable PAC assessment instrument
are not used to achieve the
standardization of patient assessment
data.
The total cost related to the proposed
measures is estimated at $4,625.46 per
IRF annually, or $5,231,398.17 for all
IRFs annually.
We intend to continue to closely
monitor the effects of this new quality
reporting program on IRF providers and
help perpetuate successful reporting
outcomes through ongoing stakeholder
education, national trainings, IRF
provider announcements, Web site
postings, CMS Open Door Forums, and
general and technical help desks.
We did not receive any comments
related to the Effects of Proposed
Requirements for the IRF QRP for FY
2018.
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 Thus, we did not consider
alternatives to updating payments using
the estimated IRF market basket
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Federal Register / Vol. 81, No. 151 / Friday, August 5, 2016 / Rules and Regulations
increase factor for FY 2017. However, 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 2017, and
sections 1886(j)(3)(C)(ii)(II) and
1886(j)(3)(D)(v) of the Act require the
Secretary to apply a 0.75 percentage
point reduction to the market basket
increase factor for FY 2017. Thus, in
accordance with section 1886(j)(3)(C) of
the Act, we update the IRF federal
prospective payments in this final rule
by 1.65 percent (which equals the 2.7
percent estimated IRF market basket
increase factor for FY 2017 reduced by
a 0.3 percentage point productivity
adjustment as required by section
1886(j)(3)(C)(ii)(I) of the Act and further
reduced by 0.75 percentage point). We
considered maintaining the existing
CMG relative weights and average
length of stay values for FY 2017.
However, in light of recently available
data and our desire to ensure that the
CMG relative weights and average
length of stay values are as reflective as
possible of recent changes in IRF
utilization and case mix, we believe that
it is appropriate to update the CMG
relative weights and average length of
stay values at this time to ensure that
IRF PPS payments continue to reflect as
accurately as possible the current costs
of care in IRFs.
We considered updating facility-level
adjustment factors for FY 2017.
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 maintaining the
existing outlier threshold amount for FY
2017. However, analysis of updated FY
2015 data indicates that estimated
outlier payments would be lower than 3
percent of total estimated payments for
FY 2017, by approximately 0.3 percent,
unless we updated the outlier threshold
amount. Consequently, we are adjusting
the outlier threshold amount in this
final rule to reflect a 0.3 percent
increase thereby setting the total outlier
payments equal to 3 percent, instead of
2.7 percent, of aggregate estimated
payments in FY 2017.
E. Accounting Statement
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 23, we have prepared an
accounting statement showing the
classification of the expenditures
associated with the provisions of this
final rule. Table 23 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 1,133 IRFs in
our database. In addition, Table 23
presents the costs associated with the
new IRF quality reporting program for
FY 2017.
TABLE 23—ACCOUNTING STATEMENT: CLASSIFICATION OF ESTIMATED EXPENDITURES
Category
Transfers
Change in Estimated Transfers from FY 2016 IRF PPS to FY 2017 IRF PPS
Annualized Monetized Transfers ..............................................................
From Whom to Whom? ............................................................................
$145 million.
Federal Government to IRF Medicare Providers.
Category
Costs
FY 2017 Cost to Updating the Quality Reporting Program
Cost for IRFs to Submit Data for the Quality Reporting Program ...........
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F. Conclusion
Overall, the estimated payments per
discharge for IRFs in FY 2017 are
projected to increase by 1.9 percent,
compared with the estimated payments
in FY 2016, as reflected in column 7 of
Table 22.
IRF payments per discharge are
estimated to increase by 2.0 percent in
urban areas and 1.2 percent in rural
areas, compared with estimated FY 2016
payments. Payments per discharge to
rehabilitation units are estimated to
increase 2.2 percent in urban areas and
1.5 percent in rural areas. Payments per
discharge to freestanding rehabilitation
hospitals are estimated to increase 1.8
percent in urban areas and 0.0 percent
in rural areas.
Overall, IRFs are estimated to
experience a net increase in payments
as a result of the proposed policies in
this final rule. The largest payment
increase is estimated to be a 3.1 percent
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$5,231,398.17.
increase for rural IRFs located in the
Pacific region.
In accordance with the provisions of
Executive Order 12866, this final rule
was reviewed by the Office of
Management and Budget.
1395hh), sec. 124 of Pub. L. 106–113 (113
Stat. 1501A–332), sec. 1206 of Pub. L. 113–
67, and sec. 112 of Pub. L. 113–93.
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:
§ 412.634 Requirements under the
Inpatient Rehabilitation Facility (IRF) Quality
Reporting Program (QRP).
PART 412—PROSPECTIVE PAYMENT
SYSTEMS FOR INPATIENT HOSPITAL
SERVICES
1. The authority citation for part 412
continues to read as follows:
■
Authority: Secs. 1102 and 1871 of the
Social Security Act (42 U.S.C. 1302 and
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2. Section 412.634 is amended by
revising paragraph (c)(2) and adding
paragraph (f) to read as follows:
■
*
*
*
*
*
(c) * * *
(2) An IRF must request an exception
or extension within 90 days of the date
that the extraordinary circumstances
occurred.
*
*
*
*
*
(f) Data Completion Thresholds. (1)
IRFs must meet or exceed two separate
data completeness thresholds: One
threshold set at 95 percent for
completion of quality measures data
collected using the IRF–PAI submitted
through the QIES and a second
threshold set at 100 percent for quality
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Federal Register / Vol. 81, No. 151 / Friday, August 5, 2016 / Rules and Regulations
measures data collected and submitted
using the CDC NHSN.
(2) These thresholds will apply to all
measures adopted into IRF QRP.
(3) An IRF must meet or exceed both
thresholds to avoid receiving a 2
percentage point reduction to their
annual payment update for a given
fiscal year, beginning with FY 2016 and
for all subsequent payment updates.
52141
Dated: July 18, 2016.
Andrew M. Slavitt,
Acting Administrator, Centers for Medicare
& Medicaid Services.
Dated: July 25, 2016.
Sylvia M. Burwell,
Secretary, Department of Health and Human
Services.
[FR Doc. 2016–18196 Filed 7–29–16; 4:15 pm]
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BILLING CODE 4120–01–P
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Agencies
[Federal Register Volume 81, Number 151 (Friday, August 5, 2016)]
[Rules and Regulations]
[Pages 52055-52141]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2016-18196]
[[Page 52055]]
Vol. 81
Friday,
No. 151
August 5, 2016
Part III
Department of Health and Human Services
-----------------------------------------------------------------------
Centers for Medicare & Medicaid Services
-----------------------------------------------------------------------
42 CFR Part 412
Medicare Program; Inpatient Rehabilitation Facility Prospective Payment
System for Federal Fiscal Year 2017; Final Rule
Federal Register / Vol. 81 , No. 151 / Friday, August 5, 2016 / Rules
and Regulations
[[Page 52056]]
-----------------------------------------------------------------------
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Medicare & Medicaid Services
42 CFR Part 412
[CMS-1647-F]
RIN 0938-AS78
Medicare Program; Inpatient Rehabilitation Facility Prospective
Payment System for Federal Fiscal Year 2017
AGENCY: Centers for Medicare & Medicaid Services (CMS), HHS.
ACTION: Final rule.
-----------------------------------------------------------------------
SUMMARY: This final rule will update the prospective payment rates for
inpatient rehabilitation facilities (IRFs) for federal fiscal year (FY)
2017 as required by the statute. As required by section 1886(j)(5) of
the Act, this rule includes the classification and weighting factors
for the IRF prospective payment system's (IRF PPS's) case-mix groups
and a description of the methodologies and data used in computing the
prospective payment rates for FY 2017. This final rule also revises and
updates quality measures and reporting requirements under the IRF
quality reporting program (QRP).
DATES:
Effective Dates: These regulations are effective on October 1,
2016.
Applicability Dates: The updated IRF prospective payment rates are
applicable for IRF discharges occurring on or after October 1, 2016,
and on or before September 30, 2017 (FY 2017). The updated quality
measures and reporting requirements under the IRF QRP are effective for
IRF discharges occurring on or after October 1, 2016.
FOR FURTHER INFORMATION CONTACT: Gwendolyn Johnson, (410) 786-6954, for
general information. Catie Kraemer, (410) 786-0179, for information
about the wage index. Christine Grose, (410) 786-1362, for information
about the quality reporting program. Kadie Derby, (410) 786-0468, or
Susanne Seagrave, (410) 786-0044, for information about the payment
policies and payment rates.
SUPPLEMENTARY INFORMATION: The IRF PPS Addenda along with other
supporting documents and tables referenced in this final rule are
available through the Internet on the CMS Web site 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 2017 (that is, for discharges occurring on or after October 1, 2016,
and on or before September 30, 2017) 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 rule includes the classification
and weighting factors for the IRF PPS's case-mix groups and a
description of the methodologies and data used in computing the
prospective payment rates for FY 2017. This final rule also finalizes
revisions and updates to the quality measures and reporting
requirements under the IRF QRP.
B. Summary of Major Provisions
In this final rule, we use the methods described in the FY 2016 IRF
PPS final rule (80 FR 47036) to update the federal prospective payment
rates for FY 2017 using updated FY 2015 IRF claims and the most recent
available IRF cost report data, which is FY 2014 IRF cost report data.
We are also finalizing revisions and updates to the quality measures
and reporting requirements under the IRF QRP.
C. Summary of Impacts
------------------------------------------------------------------------
Provision description Transfers
------------------------------------------------------------------------
FY 2017 IRF PPS payment rate The overall economic impact of this final
update. rule is an estimated $145 million in
increased payments from the Federal
government to IRFs during FY 2017.
------------------------------------------------------------------------
Provision description Costs
------------------------------------------------------------------------
New quality reporting program The total costs in FY 2017 for IRFs as a
requirements. result of the new quality reporting
requirements are estimated to be
$5,231,398.17.
------------------------------------------------------------------------
To assist readers in referencing sections contained in this
document, we are providing the following Table of Contents.
Table of Contents
I. Background
A. Historical Overview of the IRF PPS
B. Provisions of the Affordable Care Act Affecting the IRF PPS
in FY 2012 and Beyond
C. Operational Overview of the Current IRF PPS
D. Advancing Health Information Exchange
II. Summary of Provisions of the Proposed Rule
III. Analysis and Responses to Public Comments
IV. Update to the Case-Mix Group (CMG) Relative Weights and Average
Length of Stay Values for FY 2017
V. Facility-Level Adjustment Factors
VI. FY 2017 IRF PPS Payment Update
A. Background
B. FY 2017 Market Basket Update and Productivity Adjustment
C. Labor-Related Share for FY 2017
D. Wage Adjustment
E. Description of the IRF Standard Payment Conversion Factor and
Payment Rates for FY 2017
F. Example of the Methodology for Adjusting the Federal
Prospective Payment Rates
VII. Update to Payments for High-Cost Outliers Under the IRF PPS
A. Update to the Outlier Threshold Amount for FY 2017
B. Update to the IRF Cost-to-Charge Ratio Ceiling and Urban/
Rural Averages
VIII. Revisions and Updates to the IRF Quality Reporting Program
(QRP)
A. Background and Statutory Authority
B. General Considerations Used for Selection of Quality,
Resource Use, and Other Measures for the IRF QRP
C. Policy for Retention of IRF QRP Measures Adopted for Previous
Payment Determinations
D. Policy for Adopting Changes to IRF QRP Measures
E. Quality Measures Previously Finalized for and Currently Used
in the IRF QRP
F. IRF QRP Quality, Resource Use and Other Measures Finalized
for the FY 2018 Payment Determination and Subsequent Years
G. IRF QRP Quality Measure Finalized for the FY 2020 Payment
Determination and Subsequent Years
H. IRF QRP Quality Measures and Measure Concepts Under
Consideration for Future Years
I. Form, Manner, and Timing of Quality Data Submission for the
FY 2018 Payment Determination and Subsequent Years
J. IRF QRP Data Completion Thresholds for the FY 2016 Payment
Determination and Subsequent Years
K. IRF QRP Data Validation Process for the FY 2016 Payment
Determination and Subsequent Years
[[Page 52057]]
L. Previously Adopted and Codified IRF QRP Submission Exception
and Extension Policies
M. Previously Adopted and Finalized IRF QRP Reconsideration and
Appeals Procedures
N. Public Display of Measure Data for the IRF QRP & Procedures
for the Opportunity to Review and Correct Data and Information
O. Mechanism for Providing Feedback Reports to IRFs
P. Method for Applying the Reduction to the FY 2017 IRF Increase
Factor for IRFs That Fail To Meet the Quality Reporting Requirements
IX. Miscellaneous Comments
X. Provisions of the Final Regulations
XI. Collection of Information Requirements
A. Statutory Requirement for Solicitation of Comments
B. Collection of Information Requirements for Updates Related to
the IRF QRP
XII. Regulatory Impact Analysis
A. Statement of Need
B. Overall Impacts
C. Detailed Economic Analysis
D. Alternatives Considered
E. Accounting Statement
F. Conclusion
Acronyms, Abbreviations, and Short Forms
Because of the many terms to which we refer by acronym,
abbreviation, or short form in this final rule, we are listing the
acronyms, abbreviation, and short forms used and their corresponding
terms in alphabetical order.
The Act The Social Security Act
ADC Average Daily Census
ADE Adverse Drug Events
The Affordable Care Act Patient Protection and Affordable Care Act
(Pub. L. 111-148, enacted on March 23, 2010)
AHRQ Agency for Healthcare Research and Quality
APU Annual Payment Update
ASAP Assessment Submission and Processing
ASCA The Administrative Simplification Compliance Act of 2002 (Pub.
L. 107-105, enacted on December 27, 2002)
ASPE Office of the Assistant Secretary for Planning and Evaluation
BLS U.S. Bureau of Labor Statistics
BMI Body Mass Index
CAH Critical Access Hospitals
CASPER Certification and Survey Provider Enhanced Reports
CAUTI Catheter-Associated Urinary Tract Infection
CBSA Core-Based Statistical Area
CCR Cost-to-Charge Ratio
CDC The Centers for Disease Control and Prevention
CDI Clostridium difficile Infection
CFR Code of Federal Regulations
CMG Case-Mix Group
CMS Centers for Medicare & Medicaid Services
COA Care for Older Adults
CY Calendar year
DSH Disproportionate Share Hospital
DSH PP Disproportionate Share Patient Percentage
DRG Diagnosis-Related Group
eCQMs Electronically Specified Clinical Quality Measures
ESRD End-Stage Renal Disease
FFS Fee-for-Service
FR Federal Register
FY Federal Fiscal Year
GEMS General Equivalence Mapping
GPCI Geographic Practice Cost Index
HAI Healthcare Associated Infection
HCC Hierarchical Condition Category
HHA Home Health Agencies
HCP Home Care Personnel
HHS U.S. Department of Health & Human Services
HIPAA Health Insurance Portability and Accountability Act of 1996
(Pub. L. 104-191, enacted on August 21, 1996)
Hospital VBP Hospital Value-Based Purchasing Program (also HVBP)
ICD-9-CM International Classification of Diseases, 9th Revision,
Clinical Modification
ICD-10-CM International Classification of Diseases, 10th Revision,
Clinical Modification
IGC Impairment Group Code
IGI IHS Global Insight
IMPACT Act Improving Medicare Post-Acute Care Transformation Act of
2014 (Pub. L. 113-185, enacted on October 6, 2014)
IME Indirect Medical Education
IPF Inpatient Psychiatric Facility
IPPS Inpatient prospective payment system
IQR Inpatient Quality Reporting Program
IRF Inpatient Rehabilitation Facility
IRF-PAI Inpatient Rehabilitation Facility-Patient Assessment
Instrument
IRF PPS Inpatient Rehabilitation Facility Prospective Payment System
IRF QRP Inpatient Rehabilitation Facility Quality Reporting Program
IRVEN Inpatient Rehabilitation Validation and Entry
LIP Low-Income Percentage
IVS Influenza Vaccination Season
LTCH Long-Term Care Hospital
MA (Medicare Part C) Medicare Advantage
MAC Medicare Administrative Contractor
MAP Measures Application Partnership
MedPAC Medicare Payment Advisory Commission
MFP Multifactor Productivity
MMSEA Medicare, Medicaid, and SCHIP Extension Act of 2007 (Pub. L.
110-173, enacted on December 29, 2007)
MRSA Methicillin-Resistant Staphylococcus aureus
MSPB Medicare Spending per Beneficiary
MUC Measures under Consideration
NHSN National Healthcare Safety Network
NQF National Quality Forum
OMB Office of Management and Budget
ONC Office of the National Coordinator for Health Information
Technology
OPPS/ASC Outpatient Prospective Payment System/Ambulatory Surgical
Center
PAC Post-Acute Care
PAC/LTC Post-Acute Care/Long-Term Care
PAI Patient Assessment Instrument
PPR Potentially Preventable Readmissions
PPS Prospective Payment System
PRA Paperwork Reduction Act of 1995 (Pub. L. 104-13, enacted on May
22, 1995)
QIES Quality Improvement Evaluation System
QM Quality Measure
QRP Quality Reporting Program
RIA Regulatory Impact Analysis
RIC Rehabilitation Impairment Category
RFA Regulatory Flexibility Act (Pub. L. 96-354, enacted on September
19, 1980)
RN Registered Nurse
RPL Rehabilitation, Psychiatric, and Long-Term Care market basket
RSRR Risk-standardized readmission rate
SIR Standardized Infection Ratio
SNF Skilled Nursing Facilities
SRR Standardized Risk Ratio
SSI Supplemental Security Income
TEP Technical Expert Panel
I. Background
A. Historical Overview of the IRF PPS
Section 1886(j) of the Act provides for the implementation of a
per-discharge prospective payment system (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 below a
general description of the IRF PPS for FYs 2002 through 2016.
Under the IRF PPS from FY 2002 through FY 2005 the federal
prospective payment rates were computed across 100 distinct case-mix
groups (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
[[Page 52058]]
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 federal 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 federal 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.
We established a CMS Web site as a primary information resource for
the IRF PPS which is available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/. The Web site
may be accessed to download or view publications, software, data
specifications, educational materials, and other information pertinent
to the IRF PPS.
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) that we published on September
30, 2005, 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, revision and rebasing of
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 (70 FR
47880 and 70 FR 57166).
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 (71 FR 48354).
In the FY 2008 IRF PPS final rule (72 FR 44284), we updated the
federal 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 implemented for FY 2008, please refer to the FY 2008 IRF PPS
final rule (72 FR 44284), in which we published the final FY 2008 IRF
federal prospective payment rates. 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 on 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 federal prospective payment rates for each FY. Based on
the legislative change to the increase factor, we revised the FY 2008
federal prospective payment rates for IRF discharges occurring on or
after April 1, 2008. Thus, the final FY 2008 IRF federal 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
federal 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 federal prospective payment rates are
available on the CMS Web site 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 length of stay 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 (73 FR 46370), in
which we published the final FY 2009 IRF federal prospective payment
rates.
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) that we
published on October 1, 2009, we updated the federal prospective
payment rates, the CMG relative weights, the average length of stay
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
[[Page 52059]]
to the FY 2010 IRF PPS final rule (74 FR 39762 and 74 FR 50712), in
which we published the final FY 2010 IRF federal prospective payment
rates.
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 on 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 on March 30, 2010)
(collectively, hereinafter referred to as ``The Affordable Care Act''),
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 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 Affordable Care
Act, 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 federal 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 federal
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 federal prospective payment rates applied to discharges
occurring on or after April 1, 2010, and on or before September 30,
2010. The adjusted FY 2010 federal prospective payment rates are
available on the CMS Web site at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Data-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
are 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
2011 and FY 2010 IRF PPS federal 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
federal prospective payment rates, the CMG relative weights, and the
average length of stay 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 (75 FR 42836 and 75 FR 70013).
In the FY 2012 IRF PPS final rule (76 FR 47836), we updated the IRF
federal prospective payment rates, rebased and revised the RPL market
basket, and established a new quality reporting program for IRFs in
accordance with section 1886(j)(7) of the Act. We also revised
regulation text for the purpose of updating and providing greater
clarity. For more information on the policy changes implemented for FY
2012, please refer to the FY 2012 IRF PPS final rule (76 FR 47836), in
which we published the final FY 2012 IRF federal prospective payment
rates.
The FY 2013 IRF PPS notice (77 FR 44618) described the required
adjustments to the FY 2013 federal prospective 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 federal prospective payment rates, the CMG relative
weights, and the average length of stay values. For more information on
the updates for FY 2013, please refer to the FY 2013 IRF PPS notice (77
FR 44618).
In the FY 2014 IRF PPS final rule (78 FR 47860), we updated the
federal 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 Inpatient Rehabilitation Facility-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 revised and updated quality measures and
reporting requirements under the IRF quality reporting program. For
more information on the policy changes implemented for FY 2014, please
refer to the FY 2014 IRF PPS final rule (78 FR 47860), in which we
published the final FY 2014 IRF federal prospective payment rates.
In the FY 2015 IRF PPS final rule (79 FR 45872), we updated the
federal prospective payment rates, the CMG relative weights, and the
outlier threshold amount. We also further 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 revised and updated quality measures and reporting
requirements under the IRF quality reporting program. For more
information on the policy changes implemented for FY 2015, please refer
to the FY 2015 IRF PPS final rule (79 FR 45872) and the FY 2015 IRF PPS
correction notice (79 FR 59121).
In the FY 2016 IRF PPS final rule (80 FR 47036), we updated the
federal 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 one-
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 revisions and updates to
the IRF QRP. For more information on the policy changes implemented for
[[Page 52060]]
FY 2016, please refer to the FY 2016 IRF PPS final rule (80 FR 47036).
B. Provisions of the Affordable Care Act Affecting the IRF PPS in FY
2012 and Beyond
The Affordable Care Act 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 Affordable Care Act also added
section 1886(j)(3)(C)(ii)(I) (providing for a ``productivity
adjustment'' for fiscal year 2012 and each subsequent fiscal year). The
productivity adjustment for FY 2017 is discussed in section VI.B. of
this final rule. Section 3401(d) of the Affordable Care Act requires an
additional 0.75 percentage point adjustment to the IRF increase factor
for each of FYs 2017, 2018, and 2019. The applicable adjustment for FY
2017 is discussed in section VI.B. of this final rule. Section
1886(j)(3)(C)(ii)(II) of the Act notes that the application of these
adjustments 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.
Section 3004(b) of the Affordable Care Act also addressed the IRF PPS
program. It reassigned the previously designated section 1886(j)(7) of
the Act to section 1886(j)(8) and inserted a new section 1886(j)(7),
which contains requirements for the Secretary to establish a quality
reporting program 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 of the applicable market
basket increase factor for IRFs that fail to comply with the quality
data submission requirements. 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 will not be cumulative; they will
only apply for the FY involved.
Under section 1886(j)(7)(D)(i) and (ii) of the Act, the Secretary
is generally required to select quality measures for the IRF quality
reporting program from those that have been endorsed by the consensus-
based entity which holds a performance measurement contract under
section 1890(a) of the Act. This contract is currently held by the
National Quality Forum (NQF). So long as due consideration is given to
measures that have been endorsed or adopted by a consensus-based
organization, section 1886(j)(7)(D)(ii) of the Act authorizes the
Secretary to select non-endorsed measures for specified areas or
medical topics when there are no feasible or practical endorsed
measure(s).
Section 1886(j)(7)(E) of the Act requires the Secretary to
establish procedures for making the IRF PPS quality reporting data
available to the public. In so doing, the Secretary must ensure that
IRFs have the opportunity to review any such data prior to its release
to the public.
C. Operational Overview of the Current IRF PPS
As described in the FY 2002 IRF PPS final rule, 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 patient
assessment instrument (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) (formerly called Medicare Part C) patient, as described in the FY
2010 IRF PPS final rule. 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 5-character CMG number. The first
character is an alphabetic character that indicates the comorbidity
tier. The last 4 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 Web site at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Software.html.
Once a Medicare FFS Part A patient is discharged, the IRF submits a
Medicare claim as a Health Insurance Portability and Accountability Act
of 1996 (Pub. L. 104-191, enacted on August 21, 1996) (HIPAA) compliant
electronic claim or, if the Administrative Simplification Compliance
Act of 2002 (Pub. L. 107-105, enacted on 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 Medicare Advantage
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 Medicare Advantage days are included in the hospital's
Supplemental Security Income (SSI) ratio (used in calculating the IRF
low-income percentage adjustment) for fiscal year 2007 and beyond.
Claims submitted to Medicare must comply with both ASCA and HIPAA.
Section 3 of the ASCA amends 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, parts 160 and 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
[[Page 52061]]
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 & Human Services (HHS) has a number of
initiatives designed to encourage and support the adoption of health
information technology and to promote nationwide health information
exchange to improve health care. As discussed in the August 2013
Statement ``Principles and Strategies for Accelerating Health
Information Exchange'' (available at https://www.healthit.gov/sites/default/files/acceleratinghieprinciples_strategy.pdf). HHS believes
that all individuals, their families, their healthcare and social
service providers, and payers should have consistent and timely access
to health information in a standardized format that can be securely
exchanged between the patient, providers, and others involved in the
individual's care. Health IT that facilitates the secure, efficient,
and effective sharing and use of health-related information when and
where it is needed is an important tool for settings across the
continuum of care, including inpatient rehabilitation facilities. The
effective adoption and use of health information exchange and health IT
tools will be essential as IRFs seek to improve quality and lower costs
through value-based care.
The Office of the National Coordinator for Health Information
Technology (ONC) has released a document entitled ``Connecting Health
and Care for the Nation: A Shared Nationwide Interoperability Roadmap''
(available at https://https://www.healthit.gov/sites/default/files/hie-interoperability/nationwide-interoperability-roadmap-final-version-1.0.pdf). In the near term, the Roadmap focuses on actions that will
enable individuals and providers across the care continuum to send,
receive, find, and use a common set of electronic clinical information
at the nationwide level by the end of 2017. The Roadmap's goals also
align with the Improving Medicare Post-Acute Care Transformation Act of
2014 (Pub. L. 113-185, enacted on October 6, 2014) (IMPACT Act), which
requires assessment data to be standardized and interoperable to allow
for exchange of the data.
The Roadmap identifies four critical pathways that health IT
stakeholders should focus on now in order to create a foundation for
long-term success: (1) Improve technical standards and implementation
guidance for priority data domains and associated elements; (2) rapidly
shift and align federal, state, and commercial payment policies from
FFS to value-based models to stimulate the demand for interoperability;
(3) clarify and align federal and state privacy and security
requirements that enable interoperability; and (4) align and promote
the use of consistent policies and business practices that support
interoperability, in coordination with stakeholders. In addition, ONC
has released the final version of the 2016 Interoperability Standards
Advisory (available at https://www.healthit.gov/standards-advisory/2016), which provides a list of the best available standards and
implementation specifications to enable priority health information
exchange functions. Providers, payers, and vendors are encouraged to
take these ``best available standards'' into account as they implement
interoperable health information exchange across the continuum of care,
including care settings such as inpatient rehabilitation facilities.
We encourage stakeholders to utilize health information exchange
and certified health IT to effectively and efficiently help providers
improve internal care delivery practices, engage patients in their
care, support management of care across the continuum, enable the
reporting of electronically specified clinical quality measures
(eCQMs), and improve efficiencies and reduce unnecessary costs. As
adoption of certified health IT increases and interoperability
standards continue to mature, HHS will seek to reinforce standards
through relevant policies and programs. We received one comment on
health information exchange, which is summarized below.
Comment: A commenter stated that the rule focuses only on
providers, vendors, and institutions, not individuals and that sharing
information requires standardized data exchange. The commenter
suggested that CMS add a system-wide measure to assess whether robust
data standards, policies, and governance infrastructure exists to
support widespread industry and individual participation.
Response: We agree with the commenter that all individuals,
families, and healthcare providers should have consistent and timely
access to health information, in accordance with applicable law, in a
standardized format that can be securely exchanged to support the
health and wellness of individuals and shared decision-making. We agree
nationwide interoperability across the care continuum will require
stakeholders to agree to and follow a common set of standards,
services, policies and practices that facilitates the exchange and use
of interoperable health information. ONC recently requested comment on
system-wide measures of interoperability required under the Medicare
Access and CHIP Reauthorization Act of 2015 (81 FR 20651, https://federalregister.gov/a/2016-08134).
II. Summary of Provisions of the Proposed Rule
In the FY 2017 IRF PPS proposed rule (81 FR 24178), we proposed to
update the IRF federal prospective payment rates for FY 2017 and to
revise and update quality measures and reporting requirements under the
IRF QRP.
The proposed updates to the IRF federal prospective payment rates
for FY 2017 were as follows:
Update the FY 2017 IRF PPS relative weights and average
length of stay values using the most current and complete Medicare
claims and cost report data in a budget-neutral manner, as discussed in
section III of the FY 2017 IRF PPS proposed rule (81 FR 24178, 24184
through 24187).
Describe the continued use of FY 2014 facility-level
adjustment factors as discussed in section IV of the FY 2017 IRF PPS
proposed rule (81 FR 24178 at 24187).
Update the FY 2017 IRF PPS payment rates by the proposed
market basket increase factor, based upon the most current data
available, with a 0.75 percentage point reduction as required by
sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(v) of the Act and a
proposed productivity adjustment required by section
1886(j)(3)(C)(ii)(I) of the Act, as described in section V of the FY
2017 IRF PPS proposed rule (81 FR 24178, 24187 through 24189).
Update the FY 2017 IRF PPS payment rates by the FY 2017
wage index and the labor-related share in a budget-neutral manner, as
discussed in section V of the FY 2017 IRF PPS proposed rule (81 FR
24178, 24189 through 24190).
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Describe the calculation of the IRF standard payment
conversion factor for FY 2017, as discussed in section V of the FY 2017
IRF PPS proposed rule (81 FR 24178, 24190 through 24192).
Update the outlier threshold amount for FY 2017, as
discussed in section VI of the FY 2017 IRF PPS proposed rule (81 FR
24178, at 24193).
Update the cost-to-charge ratio (CCR) ceiling and urban/
rural average CCRs for FY 2017, as discussed in section VI of the FY
2017 IRF PPS proposed rule (81 FR 24178, 24193 through 24194).
Describe proposed revisions and updates to quality
measures and reporting requirements under the quality reporting program
for IRFs in accordance with section 1886(j)(7) of the Act, as discussed
in section VII of the FY 2017 IRF PPS proposed rule (81 FR 24194
through 24220).
III. Analysis and Responses to Public Comments
We received 61 timely responses from the public, many of which
contained multiple comments on the FY 2017 IRF PPS proposed rule (81 FR
24178). We received comments from various trade associations, inpatient
rehabilitation facilities, 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. Update to the Case-Mix Group (CMG) Relative Weights and Average
Length of Stay Values for FY 2017
As specified in Sec. 412.620(b)(1), we calculate a relative weight
for each CMG that is proportional to the resources needed by an average
inpatient rehabilitation case in that CMG. For example, cases in a CMG
with a relative weight of 2, on average, will cost twice as much as
cases in a CMG with a relative weight of 1. Relative weights account
for the variance in cost per discharge due to the variance in resource
utilization among the payment groups, and their use helps to ensure
that IRF PPS payments support beneficiary access to care, as well as
provider efficiency.
In the FY 2017 IRF PPS proposed rule (81 FR 24178, 24184 through
24187), we proposed to update the CMG relative weights and average
length of stay values for FY 2017. As required by statute, we always
use the most recent available data to update the CMG relative weights
and average lengths of stay. For FY 2017, we proposed to use the FY
2015 IRF claims and FY 2014 IRF cost report data. These data are the
most current and complete data available at this time.
We note that, as we typically do, we updated our data between the
FY 2017 IRF PPS proposed and final rules to ensure that we use the most
recent available data in calculating IRF PPS payments. This updated
data reflects a more complete set of claims for FY 2015 and additional
cost report data for FY 2014.
In the FY 2017 IRF PPS proposed rule, we proposed to apply these
data using the same methodologies that we have used to update the CMG
relative weights and average length of stay values each fiscal year
since we implemented an update to the methodology to use the more
detailed CCR data from the cost reports of IRF subprovider units of
primary acute care hospitals, instead of CCR data from the associated
primary care hospitals, to calculate IRFs' average costs per case, as
discussed in the FY 2009 IRF PPS final rule (73 FR 46372). 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. The process used to calculate the
CMG relative weights for this final rule is as follows:
Step 1. We estimate the effects that comorbidities have on costs.
Step 2. We adjust the cost of each Medicare discharge (case) to
reflect the effects found in the first step.
Step 3. We use the adjusted costs from the second step to calculate
CMG relative weights, using the hospital-specific relative value
method.
Step 4. We normalize the FY 2017 CMG relative weights to the same
average CMG relative weight from the CMG relative weights implemented
in the FY 2016 IRF PPS final rule (80 FR 47036).
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 CMG relative weights for FY 2017 in such a way that total
estimated aggregate payments to IRFs for FY 2017 are the same with or
without the changes (that is, 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
2017 CMG relative weights, we use the following steps:
Step 1. Calculate the estimated total amount of IRF PPS payments
for FY 2017 (with no changes to the CMG relative weights).
Step 2. Calculate the estimated total amount of IRF PPS payments
for FY 2017 by applying the changes to the CMG relative weights (as
discussed 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 (0.9992)
that would maintain the same total estimated aggregate payments in FY
2017 with and without the changes to the CMG relative weights.
Step 4. Apply the budget neutrality factor (0.9992) to the FY 2016
IRF PPS standard payment amount after the application of the budget-
neutral wage adjustment factor.
In section VI.E. of this final rule, we discuss the proposed use of
the existing methodology to calculate the standard payment conversion
factor for FY 2017.
In Table 1, ``Relative Weights and Average Length of Stay Values
for Case-Mix Groups,'' we present the CMGs, the comorbidity tiers, the
corresponding relative weights, and the average length of stay values
for each CMG and tier for FY 2017. The average length of stay 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.
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Generally, updates to the CMG relative weights result in some
increases and some decreases to the CMG relative weight values. Table 2
shows how we estimate that the application of the revisions for FY 2017
would affect particular CMG relative weight values, which would affect
the overall distribution of payments within CMGs and tiers. Note that,
because we proposed to implement the CMG relative weight revisions in a
budget-neutral manner (as previously described), total estimated
aggregate payments to IRFs for FY 2017 would not be affected as a
result of the proposed CMG relative weight revisions. However, the
proposed revisions would affect the distribution of payments within
CMGs and tiers.
Table 2--Distributional Effects of the Changes to the CMG Relative
Weights
[FY 2016 values compared with FY 2017 values]
------------------------------------------------------------------------
Percentage of
Percentage change Number of cases cases affected
affected (percent)
------------------------------------------------------------------------
Increased by 15% or more.......... 0 0.0
Increased by between 5% and 15%... 540 0.1
Changed by less than 5%........... 395,897 99.7
Decreased by between 5% and 15%... 761 0.2
Decreased by 15% or more.......... 41 0.0
------------------------------------------------------------------------
As Table 2 shows, 99.7 percent of all IRF cases are in CMGs and
tiers that would experience less than a 5 percent change (either
increase or decrease) in the CMG relative weight value as a result of
the revisions for FY 2017. The largest estimated increase in the CMG
relative weight values that affects the largest number of IRF
discharges would be a 0.7 percent change in the CMG relative weight
value for CMG 0604--Neurological, with a motor score less than 25.85--
in the ``no comorbidity'' tier. In the FY 2015 claims data, 8,572 IRF
discharges (2.2 percent of all IRF discharges) were classified into
this CMG and tier.
The largest decrease in a CMG relative weight value affecting the
largest number of IRF cases would be a 1.4 percent decrease in the CMG
relative weight for CMG 0110--Stroke, with a motor score less than
22.35 and age less than 84.5--in the ``no comorbidity'' tier. In the FY
2015 IRF claims data, this change would have affected 13,739 cases (3.5
percent of all IRF cases).
The proposed changes in the average length of stay values for FY
2017, compared with the FY 2016 average length of stay values, are
small and do not show any particular trends in IRF length of stay
patterns.
[[Page 52071]]
We received 3 comments on the proposed update to the CMG relative
weights and average length of stay values for FY 2017, which are
summarized below.
Comment: Commenters, while supportive of the methodology used to
calculate the weights, requested that we provide more detail about the
use of the CCR data in the CMG relative weight calculations.
Additionally, the commenters requested that we outline the methodology
used to calculate the average length of stay values in the FY 2017 IRF
PPS proposed rule.
Response: As we discussed, most recently, in the FY 2016 IRF PPS
final rule (80 FR 47036, 47045), a key variable used to calculate the
CMG relative weights is a facility's average cost per case, which is
obtained by averaging the estimated cost per case for every patient
discharged from the facility in a given fiscal year. To obtain the
estimated cost per case for a given IRF patient, we start by pulling
the appropriate charges from the Medicare claim for that patient. Then,
we calculate the appropriate CCRs from the Medicare cost report
submitted by the facility. The CCRs are then multiplied by the charges
from the Medicare claim to obtain the estimated IRF cost for the case.
This variable is used as the dependent variable in the regression
analysis to estimate the CMG relative weights.
As we also discussed in the FY 2016 IRF PPS final rule (80 FR
47036, 47045), the methodology for calculating the average length of
stay values is available for download from the IRF PPS Web site at
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research.html.
Final Decision: After careful consideration of the public comments,
we are finalizing our proposal to update the CMG relative weight and
average length of stay values for FY 2017, as shown in Table 1 of this
final rule. These updates are effective October 1, 2016.
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 federal 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 final rule (78 FR 47860, 47868
through 47872), in the FY 2015 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 2017, 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 2017 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, which is referred to as a market basket index.
According to section 1886(j)(3)(A)(i) of the Act, the increase factor
shall be used to update the IRF federal prospective payment rates for
each FY. Section 1886(j)(3)(C)(ii)(I) of the Act requires the
application of a productivity adjustment, as described below. In
addition, sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(v) of the
Act require the application of a 0.75 percentage point reduction to the
market basket increase factor for FY 2017. Thus, in the FY 2017 IRF PPS
proposed rule (81 FR 24178, 24187 through 24188), we proposed to update
the IRF PPS payments for FY 2017 by a market basket increase factor as
required by section 1886(j)(3)(C) of the Act, with a productivity
adjustment as required by section 1886(j)(3)(C)(ii)(I) of the Act, and
a 0.75 percentage point reduction as required by sections
1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(v) of the Act.
For FY 2015, IRF PPS payments were updated using the 2008-based RPL
market basket. Beginning with the FY 2016 IRF PPS, we created and
adopted a stand-alone IRF market basket, which was referred to as the
2012-based IRF market basket, reflecting the operating and capital cost
structures for freestanding IRFs and hospital-based IRFs. The general
structure of the 2012-based IRF market basket is similar to the 2008-
based RPL market basket; however, we made several notable changes. In
developing the 2012-based IRF market basket, we derived cost weights
from Medicare cost report data for both freestanding and hospital-based
IRFs (the 2008-based RPL market basket was based on freestanding data
only), incorporated the 2007 Input-Output data from the Bureau of
Economic Analysis (the 2008-based RPL market basket was based on the
2002 Input-Output data); used new price proxy blends for two cost
categories (Fuel, Oil, and Gasoline and Medical Instruments); added one
additional cost category (Installation, Maintenance, and Repair), which
was previously included in the residual All Other Services: Labor-
Related cost category of the 2008-based RPL market basket; and
eliminated three cost categories (Apparel, Machinery & Equipment, and
Postage). The FY 2016 IRF PPS final rule (80 FR 47046 through 47068)
contains a complete discussion of the development of the 2012-based IRF
market basket.
B. FY 2017 Market Basket Update and Productivity Adjustment
For FY 2017, we proposed to use the same methodology described in
the FY 2016 IRF PPS final rule (80 FR 47066) to compute the FY 2017
market basket increase factor 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 Insight's forecast
using the most recent available data. IHS Global Insight (IGI), Inc. is
a nationally recognized economic and financial forecasting firm with
which CMS contracts to forecast the components of the market baskets
and multifactor productivity (MFP).
Based on IGI's first quarter 2016 forecast with historical data
through the fourth quarter of 2015, we proposed that the projected
2012-based IRF market basket increase factor for FY 2017 would be 2.7
percent. We also proposed that if more recent data were subsequently
available (for example, a more recent estimate of the market basket
update), we would use such data to determine the FY 2017 update in the
final rule. Incorporating the most recent data available, based on
IGI's second quarter 2016 forecast with historical data through the
first quarter of 2016, the projected 2012-based IRF market basket
increase factor for FY 2017 is 2.7 percent.
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. 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
[[Page 52072]]
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. A complete description of the MFP
projection methodology is available on the CMS Web site at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch.html.
Using IGI's first quarter 2016 forecast, the proposed MFP
adjustment for FY 2017 (the 10-year moving average of MFP for the
period ending FY 2017) was 0.5 percent. We proposed that if more recent
data were subsequently available, we would use such data to determine
the FY 2017 MFP adjustment in the final rule. Incorporating the most
recent data available, based on IGI's second quarter 2016 forecast with
historical data through the first quarter of 2016, the projected MFP
adjustment for FY 2017 is 0.3 percent.
Thus, in accordance with section 1886(j)(3)(C) of the Act, we
proposed to base the FY 2017 market basket update, which is used to
determine the applicable percentage increase for the IRF payments, on
the most recent estimate of the 2012-based IRF market basket. We
proposed to then reduce this percentage increase by the most up-to-date
estimate of the MFP adjustment for FY 2017. Following application of
the MFP, we proposed to further reduce the applicable percentage
increase by 0.75 percentage point, as required by sections
1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(v) of the Act. Therefore, the
estimate of the FY 2017 IRF update for the proposed rule was 1.45
percent (2.7 percent market basket update, less 0.5 percentage point
MFP adjustment, less 0.75 percentage point legislative adjustment).
Incorporating the most recent data, the current estimate of the FY 2017
IRF update is 1.65 percent (2.7 percent market basket update, less 0.3
percentage point MFP adjustment, less 0.75 percentage point legislative
adjustment).
For FY 2017, the Medicare Payment Advisory Commission (MedPAC)
recommends that a 0-percent update be applied to IRF PPS payment rates.
As discussed, and in accordance with sections 1886(j)(3)(C) and
1886(j)(3)(D) of the Act, the Secretary proposed to update the IRF PPS
payment rates for FY 2017 by an adjusted market basket increase factor
of 1.45 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 2017. As noted above, incorporating the
most recent data, the current estimate of the FY 2017 IRF update is
1.65 percent.
We received 10 comments on the proposed market basket increase
update and productivity adjustment, which are summarized below.
Comment: One commenter (MedPAC) stated that it understood that CMS
is required to implement this statutory payment update; however, MedPAC
noted that after reviewing many factors--including indicators of
beneficiary access to rehabilitative services, the supply of providers,
and Medicare margins--it determined that Medicare's current payment
rates for IRFs appear to be adequate and therefore recommended no
update to IRF payment rates for FY 2017. MedPAC appreciated that CMS
cited its recommendation, even while noting that the Secretary does not
have the authority to deviate from statutorily mandated updates.
Response: As discussed, and in accordance with sections
1886(j)(3)(C) and 1886(j)(3)(D) of the Act, the Secretary is updating
IRF PPS payment rates for FY 2017 by an adjusted market basket increase
factor of 1.65 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 2017.
Comment: Several commenters requested that, with respect to the
productivity adjustment, CMS remain cognizant of the intensive labor,
time and costs required by state and/or federal regulations to which
IRFs are bound. These commenters stated that these requirements may be
barriers to IRFs achieving further gains in productivity efficiencies.
Further, some commenters stated that successful rehabilitation outcomes
require an intense labor component, including the interaction of the
full multidisciplinary treatment team, which includes physicians,
nurses, physical and occupational therapists, speech language
pathologists as well as social workers, psychologists and others. In
addition, these commenters indicated that some states have regulations
mandating increased professional staffing ratios between health care
providers and patients. A few commenters claimed that, since CMS has
stated its policy is that the majority of patient therapy should be
one-on-one, which is highly labor-intensive, then CMS should not
mandate further efficiencies such as productivity adjustments while
simultaneously implementing new regulations or interpreting existing
regulations in ways that preclude IRFs from adopting clinically
appropriate innovations that would allow for greater efficiencies.
These commenters requested that the 0.5 percentage point productivity
adjustment be ``reversed.'' In addition, several commenters requested
that CMS be mindful of the additional labor costs and quality
improvement activities that IRFs will incur as a result of the
additional items required in version 1.4 of the IRF PAI beginning on
October 1, 2016 as well as the IRF PAI proposed changes relating to the
drug regimen measure for which data would start to be collected on
October 1, 2018.
Response: Section 1886(j)(3)(C)(ii)(I) of the Act requires the
application of a productivity adjustment that must be applied to the
IRF PPS market basket update. The statute does not provide the
Secretary with the authority to ``reverse'' the productivity adjustment
or apply a different adjustment. We will continue to monitor the impact
of the payment updates, including the effects of the productivity
adjustment, on IRF provider margins as well as beneficiary access to
care.
Comment: One commenter recommended that CMS use the latest data
available in estimating the market basket in the final rule.
Response: We agree with the commenter's recommendation, and it is
consistent with the proposed rule language stating that the final IRF
PPS payment update will be based on the most recent forecast of the
market basket update and productivity adjustment. As noted above, the
most recent estimate of the 2012-based IRF market basket is based on
IGI's second quarter 2016 forecast with historical data through the
first quarter of 2016.
Final Decision: Based on careful consideration of the comments, we
are finalizing the FY 2017 market basket update for IRF payments of
1.65 percent (2.7 percent market basket update, less 0.3 percentage
point MFP adjustment, less 0.75 percentage point legislative
adjustment), which is based on the most recent forecasts of the 2012-
based IRF market basket update and the MFP adjustment.
[[Page 52073]]
C. Labor-Related Share for FY 2017
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) 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 continue to classify a cost category as labor-related
if the costs are labor-intensive and vary with the local labor market.
Based on our definition of the labor-related share and the cost
categories in the 2012-based IRF market basket, we proposed to include
in the labor-related share for FY 2017 the sum of the FY 2017 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
2012-based IRF market basket. For more details regarding the
methodology for determining specific cost categories for inclusion in
the 2012-based IRF labor-related share, see the FY 2016 IRF final rule
(80 FR 47066 through 47068).
Using this method and the IHS Global Insight, Inc. first quarter
2016 forecast for the 2012-based IRF market basket, the proposed IRF
labor-related share for FY 2017 was 71.0 percent. We proposed that if
more recent data were subsequently available, we would use such data to
determine the FY 2017 IRF labor-related share in the final rule.
Incorporating the most recent estimate of the 2012-based IRF market
basket based on IGI's second quarter 2016 forecast with historical data
through the first quarter of 2016, the sum of the relative importance
for FY 2017 operating costs (Wages and Salaries, Employee Benefits,
Professional Fees: Labor-related, Administrative and Facilities Support
Services, Installation Maintenance & Repair Services, and All Other:
Labor-related Services) using the 2012-based IRF market basket is 67.0
percent. We proposed that the portion of Capital-Related Costs that is
influenced by the local labor market is estimated to be 46 percent.
Incorporating the most recent estimate of the FY 2017 relative
importance of Capital-Related costs from the 2012-based IRF market
basket based on IGI's second quarter 2016 forecast with historical data
through the first quarter of 2016, which is 8.4 percent, we take 46
percent of 8.4 percent to determine the labor-related share of Capital
for FY 2017. As we proposed, we then add this amount (3.9 percent) to
the sum of the relative importance for FY 2017 operating costs (67.0
percent) to determine the total labor-related share for FY 2017 of 70.9
percent.
Table 3--IRF Labor-Related Share
------------------------------------------------------------------------
FY 2017 Final FY 2016 Final
labor-related labor-related
share \1\ share \2\
------------------------------------------------------------------------
Wages and Salaries................ 47.7 47.6
Employee Benefits................. 11.3 11.4
Professional Fees: Labor-related.. 3.5 3.5
Administrative and Facilities 0.8 0.8
Support Services.................
Installation, Maintenance, and 1.9 2.0
Repair...........................
All Other: Labor-related Services. 1.8 1.8
Subtotal.......................... 67.0 67.1
Labor-related portion of capital 3.9 3.9
(46%)............................
-------------------------------------
Total Labor-Related Share..... 70.9 71.0
------------------------------------------------------------------------
\1\ Based on the 2012-based IRF Market Basket, IHS Global Insight, Inc.
2nd quarter 2016 forecast.
\2\ Federal Register 80 FR 47068.
Final Decision: As we did not receive any comments on the proposed
labor-related share for FY 2017, we are finalizing the FY 2017 labor-
related share of 70.9 percent.
D. Wage Adjustment
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.
For FY 2017, we proposed to maintain the policies and methodologies
described in the FY 2016 IRF PPS final rule (80 FR 47036, 47068 through
47075) related to the labor market area definitions and the wage index
methodology for areas with wage data. Thus, we proposed to use the CBSA
labor market area definitions and the FY 2016 pre-reclassification and
pre-floor hospital wage index data. The current statistical areas which
were implemented in FY 2016 are based on OMB standards published on
February 28, 2013, in OMB Bulletin No. 13-01. For FY 2017, we are
continuing to use the new OMB delineations that we adopted beginning
with FY 2016. In accordance with section 1886(d)(3)(E) of the Act, the
FY 2016 pre-reclassification and pre-floor hospital wage index is based
on data submitted for hospital cost reporting periods beginning on or
after October 1, 2011, and before October 1, 2012 (that is, FY 2012
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
[[Page 52074]]
hospitals and, thus, no hospital wage index data on which to base the
calculation for the FY 2017 IRF PPS wage index.
We did not receive any comments on these proposals. Therefore, we
are finalizing our proposal to use the CBSA labor market area
definitions and the FY 2016 pre-reclassification and pre-floor hospital
wage index data for areas with wage data. We are also finalizing our
proposal 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.
2. Update
The wage index used for the IRF PPS is calculated using the pre-
reclassification and pre-floor acute care hospital 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. In the FY 2016
IRF PPS final rule (80 FR 47036, 47068), we established an IRF wage
index based on FY 2011 acute care hospital wage data to adjust the FY
2016 IRF payment rates. We also adopted the revised CBSAs set forth by
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, and provided guidance on the use
of the delineations of these statistical areas based on new standards
published on June 28, 2010, in the Federal Register (75 FR 37246
through 37252). A copy of this bulletin may be obtained at https://www.whitehouse.gov/sites/default/files/omb/bulletins/2013/b-13-01.pdf.
For FY 2017, we are continuing to use the new OMB delineations that we
adopted beginning with FY 2016 to calculate the area wage indexes and
the transition periods, which we discuss below.
3. Transition Period
In FY 2016, we applied a 1-year blended wage index for all IRF
providers to mitigate the impact of the wage index change due to the
implementation of the revised CBSA delineations. Under that policy, all
IRF providers are receiving a blended wage index in FY 2016 using 50
percent of their FY 2016 wage index based on the revised OMB CBSA
delineations and 50 percent of their FY 2016 wage index based on the
OMB delineations used in FY 2015. For FY 2017, we proposed to maintain
the policy established in FY 2016 IRF PPS final rule related to the
blended one-year transition wage index (see 80 FR 47036, 47073 through
47074). Thus, the 1-year blended wage index that became effective on
October 1, 2015, will expire on September 30, 2016.
We did not receive any comments on the proposal to maintain the
policy established in FY 2016 IRF PPS final rule related to the blended
one-year transition wage index.
Final decision: As we did not receive any comments on our proposal
to maintain the 1-year blended wage index for all IRF providers, we are
finalizing the expiration of this policy on September 30, 2016.
For FY 2016, in addition to the blended wage index, we also adopted
a 3-year budget neutral phase out of the rural adjustment for IRFs that
were rural in FY 2015 and became urban in FY 2016 under the revised
CBSA delineations. In FY 2016, IRFs that were designated as rural in FY
2015 and became designated as urban in FY 2016 received two-thirds of
the 2015 rural adjustment of 14.9 percent. FY 2017 represents the
second year of the 3-year phase out of the rural adjustment, in which
these same IRFs will receive one-third of the 2015 rural adjustment of
14.9 percent, as finalized in the FY 2016 IRF PPS final rule (80 FR
47036, 47073 through 47074).
For FY 2017, the wage index will be based solely on the previously
adopted revised CBSA delineations and their respective wage index
(rather than on a blended wage index). Furthermore, we will continue
the 3-year phase out of the rural adjustments for IRF providers that
changed from rural to urban status that was finalized in the FY 2016
IFR PPS final rule (80 FR 47036, 47073 through 47074).
We received one comment on our proposal to continue the 3-year
phase out of the rural adjustments for IRF providers that changed from
rural to urban status and that was finalized in the FY 2016 IFR PPS
final rule.
Comment: One commenter suggested that we implement a 5-year phase-
out of the rural adjustment or allow IRFs that are losing the FY 2015
rural adjustment due to the changes in the CBSA delineations to apply
for reclassification back to rural status for a period of 5 years.
Response: The intent of the 3-year phase-out of the rural
adjustment is to mitigate potential negative payment effects on rural
facilities that are redesignated as urban facilitates, effective FY
2016. As described in more detail in the FY 2006 IRF PPS final rule (70
FR 47880), our analysis determined that a 3-year budget-neutral
transition policy would best accomplish the goals of mitigating the
loss of the rural adjustment for existing IRFs that were rural in FY
2005 and became urban in FY 2006 under the new CBSA designations. For a
complete discussion of this policy, we refer readers to the FY 2006 IRF
PPS final rule (70 FR 47880, 47921 through 47925). As discussed in the
FY 2016 IRF PPS final rule (80 FR 47036, 47074), we continue to believe
that a 3-year budget-neutral phase-out of the rural adjustment
appropriately mitigates the adverse payment impacts for these IRFs
while also ensuring that payment rates for all IRFs are set accurately
and appropriately.
Final Decision: After careful consideration, we are finalizing the
continuation of the 3-year phase-out of the rural adjustment for IRFs
that were designated as rural in FY 2015 but changed to urban in FY
2016 under the new OMB market area delineations. For FY 2017, these
IRFs will receive the full FY 2017 wage index and one-third of the FY
2015 rural adjustment. For FY 2018, these IRFs will receive the full FY
2018 wage index with no rural adjustment.
For a full discussion of our implementation of the new OMB labor
market area delineations for the FY 2016 wage index, please refer to
the FY 2016 IRF PPS final rule (80 FR 47036, 47068 through 47076).
While conducting analysis for the FY 2017 IRF PPS final rule, an
additional IRF provider was identified as being eligible for the 3-year
phase out of the rural adjustments for IRF providers that changed from
rural to urban status. The original 19 providers were identified in FY
2014 claims data for the FY 2016 IRF PPS proposed and final rules. This
newly eligible provider was new in FY 2015 and thus had no claims data
in FY 2014. An analysis of the FY 2015 claims determined that this
provider should have received two-thirds of the rural adjustment in FY
2016. This provider will be added to the group of providers receiving
two-thirds of the rural adjustment in FY 2016 and one-third of the
rural adjustment in FY 2017. For FY 2017, 20 IRFs that were designated
as rural in FY 2015 and became designated as urban in FY 2016 will
receive the FY 2017 wage index (based solely on the revised CBSA
delineations) and one-third of the FY 2015 rural adjustment of 14.9
percent (80 FR 47036, 47073 through 47076). The wage index applicable
to FY 2017
[[Page 52075]]
is available on the CMS Web site at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Data-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 multiply the unadjusted federal
payment rate for IRFs by the FY 2017 labor-related share based on the
2012-based IRF market basket (70.9 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.C
of this final rule. We 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 through the Internet on the CMS Web
site at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Data-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 2017 IRF standard payment
conversion factor reflects the update to the wage indexes (based on the
FY 2012 hospital cost report data) and the labor-related share in a
budget-neutral manner:
Step 1. Determine the total amount of the estimated FY 2016 IRF PPS
payments, using the FY 2016 standard payment conversion factor and the
labor-related share and the wage indexes from FY 2016 (as published in
the FY 2016 IRF PPS final rule (80 FR 47036)).
Step 2. Calculate the total amount of estimated IRF PPS payments
using the FY 2017 standard payment conversion factor and the FY 2017
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 2017 budget-
neutral wage adjustment factor of 0.9992.
Step 4. Apply the FY 2017 budget-neutral wage adjustment factor
from step 3 to the FY 2016 IRF PPS standard payment conversion factor
after the application of the adjusted market basket update to determine
the FY 2017 standard payment conversion factor.
We discuss the calculation of the standard payment conversion
factor for FY 2017 in section VI.E of this final rule.
We did not receive any specific comments on the proposal to
calculate a budget-neutral wage adjustment factor.
Final Decision: As we did not receive any comments on the proposal
to calculate a budget-natural wage adjustment factor, we are finalizing
our calculation of the budget-neutral wage adjustment factor of 0.9992
for FY 2017.
We received 11 public comments on the proposed IRF wage adjustment
for FY 2017, which are summarized below.
Comment: Commenters again recommended that we develop a new
methodology for the area wage adjustment that eliminates hospital wage
index reclassifications for all hospitals and reduces the problems
associated with annual fluctuations in wage indices and across
geographic boundaries. Until such time as the new methodology may be
developed, commenters also recommended that we consider adopting
certain wage index policies currently employed under the IPPS, because
IRFs compete in a similar labor pool as acute care hospitals. Such
comments included requests that CMS grant IRFs the ability to request
reclassification and/or establish a rural floor policy. One commenter
further recommended that, until a new wage index system is implemented,
we institute a ``smoothing'' variable to the current process to reduce
the fluctuations IRFs annually experience.
Response: Consistent with our previous responses to these comments
(most recently published in our FY 2016 IRF PPS final rule (80 FR
47036, 47076)), we note that the IRF PPS does not account for
geographic reclassification under sections 1886(d)(8) and (d)(10) of
the Act, and does not apply the ``rural floor'' under section 4410 of
the BBA. Furthermore, as we do not have an IRF-specific wage index, we
are unable to determine at this time the degree, if any, to which a
geographic reclassification adjustment or a rural floor policy under
the IRF PPS would be appropriate. The rationale for our current wage
index policies is fully described in the FY 2006 IRF PPS final rule (70
FR 47880, 47926 through 47928).
Additionally, while some commenters recommended that we adopt IPPS
reclassification and/or floor policies, we note the MedPAC's June 2007
report to the Congress, titled ``Report to Congress: Promoting Greater
Efficiency in Medicare'' (available at https://www.medpac.gov/-documents-/reports), recommends that Congress ``repeal the existing
hospital wage index statute, including reclassification and exceptions,
and give the Secretary authority to establish new wage index systems.''
We continue to believe it would not be appropriate at this time to
adopt the IPPS wage index policies, such as reclassification and/or
floor policies. Therefore, we will continue to use the CBSA labor
market area definitions and the pre-reclassification and pre-floor
hospital wage index data based on 2012 cost report data as this is the
most recent final data available.
With regard to issues mentioned about ensuring that the wage index
minimizes fluctuations, matches the costs of labor in the market, and
provides for a single wage index policy, we note that section 3137(b)
of the Affordable Care Act required us to submit a report to the
Congress by December 31, 2011 that includes a plan to reform the
hospital wage index system. This report describes the concept of a
Commuting Based Wage Index as a potential replacement to the current
Medicare wage index methodology. While this report addresses the goals
of broad based Medicare wage index reform, no consensus has been
achieved regarding how best to implement a replacement system. These
concerns will be taken into consideration while CMS continues to
explore potential wage index reforms.
The report that we submitted is available online at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Wage-Index-Reform.html.
Comment: Several commenters suggested that CMS use the most current
wage data that is available and align the timeframe for the IRF wage
index with other post-acute and acute care settings. These commenters
indicated that this would position the IRF PPS to be more in line with
alternative payment models that are currently being developed and
tested.
Response: As we did not propose any changes to the methodology for
determining the wage index for IRF providers, these comments are
outside the scope of the proposed rule. We appreciate the commenters'
suggestions and agree that this issue needs to be considered within the
broader context of Medicare post-acute care payment reform efforts. We
will consider these suggestions for future analyses.
Final Decision: After careful consideration of the comments, we are
finalizing use of the FY 2016 pre-floor, pre-reclassified hospital wage
index data to derive the applicable IRF PPS wage index for FY 2017. We
are also continuing to implement the 3-year
[[Page 52076]]
phase-out of the rural adjustment for IRFs that were designated as
rural in FY 2015 but changed to urban in FY 2016 under the new OMB
market area delineations. For FY 2017, these IRFs will receive the full
FY 2017 wage index and one-third of the FY 2015 rural adjustment. For
FY 2018, these IRFs will receive the full FY 2018 wage index with no
rural adjustment.
E. Description of the IRF Standard Payment Conversion Factor and
Payment Rates for FY 2017
To calculate the standard payment conversion factor for FY 2017, as
illustrated in Table 4, we begin by applying the adjusted market basket
increase factor for FY 2017 that was adjusted in accordance with
sections 1886(j)(3)(C) and (D) of the Act, to the standard payment
conversion factor for FY 2016 ($15,478). Applying the 1.65 percent
adjusted market basket increase for FY 2017 to the standard payment
conversion factor for FY 2016 of $15,478 yields a standard payment
amount of $15,733. Then, we apply the budget neutrality factor for the
FY 2017 wage index and labor-related share of 0.9992, which results in
a standard payment amount of $15,721. We next apply the budget
neutrality factor for the revised CMG relative weights of 0.9992, which
results in the standard payment conversion factor of $15,708 for FY
2017.
Table 4--Calculations to Determine the FY 2017 Standard Payment
Conversion Factor
------------------------------------------------------------------------
Explanation for adjustment Calculations
------------------------------------------------------------------------
Standard Payment Conversion Factor for FY 2016..... $15,478
Market Basket Increase Factor for FY 2017 (2.7 x 1.0165
percent), reduced by 0.3 percentage point for the
productivity adjustment as required by section
1886(j)(3)(C)(ii)(I) of the Act, and reduced by
0.75 percentage point in accordance with
paragraphs 1886(j)(3)(C) and (D) of the Act.......
Budget Neutrality Factor for the Wage Index and x 0.9992
Labor-Related Share...............................
Budget Neutrality Factor for the Revisions to the x 0.9992
CMG Relative Weights..............................
FY 2017 Standard Payment Conversion Factor......... = 15,708
------------------------------------------------------------------------
We did not receive comments specifically on the proposed FY 2017
standard payment conversion factor. We received comments on how the FY
2016 IRF QRP relates to the proposed FY 2017 standard payment
conversion factor, which we have summarized in section IX. of this
final rule.
Final Decision: As we did not receive comments specifically on the
proposed FY 2017 standard payment conversion factor, we are finalizing
the IRF standard payment conversion factor of $15,708 for FY 2017.
After the application of the proposed CMG relative weights
described in section IV of this final rule to the FY 2017 standard
payment conversion factor ($15,708), the resulting unadjusted IRF
prospective payment rates for FY 2017 are shown in Table 5.
Table 5--FY 2017 Payment Rates
----------------------------------------------------------------------------------------------------------------
Payment rate Payment rate Payment rate Payment rate
CMG Tier 1 Tier 2 Tier 3 no comorbidity
----------------------------------------------------------------------------------------------------------------
0101............................................ $12,553.83 $11,179.38 $10,227.48 $9,762.52
0102............................................ 15,912.20 14,168.62 12,962.24 12,373.19
0103............................................ 18,591.99 16,556.23 15,145.65 14,457.64
0104............................................ 19,788.94 17,621.23 16,121.12 15,387.56
0105............................................ 22,889.70 20,382.70 18,646.97 17,798.73
0106............................................ 25,597.76 22,793.88 20,852.37 19,903.61
0107............................................ 28,568.14 25,439.11 23,271.40 22,214.25
0108............................................ 35,960.32 32,022.33 29,293.85 27,961.81
0109............................................ 32,333.35 28,791.19 26,339.17 25,140.65
0110............................................ 42,914.26 38,212.85 34,958.15 33,368.50
0201............................................ 12,178.41 9,960.44 8,977.12 8,392.78
0202............................................ 17,192.41 14,060.23 12,671.64 11,846.97
0203............................................ 19,121.35 15,637.31 14,094.79 13,175.87
0204............................................ 21,135.11 17,283.51 15,579.19 14,564.46
0205............................................ 25,484.66 20,842.95 18,785.20 17,563.11
0206............................................ 30,220.62 24,714.97 22,277.09 20,825.67
0207............................................ 39,716.11 32,481.00 29,275.00 27,369.62
0301............................................ 17,944.82 14,815.79 13,463.33 12,569.54
0302............................................ 22,090.16 18,236.99 16,573.51 15,472.38
0303............................................ 25,902.49 21,384.87 19,433.94 18,142.74
0304............................................ 33,514.59 27,668.07 25,143.80 23,474.04
0401............................................ 15,392.27 13,534.01 12,483.15 11,330.18
0402............................................ 22,072.88 19,410.38 17,900.84 16,248.36
0403............................................ 34,816.78 30,618.03 28,236.70 25,629.17
0404............................................ 60,793.10 53,459.04 49,302.70 44,750.52
0405............................................ 54,027.67 47,510.42 43,815.90 39,771.09
0501............................................ 13,389.50 10,547.92 10,045.27 9,033.67
0502............................................ 18,221.28 14,355.54 13,670.67 12,294.65
0503............................................ 22,866.14 18,015.51 17,154.71 15,428.40
0504............................................ 26,840.26 21,146.11 20,136.09 18,109.75
0505............................................ 30,798.68 24,264.15 23,104.90 20,780.11
0506............................................ 42,648.79 33,600.98 31,995.63 28,777.06
[[Page 52077]]
0601............................................ 16,260.92 12,888.41 11,901.95 10,899.78
0602............................................ 20,926.20 16,587.65 15,316.87 14,027.24
0603............................................ 25,778.40 20,432.97 18,868.45 17,280.37
0604............................................ 34,168.04 27,082.16 25,010.28 22,903.83
0701............................................ 15,693.86 12,780.03 12,200.40 11,077.28
0702............................................ 20,041.84 16,320.61 15,580.77 14,146.62
0703............................................ 24,163.62 19,677.41 18,783.63 17,055.75
0704............................................ 31,326.46 25,509.79 24,352.11 22,110.58
0801............................................ 12,539.70 10,120.66 9,358.83 8,601.70
0802............................................ 16,231.08 13,100.47 12,115.58 11,135.40
0803............................................ 21,713.17 17,523.84 16,205.94 14,894.33
0804............................................ 19,548.61 15,777.12 14,591.16 13,409.92
0805............................................ 23,257.26 18,769.49 17,358.91 15,954.62
0806............................................ 28,253.98 22,803.30 21,087.99 19,382.10
0901............................................ 15,455.10 12,472.15 11,554.80 10,513.36
0902............................................ 19,765.38 15,951.47 14,778.09 13,446.05
0903............................................ 24,834.35 20,043.41 18,568.43 16,893.95
0904............................................ 31,437.99 25,373.13 23,507.02 21,386.44
1001............................................ 16,831.12 14,840.92 12,878.99 11,623.92
1002............................................ 21,843.54 19,259.58 16,714.88 15,085.96
1003............................................ 30,848.94 27,201.54 23,607.55 21,306.33
1101............................................ 20,769.12 18,826.04 15,298.02 13,889.01
1102............................................ 29,771.37 26,987.91 21,929.94 19,911.46
1201............................................ 16,303.33 16,086.56 14,617.86 12,929.25
1202............................................ 18,945.42 18,692.52 16,985.06 15,023.13
1203............................................ 24,143.20 23,821.18 21,645.62 19,144.91
1301............................................ 18,753.78 14,754.52 13,650.25 12,577.40
1302............................................ 25,756.41 20,263.32 18,747.50 17,274.09
1303............................................ 31,753.72 24,982.00 23,114.32 21,296.91
1401............................................ 13,612.55 11,504.54 10,428.54 9,464.07
1402............................................ 18,551.15 15,678.15 14,211.03 12,897.84
1403............................................ 22,115.29 18,690.95 16,941.08 15,374.99
1404............................................ 27,968.09 23,637.40 21,425.71 19,444.93
1501............................................ 15,847.80 13,419.34 12,390.47 11,680.47
1502............................................ 20,021.42 16,953.64 15,654.59 14,756.10
1503............................................ 24,414.94 20,674.87 19,089.93 17,995.08
1504............................................ 30,426.40 25,764.26 23,789.77 22,424.74
1601............................................ 15,533.64 14,031.96 13,070.63 12,059.03
1602............................................ 20,264.89 18,306.10 17,051.03 15,731.56
1603............................................ 25,376.27 22,921.11 21,350.31 19,697.83
1701............................................ 17,820.73 14,542.47 13,383.22 12,049.61
1702............................................ 22,388.61 18,269.97 16,813.84 15,137.80
1703............................................ 26,683.18 21,774.43 20,040.27 18,042.21
1704............................................ 34,276.43 27,969.66 25,740.70 23,174.01
1801............................................ 20,313.59 16,642.63 14,456.07 12,965.38
1802............................................ 28,641.97 23,466.18 20,382.70 18,282.54
1803............................................ 45,069.39 36,924.80 32,074.17 28,767.63
1901............................................ 19,269.00 16,518.53 14,561.32 14,347.69
1902............................................ 35,009.99 30,011.70 26,456.98 26,067.43
1903............................................ 57,623.23 49,396.95 43,545.72 42,906.40
2001............................................ 14,490.63 11,878.39 10,904.49 9,872.48
2002............................................ 19,001.97 15,576.05 14,300.56 12,944.96
2003............................................ 23,756.78 19,473.21 17,877.27 16,183.95
2004............................................ 30,492.37 24,994.57 22,946.25 20,772.26
2101............................................ 26,544.95 26,544.95 23,657.82 21,697.46
5001............................................ .............. .............. .............. 2,489.72
5101............................................ .............. .............. .............. 10,657.88
5102............................................ .............. .............. .............. 26,084.70
5103............................................ .............. .............. .............. 12,569.54
5104............................................ .............. .............. .............. 33,300.96
----------------------------------------------------------------------------------------------------------------
F. Example of the Methodology for Adjusting the Federal Prospective
Payment Rates
Table 6 illustrates the methodology for adjusting the federal
prospective payments (as described in sections VI.A. through VI.F. of
this final rule). The following examples are based on two hypothetical
Medicare beneficiaries, both classified into CMG 0110 (without
comorbidities). The unadjusted federal prospective payment rate for CMG
0110 (without comorbidities) appears in Table 5.
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
[[Page 52078]]
Hospital (DSH) percentage of 5 percent (which would result in a LIP
adjustment of 1.0156), a wage index of 0.8297, 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 0.8756, and a teaching status
adjustment of 0.0784.
To calculate each IRF's labor and non-labor portion of the federal
prospective payment, we begin by taking the unadjusted federal
prospective payment rate for CMG 0110 (without comorbidities) from
Table 5. Then, we multiply the labor-related share for FY 2017 (70.9
percent) described in section VI.C. of this final rule by the
unadjusted federal prospective payment rate. To determine the non-labor
portion of the federal prospective payment rate, we subtract the labor
portion of the federal payment from the unadjusted federal prospective
payment.
To compute the wage-adjusted federal 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 CMS
Web site at https://www.cms.hhs.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/. 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.
Adjusting the wage-adjusted federal payment by the facility-level
adjustments involves several steps. First, we take the wage-adjusted
federal 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 federal
prospective payment rates. Table 6 illustrates the components of the
adjusted payment calculation.
[GRAPHIC] [TIFF OMITTED] TR05AU16.008
[[Page 52079]]
Thus, the adjusted payment for Facility A would be $34,236.98 and
the adjusted payment for Facility B would be $34,192.08.
VII. Update to Payments for High-Cost Outliers Under the IRF PPS
A. Update to the Outlier Threshold Amount for FY 2017
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 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 2016 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, and 77 FR 44618, 78 FR
47860, 79 FR 45872, 80 FR 47036, 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 2017, we proposed
to use FY 2015 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 2016. 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 2.8
percent in FY 2016. Therefore, we proposed to update the outlier
threshold amount from $8,658 for FY 2016 to $8,301 for FY 2017 to
maintain estimated outlier payments at approximately 3 percent of total
estimated aggregate IRF payments for FY 2017.
We note that, as we typically do, we updated our data between the
FY 2017 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 2015. Based on our
analysis using this updated data, we now estimate that IRF outlier
payments as a percentage of total estimated payments are approximately
2.7 percent in FY 2016. Therefore, we will update the outlier threshold
amount from $8,658 for FY 2016 to $7,984 for FY 2017 to maintain
estimated outlier payments at approximately 3 percent of total
estimated aggregate IRF payments for FY 2017.
We received 7 public comments on the proposed update to the FY 2017
outlier threshold amount to maintain estimated outlier payments at
approximately 3 percent of total estimated IRF payments, which are
summarized below.
Comment: Commenters, while supportive of maintaining estimated
payments for outlier payments at approximately 3 percent, suggested
that CMS review its methodology for setting the outlier threshold
amount and modify as needed so that the full 3 percent is paid as
outlier payments. Some commenters suggested implementing a forecast
error correction if the full amount of the outlier pool is not paid
out.
Response: 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, thereby, promote access to care for
patients who are likely to require unusually high-cost care. As we have
indicated in previous IRF PPS final rules, we do not make adjustments
to IRF PPS payment rates for the sole purpose of accounting for
differences between projected and actual outlier payments. We use the
best available data at the time to establish an outlier threshold for
IRF PPS payments prior to the beginning of each fiscal year to help
ensure that estimated outlier payments for that fiscal year will equal
3 percent of total estimated IRF PPS payments. We analyze expenditures
annually, and if there is a difference from our projection, that
information is used to make a prospective adjustment to lower or raise
the outlier threshold for the upcoming fiscal year. We believe a
retrospective adjustment would not be appropriate, given that we do not
recoup or make excess payments to hospitals.
If outlier payments for a given year turn out to be greater than
projected, we do not recoup money from hospitals; if outlier payments
for a given year are lower than projected, we do not make an adjustment
to account for the difference. Payments for a given discharge in a
given fiscal year are generally intended to reflect or address the
prospective average costs of that discharge in that year; that goal
would be undermined if we adjusted IRF PPS payments to account for
``underpayments'' or ``overpayments'' in IRF outliers in previous
years.
Comment: One commenter recommended that we expand the outlier pool
from 3 percent to 5 percent in order to ensure that payments are more
equitably distributed within the IRF payment system. However, this same
commenter noted that such an expansion in the outlier pool could
inappropriately reward facilities for inefficiencies. Several other
commenters stated that expanding the outlier pool would be
inappropriate for this same reason.
Response: We refer readers to the 2002 IRF PPS final rule (66 FR
41316, 41362 through 41363), for a discussion of 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. 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 of caring for high-cost patients, while still providing for
adequate payments for all other (non-high cost outlier)
[[Page 52080]]
cases. We believe that the outlier policy of 3 percent of total
estimated payments optimizes the extent to which we can encourage
facilities to continue to take patients that are likely to have
unusually high costs, while still providing adequate payment for all
other cases. Increasing the outlier pool would leave less money
available to cover the costs of non-outlier cases, due to the fact that
we would implement such a change in a budget-neutral manner. We believe
that our current outlier policy, to set outlier payments at 3 percent
of total payments, is consistent with the statute and the goals of the
prospective payment system.
Comment: Several commenters recommended that CMS impose a cap on
the amount of outlier payments an individual IRF can receive under the
IRF PPS.
Response: Comments regarding the amount of outlier payments an
individual IRF can receive are outside the scope of this rule. However,
any future consideration given to imposing a limit on outlier payments
would have to be carefully analyzed and would need to take into account
any effect on access to IRF care it would have for certain high-cost
populations.
Final Decision: 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 $7,984 to maintain
estimated outlier payments at approximately 3 percent of total
estimated aggregate IRF payments for FY 2017. This update is effective
October 1, 2016.
B. Update to the IRF Cost-to-Charge Ratio Ceiling and Urban/Rural
Averages
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 2017, 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 2017, as discussed below.
Other IRFs for which accurate data to calculate an overall
CCR are not available.
Specifically, for FY 2017, we proposed to estimate a national
average CCR of 0.562 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 0.435 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. We used FY 2013 IRF cost report
data for the proposed rule. (Please note that we erroneously stated in
the proposed rule that we used FY 2014 cost report data.) For this
final rule, we have used the most recent available cost report data (FY
2014). This includes all IRFs whose cost reporting periods begin on or
after October 1, 2013, and before October 1, 2014. If, for any IRF, the
FY 2014 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
2013) 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 the updated FY 2014 cost report data for
this final rule, we estimate a national average CCR of 0.522 for rural
IRFs, and a national average CCR of 0.421 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 1.36 for FY 2017. This
means that, if an individual IRF's CCR were to exceed this proposed
ceiling of 1.36 for FY 2017, 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 2014 cost report data for this final rule, we
estimate a national average CCR ceiling of 1.29, using this same
methodology.
We did not receive any comments on the proposed update to the IRF
CCR ceiling and the urban/rural averages for FY 2017.
Final Decision: As we did not receive any comments on the proposed
updates to the IRF CCR ceiling and the urban/rural averages for FY
2017, we are finalizing the national average urban CCR at 0.421, the
national average rural CCR at 0.522, and the national CCR ceiling at
1.29 for FY 2017. These updates are effective October 1, 2016.
VIII. Revisions and Updates to the IRF Quality Reporting Program (QRP)
A. Background and Statutory Authority
We seek to promote higher quality and more efficient health care
for Medicare beneficiaries, and our efforts are furthered by QRPs
coupled with public reporting of that information. Section 3004(b) of
the Affordable Care Act amended section 1886(j)(7) of the Act,
requiring the Secretary to establish the IRF QRP. This program applies
to freestanding IRFs, as well as IRF units affiliated with either acute
care facilities or critical access hospitals (CAHs). Beginning with the
FY 2014 payment determination and subsequent years, the Secretary is
required to reduce any annual update to the standard federal rate for
discharges occurring during such fiscal year by 2 percentage points for
any IRF that does not comply with the requirements established by the
Secretary. Section 1886(j)(7) of the Act requires that for the FY 2014
payment determination and subsequent years, each IRF submit data on
quality measures specified by the Secretary in a form and manner, and
at a time, specified by the Secretary. For more information on the
statutory history of the IRF QRP, please refer to the FY 2015 IRF PPS
final rule (79 FR 45908).
The Improving Medicare Post-Acute Care Transformation Act of 2014
(IMPACT Act) imposed new data reporting requirements for certain PAC
providers, including IRFs. For information on the statutory background
of the IMPACT Act, please refer to the FY 2016 IRF PPS final rule (80
FR 47080 through 47083).
In the FY 2016 IRF PPS final rule, we reviewed general activities
and finalized the general timeline and sequencing of such activities
that will occur under the
[[Page 52081]]
IRF QRP. For further information, please refer to the FY 2016 IRF PPS
final rule (80 FR 40708 through 47128). In addition, we established our
approach for identifying cross-cutting measures and process for the
adoption of measures, including the application and purpose of the
Measures Application Partnership (MAP) and the notice-and-comment
rulemaking process (80 FR 47080 through 47084). For information on
these topics, please refer to the FY 2016 IRF PPS final rule (80 FR
47080).
B. General Considerations Used for Selection of Quality, Resource Use,
and Other Measures for the IRF QRP
For a detailed discussion of the considerations we use for the
selection of IRF QRP quality measures, such as alignment with the CMS
Quality Strategy,\1\ which incorporates the 3 broad aims of the
National Quality Strategy,\2\ please refer to the FY 2015 IRF PPS final
rule (79 FR 45911) and the FY 2016 IRF PPS final rule (80 FR 47083
through 47084). Overall, we strive to promote high quality and
efficiency in the delivery of health care to the beneficiaries we
serve. Performance improvement leading to the highest-quality health
care requires continuous evaluation to identify and address performance
gaps and reduce the unintended consequences that may arise in treating
a large, vulnerable, and aging population. QRPs, coupled with public
reporting of quality information, are critical to the advancement of
health care quality improvement efforts. Valid, reliable, relevant
quality measures are fundamental to the effectiveness of our QRPs.
Therefore, selection of quality measures is a priority for us in all of
our QRPs.
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\1\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/CMS-Quality-Strategy.html.
\2\ https://www.ahrq.gov/workingforquality/nqs/nqs2011annlrpt.htm.
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In the IRF PPS FY 2017 proposed rule (81 FR 24178), we proposed to
adopt for the IRF QRP one measure that we are specifying under section
1899B(c)(1) of the Act to meet the Medication Reconciliation domain,
that is, Drug Regimen Review Conducted with Follow-Up for Identified
Issues-Post Acute Care Inpatient Rehabilitation Facility Quality
Reporting Program. Further, we proposed to adopt for the IRF QRP three
measures to meet the resource use and other measure domains identified
in section 1899B(d)(1) of the Act. These measures include: (1) Total
Estimated Medicare Spending per Beneficiary: Medicare Spending per
Beneficiary-Post Acute Care Inpatient Rehabilitation Facility Quality
Reporting Program; (2) Discharge to Community: Discharge to Community-
Post Acute Care Inpatient Rehabilitation Facility Quality Reporting
Program, and (3) Potentially Preventable 30-Day Post-Discharge
Readmission Measure for Inpatient Rehabilitation Facility Quality
Reporting Program. We also proposed an additional measure, which is not
required under the IMPACT Act: (4) Potentially Preventable Within Stay
Readmission Measure for Inpatient Rehabilitation Facilities.
In our development and specification of measures, we employed a
transparent process in which we seek input from stakeholders and
national experts and engage in a process that allows for pre-rulemaking
input on each measure, as required by section 1890A of the Act. To meet
this requirement, we provided the following opportunities for
stakeholder input: Our measure development contractor convened
technical expert panels (TEPs) that included stakeholder experts and
patient representatives on July 29, 2015, for the Drug Regimen Review
Conducted with Follow-Up for Identified Issues measures; on August 25,
2015, September 25, 2015, and October 5, 2015, for the Discharge to
Community measures; on August 12 and 13, 2015, and October 14, 2015,
for the Potentially Preventable 30-Day Post-Discharge Readmission
Measures and Potentially Preventable Within Stay Readmission Measure
for IRFs; and on October 29 and 30, 2015, for the Medicare Spending per
Beneficiary (MSPB) measures. In addition, we released draft quality
measure specifications for public comment for the Drug Regimen Review
Conducted with Follow-Up for Identified Issues measures from September
18, 2015, to October 6, 2015; for the Discharge to Community measures
from November 9, 2015, to December 8, 2015; for the Potentially
Preventable 30-Day Post-Discharge Readmission Measure for IRFs and
Potentially Preventable Within Stay Readmission Measure for IRFs from
November 2, 2015 to December 1, 2015; and for the MSPB measures from
January 13, 2016 to February 5, 2016. We implemented a public mailbox,
PACQualityInitiative@cms.hhs.gov, for the submission of public
comments. This PAC mailbox is accessible on our post-acute care quality
initiatives Web site at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-of-2014-Data-Standardization-and-Cross-Setting-MeasuresMeasures.html.
Additionally, we sought public input from the NQF-convened MAP
Post-Acute Care, Long-Term Care Workgroup during the annual in-person
meeting held December 14 and 15, 2015. The MAP, composed of multi-
stakeholder groups, is tasked to provide input on the selection of
quality and efficiency measures described in section 1890(b)(7)(B) of
the Act.
The MAP reviewed each IMPACT Act-related measure, as well as other
quality measures proposed in this rule for use in the IRF QRP. For more
information on the MAP's recommendations, please refer to the MAP 2016
Final Recommendations to HHS and CMS public report at https://www.qualityforum.org/Publications/2016/02/MAP_2016_Considerations_for_Implementing_Measures_in_Federal_Programs_-_PAC-LTC.aspx.
For measures that do not have NQF endorsement, or which are not
fully supported by the MAP for use in the IRF QRP, we proposed for the
IRF QRP for the purposes of satisfying the measure domains required
under the IMPACT Act, measures that closely align with the national
priorities identified in the National Quality Strategy (https://www.ahrq.gov/workingforquality/) and for which the MAP supports the
measure concept. Further discussion as to the importance and high-
priority status of these proposed measures in the IRF setting is
included under each quality measure in this final rule.
Although we did not solicit feedback on General Considerations Used
for Selection of Quality, Resource Use, and Other Measures for the IRF
QRP, we received a number of comments, which are summarized with our
responses below.
Comment: One commenter supported CMS's intention to select measures
that are already incorporated in various quality reporting programs to
minimize burden. One commenter commented that CMS should recognize
burden of data collection and focus on measures that are the most
clinically relevant and actionable to the facility and patients.
Additionally, the commenter recommended that CMS use minimum standards
in the development of new measures so that they are as clear and
consistent across facilities as possible.
Response: We appreciate the commenters' support of CMS's intention
to select measures that are already incorporated in the various quality
reporting programs to minimize burden. In addition, we note that we
strive to strike a balance between minimizing burden and addressing
gaps in quality
[[Page 52082]]
of care as we continue to expand the IRF QRP. We interpret the
commenter's suggestion that CMS apply minimum standards in its measure
development to suggest that we simplify our approach to quality measure
development itself. We will take these recommendations into
consideration in our future measure development.
We also received several comments related to the proposed measures,
the IMPACT Act, NQF endorsement, the NQF MAP review process, and the
use of TEPs, which are addressed below.
Comment: We received several comments supporting the goals of the
IMPACT Act and the implementation of cross-setting measures across PAC
settings as required by the IMPACT Act. One commenter appreciated the
use of TEPs and input of stakeholders. These commenters noted the
importance of functional status measures and recommended that CMS
include additional functional status measures in future iterations.
Also, one of the commenters indicated that achieving standardized and
interoperable patient assessment data will allow for better cross-
setting comparisons of quality and will support the development of
better quality measures with uniform risk standardization.
Response: We believe that standardizing patient assessment data
will allow for the exchange of data among PAC providers in order to
facilitate care coordination and improve patient outcomes. We
appreciate the importance of functional status measures and will
consider inclusion of additional measures. As with our measure
development process, we will continue to use TEPs, public comments,
open door forums, and the pre-rulemaking process in order to gather
stakeholder input on all measures under development.
Comment: One commenter recommended that CMS seek an increased level
of patient engagement in order to discern what quality measures are of
greatest value to patients.
Response: We value the patient perspective in the measure
development process. We have employed a transparent process in which we
seek input from stakeholders, as described earlier. We have also have
taken several steps to engage stakeholders, including patients, in all
TEPs, public comments, and special open door forums. In addition, a
summary of the IMPACT Act measure TEP proceedings, public comments, and
special open door forums is available on the PAC Quality Initiatives
Downloads and Videos Web site 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
Patient engagement is a priority for CMS, and we will continue to take
steps to include the patient perspective, especially with regard to
assembling TEP, which review and comment on our measure development
activities.
Comment: Several commenters recommended that CMS delay
implementation of proposed measures until NQF has completed its review
and has endorsed measures that are appropriate for the specific
characteristics of the IRF patient population. A few commenters
suggested that CMS seek NQF's formal consensus development process
instead of a time-limited endorsement, as it was perceived that the
time-limited endorsement was not sufficient.
Response: We received several comments regarding the NQF
endorsement status for the proposed measures, and acknowledge the
commenters' recommendation to submit the measures to the NQF prior to
implementation. We consider and propose appropriate measures that have
been endorsed by the NQF whenever possible. However, when this is not
feasible because there is no NQF-endorsed measure, we utilize our
statutory authority that allows the Secretary to specify a measure for
the IRF QRP that is not NQF-endorsed where, as in the case for the
proposed measures, we have not been able to identify other measures
that are endorsed or adopted by a consensus organization. While we
appreciate the importance of consensus endorsement and intend to seek
such endorsement, we must balance the need to address gaps in quality
and adhere to statutorily required timelines as in the case of the
quality and resource use measures that we proposed to address the
IMPACT Act. In regard to the comments surrounding time-limited
endorsement, NQF uses time-limited endorsement for measures that meet
all of the NQF's endorsement criteria with the exception of field
testing and are critical to advancing quality improvement. When
measures are granted this two-year endorsement rather than the
traditional three-year period, measure developers must test the measure
and return results to NQF within the two-year window to maintain the
endorsement. We wish to clarify that we have not yet sought endorsement
of the proposed measures, time-limited or otherwise.
Comment: Several commenters stated the NQF MAP committee did not
endorse the proposed measures; instead, the commenters recommended that
CMS delay measure implementation until the measures are fully developed
and tested and brought back to the NQF for further consideration. One
commenter further stated that TEP members and other stakeholders who
provided feedback in the measure development process did not support
measures moving forward without further testing.
Response: We interpret this comment to address the activities of
the Measures Application Partnership, a multi-stakeholder partnership
convened by NQF that provides input to the U.S. Department of Health
and Human Services (HHS) on its selection of measures for certain
Medicare programs. We would like to clarify that the MAP ``encouraged
continued development'' for the proposed measures. According to the
MAP, the term ``encourage continued development'' is applied when a
measure addresses a critical program objective or promotes alignment,
but is in an earlier stage of development. In contrast, the MAP uses
the phrase ``do not support'' when it does not support the measure at
all.
Since the MAP recommendation of ``encourage continued development''
for the proposed measures during the December 2015 NQF-convened PAC LTC
MAP meeting, further refinement of measure specifications and testing
of measure validity and reliability have been performed. These efforts
have included: A pilot test in 12 post-acute care settings, including
IRFs, to determine the feasibility of assessment items for use in
calculation of the Drug Regimen Review Conducted with Follow-Up for
Identified Issues measure, and further development of the risk-adjusted
models for the Discharge to Community, Medicare Spending per
Beneficiary, Potentially Preventable Readmissions, and Potentially
Preventable Within Stay Readmissions Measure for Inpatient
Rehabilitation Facilities measures. Additional information regarding
testing is further described in the specific measure sections.
Additional information regarding testing that was performed since the
MAP Meeting, TEP meetings, and public comment periods is further
described below in our responses to comments on individual proposed
measures.
For these reasons, we believe that the measures have been fully and
robustly developed, and believe they are appropriate for implementation
and should not be delayed.
Comment: Several commenters, including MedPAC, expressed concern
regarding the standardization and
[[Page 52083]]
interoperability of the proposed measures as they perceived the
measures to have different inclusion/exclusion criteria, episode
constructions and risk factors, and therefore do not meet the mandate
of the IMPACT Act. The commenters expressed further concern about
future implications of such variations and recommend delaying
implementation until measures are standardized and interoperable across
PAC settings. One commenter further indicated that the measure names
were different for each setting, pointing out the words ``IRF QRP'' or
``Inpatient Rehabilitation Facility'' were included in the measures'
titles to designate a difference in the measure in each setting. One
commenter stated implementing the quality measures in an unstandardized
fashion would result in additional costs in the future for aligning
measures between PAC providers.
MedPAC suggested that the measures use uniform definitions,
specifications, and risk-adjustment methods, conveying that findings
from their work on a unified PAC payment system suggest overlap or
similar care provided for Medicare beneficiaries with similar needs
across PAC settings. As a result of this work, MedPAC recommended that
the IMPACT Act measures be standardized to facilitate quality
comparison across PAC settings to inform Medicare beneficiary choice
and provide an opportunity for CMS to evaluate the value of PAC
services, noting that differences in rates should reflect differences
in quality of care rather than differences in the way rates are
constructed.
Response: We wish to clarify that the IMPACT Act requires that the
patient assessment instruments be modified to enable the submission of
standardized data, for purposes such as interoperability. However,
measures themselves are not ``interoperable.''
CMS, in collaboration with our measure contractors, developed the
proposed measures with the intent to standardize the measure
methodology so that we are able to detect variation among PAC providers
in order to be able to assess differences in quality of care. For
example, the proposed patient assessment-based quality measure, Drug
Regimen Review Conducted with Follow-Up for Identified Issues-PAC IRF
QRP, was developed across PAC settings with uniform definitions and
specifications. This measure is not risk adjusted. The standardized
development of this assessment-based measure follows the mandate of the
IMPACT Act to develop standardized patient assessment-based measures
for the four PAC settings (section 1899B(c)(1) of the Act). The
resource use and other measures, Discharge to the Community-PAC IRF QRP
and All-Condition Risk-Adjusted Potentially Preventable Hospital
Readmissions Rates--PAC IRF QRP were developed to be uniform across the
PAC settings in terms of their definitions, measure calculations, and
risk-adjustment approach where applicable. However, there is variation
in each measure primarily due to the data sources for each PAC setting.
Further, the risk-adjustment approach for the resource use and other
IMPACT Act measures is aligned, but is tailored to each measure based
on measure testing results. Adjusting for relevant case-mix
characteristics in each setting improves the validity and explanatory
power of risk adjustment models, and helps ensure that any differences
in measure performance reflect differences in the care provided rather
than differences in patient case-mix. We employ this approach to
measure development to enable appropriate cross-setting comparisons in
PAC settings and to maximize measure reliability and validity. It
should be noted that sections 1899B(c)(3)(B) and 1899B(d)(3)(B) of the
Act require that quality measures and resource use and other measures
be risk adjusted, as determined appropriate by the Secretary.
Comment: Several commenters expressed concerns regarding the
validity and reliability of IMPACT Act measures and encouraged CMS to
conduct further analysis of data to ensure comparability across post-
acute care settings, prior to implementation and public reporting of
data.
Response: We have tested for validity and reliability all of the
IMPACT Act measures, and the results of that testing is available at:
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
We intend to continue to monitor the reliability and validity of
the IRF QRP measures, including whether the measures are reliable and
valid for cross-setting purposes.
Comment: A few commenters voiced concern regarding the burden of
implementing the proposed measures in the IRF setting. One commenter
requested that CMS proceed cautiously to ensure new measures are
associated with minimal administrative and data collection burden. One
commenter expressed concern that the new measures increase provider
burden by increasing the time providers are ensuring data accuracy and
move the focus away from patient-centered care towards a more metric-
based focus.
Response: We appreciate the importance of avoiding undue burden on
providers and will continue to evaluate and consider any unnecessary
burden associated with the implementation of the IRF QRP. We wish to
note that the three proposed resource measures are claims-based, and
will require no additional data collection by providers and thus result
in minimal increases in burden. The measure, Drug Regimen Review
Conducted with Follow-Up for Identified Issues, is calculated using
assessment data and requires the addition of three items to the IRF-
PAI, also requiring minimal additional burden. We address the issue of
burden further under section XI.B. of this final rule.
Comment: Several commenters recommended that CMS engage in several
activities which would afford greater transparency with stakeholders
regarding proposed measure development. These commenters also requested
that measures undergo field testing with providers prior to
implementation. Commenters also requested that more detailed measure
specifications be posted in order to enable providers to evaluate
measure design decisions. Commenters requested that IRF providers be
provided with confidential preview reports as a part of a ``dry run''
process as this would enable providers to review data and provide CMS
with feedback on potential technical issues with proposed measure.
Finally, the commenters requested that measure data be provided to IRFs
on a patient level on a quarterly basis, similar to other quality
reporting programs, in order to make effective use of the data and
improve performance.
Response: With regard to the testing and analytic results provided
for this measure, since the December 2015 MAP meeting, further
refinement of measure specifications and testing of measure validity
and reliability have been performed.
We direct readers to the Measure Specifications for Measures
Adopted in the FY 2017 IRF QRP final rule are available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html, which include detailed information
regarding measure specifications, including results of the final risk
adjustment models for the resource use measures. For resource use
measures, our testing results are within range for similar outcome
measures finalized in
[[Page 52084]]
public reporting and value-based purchasing programs, including the
All-Cause Unplanned Readmission Measure for 30 Days Post Discharge from
IRFs (NQF #2502), previously adopted into the IRF QRP.
We appreciate the comment requesting that we provide performance
data on IRF QRP measures on a more frequent, such as quarterly, basis
in order to promote quality improvement. We wish to note that the
proposed claims-based measures are based on 2 consecutive years of data
in order to ensure a sufficient sample size to reliably assess IRFs'
performance. However, we will investigate the feasibility and usability
of providing IRFs with information more frequently, such as unadjusted
counts of PPRs and discharge data. We also appreciate the commenters'
suggestions related to the implementation of dry run activities, such
as confidential reports, for the purposes of identifying any technical
issues prior to public reporting, as was successfully provided in the
fall of 2015 for the All Cause Unplanned Readmission Measure for 30
Days Post Discharge from IRFs (NQF#2502). We wish to note that we
intend to provide confidential feedback reports beginning in October,
2017, as described in section VIII.O of this final rule, and we believe
that the reports could serve as an opportunity for providers to extend
to us any technical issues they may discover. We note that, as
described in section VIII.P of this final rule, we are unable at this
time to provide patient-level information for the claims-based measure,
for example, the readmission measures, because such data comes from a
separate entity. Finally, we wish to note that we intend to continue
refining specifications, and we will consider pilot testing in addition
to the performance testing that we currently conduct.
C. Policy for Retention of IRF QRP Measures Adopted for Previous
Payment Determinations
In 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 68507), we adopted a
policy that allows any quality measure adopted for use in the IRF QRP
to remain in effect until the measure was actively removed, suspended,
or replaced, when we initially adopt a measure for the IRF QRP for a
payment determination. For the purpose of streamlining the rulemaking
process, when we initially adopt a measure for the IRF QRP for a
payment determination, this measure will also be adopted for all
subsequent years or until we remove, suspend, or replace the measure.
For further information on how measures are considered for removal,
suspension, or replacement, please refer to the CY 2013 OPPS/ASC final
rule (77 FR 68500). We did not propose any changes to the policy for
retaining IRF QRP measures adopted for previous payment determinations.
D. Policy for Adopting Changes to IRF QRP Measures
In the CY 2013 OPPS/ASC final rule (77 FR 68500 through 68507), we
adopted a subregulatory process to incorporate NQF updates to IRF
quality measure specifications that do not substantively change the
nature of the measure. Substantive changes will be proposed and
finalized through rulemaking. For further information on what
constitutes a substantive versus a nonsubstantive change and the
subregulatory process for nonsubstantive changes, please refer to the
CY 2013 OPPS/ASC final rule (77 FR 68500). We did not propose any
changes to the policy for adopting changes to IRF QRP measures.
E. Quality Measures Previously Finalized for and Currently Used in the
IRF QRP
A history of the IRF QRP quality measures adopted for the FY 2014
payment determinations and subsequent years is presented in Table 7.
The year in which each quality measure was first adopted and
implemented, and then subsequently re-proposed or revised, if
applicable, is displayed. The initial and subsequent annual payment
determination years are also shown in Table 7. For more information on
a particular measure, please refer to the IRF PPS final rule and
associated page numbers referenced in Table 7.
Although we did not solicit feedback, we received a number of
comments about previously finalized measures for and currently used in
the IRF QRP. These comments are summarized and addressed below.
Comment: One commenter was generally supportive of implementing
additional quality measures in post-acute care, especially those that
are cross-setting, but recommended that CMS take steps to validate data
and assess provider experience during the first several months of
reporting. One commenter supported the retention of the NHSN measures.
With regard to the measure, Pressure Ulcers that are New or
Worsened (Short-Stay) (NQF #0678), several commenters recommended that
future updates to the measure include clinical guidance that is
consistent with the most current evidence-based processes.
We received several comments about the NHSN Facility-Wide Inpatient
Hospital-Onset Clostridium difficile Infection (CDI) Outcome Measure
(NQF #1717). Several commenters recommended that CMS revise the measure
so that it is only reported at the first site of discovery, to avoid
penalizing IRFs for the presence of the infection that started in a
previous care setting.
With regard to the measure, Application of Percent of Residents
Experiencing One or More Falls with Major Injury (NQF #0674), one
commenter had concerns that the nature of IRF treatment could lead to a
frequency of falls higher than other settings. The commenter was
concerned that including assisted falls in the definition of falls for
this quality measure was inappropriate and confusing and recommended
that CMS revisit the definition and include only falls with major
injury.
Response: With regard to the measure Pressure Ulcers that are New
or Worsened (Short-Stay) (NQF #0678), we intend to continue our ongoing
measure development and refinement activities to inform the ongoing
evaluation of this measure, to ensure that the measure remains valid
and reliable to inform quality improvement within and across each PAC
setting, and to fulfill the public reporting goals of quality reporting
programs, including the IRF QRP. Reviewing the most current evidence-
based clinical guidance is part of that process. With regard to the
comments about the NHSN Facility-Wide Inpatient Hospital-Onset CDI
Outcome Measure (NQF #1717), the scope of NQF#1717 extends to acute
care hospitals, long-term care hospitals, inpatient rehabilitation
facilities, and cancer hospitals. The same measure specifications are
used by all these facility types to report Clostridium difficile
Laboratory Identified events to NHSN, and these measure specifications
differentiate between community-onset events, which include events that
had their onset at another healthcare facility, from healthcare-
associated events, which are attributed to the facility reporting the
event. CDC reports only incident healthcare-associated events on behalf
of healthcare facilities to CMS. To limit Clostridium difficile
Laboratory Identified event reporting to the first site of discovery
offers opportunity for missed ``true'' healthcare-associated events
(those recognized on or after hospital day 4) and would require
[[Page 52085]]
additional data collection and investigation burden to users.
The measure specifications for NQF#1717, by design, align with the
NHSN LabID Event protocol, which was developed to require minimal
investigation on the part of facilities and to provide a proxy measure
of infection. Dates of admission and specimen collection are required
and can easily be collected via electronic methods and identified as
healthcare-associated (HO) or community-onset (CO). To require a
facility to determine if a CDI LabID Event had been identified in
another facility would call for manual review of medical records and
potential communication with transferring facilities. In accordance
with protocol guidelines, IRF-based events are categorized as
``incident'' (first non-duplicate event for the IRF) in addition to a
CO/HO categorization. IRF facilities are analyzed independently of any
other reporting facility, that is, are viewed as separate reporting
facilities.
With regard to the measure, An Application of Percent of Residents
Experiencing One or More Falls with Major Injury (Long Stay) (NQF
#0674), we would like to clarify that the quality measure adopted for
the IRF QRP includes only falls with a major injury, satisfying the
IMPACT Act domain, Incidence of Major Falls. Thus, falls with no
injury, such as those that may be considered near-falls, are not
included in the measure.
Additionally, we received a number of comments specifically
regarding quality measures that were finalized into the IRF QRP in the
FY 2016 IRF PPS final rule.
Comment: Many commenters indicated they had concerns about the use
of CARE items or the use of the CARE Tool. Several commenters were
concerned that the CARE items added to the IRF-PAI would be duplicative
and confusing to clinicians because they are similar to the FIM[supreg]
items. One commenter suggested the FIM[supreg] items be removed from
the IRF-PAI. Other commenters supported continued use of the
FIM[supreg] instrument, and recommended a delay in implementing the
CARE items. The commenters also had concerns about the precision of the
CARE items and the patient types with which it was tested, the
timeframe and six-point scale, as well as NQF-endorsement of CARE items
in all settings. Commenters noted that the FIM[supreg] instrument has
demonstrated increased efficiency and decreased length of stay, and
allows for comparison of functional gains across patients with similar
debility levels. Commenters had concerns about lack of credentialing of
staff for CARE items, as this is currently required for the FIM[supreg]
instrument to ensure consistent scoring.
Several commenters were concerned about the training, data
submission specifications, and support CMS has provided for items being
required on the IRF-PAI Version 1.4, effective October 1, 2016. Several
commenters were concerned that the data were collected for research
purposes. One commenter indicated there was a discrepancy between the
IRF-PAI Training Manual and the data submission specifications. Many
commenters had concerns about the need for further clarification about
the patient's usual status, and another commenter requested
clarification about the use of a dash to indicate that an item was not
assessed.
Response: As we did not propose any changes to the quality measures
finalized in the FY 2016 IRF PPS final rule, these comments are outside
the scope of the proposed rule. However, we would like to clarify that
we are not implementing the CARE Tool for the IRF QRP to meet the
mandate of the IMPACT Act. To meet the mandate, and to standardize
quality measures and data items, we retained the use of the IRF-PAI as
the collection instrument for all IRF settings. We incorporated items
from the CARE Tool into new section GG: Functional Abilities and Goals
of the IRF-PAI Version 1.4 in order to calculate the 5 function quality
measures that were adopted into the IRF QRP in the IRF PPS FY 2016
Final Rule (80 FR 47100 through 47120). The items were not added to the
IRF-PAI for research purposes.
We refer the readers to the FY 2016 final rule (80 FR 47100 through
47120) for discussion about the testing, including the rating scale,
reliability, validity and sensitivity of the function items that were
added to the IRF-PAI, as well as plans for ongoing evaluation of these
items, and concerns related to FIM[supreg] item duplication. With
regard to training and provider support, we agree with the importance
of thorough and comprehensive training. Information about and materials
from each IRF QRP training are posted on the IRF-QRP Training Web site
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/Training.html. With regard to the
comments related to the data specifications, we post data
specifications and errata on the CMS Web site as soon as we are able so
that vendors and providers are able to review and understand the valid
data codes for all items and the associated requirements: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Software.html.
Table 7--Quality Measures Previously Finalized for and Currently Used in the IRF Quality Reporting Program
----------------------------------------------------------------------------------------------------------------
Data collection start Annual payment determination: Initial
Measure title Final rule date and subsequent APU years
----------------------------------------------------------------------------------------------------------------
National Healthcare Safety Adopted an October 1, 2012....... FY 2014 and subsequent years.
Network (NHSN) Catheter- application of
Associated Urinary Tract the measure in
Infection (CAUTI) Outcome FY 2012 IRF PPS
Measure (NQF #0138). Final Rule (76
FR 47874 through
47886).
Adopted the NQF- January 1, 2013....... FY 2015 and subsequent years.
endorsed version
and expanded
measure (with
standardized
infection ratio)
in CY 2013 OPPS/
ASC Final Rule
(77 FR 68504
through 68505).
Percent of Residents or Adopted October 1, 2012....... FY 2014 and subsequent years.
Patients with Pressure Ulcers application of
That Are New or Worsened measure in FY
(Short Stay) (NQF #0678). 2012 IRF PPS
final rule (76
FR 47876 through
47878).
Adopted a non- January 1, 2013....... FY 2015 and subsequent years.
risk-adjusted
application of
the NQF-endorsed
version in CY
2013 OPPS/ASC
Final Rule (77
FR 68500 through
68507).
[[Page 52086]]
Adopted the risk October 1, 2014....... FY 2017 and subsequent years.
adjusted, NQF-
endorsed version
in FY 2014 IRF
PPS Final Rule
(78 FR 47911
through 47912).
Adopted in the FY October 1, 2015....... FY 2018 and subsequent years.
2016 IRF PPS
final rule (80
FR 47089 through
47096) to
fulfill IMPACT
Act requirements.
Percent of Residents or Adopted in FY October 1, 2014....... FY 2017 and subsequent years.
Patients Who Were Assessed 2014 IRF PPS
and Appropriately Given the final rule (78
Seasonal Influenza Vaccine FR 47906 through
(Short Stay) (NQF #0680). 47911).
Influenza Vaccination Coverage Adopted in FY October 1, 2014....... FY 2016 and subsequent years.
among Healthcare Personnel 2014 IRF PPS
(NQF #0431). final rule (78
FR 47905 through
47906).
All-Cause Unplanned Adopted in FY N/A................... FY 2017 and subsequent years.
Readmission Measure for 30 2014 IRF PPS
Days Post Discharge from final rule (78
Inpatient Rehabilitation FR 47906 through
Facilities (NQF #2502). 47910).
Adopted the NQF- N/A................... FY 2018 and subsequent years.
endorsed version
in FY 2016 IRF
PPS final rule
(80 FR 47087
through 47089).
National Healthcare Safety Adopted in the FY January 1, 2015....... FY 2017 and subsequent years.
Network (NHSN) Facility-Wide 2015 IRF PPS
Inpatient Hospital-Onset final rule (79
Methicillin-Resistant FR 45911 through
Staphylococcus aureus (MRSA) 45913).
Bacteremia Outcome Measure
(NQF #1716).
National Healthcare Safety Adopted in the FY January 1, 2015....... FY 2017 and subsequent years.
Network (NHSN) Facility-Wide 2015 IRF PPS
Inpatient Hospital-Onset final rule (79
Clostridium difficile FR 45913 through
Infection (CDI) Outcome 45914).
Measure (NQF #1717).
Application of Percent of Adopted an October 1, 2016....... FY 2018 and subsequent years.
Residents Experiencing One or application of
More Falls with Major Injury the measure in
(Long Stay) (NQF #0674). FY 2016 IRF PPS
Final Rule (80
FR 47096 through
47100).
Application of Percent of Long- Adopted an October 1, 2016....... FY 2018 and subsequent years.
Term Care Hospital Patients application of
with an Admission and the measure in
Discharge Functional the FY 2016 IRF
Assessment and a Care Plan PPS final rule
That Addresses Function (NQF (80 FR 47100
#2631). through 47111).
IRF Functional Outcome Adopted in the FY October 1, 2016....... FY 2018 and subsequent years.
Measure: Change in Self-Care 2016 IRF PPS
for Medical Rehabilitation final rule (80
Patients (NQF #2633).* FR 47111 through
47117).
IRF Functional outcome Adopted in the FY October 1, 2016....... FY 2018 and subsequent years.
Measure: Change in Mobility 2016 IRF PPS
Score for Medical final rule (80
Rehabilitation (NQF #2634).* FR 47117 through
47118).
IRF Functional Outcome Adopted in the FY October 1, 2016....... FY 2018 and subsequent years.
Measure: Discharge Self-Care 2016 IRF PPS
Score for Medical final rule (80
Rehabilitation Patients (NQF FR 47118 through
#2635). 47119).
IRF Functional Outcome Adopted in the FY October 1, 2016....... FY 2018 and subsequent years.
Measure: Discharge Mobility 2016 IRF PPS
Score for Medical final rule (80
Rehabilitation Patients (NQF FR 47119 through
#2636). 47120).
----------------------------------------------------------------------------------------------------------------
* These measures were under review at NQF when they were finalized for use in the IRF QRP. These measures are
now NQF-endorsed.
F. IRF QRP Quality, Resource Use and Other Measures Finalized for the
FY 2018 Payment Determination and Subsequent Years
For the FY 2018 payment determinations and subsequent years, in
addition to the quality measures we are retaining under our policy
described in section VIII.C. of this final rule, we proposed four new
measures. Three of these measures were developed to meet the
requirements of IMPACT Act. They are:
(1) MSPB-PAC IRF QRP,
(2) Discharge to Community-PAC IRF QRP, and
(3) Potentially Preventable 30-Day Post-Discharge Readmission
Measure for IRF QRP.
The fourth measure is: (4) Potentially Preventable Within Stay
Readmission Measure for IRFs. The measures are described in more detail
below.
For the risk-adjustment of the resource use and other measures, we
understand the important role that sociodemographic status plays in the
care of patients. However, we continue to have concerns about holding
providers to different standards for the outcomes of their patients of
diverse sociodemographic status because we do not want to mask
potential disparities or minimize incentives to improve the outcomes of
disadvantaged populations. We routinely monitor the impact of
sociodemographic status on providers' results for our measures.
The NQF is currently undertaking a two-year trial period in which
new measures and measures undergoing maintenance review will be
assessed to determine if risk-adjusting for sociodemographic factors is
appropriate. For 2 years, NQF will conduct a trial of temporarily
allowing inclusion of sociodemographic factors in the risk-adjustment
approach for some
[[Page 52087]]
performance measures. At the conclusion of the trial, NQF will issue
recommendations on future permanent inclusion of sociodemographic
factors. During the trial, measure developers are expected to submit
information such as analyses and interpretations as well as performance
scores with and without sociodemographic factors in the risk adjustment
model.
Furthermore, the Office of the Assistant Secretary for Planning and
Evaluation (ASPE) is conducting research to examine the impact of
sociodemographic status on quality measures, resource use, and other
measures under the Medicare program as directed by the IMPACT Act. We
will closely examine the findings of the ASPE reports and related
Secretarial recommendations and consider how they apply to our quality
programs at such time as they are available.
We received several comments on the impact of sociodemographic
status on quality measures, resource use, and other measures, which are
summarized with our responses below.
Comment: Several commenters indicated their support for the
inclusion of sociodemographic status adjustment in quality measures,
resource use, and other measures. Commenters suggested that failure to
account for patient characteristics could penalize IRFs for providing
care to a more medically-complex and socioeconomically disadvantaged
patient population and affect provider performance. Some commenters
expressed concerns about standardization and interoperability of the
measures as it pertain to risk-adjusting, particularly for SDS
characteristics. Many commenters recommended incorporating
socioeconomic factors as risk-adjustors for the measures, and several
commenters suggested conducting additional testing and NQF-endorsement
prior to implementation of these measures. In addition, many commenters
recommended including functionality as an additional risk-adjustment
factor, and several commenters suggested risk-adjustment for cognitive
impairment.
A few commenters, including MedPAC, did not support risk-adjustment
of measures by socioeconomic status (SES) or SDS status. One commenter
did not support risk-adjustment, stating that it can hide disparities
and create different standards of care for IRFs based on the
demographics in the facility. MedPAC reiterated that risk adjustment
can hide disparities in care and suggested that risk-adjustment reduces
pressure on providers to improve quality of care for low-income
Medicare beneficiaries. Instead, MedPAC supported peer provider group
comparisons with providers of similar low-income beneficiary
populations. Another commenter stated that SDS factors should not be
included in measures that examine the patient during an IRF stay, but
should only be considered for measures evaluating care after the IRF
discharge.
Response: We appreciate the considerations and suggestions conveyed
in relation to the measures and the importance in balancing appropriate
risk adjustment along with ensuring access to high-quality care. We
note that in the measures that are risk adjusted, we do take into
account characteristics associated with medical complexity, as well as
factors such as age where appropriate to do so. For those cross-setting
post-acute measures, such as those intended to satisfy the IMPACT Act
domains that use the patient assessment-based data elements for risk
adjustment, we have either made such items standardized, or intend to
do so as feasible. With regard to the incorporation of additional
factors, such as function, we have and will continue to take such
factors into account, which would include further testing as part of
our ongoing measure development monitoring activities. As discussed
previously, we intend to seek NQF endorsement for our measures.
We also received suggestions pertaining to the incorporation of
socioeconomic factors as risk-adjustors for the measures, including in
those measures that pertain to after the patient was discharged from
the IRF, additional testing and/or NQF endorsement prior to
implementation of these measures, and comments that pertain to
potential consequences associated with such risk adjustors and
alternative approaches to grouping comparative data. We wish to
reiterate that as previously discussed, NQF is currently undertaking a
2-year trial period in which new measures and measures undergoing
maintenance review will be assessed to determine if risk-adjusting for
sociodemographic factors is appropriate. This trial entails temporarily
allowing inclusion of sociodemographic factors in the risk-adjustment
approach for some performance measures. At the conclusion of the trial,
NQF will issue recommendations on future permanent inclusion of
sociodemographic factors. During the trial, measure developers are
encouraged to submit information such as analyses and interpretations
as well as performance scores with and without sociodemographic factors
in the risk adjustment model. Several measures developed by CMS have
been brought to NQF since the beginning of the trial. CMS, in
compliance with NQF's guidance, has tested sociodemographic factors in
the measures' risk models and made recommendations about whether or not
to include these factors in the endorsed measure. We intend to continue
engaging in the NQF process as we consider the appropriateness of
adjusting for sociodemographic factors in our outcome measures.
Furthermore, the Office of the ASPE is conducting research to
examine the impact of sociodemographic status on quality measures,
resource use, and other measures under the Medicare program as directed
by the IMPACT Act. We will closely examine the findings of the ASPE
reports and related Secretarial recommendations and consider how they
apply to our quality programs at such time as they are available.
1. Measure to Address the IMPACT Act Domain of Resource Use and Other
Measures: Total Estimated MSPB-PAC IRF QRP
We proposed an MSPB-PAC IRF QRP measure for inclusion in the IRF
QRP for the FY 2018 payment determination and subsequent years. Section
1899B(d)(1)(A) of the Act requires the Secretary to specify resource
use measures, including total estimated MSPB, on which PAC providers
consisting of Skilled Nursing Facilities (SNFs), IRFs, Long-Term Care
Hospitals (LTCHs), and Home Health Agencies (HHAs) are required to
submit necessary data specified by the Secretary.
Rising Medicare expenditures for post-acute care as well as wide
variation in spending for these services underlines the importance of
measuring resource use for providers rendering these services. Between
2001 and 2013, Medicare PAC spending grew at an annual rate of 6.1
percent and doubled to $59.4 billion, while payments to inpatient
hospitals grew at an annual rate of 1.7 percent over this same
period.\3\ A study commissioned by the Institute of Medicine discovered
that variation in PAC spending explains 73 percent of variation in
total Medicare spending across the United States.\4\
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\3\ MedPAC, ``A Data Book: Health Care Spending and the Medicare
Program,'' (2015). 114.
\4\ Institute of Medicine, ``Variation in Health Care Spending:
Target Decision Making, Not Geography,'' (Washington, DC: National
Academies 2013). 2.
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We reviewed the NQF's consensus-endorsed measures and were unable
to identify any NQF-endorsed resource use measures for PAC settings. As
such, we proposed this MSPB-PAC IRF QRP measure under the Secretary's
authority
[[Page 52088]]
to specify non-NQF-endorsed measures under section 1899B(e)(2)(B) of
the Act. Given the current lack of resource use measures for PAC
settings, our MSPB-PAC IRF QRP measure will provide valuable
information to IRF providers on their relative Medicare spending in
delivering services to approximately 338,000 Medicare beneficiaries.\5\
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\5\ Figures for 2013. MedPAC, ``Medicare Payment Policy,''
Report to the Congress (2015). xvii-xviii.
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The MSPB-PAC IRF QRP episode-based measure will provide actionable
and transparent information to support IRF providers' efforts to
promote care coordination and deliver high quality care at a lower cost
to Medicare. The MSPB-PAC IRF QRP measure holds IRF providers
accountable for the Medicare payments within an ``episode of care''
(episode), which includes the period during which a patient is directly
under the IRF's care, as well as a defined period after the end of the
IRF treatment, which may be reflective of and influenced by the
services furnished by the IRF. MSPB-PAC IRF QRP episodes, constructed
according to the methodology described below, have high levels of
Medicare spending with substantial variation. In FY 2013 and FY 2014,
Medicare FFS beneficiaries experienced 613,089 MSPB-PAC IRF QRP
episodes triggered by admission to an IRF. The mean payment-
standardized, risk-adjusted episode spending for these episodes is
$30,370. There is substantial variation in the Medicare payments for
these MSPB-PAC IRF QRP episodes--ranging from approximately $15,059 at
the 5th percentile to approximately $55,912 at the 95th percentile.
This variation is partially driven by variation in payments occurring
following IRF treatment.
Evaluating Medicare payments during an episode creates a continuum
of accountability between providers that should improve post-treatment
care planning and coordination. While some stakeholders throughout the
measure development process supported the MSPB-PAC measures and
believed that measuring Medicare spending was critical for improving
efficiency, others believed that resource use measures did not reflect
quality of care in that they do not take into account patient outcomes
or experience beyond those observable in claims data. However, IRFs
involved in the provision of high quality PAC care as well as
appropriate discharge planning and post-discharge care coordination
would be expected to perform well on this measure since beneficiaries
would likely experience fewer costly adverse events (for example,
avoidable hospitalizations, infections, and emergency room usage).
Further, it is important that the cost of care be explicitly measured
so that, in conjunction with other quality measures, we can publicly
report which IRFs provide high quality care at lower cost.
We developed an MSPB-PAC measure for each of the four PAC settings.
We proposed an LTCH-specific MSPB-PAC measure in the FY 2017 IPPS/LTCH
proposed rule (81 FR 25216 through 25220), an IRF-specific MSBP-PAC
measure in the FY 2017 IRF PPS proposed rule (81 FR 24197 through
24201), a SNF-specific MSPB-PAC measure in the FY 2017 SNF proposed
rule (81 FR 24258 through 24262), and a HHA-specific MSBP-PAC measure
in the CY 2017 HH proposed rule (81 FR 43760 through 43764). The four
setting-specific MSPB-PAC measures are closely aligned in terms of
episode construction and measure calculation. Each of the MSPB-PAC
measures assess Medicare Part A and Part B spending during an episode,
and the numerator and denominator are defined similarly for each of the
MSPB-PAC measures. However, setting-specific measures allow us to
account for differences between settings in payment policy, the types
of data available, and the underlying health characteristics of
beneficiaries. For example, we use the IRF setting-specific
rehabilitation impairment categories (RICs) in the MSPB-PAC IRF QRP
risk adjustment model, as detailed below.
The MSPB-PAC measures mirror the general construction of the
inpatient prospective payment system (IPPS) hospital MSPB measure,
which was adopted for the Hospital IQR Program beginning with the FY
2014 program, and was implemented in the Hospital VBP Program beginning
with the FY 2015 program. The measure was endorsed by the NQF on
December 6, 2013 (NQF #2158).\6\ The hospital MSPB measure evaluates
hospitals' Medicare spending relative to the Medicare spending for the
national median hospital during a hospital MSPB episode. It assesses
Medicare Part A and Part B payments for services performed by hospitals
and other healthcare providers during a hospital MSPB episode, which is
comprised of the periods immediately prior to, during, and following a
patient's hospital stay.7 8 Similarly, the MSPB-PAC measures
assess all Medicare Part A and Part B payments for FFS claims with a
start date during the episode window (which, as discussed in this
section, is the time period during which Medicare FFS Part A and Part B
services are counted towards the MSPB-PAC IRF QRP episode). There are
differences between the MSPB-PAC measures and the hospital MSPB measure
to reflect differences in payment policies and the nature of care
provided in each PAC setting. For example, the MSPB-PAC measures
exclude a limited set of services (for example, clinically unrelated
services) provided to a beneficiary during the episode window, while
the hospital MSPB measure does not exclude any services.\9\
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\6\ QualityNet, ``Measure Methodology Reports: Medicare Spending
per Beneficiary (MSPB) Measure,'' (2015). https://www.qualitynet.org/dcs/ContentServer?pagename=QnetPublic%2FPage%2FQnetTier3&cid=1228772053996.
\7\ QualityNet, ``Measure Methodology Reports: Medicare Spending
per Beneficiary (MSPB) Measure,'' (2015). https://www.qualitynet.org/dcs/ContentServer?pagename=QnetPublic%2FPage%2FQnetTier3&cid=1228772053996.
\8\ FY 2012 IPPS/LTCH PPS final rule (76 FR 51619).
\9\ National Quality Forum, Applications Partnership, ``Process
and Approach for MAP Pre-Rulemaking Deliberations, 2015-2016''
(February 2016) https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&Ote,OD=81693.
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MSPB-PAC episodes may begin within 30 days of discharge from an
inpatient hospital as part of a patient's trajectory from an acute to a
PAC setting. An IRF stay beginning within 30 days of discharge from an
inpatient hospital would therefore be included once in the hospital's
MSPB measure, and once in the IRF provider's MSPB-PAC measure. Aligning
the hospital MSPB and MSPB-PAC measures in this way creates continuous
accountability and aligns incentives to improve care planning and
coordination across inpatient and PAC settings.
We sought and considered the input of stakeholders throughout the
measure development process for the MSPB-PAC measures. We convened a
TEP consisting of 12 panelists with combined expertise in all of the
PAC settings on October 29 and 30, 2015 in Baltimore, Maryland. A
follow-up email survey was sent to TEP members on November 18, 2015 to
which seven responses were received by December 8, 2015. The MSPB-PAC
TEP Summary Report is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Technical-Expert-Panel-on-Medicare-Spending-Per-Beneficiary.pdf. The measures were also presented to the
MAP Post-Acute Care/Long-Term Care (PAC/LTC) Workgroup on December 15,
2015. As the MSPB-PAC measures were under
[[Page 52089]]
development, there were three voting options for members: Encourage
continued development, do not encourage further consideration, and
insufficient information.\10\ The MAP PAC/LTC workgroup voted to
``encourage continued development'' for each of the MSPB-PAC
measures.\11\ The MAP PAC/LTC workgroup's vote of ``encourage continued
development'' was affirmed by the MAP Coordinating Committee on January
26, 2016.\12\ The MAP's concerns about the MSPB-PAC measures, as
outlined in their final report ``MAP 2016 Considerations for
Implementing Measures in Federal Programs: Post-Acute Care and Long-
Term Care'' and Spreadsheet of Final Recommendations, were taken into
consideration during the measure development process and are discussed
as part of our responses to public comments, described
below.13 14
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\10\ National Quality Forum, Measure Applications Partnership,
``Process and Approach for MAP Pre-Rulemaking Deliberations, 2015-
2016'' (February 2016) https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=81693.
\11\ National Quality Forum, Measure Applications Partnership
Post-Acute Care/Long-Term Care Workgroup, ``Meeting Transcript--Day
2 of 2'' (December 15, 2015) 104-106. https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=81470.
\12\ National Quality Forum, Measure Applications Partnership,
``Meeting Transcript--Day 1 of 2'' (January 26, 2016) 231-232 https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=81637.
\13\ National Quality Forum, Measure Applications Partnership,
``MAP 2016. Considerations for Implementing Measures in Federal
Programs: Post-Acute Care and Long-Term Care'' Final Report,
(February 2016) https://www.qualityforum.org/Publications/2016/02/MAP_2016_Considerations_for_Implementing_Measures_in_Federal_Programs_-_PAC-LTC.aspx.
\14\ National Quality Forum, Measure Applications Partnership,
``Spreadsheet of MAP 2016 Final Recommendations'' (February 1, 2016)
https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=81593.
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Since the MAP's review and recommendation of continued development,
CMS continued to refine risk adjustment models and conduct measure
testing for the IMPACT Act measures in compliance with the MAP's
recommendations. The IMPACT Act measures are consistent with the
information submitted to the MAP and support the scientific
acceptability of these measures for use in quality reporting programs.
In addition, a public comment period, accompanied by draft measures
specifications, was open from January 13 to 27, 2016 and extended to
February 5. A total of 45 comments on the MSPB-PAC measures were
received during this 3.5 week period. The comments received also
covered each of the MAP's concerns as outlined in their Final
Recommendations.\15\ The MSPB-PAC Public Comment Summary Report is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/2016_03_24_mspb_pac_public_comment_summary_report.pdf and the MSPB-PAC
Public Comment Supplementary Materials are available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/2016_03_24_mspb_pac_public_comment_summary_report_supplementary_materials.pdf: These documents contain the public comments, along with our
responses including statistical analyses. The MSPB-PAC IRF QRP measure,
along with the other MSPB-PAC measures, as applicable, will be
submitted for NQF endorsement when feasible.
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\15\ National Quality Forum, Measure Applications Partnership,
``Spreadsheet of MAP 2016 Final Recommendations'' (February 1, 2016)
https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=81593.
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To calculate the MSPB-PAC IRF QRP measure for each IRF provider, we
first defined the construction of the MSPB-PAC IRF QRP episode,
including the length of the episode window as well as the services
included in the episode. Next, we apply the methodology for the measure
calculation. The specifications are discussed further in this section.
More detailed specifications for the MSPB-PAC measures, including the
MSPB-PAC IRF QRP measure, are available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
a. Episode Construction
An MSPB-PAC IRF QRP episode begins at the episode trigger, which is
defined as the patient's admission to an IRF. The admitting facility is
the attributed provider, for whom the MSPB-PAC IRF QRP measure is
calculated. The episode window is the time period during which Medicare
FFS Part A and Part B services are counted towards the MSPB-PAC IRF QRP
episode. Because Medicare FFS claims are already reported to the
Medicare program for payment purposes, IRF providers would not be
required to report any additional data to CMS for calculation of this
measure. Thus, there would be no additional data collection burden from
the implementation of this measure.
The episode window is comprised of a treatment period and an
associated services period. The treatment period begins at the trigger
(that is, on the day of admission to the IRF) and ends on the day of
discharge from that IRF. Readmissions to the same facility occurring
within 7 or fewer days do not trigger a new episode, and instead are
included in the treatment period of the original episode. When two
sequential stays at the same IRF occur within 7 or fewer days of one
another, the treatment period ends on the day of discharge for the
latest IRF stay. The treatment period includes those services that are
provided directly or reasonably managed by the IRF provider that are
directly related to the beneficiary's care plan. The associated
services period is the time during which Medicare Part A and Part B
services (with certain exclusions) are counted towards the episode. The
associated services period begins at the episode trigger and ends 30
days after the end of the treatment period. The distinction between the
treatment period and the associated services period is important
because clinical exclusions of services may differ for each period.
Certain services are excluded from the MSPB-PAC IRF QRP episodes
because they are clinically unrelated to IRF care, and/or because IRF
providers may have limited influence over certain Medicare services
delivered by other providers during the episode window. These limited
service-level exclusions are not counted towards a given IRF provider's
Medicare spending to ensure that beneficiaries with certain conditions
and complex care needs receive the necessary care. Certain services
that are determined to be outside of the control of an IRF provider
include planned hospital admissions, management of certain preexisting
chronic conditions (for example, dialysis for end-stage renal disease
(ESRD), and enzyme treatments for genetic conditions), treatment for
preexisting cancers, organ transplants, and preventive screenings (for
example, colonoscopy and mammograms). Exclusion of such services from
the MSPB-PAC IRF QRP episode ensures that facilities do not have
disincentives to treat patients with certain conditions or complex care
needs.
An MSPB-PAC episode may begin during the associated services period
of an MSPB-PAC IRF QRP episode in the 30 days post-treatment. One
possible scenario occurs where an IRF provider discharges a beneficiary
who is then admitted to an LTCH within 30 days. The LTCH claim will be
included once as an associated service for the attributed provider of
the first MSPB-PAC IRF QRP episode and once as a treatment service for
the attributed
[[Page 52090]]
provider of the second MSPB-PAC LTCH QRP episode. As in the case of
overlap between hospital and PAC episodes discussed earlier, this
overlap is necessary to ensure continuous accountability between
providers throughout a beneficiary's trajectory of care, as both
providers share incentives to deliver high quality care at a lower cost
to Medicare. Even within the IRF setting, one MSPB-PAC IRF QRP episode
may begin in the associated services period of another MSPB-PAC IRF QRP
episode in the 30 days post-treatment. The second IRF claim would be
included once as an associated service for the attributed IRF provider
of the first MSPB-PAC IRF QRP episode and once as a treatment service
for the attributed IRF provider of the second MSPB-PAC IRF QRP episode.
Again, this ensures that IRF providers have the same incentives
throughout both MSPB-PAC IRF QRP episodes to deliver quality care and
engage in patient-focused care planning and coordination. If the second
MSPB-PAC IRF QRP episode were excluded from the second IRF provider's
MSPB-PAC IRF QRP measure, that provider would not share the same
incentives as the first IRF provider of the first MSPB-PAC IRF QRP
episode. The MSPB-PAC IRF QRP measure was designed to benchmark the
resource use of each attributed provider against what their spending is
expected to be as predicted through risk adjustment. As discussed
further in this section, the measure takes the ratio of observed
spending to expected spending for each episode and then takes the
average of those ratios across all of the attributed provider's
episodes. The measure is not a simple sum of all costs across a
provider's episodes, thus mitigating concerns about double counting.
b. Measure Calculation
Medicare payments for Part A and Part B claims for services
included in MSPB-PAC IRF QRP episodes, defined according to the
methodology previously discussed, are used to calculate the MSPB-PAC
IRF QRP measure. Measure calculation involves determination of the
episode exclusions, the approach for standardizing payments for
geographic payment differences, the methodology for risk adjustment of
episode spending to account for differences in patient case mix, and
the specifications for the measure numerator and denominator.
(1) Exclusion Criteria
In addition to service-level exclusions that remove some payments
from individual episodes, we exclude certain episodes in their entirety
from the MSPB-PAC IRF QRP measure to ensure that the MSPB-PAC IRF QRP
measure accurately reflects resource use and facilitates fair and
meaningful comparisons between IRF providers. The episode-level
exclusions are as follows:
Any episode that is triggered by an IRF claim outside the
50 states, DC, Puerto Rico, and U.S. Territories.
Any episode where the claim(s) constituting the attributed
IRF provider's treatment have a standard allowed amount of zero or
where the standard allowed amount cannot be calculated.
Any episode in which a beneficiary is not enrolled in
Medicare FFS for the entirety of a 90-day lookback period (that is, a
90-day period prior to the episode trigger) plus episode window
(including where a beneficiary dies), or is enrolled in Part C for any
part of the lookback period plus episode window.
Any episode in which a beneficiary has a primary payer
other than Medicare for any part of the 90-day lookback period plus
episode window.
Any episode where the claim(s) constituting the attributed
IRF provider's treatment include at least one related condition code
indicating that it is not a prospective payment system bill.
(2) Standardization and Risk Adjustment
Section 1899B(d)(2)(C) of the Act requires that the MSPB-PAC
measures are adjusted for the factors described under section
1886(o)(2)(B)(ii) of the Act, which include adjustment for factors such
as age, sex, race, severity of illness, and other factors that the
Secretary determines appropriate. Medicare payments included in the
MSPB-PAC IRF QRP measure are payment-standardized and risk-adjusted.
Payment standardization removes sources of payment variation not
directly related to clinical decisions and facilitates comparisons of
resource use across geographic areas. We proposed to use the same
payment standardization methodology that was used in the NQF-endorsed
hospital MSPB measure. This methodology removes geographic payment
differences, such as wage index and geographic practice cost index
(GPCI), incentive payment adjustments, and other add-on payments that
support broader Medicare program goals including indirect graduate
medical education (IME) and hospitals serving a disproportionate share
of uninsured patients (DSH).\16\
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\16\ QualityNet, ``CMS Price (Payment) Standardization--Detailed
Methods'' (Revised May 2015) https://qualitynet.org/dcs/ContentServer?c=Page&pagename=QnetPublic%2FPage%2FQnetTier4&cid=1228772057350.
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Risk adjustment uses patient claims history to account for case-mix
variation and other factors that affect resource use but are beyond the
influence of the attributed IRF provider. To assist with risk
adjustment, we created mutually exclusive and exhaustive clinical case
mix categories using the most recent institutional claim in the 60 days
prior to the start of the MSPB-PAC IRF QRP episode. The beneficiaries
in these clinical case mix categories have a greater degree of clinical
similarity than the overall IRF patient population, and allow us to
more accurately estimate Medicare spending. Our MSPB-PAC IRF QRP
measure, adapted for the IRF setting from the NQF-endorsed hospital
MSPB measure, uses a regression framework with a 90-day hierarchical
condition category (HCC) lookback period and covariates including the
clinical case mix categories, HCC indicators, age brackets, indicators
for originally disabled, ESRD enrollment, and long-term care status,
and selected interactions of these covariates where sample size and
predictive ability make them appropriate. We sought and considered
public comment regarding the treatment of hospice services occurring
within the MSPB-PAC IRF QRP episode window. Given the comments
received, we proposed to include the Medicare spending for hospice
services but risk adjust for them, such that MSPB-PAC IRF QRP episodes
with hospice services are compared to a benchmark reflecting other
MSPB-PAC IRF QRP episodes with hospice services. We believe this
strikes a balance between the measure's intent of evaluating Medicare
spending and ensuring that providers do not have incentives against the
appropriate use of hospice services in a patient-centered continuum of
care.
We proposed to use RICs in response to commenters' concerns about
the risk adjustment approach for the MSPB-PAC IRF QRP measure.
Commenters suggested the use of case mix groups (CMGs); however, we
believed that the use of RICs may be more appropriate given that the
other covariates incorporated in the model partially account for
factors in CMGs (for example, age and certain HCC indicators). RICs do
not account for functional status as CMGs do, as the functional status
information in CMGs is based on the IRF-PAI. Given the
[[Page 52091]]
move toward standardized data that was mandated by the IMPACT Act, we
have chosen to defer risk adjustment for functional status until
standardized data become available. We sought comments on whether the
use of CMGs would be appropriate to include in the MSPB-PAC IRF QRP
risk adjustment model.
We understand the important role that sociodemographic factors,
beyond age, play in the care of patients. However, we continue to have
concerns about holding providers to different standards for the
outcomes of their patients of diverse sociodemographic status because
we do not want to mask potential disparities or minimize incentives to
improve the outcomes of disadvantaged populations. We will monitor the
impact of sociodemographic status on providers' results on our
measures.
The NQF is currently undertaking a 2-year trial period in which new
measures and measures undergoing maintenance review will be assessed to
determine if risk-adjusting for sociodemographic factors is
appropriate. For 2 years, NQF will conduct a trial of temporarily
allowing inclusion of sociodemographic factors in the risk-adjustment
approach for some performance measures. At the conclusion of the trial,
NQF will issue recommendations on future permanent inclusion of
sociodemographic factors. During the trial, measure developers are
expected to submit information such as analyses and interpretations as
well as performance scores with and without sociodemographic factors in
the risk adjustment model.
Furthermore, ASPE is conducting research to examine the impact of
sociodemographic status on quality measures, resource use, and other
measures under the Medicare program as required under the IMPACT Act.
We will closely examine the findings of the ASPE reports and related
Secretarial recommendations and consider how they apply to our quality
programs at such time as they are available.
While we conducted analyses on the impact of age by sex on the
performance of the MSPB-PAC IRF QRP risk-adjustment model, we did not
propose to adjust the MSPB-PAC IRF QRP measure for socioeconomic
factors. As this MSPB-PAC IRF QRP measure would be submitted for NQF
endorsement, we prefer to await the results of this trial and study
before deciding whether to risk adjust for socioeconomic factors. We
will monitor the results of the trial, studies, and recommendations. We
invited public comment on how socioeconomic and demographic factors
should be used in risk adjustment for the MSPB-PAC IRF QRP measure.
(3) Measure Numerator and Denominator
The MPSB-PAC IRF QRP measure is a payment-standardized, risk-
adjusted ratio that compares a given IRF provider's Medicare spending
against the Medicare spending of other IRF providers within a
performance period. Similar to the hospital MSPB measure, the ratio
allows for ease of comparison over time as it obviates the need to
adjust for inflation or policy changes.
The MSPB-PAC IRF QRP measure is calculated as the ratio of the
MSPB-PAC Amount for each IRF provider divided by the episode-weighted
median MSPB-PAC Amount across all IRF providers. To calculate the MSPB-
PAC Amount for each IRF provider, one calculates the average of the
ratio of the standardized episode spending over the expected episode
spending (as predicted in risk adjustment), and then multiplies this
quantity by the average episode spending level across all IRF providers
nationally. The denominator for an IRF provider's MSPB-PAC IRF QRP
measure is the episode-weighted national median of the MSPB-PAC Amounts
across all IRF providers. An MSPB-PAC IRF QRP measure of less than 1
indicates that a given IRF provider's Medicare spending is less than
that of the national median IRF provider during a performance period.
Mathematically, this is represented in equation (A) below:
[GRAPHIC] [TIFF OMITTED] TR05AU16.009
where
Yij = attributed standardized spending for episode i and
provider j
[Ycirc]ij = expected standardized spending for episode i
and provider j, as predicted from risk adjustment
nj = number of episodes for provider j
n = total number of episodes nationally
i [isin] {Ij{time} = all episodes i in the set of episodes
attributed to provider j.
c. Data Sources
The MSPB-PAC IRF QRP resource use measure is an administrative
claims-based measure. It uses Medicare Part A and Part B claims from
FFS beneficiaries and Medicare eligibility files.
d. Cohort
The measure cohort includes Medicare FFS beneficiaries with an IRF
treatment period ending during the data collection period.
e. Reporting
We intend to provide initial confidential feedback to providers,
prior to public reporting of this measure, based on Medicare FFS claims
data from discharges in CY 2015 and 2016. We intend to publicly report
this measure using claims data from discharges in CY 2016 and 2017.
We proposed to use a minimum of 20 episodes for reporting and
inclusion in the IRF QRP. For the reliability calculation, as described
in the measure specifications for which a link has been provided above,
we used 2 years of data (FY 2013 and FY 2014) to increase the
statistical reliability of this measure. The reliability results
support the 20 episode case minimum, and 99.74 percent of IRF providers
had moderate or high reliability (above 0.4).
We invited public comment on our proposal to adopt the MSPB-PAC IRF
QRP measure for the IRF QRP. The comments we received, with our
responses, appear below.
Comment: Several commenters expressed concern about the lack of NQF
endorsement for proposed measures; some believed that the measure
should not be finalized until NQF endorsement is obtained.
[[Page 52092]]
Response: Regarding the lack of NQF endorsement, refer to section
VIII.B. of this final rule where we also discuss this topic.
Comment: Some commenters recommended the use of uniform single
MSPB-PAC measure that could be used to compare providers' resource use
across settings, but the commenters also recognized that we do not have
a uniform PPS for all the PAC settings currently. In the absence of a
single PAC PPS, the commenters recommended a single MSPB-PAC measure
for each setting that could be used to compare providers within a
setting. Under a single measure, the episode definitions, service
inclusions/exclusions, and risk adjustment methods would be the same
across all PAC settings.
Response: The four separate MSPB-PAC measures reflect the unique
characteristics of each PAC setting and the population it serves. The
four setting-specific MSPB-PAC measures are defined as consistently as
possible across settings given the differences in the payment systems
for each setting, and types of patients served in each setting. We have
taken into consideration these differences and aligned the
specifications, such as episode definitions, service inclusions/
exclusions and risk adjustment methods for each setting, to the extent
possible while ensuring the accuracy of the measures in each PAC
setting.
Each of the measures assess Medicare Part A and Part B spending
during the episode window which begins upon admission to the provider's
care and ends 30 days after the end of the treatment period. The
service-level exclusions are harmonized across settings. The definition
of the numerator and denominator is the same across settings. However,
specifications differ between settings when necessary to ensure that
the measures accurately reflect patient care and align with each
setting's payment system. For example, Medicare pays LTCHs and IRFs a
stay-level payment based on the assigned MS-LTC-DRG and CMG,
respectively, while SNFs are paid a daily rate based on the RUG level,
and HHA providers are reimbursed based on a fixed 60-day period for
standard home health claims. While the definition of the episode window
is consistent across settings and is based on the period of time that a
beneficiary is under a given provider's care, the duration of the
treatment period varies to reflect how providers are reimbursed under
the PPS that applies to each setting. The length of the post-treatment
period is consistent between settings. There are also differences in
services covered under the PPS that applies to each setting: For
example, durable medical equipment, prosthetics, orthotics, and
supplies (DMEPOS) claims are covered LTCH, IRF, and SNF services but
are not covered HHA services. This affects the way certain first-day
service exclusions are defined for each measure.
We recognize that beneficiaries may receive similar services as
part of their overall treatment plan in different PAC settings, but
believe that there are some important differences in beneficiaries'
care profiles that are difficult to capture in a single measure that
compares resource use across settings.
Also, the risk adjustment models for the MSPB-PAC measures share
the same covariates to the greatest extent possible to account for
patient case mix. However, the measures also incorporate additional
setting-specific information where available to increase the predictive
power of the risk adjustment models. For example, the MSPB-PAC LTCH QRP
risk adjustment model uses MS-LTC-DRGs and Major Diagnostic Categories
(MDCs) and the MSPB-PAC IRF QRP model includes Rehabilitation
Impairment Categories (RICs). The HH and SNF settings do not have
analogous variables that directly reflect a patient's clinical profile.
We will continue to work towards a more uniform measure across
settings as we gain experience with these measures, and we plan to
conduct further research and analyses about comparability of resource
use measures across settings for clinically similar patients, different
treatment periods and windows, risk adjustment, service exclusions, and
other factors.
Comment: A few commenters noted that the MSPB-PAC measures are
resource use measures that are not a standalone indicator of quality.
Response: We appreciate the comment regarding the proposed MSPB-PAC
measures as resource use measures. The MSPB-PAC IRF QRP measure is one
of five QRP measures that were proposed in the FY 2017 IRF PPS proposed
rule for inclusion in the IRF QRP: In addition to the MSPB-PAC IRF QRP
measure, these proposed measures were the Discharge to Community--PAC
IRF QRP measure (81 FR 24201 through 24204), the Potentially
Preventable 30-day Post-Discharge Readmission Measure for IRF QRP (81
FR 24204 through 24206), the Potentially Preventable Within Stay
Readmission Measure for IRFs (81 FR 242096 through 24207), and the Drug
Regimen Review Conducted with Follow-Up for Identified Issues--PAC IRF
QRP measure (81 FR 24207 through 24209). As part of the IRF QRP, the
MSPB-PAC IRF QRP measure will be paired with quality measures; we
direct readers to section VIII.E. of this final for a discussion of
quality measures previously finalized for use in the IRF QRP. We
believe it is important that the cost of care be explicitly measured so
that, in conjunction with other quality measures, we can publicly
report which IRF providers are involved in the provision of high
quality care at lower cost.
Comment: One commenter recommended that proposed quality measures
obtain the support of a TEP including IRF representatives to ensure the
applicability of the measures to the IRF setting.
Response: We thank the commenter for their recommendation. As
discussed in the proposed rule (81 FR 24198), we convened a TEP
consisting of 12 panelists with combined expertise in PAC settings,
including IRFs, on October 29 and 30, 2015, in Baltimore, Maryland.
TEPs do not formally support or endorse measures. However, their
feedback on risk adjustment, episode windows, exclusions, and other key
elements of measure construction were incorporated into measure
development. The MSPB-PAC TEP Summary Report Web site is listed above
in this section.
Comment: Several commenters recommended that the risk adjustment
model for the MSPB-PAC IRF QRP measure include variables for SES/SDS
factors. A commenter recommended that a ``fairer'' approach than using
SES/SDS factors as risk adjustment variables would be to compare
resource use levels that have not been adjusted for SES/SDS factors
across peer providers (that is, providers with similar shares of
beneficiaries with similar SES characteristics).
Response: With regard to the suggestions that the model include
sociodemographic factors and the suggestion pertaining to an approach
with which to convey data comparisons, we refer readers to section
VIII.F of this final rule where we also discuss these topics.
Comment: Some commenters recommended that additional variables be
included in risk adjustment to better capture clinical complexity. A
few commenters suggested the inclusion of functional and cognitive
status, other patient assessment data and patient-reported data.
Commenters recommended that additional variables should include
obesity, amputations, CVAs (hemiplegia/paresis), ventilator status, and
discharged against medical advice.
Response: We thank the commenters for their suggestions. HCC
indicators
[[Page 52093]]
that are already included in the risk adjustment model account for
amputations, hemiplegia, and paresis. We believe that the other risk
adjustment variables adequately adjust for ventilator dependency and
obesity by accounting for HCCs, clinical case mix categories, and prior
inpatient and ICU length of stay. Excluding patients who are discharged
against medical advice may create incentives for providers to use this
discharge status code to remove high-cost patients from their MSPB-PAC
measure calculation. Patient-reported data is not currently available
on Medicare FFS claims. The addition of such data would likely be
burdensome on IRF providers and the reliability of the data would need
to be thoroughly tested before use in Medicare programs.
We recognize the importance of accounting for beneficiaries'
functional and cognitive status in the calculation of predicted episode
spending. We considered the potential use of functional status
information in the risk adjustment models for the MSPB-PAC measures.
However, we decided not to include this information derived from
current setting-specific assessment instruments given the move towards
standardized data as mandated by the IMPACT Act. We will revisit the
inclusion of functional status in these measures' risk adjustment
models in the future when the standardized functional status data
mandated by the IMPACT Act become available. Once they are available,
we will take a gradual and systematic approach in evaluating how they
might be incorporated. We intend to implement any changes if
appropriate based on testing.
Comment: A few commenters expressed concern that the measures will
give incentive to IRFs to avoid admitting medically complex patients,
which would result in unintended consequences.
Response: To mitigate the risk of creating incentives for IRFs to
avoid admitting medically complex patients, who may be at higher risk
for poor outcomes and higher costs, we have included factors related to
medical complexity in the risk adjustment methodology for the MSPB-PAC
IRF QRP measure. We also intend to conduct ongoing monitoring to assess
for potential unintended consequences associated with the
implementation of these measures.
Comment: Several commenters recommended that IRF interrupted stays
be excluded as those patients would appear more expensive for receiving
necessary care outside of the control of the IRF (that is, during the
interruption).
Response: We believe that IRFs are in a position to influence a
patient's experience and outcomes after the initial discharge from the
IRF, including the likelihood and intensity of IRF readmissions. As
noted in the proposed rule (81 FR 24197), the proposed MSPB-PAC IRF QRP
measure will support IRF providers' efforts to promote care
coordination.
Comment: Several commenters expressed concerns over the inclusion
of spending that occurs within the thirty day post-discharge timeframe
in the measure, believing that providers do not have sufficient control
over the patient in the post-treatment period.
Response: We believe that the post-treatment period may be
reflective of and influenced by the services furnished by the PAC
provider, therefore, including the 30-day post-treatment period in the
MSPB-PAC IRF QRP measure creates a continuum of accountability between
providers and may incentivize improvements in post-treatment care
planning and coordination. The MSPB-PAC measures complement the NQF-
endorsed hospital MSPB measure: As they all include a period during
which post-treatment spending is attributed to the provider, this
accountability incentivizes acute and PAC providers to engage in
appropriate discharge planning and post-treatment care coordination to
minimize the likelihood of costly adverse events, such as avoidable
hospitalizations.
Comment: Several commenters recommended first day service
exclusions for IRFs that are the same as other PAC settings, such as
SNFs.
Response: As discussed in the MSPB-PAC Measure Specifications, the
Web site that is listed above in this section, treatment services
occurring on the first day of MSPB-PAC episodes are subject to
exclusions related to prior institutional care such as discharge care
services. IRFs provide more intense hospital-level care and have
physicians or midlevel practitioners evaluate patients upon admission,
which enables the facility to influence many services delivered on the
first day of the PAC stay. As such, only a limited number of discharge
care services are excluded. Moreover, the NQF-endorsed hospital MSPB
measure includes a period during which post-treatment spending is
attributed to the provider; this accountability incentivizes acute and
PAC providers to engage in appropriate discharge planning and post-
treatment care coordination.
Comment: Several commenters recommended that short stays be
excluded from the MSPB-PAC IRF QRP measure as these patients are
identified as not being suitable for IRF care.
Response: We believe that including short stay discharges in the
measure promotes timely and accurate pre-admission screening, as well
as discharge planning and post-discharge care coordination. Including
IRF short stays maintains consistency across the MSPB-PAC measures to
the greatest extent possible. Short stays constitute a very small share
of IRF stays nationally; in FY 2014, approximately 1.8 percent of IRF
stays were short stay discharges. Moreover, the MSPB-PAC IRF QRP
measure's methodology excludes outlier episodes. Therefore, we do not
believe that inclusion of short stays in the MSPB-PAC IRF QRP measure
will unfairly disadvantage or advantage an IRF provider in their
performance on the measure. Moreover, including short stay discharges
incentivizes providers to maintain beneficiaries under their care for
the appropriate length of time, and will not incentivize IRFs to
prematurely discharge their beneficiaries. We are finalizing the MSPB-
PAC IRF QRP measure to include short stay discharges after careful
consideration of the commenter's input.
Comment: Several commenters recommended the use of CMGs for risk
adjustment instead of RICs to more fully and accurately account for and
explain variances in resource utilization and case mix in the IRF
setting. Commenters noted that CMGs incorporate functional status and
are weighted to account for patients' predicted resource requirements,
while RICs only indicate patients' overall medical condition; as such
there can be wide variation of reimbursement within a single RIC.
Response: We have carefully considered the commenters feedback and
are proceeding to finalize the measure as proposed. We believe the
beneficiary's principal diagnosis or impairment as provided by the RIC
currently supports the accurate estimation of Medicare spending while
also reflecting clinical information that is accurately and
consistently coded on IRF claims. The inclusion of RICs as variables in
the MSPB-PAC IRF QRP risk adjustment model maintains consistency
between MSPB-PAC resource use measures for each setting to the greatest
extent possible, in that the other settings' MSPB-PAC measures do not
incorporate variables reflecting the beneficiaries' functional status
information. We may reconsider how to consistently incorporate
functional status into the risk adjustment models for the MSPB-PAC
measures once standardized data mandated by the IMPACT Act become
available in the
[[Page 52094]]
future. Furthermore, the covariates incorporated in the MSPB-PAC IRF
QRP risk adjustment model partially account for two factors in CMGs--
age and co-morbidities. For co-morbidities, the risk adjustment
specifications use flags for Hierarchical Condition Categories (HCCs)
defined by scanning inpatient, Part B physician/carrier, and outpatient
claims during a 90-day lookback period. We appreciate commenters'
thoughtful input and thank them for their engagement with this measure
through the rulemaking process.
Comment: A few commenters suggested that descriptive statistics on
the measure score by provider-level characteristics (for example,
urban/rural status and bed size) would be useful to evaluate measure
design decisions.
Response: Table 8 shows the MSPB-PAC IRF provider scores by
provider characteristics, calculated using FY 2013 and FY 2014 data.
Table 8--MSPB-PAC IRF Scores by Provider Characteristics
--------------------------------------------------------------------------------------------------------------------------------------------------------
Score percentile
Provider characteristic Number of Mean ----------------------------------------------------------------------------
providers score 1st 10th 25th 50th 75th 90th 99th
--------------------------------------------------------------------------------------------------------------------------------------------------------
All Providers........................................ 1,169 0.99 0.78 0.88 0.93 0.98 1.04 1.09 1.24
Urban/Rural:
Urban............................................ 979 0.99 0.77 0.88 0.93 0.98 1.04 1.08 1.24
Rural............................................ 190 0.98 0.79 0.88 0.91 0.97 1.04 1.10 1.25
Ownership Type:
For profit....................................... 345 1.01 0.82 0.91 0.97 1.01 1.06 1.10 1.24
Non-profit....................................... 569 0.97 0.76 0.87 0.91 0.97 1.02 1.07 1.28
Government....................................... 142 0.98 0.81 0.88 0.93 0.98 1.02 1.08 1.23
Unknown.......................................... 113 0.97 0.77 0.88 0.91 0.96 1.02 1.06 1.31
Census Division:
New England...................................... 36 1.03 0.86 0.92 0.97 1.03 1.08 1.12 1.16
Middle Atlantic.................................. 153 0.99 0.79 0.89 0.93 0.98 1.05 1.09 1.30
East North Central............................... 210 0.96 0.79 0.87 0.91 0.97 1.01 1.04 1.10
West North Central............................... 103 0.94 0.76 0.83 0.90 0.94 0.99 1.03 1.14
South Atlantic................................... 162 1.00 0.80 0.90 0.95 1.00 1.05 1.09 1.24
East South Central............................... 78 1.00 0.87 0.92 0.96 0.99 1.04 1.08 1.11
West South Central............................... 226 1.01 0.85 0.91 0.95 1.02 1.05 1.12 1.24
Mountain......................................... 91 1.00 0.79 0.88 0.93 0.98 1.05 1.12 1.99
Pacific.......................................... 106 0.96 0.74 0.83 0.89 0.95 1.02 1.08 1.32
Other............................................ 4 0.88 0.74 0.74 0.79 0.90 0.97 0.98 0.98
Bed Count:
0-49............................................. 114 1.01 0.79 0.91 0.96 1.01 1.04 1.12 1.25
50-99............................................ 188 1.01 0.80 0.91 0.96 1.00 1.06 1.09 1.30
100-199.......................................... 231 0.98 0.79 0.87 0.92 0.98 1.04 1.10 1.24
200-299.......................................... 184 0.97 0.77 0.87 0.91 0.97 1.01 1.07 1.44
300 +............................................ 452 0.98 0.77 0.88 0.92 0.97 1.03 1.08 1.24
Number of Episodes:
0-99............................................. 108 1.00 0.74 0.81 0.89 0.97 1.07 1.16 1.83
100-249.......................................... 344 0.97 0.76 0.86 0.90 0.96 1.03 1.08 1.31
250-499.......................................... 327 0.98 0.82 0.88 0.92 0.97 1.03 1.08 1.23
500-1000......................................... 216 0.99 0.83 0.92 0.95 0.99 1.03 1.07 1.17
1000 +........................................... 174 1.01 0.89 0.94 0.97 1.02 1.06 1.08 1.15
Teaching:
Non-teaching..................................... 1,059 0.98 0.77 0.88 0.93 0.98 1.03 1.08 1.24
Patient to ADC less than 10%..................... 63 0.99 0.83 0.90 0.93 0.98 1.04 1.08 1.30
Patient to ADC 10%-20%........................... 36 1.02 0.83 0.89 0.95 1.00 1.06 1.11 1.83
Patient to ADC greater than 20%.................. 11 1.00 0.88 0.90 0.91 1.03 1.06 1.07 1.08
--------------------------------------------------------------------------------------------------------------------------------------------------------
Comment: One commenter recommended that a geographic-specific (for
example, state or regional) median should be used instead of the
national median, citing differences in cost, patient population, and
regulation.
Response: As noted in the proposed rule (81 FR 24199), we proposed
to use the same payment standardization methodology that used in the
NQF-endorsed hospital MSPB measure to account for variation in Medicare
spending. This methodology removes geographic payment differences, such
as wage index and geographic practice cost index (GPCI), incentive
payment adjustments, and other add-on payments that support broader
Medicare program goals including indirect graduate medical education
(IME) and hospitals serving a disproportionate share of uninsured
patients (DSH). We believe that this approach accounts for the
differences that the commenter raises while also maintaining
consistency with the NQF-endorsed hospital MSPB measure's methodology
for addressing regional variation through payment standardization.
Comment: Some commenters recommended that the measure be tested for
reliability and validity prior to finalization.
Response: The MSPB-PAC IRF QRP measure has been tested for
reliability using 2 years of data (FY 2013 and FY 2014). The
reliability results support the 20 episode case minimum, and 99.74
percent of IRF providers had moderate or high reliability (above 0.4).
Further details on the reliability calculation are provided in the
MSPB-PAC Measure Specifications Web site that is listed above in this
section.
Comment: Some commenters recommended an initial confidential
[[Page 52095]]
data preview period for providers, prior to public reporting.
Response: Providers will receive a confidential preview report with
30 days for review in advance of their data and information being
publically displayed.
Comment: A few commenters believed that the measure is a burden for
providers.
Response: We appreciate the importance of avoiding undue burden on
providers. The MSPB-PAC IRF QRP measure relies on Medicare FFS claims,
which are already reported to the Medicare program for payment
purposes. PAC providers will not be required to report additional data
to CMS for calculation of this measure
Comment: One commenter requested that if the measures are finalized
after a trial, that the same FIM Rating system be used to eliminate
confusion and ensure that providers are submitting accurate
information.
Response: The MSPB-PAC IRF QRP Measure focuses on comparing
resource use among providers within a given PAC setting and does not
measure clinical outcomes such as severity of disability.
In summary, after consideration of the public comments we received,
we are finalizing the specifications of the MSPB-PAC IRF QRP resource
use measure, as proposed. A Web site for the measure specifications has
been provided above in this section.
Specifically, we are finalizing the definition of an MSPB-PAC IRF
QRP episode, beginning from episode trigger. An episode window
comprises a treatment period beginning at the trigger and ended upon
discharge, and associated services period beginning at the trigger and
ending 30 days after the end of the treatment period. Readmissions to
the same IRF within 7 or fewer days do not trigger a new episode and
are instead included in the treatment period of the first episode.
We exclude certain services that are clinically unrelated to IRF
care and/or because IRF providers may have limited influence over
certain Medicare services delivered by other providers during the
episode window. We also exclude certain episodes in their entirety from
the MSPB-PAC IRF QRP measure, such as where a beneficiary is not
enrolled in Medicare FFS for the entirety of the lookback period plus
episode window.
We finalize the inclusion of Medicare payments for Part A and Part
B claims for services included in the MSPB-PAC IRF QRP episodes to
calculate the MSPB-PAC IRF QRP measure.
We are finalizing our proposal to risk adjust using covariates
including age brackets, HCC indicators, prior inpatient stay length,
ICU stay length, clinical case mix categories, and indicators for
originally disabled, ESRD enrollment, long-term care status, and
hospice claim in episode window. The measure also adjusts for
geographic payment differences such as wage index and GPCI, and adjust
for Medicare payment differences resulting from IME and DSH.
We calculate the individual providers' MSPB-PAC Amount which is
inclusive of MSPB-PAC IRF QRP observed episode spending over the
expected episode spending as predicted through risk adjustment.
Individual IRF providers' scores are calculated as their individual
MSPB-PAC Amount divided by the median MSPB-PAC amount across all IRFs.
2. Measure To Address the IMPACT Act Domain of Resource Use and Other
Measures: Discharge to Community-Post Acute Care (PAC) Inpatient
Rehabilitation Facility (IRF) Quality Reporting Program (QRP)
Sections 1899B(d)(1)(B) and 1899B(a)(2)(E)(ii) of the Act require
the Secretary to specify a measure to address the domain of discharge
to community by SNFs, LTCHs, and IRFs by October 1, 2016, and HHAs by
January 1, 2017. We proposed to adopt the measure, Discharge to
Community-PAC IRF QRP, for the IRF QRP for the FY 2018 payment
determination and subsequent years as a Medicare FFS claims-based
measure to meet this requirement.
This measure assesses successful discharge to the community from an
IRF setting, with successful discharge to the community including no
unplanned rehospitalizations and no death in the 31 days following
discharge from the IRF. Specifically, this measure reports an IRF's
risk-standardized rate of Medicare FFS patients who are discharged to
the community following an IRF stay, and 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. The term ``community'', for this
measure, is defined as home or self care, with or without home health
services, based on Patient Discharge Status Codes 01, 06, 81, and 86 on
the Medicare FFS claim.17 18 This measure is conceptualized
uniformly across the PAC settings, in terms of the definition of the
discharge to community outcome, the approach to risk adjustment, and
the measure calculation.
---------------------------------------------------------------------------
\17\ National Uniform Billing Committee Official UB-04 Data
Specifications Manual 2017, Version 11, July 2016, Copyright 2016,
American Hospital Association.
\18\ This definition is not intended to suggest that board and
care homes, assisted living facilities, or other settings included
in the definition of ``community'' for the purpose of this measure
are the most integrated setting for any particular individual or
group of individuals under the Americans with Disabilities Act (ADA)
and Section 504.
---------------------------------------------------------------------------
Discharge to a community setting is an important health care
outcome for many patients for whom the overall goals of post-acute care
include optimizing functional improvement, returning to a previous
level of independence, and avoiding institutionalization. Returning to
the community is also an important outcome for many patients who are
not expected to make functional improvement during their IRF stay, and
for patients who may be expected to decline functionally due to their
medical condition. The discharge to community outcome offers a multi-
dimensional view of preparation for community life, including the
cognitive, physical, and psychosocial elements involved in a discharge
to the community.19 20
---------------------------------------------------------------------------
\19\ El-Solh AA, Saltzman SK, Ramadan FH, Naughton BJ. Validity
of an artificial neural network in predicting discharge destination
from a postacute geriatric rehabilitation unit. Archives of physical
medicine and rehabilitation. 2000;81(10):1388-1393.
\20\ Tanwir S, Montgomery K, Chari V, Nesathurai S. Stroke
rehabilitation: Availability of a family member as caregiver and
discharge destination. European journal of physical and
rehabilitation medicine. 2014;50(3):355-362.
---------------------------------------------------------------------------
In addition to being an important outcome from a patient and family
perspective, patients discharged to community settings, on average,
incur lower costs over the recovery episode, compared with those
discharged to institutional settings.21 22 Given the high
costs of care in institutional settings, encouraging IRFs to prepare
patients for discharge to community, when clinically appropriate, may
have cost-saving implications for the Medicare program.\23\ Also,
providers have discovered that successful discharge to community was a
major driver of their ability to achieve savings, where capitated
payments for post-acute care
[[Page 52096]]
were in place.\24\ For patients who require long-term care due to
persistent disability, discharge to community could result in lower
long-term care costs for Medicaid and for patients' out-of-pocket
expenditures.\25\
---------------------------------------------------------------------------
\21\ Dobrez D, Heinemann AW, Deutsch A, Manheim L, Mallinson T.
Impact of Medicare's prospective payment system for inpatient
rehabilitation facilities on stroke patient outcomes. American
journal of physical medicine & rehabilitation/Association of
Academic Physiatrists. 2010;89(3):198-204.
\22\ Gage B, Morley M, Spain P, Ingber M. Examining Post Acute
Care Relationships in an Integrated Hospital System. Final Report.
RTI International;2009.
\23\ Ibid.
\24\ Doran JP, Zabinski SJ. Bundled payment initiatives for
Medicare and non-Medicare total joint arthroplasty patients at a
community hospital: Bundles in the real world. The journal of
arthroplasty. 2015;30(3):353-355.
\25\ Newcomer RJ, Ko M, Kang T, Harrington C, Hulett D, Bindman
AB. Health Care Expenditures After Initiating Long-term Services and
Supports in the Community Versus in a Nursing Facility. Medical
Care. 2016;54(3):221-228.
---------------------------------------------------------------------------
Analyses conducted for ASPE on PAC episodes, using a 5 percent
sample of 2006 Medicare claims, revealed that relatively high average,
unadjusted Medicare payments are associated with discharge to
institutional settings from IRFs, SNFs, LTCHs or HHAs, as compared with
payments associated with discharge to community settings.\26\ Average,
unadjusted Medicare payments associated with discharge to community
settings ranged from $0 to $4,017 for IRF discharges, $0 to $3,544 for
SNF discharges, $0 to $4,706 for LTCH discharges, and $0 to $992 for
HHA discharges. In contrast, payments associated with discharge to non-
community settings were considerably higher, ranging from $11,847 to
$25,364 for IRF discharges, $9,305 to $29,118 for SNF discharges,
$12,465 to $18,205 for LTCH discharges, and $7,981 to $35,192 for HHA
discharges.\27\
---------------------------------------------------------------------------
\26\ Gage B, Morley M, Spain P, Ingber M. Examining Post Acute
Care Relationships in an Integrated Hospital System. Final Report.
RTI International;2009.
\27\ Ibid.
---------------------------------------------------------------------------
Measuring and comparing facility-level discharge to community rates
is expected to help differentiate among facilities with varying
performance in this important domain, and to help avoid disparities in
care across patient groups. Variation in discharge to community rates
has been reported within and across post-acute settings; across a
variety of facility-level characteristics, such as geographic location
(for example, regional location, urban or rural location), ownership
(for example, for-profit or nonprofit), and freestanding or hospital-
based units; and across patient-level characteristics, such as race and
gender.28 29 30 31 32 33 Discharge to community rates in the
IRF setting have been reported to range from about 60 to 80
percent.34 35 36 37 38 39 Longer-term studies show that
rates of discharge to community from IRFs have decreased over time as
IRF length of stay has decreased.40 41 In the IRF Medicare
FFS population, using CY 2013 national claims data, we discovered that
approximately 69 percent of patients were discharged to the community.
Greater variation in discharge to community rates is seen in the SNF
setting, with rates ranging from 31 to 65
percent.42 43 44 45 A multi-center study of 23 LTCHs
demonstrated that 28.8 percent of 1,061 patients who were ventilator-
dependent on admission were discharged to home.\46\ A single-center
study revealed that 31 percent of LTCH hemodialysis patients were
discharged to home.\47\ One study noted that 64 percent of
beneficiaries who were discharged from the home health episode did not
use any other acute or post-acute services paid by Medicare in the 30
days after discharge.\48\ However, significant numbers of patients were
admitted to hospitals (29 percent) and lesser numbers to SNFs (7.6
percent), IRFs (1.5 percent), home health (7.2 percent) or hospice (3.3
percent).\49\
---------------------------------------------------------------------------
\28\ Reistetter TA, Karmarkar AM, Graham JE, et al. Regional
variation in stroke rehabilitation outcomes. Archives of physical
medicine and rehabilitation. 2014;95(1):29-38.
\29\ El-Solh AA, Saltzman SK, Ramadan FH, Naughton BJ. Validity
of an artificial neural network in predicting discharge destination
from a postacute geriatric rehabilitation unit. Archives of physical
medicine and rehabilitation. 2000;81(10):1388-1393.
\30\ March 2015 Report to the Congress: Medicare Payment Policy.
Medicare Payment Advisory Commission; 2015.
\31\ Bhandari VK, Kushel M, Price L, Schillinger D. Racial
disparities in outcomes of inpatient stroke rehabilitation. Archives
of physical medicine and rehabilitation. 2005;86(11):2081-2086.
\32\ Chang PF, Ostir GV, Kuo YF, Granger CV, Ottenbacher KJ.
Ethnic differences in discharge destination among older patients
with traumatic brain injury. Archives of physical medicine and
rehabilitation. 2008;89(2):231-236.
\33\ Berges IM, Kuo YF, Ostir GV, Granger CV, Graham JE,
Ottenbacher KJ. Gender and ethnic differences in rehabilitation
outcomes after hip-replacement surgery. American journal of physical
medicine & rehabilitation/Association of Academic Physiatrists.
2008;87(7):567-572.
\34\ Galloway RV, Granger CV, Karmarkar AM, et al. The Uniform
Data System for Medical Rehabilitation: Report of patients with
debility discharged from inpatient rehabilitation programs in 2000-
2010. American journal of physical medicine & rehabilitation/
Association of Academic Physiatrists. 2013;92(1):14-27.
\35\ Morley MA, Coots LA, Forgues AL, Gage BJ. Inpatient
rehabilitation utilization for Medicare beneficiaries with multiple
sclerosis. Archives of physical medicine and rehabilitation.
2012;93(8):1377-1383.
\36\ Reistetter TA, Graham JE, Deutsch A, Granger CV, Markello
S, Ottenbacher KJ. Utility of functional status for classifying
community versus institutional discharges after inpatient
rehabilitation for stroke. Archives of physical medicine and
rehabilitation. 2010;91(3):345-350.
\37\ Gagnon D, Nadeau S, Tam V. Clinical and administrative
outcomes during publicly-funded inpatient stroke rehabilitation
based on a case-mix group classification model. Journal of
rehabilitation medicine. 2005;37(1):45-52.
\38\ DaVanzo J, El-Gamil A, Li J, Shimer M, Manolov N, Dobson A.
Assessment of patient outcomes of rehabilitative care provided in
inpatient rehabilitation facilities (IRFs) and after discharge.
Vienna, VA: Dobson DaVanzo & Associates, LLC;2014.
\39\ Kushner DS, Peters KM, Johnson-Greene D. Evaluating Siebens
Domain Management Model for Inpatient Rehabilitation to Increase
Functional Independence and Discharge Rate to Home in Geriatric
Patients. Archives of physical medicine and rehabilitation.
2015;96(7):1310-1318.
\40\ Galloway RV, Granger CV, Karmarkar AM, et al. The Uniform
Data System for Medical Rehabilitation: Report of patients with
debility discharged from inpatient rehabilitation programs in 2000-
2010. American journal of physical medicine & rehabilitation/
Association of Academic Physiatrists. 2013;92(1):14-27.
\41\ Mallinson T, Deutsch A, Bateman J, et al. Comparison of
discharge functional status after rehabilitation in skilled nursing,
home health, and medical rehabilitation settings for patients after
hip fracture repair. Archives of physical medicine and
rehabilitation. 2014;95(2):209-217.
\42\ El-Solh AA, Saltzman SK, Ramadan FH, Naughton BJ. Validity
of an artificial neural network in predicting discharge destination
from a postacute geriatric rehabilitation unit. Archives of physical
medicine and rehabilitation. 2000;81(10):1388-1393.
\43\ Hall RK, Toles M, Massing M, et al. Utilization of acute
care among patients with ESRD discharged home from skilled nursing
facilities. Clinical journal of the American Society of Nephrology:
CJASN. 2015;10(3):428-434.
\44\ Stearns SC, Dalton K, Holmes GM, Seagrave SM. Using
propensity stratification to compare patient outcomes in hospital-
based versus freestanding skilled-nursing facilities. Medical care
research and review: MCRR. 2006;63(5):599-622.
\45\ Wodchis WP, Teare GF, Naglie G, et al. Skilled nursing
facility rehabilitation and discharge to home after stroke. Archives
of physical medicine and rehabilitation. 2005;86(3):442-448.
\46\ Scheinhorn DJ, Hassenpflug MS, Votto JJ, et al. Post-ICU
mechanical ventilation at 23 long-term care hospitals: A multicenter
outcomes study. Chest. 2007;131(1):85-93.
\47\ Thakar CV, Quate-Operacz M, Leonard AC, Eckman MH. Outcomes
of hemodialysis patients in a long-term care hospital setting: A
single-center study. American journal of kidney diseases: The
official journal of the National Kidney Foundation. 2010;55(2):300-
306.
\48\ Wolff JL, Meadow A, Weiss CO, Boyd CM, Leff B. Medicare
home health patients' transitions through acute and post-acute care
settings. Medical care. 2008;46(11):1188-1193.
\49\ Ibid.
---------------------------------------------------------------------------
Discharge to community is an actionable health care outcome, as
targeted interventions have been shown to successfully increase
discharge to community rates in a variety of post-acute
settings.50 51 52 53 Many of these
[[Page 52097]]
interventions involve discharge planning or specific rehabilitation
strategies, such as addressing discharge barriers and improving medical
and functional status.54 55 56 57 The effectiveness of these
interventions suggests that improvement in discharge to community rates
among post-acute care patients is possible through modifying provider-
led processes and interventions.
---------------------------------------------------------------------------
\50\ Kushner DS, Peters KM, Johnson-Greene D. Evaluating Siebens
Domain Management Model for Inpatient Rehabilitation to Increase
Functional Independence and Discharge Rate to Home in Geriatric
Patients. Archives of physical medicine and rehabilitation.
2015;96(7):1310-1318.
\51\ Wodchis WP, Teare GF, Naglie G, et al. Skilled nursing
facility rehabilitation and discharge to home after stroke. Archives
of physical medicine and rehabilitation. 2005;86(3):442-448.
\52\ Berkowitz RE, Jones RN, Rieder R, et al. Improving
disposition outcomes for patients in a geriatric skilled nursing
facility. Journal of the American Geriatrics Society.
2011;59(6):1130-1136.
\53\ Kushner DS, Peters KM, Johnson-Greene D. Evaluating use of
the Siebens Domain Management Model during inpatient rehabilitation
to increase functional independence and discharge rate to home in
stroke patients. PM & R: The journal of injury, function, and
rehabilitation. 2015;7(4):354-364.
\54\ Kushner DS, Peters KM, Johnson-Greene D. Evaluating Siebens
Domain Management Model for Inpatient Rehabilitation to Increase
Functional Independence and Discharge Rate to Home in Geriatric
Patients. Archives of physical medicine and rehabilitation.
2015;96(7):1310-1318.
\55\ Wodchis WP, Teare GF, Naglie G, et al. Skilled nursing
facility rehabilitation and discharge to home after stroke. Archives
of physical medicine and rehabilitation. 2005;86(3):442-448.
\56\ Berkowitz RE, Jones RN, Rieder R, et al. Improving
disposition outcomes for patients in a geriatric skilled nursing
facility. Journal of the American Geriatrics Society.
2011;59(6):1130-1136.
\57\ Kushner DS, Peters KM, Johnson-Greene D. Evaluating use of
the Siebens Domain Management Model during inpatient rehabilitation
to increase functional independence and discharge rate to home in
stroke patients. PM & R: The journal of injury, function, and
rehabilitation. 2015;7(4):354-364.
---------------------------------------------------------------------------
A TEP convened by our measure development contractor was strongly
supportive of the importance of measuring discharge to community
outcomes, and implementing the measure, Discharge to Community-PAC IRF
QRP in the IRF QRP. The panel provided input on the technical
specifications of this measure, including the feasibility of
implementing the measure, as well as the overall measure reliability
and validity. A summary of the TEP proceedings is available on the PAC
Quality Initiatives Downloads and Videos Web site 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 solicited stakeholder feedback on the development of this
measure through a public comment period held from November 9, 2015,
through December 8, 2015. Several stakeholders and organizations,
including the MedPAC, among others, supported this measure for
implementation. The public comment summary report for the measure is
available on our Web site 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 NQF-convened MAP met on December 14 and 15, 2015, and provided
input on the use of this Discharge to Community-PAC IRF QRP measure in
the IRF QRP. The MAP encouraged continued development of the measure to
meet the mandate of the IMPACT Act. The MAP supported the alignment of
this measure across PAC settings, using standardized claims data. More
information about the MAP's recommendations for this measure is
available at https://www.qualityforum.org/Publications/2016/02/MAP_2016_Considerations_for_Implementing_Measures_in_Federal_Programs_-_PAC-LTC.aspx.
Since the MAP's review and recommendation of continued development,
we have continued to refine risk-adjustment models and conduct measure
testing for this measure, as recommended by the MAP. This measure is
consistent with the information submitted to the MAP, and the original
MAP submission and our continued refinements support its scientific
acceptability for use in quality reporting programs. As discussed with
the MAP, we fully anticipate that additional analyses will continue as
we submit this measure to the ongoing measure maintenance process.
We reviewed the NQF's consensus-endorsed measures and were unable
to identify any NQF-endorsed resource use or other measures for post-
acute care focused on discharge to community. In addition, we are
unaware of any other post-acute care measures for discharge to
community that have been endorsed or adopted by other consensus
organizations. Therefore, we proposed the measure, Discharge to
Community-PAC IRF QRP, under the Secretary's authority to specify non-
NQF-endorsed measures under section 1899B(e)(2)(B) of the Act.
We proposed to use data from the Medicare FFS claims and Medicare
eligibility files to calculate this measure. We proposed to use data
from the ``Patient Discharge Status Code'' on Medicare FFS claims to
determine whether a patient was discharged to a community setting for
calculation of this measure. In all PAC settings, we tested the
accuracy of determining discharge to a community setting using the
``Patient Discharge Status Code'' on the PAC claim by examining whether
discharge to community coding based on PAC claim data agreed with
discharge to community coding based on PAC assessment data. We found
excellent agreement between the two data sources in all PAC settings,
ranging from 94.6 percent to 98.8 percent. Specifically, in the IRF
setting, using 2013 data, we found 98.8 percent agreement in coding of
community and non-community discharges when comparing discharge status
codes on claims and the Discharge to Living Setting (item 44A) codes on
the IRF-PAI. We further examined the accuracy of the ``Patient
Discharge Status Code'' on the PAC claim by assessing how frequently
discharges to an acute care hospital were confirmed by follow-up acute
care claims. We discovered that 88 percent to 91 percent of IRF, LTCH,
and SNF claims with acute care discharge status codes were followed by
an acute care claim on the day of, or day after, PAC discharge. We
believed these data support the use of the claims ``Patient Discharge
Status Code'' for determining discharge to a community setting for this
measure. In addition, this measure can feasibly be implemented in the
IRF QRP because all data used for measure calculation are derived from
Medicare FFS claims and eligibility files, which are already available
to CMS.
Based on the evidence discussed above, we proposed to adopt the
measure, Discharge to Community-PAC IRF QRP, for the IRF QRP for FY
2018 payment determination and subsequent years. This measure is
calculated using 2 years of data. We proposed a minimum of 25 eligible
stays in a given IRF for public reporting of the measure for that IRF.
Since Medicare FFS claims data are already reported to the Medicare
program for payment purposes, and Medicare eligibility files are also
available, IRFs will not be required to report any additional data to
us for calculation of this measure. The measure denominator is the
risk-adjusted expected number of discharges to community. The measure
numerator is the risk-adjusted estimate of the number of patients who
are discharged to the community, do not have an unplanned readmission
to an acute care hospital or LTCH in the 31-day post-discharge
observation window, and who remain alive during the post-discharge
observation window. The measure is risk-adjusted for variables such as
age and sex, principal diagnosis, comorbidities, ESRD status, and
dialysis, among other variables. For technical information about the
proposed measure, including information about the measure calculation,
risk adjustment, and denominator exclusions, we referred readers to the
document titled, Proposed Measure Specifications for Measures Proposed
in the FY 2017 IRF QRP proposed rule, available at https://
[[Page 52098]]
www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-
Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-
Measures-Information-.html.
We stated in the proposed rule that we intend to provide initial
confidential feedback to IRFs, prior to public reporting of this
measure, based on Medicare FFS claims data from discharges in CY 2015
and 2016. We intend to publicly report this measure using claims data
from discharges in CY 2016 and 2017. We will submit this measure to the
NQF for consideration for endorsement.
In the CY 2013 OPPS/ASC final rule (77 FR 68500), we finalized our
policy to use a subregulatory approach to incorporate non-substantive
changes to measures adopted in the IRF QRP, including changes to
exclusions. In that rule, we noted that we expect to make this
determination on a measure-by-measure basis and that examples of non-
substantive changes to measures might include exclusions for a measure.
For the proposed Discharge to Community-IRF QRP measure, we have added
an exclusion of patients/residents with a hospice benefit in the post-
discharge observation window, in response to comments received during
measure development and our ongoing analysis and testing. The rationale
for the exclusion of patients/residents with a hospice benefit in the
post-discharge observation window aligns with the rationale for
exclusion of discharges to hospice. Based on testing, we found that
patients/residents with a post-discharge hospice benefit have a much
higher death rate in the post-discharge observation window compared
with patients/residents without a hospice benefit. We determined that
the addition of this hospice exclusion enhances the measure by
excluding patients/residents with a high likelihood of post-discharge
death and improves the national observed discharge to community rate
for IRFs by approximately 0.8 percent. With the addition of this
hospice exclusion, we do not believe burden is added, nor that the
addition of this exclusion is a substantive change to the overall
measure. Failure to include this hospice exclusion could lead to
unintended consequences and access issues for terminally-ill patients/
residents in our PAC populations.
We invited public comment on our proposal to adopt the measure,
Discharge to Community-PAC IRF QRP, for the IRF QRP. The comments we
received on this topic, with our responses, appear below.
Comment: Multiple commenters, including MedPAC, supported the
Discharge to Community-PAC IRF QRP measure, noting that it is a
critical measure assessing the ability of PAC providers to avoid
patient institutionalization. One commenter noted that measuring the
rate that the various PAC settings discharge patients to the community,
without an admission (or readmission) to an acute care hospital within
30 days, is one of the most relevant patient-centered measures that
exists in the post-acute care area. One commenter conveyed that
successful transitions to the community are expected to decrease
potentially preventable readmissions, while another was appreciative
that the measure did not place additional data collection burden on
facilities. One commenter stated that achieving a standardized and
interoperable patient assessment data set and stable quality measures
as quickly as possible will allow for better cross-setting comparisons
and the evolution of better quality measures with uniform risk
standardization.
Response: We thank the commenters for their support of the
Discharge to Community-PAC IRF QRP measure, and their recognition of
the patient-centeredness of this measure, its potential to decrease
post-discharge readmissions, and its lack of data collection burden. We
also thank the commenter for their support of standardized and
interoperable patient assessment data and quality measures. As mandated
by the IMPACT Act, we are moving toward the goal of standardized
patient assessment data and quality measures across PAC settings.
Comment: One commenter interpreted our measure proposal language as
suggesting that functional improvement is not a requirement, and
encouraged that Medicare coverage for maintenance nursing and therapy
be ensured and reflected by the measure.
Response: Our intent in the measure proposal was to acknowledge
that discharge to community can be an important goal even for patients
who may not be able to make functional improvement. This measure does
not impact Medicare coverage rules for maintenance nursing and therapy.
Comment: Several commenters expressed concerns regarding the use of
the Patient Discharge Status Code variable to define community
discharges. Commenters emphasized that it was important to ensure that
only home and community based settings were included in the definition
of community, and were concerned that Code 01 (Discharge to home or
self-care) included institutional settings such as jail or law
enforcement. One commenter expressed that many settings included under
Code 01 do not satisfy the home and community based settings rule, and
may be inconsistent with the integration mandate of the Americans with
Disabilities Act. Commenters strongly recommended that CMS either
revise Patient Discharge Status Code 01 to exclude non community-based
settings, or use alternative variables to capture discharge to
community.
Response: We agree with the commenters that the discharge to
community measure should only capture discharges to home and community
based settings. We believe that the comment referring to the ``home and
community based settings rule'' refers to Medicaid regulations
applicable to services authorized under sections 1915(c), 1915(i) and
1915(k) of the Social Security Act (the Act), which are provided
through waivers or state plans amendments approved by CMS. We would
like to clarify that this measure only captures discharges to home and
community based settings, not to institutional settings, and is
consistent with both Medicaid regulations requiring home and community
based settings to support integration, and also with the Americans with
Disabilities Act (ADA), based on Patient Discharge Status Codes 01, 06,
81, and 86 on the Medicare FFS PAC claim.\58\ Discharges to court or
law enforcement are not included under Code 01 of the Patient Discharge
Status Code; rather these are included under Code 21 (Discharged/
transferred to Court/Law Enforcement).
---------------------------------------------------------------------------
\58\ National Uniform Billing Committee Official UB-04 Data
Specifications Manual 2017, Version 11, July 2016, Copyright 2016,
American Hospital Association.
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We also note that Title II of the ADA requires public entities to
administer services, programs, and activities in the most integrated
setting appropriate to the needs of qualified individuals with
disabilities (28 CFR 35.130(d)). The preamble discussion of the
``integration regulation'' explains that ``the most integrated
setting'' is one that enables individuals with disabilities to interact
with nondisabled persons to the fullest extent possible. Integrated
settings are those that provide individuals with disabilities
opportunities to live, work, and receive services in the greater
community, like individuals without disabilities (28 CFR part 35, app.
A (2010) (addressing Sec. 35.130)).
Comment: Several commenters stated that PAC patients/residents
discharged to a nursing facility as long-term care
[[Page 52099]]
residents should not be considered discharges to community,
particularly if they were discharged to the nursing facility from the
Medicare-certified skilled nursing part of the same nursing home, and
even if they resided in a long-term nursing facility at baseline.
Commenters emphasized that a nursing home does not represent an
individual's own home in their own community. These commenters
interpreted the measure specifications as allowing these discharges to
nursing facility to be coded as ``group home'', ``foster care'', or
``other residential care arrangement'' under discharge status code 01.
Commenters expressed concern that coding discharges from the SNF to
residential/long-term care facility within the same nursing home as
discharges to community would unfairly advantage SNFs and artificially
inflate their discharge to community rates, would disadvantage other
PAC providers, and would miscommunicate a facility's actual discharge
to community performance to the average Medicare beneficiary. One
commenter suggested exclusion of patients discharged to a non-Medicare
certified residence, such as a ``group home'' or ``foster care'' or
other arrangement.
Response: We agree with the commenters that discharges to long-term
care nursing facilities, or any other institutional settings, should
not be coded as discharges to community. We also recognize the
differences in required discharge planning processes and resources for
discharging a patient/resident to the community compared with
discharging to a long-term nursing facility. The discharge to community
measure only captures discharges to home and community based settings
as discharges to community, based on Patient Discharge Status Codes 01,
06, 81, and 86 on the Medicare FFS PAC claim.\59\ These codes do not
include discharges to long-term care nursing facilities or any other
institutional setting that may violate the integration mandate of Title
II of the ADA. Instead, depending on the nature of the facility to
which patients/residents are discharged, such discharges may be coded
on the Medicare FFS claim as 04, 64, 84, 92, or another appropriate
code for an institutional discharge.
---------------------------------------------------------------------------
\59\ Ibid.
---------------------------------------------------------------------------
In response to the commenters' concerns that SNFs may be unfairly
advantaged by this measure as compared with other PAC providers, we
would like to note that, in our measure development samples, the
national discharge to community rate for SNFs was 47.26 percent, while
this rate for IRFs was considerably higher (69.51 percent). Further,
using an MDS-claims linked longitudinal file, we found that of the SNF
stays that had a pre-hospitalization non-PPS MDS assessment suggesting
prior nursing facility residence, two-thirds had a discharge status
code of 30 (still patient), and approximately 18 percent had a
discharge status code of 02 (acute hospital). Less than 5 percent of
these patients had a Discharge Status Code of 01 (discharge to home or
self care). Thus, the commenters' concerns that discharges from SNF to
nursing facility are largely coded as Patient Discharge Status Code 01
are not reflected in our data.
Comment: Some commenters expressed concern that the discharge to
community measure fails to distinguish patients/residents who lived in
a long-term care nursing facility at baseline and returned to the
nursing facility after their PAC stay. Commenters recommended that
baseline long-stay nursing facility residents be excluded from the
discharge to community measure, as they could not be reasonably
expected to discharge back to the community. One commenter noted that
these residents have a very different discharge process back to the
nursing facility compared with patients discharged to the community.
The commenter recommended that different measures be developed for the
baseline nursing facility resident population, such as return to prior
level of function, improvement in function, prevention of further
functional decline, development of pressure ulcers, or accidental
falls. The commenter also recognized CMS's current efforts in
monitoring transitions of care and quality requirements in long-term
care facilities. Commenters suggested that CMS could use longitudinal
Minimum Data Set-linkage to identify and exclude baseline nursing
facility residents.
Response: We appreciate the commenters' concerns and their
recommendation to exclude baseline nursing facility residents from the
discharge to community measure, and to distinguish baseline custodial
nursing facility residents who are discharged back to the nursing
facility after their PAC stay. We recognize that patients/residents who
permanently lived in a nursing facility at baseline may not be expected
to discharge back to a home and community based setting after their PAC
stay. We also recognize that, for baseline nursing facility residents,
a discharge back to their nursing facility represents a discharge to
their baseline residence. We agree with the commenter about the
differences in discharge planning processes when discharging a patient/
resident to the community compared with discharging to a long-term
nursing facility. However, using Medicare FFS claims alone, we are
unable to accurately identify baseline nursing facility residents.
Potential future modifications of the measure could include the
assessment of the feasibility and impact of excluding baseline nursing
facility residents from the measure through the addition of patient
assessment-based data. However, we note that, currently, the IRF-PAI is
the only PAC assessment that contains an item related to pre-hospital
baseline living setting.
Comment: A few commenters questioned the inclusion of only Medicare
FFS patients/residents in the measure, and stated whether the measure
would be expanded to include patients/residents with other payers or
plan types. One commenter recommended that the patient populations be
consistent across IRF measures, and not vary by payer or plan type,
stating that consistency in measure populations across IRF measures was
important for facilities to understand their quality metrics. Other
commenters recommended that the discharge to community measure include
other payer populations, and particularly emphasized the importance of
including Medicare Advantage patients in the measure, highlighting that
Medicare Advantage patients were included in the IRF Drug Regimen
Review measure. The commenters noted that the Medicare Advantage
population was a rapidly growing Medicare population, warranting their
inclusion in quality measures.
Response: We agree that is it important to monitor quality and
resource use outcomes of all post-acute care patients/residents, not
just Medicare FFS patients/residents. The discharge to community
measure is limited to the Medicare FFS population through the use of a
Medicare FFS claim, but we will consider the appropriateness and
feasibility of including Managed Care patients/residents in future
modifications of the measure. We would like to note that further
expansion of the measure to include Medicare Managed Care or other
payer populations would require standardized data collection across all
settings and payer populations.
Comment: MedPAC recommended that CMS confirm discharge to a
community setting with the absence of a subsequent claim to a hospital,
IRF, SNF, or LTCH, to ensure that discharge to community rates reflect
actual facility performance. Other commenters also
[[Page 52100]]
recommended that CMS assess the reliability and validity of the Patient
Discharge Status Code on PAC claims. Commenters cited MedPAC and other
studies, noting that Patient Discharge Status Codes often have low
reliability, and that this could impact accurate portrayal of measure
performance.
Response: We are committed to developing measures based on reliable
and valid data. This measure does confirm the absence of hospital or
LTCH claims following discharge to a community setting. Unplanned
hospital and LTCH readmissions following the discharge to community,
including those on the day of IRF discharge, are considered an
unfavorable outcome. We will consider verifying the absence of IRF and
SNF claims following discharge to a community setting, as we continue
to refine this measure. Nonetheless, we would like to note that an ASPE
report on post-acute care relationships found that, following discharge
to community settings from IRFs, LTCHs, or SNFs in a 5 percent Medicare
sample, IRFs or SNFs were very infrequently reported as the next site
of post-acute care.\60\
---------------------------------------------------------------------------
\60\ Gage B, Morley M, Spain P, Ingber M. Examining Post Acute
Care Relationships in an Integrated Hospital System Final Report.
RTI International; 2009.
---------------------------------------------------------------------------
Because the discharge to community measure is a measure of
discharge destination from the PAC setting, we have chosen to use the
PAC-reported discharge destination (from the Medicare FFS claims) to
determine whether a patient/resident was discharged to the community
(based on discharge status codes 01, 06, 81, 86). We assessed the
reliability of the claims discharge status code(s) by examining
agreement between discharge status on claims and assessment instruments
in all four PAC settings. We found between 94 and 99 percent agreement
in coding of community discharges on matched claims and assessments in
each of the PAC settings. We also assessed how frequently discharges to
acute care, as indicated on the PAC claim, were confirmed by follow-up
acute care claims, and found that 88 percent to 91 percent of IRF,
LTCH, and SNF claims indicating acute care discharge were followed by
an acute care claim on the day of, or day after, PAC discharge. We
believe that these data support the use of the ``Patient Discharge
Status Code'' from the PAC claim for determining discharge to a
community setting for this measure.
The use of the claims discharge status code to identify discharges
to the community was discussed at length with the TEP convened by our
measure development contractor. TEP members did not express significant
concerns regarding the accuracy of the claims discharge status code in
coding community discharges, nor about our use of the discharge status
code for defining this quality measure. A summary of the TEP
proceedings is available on the PAC Quality Initiatives Downloads and
Videos Web site 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: A few commenters conveyed the importance of ensuring
consistency in coding of discharge status codes across PAC settings,
and requested a clear definition of community discharge for purposes of
this measure.
Response: This measure captures discharges to home and community
based settings, with or without home health services. Community, for
this measure, is defined as Patient Discharge Status codes 01, 06, 81,
and 86 on the PAC claim. Code 01 refers to discharge to home or self
care; Code 06 refers to discharge with home health services; Code 81
refers to discharge to home or self care with a planned acute care
readmission; and Code 86 refers to discharge with home health services
with a planned acute care readmission. We refer readers to the National
Uniform Billing Committee Data Specifications Manual for coding
instructions.\61\ For further details on measure specifications,
including the definition of community, we refer readers to the Measure
Specifications for Measures Adopted in the FY 2017 IRF QRP final rule,
posted on the CMS IRF QRP Web site at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
---------------------------------------------------------------------------
\61\ National Uniform Billing Committee Official UB-04 Data
Specifications Manual 2017, Version 11, July 2016, Copyright 2016,
American Hospital Association.
---------------------------------------------------------------------------
Comment: Some commenters were concerned about overlap between the
discharge to community and readmissions measures, specifically
expressing concern that a single post-discharge readmission would
affect a facility's performance on two measures. One commenter
expressed that the discharge to community measure essentially
functioned as a readmission measure, and that different definitions of
readmissions could be confusing for providers and patients, lead to
unintended differences in the data CMS receives, and skew the data. One
commenter indicated that the IMPACT Act measures overemphasized
reducing readmissions and did not adequately address the domains they
are meant to measure. This commenter suggested that quality measures
should exclude aspects measured by other domains and/or quality
measures, and instead should measure unique domains. This commenter
further recommended that the Secretary suspend this measure until CMS
can evaluate whether the inclusion of readmissions within each quality
measure is necessary, and whether it produces duplicative results
within the various quality reporting programs.
Response: There are distinct differences between the discharge to
community and readmission measures under the IRF QRP. Although there
may be some overlap in the outcomes captured across the two measures
(for example, patients who have a post-discharge readmission also have
an unsuccessful discharge to community), the discharge to community and
readmission measures each have a distinct purpose, outcome definition,
and measure population. For example, the discharge to community measure
assesses the rate of successful discharges to the community, defined as
discharge to a community setting without post-discharge unplanned
readmissions or death, while the readmission measures assess the rate
of readmissions for patients discharged to lower levels of care from
the IRF.
Our goal is to develop measures that are meaningful to patients and
consumers, and assist them in making informed choices when selecting
post-acute providers. Since the goal of PAC for most patients and
family members is to be discharged to the community and remain in the
community, from a patient/consumer perspective, it is important to
assess whether a patient remained in the community after discharge and
to separately report discharge to community rates. In addition to
assessing the success of community discharges, the inclusion of post-
discharge readmission and death outcomes in this measure is intended to
avoid the potential unintended consequence of inappropriate discharges
to the community.
Comment: Several commenters expressed concern that the discharge to
community measure holds IRFs accountable for post-discharge adverse
outcomes, including unplanned readmissions and death. Commenters
expressed that IRFs have little control
[[Page 52101]]
over patient behavior or adherence once the patient is discharged from
the facility, and should not be penalized for post-discharge events. We
received recommendations to exclude patients who have been discharged
to the community and then expire within the post-discharge window; this
recommendation was based on the explanation that the types of patients
treated in IRFs greatly varied and that including post-discharge death
in the measure could lead to an inaccurate reflection of the quality of
care furnished by the IRF.
Response: We monitor 31-day post-discharge unplanned readmissions
and death in the measure to more accurately capture successful
discharge to community outcomes, and to avoid the potential unintended
consequence of inappropriate discharges to the community. We expect
that improved care transitions and care coordination across providers
will reduce these post-discharge adverse outcomes. Members of our TEP
unanimously believed that the definition of discharge to community
should be broader than discharge destination alone, and should
incorporate indicators of post-discharge patient outcomes. TEP members
agreed with the inclusion of both post-discharge readmissions and death
in the discharge to community measure.
We found, through our analyses in our measure development sample,
that death in the 31 days following discharge to community is an
infrequent event, with only 0.9 percent of IRF Medicare FFS
beneficiaries dying during that period. By risk adjusting for prior
service use (that is, number of hospitalizations in the past year), our
intent is to adjust for patient characteristics, such as access,
patient compliance, or sociodemographic and socioeconomic factors that
may influence the likelihood of post-discharge readmissions.
Additionally, by excluding patients discharged against medical advice
from the measure, we are excluding patients who demonstrate non-
compliance or non-adherence during the PAC stay.
We would like to note that we do not expect facilities to achieve a
0 percent readmission or death rate in the measure's post-discharge
observation window; the focus is to identify facilities with
unexpectedly high rates of unplanned readmissions and death for quality
monitoring purposes.
Comment: Multiple commenters suggested that the measure include
risk adjustment for sociodemographic factors such as home and community
caregivers and supports, and socioeconomic factors of patients and
communities.
Response: We understand the importance of home and community
supports, sociodemographic factors, and socioeconomic factors in
ensuring a successful discharge to community outcome. The discharge to
community measure is a claims-based measure in its first phase of
development. Currently, there are no standardized data on variables
such as living status or family and caregiver supports across the four
PAC settings. As we refine the measure in the future, we will consider
testing and adding additional relevant data sources and standardized
items for risk adjustment of this measure. We refer readers to section
VIII.F of this final rule for a more detailed discussion of the role of
SES/SDS factors in risk adjustment of our measures.
Comment: A few commenters emphasized the relationship between
functional gains during the IRF stay and the ability to discharge to
the community, stating that functional status measures are important
indicators of recovery and achievement of rehabilitation goals and
should be more intimately embedded in the proposed discharge to
community measure. One commenter stated that return to one's previous
home represents part of the goal of care. The commenter noted that,
additionally, it is also important that the patient is able to function
to the greatest possible extent in the home and community setting and
achieve the highest quality of life possible. The commenter recommended
that CMS delay adopting this measure until it incorporated metrics that
assess whether patients achieved their functional and independence
goals based on their plan of care and their specific condition.
Multiple commenters suggested that the measure include risk
adjustment for functional status in all settings, as it is closely
associated with patients' discharge destination. One commenter noted
that functional status is associated with increased risk of 30-day all-
cause hospital readmissions, and since readmissions and discharge to
community are closely related, functional status risk adjustment is
also important for this measure. One commenter suggested that the SNF
and LTCH measures include risk adjustment that is similar to the risk
adjustment for CMGs in the IRF setting and Activities of Daily Living
in the HHA setting. One commenter interpreted the measure proposal as
stating that CMS will not adjust the quality measures, including the
discharge to community measure, to account for functional status of
beneficiaries until such data are collected under the IMPACT Act.
Response: We agree that it is important to assess various aspects
of patient outcomes that are indicative of successful discharge from
the IRF setting. We also agree that functional status may be related to
discharge to community outcomes, and that it is important to test
admission functional status risk adjustment when assessing discharge to
community outcomes. The discharge to community measure does include
functional status risk adjustment in the IRF setting using CMGs from
claims, and in the home health setting using Activities of Daily Living
from claims.
As mandated by the IMPACT Act, we are moving toward the goal of
collecting standardized patient assessment data for functional status
across PAC settings. The IRF QRP includes five NQF-endorsed functional
status quality measures, with a data collection start date of October
1, 2016. Two measures are related to mobility functional outcomes, two
are related to self-care functional outcomes, and one is a process
measure. Once standardized functional status data become available
across settings, it is our intent to use these data to assess patients'
functional gains during their PAC stay, and to examine the relationship
between functional status, discharge destination, and patients' ability
to discharge to the community. As we examine these relationships
between functional outcomes and discharge to community outcomes in the
future, we will assess the feasibility of leveraging these standardized
patient assessment data to incorporate functional outcomes into the
discharge to community measure. Standardized cross-setting patient
assessment data will also allow us to examine interrelationships
between the quality and resource use measures in each PAC setting, and
to understand how these measures are correlated.
Comment: One commenter questioned the appropriateness of using HCCs
for risk adjustment in the new quality measures proposed for the IRF
QRP. The commenters noted that HCCs were initially developed for
setting payment benchmarks for the Medicare Advantage program, and
broad application of HCCs across quality measures may be beyond the
scope of their appropriate use. The commenter cited reports suggesting
that the HCC risk model was inaccurate at cost-estimation, and
recommended that CMS reconsider the validity and reliability of the HCC
risk-adjustment model. The commenter suggested that CMS instead develop
a refined model that encompasses the diversity and complexity of PAC
patients to a greater
[[Page 52102]]
extent, and is more sensitive to their levels of resource use.
Response: We agree that comorbidities are important risk adjusters
when examining quality and resource use measures. The HCCs were
developed to separate clinically-related codes by Medicare utilization
implications; they represent diagnosis-based, clinically meaningful
clusters of ICD codes that have also been grouped by cost implications.
When we apply HCCs for risk adjustment of quality or resources use
measures, we do not use the HCC models applied to payment. In our
measure development, we typically test individual HCCs that are
relevant to the outcome of interest; we estimate the effects of the
individual HCCs or clusters on the dependent variable in the particular
model and retain those that are significant or meaningful predictors of
outcomes. We believe that risk adjusting for individual HCCs or small
clusters provides greater sensitivity than using a single comorbidity
index, which is based on selected diagnoses. Our approach accounts for
an average effect for each comorbidity or comorbidity group, rather
than an overall burden of comorbidities.
The HCCs are more comprehensive than the simpler diagnosis-based
systems, such as the Elixhauser Comorbidity Index or Charlson
Comorbidity Index, which were targeted for predicting specific outcomes
(for example, hospital mortality). We believe that HCCs provide a good
representation of health risk, and their use to examine outcomes other
than costs is supported in the literature.62 63 A study
comparing the ability of five comorbidity indices to predict discharge
functional status of IRF patients found that HCCs slightly outperformed
other comorbidity indices.\64\ The superior performance of HCCs was
hypothesized to be related to the inclusion of more medical conditions,
and the inclusion of more ICD codes per condition in HCCs, making them
a slightly more sensitive index for predicting clinical outcomes
compared with other comorbidity indices.\65\
---------------------------------------------------------------------------
\62\ Li P, Kim MM, Doshi JA. Comparison of the performance of
the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with
the Charlson and Elixhauser comorbidity measures in predicting
mortality. BMC Health Serv Res. 2010 Aug 20;10:245. doi: 10.1186/
1472-6963-10-245.
\63\ Kumar A, Graham JE, Resnik L, Karmarkar AM, Tan A, Deutsch
A, Ottenbacher KJ. Comparing Comorbidity Indices to Predict Post-
Acute Rehabilitation Outcomes in Older Adults. Am J Phys Med
Rehabil. 2016 May 4. [Epub ahead of print]
\64\ Ibid.
\65\ Ibid.
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We have successfully used HCCs as risk adjusters in several other
quality measures, such as the readmissions and functional status
measures for post-acute care. We have found HCCs to be significant and
important predictors of outcomes across these quality measures.
Comment: One commenter stated that ventilator use is included as a
risk adjuster in the LTCH setting only, but should be used across all
settings. This commenter also requested information on the hierarchical
logistic regression modeling and variables that will be used for risk
adjustment.
Response: We would like to clarify that risk adjustment for
ventilator use is included in both LTCH and SNF settings. We
investigated the need for risk adjustment for ventilator use in IRFs,
but found that less than 0.01 percent of the IRF population (19 patient
stays in 2012, and 9 patient stays in 2013) had ventilator use in the
IRF. Given the low frequency of ventilator use in IRFs, any associated
estimates would not be reliable, and therefore, ventilator use is not
included as a risk adjuster in the IRF setting measure. However, we
will continue to assess this risk adjuster for inclusion in the IRF
model for this measure.
For details on measure specifications, modeling, and calculations,
we refer readers to the Measure Specifications for Measures Adopted in
the FY 2017 IRF QRP final rule, posted on the CMS IRF QRP Web page at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
Comment: Two commenters requested clarification on the dual status
of IRFs as qualifying hospitals for the purposes of the SNF ``3-Day
Stay'' rule, and PAC providers for purposes of the discharge to
community measure. Specifically, the commenters questioned whether a
discharge from a SNF back to an IRF would count as a readmission under
this measure (and thus result in a ``failed'' community discharge for
the SNF), or whether it would only count as a non-community discharge.
Response: For the discharge to community measure, a PAC stay must
be preceded by an acute care stay in the past 30 days to be included in
the measure. IRF stays are not considered qualifying stays for the
purposes of inclusion in the discharge to community measure. When
examining discharge destination from PAC, a discharge to an IRF would
be considered a non-community discharge. Additionally, in the current
measure specification, if a patient is discharged from PAC to the
community and has a subsequent IRF admission in the post-discharge
observation window, this IRF admission does not translate into a failed
community discharge. In future measure work, we will assess the impact
of flagging IRF admissions in the post-discharge window as failed
discharges to community.
Comment: One commenter encouraged CMS to provide PAC settings with
access to measure performance data as early as possible so providers
have time to adequately review these data, and implement strategies to
decrease readmissions where necessary.
Response: We intend to provide initial confidential feedback to PAC
providers, prior to public reporting of this measure, based on Medicare
FFS claims data from discharges in CY 2015 and 2016.
Comment: A few commenters were concerned about potential unintended
consequences associated with perceived conflicting incentives of
measures within the IRF QRP. One commenter noted that while the
discharge to community measure may incentivize IRFs to discharge
patients with home health services in order to continue their recovery
and function in a safe, lower cost setting, the MSPB measure may create
an opposite incentive for IRFs to avoid the use of home health to
reduce post-discharge resource utilization. Another commenter conveyed
that IRFs may not be incentivized to discharge patients to the
community as there is a risk of post-discharge readmissions affecting
their measure performance. The commenter expressed that decreased
discharge to community rates may result in increased costs.
Response: We expect that, on average, discharges to community
settings rather than institutional settings, will result in lower
healthcare costs. We choose measures for our quality reporting programs
that reflect patient-centeredness, and assess healthcare outcomes and
utilization that may be indicators of poor quality of care or
inefficient resource use. As with all our measures, we will monitor for
unintended consequences as part of measure monitoring and evaluation to
ensure that measures do not reduce quality of care or access for
patients.
Comment: Several commenters expressed concern that the discharge to
community measure had not been endorsed by the NQF, and had not been
fully developed and tested when presented to the NQF MAP. Some
commenters recommended that CMS delay measure implementation and seek
[[Page 52103]]
NQF endorsement before measure adoption, while others recommended that
CMS submit the measures for NQF endorsement as soon as feasible after
measure adoption. A few commenters suggested that CMS obtain the
support of a TEP before deciding whether to implement new quality
measures, and that the TEP include IRF setting representatives.
Response: We would like to clarify that the discharge to community
measure has been fully developed and tested. We plan to submit the
Discharge to Community-PAC IRF QRP measure to the NQF for consideration
for endorsement.
As with all measure development, our measure development contractor
held three TEP meetings to seek input to guide development of the
Discharge to Community measure. The TEP represented members of IRF,
LTCH, SNF and home health agency settings. A summary of the TEP
proceedings is available on the PAC Quality Initiatives Downloads and
Videos Web site 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. TEP members
were very supportive of the discharge to community measure concept
across all PAC settings. We incorporated various TEP member
recommendations into the measure specifications.
Final Decision: After careful consideration of the public comments,
we are finalizing our proposal to adopt the measure, Discharge to
Community-PAC IRF QRP as a Medicare FFS claims-based measure for the FY
2018 payment determination and subsequent years, with the added
exclusion of patients with a hospice benefit in the 31-day post-
discharge observation window.
For measure specifications, we refer readers to the Measure
Specifications for Measures Adopted in the FY 2017 IRF QRP final rule,
posted on the CMS IRF QRP Web site at: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
3. Measure To Address the IMPACT Act Domain of Resource Use and Other
Measures: Potentially Preventable 30-Day Post-Discharge Readmission
Measure for Inpatient Rehabilitation Facility Quality Reporting Program
Sections 1899B(a)(2)(E)(ii) and 1899B(d)(1)(C) of the Act require
the Secretary to specify measures to address the domain of all-
condition risk-adjusted potentially preventable hospital readmission
rates by SNFs, LTCHs, and IRFs by October 1, 2016, and HHAs by January
1, 2017. We proposed the measure Potentially Preventable 30-Day Post-
Discharge Readmission Measure for IRF QRP as a Medicare FFS claims-
based measure to meet this requirement for the FY 2018 payment
determination and subsequent years.
The measure assesses the facility-level risk-standardized rate of
unplanned, potentially preventable hospital readmissions for Medicare
FFS beneficiaries in the 30 days post IRF discharge. The IRF admission
must have occurred within up to 30 days of discharge from a prior
proximal hospital stay which is defined as an inpatient admission to an
acute care hospital (including IPPS, CAH, or a psychiatric hospital).
Hospital readmissions include readmissions to a short-stay acute-care
hospital or an LTCH, with a diagnosis considered to be unplanned and
potentially preventable. This measure is claims-based, requiring no
additional data collection or submission burden for IRFs. Because the
measure denominator is based on IRF admissions, each Medicare
beneficiary may be included in the measure multiple times within the
measurement period. Readmissions counted in this measure are identified
by examining Medicare FFS claims data for readmissions to either acute
care hospitals (IPPS or CAH) or LTCHs that occur during a 30-day window
beginning 2 days after IRF discharge. This measure is conceptualized
uniformly across the PAC settings, in terms of the measure definition,
the approach to risk adjustment, and the measure calculation. Our
approach for defining potentially preventable hospital readmissions is
described in more detail below.
Hospital readmissions among the Medicare population, including
beneficiaries that utilize PAC, are common, costly, and often
preventable.66 67 MedPAC and a study by Jencks et al.
estimated that 17 to 20 percent of Medicare beneficiaries discharged
from the hospital were readmitted within 30 days. MedPAC found that
more than 75 percent of 30-day and 15-day readmissions and 84 percent
of 7-day readmissions were considered ``potentially preventable.'' \68\
In addition, MedPAC calculated that annual Medicare spending on
potentially preventable readmissions were $12 billion for 30-day, $8
billion for 15-day, and $5 billion for 7-day readmissions in 2005.\69\
For hospital readmissions from one post-acute care setting, SNFs,
MedPAC deemed 76 percent of these readmissions as ``potentially
avoidable''-associated with $12 billion in Medicare expenditures.\70\
Mor et al. analyzed 2006 Medicare claims and SNF assessment data
(Minimum Data Set), and reported a 23.5 percent readmission rate from
SNFs, associated with $4.3 billion in expenditures.\71\ Fewer studies
have investigated potentially preventable readmission rates from the
remaining post-acute care settings.
---------------------------------------------------------------------------
\66\ Friedman, B., and Basu, J.: The rate and cost of hospital
readmissions for preventable conditions. Med. Care Res. Rev.
61(2):225-240, 2004. doi:10.1177/1077558704263799.
\67\ Jencks, S.F., Williams, M.V., and Coleman, E.A.:
Rehospitalizations among patients in the Medicare Fee-for-Service
Program. N. Engl. J. Med. 360(14):1418-1428, 2009. doi:10.1016/
j.jvs.2009.05.045.
\68\ MedPAC: Payment policy for inpatient readmissions, in
Report to the Congress: Promoting Greater Efficiency in Medicare.
Washington, DC, pp. 103-120, 2007. Available from https://www.medpac.gov/documents/reports/Jun07_EntireReport.pdf.
\69\ ibid.
\70\ ibid.
\71\ Mor, V., Intrator, O., Feng, Z., et al.: The revolving door
of rehospitalization from skilled nursing facilities. Health Aff.
29(1):57-64, 2010. doi:10.1377/hlthaff.2009.0629.
---------------------------------------------------------------------------
We have addressed the high rates of hospital readmissions in the
acute care setting as well as in PAC. For example, we developed the
following measure: All-Cause Unplanned Readmission Measure for 30 Days
Post-Discharge from IRFs (NQF #2502), as well as similar measures for
other PAC providers (NQF #2512 for LTCHs and NQF #2510 for SNFs).\72\
These measures are endorsed by the NQF, and the NQF-endorsed IRF
measure (NQF #2502) was adopted into the IRF QRP in the FY 2016 IRF PPS
final rule (80 FR 47087 through 47089). Note that these NQF-endorsed
measures assess all-cause unplanned readmissions.
---------------------------------------------------------------------------
\72\ National Quality Forum: All-Cause Admissions and
Readmissions Measures. pp. 1-319, April 2015. Available from https://www.qualityforum.org/Publications/2015/04/All-Cause_Admissions_and_Readmissions_Measures_-_Final_Report.aspx.
---------------------------------------------------------------------------
Several general methods and algorithms have been developed to
assess potentially avoidable or preventable hospitalizations and
readmissions for the Medicare population. These include the Agency for
Healthcare Research and Quality's (AHRQ's) Prevention Quality
Indicators, approaches developed by MedPAC, and proprietary approaches,
such as the 3M\TM\ algorithm for Potentially Preventable
Readmissions.73 74 75 Recent
[[Page 52104]]
work led by Kramer et al. for MedPAC identified 13 conditions for which
readmissions were deemed as potentially preventable among SNF and IRF
populations.76 77 Although much of the existing literature
addresses hospital readmissions more broadly and potentially avoidable
hospitalizations for specific settings like long-term care, these
findings are relevant to the development of potentially preventable
readmission measures for PAC.78 79 80
---------------------------------------------------------------------------
\73\ Goldfield, N.I., McCullough, E.C., Hughes, J.S., et al.:
Identifying potentially preventable readmissions. Health Care Finan.
Rev. 30(1):75-91, 2008. Available from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4195042/.
\74\ National Quality Forum: Prevention Quality Indicators
Overview. 2008.
\75\ MedPAC: Online Appendix C: Medicare Ambulatory Care
Indicators for the Elderly. pp. 1-12, prepared for Chapter 4, 2011.
Available from https://www.medpac.gov/documents/reports/Mar11_Ch04_APPENDIX.pdf?sfvrsn=0.
\76\ Kramer, A., Lin, M., Fish, R., et al.: Development of
Inpatient Rehabilitation Facility Quality Measures: Potentially
Avoidable Readmissions, Community Discharge, and Functional
Improvement. pp. 1-42, 2015. Available from https://www.medpac.gov/documents/contractor-reports/development-of-inpatient-rehabilitation-facility-quality-measures-potentially-avoidable-readmissions-community-discharge-and-functional-improvement.pdf?sfvrsn=0.
\77\ Kramer, A., Lin, M., Fish, R., et al.: Development of
Potentially Avoidable Readmission and Functional Outcome SNF Quality
Measures. pp. 1-75, 2014. Available from https://www.medpac.gov/documents/contractor-reports/mar14_snfqualitymeasures_contractor.pdf?sfvrsn=0.
\78\ Allaudeen, N., Vidyarthi, A., Maselli, J., et al.:
Redefining readmission risk factors for general medicine patients.
J. Hosp. Med. 6(2):54-60, 2011. doi:10.1002/jhm.805.
\79\ \4\ Gao, J., Moran, E., Li, Y.-F., et al.: Predicting
potentially avoidable hospitalizations. Med. Care 52(2):164-171,
2014. doi:10.1097/MLR.0000000000000041.
\80\ Walsh, E.G., Wiener, J.M., Haber, S., et al.: Potentially
avoidable hospitalizations of dually eligible Medicare and Medicaid
beneficiaries from nursing facility and home[hyphen]and
community[hyphen]based services waiver programs. J. Am. Geriatr.
Soc. 60(5):821-829, 2012. doi:10.1111/j.1532-5415.2012.03920.x.
---------------------------------------------------------------------------
Potentially Preventable Readmission Measure Definition: We
conducted a comprehensive environmental scan, analyzed claims data, and
obtained input from a TEP to develop a definition and list of
conditions for which hospital readmissions are potentially preventable.
The Ambulatory Care Sensitive Conditions and Prevention Quality
Indicators, developed by AHRQ, served as the starting point in this
work. For patients in the 30-day post-PAC discharge period, a
potentially preventable readmission refers to a readmission for which
the probability of occurrence could be minimized with adequately
planned, explained, and implemented post-discharge instructions,
including the establishment of appropriate follow-up ambulatory care.
Our list of PPR conditions is categorized by 3 clinical rationale
groupings:
Inadequate management of chronic conditions;
Inadequate management of infections; and
Inadequate management of other unplanned events.
Additional details regarding the definition for potentially
preventable readmissions are available in the document titled, Proposed
Measure Specifications for Measures Proposed in the FY 2017 IRF QRP
proposed rule, available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
This measure focuses on readmissions that are potentially
preventable and also unplanned. Similar to the All-Cause Unplanned
Readmission Measure for 30 Days Post-Discharge from IRFs (NQF #2502),
this measure uses the current version of the CMS Planned Readmission
Algorithm as the main component for identifying planned readmissions. A
complete description of the CMS Planned Readmission Algorithm, which
includes lists of planned diagnoses and procedures, can be found on the
CMS Web site https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/Measure-Methodology.html.
In addition to the CMS Planned Readmission Algorithm, this measure
incorporates procedures that are considered planned in post-acute care
settings, as identified in consultation with TEPs. Full details on the
planned readmissions criteria used, including the CMS Planned
Readmission Algorithm and additional procedures considered planned for
post-acute care, can be found in the document titled, Proposed Measure
Specifications for Measures Proposed in the FY 2017 IRF QRP proposed
rule, available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
The measure, Potentially Preventable 30-Day Post-Discharge
Readmission Measure for IRF QRP, assesses potentially preventable
readmission rates while accounting for patient demographics, principal
diagnosis in the prior hospital stay, comorbidities, and other patient
factors. While estimating the predictive power of patient
characteristics, the model also estimates a facility-specific effect,
common to patients treated in each facility. This measure is calculated
for each IRF based on the ratio of the predicted number of risk-
adjusted, unplanned, potentially preventable hospital readmissions that
occur within 30 days after an IRF discharge, including the estimated
facility effect, to the estimated predicted number of risk-adjusted,
unplanned inpatient hospital readmissions for the same patients treated
at the average IRF. A ratio above 1.0 indicates a higher than expected
readmission rate (worse) while a ratio below 1.0 indicates a lower than
expected readmission rate (better). This ratio is referred to as the
standardized risk ratio (SRR). The SRR is then multiplied by the
overall national raw rate of potentially preventable readmissions for
all IRF stays. The resulting rate is the risk-standardized readmission
rate (RSRR) of potentially preventable readmissions.
An eligible IRF stay is followed until: (1) The 30-day post-
discharge period ends; or (2) the patient is readmitted to an acute
care hospital (IPPS or CAH) or LTCH. If the readmission is unplanned
and potentially preventable, it is counted as a readmission in the
measure calculation. If the readmission is planned, the readmission is
not counted in the measure rate. This measure is risk adjusted. The
risk adjustment modeling estimates the effects of patient
characteristics, comorbidities, and select health care variables on the
probability of readmission. More specifically, the risk-adjustment
model for IRFs accounts for demographic characteristics (age, sex,
original reason for Medicare entitlement), principal diagnosis during
the prior proximal hospital stay, body system specific surgical
indicators, IRF case-mix groups which capture motor function,
comorbidities, and number of acute care hospitalizations in the
preceding 365 days.
The measure is calculated using 2 consecutive calendar years of FFS
claims data, to ensure the statistical reliability of this measure for
facilities. In addition, we proposed a minimum of 25 eligible stays for
public reporting of the measure.
A TEP convened by our measure contractor provided recommendations
on the technical specifications of this measure, including the
development of an approach to define potentially preventable hospital
readmission for PAC. Details from the TEP meetings, including TEP
members' ratings of conditions proposed as being potentially
preventable, are available in the TEP summary report available on the
CMS Web site at https://www.cms.gov/Medicare/Quality-Initiatives-
Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/
IMPACT-Act-of-2014/
[[Page 52105]]
IMPACT-Act-Downloads-and-Videos.html. We also solicited stakeholder
feedback on the development of this measure through a public comment
period held from November 2 through December 1, 2015. Comments on the
measure varied, with some commenters supportive of the measure, while
others either were not in favor of the measure, or suggested potential
modifications to the measure specifications, such as including
standardized function data. A summary of the public comments is also
available on our Web site 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 MAP encouraged continued development of the proposed measure.
Specifically, the MAP stressed the need to promote shared
accountability and ensure effective care transitions. More information
about the MAP's recommendations for this measure is available at https://www.qualityforum.org/Publications/2016/02/MAP_2016_Considerations_for_Implementing_Measures_in_Federal_Programs_-_PAC-LTC.aspx. At the time, the risk-adjustment model was still under
development. Following completion of that development work, we were
able to test for measure validity and reliability as identified in the
measure specifications document provided above. Testing results are
within range for similar outcome measures finalized in public reporting
and value-based purchasing programs, including the All-Cause Unplanned
Readmission Measure for 30 Days Post Discharge from IRFs (NQF #2502)
adopted into the IRF QRP.
We reviewed the NQF's consensus endorsed measures and were unable
to identify any NQF-endorsed measures focused on potentially
preventable hospital readmissions. We are unaware of any other measures
for this IMPACT Act domain that have been endorsed or adopted by other
consensus organizations. Therefore, we proposed the Potentially
Preventable 30-Day Post-Discharge Readmission Measure for IRF QRP,
under the Secretary's authority to specify non-NQF-endorsed measures
under section 1899B(e)(2)(B) of the Act, for the IRF QRP for the FY
2018 payment determination and subsequent years, given the evidence
previously discussed above.
We plan to submit the measure to the NQF for consideration of
endorsement. We stated in the proposed rule that we intended to provide
initial confidential feedback to providers, prior to public reporting
of this measure, based on 2 calendar years of data from discharges in
CY 2015 and 2016. We also stated that we intended to publicly report
this measure using data from CY 2016 and 2017.
We invited public comment on our proposal to adopt the measure,
Potentially Preventable 30-Day Post-Discharge Readmission Measure for
IRF QRP. We received several comments, which are summarized with our
responses below.
Comment: We received several comments in support of the proposed
Potentially Preventable 30-Day Post-Discharge Readmission Measure for
IRF QRP. In particular, MedPAC supported this measure and believes that
IRFs should be held accountable for readmissions in the post-discharge
readmission window. Some commenters preferred a potentially preventable
readmission measure over an all-cause readmission measure.
Response: We thank commenters for their support of this measure.
Comment: One commenter specifically supported the inclusion of
infectious conditions in the inadequate management of infections and
inadequate management of other unplanned events categories in the
measure's definition of potentially preventable hospital readmissions.
Another commenter expressed concern over being ``penalized'' for
readmissions that are clinically unrelated to a patient's original
reason for IRF admission. One commenter recommended that CMS continue
evaluating and testing the measure to ensure that the codes used for
the PPR definition are clinically relevant. Another commenter expressed
concern over using DRGs as the basis for defining the reasons for
receiving inpatient rehabilitation or the reason for a subsequent
hospital readmission given variation in coding practices in acute care
hospitals.
Response: As described in the proposed rule, the definition for
potentially preventable readmissions for this measure was developed
based on existing evidence and was vetted by a TEP, which included
clinicians and post-acute care experts. We also conducted a
comprehensive environmental scan to identify conditions for which
readmissions may be considered potentially preventable. Results of this
environmental scan and details of the TEP input received were made
available in the PPR TEP summary report available on the CMS Web site
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 solicited stakeholder
feedback on the development of this measure through a public comment
period held from November 2 through December 1, 2015. Comments on the
measure varied, with some commenters supportive of the proposed
measure, while others either were not in favor of the measure, or
suggested potential modifications to the measure specifications, such
as including standardized function data. A summary of the public
comments is also available on the CMS Web site 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.
Though readmissions may be considered potentially preventable even
if they may not appear to be clinically related to the patient's
original reason for IRF admission, there is substantial evidence that
the conditions included in the definition may be preventable with
adequately planned, explained, and implemented post-discharge
instructions, including the establishment of appropriate follow-up
ambulatory care. Furthermore, this measure is based on Medicare FFS
claims data and it may not always be feasible to determine whether a
subsequent readmission is or is not clinically related to the reason
why the patient was receiving inpatient rehabilitation. We intend to
conduct ongoing evaluation and monitoring of this measure, and will
take these comments into consideration.
With regard to the comment related to DRGs, we wish to clarify that
this measure does not use hospital DRGs to define PPRs or in the risk
adjustment. Potentially preventable hospital readmissions are defined
by the principal diagnosis on the readmission claim. Our risk-
adjustment model uses diagnoses (not DRGs) from the prior hospital
claim as risk adjusters. Though there may be variation in coding
practices, claims data are the most reliable source to identify
potentially preventable hospital readmissions post-IRF discharge. We
would also like to clarify that the reason for receiving inpatient
rehabilitation is captured as a risk adjuster by the use of the IRF PPS
CMGs which also incorporate the RICs as well as function.
Comment: Several commenters expressed support for the cross-setting
standardization of the inclusion and exclusion criteria for the PPR
measures. MedPAC and another commenter
[[Page 52106]]
commented that the measure definition and risk adjustment should be
identical across PAC settings so that potentially preventable
readmission rates can be compared across settings. One commenter
expressed concern over the ``nonalignment'' specifically between the
IRF and SNF versions of the measure, adding that this may lead to
confusion. Another commenter suggested a single or harmonized measure
to better inform patients, caregivers, and payers. One comment
encouraged CMS to assess readmission measures across the agency's
programs to ensure that they promote collaboration and support
readmission reduction efforts.
Response: The PPR definition (that is, list of conditions for which
readmissions would be considered potentially preventable) is aligned
for measures with the same readmission window, regardless of PAC
setting. Specifically, the post-PAC discharge PPR measures that were
developed for each of the PAC settings contain the same list of PPR
conditions. Although there are some minor differences in the
specifications across these potentially preventable readmissions
measures (for example, years of data used to calculate the measures to
ensure reliability and some of the measure exclusions necessary to
attribute responsibility to the individual settings), the IMPACT Act
PPR measures are standardized. As described for all IMPACT Act measures
in section VIII.B in this final rule, the statistical approach for risk
adjustment is also aligned across the measures; however, there is
variation in the exact risk adjusters. The risk-adjustment models are
empirically driven and differ between measures as a consequence of case
mix differences, which is necessary to ensure that the estimates are
valid. We appreciate the comment that the readmission measures across
our programs be assessed to ensure they promote collaboration and
support readmission reduction efforts. As we continually evaluate and
monitor the PAC quality reporting and other CMS programs, we will take
the commenter's suggestion into consideration.
Comment: Several commenters expressed concern that this measure
would capture outcomes that are outside of PAC providers' control,
specifically with respect to chronically ill patients, instances of
poor patient compliance, unhealthy choices, and various SDS factors,
such as lack of resources or limited access to follow up or primary
care. One commenter also expressed concern over the added risk of
caring for a high volume of transplant patients that other IRFs may
choose not to admit. Another commenter noted that even though the risk
adjustment will account for some of these circumstances, it is
difficult for providers to fully evaluate the risk-adjustment model
because the testing and risk-adjustment coefficients have not been
finalized. A few commenters recommend these measures be suspended until
CMS explains how the measures will treat each of these scenarios.
Response: As noted by one commenter, the measure's comprehensive
risk-adjustment approach and exclusion criteria are intended to capture
many of these factors. As described above, there is substantial
evidence that the conditions included in the definition may be
preventable with adequately planned, explained, and implemented post-
discharge instructions, including the establishment of appropriate
follow-up ambulatory care. We would like to clarify that the focus of
the PPR measure is to identify excess PPR rates for the purposes of
quality improvement.
We would also like to clarify that the finalized risk-adjustment
models and coefficients are included in the measure specifications
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
Comment: Several commenters expressed concern over the overlap
between the proposed PPR measure and other IRF QRP measures, including
the existing all-cause readmission measure. Commenters expressed
concern that public reporting of more than one hospital readmission
measure for IRFs may result in confusion among the public; the
commenters also noted providers could face confusion over two distinct
but similar measures, which could potentially pose challenges for
quality improvement efforts. One commenter noted that the proposed PPR
measures and the existing all-cause measure are distinct yet
overlapping, adding that the PPR measure is a subset of the all-cause
readmission measure. Given this overlap, one commenter was concerned
that providers who perform poorly on the all-cause readmission measure
are likely to do so for the proposed PPR measure as well, and suggested
CMS suspend the measure until it could evaluate the necessity of each
measure. Some commenters requested that CMS clarify the overlap and
intent of these measures, and provide more education to providers and
the public on the multiple IRF QRP readmission measures. Another
commenter suggested that CMS conduct dry runs of the readmission
measures, similar to those conducted for the all-cause measure.
One commenter supported the use of Medicare claims data to
calculate these measures because it does not require the submission of
additional data by IRFs. Another commenter noted that despite the lack
of a data collection burden for providers, multiple readmission
measures in the program will create burden on the part of providers to
track and improve performance. Another commenter expressed concern that
the measures are ``extensive'' and will place additional financial
burden on providers.
Response: The All-Cause Unplanned Hospital Readmission Measure for
30 Days Post-IRF Discharge (NQF #2502) was adopted for the IRF QRP
prior to the IMPACT Act. The measure of potentially preventable
hospital readmissions was developed in response to the statutory
mandate of the IMPACT Act. We would like to clarify that providers are
not held financially accountable for their performance on these
measures, but only whether they report the necessary data for the IRF
QRP.
With regard to overlap with the existing IRF QRP readmission
measure, retaining the all-cause measure will allow us to monitor
trends in both all-cause and PPR rates in order to assess the extent to
which changes in facility performance for one measure are reflected in
the other. We are committed to ensuring that measures in the IRF QRP
are useful in assessing quality and will continue to evaluate all
readmission measures over time.
We thank commenters for their feedback related to provider burden
on the measure. We would like to note that the PPR measure uses
Medicare claims data and is not collected by means of an assessment
instrument. Therefore, the measure does not increase data collection
burden on the provider as this data is currently collected by
providers. Despite the lack of data collection burden, we appreciate
the comments that more education will be required for the public and
providers to understand the differences between the readmission
measures in the IRF QRP.
Comment: Several commenters raised concerns over the risk-
adjustment approach for the PPR measure. One commenter expressed
concern that the HCC risk-adjustment method is insufficient at
predicting costs for certain patient populations. The commenter
suggested CMS research and develop a refined risk-adjustment model that
encompasses more of the diversity
[[Page 52107]]
and complexity of PAC patients and is more sensitive to their levels of
resource use. Several commenters expressed concern that the proposed
measure is not adjusted for socio-economic factors, and a couple
commenters, including MedPAC, suggested the use of peer group
comparisons of performance rates to address this issue.
Another commenter supported the proposed risk-adjustment
methodology commenting it will provide a valid assessment of quality of
care in preventing unplanned, preventable hospital readmissions. One
commenter also suggested that, in addition to the measure exclusion for
non-surgical treatment of cancer, that other conditions with similar
disease trajectories be excluded from the measure, citing end-stage
Multiple Sclerosis (MS), motor neuron disease, and Alzheimer's disease.
Response: We would like to note that the measure is fully developed
and the finalized risk-adjustment model and coefficients are included
in the measure specifications available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
The HCCs were developed to separate clinically-related codes by
Medicare utilization implications; they represent diagnosis-based,
clinically meaningful clusters of ICD codes that have also been grouped
by cost implications. When we apply HCCs for risk adjustment of quality
or resources use measures, we do not use the HCC models applied to
payment. In our measure development, we typically test individual HCCs
that are relevant to the outcome of interest; we estimate the effects
of the individual HCCs or clusters on the dependent variable in the
particular model and retain those that are significant or meaningful
predictors of outcomes. We believe that risk adjusting for individual
HCCs or small clusters provides greater sensitivity than using a single
comorbidity index, which is based on selected diagnoses. Our approach
accounts for an average effect for each comorbidity or comorbidity
group, rather than an overall burden of comorbidities.
The HCCs are more comprehensive than the simpler diagnosis-based
systems, such as the Elixhauser Comorbidity Index or Charlson
Comorbidity Index, which were targeted for predicting specific outcomes
(for example, hospital mortality). We believe that HCCs provide a good
representation of health risk, and their use to examine outcomes other
than costs is supported in the literature.81 82 A study
comparing the ability of five comorbidity indices to predict discharge
functional status of IRF patients found that HCCs slightly outperformed
other comorbidity indices.\83\ The superior performance of HCCs was
hypothesized to be related to the inclusion of more medical conditions
in HCCs, and the inclusion of more ICD codes per condition in HCCs,
making them a slightly more sensitive index for predicting clinical
outcomes compared with other comorbidity indices.\84\
---------------------------------------------------------------------------
\81\ Kumar A, Graham JE, Resnik L, Karmarkar AM, Tan A, Deutsch
A, Ottenbacher KJ. Comparing Comorbidity Indices to Predict Post-
Acute Rehabilitation Outcomes in Older Adults. Am J Phys Med
Rehabil. 2016 May 4. [Epub ahead of print]
\82\ Li P, Kim MM, Doshi JA. Comparison of the performance of
the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with
the Charlson and Elixhauser comorbidity measures in predicting
mortality. BMC Health Serv Res. 2010 Aug 20;10:245. doi: 10.1186/
1472-6963-10-245.
\83\ Kumar A, Graham JE, Resnik L, Karmarkar AM, Tan A, Deutsch
A, Ottenbacher KJ. Comparing Comorbidity Indices to Predict Post-
Acute Rehabilitation Outcomes in Older Adults. Am J Phys Med
Rehabil. 2016 May 4. [Epub ahead of print]
\84\ Kumar A, Graham JE, Resnik L, Karmarkar AM, Tan A, Deutsch
A, Ottenbacher KJ. Comparing Comorbidity Indices to Predict Post-
Acute Rehabilitation Outcomes in Older Adults. Am J Phys Med
Rehabil. 2016 May 4. [Epub ahead of print]
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We wish to clarify that the model included in the specifications
using HCCs as risk adjusters for comorbidities posted for the proposed
rule demonstrated sufficient discrimination power. The model had a c-
statistic of 0.74 which is within range, if not higher than, similar
readmission measures finalized in public reporting programs, including
the All-Cause Unplanned Readmission Measure for 30 Days Post-Discharge
from IRFs (NQF #2502) previously adopted for the IRF QRP.
With regard to the suggestions that the model include
sociodemographic factors and the suggestion pertaining to an approach
with which to convey data comparisons, we refer the readers to section
VIII.F of this final rule where we also discuss these topics. In
response to the suggestion to include additional conditions from the
measure, such as end-stage MS, motor neuron disease, and Alzheimer's
disease, we wish to clarify that we risk adjust for these clinical
characteristics in our risk-adjustment model. These are low prevalence
conditions and the claims data are limited in their ability to identify
disease progression. However, we will take this suggestion into
consideration.
Comment: Several commenters expressed concern that the measures are
not NQF-endorsed, and some had additional concerns over measure testing
and development. Some of these commenters recommended that CMS should
adopt measures endorsed by the NQF in quality reporting programs or
recommended that CMS submit the measures through the NQF endorsement
process as soon as feasible.
Response: With regard to NQF endorsement, as noted in the proposed
rule, we intend to submit this measure to NQF for consideration of
endorsement. In addition, we noted that we reviewed the NQF's consensus
endorsed measures and were unable to identify any NQF endorsed measures
focused on potentially preventable hospital readmissions. We are
unaware of any other measures for this IMPACT Act domain that have been
endorsed or adopted by other consensus organizations. Therefore, we
proposed the Potentially Preventable 30-Day Post-Discharge Readmission
Measure for IRF QRP, under the Secretary's authority to specify non-NQF
endorsed measures under section 1899B(e)(2)(B) of the Act, for the IRF
QRP.
We would also like to clarify that the finalized risk-adjustment
models and coefficients are included in the measure specifications
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html. We will make additional testing
results available in the future.
We would like to clarify that the MAP encouraged continued
development of the proposed measure. More information about the MAP's
recommendations for this measure is available at https://www.qualityforum.org/Publications/2016/02/MAP_2016_Considerations_for_Implementing_Measures_in_Federal_Programs_-_PAC-LTC.aspx.
Comment: Some commenters raised concerns over unintended
consequences of the measure. One commenter was concerned that the
measure could create an incentive for IRFs to be selective about the
types of patients they admit (that is, ``cherry pick'' their patients)
in order to reduce the risk of PPRs. Another comment suggested that
IRFs should not be held accountable for IRF patients with planned
procedures that are not admitted and treated as observation stays and
requested that CMS provide clarification on how these types of patients
will be assessed by the measure.
Response: We intend to conduct ongoing monitoring to assess for
potential unintended consequences
[[Page 52108]]
associated with the implementation of this measure and will take these
suggestions into account.
In response to the concern regarding holding an IRF accountable for
planned procedures that are treated as observation stays instead of
planned hospital readmissions, we appreciate the commenter's concern
and expect that this is a relatively infrequent occurrence given that
most of the planned procedures are invasive surgical procedures. The
measure is of hospital readmissions and does not count planned
procedures that are treated as observation stays. We will take this
issue into consideration for future measure development.
Comment: One commenter expressed concern over using claims data for
hospital readmissions, noting that these data may not be accurate.
Response: We appreciate the commenter's concern over the accuracy
of claims data. However, we wish to clarify that claims data have been
validated for the purposes of assessing hospital readmissions and are
used for several NQF-endorsed measures adopted for CMS programs,
including the IRF QRP. Multiple studies have been conducted to examine
the validity of using Medicare hospital claims to calculate several
NQF-endorsed quality measures for public
reporting.85 86 87Additionally, although assessment and
other data sources may be valuable for risk adjustment, we are not
aware of any other data source aside from Medicare claims data that
could be used to reliably assess potentially preventable hospital
readmissions for this measure.
---------------------------------------------------------------------------
\85\ Bratzler DW, Normand SL, Wang Y, et al. An administrative
claims model for profiling hospital 30-day mortality rates for
pneumonia patients. PLoS One 2011;6(4):e17401.
\86\ Keenan PS, Normand SL, Lin Z, et al. An administrative
claims measure suitable for profiling hospital performance on the
basis of 30-day all-cause readmission rates among patients with
heart failure. Circulation 2008;1(1):29-37.
\87\ Krumholz HM, Wang Y, Mattera JA, et al. An administrative
claims model suitable for profiling hospital performance based on
30-day mortality rates among patients with heart failure.
Circulation 2006;113:1693-1701.
---------------------------------------------------------------------------
Final Decision: After careful consideration of the public comments,
we are finalizing our proposal to adopt the measure, Potentially
Preventable 30-Day Post-Discharge Readmission Measure for IRF QRP.
Measure Specifications for Measures Adopted in the FY 2017 IRF QRP
final rule are available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
4. Potentially Preventable Within Stay Readmission Measure for
Inpatient Rehabilitation Facilities
In addition to the measure finalized in section VIII.F.3. of this
final rule, Potentially Preventable 30-Day Post-Discharge Readmission
Measure for IRF QRP, we proposed the Potentially Preventable Within
Stay Readmission Measure for IRFs for the FY 2018 payment determination
and subsequent years. This measure is similar to the Potentially
Preventable 30-Day Post-Discharge Readmission Measure for IRF QRP;
however, the readmission window for this measure focuses on potentially
preventable hospital readmissions that take place during the IRF stay
as opposed to during the 30-day post-discharge period. The two PPR
measures are intended to function in tandem, covering readmissions
during the IRF stay and for 30 days following discharge from the IRF.
Utilizing two PPR measures in the IRF QRP will enable us to assess
different aspects of care and care coordination. The within stay
measure focuses on the care transition into inpatient rehabilitation as
well as the care provided during the IRF stay, whereas the 30-day post-
IRF discharge measure focuses on transitions from the IRF into less-
intensive levels of care or the community.
Similar to the Potentially Preventable 30-Day Post-Discharge
Readmission Measure for IRF QRP measure for IRFs, this measure assesses
the facility-level risk-standardized rate of unplanned, potentially
preventable hospital readmissions during the IRF stay. Hospital
readmissions include readmissions to a short-stay acute-care hospital
or an LTCH, with a diagnosis considered to be unplanned and potentially
preventable. This Medicare FFS measure is claims-based, requiring no
additional data collection or submission burden for IRFs. As described
in section VIII.F.3. of this final rule, we developed the approach for
defining PPR measure based on a comprehensive environmental scan,
analysis of claims data, and TEP input. Also, we obtained public
comment.
The definition for PPRs differs by readmission window. For the
within-IRF stay window, PPRs should be avoidable with sufficient
medical monitoring and appropriate patient treatment. The list of PPR
conditions for the Potentially Preventable Within Stay Readmission
Measure for IRFs are categorized by 4 clinical rationale groupings:
Inadequate management of chronic conditions;
Inadequate management of infections;
Inadequate management of other unplanned events; and
Inadequate injury prevention.
Additional details regarding the definition for PPRs are available
in our document titled, Proposed Measure Specifications for Measures
Proposed in the FY 2017 IRF QRP proposed rule available on our Web site
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
Section VIII.F of this final rule discusses the relevant background
and details that are also relevant for this measure. This measure
defines planned readmissions in the same manner as described in section
VIII.F.3 of this final rule, for the Potentially Preventable 30-Day
Post-Discharge Readmission Measure for IRF QRP. In addition, similar to
the Potentially Preventable 30-Day Post-Discharge Readmission Measure
for IRF QRP measure, this measure uses the same risk-adjustment and
statistical approach as described in section VIII.F.3 of this final
rule. Note the full methodology is detailed in the document titled,
Proposed Measure Specifications for Measures Proposed in the FY 2017
IRF QRP proposed rule, at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html. This measure is
also based on 2 consecutive calendar years of Medicare FFS claims data.
A TEP convened by our measure contractor provided recommendations
on the technical specifications of this measure, including the
development of an approach to define potentially preventable hospital
readmission for PAC. Details from the TEP meetings, including TEP
members' ratings of conditions proposed as being potentially
preventable, are available in the TEP Summary Report available on our
Web site 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 solicited
stakeholder feedback on the development of this measure through a
public comment period held from November 2 through December 1, 2015.
Comments on this and other PAC measures of PPR measures varied, with
some commenters supportive of the proposed measure, while others either
were not in favor of the measure, or suggested potential modifications
to the
[[Page 52109]]
measure specifications, such as including standardized function data. A
summary of our public comment period is also available on our Web site
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 MAP encouraged continued development of the proposed measure.
Specifically, the MAP stressed the need to promote shared
accountability and ensure effective care transitions. More information
about the MAP's recommendations for this measure is available at https://www.qualityforum.org/Publications/2016/02/MAP_2016_Considerations_for_Implementing_Measures_in_Federal_Programs_-_PAC-LTC.aspx. At the time, the risk-adjustment model was still under
development. Following completion of that development work, we were
able to test for measure validity and reliability as described in the
measure specifications document provided above. Testing results are
within range for similar outcome measures finalized in public reporting
and value-based purchasing programs, including the All-Cause Unplanned
Readmission Measure for 30 Days Post-Discharge from IRFs (NQF #2502)
that we previously adopted into the IRF QRP.
We plan to submit the measure to the NQF for consideration of
endorsement. We stated in the proposed rule that we intended to provide
initial confidential feedback to providers, prior to public reporting
of this measure, based on 2 calendar years of claims data from
discharges in 2015 and 2016. We proposed a minimum of 25 eligible stays
in a given IRF for public reporting of the measure for that IRF. We
also stated that we intended to publicly report this measure using
claims data from calendar years 2016 and 2017.
We invited public comment on our proposal to adopt this measure,
Potentially Preventable Within Stay Readmission Measure for IRFs. We
received several comments, which are summarized with our responses
below.
Comment: CMS received comments in support of this measure. In
particular, MedPAC supported this measure, and further suggested that
it should be applied identically across the four PAC settings so that
post-discharge rates can be meaningfully compared.
Response: We wish to clarify that this particular measure,
developed and proposed for use in the IRF QRP, is unique in that it is
a within stay readmission measure. Analogous measures applicable to
other PAC settings may be considered in future rulemaking.
Comment: Several commenters expressed concern over cross-setting
alignment of measures, some urging CMS to delay implementation of this
measure until there are equivalent within stay PPR measures for each
PAC setting. Commenters noted this measure is not required by the
IMPACT Act and that incongruences between measures in the different PAC
settings present concerns for cross-setting comparisons and potential
confusion for IRFs about their quality performance. One commenter was
particularly concerned about the differences between the IRF within
stay measure and the SNF PPR measure proposed for the SNF VBP Program
that assess PPRs 30 days after discharge from the prior hospital.
Response: We are clarifying that though this within-stay PPR
measure is not required by the IMPACT Act, capturing potentially
preventable readmission measures during an IRF stay assesses important
aspects of inpatient post-acute care. The measure is a starting point
for this work, which is being conducted in phases, and additional
measures that calculate PPRs using different readmission windows in
other PAC settings will be considered in the future. We will take this
comment into consideration.
Comment: Some commenters expressed that IRFs may not be able to
control or prevent hospital readmissions that take place during an IRF
stay, especially within the first few days of admission, if patients
are admitted to IRFs prior to the availability of diagnostic testing
results, or if they did not receive adequate acute care. One commenter
cited the example of patients with leukemia, who are often readmitted
to the hospital for treatment. Another commenter noted that even though
the risk adjustment will account for some of these circumstances, it is
difficult for providers to fully evaluate the risk-adjustment model
because the testing and risk-adjustment coefficients have not been
finalized. The commenter recommended these measures be suspended until
CMS explains how the measures will treat each of these scenarios.
Commenters suggested that the IRF within-stay PPR measure should
account for the three-day, short-stay and transfer care policies that
exist in the IRF PPS. One commenter expressed concern that the proposed
measure's readmission window and IRF payment rules would cause a
``double penalty'' for short-stay episodes that end in a readmission.
Commenters noted that the home health measures account for short-stay
payment policies and that the IRF measure should be designed in a
similar manner.
Response: We recognize the concerns raised related to potential
delays in receiving diagnostic information and/or inadequate care
provided in the prior acute setting for some patients. However, we wish
to clarify that this measure is intended to address potentially
preventable hospital readmissions and does not count all hospital
readmissions that take place during the IRF stay. The goal of this
measure is to improve care transitions and coordination of care, which
is important for all patients. Furthermore, providers assume the
responsibility for this outcome for all patients that they admit into
their facility, including those with shorter lengths of stay.
We would like to clarify that for the commenter's example regarding
patients with leukemia, these patients would most likely be excluded
from the measure because non-surgical treatment of cancer is a measure
exclusion. Based on analysis of data from 2013, 0.5 percent of the IRF
sample was excluded because the prior short-term acute-care stay was
for nonsurgical treatment of cancer which includes leukemia. In
addition, leukemia and other cancer patients that are not excluded from
the measure are more likely being readmitted for planned procedures and
treatments; however, this is a measure of potentially preventable
hospital readmissions that are also unplanned.
With regard to excluding readmissions during the first three days
of an IRF stay, we would like to clarify that the policy cited is for
IRF payment determination and is not related to measurement of quality
of care. This measure focuses on care transitions and coordination
which is relevant to all patients, including those with shorter lengths
of stay. Furthermore, excluding readmissions during the first three
days of an IRF stay may result in transferring patients back sooner in
order to exclude patients from the measure.
We would also like to clarify that the finalized risk-adjustment
models and coefficients are included in the measure specifications
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
Comment: Some commenters expressed concern over the
``multiplicity'' of the IRF QRP's readmission measures, adding that
this may lead to confusion and make it difficult for IRFs to track and
improve performance. There was also concern
[[Page 52110]]
that this IRF within stay PPR measure was not required by the IMPACT
Act, nor did it align with a domain in CMS's National Quality Strategy.
Several commenters expressed concern over the overlap between the PPR
measure and the existing all-cause readmission measures adopted for the
IRF QRP. A few commenters recommended CMS not to adopt this measure, or
to postpone implementation, commenting that the purpose and
implications of the measure were ambiguous and its introduction was
premature. The commenters respectfully recommended CMS not to adopt
this measure, and some commenters suggested postponing the
implementation of this measure pending further development or use in a
cross-setting and standardized manner.
Response: We appreciate the comment related to the potential
challenges that may be associated with proposing multiple readmission
measures for the program. However, given that each measure focuses on a
different aspect of care, we believe that each measure provides value
in the program. We are committed to ensuring that measures in the IRF
QRP are useful in assessing quality and will evaluate the readmission
measures in the future.
In addition, we wish to clarify that though this measure is not
required by the IMPACT Act, capturing potentially preventable
readmission measures during an IRF stay assesses important aspects of
inpatient post-acute care, including care coordination. Like other
hospital readmission measures for post-acute care, the measure fits
within the National Quality Strategy communication and care
coordination priority area. We also wish to clarify that this measure
does not overlap readmission captured in other readmission measures
proposed or adopted for the IRF QRP.
We would also like to clarify that the full measure specifications
including preliminary results were made available at the time of the
proposed rule's display. The measure is fully developed and the final
measure specifications, including the finalized risk-adjustment models
and descriptive statistics on IRFs' risk-standardized within-stay PPR
rates, are available are included in the measure specifications
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
Comment: One commenter specifically supported the inclusion of
infectious conditions in the inadequate management of infections and
inadequate management of other unplanned events categories in the
measure's definition of potentially preventable hospital readmissions.
Another commenter expressed support for the inclusion of chronic
conditions and infections as conditions for which readmissions would be
considered potentially preventable, citing infection prevention and
other interventions that are effective in preventing such readmissions.
Another commenter expressed appreciation for the focus on preventable
readmissions, but recommended that CMS continue evaluating and testing
the measure to ensure that the codes used for the PPR definition are
clinically relevant. One commenter expressed concern over being
``penalized'' for readmissions that are clinically unrelated to a
patient's original reason for IRF admission.
Response: As described in the proposed rule, the definition for
potentially preventable readmissions for this measure was developed
based on existing evidence and was vetted by a TEP, which included
clinicians and post-acute care experts. We also conducted a
comprehensive environmental scan to identify conditions for which
readmissions may be considered potentially preventable. Results of this
environmental scan and details of the TEP input received were made
available in the PPR TEP summary report available on the CMS Web site
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.
Though readmissions may be considered potentially preventable even
if they may not appear to be clinically related to the patient's
original reason for IRF admission, there is substantial evidence that
the conditions included in the definition may be preventable with
sufficient medical monitoring and appropriate patient treatment.
Furthermore, this measure is based on Medicare claims data and it may
not always be feasible to determine whether a subsequent readmission is
or is not clinically related to the reason why the patient was
receiving inpatient rehabilitation. We intend to conduct ongoing
evaluation and monitoring of this measure, and will take these comments
into consideration.
Comment: One commenter expressed concern that the measure could
create an incentive for IRFs to be selective about the types of
patients they admit in order to reduce the risk of PPRs (that is,
``cherry pick'' less complex patients for IRF admission). Another
commenter noted this measure could incentivize longer acute hospital
stays and delay admission to IRFs, expressing concern over being
penalized for brief readmissions for follow-up procedures.
Response: We wish to clarify that this measure does not count
planned procedures as these types of readmissions do not reflect
quality of care or care transitions. We intend to conduct ongoing
monitoring to assess for potential unintended consequences associated
with the implementation of this measure, and will take these
suggestions into account.
Comment: One commenter raised concerns over the risk-adjustment
approach for the within-stay PPR measure. The commenter expressed
concern that the HCC risk-adjustment method is insufficient at
predicting costs for certain patient populations. The commenters
suggested CMS reconsider the validity and reliability of the HCC risk-
adjustment model, and research and develop a refined risk-adjustment
model that encompasses more of the diversity and complexity of PAC
patients and is more sensitive to their levels of resource use. The
commenter also expressed concern that the proposed measure is not
adjusted for socio-economic factors.
Response: We appreciate the comment received regarding the risk-
adjustment model and will take this comment into consideration. We
refer readers to our response on the use of HCCs as described in
section VIII.F.3. of this final rule. We wish to clarify that the model
included in the specifications using HCCs as risk adjusters for
comorbidities posted for the proposed rule demonstrated more than
adequate discrimination power. The model had a c-statistic of 0.74
which is within range if not higher for similar readmission measures
finalized in public reporting programs, including the All-Cause
Unplanned Readmission Measure for 30 Days Post-Discharge from IRFs (NQF
#2502) previously adopted for the IRF QRP. We would also like to
clarify that the finalized risk-adjustment models and coefficients are
included in the measure specifications available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
With regard to the suggestions that the model include
sociodemographic factors and the suggestion pertaining to an approach
with which to convey data comparisons, we refer the readers to section
VIII.F of this final rule where we also discuss these topics.
[[Page 52111]]
Comment: Some commenters expressed concern over provider burden and
questioned CMS's intention of applying both all-cause and potentially
preventable readmission measures. The commenters also noted that with
the finalization of all required measures by the IMPACT Act, the
industry would be subject to significant changes and an increased data
reporting burden with regard to the quality reporting program. Some
commenters noted that there would not be an additional reporting or
data collection burden given the measure is claim-based; however,
providers would take on additional burdens, including understanding the
measure design, evaluating its implications, and reconciling the CASPER
Quality Measure feedback data.
Response: We would like to note that the within-stay PPR measures
use a data source of claims data and are not collected by means of an
assessment instrument. Therefore, the measure does not increase data
collection burden on the provider as this data is currently collected
by providers. Despite the lack of data collection burden, we appreciate
the comments that more education will be required for the public and
providers to understand the differences between the readmission
measures in the IRF QRP. We also wish to clarify that the within-stay
readmission measure does not overlap any existing readmission measures.
Comment: Several commenters expressed concern that the measures are
not NQF-endorsed, some with additional concerns over measure testing
and development. Some of these commenters recommended that CMS should
adopt measures endorsed by the NQF in quality reporting programs or
recommended that CMS submit the measures through the NQF endorsement
process as soon as feasible.
Response: With regard to NQF endorsement, as noted in the proposed
rule, we intend to submit this measure to NQF for consideration of
endorsement. We are unaware of any other measures that assess
potentially preventable readmissions during an IRF stay. We appreciate
the comments related to the measure's testing. We would also like to
clarify that the finalized risk-adjustment models and coefficients are
included in the measure specifications available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html. We will make results of additional testing
and evaluation of the measure beyond those provided in the final
measure specifications available in the future.
Final Decision: After careful consideration of the public comments,
we are finalizing our proposal to adopt this measure, Potentially
Preventable Within Stay Readmission Measure for IRFs. Measure
Specifications for Measures Adopted in the FY 2017 IRF QRP Final Rule
are available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
G. IRF QRP Quality Measure Finalized for the FY 2020 Payment
Determination and Subsequent Years
We proposed to adopt one new quality measure to meet the
requirements of the IMPACT Act beginning with the FY 2020 payment
determination and subsequent years. The measure, Drug Regimen Review
Conducted with Follow-Up for Identified Issues--PAC IRF QRP, addresses
the IMPACT Act quality domain of Medication Reconciliation.
1. Quality Measure Addressing the IMPACT Act Domain of Medication
Reconciliation: Drug Regimen Review Conducted With Follow-Up for
Identified Issues--Post Acute Care Inpatient Rehabilitation Facility
Quality Reporting Program
Sections 1899B(a)(2)(E)(i)(III) and 1899B(c)(1)(C) of the Act, as
added by the IMPACT Act, require the Secretary to specify a quality
measure to address the quality domain of medication reconciliation by
October 1, 2018 for IRFs, LTCHs and SNFs by January 1, 2017 for HHAs.
We proposed to adopt the quality measure, Drug Regimen Review Conducted
with Follow-Up for Identified Issues--PAC IRF QRP, for the IRF QRP as a
patient-assessment based, cross-setting quality measure to meet the
IMPACT Act requirements with data collection beginning October 1, 2018
for the FY 2020 payment determinations and subsequent years.
This measure assesses whether PAC providers were responsive to
potential or actual clinically significant medication issue(s) when
such issues were identified. Specifically, the quality measure reports
the percentage of patient stays in which a drug regimen review was
conducted at the time of admission and timely follow-up with a
physician occurred each time potential clinically significant
medication issues were identified throughout that stay.
For this quality measure, drug regimen review is defined as the
review of all medications or drugs the patient is taking to identify
any potential clinically significant medication issues. The quality
measure utilizes both the processes of medication reconciliation and a
drug regimen review, in the event an actual or potential medication
issue occurred. The measure informs whether the PAC facility identified
and addressed each clinically significant medication issue and if the
facility responded or addressed the medication issue in a timely
manner. Of note, drug regimen review in PAC settings is generally
considered to include medication reconciliation and review of the
patient's drug regimen to identify potential clinically significant
medication issues.\88\ This measure is applied uniformly across the PAC
settings.
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\88\ Institute of Medicine. Preventing Medication Errors.
Washington DC: National Academies Press; 2006.
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Medication reconciliation is a process of reviewing an individual's
complete and current medication list. Medication reconciliation is a
recognized process for reducing the occurrence of medication
discrepancies that may lead to Adverse Drug Events (ADEs).\89\
Medication discrepancies occur when there is conflicting information
documented in the medical records. The World Health Organization
regards medication reconciliation as a standard operating protocol
necessary to reduce the potential for ADEs that cause harm to patients.
Medication reconciliation is an important patient safety process that
addresses medication accuracy during transitions in patient care and in
identifying preventable ADEs.\90\ The Joint Commission added medication
reconciliation to its list of National Patient Safety Goals (2005),
suggesting that medication reconciliation is an integral component of
medication safety.\91\ The Society of Hospital Medicine published a
statement in agreement of the Joint Commission's emphasis and value of
medication reconciliation as a patient safety goal.\92\ There is
universal agreement that medication reconciliation directly addresses
patient safety issues that can result from medication
[[Page 52112]]
miscommunication and unavailable or incorrect
information.93 94 95
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\89\ Ibid.
\90\ Leotsakos A., et al. Standardization in patient safety: The
WHO High 5s project. Int J Qual Health Care. 2014:26(2):109-116.
\91\ The Joint Commission. 2016 Long Term Care: National Patient
Safety Goals Medicare/Medicaid Certification-based Option.
(NPSG.03.06.01).
\92\ Greenwald, J.L., Halasyamani, L., Greene, J., LaCivita, C.,
et al. (2010). Making inpatient medication reconciliation patient
centered, clinically relevant and implementable: A consensus
statement on key principles and necessary first steps. Journal of
Hospital Medicine, 5(8), 477-485.
\93\ Leotsakos A., et al. Standardization in patient safety: The
WHO High 5s project. Int J Qual Health Care. 2014:26(2):109-116.
\94\ The Joint Commission. 2016 Long Term Care: National Patient
Safety Goals Medicare/Medicaid Certification-based Option.
(NPSG.03.06.01).
\95\ IHI. Medication Reconciliation to Prevent Adverse Drug
Events [Internet]. Cambridge, MA: Institute for Healthcare
Improvement; [cited 2016 Jan 11]. Available from: https://www.ihi.org/topics/adesmedicationreconciliation/Pages/default.aspx.
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The performance of timely medication reconciliation is valuable to
the process of drug regimen review. Preventing and responding to ADEs
is of critical importance as ADEs account for significant increases in
health services utilization and costs 96 97 98 including
subsequent emergency room visits and re-hospitalizations.\99\ Annual
health care costs in the United States from ADEs are estimated at $3.5
billion, resulting in 7,000 deaths annually.100 101
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\96\ Institute of Medicine. Preventing Medication Errors.
Washington DC: National Academies Press; 2006.
\97\ Jha AK, Kuperman GJ, Rittenberg E, et al. Identifying
hospital admissions due to adverse drug events using a computer-
based monitor. Pharmacoepidemiol Drug Saf. 2001;10(2):113-119.
\98\ Hohl CM, Nosyk B, Kuramoto L, et al. Outcomes of emergency
department patients presenting with adverse drug events. Ann Emerg
Med. 2011;58:270-279.
\99\ Kohn LT, Corrigan JM, Donaldson MS. To Err Is Human:
Building a Safer Health System Washington, DC: National Academies
Press; 1999.
\100\ Greenwald, J.L., Halasyamani, L., Greene, J., LaCivita,
C., et al. (2010). Making inpatient medication reconciliation
patient centered, clinically relevant and implementable: A consensus
statement on key principles and necessary first steps. Journal of
Hospital Medicine, 5(8), 477-485.
\101\ Phillips, David P.; Christenfeld, Nicholas; and Glynn,
Laura M. Increase in US Medication-Error Deaths between 1983 and
1993. The Lancet. 351:643-644, 1998.
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Medication errors include the duplication of medications, delivery
of an incorrect drug, inappropriate drug omissions, or errors in the
dosage, route, frequency, and duration of medications. Medication
errors are one of the most common types of medical error and can occur
at any point in the process of ordering and delivering a medication.
Medication errors have the potential to result in an
ADE.102 103 104 105 106 107 Inappropriately prescribed
medications are also considered a major healthcare concern in the
United States for the elderly population, with costs of roughly $7.2
billion annually.\108\
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\102\ Institute of Medicine. To err is human: Building a safer
health system. Washington, DC: National Academies Press; 2000.
\103\ Lesar TS, Briceland L, Stein DS. Factors related to errors
in medication prescribing. JAMA. 1997:277(4): 312-317.
\104\ Bond CA, Raehl CL, & Franke T. Clinical pharmacy services,
hospital pharmacy staffing, and medication errors in United States
hospitals. Pharmacotherapy. 2002:22(2): 134-147.
\105\ Bates DW, Cullen DJ, Laird N, Petersen LA, Small SD, et
al. Incidence of adverse drug events and potential adverse drug
events. Implications for prevention. JAMA. 1995:274(1): 29-34.
\106\ Barker KN, Flynn EA, Pepper GA, Bates DW, & Mikeal RL.
Medication errors observed in 36 health care facilities. JAMA. 2002:
162(16):1897-1903.
\107\ Bates DW, Boyle DL, Vander Vliet MB, Schneider J, & Leape
L. Relationship between medication errors and adverse drug events. J
Gen Intern Med. 1995:10(4): 199-205.
\108\ Fu, Alex Z., et al. ``Potentially inappropriate medication
use and healthcare expenditures in the US community-dwelling
elderly.'' Medical care 45.5 (2007): 472-476.
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There is strong evidence that medication discrepancies occur during
transfers from acute care facilities to post-acute care facilities.
Discrepancies occur when there is conflicting information documented in
the medial records. Almost one-third of medication discrepancies have
the potential to cause patient harm.\109\ An estimated 50 percent of
patients experienced a clinically important medication error after
hospital discharge in an analysis of two tertiary care academic
hospitals.\110\
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\109\ Wong, Jacqueline D., et al. ``Medication reconciliation at
hospital discharge: Evaluating discrepancies.'' Annals of
Pharmacotherapy 42.10 (2008): 1373-1379.
\110\ Kripalani S, Roumie CL, Dalal AK, et al. Effect of a
pharmacist intervention on clinically important medication errors
after hospital discharge: A randomized controlled trial. Ann Intern
Med. 2012:157(1):1-10.
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Medication reconciliation has been identified as an area for
improvement during transfer from the acute care facility to the
receiving post-acute care facility. PAC facilities report gaps in
medication information between the acute care hospital and the
receiving post-acute-care setting when performing medication
reconciliation.111 112 Hospital discharge has been
identified as a particularly high risk time point, with evidence that
medication reconciliation identifies high levels of
discrepancy.113 114 115 116 117 118 Also, there is evidence
that medication reconciliation discrepancies occur throughout the
patient stay.119 120 For older patients, who may have
multiple comorbid conditions and thus multiple medications, transitions
between acute and post-acute care settings can be further
complicated,\121\ and medication reconciliation and patient knowledge
(medication literacy) can be inadequate post-discharge.\122\ The
quality measure, Drug Regimen Review Conducted with Follow-Up for
Identified Issues-PAC IRF QRP, evaluates an important component of care
coordination for PAC settings and will affect a large proportion of the
Medicare population who transfer from hospitals into PAC services each
year. For example, in 2013, 1.7 million Medicare FFS beneficiaries had
SNF stays, 338,000 beneficiaries had IRF stays, and 122,000
beneficiaries had LTCH stays.\123\
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\111\ Gandara, Esteban, et al. ``Communication and information
deficits in patients discharged to rehabilitation facilities: An
evaluation of five acute care hospitals.'' Journal of Hospital
Medicine 4.8 (2009): E28-E33.
\112\ Gandara, Esteban, et al. ``Deficits in discharge
documentation in patients transferred to rehabilitation facilities
on anticoagulation: Results of a system wide evaluation.'' Joint
Commission Journal on Quality and Patient Safety 34.8 (2008): 460-
463.
\113\ Coleman EA, Smith JD, Raha D, Min SJ. Post hospital
medication discrepancies: Prevalence and contributing factors. Arch
Intern Med. 2005 165(16):1842-1847.
\114\ Wong JD, Bajcar JM, Wong GG, et al. Medication
reconciliation at hospital discharge: Evaluating discrepancies. Ann
Pharmacother. 2008 42(10):1373-1379.
\115\ Hawes EM, Maxwell WD, White SF, Mangun J, Lin FC. Impact
of an outpatient pharmacist intervention on medication discrepancies
and health care resource utilization in post hospitalization care
transitions. Journal of Primary Care & Community Health. 2014;
5(1):14-18.
\116\ Foust JB, Naylor MD, Bixby MB, Ratcliffe SJ. Medication
problems occurring at hospital discharge among older adults with
heart failure. Research in Gerontological Nursing. 2012, 5(1): 25-
33.
\117\ Pherson EC, Shermock KM, Efird LE, et al. Development and
implementation of a post discharge home-based medication management
service. Am J Health Syst Pharm. 2014; 71(18): 1576-1583.
\118\ Pronovosta P, Weasta B, Scwarza M, et al. Medication
reconciliation: A practical tool to reduce the risk of medication
errors. J Crit Care. 2003; 18(4): 201-205.
\119\ Bates DW, Cullen DJ, Laird N, Petersen LA, Small SD, et
al. Incidence of adverse drug events and potential adverse drug
events. Implications for prevention. JAMA. 1995:274(1): 29-34.
\120\ Himmel, W., M. Tabache, and M. M. Kochen. ``What happens
to long-term medication when general practice patients are referred
to hospital?.''European journal of clinical pharmacology 50.4
(1996): 253-257.
\121\ Chhabra, P.T., et al. (2012). ``Medication reconciliation
during the transition to and from long-term care settings: A
systematic review.'' Res Social Adm Pharm 8(1): 60-75.
\122\ Kripalani S, Roumie CL, Dalal AK, et al. Effect of a
pharmacist intervention on clinically important medication errors
after hospital discharge: A randomized controlled trial. Ann Intern
Med. 2012:157(1):1-10.
\123\ March 2015 Report to the Congress: Medicare Payment
Policy. Medicare Payment Advisory Commission; 2015.
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A TEP convened by our measure development contractor provided input
on the technical specifications of this quality measure, Drug Regimen
Review Conducted with Follow-Up for Identified Issues-PAC IRF QRP,
including components of reliability, validity, and the feasibility of
implementing the measure across PAC settings. The TEP supported the
measure's implementation across PAC settings and was supportive of our
plans to standardize this measure for cross-setting development. A
summary of the TEP proceedings is available on the PAC Quality
Initiatives Downloads and
[[Page 52113]]
Video Web site 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 stakeholder feedback on the development of this
measure by means of a public comment period held from September 18
through October 6, 2015. Through public comments submitted by several
stakeholders and organizations, we received support for implementation
of this measure. The public comment summary report for the measure is
available on the CMS Web site 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 NQF-convened MAP met on December 14 and 15, 2015, and provided
input on the use of this measure, Drug Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF QRP. The MAP encouraged
continued development of the quality measure to meet the mandate added
by the IMPACT Act. The MAP agreed with the measure gaps identified by
CMS, including medication reconciliation, and stressed that medication
reconciliation be present as an ongoing process. More information about
the MAPs recommendations for this measure is available at https://www.qualityforum.org/Publications/2016/02/MAP_2016_Considerations_for_Implementing_Measures_in_Federal_Programs_-_PAC-LTC.aspx.
Since the MAP's review and recommendation of continued development,
we have continued to refine this measure in compliance with the MAP's
recommendations. The measure is consistent with the information
submitted to the MAP and supports its scientific acceptability for use
in quality reporting programs. Therefore, we proposed this measure for
implementation in the IRF QRP as required by the IMPACT Act.
We reviewed the NQF's endorsed measures and identified one NQF-
endorsed cross-setting and quality measure related to medication
reconciliation, which applies to the SNF, LTCH, IRF, and HHA settings
of care: Care for Older Adults (COA), (NQF #0553). The quality measure,
Care for Older Adults (COA), (NQF #0553) assesses the percentage of
adults 66 years and older who had a medication review. The Care for
Older Adults (COA), (NQF #0553) measure requires at least one
medication review conducted by a prescribing practitioner or clinical
pharmacist during the measurement year and the presence of a medication
list in the medical record. This is in contrast to the quality measure,
Drug Regimen Review Conducted with Follow-Up for Identified Issues-PAC
IRF QRP, which reports the percentage of patient stays in which a drug
regimen review was conducted at the time of admission and that timely
follow-up with a physician occurred each time one or more potential
clinically significant medication issues were identified throughout
that stay.
After careful review of both quality measures, we decided to
propose the quality measure, Drug Regimen Review Conducted with Follow-
Up for Identified Issues-PAC IRF QRP for the following reasons:
The IMPACT Act requires the implementation of quality
measures, using patient assessment data that are standardized and
interoperable across PAC settings. The quality measure, Drug Regimen
Review Conducted with Follow-Up for Identified Issues-PAC IRF QRP,
employs three standardized patient-assessment data elements for each of
the four PAC settings so that data are standardized, interoperable, and
comparable; whereas, the Care for Older Adults (COA), (NQF #0553)
quality measure does not contain data elements that are standardized
across all four PAC settings.
The quality measure, Drug Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF QRP, requires the
identification of potential clinically significant medication issues at
the beginning, during, and at the end of the patient's stay to capture
data on each patient's complete PAC stay; whereas, the Care for Older
Adults (COA), (NQF #0553) quality measure only requires annual
documentation in the form of a medication list in the medical record of
the target population.
The quality measure, Drug Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF QRP, includes identification of
the potential clinically significant medication issues and
communication with the physician (or physician designee) as well as
resolution of the issue(s) within a rapid timeframe (by midnight of the
next calendar day); whereas, the Care for Older Adults (COA), (NQF
#0553) quality measure does not include any follow-up or timeframe in
which the follow-up would need to occur.
The quality measure, Drug Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF QRP, does not have age
exclusions; whereas, the Care for Older Adults (COA), (NQF #0553)
quality measure limits the measure's population to patients aged 66 and
older.
The quality measure, Drug Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF QRP, will be reported to IRFs
quarterly to facilitate internal quality monitoring and quality
improvement in areas such as patient safety, care coordination, and
patient satisfaction; whereas, the Care for Older Adults (COA), (NQF
#0553) quality measure would not enable quarterly quality updates, and
thus data comparisons within and across PAC providers would be
difficult due to the limited data and scope of the data collected.
Therefore, based on the evidence discussed above, we proposed to
adopt the quality measure entitled, Drug Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF QRP, for the IRF QRP for FY
2020 payment determination and subsequent years. We plan to submit the
quality measure to the NQF for consideration for endorsement.
The calculation of the quality measure is based on the data
collection of three standardized items to be included in the IRF-PAI.
The collection of data by means of the standardized items will be
obtained at admission and discharge. For more information about the
data submission required for this measure, we refer readers to section
VIII.I.c of this final rule.
The standardized items used to calculate this quality measure do
not duplicate existing items currently used for data collection within
the IRF-PAI. The measure denominator is the number of patient stays
with a discharge assessment during the reporting period. The measure
numerator is the number of stays in the denominator where the medical
record contains documentation of a drug regimen review conducted at:
(1) Admission and (2) discharge with a lookback through the entire
patient stay with all potential clinically significant medication
issues identified during the course of care and followed up with a
physician or physician designee by midnight of the next calendar day.
This measure is not risk adjusted. For technical information about this
measure, including information about the measure calculation and
discussion pertaining to the standardized items used to calculate this
measure, we refer readers to the document titled, Proposed Measure
Specifications for Measures Proposed in the FY 2017 IRF QRP proposed
rule available at https://www.cms.gov/Medicare/Quality-Initiatives-
Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-
[[Page 52114]]
Quality-Reporting-Program-Measures-Information-.html.
Data for the quality measure, Drug Regimen Review Conducted with
Follow-Up for Identified Issues-PAC IRF QRP, will be collected using
the IRF-PAI with submission through the Quality Improvement Evaluation
System (QIES) Assessment Submission and Processing (ASAP) system.
We invited public comment on our proposal to adopt the quality
measure, Drug Regimen Review Conducted with Follow-Up for Identified
Issues-PAC IRF QRP for the IRF QRP. We received several comments, which
are summarized with our responses below.
Comment: Several commenters, including MedPAC, expressed support
for the quality measure. Commenters supported the medication
reconciliation concept, and one commenter conveyed that preventing and
responding to ADEs that account for increases in health services
utilization and cost is critically important. MedPAC further noted that
the medication reconciliation and follow-up process can help reduce
medication errors that are especially common among patients who have
multiple health care providers and multiple comorbidities.
Response: We agree that medication reconciliation is an important
patient safety process for addressing medication accuracy during
transitions in patient care and identifying preventable ADEs, which may
lead to reduced health services utilization and associated costs.
Comment: Several commenters recommended that CMS add an additional
response option, to indicate that the item N2003 Medication Follow-up
(completed at admission) is not applicable if a patient does not take
any medication. Alternatively, commenters suggested that CMS clarify
whether this item would be mandatory in the event that a patient is not
taking any medications.
Response: We wish to point out that Measure item N2003 has a skip
pattern that allows the user to skip over this item if the patient does
not take medication. Additional guidance will be included in the IRF-
PAI training manual.
Comment: We received several comments regarding concerns about
whether the measure has been fully developed and tested. Many
commenters noted that the NQF-convened MAP recommended continued
development for the measure and requested testing of the measure to
ensure that it is appropriate for the IRF setting. Several commenters
expressed concern that the measure was not NQF-endorsed.
Response: Since the time of the NQF-convened MAP, with our measure
contractor, we tested this measure in a pilot test involving twelve
post-acute care facilities (IRF, SNF, LTCH), representing variation
across geographic location, size, profit status, and clinical records
system. Two clinicians in each facility collected data on a sample of
10 to 20 patients for a total of 298 records (147 qualifying pairs).
Analysis of agreement between coders within each participating facility
indicated a 71 percent agreement for item DRR-01 \124\ Drug Regimen
Review (admission); 69 percent agreement for item DRR-02 \125\
Medication Follow-up (admission); and 61 percent agreement for DRR-03
\126\ Medication Intervention (during stay and discharge). Overall,
pilot testing enabled us to verify feasibility of the measure.
Furthermore, measure development included convening a TEP to provide
input on the technical specifications of this quality measure,
including components of reliability, validity and the feasibility of
implementing the measure across PAC settings. The TEP included
stakeholders from the IRF setting and was supportive of our plans to
standardize this measure for cross-setting development. A summary of
the TEP proceedings is available on the PAC Quality Initiatives
Downloads and Videos Web site 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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\124\ DRR pilot items DRR-01, DRR-02 and DRR-03 are equivalent
to the proposed rule DRR PAC instrument items N. 2001, N. 2003 and
N. 2005
\125\ DRR pilot items DRR-01, DRR-02 and DRR-03 are equivalent
to the proposed rule DRR PAC instrument items N. 2001, N. 2003 and
N. 2005
\126\ DRR pilot items DRR-01, DRR-02 and DRR-03 are equivalent
to the proposed rule DRR PAC instrument items N. 2001, N. 2003 and
N. 2005
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As noted above, we plan to conduct further testing on this measure
once we have started collecting data from the PAC settings. Once we
have completed this additional measure performance testing, we plan to
submit the measure to NQF for endorsement.
Comment: We received several comments about guidance and training.
One commenter requested clear and consistent information for training
staff and resources to meet the requirements of the measure. We
received several comments requesting guidance regarding the definition
of ``clinically significant medication issues.'' Several commenters
were concerned that the phrase could be interpreted differently by the
many providers involved in a patient's treatment and that this could
result in a challenge to collect reliable and accurate data for this
quality measure. One commenter further conveyed that there are likely
to be variations in measure performance that are not based on
differences in care, but rather on differences in data collection. In
addition, one commenter requested a specific definition in the measure
specifications for the word ``potential,'' and another commenter
requested further guidance on what would be considered an ``adequate
response'' to a clinically significant medication issue.
Response: For this measure, potential clinically significant
medication issues are defined as those issues that, in the clinician's
professional judgment, warrant interventions, such as alerting the
physician and/or others, and the timely completion of any recommended
actions (by midnight of the next calendar day) so as to avoid and
mitigate any untoward or adverse outcomes. The definition of
``clinically significant'' in this measure was conceptualized during
the measure development process. For purposes of the measure, the
decision regarding whether or not a medication issue is ``clinically
significant'' will need to be made on a case-by-case basis, but we also
intend to provide additional guidance and training on this issue.
Comment: We received several comments regarding the patient
populations for the measure, specifically conveying concern that the
populations are not standardized across PAC settings. For example, many
commenters noted that IRF QRP measure includes data collection for
Medicare Fee for Service and Medicare Advantage patients, while the SNF
QRP measure only includes Medicare Part A patients, and the LTCH QRP
includes all patients. Commenters were concerned that this could result
in selective sampling of the patient population that would skew the
collected data and distort or otherwise invalidate meaningful
comparisons across measures and across settings, thereby falling short
of the PAC standardization goals of the IMPACT Act. Several commenters
suggested that CMS exclude Medicare Advantage patients, while others
recommended that they be included for all measures across all PAC
settings.
Response: We are working to standardize all measures as mandated by
the IMPACT Act to increase data comparability and interoperability. We
will take the commenter's comments and concerns into consideration as
we work to standardize the proposed measure.
[[Page 52115]]
Comment: We received several comments regarding the time period for
the proposed measure. One commenter disagreed with the measure's
requirement that a facility must respond to urgent medication issues
within one calendar day, noting that some medication issues may need to
be resolved much more quickly for the patient's well-being. Another
commenter was concerned that the measure tracks medication issues
during any point of the patient's stay, citing that medication
reconciliation occurs only during transitions of care such as
admission, transfer and discharge. Therefore, this commenter had
concerns that this drug regimen review process was fundamentally
different than a medication reconciliation measure that focused only on
care transitions.
Response: We appreciate the challenges in coordinating patient care
in IRF settings. However, we chose to set the intervention timeline as
midnight of the next calendar day because we believe this timeline is
consistent with current standard clinical practice where a clinically
significant medication issue arises. The measure evaluates
responsiveness to potential or actual clinically significant medication
issues when such issues are identified. The measure evaluates
responsiveness to potential or actual clinically significant medication
issues when such issues are identified. We would like to note that the
measure is simply assessing responsiveness to issues and does not
prevent clinicians from acting more quickly when an issue is
identified.
We agree that medication discrepancies can occur during patient
admissions, transfers, and discharges. We wish to clarify that the
quality measure requires the identification of potential clinically
significant medication issues for each patient's complete IRF stay,
from admission to discharge. Medication reconciliation and drug regimen
review are interrelated activities; while medication reconciliation is
a process that identifies the most accurate and current list of
medications, particularly during transitions of care, it also includes
the evaluation of the name, dosage, frequency, and route. Drug regimen
review is a process that necessitates and includes the review of all
medications for additional purposes such as the identification of
potential adverse effects. The process of drug regimen review includes
medication reconciliation at the time of patient transitions and
throughout the patient's stay.
Comment: We received several comments pertaining to the scope of
the measure. Several commenters commented that medication
reconciliation and drug regimen review are distinct processes. Several
commenters were concerned that the measure does not meet the medication
reconciliation domain of the IMPACT Act. Commenters maintained that the
services provided as part of drug regimen review are distinctly
different from the services provided as part of medication
reconciliation, and that they are completed by different members of the
care team. These commenters believe that the measure goes beyond the
statutory mandate of the medication reconciliation domain of the IMPACT
Act. One commenter was also concerned that, according to the definition
provided in the Home Health Conditions of Participation, drug regimen
review includes taking into consideration a patient's noncompliance
with drug therapy, significant side effects, and ineffective drug
therapy, which are not feasible for a facility to assess during
admission. The commenter conveyed that this was distinct from
medication reconciliation. Many commenters were concerned that the
measure only evaluates whether the patient's current medications are
being reviewed and does not determine whether this review affects the
patient's quality of care.
Response: We disagree with the commenters' suggestion that the
measure does not meet the requirements of the IMPACT Act. Medication
reconciliation and drug regimen review are interrelated activities;
while medication reconciliation is a process that identifies the most
accurate and current list of medications, particularly during
transitions in care, it also includes the evaluation of the name,
dosage, frequency, and route. Drug regimen review is a process that
necessitates, and includes the review of all medications for additional
purposes, such as the identification of potential adverse effects. The
process of drug regimen review includes medication reconciliation at
the time of patient transitions and throughout the patient's stay.
Therefore, we believe that medication reconciliation and drug regimen
review are processes that are appropriate to combine into a single
measure for purposes of the IRF QRP. We would also like to note that
during the development of the measure, the definitions of medication
reconciliation and drug regimen review, as detailed in the State
Operations Manual (SOM), which includes the Conditions of
Participation, were taken into consideration. We do not believe that
the measure's use of the term ``clinically significant'' overrides or
conflicts with the guidance as outlined in the SOM. Further, we wish to
clarify that the specification of the measure does not preclude the
activities of drug regimen reviews that are consistent with the SOM.
The measure encompasses the IMPACT Act's medication reconciliation
domain.
Comment: Several commenters were concerned that the measure does
not specify which healthcare provider is required to perform the drug
regimen review, or the level of clinical training required to do so.
The commenters were concerned that this lack of standardization could
lead to differences across the PAC settings. Many commenters conveyed
that in the IRF setting, medication reconciliation is complicated and
time consuming, as IRF patients with multiple clinical needs often
arrive from an acute hospital where many physicians, including
specialists, have made changes to patients' prescriptions. One
commenter noted that patient medications may be adjusted more
frequently in an IRF due to the high level of physician supervision and
was concerned that the measure would not count the extensive drug
regimen review being done if a clinically significant medication issue
was not identified during the stay. However, commenters note that other
PAC settings may lack the clinical expertise required to perform such
thorough medication reviews. Commenters were concerned that the
assessment items proposed do not capture the intense involvement of a
pharmacist, physician, and nurse that occurs in complex cases.
Response: We wish to clarify that the measure does not override,
supersede or conflict with current CMS guidance or regulations related
to drug regimen review. The measure also does not specify what clinical
professional is required to perform these activities. We do not
prescribe guidance on which clinician may complete patient assessments.
We also appreciate concerns about standardization across the PAC
settings and acknowledge the complexity of drug regimen review in the
IRF settings. While we agree that this measure does not capture every
aspect of the drug regimen review process undertaken for each IRF
patient, we emphasize that it is intended to assess whether PAC
providers were responsive to potential or actual clinically significant
medication issue(s) when such issues were identified. As noted in the
measure specifications, the
[[Page 52116]]
measure's assessment items are standardized.
Comment: Many commenters, including MedPAC, encouraged CMS to
develop a measure to evaluate medication reconciliation throughout the
care continuum. Commenters, including MedPAC, suggested CMS focus on
discharge from the PAC setting and evaluate whether the PAC sends a
medication list to the patient's primary care physician or to the next
PAC provider. One commenter recommended that CMS not proceed with the
measure and instead focus on medication reconciliation at discharge.
Response: PAC facilities are expected to document information
pertaining to the process of a drug regimen review, which includes
medication reconciliation, in the patient's discharge medical record.
Further, it is standard practice for patient discharge records to
include a medication list to be transferred to the admitting PAC
facility. We appreciate MedPAC and other commenters' recommendation for
a quality measure that assesses post-discharge medication communication
with primary care providers for patients discharged to home. We will
take the recommendation into consideration for future measure
development in accordance with the IMPACT Act, which emphasizes the
transfer of interoperable patient information across the continuum of
care.
Comment: We received a number of comments related to unintended
consequences of the measure. One commenter expressed concern that the
measure would discourage PAC clinicians from reporting and correcting
medication errors. Another commenter was concerned that the measure
does not require an IRF to take steps to identify clinically
significant medication issues, but instead measures whether steps were
taken once an issue was identified, which could be abused by PAC
providers who limit the identification of clinically significant
medication issues in order to artificially increase their score.
Response: Since it is a professional standard of practice for all
providers to address potential clinically significant medication issues
before they lead to avoidable harm to the patient, we do not believe
that the measure will discourage a clinician from reporting a
significant medication issue. We reiterate that the quality measure
encourages PAC providers to conduct thorough drug regimen review to
identify, address, and follow up for all clinically significant
medication errors. The measure was informed by current evidence
surrounding medication reconciliation and drug regimen review, as well
as a review of best practice and professional standards of care.
Comment: We received multiple comments related to burden and
expenses related to this measure. One commenter expressed concern that
the requirements required increased resources without clear benefit or
increase in pay to providers for additional expenses. One commenter
conveyed concern that providers' existing electronic medical record
systems (EMRs) likely do not include data collection and reporting
capabilities required by the measure. The commenter conveyed the
challenge of collecting the data for this measure manually and had
concerns about the cost of doing so, and resulting data inaccuracy.
Response: We are very sensitive to the issue of burden associated
with data collection and have proposed only the minimal number of items
needed to calculate the quality measures. We emphasize that this
measure follows standard clinical practice requirements of ongoing
review, documentation, and timely reconciliation of all patient
medications, with appropriate follow-up to address all clinically
significant medication concerns. While we support the use of EMRs, we
do not require that providers use EMRs to populate assessment data.
Comment: One commenter suggested that CMS exclude patients from the
measure who were unexpectedly discharged before the medication
reconciliation process is completed.
Response: We would like to clarify that this IRF measure includes
all Medicare Part A and Medicare Advantage patient stays, including
stays where a patient has an unexpected discharge. Data for coding
N2005 Medicare Interventions can be obtained from the patient's medical
records, so it is feasible to code the measure item when a patient has
an unexpected discharge.
Comment: One commenter conveyed concern that drug regimen review
occurs differently across the care settings. The commenter specifically
expressed that inpatient settings may handle clinically significant
medication issues more immediately than home health agencies.
Response: We believe that this comment is immaterial to the intent
of the measure. It should be noted that we strive for consistency in
the collection and application of the measure across all PAC settings.
Comment: One commenter requested for clarification about whether
the measure is intended to include instances where a drug was reviewed
for potential adverse effects and drug reactions prior to being
ordered. The commenter conveyed that the measure only included
medications that have been ordered for the patient but not those that
were prevented from being ordered by a drug regimen process.
Response: We appreciate the commenter's concern regarding
medications that were prevented from being ordered by the drug regimen
review process. If finalized, we would provide guidance on these and
other clinical examples as part of the training efforts.
Final Decision: After careful consideration of the public comments,
we are finalizing our proposal to adopt the quality measure, Drug
Regimen Review Conducted with Follow-Up for Identified Issues-PAC IRF
QRP measure for the IRF QRP for FY 2020 payment determination and
subsequent years, as described in the Measure Specifications for
Measures Adopted in the FY 2017 IRF QRP final rule, available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/Technical-Information.html.
H. IRF QRP Quality Measures and Measure Concepts under Consideration
for Future Years
We invited comment on the importance, relevance, appropriateness,
and applicability of each of the quality measures listed in Table 8 for
future years in the IRF QRP. We are developing a measure related to the
IMPACT Act domain, ``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 furnishing items and services to the
individual, when the individual transitions.'' We considered the
possibility of adding quality measures that rely on the patient's
perspective; that is, measures that include patient-reported experience
of care and health status data. We recently posted a ``Request for
Information to Aid in the Design and Development of a Survey Regarding
Patient and Family Member Experiences with Care Received in Inpatient
Rehabilitation Facilities'' (80 FR 72725). Also, we are considering a
measure focused on pain that relies on the collection of patient-
reported pain data. Finally, we are considering a measure related to
patient safety, Venous Thromboembolism Prophylaxis.
We received several comments about IRF QRP quality measures under
[[Page 52117]]
consideration for future years which are summarized with our responses
below.
Comment: Commenters had concerns about the current process for
seeking stakeholder feedback, noting that seven- and fourteen-day
public comment periods are unreasonable for stakeholders. Other
commenters did not support the addition of process measures, citing
administrative burden and expense, and recommended that CMS focus on
outcome measures and postpone any measures outside the requirements of
the IMPACT Act.
Many commenters remarked on the limited number of items in the IRF-
PAI related to communication, cognition, and swallowing and noted that
these domains are important in treating individuals with neurological
disorders. One commenter encouraged CMS to adopt a specific screening
instrument (Montreal Cognitive Assessment (MoCA)) or similar screening
tools and assessment tools (such as the Continuity Assessment Record
and Evaluation-Community, or CARE-C) to best meet the needs of Medicare
beneficiaries and the intent of the IMPACT Act. Another commenter
requested that CMS add a functional cognition assessment item to the
IRF discharge assessment and that this information be provided to the
next provider when a patient is transferred. The commenters offered to
collaborate with CMS to develop future measures in the area of
cognitive function.
Response: We wish to note that several of the measures currently
adopted in the IRF QRP are outcome measures, including: Percent of
Residents or Patients with Pressure Ulcers that are New or Worsened
(Short-Stay) (NQF #0678), NHSN CAUTI Outcome Measure (NQF #0138), All-
Cause Unplanned Readmission Measure for 30 Days Post Discharge from an
IRF (NQF #2502), NHSN Facility-wide Inpatient Hospital-onset MRSA
Bacteremia Outcome Measure (NQF #1716), and NHSN Facility-wide
Inpatient Hospital-onset CDI Outcome Measure (NQF #1717). Measures that
have been finalized for implementation October 1, 2016 also include
outcome measures: Application of Percent of Residents Experiencing One
or More Falls with Major Injury (NQF #0674), IRF Functional Outcome
Measure: Change in Self-Care Score for Medical Rehabilitation Patients
(NQF #2633), IRF Functional Outcome Measure: Change in Mobility Score
for Medical Rehabilitation Patients (NQF #2634), Discharge Self-Care
Score for Medical Rehabilitation Patients (NQF #2635), Discharge
Mobility Score for Medical Rehabilitation Patients (NQF #2636) We agree
that future development of outcome measures should include other areas
of function, such as communication, cognition and swallowing, and are
important components of functional assessment and improvement for
patients who receive care in PAC settings, including IRFs. We
appreciate comments related to the public comment periods during the
measure development and stakeholder feedback process, and will continue
to engage stakeholders as we develop and implement quality measures to
meet the requirements of the IMPACT Act.
Comment: Several commenters supported a Venous Thromboembolism
(VTE) Prophylaxis measure but suggested that the measure take into
account that not all VTEs can be prevented due to its complexity. Some
commenters did not support a process measure, since VTE prophylaxis is
already a standard of practice and the measure would add burden, but
have no clinical significance. These commenters do support the
development of a VTE outcome measure.
Response: We thank the commenters for their comments on the VTE
Prophylaxis measure under consideration for future implementation in
the IRF QRP and will take into consideration the commenters'
recommendations.
Comment: Several commenters recommended that a pain measure take
into consideration pain that might be experienced as the result of
intense therapy. One commenter suggested that pain management was a
more meaningful measure for IRF patients and requested guidance on the
definitions of moderate and severe pain.
Response: We will take these suggested quality measure concepts and
recommendations regarding measure specifications into consideration in
our ongoing measure development and testing efforts.
Comment: We received several comments regarding the patient
experience of care measure. Several commenters had concerns about
survey fatigue across the continuum of care. Many commenters were
concerned that for one episode of care, a patient could receive a
survey from each setting which could result in confusion in responses
and inaccurate results. Many commenters were concerned that since many
IRFs are small units, their data may not be statistically
representative or may show high variability. The commenters recommended
that CMS take a systems-based approach with patient experience surveys
to avoid these problems.
Many commenters supported a patient experience of care measure, and
supported accepting proxy response from family members and caregivers
to support accurate and reliable results at the facility level. Other
commenters supported a measure of patient experience, instead of only
patient satisfaction, and recommended that it include several aspects
unique to IRF care, including goal setting and discharge planning.
Commenters recommended that CMS implement the survey as a voluntary
tool prior to requiring it, which would allow IRFs to transition
operationally and find a vendor, if needed. Commenters also recommended
that the quality measure adjust for factors already in place for
existing CAHPS[supreg] surveys, including adjusting for mode of survey
administration, as well as IRF-specific patient-mix adjustment. The
commenter also suggested converting responses to a 0 to 100 linear-
scaled score. Several commenters recommended that CMS seek stakeholder
input on the development of a patient experience of care measure.
Response: We will take these recommendations regarding measure
specifications and survey fatigue across the care continuum into
consideration in our ongoing measure development and testing efforts,
and will continue to engage stakeholders in the development process.
Comment: We received several comments regarding the transfer of
health information and care preferences measure. Many commenters
recommended that development efforts for this measure should recognize
that there is a large amount of variation in the different health
information systems used by different IRFs to record, store, retrieve,
and share patient information. The commenter noted that hospitals are
already required to transfer health information and care preferences as
part of their Medicare Conditions of Participation, and posited that
adding such a measure to the IRF QRP would rely on receiving accurate
and complete discharge information from a prior level of care, which
may be out of the IRF's control.
Response: As we move through the development of this measure
concept, we will consider the variation in health information systems
used by different IRFs, as well as the concerns about receiving
complete discharge information from a prior level of care for these
measure concepts.
[[Page 52118]]
Table 9--IRF QRP Quality Measures Under Consideration for Future Years
------------------------------------------------------------------------
------------------------------------------------------------------------
IMPACT Act Domain................. 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 furnishing items and
services to the individual, when
the individual transitions.
IMPACT Act Measure............ Transfer of health
information and care preferences
when an individual transitions.
NQS Priority...................... Patient- and Caregiver-Centered
Care.
Measures...................... Patient Experience of
Care.
Percent of Patients with
Moderate to Severe Pain.
NQS Priority...................... Patient Safety.
Measure....................... Venous Thromboembolism
Prophylaxis.
------------------------------------------------------------------------
I. Form, Manner, and Timing of Quality Data Submission for the FY 2018
Payment Determination and Subsequent Years
1. Background
Section 1886(j)(7)(C) of the Act requires that, for the FY 2014
payment determination and subsequent years, each IRF submit to the
Secretary data on quality measures specified by the Secretary. In
addition, section 1886(j)(7)(F) of the Act requires that, for the
fiscal year beginning on the specified application date, as defined in
section 1899B(a)(2)(E) of the Act, and each subsequent year, each IRF
submit to the Secretary data on measures specified by the Secretary
under section 1899B of the Act. The data required under section
1886(j)(7)(C) and (F) of the Act must be submitted in a form and
manner, and at a time, specified by the Secretary. As required by
section 1886(j)(7)(A)(i) of the Act, for any IRF that does not submit
data in accordance with section 1886(j)(7)(C) and (F) of the Act for a
given fiscal year, the annual increase factor for payments for
discharges occurring during the fiscal year must be reduced by 2
percentage points.
a. Timeline for Data Submission Under the IRF QRP for the FY 2018, FY
2019 and Subsequent Year Payment Determinations
Tables 10 through 18 represent our finalized data collection and
data submission quarterly reporting periods, as well as the quarterly
review and correction periods and submission deadlines for the quality
measure data submitted via the IRF-PAI and the CDC/NHSN affecting the
FY 2018 and subsequent year payment determinations. We also provide in
Table 10 our previously finalized claims-based measures for FY 2018 and
subsequent years, although we note that, for claims-based measures,
there is no corresponding quarterly-based data collection or submission
reporting periods with quarterly-based review and correction deadline
periods.
Further, in the FY 2016 IRF PPS final rule (80 FR 47122 through
47123), we established that the IRF-PAI-based measures finalized for
adoption into the IRF QRP will transition from reporting based on the
fiscal year to an annual schedule consistent with the calendar year,
with quarterly reporting periods followed by quarterly review and
correction periods and submission deadlines, unless there is a clinical
reason for an alternative data collection time frame. The pattern for
annual, calendar year-based data reporting, in which we use 4 quarters
of data, is illustrated in Table 10 and is in place for all Annual
Payment Update (APU) years except for the measure in Table 10 for which
the FY 2018 APU determination will be based on 5 calendar year quarters
in order to transition this measure from FY to CY reporting. We also
wish to clarify that payment determinations for the measures finalized
for use in the IRF QRP that use the IRF-PAI or CDC NHSN data sources
will subsequently use the quarterly data collection/submission and
review, correction and submission deadlines described in Table 10
unless otherwise specified, as is with the measure NQF #0680: Percent
of Residents or Patients Who Were Assessed and Appropriately Given the
Seasonal Influenza Vaccine. For this measure, we clarify in a
subsequent discussion that the data collection and reporting periods,
which are based on the Influenza Season, span 2 consecutive years from
July 1 through June 30th and we therefore separately illustrate those
collection/submission quarterly reporting periods, review and
correction periods, and submission deadlines for FY 2019 and subsequent
years in Table 10. We also separately distinguish the reporting periods
and data submission timeframes for the finalized measure Influenza
Vaccination Coverage among Healthcare Personnel which spans 2
consecutive years, as this measure is based on the Influenza
vaccination season, in Table 10.
Table 10--Annual QRP CY IRF-PAI & CDC/NHSN Data Collection/submission Reporting Periods and Data Submission/
Correction Deadlines ** Payment Determinations [supcaret]
----------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------
Proposed CY data collection quarter Data Collection/ QRP Quarterly review and correction periods data
submission quarterly submission deadlines for payment determination
reporting period **
----------------------------------------------------------------------------------------------------------------
Quarter 1............................ January 1-March 31 *... April 1-August 15 *.... Deadline: August 15.*
Quarter 2............................ April 1-June 30........ July 1-November 15..... Deadline: November 15.
Quarter 3............................ July 1-September 30.... October 1-February 15.. Deadline: February 15.
Quarter 4............................ October 1-December 31 * January 1-May 15 *..... Deadline: May 15.*
----------------------------------------------------------------------------------------------------------------
* We refer readers to Table 10 for the annual data collection time frame for the measure, Influenza Vaccination
Coverage among Healthcare Personnel
** We note that the submission of IRF-PAI data must also adhere to the IRF PPS deadlines
[supcaret] We refer readers to Table 10 for the 12 month (July-June) data collection/submission quarterly
reporting periods, review and correction periods and submission deadlines for APU determinations for the
measure NQF #0680: Percent of Residents or Patients Who Were Assessed and Appropriately Given the Seasonal
Influenza Vaccine
[[Page 52119]]
Table 11--Summary Details on Data Collection Period and Data Submission Timeline for Previously Adopted Quality
Measure Affecting the FY 2018 Payment Determination That Will Use 5 CY Quarters in Order To Transition From a FY
to a CY Reporting Cycle
----------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------
Finalized Measure:
NQF # 0678 Percent of Residents or Patients with Pressure Ulcers That Are New or Worsened (Short Stay)
(80 FR 47122)
----------------------------------------------------------------------------------------------------------------
Submission method Data collection/ Quarterly review and APU Determination
submission quarterly correction periods affected
reporting period(s) data submission
deadlines for payment
determination */**
----------------------------------------------------------------------------------------------------------------
IRF-PAI/QIES ASAP System............ CY 15 Q4, 10/1/15-12/31/ 1/1/2016-5/15/16 FY 2018.
15. deadline.
CY 16 Q1, 1/1/16-3/31/ 4/1/2016-8/15/16
16. deadline..
CY 16 Q2, 4/1/16-6/30/ 7/1/16-11/15/16
16. deadline.
CY 16 Q3, 7/1/16-9/30/ 10/1/16-2/15/17
16. deadline.
CY 16 Q4, 10/01/16-12/ 1/1/17-5/15/17 deadline
31/16.
----------------------------------------------------------------------------------------------------------------
* We refer readers to the Table 11 for an illustration of the data collection/submission quarterly reporting
periods and correction and submission deadlines
** We note that the submission of IRF-PAI data must also adhere to the IRF PPS deadlines
Table 12--Summary Details on Data Collection Period and Data Submission Timeline for Previously Adopted IRF-PAI
Quality Measure, NQF #0680 Percent of Residents or Patients Who Were Assessed and Appropriately Given the
Seasonal Influenza Vaccine, Affecting the FY 2018 Payment Determination
----------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------
Finalized Measure:
NQF #0680 Percent of Residents or Patients Who Were Assessed and Appropriately Given the Seasonal
Influenza Vaccine (80 FR 47122)
----------------------------------------------------------------------------------------------------------------
Submission method Data collection/ Quarterly review and APU Determination
submission quarterly correction periods affected
reporting period(s) data submission
deadlines for payment
determination *
----------------------------------------------------------------------------------------------------------------
IRF-PAI/QIES ASAP System............ CY 15 Q4, 10/1/15-12/31/ 1/1/2016-5/15/16 FY 2018.
15. deadline.
CY 16 Q1, 1/1/16-3/31/ 4/1/2016-8/15/16
16. deadline.
CY 16 Q2, 4/1/16-6/30/ 7/1/16-11/15/16
16. deadline.
----------------------------------------------------------------------------------------------------------------
* We note that the submission of IRF-PAI data must also adhere to the IRF PPS deadlines
Table 13--Summary Details on Data Collection Period and Data Submission Timeline for Previously Adopted Quality
Measures Affecting the FY 2018 Payment Determination That Will Use Only 1 CY Quarter of Data Initially for the
Purpose of Determining Provider Compliance
----------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------
Finalized Measure:
NQF #0674 Application of Percent of Residents Experiencing One or More Falls with Major Injury (Long
Stay) (80 FR 47122)
NQF #2631 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 47122)
NQF #2633 IRF Functional Outcome Measure: Change in Self-Care Score for Medical Rehabilitation Patients
(80 FR 47122)
NQF #2634 IRF Functional Outcome Measure: Change in Mobility Score for Medical Rehabilitation Patients
(80 FR 47122)
NQF #2635 IRF Functional Outcome Measure: Discharge Self-Care Score for Medical Rehabilitation Patients
(80 FR 47122)
NQF #2636 IRF Functional Outcome Measure: Discharge Mobility Score for Medical Rehabilitation Patients
(80 FR 47122)
----------------------------------------------------------------------------------------------------------------
Submission method Data collection/ Quarterly review and APU Determination
submission quarterly correction periods affected
reporting period(s) data submission
deadlines for payment
determination */**
----------------------------------------------------------------------------------------------------------------
IRF-PAI/QIES ASAP System............ CY 16 Q4, 10/1/16-12/31/ 1/1/2017-5/15/17....... FY 2018.
16.
----------------------------------------------------------------------------------------------------------------
* We refer readers to the Table 12 for an illustration of the data collection/submission quarterly reporting
periods and correction and submission deadlines, which will be followed for the above measures, for all
payment determinations subsequent to that of FY 2018.
** We note that the submission of IRF-PAI data must also adhere to the IRF PPS deadlines.
Table 14--Summary Details on Data Collection Period and Data Submission Timeline for Previously Adopted CDC/NHSN
Quality Measures Affecting the FY 2018 Payment Determination and Subsequent Years That Will Use 4 CY quarters *
----------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------
Finalized Measures:
NQF #0138 NHSN Catheter-Associated Urinary Tract Infection (CAUTI) Outcome Measure (80 FR 47122 through
47123)
NQF #1716 NHSN Facility-wide Inpatient Hospital-onset Methicillin-resistant Staphylococcus aureus
(MRSA) Bacteremia Outcome Measure (80 FR 47122 through 47123)
NQF #1717 NHSN Facility-wide Inpatient Hospital-onset Clostridium difficile Infection (CDI) Outcome
Measure (79 FR 45917)
----------------------------------------------------------------------------------------------------------------
[[Page 52120]]
Submission method Data Collection/ Quarterly Review and APU determination
submission Quarterly Correction Periods affected
Reporting Period(s) Data Submission
Deadlines for Payment
Determination
----------------------------------------------------------------------------------------------------------------
CDC/NHSN............................. CY 16 Q1, 1/1/16-3/31/ 4/1/2016-8/15/16 ** and FY 2018 and subsequent
16 and Q1 of 4/1-8/15 of subsequent years.**
subsequent Calendar years.
Years.
CY 16 Q2, 4/1/16-6/30/ 7/1/16-11/15/16 **and 7/
16 and Q2 of 1-11/15 of subsequent
subsequent Calendar years.
Years.
CY 16 Q3, 7/1/16-9/30/ 10/1/16-2/15/17 ** and
16 and Q3 of 10/1-2/15 of
subsequent Calendar subsequent years.
Years.
CY 16 Q4, 10/1/16-12/31/ 1/1/17-5/15/17 ** and 1/
16 and Q4 of 1-5/15 of subsequent
subsequent Calendar years.
Years.
----------------------------------------------------------------------------------------------------------------
* We refer readers to the Table 14 for an illustration of the data collection/submission quarterly reporting
periods and correction and submission deadlines.
** As is illustrated in Table 14: Subsequent years follow the same CY Quarterly Data Collection/submission
Quarterly Reporting Periods and Quarterly Review and Correction Periods Deadlines for Payment Determination in
which every CY quarter is followed by approximately 135 days for IRFs to review and correct their data until
midnight on the final submission deadline dates.
Table 15--Summary Details on Data Collection Period and Data Submission Timeline for Previously Adopted IRF-PAI
Quality Measures Affecting the FY 2019 Payment Determination and Subsequent Years That Will Use 4 CY Quarters
----------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------
Finalized Measures:
NQF #0678 Percent of Residents or Patients with Pressure Ulcers That Are New or Worsened (Short Stay)
(80 FR 47122)
NQF #0674 Application of Percent of Residents Experiencing One or More Falls with Major Injury (Long
Stay) (80 FR 47122)
NQF #2631 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 47122)
NQF #2633 IRF Functional Outcome Measure: Change in Self-Care Score for Medical Rehabilitation Patients
(80 FR 47122)
NQF #2634 IRF Functional Outcome Measure: Change in Mobility Score for Medical Rehabilitation Patients
(80 FR 47122)
NQF #2635 IRF Functional Outcome Measure: Discharge Self-Care Score for Medical Rehabilitation Patients
(80 FR 47122)
NQF #2636 IRF Functional Outcome Measure: Discharge Mobility Score for Medical Rehabilitation Patients
(80 FR 47122)
----------------------------------------------------------------------------------------------------------------
Submission method Data Collection/ Quarterly Review and APU determination
submission Quarterly Correction Periods affected
Reporting Period(s) Data Submission
Deadlines for Payment
Determination */**
----------------------------------------------------------------------------------------------------------------
IRF-PAI/QIES ASAP System............. CY 17 Q1, 1/1/17-3/31/ 4/1/2017-8/15/17 *** FY 2019 and subsequent
17 and Q1 of and 4/1-8/15 of years.***
subsequent Calendar subsequent years.
Years.
CY 17 Q2, 4/1/17-6/30/ 7/1/17-11/15/17 *** and
17 and Q2 of 7/1-11/15 of
subsequent Calendar subsequent years.
Years.
CY 17 Q3, 7/1/17-9/30/ 10/1/17-2/15/18 *** and
17 and Q3 of 10/1-1/15 of
subsequent Calendar subsequent years.
Years.
CY 17 Q4, 10/1/17-12/31/ 1/1/18-5/15/18 *** and
17 and Q4 of 1/1-5/15 of subsequent
subsequent Calendar years.
Years.
----------------------------------------------------------------------------------------------------------------
We refer readers to the Table 15 for an illustration of the data collection/submission quarterly reporting
periods and correction and submission deadlines.
** We note that the submission of IRF-PAI data must also adhere to the IRF PPS deadlines.
*** As is illustrated in Table 15: Subsequent years follow the same CY Quarterly Data Collection/submission
Quarterly Reporting Periods and Quarterly Review and Correction Periods) and Data Submission Deadlines for
Payment Determination in which every CY quarter is followed by approximately 135 days for IRFs to review and
correct their data until midnight on the final submission deadline dates.
In the FY 2014 IRF PPS final rule, we adopted the Percent of
Residents or Patients Who Were Assessed and Appropriately Given the
Seasonal Influenza Vaccine (Short Stay) (NQF #0680) measure for the FY
2017 payment determination and subsequent years (78 FR 47910 through
47911). In the FY 2014 IRF PPS final rule (78 FR 47917 through 47919),
we finalized the data submission timelines and submission deadlines for
the measures for FY 2017 payment determination. Refer to the FY 2014
final rule (78 FR 47917 through 47919) for a more detailed discussion
of these timelines and deadlines.
We want to clarify that this measure includes all patients in the
IRF one or more days during the influenza vaccination season (IVS)
(October 1 of
[[Page 52121]]
any given CY through March 31 of the subsequent CY). This includes, for
example, a patient is admitted September 15, 2015, and discharged April
1, 2016 (thus, the patient was in the IRF during the 2015-2016
influenza vaccination season). If a patient's stay did not include one
or more days in the IRF during the IVS, IRFs must also complete the
influenza items. For example, if a patient was admitted after April 1,
2016, and discharged September 30, 2016, and the patient did not
receive the influenza vaccine during the IVS, IRFs should code the
reason the patient did not receive the influenza vaccination as
``patient was not in the facility during this year's influenza
vaccination season.''
Further, we wish to clarify that the data submission timeline for
this measure includes 4 calendar quarters and is based on the influenza
season (July 1 through June 30 of the subsequent year), rather than on
the calendar year. For the purposes of APU determination and for public
reporting, data calculation and analysis uses data from an influenza
vaccination season that is within the influenza season itself. While
the influenza vaccination season is October 1 of a given year (or when
the vaccine becomes available) through March 31 of the subsequent year,
this timeframe rests within a greater time period of the influenza
season which spans 12 months--that is July 1 of a given year through
June 30 of the subsequent year. Thus for this measure, we utilize data
from a timeframe of 12 months that mirrors the influenza season which
is July 1 of a given year through June 30th of the subsequent year.
Additionally, for the APU determination, we review data that has been
submitted beginning on July 1 of the calendar year 2 years prior to the
calendar year of the APU effective date and ending June 30 of the
subsequent calendar year, one year prior to the calendar year of the
APU effective date. For example, and as provided in Table 14 for the FY
2019 (October 1, 2018) APU determination, we review data submission
beginning July 1 of 2016 through June 30th of June 2017 for the 2016/
2017 influenza vaccination season, so as to capture all data that an
IRF will have submitted with regard to the 2016/2017 Influenza season
itself. We will use assessment data for that time period as well for
public reporting. Further, because we enable the opportunity to review
and correct data for all assessment based IRF-PAI measures within the
IRF QRP, we continue to follow quarterly calendar data collection/
submission quarterly reporting period(s) and their subsequent quarterly
review and correction periods with data submission deadlines for public
reporting and payment determinations. However, rather than using CY
timeframe, these quarterly data collection/submission periods and their
subsequent quarterly review and correction periods and submission
deadlines begin with CY quarter 3, July 1, of a given year and end June
30th, CY quarter 2, of the following year. For further information on
data collection for this measure, please refer to section 4 of the IRF-
PAI training manual, which is available on the CMS IRF QRP Measures
Information Web site at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html, under the
downloads section. For further information on data submission of the
IRF-PAI, please refer to the IRF-PAI Data Specifications Version 1.12.1
(FINAL)--in effect on October 1, 2015, available for download at
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Software.html.
Refer to Table 16 for details about the quarterly data collection/
submission and the review and correction deadlines for FY 2019 and
subsequent years for NQF #0680 Percent of Residents or Patients Who
Were Assessed and Appropriately Given the Seasonal Influenza Vaccine.
Table 16--Summary Details on Data Collection Period and Data Submission Timeline for Previously Adopted IRF-PAI
Quality Measure, NQF #0680 Percent of Residents or Patients Who Were Assessed and Appropriately Given the
Seasonal Influenza Vaccine, Affecting the FY 2019 Payment Determination and Subsequent Years *
----------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------
Finalized Measure:
NQF #0680 Percent of Residents or Patients Who Were Assessed and Appropriately Given the Seasonal
Influenza Vaccine (80 FR 47122)
----------------------------------------------------------------------------------------------------------------
Submission method Data collection/ Quarterly review and APU determination
submission Quarterly correction periods affected
Reporting Period(s) data submission
deadlines for payment
determination **
----------------------------------------------------------------------------------------------------------------
IRF-PAI/QIES ASAP System............. CY 16 Q3, 7/1/16-9/30/ 10/1/16-2/15/17 ** and FY 2019 and subsequent
16 and Q3 of 10/1-2/15 of years.**
subsequent Calendar subsequent years.
Years.
CY 16 Q4, 10/1/16-12/31/ 1/1/17-5/15/17 ** and 1/
16 and Q4 of 1-5/15 of subsequent
subsequent Calendar years.
Years.
CY 17 Q1, 1/1/17-3/31/ 4/1/17-8/15/17 ** and 4/
17 and Q1 of 1-8/15 of subsequent
subsequent Calendar years.
Years.
CY 17 Q2, 4/1/17-6/30/ 7/1/17-11/15/17 ** and
17 and Q2 of 7/1-11/15 of
subsequent Calendar subsequent years.
Years.
----------------------------------------------------------------------------------------------------------------
* We note that the submission of IRF-PAI data must also adhere to the IRF PPS deadlines.
** As is illustrated in Table 16: Subsequent years follow the same CY Quarterly Data Collection/submission
Quarterly Reporting Periods and Quarterly Review and Correction Periods (IRF-PAI) and Data Submission (CDC/
NHSN) Deadlines for Payment Determination in which every CY quarter is followed by approximately 135 days for
IRFs to review and correct their data until midnight on the final submission deadline dates.
We finalized in the FY 2014 IRF PPS final rule (78 FR 47905 through
47906) that for FY 2016 and subsequent years IRFs will submit data on
the quality measure Influenza Vaccination Coverage among Healthcare
Personnel (NQF
[[Page 52122]]
#0431) beginning with data submission starting October 1, 2014 (or when
the influenza vaccine becomes available). To clarify that while the
data collected by IRFs for this measure includes vaccination
information for a flu vaccination season that begins October 1 (or when
the vaccine becomes available) of a given year through March 31 of the
subsequent year, the CDC/NHSN system only allows for the submission of
the corresponding data any time between October 1 of a given year until
March 31 of the subsequent year; however, corrections can be made to
such data until May 15th of that year. Quality data for this measure
are only required to be submitted once per IVS (Oct 1 through March
31), but must be submitted prior to the May 15 deadline for the year in
which the IVS ends; quarterly reporting is not required. For example,
for FY 2018 payment determinations, while IRFs can begin immunizing
their staff when the vaccine is available throughout the influenza
vaccination season which ends on March 31, 2016, IRFs can only begin
submitting the data for this measure via the CDC/NHSN system starting
on October 1, 2015, and may do so up until May 15 of 2016.
Table 17--Summary Details on the Data Submission Timeline and Correction Deadline Timeline for the Previously
Adopted Influenza Vaccination Coverage Among Healthcare Personnel Affecting CY 2018 and Subsequent Years
----------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------
Influenza Vaccination Coverage Among Data submission Period Review and Correction Periods Data Submission
Healthcare Personnel Data submission (CDC/NHSN) Deadlines for payment determination
Quarters + ++
----------------------------------------------------------------------------------------------------------------
CY QTR 4 through Subsequent CY QTR 1. 10/1/15-3/31/16 and 10/ 4/1/16-5/15/16 and 4/1- Deadline: May 15, 2016
1-3/31 of subsequent 5/15 of subsequent and May 15 of
years. years. subsequent years.
----------------------------------------------------------------------------------------------------------------
+ Data on this measure may be submitted via the CDC/NHSN system from October 1 of a given year through May 15 of
the subsequent year.
++ A time period of April 1-May 15th is also allotted for the submission, review, and corrections.
Table 18--Finalized IRF QRP Claims-Based Measure Affecting FY 2018 and
Subsequent Years
------------------------------------------------------------------------
Data submission
Quality measure method Performance period
------------------------------------------------------------------------
NQF #2502 All-Cause Unplanned Medicare FFS CY 2013 and 2014
Readmission Measure for 30 Days Claims. for public
Post-Discharge from Inpatient reporting in
Rehabilitation Facilities (80 2016.
FR 47087 through 47089). CY 2014 and 2015
for public
reporting in
2017.
------------------------------------------------------------------------
Although we did not solicit feedback, we received several comments
about the previously finalized policy to adopt calendar year data
collection time frames, unless there is a clinical reason for an
alternative data collection time frame, which are summarized with our
responses below.
Comment: Several commenters supported these data collection
timelines to simplify the data collection and reporting process, as
summarized in the FY 2016 IRF PPS Final Rule (80 FR 47122 through
47123).
Response: We thank these commenters for their support.
Comment: One commenter generally supported the change to calendar
year, but was concerned that the IRF-PAI versions aligned with the
fiscal year. Several others also commented that since updates are made
to the IRF-PAI on a FY basis, this change would create a discrepancy
within a single calendar year's data set. Many commenters were
concerned that variations in FY 2018 APU data collection periods placed
an increased burden on IRFs to maintain compliance and requested that
CMS grant some leniency to an IRF the first time it is not compliant
with quality reporting due to the new CY-based deadlines.
Response: When we finalized this change in the FY 2016 IRF PPS
final rule (80 FR 47122 through 47123), we posited this change would
simplify the data collection and submission time frame under the IRF
QRP for IRF providers. It would also eliminate the situation in which
data collection during a quarter in the same calendar year can affect 2
different years of annual payment update determination (that is,
October 1 to December 31 is the first quarter of data collection for
quality measures with a FY-based data collection time frame and the
last quarter of data collection for quality measures with a CY-based
data collection time frame). This change means that when additional
quality measures that use IRF-PAI as the data collection mechanism,
such as the measure Drug Regimen Review Conducted with Follow-Up for
Identified Issues, are adopted for future use in the IRF QRP, the first
data collection time frame for those newly-adopted measures will be 3
months (October to December) and subsequent data collection time frames
would follow a calendar year data collection time frame. This policy
only affects IRFs insofar as for these newly adopted measures,
compliance determinations for the applicable FY APU will only reflect
data collection and submission for Q4 of the CY in which data
collection begins. This does not create a discrepancy in the data set,
as stated by the commenter, as we would use the following CY of data
for APU analysis and public reporting purposes, should state measures
be proposed and finalized for public display in the future.
With regard to concerns about increased burden with the change in
data collection periods and requests for leniency regarding submission
deadlines, we disagree that leniency is warranted, given that there is
no discrepancy in the data set and the policy only affects the first
quarter of data collection for newly adopted measures. We have ongoing
education regarding data submission deadlines, including quarterly
email reminders of upcoming deadlines. We also remind the reader of the
availability of the reconsideration process, in which IRFs may file for
reconsideration if they believe the finding of non-compliance is in
error, or they have evidence of the impact of extraordinary
circumstances which prevented timely submission of data.
[[Page 52123]]
b. Timeline and Data Submission Mechanisms for the FY 2018 Payment
Determination and Subsequent Years for the IRF QRP Resource Use and
Other Measures Claims-Based Measures
The MSPB PAC IRF QRP measure; Discharge to Community PAC IRF QRP
measure; Potentially Preventable 30-Day Post-Discharge Readmission
Measure for IRF QRP; and Potentially Preventable Within Stay
Readmission Measure for IRFs, which we are finalizing in this final
rule, are Medicare FFS claims-based measures. Because claims-based
measures can be calculated based on data that are already reported to
the Medicare program for payment purposes, no additional information
collection will be required from IRFs. As discussed in section VIII.F
of this final rule, these measures will use 2 years of claims-based
data beginning with CY 2015 and CY 2016 claims to inform confidential
feedback reports for IRFs, and CYs 2016 and 2017 claims data for public
reporting.
We invited public comments on this proposal. We did not receive
comments related to data submission mechanisms for these measures. For
comments related to the measures, please see section VIII.F of this
final rule. For comments related to the future public display of these
measures, please see section VIII.N of this final rule.
We finalize the timeline and data submission mechanisms for FY 2018
payment determination and subsequent years as proposed.
c. Timeline and Data Submission Mechanisms for the IRF QRP Quality
Measure for the FY 2020 Payment Determination and Subsequent Years
As discussed in section VIII.F of this final rule, we proposed that
the data for the quality measure, Drug Regimen Review Conducted with
Follow-Up for Identified Issues--PAC IRF QRP, affecting FY 2020 payment
determination and subsequent years, be collected by completing data
elements that will be added to the IRF-PAI with submission through the
QIES-ASAP system. Data collection will begin on October 1, 2018. More
information on IRF reporting using the QIES-ASAP system is located at
the Web site at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRFPAI.html.
For the FY 2020 payment determinations, we proposed to use CY 2018
4th quarter data, that is, beginning with discharges on October 1,
2018, through discharges on December 31, 2018, to remain consistent
with the usual October release schedule for the IRF-PAI, to give IRFs
sufficient time to update their systems so that they can comply with
the new data reporting requirements, and to give us sufficient time to
determine compliance for the FY 2020 program. The proposed use of 1
quarter of data for the initial year of assessment data reporting in
the IRF QRP, to make compliance determinations related to the
applicable FY APU, is consistent with the approach we used previously
for the SNF, LTCH, and Hospice QRPs.
Table 18 presents the proposed data collection period and data
submission timelines for the new IRF QRP quality measure, Drug Regimen
Review Conducted with Follow-Up for Identified Issues--PAC IRF QRP, for
the FY 2020 Payment Determination. We invited public comments on this
proposal.
Table 19--Details on the Proposed Data Collection Period and Data Submission Timeline for Resource Use and Other
Measures Affecting the FY 2020 Payment Determination
----------------------------------------------------------------------------------------------------------------
APU
Quality measure Submission method Data collection Data correction determination
period deadlines * affected
----------------------------------------------------------------------------------------------------------------
Drug Regimen Review Conducted IRF-PAI/QIES ASAP CY 2018 Q4, 10/1/ 5/15/19 FY 2020.
with Follow-Up for 18-12/31/18; Quarterly
Identified Issues PAC IRF Quarterly for approximately
QRP. each subsequent 135 days after
calendar year. the end of each
quarter for
subsequent
years..
----------------------------------------------------------------------------------------------------------------
* We note that the submission of IRF-PAI data must also adhere to the IRF PPS deadlines.
Following the close of the reporting quarter, October 1, 2018,
through December 31, 2018, for the FY 2020 payment determination, IRFs
will have the already established additional 4.5 months to correct
their quality data and that the final deadline for correcting data for
the FY 2020 payment determination will be May 15, 2019 for these
measures. We further proposed that for the FY 2021 payment
determination and subsequent years, we will collect data using the
calendar year reporting cycle as described in section VIII.I.c of this
final rule, and illustrated in Table 20. We invited public comments on
this proposal.
We did not receive any comments on the proposed data collection
periods and data submission timelines for the new proposed IRF QRP
quality measure for the FY 2020 and FY 2021 payment determination and
subsequent years.
Final Decision: We finalize the timeline and data submission
mechanisms for FY 2020 and FY2021 payment determinations and subsequent
years as proposed, as described in Table 19. For comments related to
the measure, Drug Regimen Review Conducted with Follow-Up for
Identified Issues PAC IRF QRP, please see section VIII.G of final rule.
[[Page 52124]]
Table 20--Proposed Data Collection Period and Data Correction Deadlines * Affecting the FY 2021 Payment
Determination and Subsequent Years
----------------------------------------------------------------------------------------------------------------
Proposed
quarterly
review and data
Proposed CY data Proposed data correction
Quality measure Submission method collection collection periods *
quarter period deadlines for
payment
determination
----------------------------------------------------------------------------------------------------------------
Drug Regimen Review Conducted IRF-PAI/QIES ASAP Quarter 1........ January 1-March April 1- August
with Follow-Up for Quarter 2........ 31. 15.
Identified Issues PAC IRF Quarter 3........ April 1-June 30. July 1-November
QRP. Quarter 4........ July 1-September 15.
30. October 1-
October 1- February 15.
December 31. January 1-May
15.
----------------------------------------------------------------------------------------------------------------
* We note that the submission of IRF-PAI data must also adhere to the IRF PPS deadlines.
J. IRF QRP Data Completion Thresholds for the FY 2016 Payment
Determination and Subsequent Years
In the FY 2015 IRF PPS final rule (79 FR 45921 through 45923), we
finalized IRF QRP thresholds for completeness of IRF data submissions.
To ensure that IRFs are meeting an acceptable standard for completeness
of submitted data, we finalized the policy that, beginning with the FY
2016 payment determination and for each subsequent year, IRFs must meet
or exceed two separate data completeness thresholds: One threshold set
at 95 percent for completion of quality measures data collected using
the IRF-PAI submitted through the QIES and a second threshold set at
100 percent for quality measures data collected and submitted using the
CDC NHSN.
Additionally, we stated that we will apply the same thresholds to
all measures adopted as the IRF QRP expands and IRFs begin reporting
data on previously finalized measure sets. That is, as we finalize new
measures through the regulatory process, IRFs will be held accountable
for meeting the previously finalized data completion threshold
requirements for each measure until such time that updated threshold
requirements are proposed and finalized through a subsequent regulatory
cycle.
Further, we finalized the requirement that an IRF must meet or
exceed both thresholds to avoid receiving a 2 percentage point
reduction to their annual payment update for a given fiscal year,
beginning with FY 2016 and for all subsequent payment updates. For a
detailed discussion of the finalized IRF QRP data completion
requirements, please refer to the FY 2015 IRF PPS final rule (79 FR
45921 through 45923). We proposed to codify the IRF QRP Data Completion
Thresholds at Sec. 412.634. We invited public comments on this
proposal.
We received several comments with concerns about the proposal to
codify the IRF QRP Data Completion Thresholds at Sec. 412.634, which
are summarized below.
Comment: One commenter supported the 100 percent standard, but had
concerns regarding technical errors with the NHSN that IRFs have
experienced in the past year. Several commenters expressed concern
about the threshold set at 100 percent for quality measures data
collected and submitted using the CDC NHSN, citing significant burden
on infection preventionists to review and complete reports in NHSN. One
commenter expressed concern that the data completion threshold would be
applied to data collected in FY 2014, having a retroactive impact on
payment. One commenter recommended changes to the NHSN that could
alleviate the reporting requirement, including minimize the reporting
of elements outside of CMS regulatory requirements, as well as altering
the system to remove monthly reporting plans or allowing them to be
submitted electronically.
Response: We wish to clarify that the IRF QRP thresholds for
completeness of IRF data submissions were finalized in the FY 2015 IRF
PPS final rule (79 FR 45921 through 45923), beginning with FY 2016,
which considered quality data submitted during CY 2014. We have
continually maintained that providers should be submitting complete and
accurate data, and the adoption of the data completion thresholds in
the FY 2015 IRF PPS final rule did not change this policy. We believe
that both data completion thresholds are achievable, as evidenced by
the 91 percent of IRFs that were able to achieve these thresholds for
purposes of the FY 2016 payment determination. We have also taken
strides to assist providers achieve compliance, including regular
notification of upcoming deadlines, updated guidance documents,
increased outreach to providers with incomplete data submissions, and
the development of several reports which will help providers better
determine where they stand with respect to compliance throughout the
year. We appreciate the commenters' concerns related to burden and have
taken this into consideration when issuing data completion thresholds.
Final Decision: We are finalizing our proposal to codify the IRF
QRP data completion thresholds at Sec. 412.634.
K. IRF QRP Data Validation Process for the FY 2016 Payment
Determination and Subsequent Years
Validation is intended to provide added assurance of the accuracy
of the data that will be reported to the public as required by sections
1886(j)(7)(E) and 1899B(g) of the Act. In the FY 2015 IRF PPS rule (79
FR 45923), we finalized, for the FY 2016 adjustments to the IRF PPS
annual increase factor and subsequent years, a process to validate the
data submitted for quality purposes. However, in the FY 2016 IRF PPS
final rule (80 FR 47124), we finalized our decision to temporarily
suspend the implementation of this policy. We did not propose a data
validation policy in the FY 2017 IRF PPS proposed rule, as we are
developing a policy that could be applied to several PAC QRPs. We
intend to propose a data validation policy through future rulemaking.
L. Previously Adopted and Codified IRF QRP Submission Exception and
Extension Policies
Refer to Sec. 412.634 for requirements pertaining to submission
exception and extension for the FY 2017 payment determination and
subsequent years. We proposed to revise Sec. 412.634 to change the
timing for submission of these exception and extension requests from 30
days to 90 days from the date of the qualifying event which is
preventing an IRF from submitting their quality data for the IRF QRP.
We proposed the increased time allotted for the submission of the
requests from 30 to 90 days to be consistent with other quality
reporting programs; for example, the Hospital Inpatient Quality
Reporting (IQR) Program also proposed to extend the deadline to 90 days
in the FY 2017 IPPS/LTCH PPS proposed rule (81 FR 25205). We believe
that this increased time will assist providers experiencing
[[Page 52125]]
an event in having the time needed to submit such a request. We believe
that allowing only 30 days was insufficient. With the exception of this
one change, we did not propose any additional changes to the exception
and extension policies for the IRF QRP at this time.
We invited public comments on the proposal to revise Sec. 412.634
to change the timing for submission of these exception and extension
requests from 30 days to 90 days from the date of the qualifying event
which is preventing an IRF from submitting their quality data for the
IRF QRP. We received one comment on this proposal, which is summarized
and addressed below in this section.
Comment: One commenter supported changing the timing for submission
of exception and extension requests from 30 days to 90 days from the
date of the qualifying event preventing an IRF from submitting their
IRF QRP data.
Response: We thank the commenter for their support.
Final Decision: After careful consideration of the public comments,
we are finalizing our proposal to revise Sec. 412.634 to change the
timing for submission of these exception and extension requests from 30
days to 90 days from the date of the qualifying event which is
preventing an IRF from submitting their quality data for the IRF QRP.
M. Previously Adopted and Finalized IRF QRP Reconsideration and Appeals
Procedures
Refer to Sec. 412.634 for a summary of our finalized
reconsideration and appeals procedures for the IRF QRP for FY 2017
payment determination and subsequent years. We did not propose any
changes to this policy. However, we wish to clarify that in order to
notify IRFs found to be non-compliant with the reporting requirements
set forth for a given payment determination, we may include the QIES
mechanism in addition to U.S. Mail, and we may elect to utilize the
MACs to administer such notifications.
We received several comments about the previously adopted and
finalized IRF QRP reconsideration and appeals procedures, which are
summarized below.
Comment: One commenter requested that the notification also include
the reason for non-compliance. Multiple commenters appreciated that CMS
is using both U.S. Mail and the QIES system to notify IRFs found to be
non-compliant. Another commenter recommended that CMS continue using
the U.S. Mail method, noting that QIES may not be a reliable way to
distribute time-sensitive information. Several commenters were
concerned about the possibility of using MACs to administer
notifications, citing their lack of expertise in quality reporting, and
requested that CMS clarify the authority that MACs would have to
consider IRF QRP compliance and levy corrective action.
Response: We intend to retain this method of notification in
addition to the use of QIES. We wish to clarify that the role of the
MACs is for notification purposes only. They do not have a role in
determining provider compliance in meeting the IRF QRP reporting
requirements. We intend to include the reason for non-compliance in the
notifications distributed via the CASPER folders; however, we wish to
remind facilities that there are reports available in QIES (more
information at: https://www.qtso.com/irfpai.html) and NHSN (more
information at: https://www.cdc.gov/nhsn/cms/) that can be utilized to
confirm quality measure data submissions. Additional information
regarding non-compliance is also available on the IRF QRP
Reconsiderations Web site at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Reconsideration-and-Exception-and-Extension.html.
N. Public Display of Measure Data for the IRF QRP & Procedures for the
Opportunity to Review and Correct Data and Information
1. Public Display of Measures
Section 1886(j)(7)(E) of the Act requires the Secretary to
establish procedures for making the IRF QRP data available to the
public. In the FY 2016 IRF PPS final rule (80 FR 47126 through 47127),
we finalized our proposals to display performance data for the IRF QRP
quality measures by Fall 2016 on a CMS Web site, such as the Hospital
Compare, after a 30-day preview period, and to give providers an
opportunity to review and correct data submitted to the QIES-ASAP
system or to the CDC NHSN. The procedures for the opportunity to review
and correct data are provided in section VIII.N.2 of this final rule.
In addition, we finalized the proposal to publish a list of IRFs that
successfully meet the reporting requirements for the applicable payment
determination on the IRF QRP Web site at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/Spotlights-Announcements.html. In the FY 2016 IRF PPS final
rule, we finalized that we will update the list after the
reconsideration requests are processed on an annual basis.
Also, in the FY 2016 IRF PPS final rule (80 FR 47126 through
47127), we also finalized that the display of information for fall 2016
contains performance data on three quality measures:
Percent of Residents or Patients with Pressure Ulcers That
Are New or Worsened (Short Stay) (NQF #0678);
NHSN CAUTI Outcome Measure (NQF #0138); and
All-Cause Unplanned Readmission Measure for 30 Days Post-
Discharge from IRFs (NQF #2502).
The measures Percent of Residents or Patients with Pressure Ulcers
That Are New or Worsened (Short Stay) (NQF #0678) and NHSN CAUTI
Outcome Measure (NQF #0138) are based on data collected beginning with
the first quarter of 2015 or discharges beginning on January 1, 2015.
With the exception of the All-Cause Unplanned Readmission Measure for
30 Days Post-Discharge from IRFs (NQF #2502), rates are displayed based
on 4 rolling quarters of data and will initially use discharges from
January 1, 2015, through December 31, 2015 (CY 2015) for Percent of
Residents or Patients with Pressure Ulcers That Are New or Worsened
(Short Stay) (NQF #0678) and data collected from January 1, 2015,
through December 31, 2015 (CY 2015) for NHSN CAUTI Outcome Measure (NQF
#0138). For the readmissions measure, data will be publicly report
beginning with data collected for discharges beginning January 1, 2013,
and rates will be displayed based on 2 consecutive years of data. For
IRFs with fewer than 25 eligible cases, we proposed to assign the IRF
to a separate category: ``The number of cases is too small (fewer than
25) to reliably tell how well the IRF is performing.'' If an IRF has
fewer than 25 eligible cases, the IRF's readmission rates and interval
estimates will not be publicly reported for the measure.
Calculations for all three measures are discussed in detail in the
FY 2016 IRF PPS final rule (80 FR 47126 through 47127).
Pending the availability of data, we proposed to publicly report
data in CY 2017 on 4 additional measures beginning with data collected
on these measures for the first quarter of 2015, or discharges
beginning on January 1, 2015: (1) Facility-wide Inpatient Hospital-
onset Methicillin-resistant Staphylococcus aureus (MRSA) Bacteremia
Outcome Measure (NQF #1716) ; (2) Facility-wide Inpatient Hospital-
onset Clostridium difficile Infection (CDI) Outcome Measure (NQF #1717)
and, beginning with the 2015-16
[[Page 52126]]
influenza vaccination season, these two measures; (3) Influenza
Vaccination Coverage Among Healthcare Personnel (NQF #0431); and (4)
Percent of Residents or Patients Who Were Assessed and Appropriately
Given the Seasonal Influenza Vaccine (NQF #0680).
Standardized infection ratios (SIRs) for the Facility-wide
Inpatient Hospital-onset Methicillin-resistant Staphylococcus aureus
(MRSA) Bacteremia Outcome Measure (NQF #1716) and Facility-wide
Inpatient Hospital-onset Clostridium difficile Infection (CDI) Outcome
Measure (NQF #1717) will be displayed based on 4 rolling quarters of
data and will initially use MRSA bacteremia and CDI events that
occurred from January 1, 2015 through December 31, 2015 (CY 2015), for
calculations. We proposed that the display of these ratios will be
updated quarterly. Rates for the Influenza Vaccination Coverage Among
Healthcare Personnel (NQF #0431) will initially be displayed for
personnel working in the reporting facility October 1, 2015 through
March 31, 2016. Rates for the Percent of Residents or Patients Who Were
Assessed and Appropriately Given the Seasonal Influenza Vaccine (NQF
#0680) will also initially be displayed for patients in the IRF during
the influenza vaccination season, from October 1, 2015, through March
31, 2016. We proposed that the display of these rates will be updated
annually for subsequent influenza vaccination seasons.
Calculations for the MRSA and CDI Healthcare Associated Infection
(HAI) measures adjust for differences in the characteristics of
hospitals and patients using a SIR. The SIR is a summary measure that
takes into account differences in the types of patients that a hospital
treats. For a more detailed discussion of the SIR, please refer to the
FY 2016 IRF PPS final rule (80 FR 47126 through 47127). The MRSA and
CDI SIRs may take into account the laboratory methods, bed size of the
hospital, and other facility-level factors. It compares the actual
number of HAIs in a facility or state to a national benchmark based on
previous years of reported data and adjusts the data based on several
factors. A confidence interval with a lower and upper limit is
displayed around each SIR to indicate that there is a high degree of
confidence that the true value of the SIR lies within that interval. A
SIR with a lower limit that is greater than 1.0 means that there were
more HAIs in a facility or state than were predicted, and the facility
is classified as ``Worse than the U.S. National Benchmark.'' If the SIR
has an upper limit that is less than 1, the facility had fewer HAIs
than were predicted and is classified as ``Better than the U.S.
National Benchmark.'' If the confidence interval includes the value of
1, there is no statistical difference between the actual number of HAIs
and the number predicted, and the facility is classified as ``No
Different than U.S. National Benchmark.'' If the number of predicted
infections is less than 1.0, the SIR and confidence interval are not
calculated by CDC.
Calculations for the Influenza Vaccination Coverage Among
Healthcare Personnel (NQF #0431) are based on reported numbers of
personnel who received an influenza vaccine at the reporting facility
or who provided written documentation of influenza vaccination outside
the reporting facility. The sum of these two numbers is divided by the
total number of personnel working at the facility for at least 1 day
from October 1 through March 31 of the following year, and the result
is multiplied by 100 to produce a compliance percentage (vaccination
coverage). No risk adjustment is applicable to these calculations. More
information on these calculations and measure specifications is
available at https://www.cdc.gov/nhsn/pdfs/hps-manual/vaccination/4-hcp-vaccination-module.pdf. We proposed that this data will be displayed on
an annual basis and will include data submitted by IRFs for a specific,
annual influenza vaccination season. A single compliance (vaccination
coverage) percentage for all eligible healthcare personnel will be
displayed for each facility.
We invited public comment on our proposal to begin publicly
reporting in CY 2017, pending the availability of data, on Facility-
wide Inpatient Hospital-onset MRSA Bacteremia Outcome Measure (NQF
#1716); Facility-wide Inpatient Hospital-onset CDI Outcome Measure (NQF
#1717); and Influenza Vaccination Coverage Among Healthcare Personnel
(NQF #0431). These comments are summarized and addressed below.
Comment: Several commenters, including MedPAC, supported public
reporting of quality measures. MedPAC encouraged ongoing development
and public reporting of cross-cutting measures for all provider
settings.
Response: We will continue to move forward with cross-setting
measure development and public reporting of these measures to meet the
mandate of the IMPACT Act.
Comment: Several commenters stated CMS should risk-adjust IRFs'
publicly displayed data for Percent of Residents or Patients with
Pressure Ulcers That Are New or Worsened (Short Stay) (NQF #0678) for
the number of patients that have pressure ulcers.
Response: We refer commenters to the FY 2016 IRF PPS final rule (80
FR 47126 through 47127) that finalized public display of the risk-
adjusted quality measure, the Percent of Residents or Patients with
Pressure Ulcers That Are New or Worsened (Short Stay) (NQF #0678)
Comment: One commenter expressed concerns that CMS will utilize
data from the CARE Tool or IRF-PAI for public reporting of the quality
measures and that such data is subjective and non-evidence based and
there is a lack of ability to access the competency of staff completing
the tool either within or across PAC settings. Therefore, the commenter
is concerned that the publicly reported data will not represent the
quality of care provided in IRFs and comparing across IRFs.
Response: We appreciate the comment expressing concern regarding
the CARE Tool and IRF-PAI data for public reporting. We would like to
clarify that quality measures set for public display have already been
finalized, and the Secretary has a statutory obligation under sections
1886(j)(7)(E) and 1899B(g) of the Act to establish procedures to make
the data publicly available.
Comment: Several commenters expressed concern that the public
display of quality measure information is based on measures that do not
exemplify the IRF experience, target very small populations of cases,
and are not a good indicator of the overall quality of IRFs. Many
commenters conveyed that the goals of IRFs are to provide medically
necessary rehabilitation therapies to bring about recovery and improved
function and the measures fail to assess IRFs success at achieving
these goals.
Response: Section 3004 of the Affordable Care Act and the IMPACT
Act require the Secretary of Health and Human Services to publish the
data on the quality measures implemented in the IRF QRP through
rulemaking. The public reporting of the three measures finalized for
public reporting in the FY 2016 IRF PPS final rule and the four
measures proposed for public reporting in the FY 2017 IRF PPS proposed
rule supports the goals of the National Quality Strategy, the CMS
Quality Strategy, the HHS HAI Action Plan, and the Hospital Acquired
Condition Reduction Program. It is both a CMS and an HHS priority to
ensure the delivery of high quality, patient-centered, and safe care
across all care settings. While the main focus of care in
[[Page 52127]]
an IRF may be centered on restoration of a patient's functional status,
we believe that this cannot be achieved without attention to the basic
tenants of patient care, which speak to prevention and patient safety,
and believe that our quality measures reflect these aspects of quality.
The IMPACT Act requires us to address the domain of functional status
and requires the public reporting of this data within 2 years of a
finalized quality, resource use, and other measure's specified
application date. We believe that the addition of these measures to the
public display of IRF quality data will help to address any concerns
relayed by the commenter.
Comment: One commenter expressed concerns that the NHSN Facility-
Wide Inpatient Hospital-Onset MRSA Bacteremia Outcome Measure (NQF
#1716) does not reflect care provided in an IRF, specifically,
rehabilitation provided to promote functional recovery and achievement
of goals. The commenter also noted that the incidence of MRSA is rare,
and generally, if a patient in rehabilitation has MRSA, the infection
is present upon admission to the rehabilitation facility following
transfer from the acute care facility. Finally, the commenter noted
that the inclusion of the NHSN Facility-Wide Inpatient Hospital-Onset
MRSA Bacteremia Outcome Measure (NQF #1716) within the IRF QRP may
cause rehabilitation facilities to inappropriately screen for this
condition, resulting in unnecessary costs to the Medicare program.
Response: Section 3004 of the Affordable Care Act and the IMPACT
Act requires the Secretary of Health and Human Services to publish the
data on the quality measures implemented in the IRF QRP through
rulemaking. The public reporting of the NHSN Facility-Wide Inpatient
Hospital-Onset MRSA Bacteremia Outcome Measure (NQF #1716) support the
goals of the National Quality Strategy, the CMS Quality Strategy, the
HHS HAI Action Plan, and the Hospital Acquired Condition Reduction
Program. It is both a CMS and an HHS priority to ensure the delivery of
high quality, patient-centered, and safe care across all care settings.
According to the CDC, the steward of this quality measure, cases
defined by NHSN as Community-onset MRSA Bacteremia are excluded from
the data that is provided by NHSN to CMS. Only those cases that meet
the NHSN definition of Incident and Healthcare Facility-onset are
reported as a part of the CMS IRF QRP. For IRF units within a hospital
that participate in the CMS IRF QRP will be given a single MRSA
bacteremia LabID SIR for each type of CMS-certified IRF unit (adult and
pediatric) mapped within the hospital according to CMS Certification
Number (CCN). The MRSA Bacteremia LabID SIR is calculated as: Number of
all incident blood source MRSA LabID events identified >3 days after
admission to an IRF unit and where the patient had no positive MRSA
bacteremia LabID events in the prior 14 days in any CMS-certified IRF
unit of that type divided by the total number of predicted incident
healthcare facility-onset blood source MRSA LabID events. Clinicians
should base decisions about diagnostic testing on the needs and
clinical picture of the patient. Patients with MRSA bacteremia would be
expected to be symptomatic. Routine collection of blood cultures on
patients not suspected of being bacteremic would be outside of the
standards of medical care. For additional information on the
specifications for this measure, please refer to the CDC reference:
https://www.cdc.gov/nhsn/pdfs/cms/irfs/linelists_irfunits_indicators.pdf.
Comment: Several commenters recommended that CMS revise the
Facility-wide Inpatient Hospital-onset CDI Outcome Measure (NQF #1717)
because there are multiple C. difficile quality measures for Medicare
providers across the continuum of care (acute care hospitals, IRFs,
etc.) and one incident of C. difficile onset may be reported by three
providers and effectively, and unreasonably, be a ``triple hit'' for
multiple providers so that it is only reported at the first site of
discovery.
Response: The Facility-wide Inpatient Hospital-onset CDI Outcome
Measure (NQF #1717) was adopted in the IRF QRP and finalized in the FY
2015 IRF PPS final rule (79 FR 45913 through 45914). The CDC, the
steward of this measure, noted that the measure specifications for NQF
#1717, by design, align with the NHSN LabID Event protocol, which was
developed to require minimal investigation on the part of healthcare
facilities and to provide a proxy measure of infection. Dates of
admission and specimen collection are required and can easily be
collected via electronic methods. These dates enable differentiation of
healthcare-associated and community-onset events. To require a facility
to determine if a CDI LabID Event had been identified in another
facility would call for manual review of medical records and potential
communication with transferring facilities. The design of LabID event
reporting via NHSN is by single facility, which means that events are
reported for the facility where they occur. Analysis is by single
facility identifier (NHSN organizational ID) and does not cross
admissions to a different NHSN facility (or a different type reporting
facility such as nursing home to acute care facility) or transfer from
facility A to facility B. Cases defined by NHSN as community-onset
Clostridium difficile are excluded from the data that is provided by
NHSN to CMS. Only those cases that meet the NHSN definitions of an
Incident (non-duplicate) Healthcare Facility-onset are reported as a
part of the CMS IRF QRP. Therefore, cases that are identified during
the first 3 days of admission to a facility, and which may be related
to a discharge from another hospital, will not be included in the
Clostridium difficile LabID Event data reported for the admitting
facility.
Comment: The commenter was concerned that the public display of
these measures will provide misleading interpretations of quality, as
almost all the measures will be based on different time frames and will
use different minimum patient thresholds and potentially varying
patient populations. The commenter recommends that CMS suspend public
display of IRF QRP data until (1) all IMPACT Act domains are
implemented and (2) the patient populations for each measure are
standardized.
Response: The Secretary has a statutory obligation under section
1899B(g) and 1886(j)(7)(E) of the Act to make the data available to the
public. We are transitioning towards aligning the data collection
periods to follow the calendar year. Once this is achieved, the only
measure that will not be in alignment is the influenza measure since
these measures require taking into account the influenza season and
vaccination season for the data collection period.
Minimum patient thresholds and populations are dependent on the
specific measure. Each measure is specifically applied in public
reporting so that there is enough volume of cases reported to protect
anonymity and provide meaningful results with representative sample
size. Public reporting must comply with applicable privacy laws and
provide minimum sample sizes in order for facilities to compare their
performance with other IRFs. If the sample size is too small, the
results will not reflect their facility performance for comparison
purposes.
Final Decision: After careful consideration of the public comments,
we are finalizing our proposal to begin publicly reporting in CY 2017,
pending the availability of data, on Facility-wide Inpatient Hospital-
onset MRSA Bacteremia Outcome Measure (NQF
[[Page 52128]]
#1716); Facility-wide Inpatient Hospital-onset CDI Outcome Measure (NQF
#1717); and Influenza Vaccination Coverage Among Healthcare Personnel
(NQF #0431).
For the Percent of Residents or Patients Who Were Assessed and
Appropriately Given the Seasonal Influenza Vaccine (Short Stay) (NQF
#0680), we proposed to display rates annually based on the influenza
season to avoid reporting for more than one influenza vaccination
within a CY. For example, in 2017 we will display rates for the patient
vaccination measure based on discharges starting on July 1, 2015, to
June 30, 2016. This is proposed because it includes the entire
influenza vaccination season (October 1, 2015, to March 31, 2016).
Calculations for Percent of Residents or Patients Who Were Assessed
and Appropriately Given the Seasonal Influenza Vaccine (Short Stay)
(NQF #0680) will be based on patients meeting any one of the following
criteria: Patients who received the influenza vaccine during the
influenza season, patients who were offered and declined the influenza
vaccine, and patients who were ineligible for the influenza vaccine due
to contraindication(s). The facility's summary observed score will be
calculated by combining the observed counts of all the criteria. This
is consistent with the publicly reported patient influenza vaccination
measure for Nursing Home Compare. Additionally, for the patient
influenza measure, we will exclude IRFs with fewer than 20 stays in the
measure denominator. For additional information on the specifications
for this measure, please refer to the IRF Quality Reporting Measures
Information Web page at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/IRF-Quality-Reporting-Program-Measures-Information-.html.
We invited public comments on our proposal to begin publicly
reporting the Percent of Residents or Patients Who Were Assessed and
Appropriately Given the Seasonal Influenza Vaccine (Short Stay) (NQF
#0680) measure on discharges from July 1st of the previous calendar
year to June 30th of the current calendar year. We invited comments on
the public display of the measure Percent of Residents or Patients Who
Were Assessed and Appropriately Given the Seasonal Influenza Vaccine
(NQF #0680) in 2017 pending the availability of data.
We received several comments, which are summarized below.
Comment: Several commenters expressed concern that the Percent of
Residents or Patients Who Were Assessed and Appropriately Given the
Seasonal Influenza Vaccine (Short-Stay) (NQF #0680) is not a true
indicator of the quality of care provided in IRFs, which focuses on
functional recovery so that patients are able to function to their
maximum potential in the least restrictive environment. Commenters
expressed concern that the influenza vaccination rates do not
adequately assess whether quality care was provided and that CMS has
not provided any evidence in the IRF QRP that differences in influenza
vaccination rates between facilities affect the quality of outcomes or
the patient experience.
Response: We appreciate the concerns by several commenters in
regard to the Percent of Residents or Patients Who Were Assessed and
Appropriately Given the Seasonal Influenza Vaccine (Short-Stay) (NQF
#0680). However, this quality measure was adopted in the IRF QRP and
finalized in the FY 2014 IRF PPS final rule (78 FR 47906 through
47911).
Final Decision: After careful consideration of the public comments,
we are finalizing our proposal to begin publicly reporting the Percent
of Residents or Patients Who Were Assessed and Appropriately Given the
Seasonal Influenza Vaccine (Short Stay) (NQF #0680) measure, pending
the availability of data, on discharges from July 1st of the previous
calendar year to June 30th of the current calendar year.
Additionally, we requested public comments on whether to include,
in the future, public display comparison rates based on CMS regions or
US census regions for Percent of Residents or Patients with Pressure
Ulcers That Are New or Worsened (Short Stay) (NQF #0678); All-Cause
Unplanned Readmission Measure for 30 Days Post-Discharge from IRFs (NQF
#2502); and Percent of Residents or Patients Who Were Assessed and
Appropriately Given the Seasonal Influenza Vaccine (Short Stay) (NQF
#0680) for CY 2017 public display.
We did not receive any comments about whether to include, in the
future, public display comparison rates based on CMS regions or US
census regions for CY 2017 public display.
2. Procedures for the Opportunity To Review and Correct Data and
Information
Section 1899B(g) of the Act requires the Secretary to establish
procedures for public reporting of IRFs' performance, including the
performance of individual IRFs, on quality measures specified under
section 1899B(c)(1) of the Act and resource use and other measures
specified under section 1899B(d)(1) of the Act (collectively, IMPACT
Act measures) beginning not later than 2 years after the applicable
specified application date under section 1899B(a)(2)(E) of the Act.
Under section 1899B(g)(2) of the Act, the procedures must ensure,
including through a process consistent with the process applied under
section 1886(b)(3)(B)(viii)(VII) of the Act, which refers to public
display and review requirements in the Hospital IQR Program, that each
IRF has the opportunity to review and submit corrections to its data
and information that are to be made public prior to the information
being made public.
In the FY 2016 IRF PPS final rule (80 FR 47126 through 47128), and
as illustrated in Table 10 in section VIII.I.a of this final rule, we
finalized that once the provider has an opportunity to review and
correct quarterly data related to measures submitted via the QIES-ASAP
system or CDC NHSN, we will consider the provider to have been given
the opportunity to review and correct this data. We wish to clarify
that although the correction of data (including claims) can occur after
the submission deadline, if such corrections are made after a
particular quarter's submission and correction deadline, such
corrections will not be captured in the file that contains data for
calculation of measures for public reporting purposes. To have publicly
displayed performance data that is based on accurate underlying data,
it will be necessary for IRFs to review and correct this data before
the quarterly submission and correction deadline.
We restated and proposed additional details surrounding procedures
that will allow individual IRFs to review and correct their data and
information on measures that are to be made public before those measure
data are made public.
For assessment-based measures, we proposed a process by which we
will provide each IRF with a confidential feedback report that will
allow the IRF to review its performance on such measures and, during a
review and correction period, to review and correct the data the IRF
submitted to CMS via the CMS QIES-ASAP system for each such measure. In
addition, during the review and correction period, the IRF will be able
to request correction of any errors in the assessment-based measure
rate calculations.
We proposed that these confidential feedback reports will be
available to each IRF using the CASPER system. We
[[Page 52129]]
refer to these reports as the IRF Quality Measure (QM) Reports. We
proposed to provide monthly updates to the data contained in these
reports as data become available. We proposed to provide the reports so
that providers will be able to view their data and information at both
the facility and patient level for its quality measures. The CASPER
facility level QM Reports may contain information such as the
numerator, denominator, facility rate, and national rate. The CASPER
patient-level QM Reports may contain individual patient information
which will provide information related to which patients were included
in the quality measures to identify any potential errors for those
measures in which we receive patient-level data. Currently, we do not
receive patient-level data on the CDC measure data received via the
NHSN system. In addition, we will make other reports available in the
CASPER system, such as IRF-PAI assessment data submission reports and
provider validation reports, which will disclose the IRFs data
submission status providing details on all items submitted for a
selected assessment and the status of records submitted. We refer
providers to the CDC/NHSN system Web site for information on obtaining
reports specific to NHSN submitted data at https://www.cdc.gov/nhsn/inpatient-rehab/. Additional information regarding the
content and availability of these confidential feedback reports will be
provided on an ongoing basis on our Web site(s) at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/.
As previously finalized in the FY 2016 IRF PPS final rule and
illustrated in Table 18 in section VIII.I.c of this final rule, IRFs
will have approximately 4.5 months after the reporting quarter to
correct any errors of their assessment-based data (that appear on the
CASPER generated QM reports) and NHSN data used to calculate the
measures. During the time of data submission for a given quarterly
reporting period and up until the quarterly submission deadline, IRFs
could review and perform corrections to errors in the assessment data
used to calculate the measures and could request correction of measure
calculations. However, as already established, once the quarterly
submission deadline occurs, the data is ``frozen'' and calculated for
public reporting and providers can no longer submit any corrections. We
will encourage IRFs to submit timely assessment data during a given
quarterly reporting period and review their data and information early
during the review and correction period so that they can identify
errors and resubmit data before the data submission deadline.
As noted above, the assessment data will be populated into the
confidential feedback reports, and we intend to update the reports
monthly with all data that have been submitted and are available. We
believe that the data collection/submission quarterly reporting periods
plus 4.5 months to review correct and review the data is sufficient
time for IRFs to submit, review and, where necessary, correct their
data and information. These time frames and deadlines for review and
correction of such measures and data satisfy the statutory requirement
that IRFs be provided the opportunity to review and correct their data
and information and are consistent with the informal process hospitals
follow in the Hospital IQR Program.
In FY 2016 IRF PPS final rule (80 FR 47126 through 47128), we
finalized the data submission/correction and review period. Also, we
afford IRFs a 30-day preview period prior to public display during
which IRFs may preview the performance information on their measures
that will be made public. We want to clarify that we will provide the
preview report using the CASPER system, with which IRFs are familiar.
The CASPER preview reports inform providers of their performance on
each measure which will be publicly reported. Please note that the
CASPER preview reports for the reporting quarter will be available
after the 4.5 month correction period and the applicable data
submission/correction deadline have passed and are refreshed on a
quarterly basis for those measures publicly reported quarterly, and
annually for those measure publicly reported annually. We proposed to
give IRFs 30 days to review the preview report beginning from the date
on which they can access the report. As already finalized, corrections
to the underlying data will not be permitted during this time; however,
IRFs may ask for a correction to their measure calculations during the
30-day preview period, should they believe the calculation is
inaccurate. We proposed that if we agree that the measure, as it is
displayed in the preview report, contains a calculation error, we could
suppress the data on the public reporting Web site, recalculate the
measure and publish it at the time of the next scheduled public display
date. This process will be consistent with informal processes used in
the Hospital IQR Program. If finalized, we intend to utilize a
subregulatory mechanism, such as our IRF QRP Web site, to provide more
information about the preview reports, such as when they will be made
available and explain the process for how and when providers may ask
for a correction to their measure calculations. We invited public
comment on these proposals to provide preview reports using the CASPER
system, giving IRFs 30 days review the preview report and ask for a
correction, and to use a subregulatory mechanism to explain the process
for how and when providers may ask for a correction.
In addition to assessment-based measures and CDC measure data
received via the NHSN system, we have also proposed claims-based
measures for the IRF QRP. The claims-based measures include those
proposed to meet the requirements of the IMPACT Act as well as the All-
Cause Unplanned Readmission Measure for 30 Days Post-Discharge from
IRFs (NQF #2502) which was finalized for public display in the FY 2016
IRF PPS final rule (80 FR 47126 through 47127). As noted in section
VII.N.2. of this final rule, section 1899B(g)(2) of the Act requires
prepublication provider review and correction procedures that are
consistent with those followed in the Hospital IQR Program. Under the
Hospital IQR Program's informal procedures, for claims-based measures,
we provide hospitals 30 days to preview their claims-based measures and
data in a preview report containing aggregate hospital-level data. We
proposed to adopt a similar process for the IRF QRP.
Prior to the public display of our claims-based measures, in
alignment with the Hospital IQR, HAC and Hospital VBP Programs, we
proposed to make available through the CASPER system, a confidential
preview report that will contain information pertaining to claims-based
measure rate calculations, for example, facility and national rates.
The data and information will be for feedback purposes only and could
not be corrected. This information will be accompanied by additional
confidential information based on the most recent administrative data
available at the time we extract the claims data for purposes of
calculating the measures. Because the claims-based measures are
recalculated on an annual basis, these confidential CASPER QM reports
for claims-based measures will be refreshed annually. As previously
finalized in the FY 2016 IRF PPS final rule (80 FR 47126 through
47128), IRFs will have 30 days from the date the preview report is made
available in which to review this information. The
[[Page 52130]]
30-day preview period is the only time when IRFs will be able to see
claims-based measures before they are publicly displayed. IRFs will not
be able to make corrections to underlying claims data during this
preview period, nor will they be able to add new claims to the data
extract. However, IRFs may request that we correct our measure
calculation if the IRF believes it is incorrect during the 30 day
preview period. We proposed that if we agree that the measure, as it is
displayed in the preview report, contains a calculation error, we could
suppress the data on the public reporting Web site, recalculate the
measure, and publish it at the time of the next scheduled public
display date. This process will be consistent with informal policies
followed in the Hospital IQR Program. If finalized, we intend to
utilize a subregulatory mechanism, such as our IRF QRP Web site, to
explain the process for how and when providers may contest their
measure calculations
The proposed claims-based measures--The MSPB-PAC IRF QRP measure;
Discharge to Community--PAC, Potentially Preventable 30-Day Post-
Discharge Readmission Measure for IRF QRP, and Potentially Preventable
Within Stay Readmission Measure for IRFs--use Medicare administrative
data from hospitalizations for Medicare FFS beneficiaries. Public
reporting of data will be based on 2 consecutive calendar years of
data, which is consistent with the specifications of the proposed
measures. We proposed to create data extracts using claims data for the
proposed claims-based measures-The MSPB-PAC IRF QRP measure; Discharge
to Community--PAC, Potentially Preventable 30-Day Post-Discharge
Readmission Measure for IRF QRP, and Potentially Preventable Within
Stay Readmission Measure for IRFs--at least 90 days after the last
discharge date in the applicable period, which we will use for the
calculations. For example, if the last discharge date in the applicable
period for a measure is December 31, 2017, for data collection January
1, 2016, through December 31, 2017, we will create the data extract on
approximately March 31, 2018, at the earliest, and use that data to
calculate the claims-based measures for that applicable period. Since
IRFs will not be able to submit corrections to the underlying claims
snapshot nor add claims (for measures that use IRF claims) to this data
set at the conclusion of the at least the 90-day period following the
last date of discharge used in the applicable period, at that time we
will consider IRF claims data to be complete for purposes of
calculating the claims-based measures.
We proposed that beginning with data that will be publicly
displayed in 2018, claims-based measures will be calculated using
claims data at least 90 days after the last discharge date in the
applicable period, at which time we will create a data extract or
snapshot of the available claims data to use for the measures
calculation. This timeframe allows us to balance the need to provide
timely program information to IRFs with the need to calculate the
claims-based measures using as complete a data set as possible. As
noted, under this procedure, during the 30-day preview period, IRFs
will not be able to submit corrections to the underlying claims data or
to add new claims to the data extract. This is for two reasons: First,
for certain measures, the claims data used to calculate the measure is
derived not from the IRF's claims, but from the claims of another
provider. For example, the proposed measure Potentially Preventable 30-
Day Post-Discharge Readmission Measure for IRF QRP uses claims data
submitted by the hospital to which the patient was readmitted. The
claims are not those of the IRF and, therefore, the IRF could not make
corrections to them. Second, even where the claims used to calculate
the measures are those of the IRF, it will not be not possible to
correct the data after it is extracted for the measures calculation.
This is because it is necessary to take a static ``snapshot'' of the
claims in order to perform the necessary measure calculations.
We seek to have as complete a data set as possible. We recognize
that the at least 90-day ``run-out'' period, when we will take the data
extract to calculate the claims-based measures, is less than the
Medicare program's current timely claims filing policy under which
providers have up to 1 year from the date of discharge to submit
claims. We considered a number of factors in determining that the
proposed at least 90-day run-out period is appropriate to calculate the
claims-based measures. After the data extract is created, it takes
several months to incorporate other data needed for the calculations
(particularly in the case of risk-adjusted or episode-based measures).
We then need to generate and check the calculations. Because several
months lead time is necessary after acquiring the data to generate the
claims-based calculations, if we were to delay our data extraction
point to 12 months after the last date of the last discharge in the
applicable period, we will not be able to deliver the calculations to
IRFs sooner than 18 to 24 months after the last discharge. We believe
this will create an unacceptably long delay both for IRFs and for us to
deliver timely calculations to IRFs for quality improvement.
We invited public comment on these proposals. We received a number
of comments, which are summarized below.
Comment: Several commenters expressed concern that for claims-based
measures, CMS proposes to calculate claims-based measures on an annual
basis and the CASPER QM provider reports for these measures would only
be available annually. Commenters also expressed concern that CMS does
not propose to allow providers to correct their metrics on claims-based
measures; reports would be for feedback purposes only. Several
commenters requested CMS provide claims-based feedback reports at least
twice a year as well as providing patient-level data.
Response: We appreciate the commenters' concerns and suggestions to
provide feedback reports at least twice a year as well as providing
patient-level data for claims-based measures. As discussed previously,
the All-Cause Unplanned Readmission Measure for 30 Days Post-Discharge
from IRFs (NQF #2502) is based on 2 consecutive years of data in order
to ensure a sufficient sample size to reliably assess IRFs'
performance. The decision to update claims-based measures on an annual
basis was to ensure that the amount of data received during the
reporting period was sufficient to generate reliable measure rates.
However, we will explore the feasibility of providing IRFs with
information more frequently. We believe that we are limited in our
ability to provide patient level information that stems from claims
submitted by providers other than IRF, but we will explore the
feasibility of providing patient-level data. With regard to the concern
for the correction of claims-based measures and the IRF's ability to
correct their metrics, and that the reports we provide will be for
feedback purposes only, we interpret the commenter to be referring to
both the preview reports and the QM reports we discussed. The
limitation on claims-based data and corrections is that the measures
are calculated after the claims file has been obtained. If the IRF
determines there are errors in the claims data they submitted, then
they can correct such data. The corrections to the claims data will be
reflected in the subsequent measure calculation. We urge IRFs to submit
timely and accurate claims-based data.
Comment: One commenter expressed concern that 30 days is inadequate
to
[[Page 52131]]
preview and assess the QM reports and recommends 60 days and that CMS
should establish a process to discuss and reconcile issues or
incongruities between CMS's and the provider's data.
Response: We interpret the commenter to be referring to the preview
reports we will provide prior to public reporting and appreciate their
concern for the 30-day timeframe for which IRFs have to review and
assess the preview reports. The 30-day preview period, previously
finalized, is consistent with other public reporting programmatic
procedures. As described, this timeframe is for providers to evaluate
their data that will be published and alert us to any discrepancies
they may find. In addition, as described, IRFs will have an opportunity
to review their information and data using various reports, which are
provided through the CASPER system and can be used to inform data
correction needs on behalf of the IRF. For example, as discussed, we
intend to provide IRF QM Reports that will provide monthly reporting on
both facility-level and patient-level CMS assessment-based data.
Further, we refer the commenter to the discussion we provide in which
IRFs will have 4.5 months to review and correct data prior to the
quarterly freeze dates and posting of the final preview reports in
QIES.
Final Decision: After careful consideration of the public comments,
we are finalizing our proposals related to procedures for the
opportunity to review and correct data and information. We are
finalizing as proposed, our policies and procedures pertaining to
public reporting and the opportunity to review and correct data and
information. We are also finalizing as proposed, our policies and
procedures for claims-based measures for public reporting.
O. Mechanism for Providing Feedback Reports to IRFs
Section 1899B(f) of the Act requires the Secretary to provide
confidential feedback reports to post-acute care providers on their
performance to the measures specified under section 1899B(c)(1) and
(d)(1) of the Act, beginning 1 year after the specified application
date that applies to such measures and PAC providers. As discussed
earlier, the reports we proposed to provide for use by IRFs to review
their data and information will be confidential feedback reports that
will enable IRFs to review their performance on the measures required
under the IRF QRP. We proposed that these confidential feedback reports
will be available to each IRF using the CASPER system. Data contained
within these CASPER reports will be updated as previously described, on
a monthly basis as the data become available except for our claims-
based measures, which are only updated on an annual basis.
We intend to provide detailed procedures to IRFs on how to obtain
their confidential feedback CASPER reports on the IRF QRP Web site at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting/. We proposed to use the
CMS QIES-ASAP system to provide quality measure reports in a manner
consistent with how providers obtain various reports to date. The QIES-
ASAP system is a confidential and secure system with access granted to
providers, or their designees.
We sought public comment on this proposal to satisfy the
requirement to provide confidential feedback reports to IRFs. We
received several comments, which are summarized are below.
Comment: Several commenters recommended CMS provide more frequent
feedback, such as quarterly, for assessment-based measures and every
six months reporting for claims-based measures.
Response: We appreciate commenters' suggestion for CMS to provide
more frequent feedback, such as quarterly, for assessment-based
measures and every 6 months for claims-based measures.
As previously discussed, IRFs will have an opportunity to review
and utilize their data using confidential reports provided through the
CASPER system. The decision to update claims-based measures on an
annual basis was basis was explained previously in response to the
comment concerning providing feedback reports at least twice a year.
Comment: One commenter recommended CMS conduct a ``dry run'' in
which providers receive confidential preview reports prior to publicly
reporting measures so that providers can become familiar with the
methodology, understand the measure results, know how well they are
performing, and have an opportunity to give CMS feedback on potential
technical issues with the measures.
Response: We intend to offer providers information related to their
measures so that they become familiar with the measure's methodology
and can utilize their confidential preview reports which they will
receive prior to the public reporting of new IRF QRP measures. IRFs
will also receive other confidential reports such as the IRF facility
and patient level QM Reports as well as an additional confidential
facility-level report to incorporate the quarterly freeze dates, for
example, the Review and Correct Report. We believe that these various
reports will provide an indication on how well the IRF is performing as
well as opportunities to provide CMS feedback on technical issues with
the measures. Therefore, no additional dry run period is warranted.
Final Decision: After careful consideration of the public comments,
we are finalizing our proposal to provide confidential feedback reports
to IRFs, as proposed.
P. Method for Applying the Reduction to the FY 2017 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 IRFs that fail to comply with the
quality data submission requirements. In compliance with section
1886(j)(7)(A)(i) of the Act, we proposed to apply a 2-percentage point
reduction to the applicable FY 2017 market basket increase factor in
calculating a proposed adjusted FY 2017 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 2017 IRF increase factor for IRFs that fail to meet
the quality reporting requirements. We did not receive any comments on
this proposal.
Final Decision: We are finalizing our proposed method for applying
the reduction to the FY 2017 IRF increase factor for IRFs that fail to
meet the quality reporting requirements.
Table 21 shows the calculation of the adjusted FY 2017 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(s).
[[Page 52132]]
Table 21--Calculations To Determine the Adjusted FY 2017 Standard
Payment Conversion Factor for IRFs That Failed To Meet the Quality
Reporting Requirement
------------------------------------------------------------------------
Explanation for adjustment Calculations
------------------------------------------------------------------------
Standard Payment Conversion Factor for $15,478.
FY 2016.
Market Basket Increase Factor for FY x 0.9965.
2017 (2.7 percent), reduced by 0.3
percentage point for the productivity
adjustment as required by section
1886(j)(3)(C)(ii)(I) of the Act,
reduced by 0.75 percentage point in
accordance with sections 1886(j)(3)(C)
and (D) of the Act and further reduced
by 2 percentage points for IRFs that
failed to meet the quality reporting
requirement.
Budget Neutrality Factor for the Wage x 0.9992.
Index and Labor-Related Share.
Budget Neutrality Factor for the x 0.9992.
Revisions to the CMG Relative Weights.
Adjusted FY 2017 Standard Payment = 15,399.
Conversion Factor.
------------------------------------------------------------------------
IX. Miscellaneous Comments
Comment: Several commenters were supportive of our continued use of
the FY 2014 facility-level adjustments and recommended that CMS
continue monitoring the adjustments. Other commenters suggested that
CMS be more transparent about the methodology and the factors it
utilizes for calculating facility adjustment payments to IRFs. Several
commenters suggested that CMS should establish a three-year minimum
interval for any change in the IRF provider-level adjustment factors
and recommended that if any factor varies by a minimum amount, the
factor should be adjusted. Some commenters also recommended that CMS
monitor the facility-level adjustment factors annually and adjust them
if there is a change in excess of 5 to 10 percent.
Response: As we did not propose any changes to the facility-level
adjustments, these comments are outside the scope of the proposed rule.
In the FY 2017 IRF PPS proposed rule (81 FR 24177), we noted that, in
the FY 2015 IRF PPS final rule (79 FR 45872 at 45882), we froze the
facility-level adjustments at 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). We will continue to
monitor the facility-level adjustments and update them as necessary
through rulemaking to ensure the continued accuracy of IRF PPS
payments.
Comment: Several commenters expressed concerns about the impact of
the changes to the 60 percent rule compliance methodology that we
finalized in the FY 2014 and FY 2015 IRF PPS final rules on beneficiary
access to IRF services, and suggested that we revisit them. These
commenters further stated that the translation of International
Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-
CM) codes to International Classification of Diseases, 10th Revision,
Clinical Modification (ICD-10-CM) codes using the General Equivalence
Mapping (GEMS) tool may have unintentionally caused some diagnoses to
now be excluded from counting under the presumptive compliance
methodology. In particular, the commenters suggested that we review the
codes excluded under the IGCs for traumatic brain injury, hip fracture,
and major multiple trauma, and add these cases back in as presumptively
compliant cases under the 60 percent rule. Some commenters suggested
that we issue clarifications to MACs and CMS Regional Offices that
these codes are considered presumptively compliant. Further, one
commenter suggested that we revisit our decision on no longer
considering presumptively compliant diagnoses codes for rheumatoid
myopathy and polyneuropathy, unilateral amputations, and amputation
status/aftercare.
Response: As we did not propose any changes to the methodology for
determining IRFs' compliance with the 60 percent rule in the FY 2017
IRF PPS proposed rule, these comments are outside the scope of the
proposed rule. We appreciate the commenter's suggestions, and will
continue to monitor and assess the implications of the changes to the
presumptive methodology that we finalized in the FY 2014 and FY 2015
IRF PPS final rules to determine if any further refinements to the
methodology are needed. We intend to take a comprehensive look at the
ICD-10-CM codes to identify any diagnosis codes that may need to be
added to the presumptive compliance methodology, as well as any codes
that may need to be removed.
Comment: Several commenters suggested that, as height and weight
are now required information on the IRF-PAI (beginning October 1,
2014), CMS should now use this information to identify patients with
unilateral joint replacements and body mass indexes (BMI) greater than
50 for presumptive compliance with the 60 percent rule requirements.
Response: As we did not propose any changes to the methodology for
determining IRFs' compliance with the 60 percent rule, these comments
are outside the scope of the proposed rule. However, we will take these
suggestions into consideration.
Comment: One commenter stated that the translation to ICD-10-CM has
created a problem with the grouping of rehabilitation diagnosis-related
groups (DRGs) in rehabilitation units due to the loss of the ``V code''
under ICD-10-CM. The commenter expressed concern that rehabilitation
patients may not be reimbursed appropriately and in many instances
would be paid under the Hospital IPPS MS-DRGs.
Response: As payment under the IRF PPS is not based on diagnosis-
related groups, this comment is outside the scope of the proposed rule.
This final rule only applies to rehabilitation units that are paid
under the IRF PPS, not to other types of rehabilitation units which may
be present in an acute care hospital but that are paid under other
Medicare payment systems.
Comment: One commenter stated that CMS should review its policy
regarding the use of ``D-subsequent encounter'' as an eligible 7th
character for traumatic injury diagnosis codes as advised by the AHA
Coding Clinic for ICD-10-CM and ICD-10-PCS Editorial Advisory Board
(reference material for this can be found at https://www.ahacentraloffice.org/codes/Resources.shtml). The commenter stated
that ``subsequent encounter'' is an appropriate option for
rehabilitation services and that CMS should allow the ``D'' as an
eligible 7th character for traumatic injury diagnosis codes.
Response: IRFs are permitted to use ``D'' as an eligible 7th
character for traumatic injury diagnosis codes on both the IRF claim
and the IRF-PAI. However, for the reasons indicated in the FY 2015 IRF
PPS final rule (79 FR 45872, 45907), effective with discharges
occurring on or after October 1, 2015, ICD-10-CM codes with the seventh
character extension of ``D'' are not included in the ICD-10-CM versions
of the ``List of Comorbidities,'' ``ICD-10-CM Codes That Meet
Presumptive Compliance Criteria,'' or ``Impairment
[[Page 52133]]
Group Codes That Meet Presumptive Compliance Criteria.'' Whereas the
AHA Coding Clinic for ICD-10-CM and ICD-10 PCS (Vol. 2, No. 1)
guidelines instruct providers to use the 7th character ``D'' for
traumatic injury diagnosis codes used in an IRF setting, the guidelines
specifically say that the AHA Coding Clinic guidelines only apply to
the IRF claim and that providers should refer to the instructions
provided in the IRF-PAI training manual, which is available for
download from the IRF PPS Web site at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRFPAI.html, for
instructions on how to code the IRF-PAI. Thus, ICD-10-CM diagnosis
codes with the 7th character ``D,'' if used for traumatic injury
diagnosis codes on the IRF-PAI, will not result in a tier payment or
result in a case being presumptively compliant with the IRF 60 percent
rule for the reasons stated in the FY 2015 IRF PPS final rule (79 FR
45872, 45907).
Comment: Several commenters stated that the FY 2017 update to the
standard payment conversion factor does not include additional payment
to IRFs for the time and resources needed to complete assessments for
quality reporting. These commenters further stated that the additional
quality reporting elements in the FY 2016 IRF PPS final rule will add
time spent collecting information while decreasing the time available
for direct patient care. Several commenters stated that the proposed
increase does not cover the costs of medical inflation, or of the
technical implementation, training, and data collection related to the
quality reporting measures even though costs will be significant.
Several commenters stated that the ``minimal increase'' does not
adequately take into account the estimated costs of implementing the
quality reporting measures and request that CMS add the estimated costs
of these measures to the FY 2017 payment update.
Response: We refer readers to the FY 2016 IRF PPS final rule (80 FR
47129 through 47137) for details regarding the Collection of
Information Requirements and Regulatory Impact Analysis for the
finalized measures. We would also like to clarify that quality program
reporting requirements are not included in the standard payment
conversion factor. However, in accordance with section 1886(j)(7)(A) of
the Act, the applicable annual increase factor for any IRF that does
not submit the required data to CMS must be reduced by two percentage
points.
Comment: One commenter reiterated MedPAC's March 2016
recommendation that we should analyze patterns of coding across IRFs
and reassess the inter-rater reliability of the IRF-PAI.
Response: This comment involves data monitoring activities that are
not discussed in the proposed rule, and are therefore outside the scope
of the rule. However, we will share this recommendation with the
appropriate components within CMS for their consideration of these
issues.
X. Provisions of the Final Regulations
In this final rule, we are adopting the provisions set forth in the
FY 2017 IRF PPS proposed rule (81 FR 24178). Specifically:
We will update the FY 2017 IRF PPS relative weights and
average length of stay values using the most current and complete
Medicare claims and cost report data in a budget-neutral manner, as
discussed in section IV of this final rule.
As established in the FY 2015 IRF PPS final rule (79 FR
45872 at 45882), the facility-level adjustments will remain frozen at
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), as discussed in section V of this final rule.
We will update the FY 2017 IRF PPS payment rates by the
market basket increase factor, based upon the most current data
available, with a 0.75 percentage point reduction as required by
sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(v) of the Act and the
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 the FY 2017 IRF PPS payment rates by the FY
2017 wage index and the labor-related share in a budget-neutral manner
and continue the phase-out of the rural adjustment as discussed in
section VI of this final rule.
We will calculate the final IRF standard payment
conversion factor for FY 2017, as discussed in section VI of this final
rule.
We will update the outlier threshold amount for FY 2017,
as discussed in section VII of this final rule.
We will update the cost-to-charge ratio (CCR) ceiling and
urban/rural average CCRs for FY 2017, as discussed in section VII of
this final rule.
We will adopt revisions and updates to quality measures
and reporting requirements under the quality reporting program for IRFs
in accordance with section 1886(j)(7) of the Act, as discussed in
section VIII of this final rule.
XI. 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
Failure to submit data required under section 1886(j)(7)(C) and (F)
of the Act will result in the reduction of the annual update to the
standard federal rate for discharges occurring during such fiscal year
by 2 percentage points for any IRF that does not comply with the
requirements established by the Secretary. At the time that this
analysis was prepared, 91, or approximately 8 percent, of the 1166
active Medicare-certified IRFs did not receive the full annual
percentage increase for the FY 2016 annual payment update
determination. Information is not available to determine the precise
number of IRFs that will not meet the requirements to receive the full
annual percentage increase for the FY 2017 payment determination.
We believe that the burden associated with the IRF QRP is the time
and effort associated with data collection and reporting. As of
February 1, 2016 there are approximately 1131 IRFs currently reporting
quality data to CMS. In this final rule, we are adopting 5 measures.
For the FY 2018 payment determinations and subsequent years, we
proposed four new measures: (1)
[[Page 52134]]
MSPB-PAC IRF QRP; (2) Discharge to Community-PAC IRF QRP, and (3)
Potentially Preventable 30-Day Post-Discharge Readmission Measure for
IRF QRP; (4) Potentially Preventable 30-Day Within Stay Readmission
Measure for IRF QRP. These four measures are Medicare claims-based
measures. Because claims-based measures can be calculated based on data
that are already reported to the Medicare program for payment purposes,
we believe there will be no additional impact.
For the FY 2020 payment determination and subsequent years, we
proposed one measure: Drug Regimen Review Conducted with Follow-Up for
Identified Issues-PAC IRF QRP. Additionally, we proposed that data for
this new measure will be collected and reported using the IRF-PAI
(version effective October 1, 2018).
Our burden calculations take into account all ``new'' items
required on the IRF-PAI (version effective October 1, 2018) to support
data collection and reporting for this measure. The addition of the new
items required to collect the newly proposed measure is for the purpose
of achieving standardization of data elements.
We estimate the additional elements for the newly proposed Drug
Regimen Review Conducted with Follow-Up for Identified Issues-PAC IRF
QRP measure will take 6 minutes of nursing/clinical staff time to
report data on admission and 4 minutes of nursing/clinical staff time
to report data on discharge, for a total of 10 minutes. We estimate
that the additional IRF-PAI items we proposed will be completed by
Registered Nurses (RN) for approximately 75 percent of the time
required, and Pharmacists for approximately 25 percent of the time
required. Individual providers determine the staffing resources
necessary. In accordance with OMB control number 0938-0842, we estimate
398,254 discharges from all IRFs annually, with an additional burden of
10 minutes. This will equate to 66,375.67 total hours or 58.69 hours
per IRF. We believe this work will be completed by RNs (75 percent) and
Pharmacists (25 percent). We obtained mean hourly wages for these staff
from the U.S. Bureau of Labor Statistics' May 2014 National
Occupational Employment and Wage Estimates (https://www.bls.gov/oes/current/oes_nat.htm), and to account for overhead and fringe benefits,
we have doubled the mean hourly wage. Per the U.S. Bureau of Labor and
Statistics, the mean hourly wage for a RN is $33.55. However, to
account for overhead and fringe benefits, we have doubled the mean
hourly wage, making it $67.10 for an RN. Per the U.S. Bureau of Labor
and Statistics, the mean hourly wage for a pharmacist is $56.98.
However, to account for overhead and fringe benefits, we have doubled
the mean hourly wage, making it $113.96 for a pharmacist. Given these
wages and time estimates, the total cost related to the newly proposed
measures is estimated at $4,625.46 per IRF annually, or $5,231,398.17
for all IRFs annually.
For the quality reporting during extraordinary circumstances, in
section VIII.L of this final rule, we add a previously finalized
process that IRFs may request an exception or extension from the FY
2019 payment determination and that of subsequent payment
determinations. The request must be submitted by email within 90 days
from the date that the extraordinary circumstances occurred.
While the preparation and submission of the request is an
information collection, unlike the aforementioned temporary exemption
of the data collection requirements for the new drug regimen review
measure, the request is not expected to be submitted to OMB for formal
review and approval since we estimate less than two requests (total)
per year. Since we estimate fewer than 10 respondents annually, the
information collection requirement and associated burden is not subject
as stated in 5 CFR 1320.3(c) of the implementing regulations of the
Paperwork Reduction Act of 1995.
As discussed in section VIII.M of this final rule, we add a
previously finalized process that will enable IRFs to request
reconsiderations of our initial non-compliance decision in the event
that it believes that it was incorrectly identified as being subject to
the 2-percentage point reduction to its annual increase factor due to
non-compliance with the IRF QRP reporting requirements. While there is
burden associated with filing a reconsideration request, 5 CFR 1320.4
of OMB's implementing regulations for PRA excludes activities during
the conduct of administrative actions such as reconsiderations.
We received comments about the collection of information
requirements associated with measures being proposed for the IRF QRP,
which are summarized and addressed below.
Comment: One commenter appreciated that the claims-based measures
being proposed do not place additional burden on the facilities and
their staff. Other commenters had concerns about the claims-based
measures, noting that while they had no data collection burden, they
were associated with time and resources needed to compile and verify
data for submission. One commenter expressed concerns that the burden
estimate doubles the resources required to collect data but doesn't
take into consideration limited resources smaller organizations have.
Response: We recognize the commenter's concern pertaining to burden
due to the requirements being added to the IRF Quality Reporting
Program. We are very sensitive to the issue of burden associated with
data collection and have proposed only the minimal number of additional
items (3) needed to calculate the proposed quality measure. Though we
recognize that new IRF-PAI items will require additional activities and
efforts by providers, we would like to clarify that burden estimates
are intended to reflect only the time needed to complete IRF-PAI items,
independent of clinical time spent assessing the patient. Similarly,
burden estimates are not indented to reflect costs of training and
operational processes; these are considered part of the operating costs
for an IRF. Time estimates for coding required items being added for
the Drug Regimen Review measure were based on a Drug Regimen Review
pilot testing conducted in November and December 2015. It should be
noted that with each assessment release, we provide free software to
our providers that allows for the completion and submission of any
required assessment data. Free downloads of the Inpatient
Rehabilitation Validation and Entry (IRVEN) software product are
available on the CMS Web site at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Software.html.
We also wish to note that, as pointed out by one commenter, four of
the five measures proposed are claims-based and have no additional data
collection burden to providers. Since the data source for these
measures is claims data, and is not collected by means of an assessment
instrument, the measure does not increase data collection burden on the
provider as this data is currently collected by providers. We also note
that providers will be given a chance to review their claims-based
measure data via feedback provided in the CASPER system. Despite the
lack of data collection burden, we appreciate the comments that more
education will be required for the public and providers to understand
the claims-based measures and the feedback mechanism. We will be
providing additional training for the reports that are, and will be,
available for providers for reviewing their data.
[[Page 52135]]
Although we did not solicit feedback on the burden associated with
the measures finalized in the FY 2016 IRF PPS final rule (80 FR 47100
through 47120), including functional status measures, which will be
collected via the IRF-PAI Version 1.4 effective October 1, 2016, we
received several comments, which are summarized below.
Comment: Several commenters were concerned that the additional 41.5
minutes required to collect new required data elements finalized in the
FY 2016 IRF PPS final rule, including training staff and updating
medical records, led to increased costs to IRFs that are not covered in
the update to the standard payment conversion factor proposed for IRFs.
One commenter also noted that delays in training led to additional
expenses for preparing staff and electronic health records.
Response: We refer the reader to our discussion of burden due to
data set revisions, data collection, or training of staff due to the
revisions in the IRF-PAI Version 1.4 in the FY 2016 IRF PPS final rule
(80 FR 47086 through 47120). Feedback relating to provider burden will
be taken into account as we consider future updates to the IRF QRP.
With regards to comments about the updated SPCF, we refer readers
to the IRF PPS FY 2016 final rule (80 FR 47129 through 47137) for
details regarding the Collection of Information Requirements and
Regulatory Impact Analysis for the measures finalized in FY 2016. We
would also like to clarify that QRP requirements are not included in
the SPCF, however, per statutory requirements, the applicable annual
increase factor for any IRF that does not submit the required data to
CMS is reduced by 2 percentage points. Additional responses to these
comments are included in sections VI.E and IX. of this final rule.
XII. Regulatory Impact Analysis
A. Statement of Need
This final rule updates the IRF prospective payment rates for FY
2017 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 case-mix groups 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) and (D) of
the Act. Section 1886(j)(3)(C)(ii)(I) of the Act requires the Secretary
to apply a multi-factor productivity 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 through 2019.
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 will revise and update the quality
measures and reporting requirements under the IRF quality reporting
program.
B. Overall Impacts
We have examined the impacts of this final rule as required by
Executive Order 12866 (September 30, 1993, Regulatory Planning and
Review), Executive Order 13563 on Improving Regulation and Regulatory
Review (January 18, 2011), the Regulatory Flexibility Act (September
19, 1980, Pub. L. 96-354) (RFA), section 1102(b) of the Act, section
202 of the Unfunded Mandates Reform Act of 1995 (Pub. L. 104-4),
Executive Order 13132 on Federalism (August 4, 1999), and the
Congressional Review Act (5 U.S.C. 804(2)).
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). Executive
Order 13563 emphasizes the importance of quantifying both costs and
benefits, of reducing costs, of harmonizing rules, and of promoting
flexibility. A regulatory impact analysis (RIA) must be prepared for a
major final rule 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
2017 with those in FY 2016. This analysis results in an estimated $145
million increase for FY 2017 IRF PPS payments. As a result, this final
rule is designated as economically ``significant'' under section
3(f)(1) of Executive Order 12866, and hence a major rule under the
Congressional Review Act. Also, the rule has been reviewed by OMB.
The Regulatory Flexibility Act (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 1,100 IRFs, of which approximately 60
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 22, we estimate that the net
revenue impact of this final rule on all IRFs is to increase estimated
payments by approximately 1.9 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 140
rural units and 11 rural hospitals in our database of 1,133 IRFs for
which data were available.
Section 202 of the Unfunded Mandates Reform Act of 1995 (Pub. L.
104-04, enacted on March 22, 1995) also requires that agencies assess
anticipated costs and benefits before
[[Page 52136]]
issuing any rule whose mandates require spending in any 1 year of $100
million in 1995 dollars, updated annually for inflation. In 2016, that
threshold level is approximately $146 million. This final rule will not
mandate spending costs on state, local, or tribal governments, in the
aggregate, or by the private sector, of greater than $146 million.
Executive Order 13132 establishes certain requirements that an
agency must meet when it promulgates a 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.
C. Detailed Economic Analysis
1. Basis and Methodology of Estimates
This final rule updates to the IRF PPS rates contained in the FY
2016 IRF PPS final rule (80 FR 47036). Specifically, this final rule
updates the CMG relative weights and average length of stay values, the
wage index, and the outlier threshold for high-cost cases. This final
rule applies a MFP adjustment to the FY 2017 IRF market basket increase
factor in accordance with section 1886(j)(3)(C)(ii)(I) of the Act, and
a 0.75 percentage point reduction to the FY 2017 IRF market basket
increase factor in accordance with sections 1886(j)(3)(C)(ii)(II) and
(D)(v) of the Act. Further, this final rule contains revisions to the
IRF quality reporting requirements that are expected to result in some
additional financial effects on IRFs. In addition, section VIII of this
final rule discusses 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, in accordance
with section 1886(j)(7) of the Act.
We estimate that the impact of the changes and updates described in
this final rule will be a net estimated increase of $145 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 XII.C.6. of
this final rule). The impact analysis in Table 22 of this final rule
represents the projected effects of the updates to IRF PPS payments for
FY 2017 compared with the estimated IRF PPS payments in FY 2016. 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, 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 2017, 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 2017 IRF
market basket increase factor in accordance with section
1886(j)(3)(C)(ii)(I) of the Act, and a 0.75 percentage point reduction
to the FY 2017 IRF market basket increase factor in accordance with
sections 1886(j)(3)(C)(ii)(II) and (D)(v) of the Act. We estimate the
total increase in payments to IRFs in FY 2017, relative to FY 2016,
will be approximately $145 million.
This estimate is derived from the application of the FY 2017 IRF
market basket increase factor, as reduced by a productivity adjustment
in accordance with section 1886(j)(3)(C)(ii)(I) of the Act, and a 0.75
percentage point reduction in accordance with sections
1886(j)(3)(C)(ii)(II) and (D)(v) of the Act, which yields an estimated
increase in aggregate payments to IRFs of $125 million. Furthermore,
there is an additional estimated $20 million increase in aggregate
payments to IRFs due to the update of the outlier threshold amount.
Outlier payments are estimated to increase from approximately 2.7
percent in FY 2016 to 3.0 percent in FY 2017. Therefore, summed
together, we estimate that these updates will result in a net increase
in estimated payments of $145 million from FY 2016 to FY 2017.
The effects of the updates that impact IRF PPS payment rates are
shown in Table 22. 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 2.7 percent to 3.0 percent of total estimated
payments for FY 2017, 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 section
1886(j)(3)(A)(i) and sections 1886(j)(3)(C) and (D) of the Act,
including a productivity adjustment in accordance with section
1886(j)(3)(C)(i)(I) of the Act, and a 0.75 percentage point reduction
in accordance with sections 1886(j)(3)(C)(ii)(II) and (D)(v) 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 CMG
relative weights and average length of stay values, under the authority
of section 1886(j)(2)(C)(i) of the Act.
The total change in estimated payments based on the FY
2017 payment changes relative to the estimated FY 2016 payments.
2. Description of Table 22
Table 22 categorizes IRFs by geographic location, including urban
or rural location, and location for CMS's 9 Census divisions (as
defined on the cost report) of the country. In addition, the table
divides IRFs into those that are separate rehabilitation hospitals
(otherwise called freestanding hospitals in this section), those that
are rehabilitation units of a hospital (otherwise called hospital units
in this section), rural or urban facilities, ownership (otherwise
called for-profit, non-profit, and government), by teaching status, and
by disproportionate share patient percentage (DSH PP). The top row of
Table 22 shows the overall impact on the 1,133 IRFs included in the
analysis.
The next 12 rows of Table 22 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 982 IRFs located in
urban areas included in
[[Page 52137]]
our analysis. Among these, there are 730 IRF units of hospitals located
in urban areas and 252 freestanding IRF hospitals located in urban
areas. There are 151 IRFs located in rural areas included in our
analysis. Among these, there are 140 IRF units of hospitals located in
rural areas and 11 freestanding IRF hospitals located in rural areas.
There are 409 for-profit IRFs. Among these, there are 356 IRFs in urban
areas and 53 IRFs in rural areas. There are 653 non-profit IRFs. Among
these, there are 564 urban IRFs and 89 rural IRFs. There are 71
government-owned IRFs. Among these, there are 62 urban IRFs and 9 rural
IRFs.
The remaining four parts of Table 22 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 final rule
to the facility categories listed are shown in the columns of Table 22.
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 2016 analysis file.
Column (3) shows the number of cases in each category in
our FY 2016 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
CMG relative weights and average length of stay 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 2017 to our estimates of payments per discharge in FY 2016.
The average estimated increase for all IRFs is approximately 1.9
percent. This estimated net increase includes the effects of the IRF
market basket increase factor for FY 2017 of 2.7 percent, reduced by a
productivity adjustment of 0.3 percentage point in accordance with
section 1886(j)(3)(C)(ii)(I) of the Act, and further reduced by 0.75
percentage point in accordance with sections 1886(j)(3)(C)(ii)(II) and
(D)(v) of the Act. It also includes the approximate 0.3 percent overall
increase 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.
[[Page 52138]]
[GRAPHIC] [TIFF OMITTED] TR05AU16.010
[[Page 52139]]
3. 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 22.
For the FY 2017 IRF PPS proposed rule, we used preliminary FY 2015
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 2.8 percent in FY 2016 (81 FR 24178, 24193). As we
typically do between the proposed and final rules each year, we updated
our FY 2015 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 estimated
IRF payments are 2.7 percent in FY 2016. Thus, we are adjusting the
outlier threshold amount in this final rule to set total estimated
outlier payments equal to 3 percent of total estimated payments in FY
2017. The estimated change in total IRF payments for FY 2017,
therefore, includes an approximate 0.3 percent increase in payments
because the estimated outlier portion of total payments is estimated to
increase from approximately 2.7 percent to 3 percent.
The impact of this outlier adjustment update (as shown in column 4
of Table 22) is to increase estimated overall payments to IRFs by about
0.3 percent. We estimate the largest increase in payments from the
update to the outlier threshold amount to be 1.4 percent for rural IRFs
in the Pacific region.
4. Impact of the CBSA Wage Index and Labor-Related Share
In column 5 of Table 22, 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.C. of this final rule, we will
decrease the labor-related share from 71.0 percent in FY 2016 to 70.9
percent in FY 2017.
5. Impact of the Update to the CMG Relative Weights and Average Length
of Stay Values
In column 6 of Table 22, we present the effects of the budget-
neutral update of the CMG relative weights and average length of stay
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. The largest estimated
increase in payments is a 0.1 percent increase for rural IRFs in the
Middle Atlantic region, and urban IRFs in the New England and East
North Central regions. Rural IRFs in the Pacific region and urban IRFs
in the East south Central regions are estimated to experience a 0.1
percent decrease in payments due to the CMG relative weights change.
6. Effects of Requirements for the IRF QRP for FY 2018
In accordance with section 1886(j)(7) of the Act, we will implement
a 2 percentage point reduction in the FY 2018 increase factor for IRFs
that have failed to report the required quality reporting data to us
during the most recent IRF quality reporting period. In section VIII.P
of this final rule, we discuss the proposed method for applying the 2
percentage point reduction to IRFs that fail to meet the IRF QRP
requirements. At the time that this analysis was prepared, 91, or
approximately 8 percent, of the 1166 active Medicare-certified IRFs did
not receive the full annual percentage increase for the FY 2016 annual
payment update determination. Information is not available to determine
the precise number of IRFs that will not meet the requirements to
receive the full annual percentage increase for the FY 2017 payment
determination.
In section VIII.L of this final rule, we discuss our proposal to
suspend the previously finalized data accuracy validation policy for
IRFs. While we cannot estimate the change in the number of IRFs that
will meet IRF QRP compliance standards at this time, we believe that
this number will increase due to the temporary suspension of this
policy. Thus, we estimate that the suspension of this policy will
decrease impact on overall IRF payments, by increasing the rate of
compliance, in addition to decreasing the cost of the IRF QRP to each
IRF provider by approximately $47,320 per IRF, which was the estimated
cost to each IRF provider to the implement the previously finalized
policy.
In section VIII.F of this final rule, we are finalizing four
measures for the FY 2018 payment determinations and subsequent years:
(1) MSPB-PAC IRF QRP; (2) Discharge to Community-PAC IRF QRP, and (3)
Potentially Preventable 30-Day Post-Discharge Readmission Measure for
IRF QRP; (4) Potentially Preventable Within Stay Readmission Measure
IRFs. These four measures are Medicare claims-based measures; because
claims-based measures can be calculated based on data that are already
reported to the Medicare program for payment purposes, we believe there
will be no additional impact.
In section VIII.G of this final rule, we are also finalizing one
measure for the FY 2020 payment determination and subsequent years:
Drug Regimen Review Conducted with Follow-Up for Identified Issues-PAC
IRF QRP. Additionally, data for this measure will be collected and
reported using the IRF-PAI (version effective October 1, 2018). While
the reporting of data on quality measures is an information collection,
we believe that the burden associated with modifications to the IRF-PAI
discussed in this final rule fall under the PRA exceptions provided in
1899B(m) of the Act because they are required to achieve the
standardization of patient assessment data. Section 1899B(m) of the Act
provides that the PRA does not apply to section 1899B and the sections
referenced in section 1899B(a)(2)(B) of the Act that require
modification to achieve the standardization of patient assessment data.
The requirement and burden will, however, be submitted to OMB for
review and approval when the modifications to the IRF-PAI or other
applicable PAC assessment instrument are not used to achieve the
standardization of patient assessment data.
The total cost related to the proposed measures is estimated at
$4,625.46 per IRF annually, or $5,231,398.17 for all IRFs annually.
We intend to continue to closely monitor the effects of this new
quality reporting program on IRF providers and help perpetuate
successful reporting outcomes through ongoing stakeholder education,
national trainings, IRF provider announcements, Web site postings, CMS
Open Door Forums, and general and technical help desks.
We did not receive any comments related to the Effects of Proposed
Requirements for the IRF QRP for FY 2018.
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 Thus, we did not consider
alternatives to updating payments using the estimated IRF market basket
[[Page 52140]]
increase factor for FY 2017. However, 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 2017, and sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(v) of
the Act require the Secretary to apply a 0.75 percentage point
reduction to the market basket increase factor for FY 2017. Thus, in
accordance with section 1886(j)(3)(C) of the Act, we update the IRF
federal prospective payments in this final rule by 1.65 percent (which
equals the 2.7 percent estimated IRF market basket increase factor for
FY 2017 reduced by a 0.3 percentage point productivity adjustment as
required by section 1886(j)(3)(C)(ii)(I) of the Act and further reduced
by 0.75 percentage point). We considered maintaining the existing CMG
relative weights and average length of stay values for FY 2017.
However, in light of recently available data and our desire to ensure
that the CMG relative weights and average length of stay values are as
reflective as possible of recent changes in IRF utilization and case
mix, we believe that it is appropriate to update the CMG relative
weights and average length of stay values at this time to ensure that
IRF PPS payments continue to reflect as accurately as possible the
current costs of care in IRFs.
We considered updating facility-level adjustment factors for FY
2017. 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 maintaining the existing outlier threshold amount for
FY 2017. However, analysis of updated FY 2015 data indicates that
estimated outlier payments would be lower than 3 percent of total
estimated payments for FY 2017, by approximately 0.3 percent, unless we
updated the outlier threshold amount. Consequently, we are adjusting
the outlier threshold amount in this final rule to reflect a 0.3
percent increase thereby setting the total outlier payments equal to 3
percent, instead of 2.7 percent, of aggregate estimated payments in FY
2017.
E. Accounting Statement
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 23, we have prepared an accounting statement showing
the classification of the expenditures associated with the provisions
of this final rule. Table 23 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 1,133 IRFs in our
database. In addition, Table 23 presents the costs associated with the
new IRF quality reporting program for FY 2017.
Table 23--Accounting Statement: Classification of Estimated Expenditures
------------------------------------------------------------------------
Category Transfers
------------------------------------------------------------------------
Change in Estimated Transfers from FY 2016 IRF PPS to FY 2017 IRF PPS
------------------------------------------------------------------------
Annualized Monetized Transfers......... $145 million.
From Whom to Whom?..................... Federal Government to IRF
Medicare Providers.
------------------------------------------------------------------------
Category Costs
------------------------------------------------------------------------
FY 2017 Cost to Updating the Quality Reporting Program
------------------------------------------------------------------------
Cost for IRFs to Submit Data for the $5,231,398.17.
Quality Reporting Program.
------------------------------------------------------------------------
F. Conclusion
Overall, the estimated payments per discharge for IRFs in FY 2017
are projected to increase by 1.9 percent, compared with the estimated
payments in FY 2016, as reflected in column 7 of Table 22.
IRF payments per discharge are estimated to increase by 2.0 percent
in urban areas and 1.2 percent in rural areas, compared with estimated
FY 2016 payments. Payments per discharge to rehabilitation units are
estimated to increase 2.2 percent in urban areas and 1.5 percent in
rural areas. Payments per discharge to freestanding rehabilitation
hospitals are estimated to increase 1.8 percent in urban areas and 0.0
percent in rural areas.
Overall, IRFs are estimated to experience a net increase in
payments as a result of the proposed policies in this final rule. The
largest payment increase is estimated to be a 3.1 percent increase for
rural IRFs located in the Pacific region.
In accordance with the provisions of Executive Order 12866, this
final rule 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 continues to read as follows:
Authority: Secs. 1102 and 1871 of the Social Security Act (42
U.S.C. 1302 and 1395hh), sec. 124 of Pub. L. 106-113 (113 Stat.
1501A-332), sec. 1206 of Pub. L. 113-67, and sec. 112 of Pub. L.
113-93.
0
2. Section 412.634 is amended by revising paragraph (c)(2) and adding
paragraph (f) to read as follows:
Sec. 412.634 Requirements under the Inpatient Rehabilitation Facility
(IRF) Quality Reporting Program (QRP).
* * * * *
(c) * * *
(2) An IRF must request an exception or extension within 90 days of
the date that the extraordinary circumstances occurred.
* * * * *
(f) Data Completion Thresholds. (1) IRFs must meet or exceed two
separate data completeness thresholds: One threshold set at 95 percent
for completion of quality measures data collected using the IRF-PAI
submitted through the QIES and a second threshold set at 100 percent
for quality
[[Page 52141]]
measures data collected and submitted using the CDC NHSN.
(2) These thresholds will apply to all measures adopted into IRF
QRP.
(3) An IRF must meet or exceed both thresholds to avoid receiving a
2 percentage point reduction to their annual payment update for a given
fiscal year, beginning with FY 2016 and for all subsequent payment
updates.
Dated: July 18, 2016.
Andrew M. Slavitt,
Acting Administrator, Centers for Medicare & Medicaid Services.
Dated: July 25, 2016.
Sylvia M. Burwell,
Secretary, Department of Health and Human Services.
[FR Doc. 2016-18196 Filed 7-29-16; 4:15 pm]
BILLING CODE 4120-01-P