Medicare Program; Inpatient Rehabilitation Facility (IRF) Prospective Payment System for Federal Fiscal Year 2020 and Updates to the IRF Quality Reporting Program, 17244-17335 [2019-07885]

Agencies

• Centers for Medicare & Medicaid Services
• DEPARTMENT OF HEALTH AND HUMAN SERVICES

[Federal Register Volume 84, Number 79 (Wednesday, April 24, 2019)]
[Proposed Rules]
[Pages 17244-17335]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2019-07885]



[[Page 17243]]

Vol. 84

Wednesday,

No. 79

April 24, 2019

Part II





Department of Health and Human Services





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Centers for Medicare & Medicaid Services





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42 CFR Part 412





Medicare Program; Inpatient Rehabilitation Facility (IRF) Prospective 
Payment System for Federal Fiscal Year 2020 and Updates to the IRF 
Quality Reporting Program; Proposed Rule

Federal Register / Vol. 84 , No. 79 / Wednesday, April 24, 2019 / 
Proposed Rules

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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Medicare & Medicaid Services

42 CFR Part 412

[CMS-1710-P]
RIN 0938-AT67


Medicare Program; Inpatient Rehabilitation Facility (IRF) 
Prospective Payment System for Federal Fiscal Year 2020 and Updates to 
the IRF Quality Reporting Program

AGENCY: Centers for Medicare & Medicaid Services (CMS), HHS.

ACTION: Proposed rule.

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SUMMARY: This proposed rule would update the prospective payment rates 
for inpatient rehabilitation facilities (IRFs) for federal fiscal year 
(FY) 2020. As required by the Social Security Act (the Act), this 
proposed rule includes the classification and weighting factors for the 
IRF prospective payment system's (PPS) case-mix groups (CMGs) and a 
description of the methodologies and data used in computing the 
prospective payment rates for FY 2020. We are proposing to rebase and 
revise the IRF market basket to reflect a 2016 base year rather than 
the current 2012 base year. Additionally, we are proposing to replace 
the previously finalized unweighted motor score with a weighted motor 
score to assign patients to CMGs and remove one item from the score 
beginning with FY 2020 and to revise the CMGs and update the CMG 
relative weights and average length of stay values beginning with FY 
2020, based on analysis of 2 years of data (FY 2017 and FY 2018). We 
are proposing to update the IRF wage index to use the concurrent FY 
inpatient prospective payment system (IPPS) wage index beginning with 
FY 2020. We are soliciting comments on stakeholder concerns regarding 
the appropriateness of the wage index used to adjust IRF payments. We 
are proposing to amend the regulations to clarify that the 
determination as to whether a physician qualifies as a rehabilitation 
physician (that is, a licensed physician with specialized training and 
experience in inpatient rehabilitation) is made by the IRF. For the IRF 
Quality Reporting Program (QRP), we are proposing to adopt two new 
measures, modify an existing measure, and adopt new standardized 
patient assessment data elements. We also propose to expand data 
collection to all patients, regardless of payer, as well as proposing 
updates related to the system used for the submission of data and 
related regulation text.

DATES: To be assured consideration, comments must be received at one of 
the addresses provided below, not later than 5 p.m. on June 17, 2019.

ADDRESSES: In commenting, please refer to file code CMS-1710-P. Because 
of staff and resource limitations, we cannot accept comments by 
facsimile (FAX) transmission.
    Comments, including mass comment submissions, must be submitted in 
one of the following three ways (please choose only one of the ways 
listed):
    1. Electronically. You may submit electronic comments on this 
regulation to https://www.regulations.gov. Follow the ``Submit a 
comment'' instructions.
    2. By regular mail. You may mail written comments to the following 
address ONLY: Centers for Medicare & Medicaid Services, Department of 
Health and Human Services, Attention: CMS-1710-P, P.O. Box 8016, 
Baltimore, MD 21244-8016.
    Please allow sufficient time for mailed comments to be received 
before the close of the comment period.
    3. By express or overnight mail. You may send written comments to 
the following address ONLY: Centers for Medicare & Medicaid Services, 
Department of Health and Human Services, Attention: CMS-1710-P, Mail 
Stop C4-26-05, 7500 Security Boulevard, Baltimore, MD 21244-1850.
    For information on viewing public comments, see the beginning of 
the SUPPLEMENTARY INFORMATION section.

FOR FURTHER INFORMATION CONTACT: 
    Gwendolyn Johnson, (410) 786-6954, for general information.
    Catie Kraemer, (410) 786-0179, for information about the IRF 
payment policies and payment rates.
    Kadie Derby, (410) 786-0468, for information about the IRF coverage 
policies.
    Kate Brooks, (410) 786-7877, for information about the IRF quality 
reporting program.

SUPPLEMENTARY INFORMATION: The IRF PPS Addenda along with other 
supporting documents and tables referenced in this proposed rule are 
available through the internet on the CMS website at https://www.cms.hhs.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/.

Executive Summary

A. Purpose

    This proposed rule would update the prospective payment rates for 
IRFs for FY 2020 (that is, for discharges occurring on or after October 
1, 2019, and on or before September 30, 2020) as required under section 
1886(j)(3)(C) of the Act. As required by section 1886(j)(5) of the Act, 
this proposed 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 2020. This proposed rule would also rebase and revise the IRF 
market basket to reflect a 2016 base year, rather than the current 2012 
base year. Additionally, this proposed rule proposes to replace the 
previously finalized unweighted motor score with a weighted motor score 
to assign patients to CMGs and remove one item from the score beginning 
in FY 2020 and to revise the CMGs and update the CMG relative weights 
and average length of stay values beginning with FY 2020, based on 
analysis of 2 years of data (FY 2017 and FY 2018). We are also 
proposing to update the IRF wage index to use the concurrent IPPS wage 
index for the IRF PPS beginning with FY 2020. We are also soliciting 
comments on stakeholder concerns regarding the appropriateness of the 
wage index used to adjust IRF payments. We are also proposing to amend 
the regulations at Sec.  412.622 to clarify that the determination as 
to whether a physician qualifies as a rehabilitation physician (that 
is, a licensed physician with specialized training and experience in 
inpatient rehabilitation) is made by the IRF. For the IRF Quality 
Reporting Program (QRP), we are proposing to adopt two new measures, 
modify an existing measure, and adopt new standardized patient 
assessment data elements. We also propose to expand data collection to 
all patients, regardless of payer, as well as proposing updates related 
to the system used for the submission of data and related regulation 
text.

B. Summary of Major Provisions

    In this proposed rule, we use the methods described in the FY 2019 
IRF PPS final rule (83 FR 38514) to update the prospective payment 
rates for FY 2020 using updated FY 2018 IRF claims and the most recent 
available IRF cost report data, which is FY 2017 IRF cost report data. 
This proposed rule also proposes to rebase and revise the IRF market 
basket to reflect a 2016 base year rather than the current 2012 base 
year. Additionally, this proposed rule proposes to replace the 
previously finalized unweighted motor score with a weighted motor score 
to assign patients to CMGs and remove one item

[[Page 17245]]

from the score beginning with FY 2020 and to revise the CMGs and update 
the CMG relative weights and average length of stay values beginning 
with FY 2020, based on analysis of 2 years of data (FY 2017 and FY 
2018). We are also proposing to use the concurrent IPPS wage index for 
the IRF PPS beginning in FY 2020. We are also soliciting comments on 
stakeholder concerns regarding the appropriateness of the wage index 
used to adjust IRF payments. We are also proposing to amend the 
regulations at Sec.  412.622 to clarify that the determination as to 
whether a physician qualifies as a rehabilitation physician (that is, a 
licensed physician with specialized training and experience in 
inpatient rehabilitation) is made by the IRF. We are also proposing to 
update requirements for the IRF QRP.

C. Summary of Impacts
[GRAPHIC] [TIFF OMITTED] TP24AP19.000

I. Background

A. Historical Overview of the IRF PPS

    Section 1886(j) of the Act provides for the implementation of a 
per-discharge PPS for inpatient rehabilitation hospitals and inpatient 
rehabilitation units of a hospital (collectively, hereinafter referred 
to as IRFs). Payments under the IRF PPS encompass inpatient operating 
and capital costs of furnishing covered rehabilitation services (that 
is, routine, ancillary, and capital costs), but not direct graduate 
medical education costs, costs of approved nursing and allied health 
education activities, bad debts, and other services or items outside 
the scope of the IRF PPS. Although a complete discussion of the IRF PPS 
provisions appears in the original FY 2002 IRF PPS final rule (66 FR 
41316) and the FY 2006 IRF PPS final rule (70 FR 47880), we are 
providing a general description of the IRF PPS for FYs 2002 through 
2019.
    Under the IRF PPS from FY 2002 through FY 2005, the prospective 
payment rates were computed across 100 distinct CMGs, as described in 
the FY 2002 IRF PPS final rule (66 FR 41316). We constructed 95 CMGs 
using rehabilitation impairment categories (RICs), functional status 
(both motor and cognitive), and age (in some cases, cognitive status 
and age may not be a factor in defining a CMG). In addition, we 
constructed five special CMGs to account for very short stays and for 
patients who expire in the IRF.
    For each of the CMGs, we developed relative weighting factors to 
account for a patient's clinical characteristics and expected resource 
needs. Thus, the weighting factors accounted for the relative 
difference in resource use across all CMGs. Within each CMG, we created 
tiers based on the estimated effects that certain comorbidities would 
have on resource use.
    We established the federal PPS rates using a standardized payment 
conversion factor (formerly referred to as the budget-neutral 
conversion factor). For a detailed discussion of the budget-neutral 
conversion factor, please refer to our FY 2004 IRF PPS final rule (68 
FR 45684 through 45685). In the FY 2006 IRF PPS final rule (70 FR 
47880), we discussed in detail the methodology for determining the 
standard payment conversion factor.
    We applied the relative weighting factors to the standard payment 
conversion factor to compute the unadjusted prospective payment rates 
under the IRF PPS from FYs 2002 through 2005. Within the structure of 
the payment system, we then made adjustments to account for interrupted 
stays, transfers, short stays, and deaths. Finally, we applied the 
applicable adjustments to account for geographic variations in wages 
(wage index), the percentage of low-income patients, location in a 
rural area (if applicable), and outlier payments (if applicable) to the 
IRFs' unadjusted prospective payment rates.
    For cost reporting periods that began on or after January 1, 2002, 
and before October 1, 2002, we determined the final prospective payment 
amounts using the transition methodology prescribed in section 
1886(j)(1) of the Act. Under this provision, IRFs transitioning into 
the PPS were paid a blend of the federal IRF PPS rate and the payment 
that the IRFs would have received had the IRF PPS not been implemented. 
This provision also allowed IRFs to elect to bypass this blended 
payment and immediately be paid 100 percent of the federal IRF PPS 
rate. The transition methodology expired as of cost reporting periods 
beginning on or after October 1, 2002 (FY 2003), and payments for all 
IRFs now consist of 100 percent of the federal IRF PPS rate.
    Section 1886(j) of the Act confers broad statutory authority upon 
the Secretary to propose refinements to the IRF PPS. In the FY 2006 IRF 
PPS final rule (70 FR 47880) and in correcting amendments to the FY 
2006 IRF PPS final rule (70 FR 57166), we finalized a number of 
refinements to the IRF PPS case-mix classification system (the CMGs and 
the corresponding relative weights) and the case-level and facility-
level adjustments. These refinements included the adoption of the 
Office of Management and Budget's (OMB) Core-Based Statistical Area 
(CBSA) market definitions; modifications to the CMGs, tier 
comorbidities; and CMG relative weights, implementation of a new 
teaching status adjustment for IRFs; rebasing and revising the market 
basket index used to update IRF payments, and updates to the rural, 
low-income percentage (LIP), and high-cost outlier adjustments. 
Beginning with the FY 2006 IRF PPS final rule (70 FR 47908 through 
47917), the market basket index used to update IRF payments was a 
market basket reflecting the operating and capital cost structures for 
freestanding IRFs, freestanding inpatient psychiatric facilities 
(IPFs), and long-term care hospitals (LTCHs) (hereinafter referred to 
as the rehabilitation, psychiatric, and long-term care (RPL) market 
basket). Any reference to the FY 2006 IRF PPS final rule in this 
proposed rule also includes the provisions effective in the correcting 
amendments. For a detailed discussion of the final key policy changes 
for FY 2006, please refer to the FY 2006 IRF PPS final rule.
    In the FY 2007 IRF PPS final rule (71 FR 48354), we further refined 
the IRF PPS case-mix classification system (the

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CMG relative weights) and the case-level adjustments, to ensure that 
IRF PPS payments would continue to reflect as accurately as possible 
the costs of care. For a detailed discussion of the FY 2007 policy 
revisions, please refer to the FY 2007 IRF PPS final rule.
    In the FY 2008 IRF PPS final rule (72 FR 44284), we updated the 
prospective payment rates and the outlier threshold, revised the IRF 
wage index policy, and clarified how we determine high-cost outlier 
payments for transfer cases. For more information on the policy changes 
implemented for FY 2008, please refer to the FY 2008 IRF PPS final 
rule.
    After publication of the FY 2008 IRF PPS final rule (72 FR 44284), 
section 115 of the Medicare, Medicaid, and SCHIP Extension Act of 2007 
(Pub. L. 110-173, enacted 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 prospective payment 
rates for each FY. Based on the legislative change to the increase 
factor, we revised the FY 2008 prospective payment rates for IRF 
discharges occurring on or after April 1, 2008. Thus, the final FY 2008 
IRF prospective payment rates that were published in the FY 2008 IRF 
PPS final rule (72 FR 44284) were effective for discharges occurring on 
or after October 1, 2007, and on or before March 31, 2008, and the 
revised FY 2008 IRF prospective payment rates were effective for 
discharges occurring on or after April 1, 2008, and on or before 
September 30, 2008. The revised FY 2008 prospective payment rates are 
available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Data-Files.html.
    In the FY 2009 IRF PPS final rule (73 FR 46370), we updated the CMG 
relative weights, the average 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.
    In the FY 2010 IRF PPS final rule (74 FR 39762) and in correcting 
amendments to the FY 2010 IRF PPS final rule (74 FR 50712), we updated 
the prospective payment rates, the CMG relative weights, the average 
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 proposed rule 
also includes the provisions effective in the correcting amendments. 
For more information on the policy changes implemented for FY 2010, 
please refer to the FY 2010 IRF PPS final rule.
    After publication of the FY 2010 IRF PPS final rule (74 FR 39762), 
section 3401(d) of the Patient Protection and Affordable Care Act (Pub. 
L. 111-148, enacted 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 ``PPACA''), amended section 
1886(j)(3)(C) of the Act and added section 1886(j)(3)(D) of the Act. 
Section 1886(j)(3)(C) of the Act requires the Secretary to estimate a 
multifactor productivity (MFP) adjustment to the market basket increase 
factor, and to apply other adjustments as defined by the Act. The 
productivity adjustment applies to FYs from 2012 forward. The other 
adjustments apply to FYs 2010 to 2019.
    Sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(i) of the Act 
defined the adjustments that were to be applied to the market basket 
increase factors in FYs 2010 and 2011. Under these provisions, the 
Secretary was required to reduce the market basket increase factor in 
FY 2010 by a 0.25 percentage point adjustment. Notwithstanding this 
provision, in accordance with section 3401(p) of the PPACA, the 
adjusted FY 2010 rate was only to be applied to discharges occurring on 
or after April 1, 2010. Based on the self-implementing legislative 
changes to section 1886(j)(3) of the Act, we adjusted the FY 2010 
prospective payment rates as required, and applied these rates to IRF 
discharges occurring on or after April 1, 2010, and on or before 
September 30, 2010. Thus, the final FY 2010 IRF prospective payment 
rates that were published in the FY 2010 IRF PPS final rule (74 FR 
39762) were used for discharges occurring on or after October 1, 2009, 
and on or before March 31, 2010, and the adjusted FY 2010 IRF 
prospective payment rates applied to discharges occurring on or after 
April 1, 2010, and on or before September 30, 2010. The adjusted FY 
2010 prospective payment rates are available on the CMS website at 
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
    In addition, sections 1886(j)(3)(C) and (D) of the Act also 
affected the FY 2010 IRF outlier threshold amount because they required 
an adjustment to the FY 2010 RPL market basket increase factor, which 
changed the standard payment conversion factor for FY 2010. 
Specifically, the original FY 2010 IRF outlier threshold amount was 
determined based on the original estimated FY 2010 RPL market basket 
increase factor of 2.5 percent and the standard payment conversion 
factor of $13,661. However, as adjusted, the IRF prospective payments 
were based on the adjusted RPL market basket increase factor of 2.25 
percent and the revised standard payment conversion factor of $13,627. 
To maintain estimated outlier payments for FY 2010 equal to the 
established standard of 3 percent of total estimated IRF PPS payments 
for FY 2010, we revised the IRF outlier threshold amount for FY 2010 
for discharges occurring on or after April 1, 2010, and on or before 
September 30, 2010. The revised IRF outlier threshold amount for FY 
2010 was $10,721.
    Sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(i) of the Act also 
required the Secretary to reduce the market basket increase factor in 
FY 2011 by a 0.25 percentage point adjustment. The FY 2011 IRF PPS 
notice (75 FR 42836) and the correcting amendments to the FY 2011 IRF 
PPS notice (75 FR 70013) described the required adjustments to the FY 
2010 and FY 2011 IRF PPS prospective payment rates and outlier 
threshold amount for IRF discharges occurring on or after April 1, 
2010, and on or before September 30, 2011. It also updated the FY 2011 
prospective payment rates, the CMG relative weights, and the average 
length of stay values. Any reference to the FY 2011 IRF PPS notice in 
this proposed 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.

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    In the FY 2012 IRF PPS final rule (76 FR 47836), we updated the IRF 
prospective payment rates, rebased and revised the RPL market basket, 
and established a new QRP for IRFs in accordance with section 
1886(j)(7) of the Act. We also consolidated, clarified, and revised 
existing policies regarding IRF hospitals and IRF units of hospitals to 
eliminate unnecessary confusion and enhance consistency. For more 
information on the policy changes implemented for FY 2012, please refer 
to the FY 2012 IRF PPS final rule.
    The FY 2013 IRF PPS notice (77 FR 44618) described the required 
adjustments to the FY 2013 prospective payment rates and outlier 
threshold amount for IRF discharges occurring on or after October 1, 
2012, and on or before September 30, 2013. It also updated the FY 2013 
prospective payment rates, the CMG relative weights, and the average 
length of stay values. For more information on the updates for FY 2013, 
please refer to the FY 2013 IRF PPS notice.
    In the FY 2014 IRF PPS final rule (78 FR 47860), we updated the 
prospective payment rates, the CMG relative weights, and the outlier 
threshold amount. We also updated the facility-level adjustment factors 
using an enhanced estimation methodology, revised the list of diagnosis 
codes that count toward an IRF's 60 percent rule compliance calculation 
to determine ``presumptive compliance,'' revised sections of the 
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 updated requirements for the IRF QRP. For more 
information on the policy changes implemented for FY 2014, please refer 
to the FY 2014 IRF PPS final rule.
    In the FY 2015 IRF PPS final rule (79 FR 45872) and the correcting 
amendments to the FY 2015 IRF PPS final rule (79 FR 59121), we updated 
the prospective payment rates, the CMG relative weights, and the 
outlier threshold amount. We also revised the list of diagnosis codes 
that count toward an IRF's 60 percent rule compliance calculation to 
determine ``presumptive compliance,'' revised sections of the IRF-PAI, 
and updated requirements for the IRF QRP. Any reference to the FY 2015 
IRF PPS final rule in this proposed rule also includes the provisions 
effective in the correcting amendments. For more information on the 
policy changes implemented for FY 2015, please refer to the FY 2015 IRF 
PPS final rule.
    In the FY 2016 IRF PPS final rule (80 FR 47036), we updated the 
prospective payment rates, the CMG relative weights, and the outlier 
threshold amount. We also adopted an IRF-specific market basket that 
reflects the cost structures of only IRF providers, a blended 1-year 
transition wage index based on the adoption of new OMB area 
delineations, a 3-year phase-out of the rural adjustment for certain 
IRFs due to the new OMB area delineations, and updates for the IRF QRP. 
For more information on the policy changes implemented for FY 2016, 
please refer to the FY 2016 IRF PPS final rule.
    In the FY 2017 IRF PPS final rule (81 FR 52056) and the correcting 
amendments to the FY 2017 IRF PPS final rule (81 FR 59901), we updated 
the prospective payment rates, the CMG relative weights, and the 
outlier threshold amount. We also updated requirements for the IRF QRP. 
Any reference to the FY 2017 IRF PPS final rule in this proposed rule 
also includes the provisions effective in the correcting amendments. 
For more information on the policy changes implemented for FY 2017, 
please refer to the FY 2017 IRF PPS final rule.
    In the FY 2018 IRF PPS final rule (82 FR 36238), we updated the 
prospective payment rates, the CMG relative weights, and the outlier 
threshold amount. We also revised the International Classification of 
Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis 
codes that are used to determine presumptive compliance under the ``60 
percent rule,'' removed the 25 percent payment penalty for IRF-PAI late 
transmissions, removed the voluntary swallowing status item (Item 27) 
from the IRF-PAI, summarized comments regarding the criteria used to 
classify facilities for payment under the IRF PPS, provided for a 
subregulatory process for certain annual updates to the presumptive 
methodology diagnosis code lists, adopted the use of height/weight 
items on the IRF-PAI to determine patient body mass index (BMI) greater 
than 50 for cases of single-joint replacement under the presumptive 
methodology, and updated requirements for the IRF QRP. For more 
information on the policy changes implemented for FY 2018, please refer 
to the FY 2018 IRF PPS final rule.
    In the FY 2019 IRF PPS final rule (83 FR 38514), we updated the 
prospective payment rates, the CMG relative weights, and the outlier 
threshold amount. We also alleviated administrative burden for IRFs by 
removing the FIMTM instrument and associated Function 
Modifiers from the IRF-PAI beginning in FY 2020 and revised certain IRF 
coverage requirements to reduce the amount of required paperwork in the 
IRF setting beginning in FY 2019. Additionally, we incorporated certain 
data items located in the Quality Indicators section of the IRF-PAI 
into the IRF case-mix classification system using analysis of 2 years 
of data (FY 2017 and FY 2018) beginning in FY 2020. For the IRF QRP, we 
adopted a new measure removal factor, removed two measures from the IRF 
QRP measure set, and codified a number of program requirements in our 
regulations. For more information on the policy changes implemented for 
FY 2019, please refer to the FY 2019 IRF PPS final rule.

B. Provisions of the PPACA Affecting the IRF PPS in FY 2012 and Beyond

    The PPACA included several provisions that affect the IRF PPS in 
FYs 2012 and beyond. In addition to what was previously discussed, 
section 3401(d) of the PPACA also added section 1886(j)(3)(C)(ii)(I) of 
the Act (providing for a ``productivity adjustment'' for fiscal year 
2012 and each subsequent fiscal year). The productivity adjustment for 
FY 2020 is discussed in section V.D. of this proposed rule. Section 
1886(j)(3)(C)(ii)(II) of the Act provides that the application of the 
productivity adjustment to the market basket update may result in an 
update that is less than 0.0 for a fiscal year and in payment rates for 
a fiscal year being less than such payment rates for the preceding 
fiscal year.
    Sections 3004(b) of the PPACA and section 411(b) of the Medicare 
Access and CHIP Reauthorization Act of 2015 (Pub. L. 114-10, enacted on 
April 16, 2015) (MACRA) also addressed the IRF PPS. Section 3004(b) of 
PPACA reassigned the previously designated section 1886(j)(7) of the 
Act to section 1886(j)(8) of the Act and inserted a new section 
1886(j)(7) of the Act, which contains requirements for the Secretary to 
establish a QRP for IRFs. Under that program, data must be submitted in 
a form and manner and at a time specified by the Secretary. Beginning 
in FY 2014, section 1886(j)(7)(A)(i) of the Act requires the 
application of a 2 percentage point reduction to the market basket 
increase factor otherwise applicable to an IRF (after application of 
subparagraphs (C)(iii) and (D) of section 1886(j)(3) of the Act) for a 
fiscal year if the IRF does not comply with the requirements of the IRF 
QRP for that fiscal year. Application of the 2

[[Page 17248]]

percentage point reduction may result in an update that is less than 
0.0 for a fiscal year and in payment rates for a fiscal year being less 
than such payment rates for the preceding fiscal year. Reporting-based 
reductions to the market basket increase factor are not cumulative; 
they only apply for the FY involved. Section 411(b) of MACRA amended 
section 1886(j)(3)(C) of the Act by adding clause (iii), which required 
us to apply for FY 2018, after the application of section 
1886(j)(3)(C)(ii) of the Act, an increase factor of 1.0 percent to 
update the IRF prospective payment rates.

C. Operational Overview of the Current IRF PPS

    As described in the FY 2002 IRF PPS final rule (66 FR 41316), upon 
the admission and discharge of a Medicare Part A Fee-for-Service (FFS) 
patient, the IRF is required to complete the appropriate sections of a 
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) patient, as described in the FY 2010 IRF PPS final rule 
(74 FR 39762 and 74 FR 50712). All required data must be electronically 
encoded into the IRF-PAI software product. Generally, the software 
product includes patient classification programming called the Grouper 
software. The Grouper software uses specific IRF-PAI data elements to 
classify (or group) patients into distinct CMGs and account for the 
existence of any relevant comorbidities.
    The Grouper software produces a five-character CMG number. The 
first character is an alphabetic character that indicates the 
comorbidity tier. The last four characters are numeric characters that 
represent the distinct CMG number. Free downloads of the Inpatient 
Rehabilitation Validation and Entry (IRVEN) software product, including 
the Grouper software, are available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Software.html.
    Once a Medicare Part A FFS patient is discharged, the IRF submits a 
Medicare claim as a Health Insurance Portability and Accountability Act 
of 1996 (Pub. L. 104-191, enacted 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 MA patient is 
discharged, in accordance with the Medicare Claims Processing Manual, 
chapter 3, section 20.3 (Pub. L. 100-04), hospitals (including IRFs) 
must submit an informational-only bill (Type of Bill (TOB) 111), which 
includes Condition Code 04 to their MAC. This will ensure that the MA 
days are included in the hospital's Supplemental Security Income (SSI) 
ratio (used in calculating the IRF LIP adjustment) for fiscal year 2007 
and beyond. Claims submitted to Medicare must comply with both ASCA and 
HIPAA.
    Section 3 of the ASCA amended section 1862(a) of the Act by adding 
paragraph (22), which requires the Medicare program, subject to section 
1862(h) of the Act, to deny payment under Part A or Part B for any 
expenses for items or services for which a claim is submitted other 
than in an electronic form specified by the Secretary. Section 1862(h) 
of the Act, in turn, provides that the Secretary shall waive such 
denial in situations in which there is no method available for the 
submission of claims in an electronic form or the entity submitting the 
claim is a small provider. In addition, the Secretary also has the 
authority to waive such denial in such unusual cases as the Secretary 
finds appropriate. For more information, see the ``Medicare Program; 
Electronic Submission of Medicare Claims'' final rule (70 FR 71008). 
Our instructions for the limited number of Medicare claims submitted on 
paper are available at https://www.cms.gov/manuals/downloads/clm104c25.pdf.
    Section 3 of the ASCA operates in the context of the administrative 
simplification provisions of HIPAA, which include, among others, the 
requirements for transaction standards and code sets codified in 45 CFR 
part 160 and part 162, subparts A and I through R (generally known as 
the Transactions Rule). The Transactions Rule requires covered 
entities, including covered health care providers, to conduct covered 
electronic transactions according to the applicable transaction 
standards. (See the CMS program claim memoranda at https://www.cms.gov/ElectronicBillingEDITrans/ and listed in the addenda to the Medicare 
Intermediary Manual, Part 3, section 3600).
    The MAC processes the claim through its software system. This 
software system includes pricing programming called the ``Pricer'' 
software. The Pricer software uses the CMG number, along with other 
specific claim data elements and provider-specific data, to adjust the 
IRF's prospective payment for interrupted stays, transfers, short 
stays, and deaths, and then applies the applicable adjustments to 
account for the IRF's wage index, percentage of low-income patients, 
rural location, and outlier payments. For discharges occurring on or 
after October 1, 2005, the IRF PPS payment also reflects the teaching 
status adjustment that became effective as of FY 2006, as discussed in 
the FY 2006 IRF PPS final rule (70 FR 47880).

D. Advancing Health Information Exchange

    The Department of Health and Human Services (HHS) has a number of 
initiatives designed to encourage and support the adoption of 
interoperable health information technology and to promote nationwide 
health information exchange to improve health care. The Office of the 
National Coordinator for Health Information Technology (ONC) and CMS 
work collaboratively to advance interoperability across settings of 
care, including post-acute care.
    To further interoperability in post-acute care, we developed a Data 
Element Library (DEL) to serve as a publicly-available centralized, 
authoritative resource for standardized data elements and their 
associated mappings to health IT standards. The DEL furthers CMS' goal 
of data standardization and interoperability, which is also a goal of 
the Improving Medicare Post-Acute Care Transformation Act of 2014 
(IMPACT Act). These interoperable data elements can reduce provider 
burden by allowing the use and exchange of healthcare data, support 
provider exchange of electronic health information for care 
coordination, person-centered care, and support real-time, data driven, 
clinical decision making. Standards in the Data Element Library 
(https://del.cms.gov/) can be referenced on the CMS website and in the 
ONC Interoperability Standards Advisory (ISA). The 2019 ISA is 
available at https://www.healthit.gov/isa.
    The 21st Century Cures Act (Pub. L. 114-255, enacted on December 
13, 2016) (Cures Act), requires HHS to take new steps to enable the 
electronic sharing of health information ensuring interoperability for 
providers and settings across the care continuum. In another important 
provision, Congress defined ``information blocking'' as practices 
likely to interfere with, prevent, or materially discourage access, 
exchange, or use of electronic health

[[Page 17249]]

information, and established new authority for HHS to discourage these 
practices. In March 2019, ONC and CMS published the proposed rules, 
``21st Century Cures Act: Interoperability, Information Blocking, and 
the ONC Health IT Certification Program,'' (84 FR 7424) and 
``Interoperability and Patient Access'' (84 FR 7610) to promote secure 
and more immediate access to health information for patients and 
healthcare providers through the implementation of information blocking 
provisions of the Cures Act and the use of standardized application 
programming interfaces (APIs) that enable easier access to electronic 
health information. These two proposed rules are open for public 
comment at www.regulations.gov. We invite providers to learn more about 
these important developments and how they are likely to affect IRFs.

II. Summary of Provisions of the Proposed Rule

    In this proposed rule, we propose to update the IRF prospective 
payment rates for FY 2020 and to rebase and revise the IRF market 
basket to reflect a 2016 base year rather than the current 2012 base 
year. We are also proposing to replace the previously finalized 
unweighted motor score with a weighted motor score to assign patients 
to CMGs and remove one item from the score beginning with FY 2020 and 
to revise the CMGs and update the CMG relative weights and average 
length of stay values beginning with FY 2020, based on analysis of 2 
years of data (FY 2017 and FY 2018). We are also proposing to use the 
concurrent IPPS wage index for the IRF PPS beginning with FY 2020. We 
are also soliciting comments on stakeholder concerns regarding the 
appropriateness of the wage index used to adjust IRF payments. We are 
proposing to amend the regulations at Sec.  412.622 to clarify that the 
determination as to whether a physician qualifies as a rehabilitation 
physician (that is, a licensed physician with specialized training and 
experience in inpatient rehabilitation) is made by the IRF.
    The proposed policy changes and updates to the IRF prospective 
payment rates for FY 2020 are as follows:
     Describe a proposed weighted motor score to replace the 
previously finalized unweighted motor score to assign a patient to a 
CMG, the removal of one item from the score, and revisions to the CMGs 
beginning on October 1, 2019, based on analysis of 2 years of data (FY 
2017 and FY 2018) using the Quality Indicator items in the IRF-PAI. 
This includes proposed revisions to the CMG relative weights and 
average length of stay values for FY 2020, in a budget neutral manner, 
as discussed in section III. of this proposed rule.
     Describe the proposed rebased and revised IRF market 
basket to reflect a 2016 base year rather than the current 2012 base 
year as discussed in section V. of this proposed rule.
     Update the IRF PPS payment rates for FY 2020 by the 
proposed market basket increase factor, based upon the most current 
data available, with a proposed productivity adjustment required by 
section 1886(j)(3)(C)(ii)(I) of the Act, as described in section V. of 
this proposed rule.
     Describe the proposed update to the IRF wage index to use 
the concurrent IPPS wage index and the FY 2020 proposed labor-related 
share in a budget-neutral manner, as described in section V. of this 
proposed rule.
     Describe the continued use of FY 2014 facility-level 
adjustment factors, as discussed in section IV. of this proposed rule.
     Describe the calculation of the IRF standard payment 
conversion factor for FY 2020, as discussed in section V. of this 
proposed rule.
     Update the outlier threshold amount for FY 2020, as 
discussed in section VI. of this proposed rule.
     Update the cost-to-charge ratio (CCR) ceiling and urban/
rural average CCRs for FY 2020, as discussed in section VI. of this 
proposed rule.
     Describe the proposed amendments to the regulations at 
Sec.  412.622 to clarify that the determination as to whether a 
physician qualifies as a rehabilitation physician (that is, a licensed 
physician with specialized training and experience in inpatient 
rehabilitation) is made by the IRF, as discussed in section VII. of 
this proposed rule.
     Updates to the requirements for the IRF QRP, as discussed 
in section VIII. of this proposed rule.

III. Proposed Refinements to the Case-Mix Classification System 
Beginning With FY 2020

A. Background

    Section 1886(j)(2)(A) of the Act requires the Secretary to 
establish case-mix groups for payment under the IRF PPS and a method of 
classifying specific IRF patients within these groups. Under section 
1886(j)(2)(B) of the Act, the Secretary must assign each case-mix group 
an appropriate weighting factor that reflects the relative facility 
resources used for patients classified within the group as compared to 
patients classified within other groups. Additionally, section 
1886(j)(2)(C)(i) of the Act requires the Secretary from time to time to 
adjust the established classifications and weighting factors as 
appropriate to reflect changes in treatment patterns, technology, case-
mix, number of payment units for which payment is made under title 
XVIII of the Act, and other factors which may affect the relative use 
of resources. Such adjustments must be made in a manner so that changes 
in aggregate payments under the classification system are a result of 
real changes and are not a result of changes in coding that are 
unrelated to real changes in case mix.
    In the FY 2019 IRF PPS final rule (83 FR 38533 through 38549), we 
finalized the removal of the Functional Independence Measure 
(FIMTM) instrument and associated Function Modifiers from 
the IRF-PAI and the incorporation of an unweighted additive motor score 
derived from 19 data items located in the Quality Indicators section of 
the IRF-PAI beginning with FY 2020 (83 FR 38535 through 38536, 38549). 
As discussed in section III.B of this proposed rule, based on further 
analysis to examine the potential impact of weighting the motor score, 
we are proposing to replace the previously finalized unweighted motor 
score with a weighted motor score and remove one item from the score 
beginning with FY 2020.
    Additionally, as noted in the FY 2019 IRF PPS final rule (83 FR 
38534), the incorporation of the data items from the Quality Indicator 
section of the IRF-PAI into the IRF case-mix classification system 
necessitates revisions to the CMGs to ensure that IRF payments are 
calculated accurately. We finalized the use of data items from the 
Quality Indicators section of the IRF-PAI to construct the functional 
status scores used to classify IRF patients in the IRF case-mix 
classification system for purposes of establishing payment under the 
IRF PPS beginning with FY 2020, but modified our proposal based on 
public comments to incorporate two years of data (FYs 2017 and 2018) 
into our analyses used to revise the CMG definitions (83 FR 38549). We 
stated that any changes to the proposed CMG definitions resulting from 
the incorporation of an additional year of data (FY 2018) into the 
analysis would be addressed in future rulemaking prior to their 
implementation beginning in FY 2020. As discussed in section III.C of 
this proposed rule, we are proposing to revise the CMGs based on 
analysis of 2 years of data (FYs 2017 and 2018) beginning with FY 2020. 
We are also proposing to update the relative weights and average length 
of stay values

[[Page 17250]]

associated with the revised CMGs beginning with FY 2020.

B. Proposed Use of a Weighted Motor Score Beginning With FY 2020

    As noted in the FY 2019 IRF PPS final rule (83 FR 38535), the IRF 
case-mix classification system currently uses a weighted motor score 
based on FIMTM data items to assign patients to CMGs under 
the IRF PPS through FY 2019. More information on the development and 
implementation of this motor score can be found in the FY 2006 IRF PPS 
final rule (70 FR 47896 through 47900). In the FY 2019 IRF PPS final 
rule (83 FR 38535 through 38536, 38549), we finalized the incorporation 
of an unweighted additive motor score derived from 19 data items 
located in the Quality Indicators section of the IRF-PAI beginning with 
FY 2020. We did not propose a weighted motor score at the time, because 
we believed that the unweighted motor score would facilitate greater 
understanding among the provider community, as it is less complex. 
However, we also noted that we would take comments in favor of a 
weighted motor score into consideration in future analysis. In response 
to feedback we received from various stakeholders and professional 
organizations regarding the use of an unweighted motor score and 
requesting that we consider weighting the motor score, we extended our 
contract with Research Triangle Institute, International (RTI) to 
examine the potential impact of weighting the motor score. Based on 
this analysis, discussed further below, we now believe that a weighted 
motor score would improve the accuracy of payments to IRFs, and we are 
proposing to replace the previously finalized unweighted motor score 
with a weighted motor score to assign patients to CMGs beginning with 
FY 2020.
    The previously finalized motor score is calculated by summing the 
scores of the 19 data items, with equal weight applied to each item. 
The 19 data items are (83 FR 38535):

 GG0130A1 Eating.
 GG0130B1 Oral hygiene.
 GG0130C1 Toileting hygiene.
 GG0130E1 Shower/bathe self.
 GG0130F1 Upper-body dressing.
 GG0130G1 Lower-body dressing.
 GG0130H1 Putting on/taking off footwear.
 GG0170A1 Roll left and right.
 GG0170B1 Sit to lying.
 GG0170C1 Lying to sitting on side of bed.
 GG0170D1 Sit to stand.
 GG0170E1 Chair/bed-to-chair transfer.
 GG0170F1 Toilet transfer.
 GG0170I1 Walk 10 feet.
 GG0170J1 Walk 50 feet with two turns.
 GG0170K1 Walk 150 feet.
 GG0170M1 One step curb.
 H0350 Bladder continence.
 H0400 Bowel continence.

    In response to feedback we received from various stakeholders and 
professional organizations requesting that we consider applying weights 
to the motor score, we extended our contract with RTI to explore the 
potential of applying unique weights to each of the 19 items in the 
motor score.
    As part of their analysis, RTI examined the degree to which the 
items used to construct the motor score were related to one another and 
adjusted their weighting methodology to account for their findings. RTI 
considered a number of different weighting methodologies to develop a 
weighted index that would increase the predictive power of the IRF 
case-mix classification system while at the same time maintaining 
simplicity. RTI used regression analysis to explore the relationship of 
the motor score items to costs. This analysis was undertaken to 
determine the impact of each of the items on cost and then to weight 
each item in the index according to its relative impact on cost. Based 
on findings from this analysis, we are proposing to remove the item 
GG0170A1 Roll left and right from the motor score as this item was 
found to have a high degree of multicollinearity with other items in 
the motor score and behaved unexpectedly across the regression models 
considered in the development of the weighted index. Using the revised 
motor score composed of the remaining 18 items identified above, RTI 
designed a weighting methodology for the motor score that could be 
applied uniformly across all RICs. For a more detailed discussion of 
the analysis used to construct the weighted motor score, we refer 
readers to the March 2019 technical report entitled ``Analyses to 
Inform the Use of Standardized Patient Assessment Data Elements in the 
Inpatient Rehabilitation Facility Prospective Payment System'', 
available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research.html. Findings from this analysis 
suggest that the use of a weighted motor score index slightly improves 
the ability of the IRF PPS to predict patient costs. Based on this 
analysis, we believe it is appropriate to utilize a weighted motor 
score for the purpose of determining IRF payments.
    Table 1 shows the proposed weights for each component of the motor 
score, averaged to 1, obtained through the regression analysis.

               Table 1--Proposed Motor Score Weight Index
------------------------------------------------------------------------
                              Item                                Weight
------------------------------------------------------------------------
GG0130A1--Eating...............................................      2.7
GG0130B1--Oral hygiene.........................................      0.3
GG0130C1--Toileting hygiene....................................      2.0
GG0130E1--Shower bathe self....................................      0.7
GG0130F1--Upper-body dressing..................................      0.5
GG0130G1--Lower-body dressing..................................      1.0
GG0130H1--Putting on/taking off footwear.......................      1.0
GG0170B1--Sit to lying.........................................      0.1
GG0170C1--Lying to sitting on side of bed......................      0.1
GG0170D1--Sit to stand.........................................      1.1
GG0170E1--Chair/bed-to-chair transfer..........................      1.1
GG0170F1--Toilet transfer......................................      1.6
GG0170I1--Walk 10 feet.........................................      0.8
GG0170J1--Walk 50 feet with two turns..........................      0.8
GG0170K1--Walk 150 feet........................................      0.8
GG0170M1--One-step curb........................................      1.4
H0350--Bladder Continence......................................      1.3
H0400--Bowel Continence........................................      0.7
------------------------------------------------------------------------

    We are proposing to determine the motor score by applying each of 
the weights indicated in Table 1 to the score of each corresponding 
item, as finalized in the FY 2019 IRF PPS final rule (83 FR 38535 
through 38537), and then summing the weighted scores for each of the 18 
items that compose the motor score.
    We invite public comments on the proposal to replace the previously 
finalized unweighted motor score with a weighted motor score to assign 
patients to CMGs under the IRF PPS and our proposal to remove the item 
GG0170A1 Roll left and right from the calculation of the motor score 
beginning with FY 2020, that is, for all discharges beginning on or 
after October 1, 2019.

C. Proposed Revisions to the CMGs and Proposed Updates to the CMG 
Relative Weights and Average Length of Stay Values Beginning With FY 
2020

    In the FY 2019 IRF PPS final rule (83 FR 38549), we finalized the 
use of data items from the Quality Indicators section of the IRF-PAI to 
construct the functional status scores used to classify IRF patients in 
the IRF case-mix classification system for purposes of establishing 
payment under the IRF PPS beginning with FY 2020, but modified our 
proposal based on public comments to incorporate two years of data (FY 
2017 and FY 2018) into our analyses used to revise the CMG definitions. 
We stated that any changes to the proposed CMG definitions resulting 
from the incorporation of an additional year of data (FY 2018) into the 
analysis would be addressed in future rulemaking prior to their 
implementation beginning in FY 2020. Additionally, we stated that we 
would also update the relative weights and average length of stay 
values

[[Page 17251]]

associated with any revised CMG definitions in future rulemaking.
    We have continued our contract with RTI to support us in developing 
proposed revisions to the CMGs used under the IRF PPS based on analysis 
of 2 years of data (FY 2017 and FY 2018). The process RTI uses for its 
analysis, which is based on a Classification and Regression Tree (CART) 
algorithm, is described in detail in the FY 2019 IRF PPS final rule (83 
FR 38536 through 38540). RTI has used this analysis to revise the CMGs 
utilizing FY 2017 and FY 2018 claim and assessment data and to develop 
revised CMGs that reflect the use of the data items collected in the 
Quality Indicators section of the IRF-PAI, incorporating the proposed 
weighted motor score, described in section III.B of this proposed rule. 
To develop the proposed revised CMGs, RTI used CART analysis to divide 
patients into payment groups based on similarities in their clinical 
characteristics and relative costs. As part of this analysis, RTI 
imposed some typically-used constraints on the payment group divisions 
(for example, on the minimum number of cases that could be in the 
resulting payment groups and the minimum dollar payment amount 
differences between groups) to identify the optimal set of payment 
groups. For a more detailed discussion of the analysis used to revise 
the CMGs for FY 2020, we refer readers to the March 2019 technical 
report entitled, ``Analyses to Inform the Use of Standardized Patient 
Assessment Data Elements in the Inpatient Rehabilitation Facility 
Prospective Payment System'' available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research.html.
    As noted in the FY 2019 IRF PPS final rule (83 FR 38533 through 
38549), we finalized the construction of a motor score, a memory score, 
and a communication score to be considered for use in our ongoing 
analysis to revise the CMGs based on FY 2017 and FY 2018 data. In 
developing the proposed CMGs using both FY 2017 and FY 2018 data, 
cognitive status as reflected through the communication score emerged 
as a potential split point for CMGs in RICs 12 and 16 as shown in Table 
2.
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    As similarly discussed in the FY 2019 IRF PPS final rule (83 FR 
38537 through 38546), the inclusion of the communication score in these 
CMG definitions would result in lower payments for patients with higher 
cognitive deficits. As we believe it would be inappropriate to 
establish lower payments for patients with higher cognitive 
impairments, we are proposing to combine the CMGs within these RICs as 
shown in Table 3. As the CMGs we are proposing to combine within these 
RICs are only differentiated by a communication score, our proposal to 
consolidate the CMGs in these 2 RICs results in the exclusion of the 
communication score from the definitions of the proposed CMGs presented 
in Table 3 of this proposed rule. We would like to note that while the 
memory score did not emerge as a potential split point in the CART 
analysis and the communication score was not ultimately selected as a 
determinant for the proposed CMGs, both scores were considered as 
possible elements in developing the proposed CMGs.
    After developing the revised CMGs, RTI calculated the relative 
weights and average length of stay values for each revised CMG using 
the same methodologies that we have used to update the CMG relative 
weights and average length of stay values each fiscal year since 2009 
when we implemented an update to this methodology. More information 
about the methodology used to update the CMG relative weights can be 
found in the FY 2009 IRF PPS final rule (73 FR 46372 through 46374). 
For FY 2020, we propose to use the FY 2017 and FY 2018 IRF claims and 
FY 2017 IRF cost report data to update the CMG relative weights and 
average length of stay values. In calculating the CMG relative weights, 
we use a hospital-specific relative value method to estimate operating 
(routine and ancillary services) and capital costs of IRFs. As noted in 
the FY 2019 IRF PPS final rule (83 FR 38521), this is the same 
methodology that we have used to update the CMG relative weights and 
average length of stay values each fiscal year since we implemented an 
update to the methodology in the FY 2009 IRF PPS final rule (73 FR 
46372 through 46374). More information on the methodology used to 
update calculate the CMG relative weights and average length of stay 
values can found in the March 2019 technical report entitled ``Analyses 
to Inform the Use of Standardized Patient Assessment Data Elements in 
the Inpatient Rehabilitation Facility Prospective Payment System'' 
available at https://www.cms.gov/

[[Page 17252]]

Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/
Research.html. Consistent with the methodology that we have used to 
update the IRF classification system in each instance in the past, we 
are proposing to update the relative weights associated with the 
revised CMGs for FY 2020 in a budget neutral manner by applying a 
budget neutrality factor to the standard payment amount. To calculate 
the appropriate budget neutrality factor for use in updating the FY 
2020 CMG relative weights, we use the following steps:
    Step 1. Calculate the estimated total amount of IRF PPS payments 
for FY 2020 (with no changes to the CMG relative weights).
    Step 2. Calculate the estimated total amount of IRF PPS payments 
for FY 2020 by applying the changes to the CMGs and the associated CMG 
relative weights (as described in this proposed rule).
    Step 3. Divide the amount calculated in step 1 by the amount 
calculated in step 2 to determine the budget neutrality factor (1.0016) 
that would maintain the same total estimated aggregate payments in FY 
2020 with and without the changes to the CMGs and the associated CMG 
relative weights.
    Step 4. Apply the budget neutrality factor (1.0016) to the FY 2019 
IRF PPS standard payment amount after the application of the budget-
neutral wage adjustment factor.
    In section V.H. of this proposed rule, we discuss the proposed use 
of the existing methodology to calculate the standard payment 
conversion factor for FY 2020.
    In Table 3, we present the proposed revised CMGs and their 
respective descriptions, as well as the comorbidity tiers, 
corresponding relative weights and the average length of stay values 
for each proposed CMG and tier for FY 2020. 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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    A list of the FY 2019 CMGs can be found in the FY 2019 IRF PPS 
final rule (83 FR 38521 through 38523). The following would be the most 
significant differences between the FY 2019 CMGs and the proposed 
revised CMGs:
     There would be more CMGs than before (97 instead of 92 
currently).
     There would be fewer CMGs in RICs 1, 2, 5, and 8 while 
there would be more CMGs in RICs 3, 4, 10, 11, 12, 13, 16, 18, 19, and 
21.
     A patient's age would affect assignment for CMGs in RICs 
1, 3, 4, 12, 13, 16, and 20 whereas it currently affects assignment for 
CMGs in RICs 1, 4, and 8.
    We are proposing to utilize the CMGs identified in Table 3 to 
classify IRF patients for purposes of establishing payment under the 
IRF PPS beginning with FY 2020, that is, for all discharges on or after 
October 1, 2019. We are proposing to implement these revisions in a 
budget neutral manner. For more information on the specific impacts of 
this proposal, we refer readers to Table 4. We are also proposing to 
update the CMG relative weights and average length of stay values 
associated with the proposed CMGs based on the data items from the 
Quality Indicators section of the IRF-PAI.

                       Table 4--Distributional Effects of the Proposed Changes to the CMGs
----------------------------------------------------------------------------------------------------------------
                                                                                                     Estimated
                                                                                     Number of       impact of
                     Facility classification                      Number of IRFs       cases       proposed CMG
                                                                                                     revisions
(1)                                                                          (2)             (3)             (4)
----------------------------------------------------------------------------------------------------------------
Total...........................................................           1,119         409,982             0.0
Urban unit......................................................             696         166,872             2.5
Rural unit......................................................             136          21,700             2.9
Urban hospital..................................................             276         216,894            -2.2
Rural hospital..................................................              11           4,516            -3.6
Urban For-Profit................................................             357         211,280            -1.8
Rural For-Profit................................................              36           7,920             0.1
Urban Non-Profit................................................             522         150,310             1.6
Rural Non-Profit................................................              90          15,166             2.2
Urban Government................................................              93          22,176             3.1
Rural Government................................................              21           3,130             4.1
Urban...........................................................             972         383,766            -0.1

[[Page 17260]]

 
Rural...........................................................             147          26,216             1.8
----------------------------------------------------------------------------------------------------------------
                                                 Urban by region
----------------------------------------------------------------------------------------------------------------
Urban New England...............................................              29          16,260            -2.3
Urban Middle Atlantic...........................................             135          51,539            -1.6
Urban South Atlantic............................................             147          77,315            -0.5
Urban East North Central........................................             165          50,466             2.3
Urban East South Central........................................              56          27,966            -0.6
Urban West North Central........................................              74          20,822             1.0
Urban West South Central........................................             184          84,068            -0.5
Urban Mountain..................................................              83          30,294            -0.6
Urban Pacific...................................................              99          25,036             2.1
----------------------------------------------------------------------------------------------------------------
                                                 Rural by region
----------------------------------------------------------------------------------------------------------------
Rural New England...............................................               5           1,317            -2.4
Rural Middle Atlantic...........................................              12           1,248             1.2
Rural South Atlantic............................................              16           3,639            -2.4
Rural East North Central........................................              23           4,061             1.5
Rural East South Central........................................              21           4,523             3.9
Rural West North Central........................................              22           3,178             2.4
Rural West South Central........................................              40           7,332             3.6
Rural Mountain..................................................               5             626             1.8
Rural Pacific...................................................               3             292             3.0
----------------------------------------------------------------------------------------------------------------
                                                 Teaching status
----------------------------------------------------------------------------------------------------------------
Non-teaching....................................................           1,014         362,675            -0.2
Resident to ADC less than 10%...................................              60          34,000             0.7
Resident to ADC 10%-19%.........................................              31          11,784             2.6
Resident to ADC greater than 19%................................              14           1,523             4.3
----------------------------------------------------------------------------------------------------------------
                               Disproportionate share patient percentage (DSH PP)
----------------------------------------------------------------------------------------------------------------
DSH PP = 0%.....................................................              29           5,300            -1.3
DSH PP <5%......................................................             139          60,003            -1.6
DSH PP 5%-10%...................................................             299         127,442            -0.7
DSH PP 10%-20%..................................................             371         139,001             0.0
DSH PP greater than 20%.........................................             281          78,236             2.1
----------------------------------------------------------------------------------------------------------------

    Table 4 shows how we estimate that the application of the proposed 
revisions to the case-mix system for FY 2020 would affect particular 
groups. Table 4 categorizes IRFs by geographic location, including 
urban or rural location, and location for CMS's 9 Census divisions of 
the country. In addition, Table 4 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 proposed changes to the case-mix 
classification system are expected to affect the overall distribution 
of payments across CMGs. Note that, because we propose to implement the 
revisions to the case-mix classification system in a budget-neutral 
manner, total estimated aggregate payments to IRFs would not be 
affected as a result of the proposed revisions to the CMGs and the CMG 
relative weights. However, these proposed revisions may affect the 
distribution of payments across CMGs. For a provider specific impact 
analysis of this proposed change, we refer readers to the CMS website 
at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
    We invite public comment on the proposed revisions to the CMGs 
based on analysis of 2 years of data (FYs 2017 and 2018) and the 
proposed updates to the relative weights and average length of stay 
values associated with the revised CMGs beginning with FY 2020, that 
is, for all discharges beginning on or after October 1, 2019.

IV. Facility-Level Adjustment Factors

    Section 1886(j)(3)(A)(v) of the Act confers broad authority upon 
the Secretary to adjust the per unit payment rate by such factors as 
the Secretary determines are necessary to properly reflect variations 
in necessary costs of treatment among rehabilitation facilities. Under 
this authority, we currently adjust the prospective payment amount 
associated with a CMG to account for facility-level characteristics 
such as an IRF's LIP, teaching status, and location in a rural area, if 
applicable, as described in Sec.  412.624(e).

[[Page 17261]]

    Based on the substantive changes to the facility-level adjustment 
factors that were adopted in the FY 2014 IRF PPS final rule (78 FR 
47860, 47868 through 47872), in the FY 2015 IRF PPS final rule (79 FR 
45872, 45882 through 45883), we froze the facility-level adjustment 
factors at the FY 2014 levels for FY 2015 and all subsequent years 
(unless and until we propose to update them again through future 
notice-and-comment rulemaking). For FY 2020, we will continue to hold 
the adjustment factors at the FY 2014 levels as we continue to monitor 
the most current IRF claims data available and continue to evaluate and 
monitor the effects of the FY 2014 changes.

V. Proposed FY 2020 IRF PPS Payment Update

A. Background

    Section 1886(j)(3)(C) of the Act requires the Secretary to 
establish an increase factor that reflects changes over time in the 
prices of an appropriate mix of goods and services included in the 
covered IRF services. According to section 1886(j)(3)(A)(i) of the Act, 
the increase factor shall be used to update the IRF prospective payment 
rates for each FY. Section 1886(j)(3)(C)(ii)(I) of the Act requires the 
application of a productivity adjustment. Thus, we propose to update 
the IRF PPS payments for FY 2020 by a market basket increase factor as 
required by section 1886(j)(3)(C) of the Act based upon the most 
current data available, with a productivity adjustment as required by 
section 1886(j)(3)(C)(ii)(I) of the Act.
    We have utilized various market baskets through the years in the 
IRF PPS. For a discussion of these market baskets, we refer readers to 
the FY 2016 IRF PPS final rule (80 FR 47046).
    Beginning with FY 2016, we finalized the use of a 2012-based IRF 
market basket, using Medicare cost report data for both freestanding 
and hospital-based IRFs (80 FR 47049 through 47068). Beginning with FY 
2020, we are proposing to rebase and revise the IRF market basket to 
reflect a 2016 base year. In the following discussion, we provide an 
overview of the proposed market basket and describe the methodologies 
used to determine the operating and capital portions of the proposed 
2016-based IRF market basket.

B. Overview of the Proposed 2016-Based IRF Market Basket

    The proposed 2016-based IRF market basket is a fixed-weight, 
Laspeyres-type price index. A Laspeyres price index measures the change 
in price, over time, of the same mix of goods and services purchased in 
the base period. Any changes in the quantity or mix of goods and 
services (that is, intensity) purchased over time relative to a base 
period are not measured.
    The index itself is constructed in three steps. First, a base 
period is selected (in this proposed rule, the base period is 2016), 
total base period costs are estimated for a set of mutually exclusive 
and exhaustive cost categories, and each category is calculated as a 
proportion of total costs. These proportions are called cost weights. 
Second, each cost category is matched to an appropriate price or wage 
variable, referred to as a price proxy. In nearly every instance where 
we have selected price proxies for the various market baskets, these 
price proxies are derived from publicly available statistical series 
that are published on a consistent schedule (preferably at least on a 
quarterly basis). In cases where a publicly available price series is 
not available (for example, a price index for malpractice insurance), 
we have collected price data from other sources and subsequently 
developed our own index to capture changes in prices for these types of 
costs. Finally, the cost weight for each cost category is multiplied by 
the established price proxy. The sum of these products (that is, the 
cost weights multiplied by their price levels) for all cost categories 
yields the composite index level of the market basket for the given 
time period. Repeating this step for other periods produces a series of 
market basket levels over time. Dividing the composite index level of 
one period by the composite index level for an earlier period produces 
a rate of growth in the input price index over that timeframe.
    As previously noted, the market basket is described as a fixed-
weight index because it represents the change in price over time of a 
constant mix (quantity and intensity) of goods and services needed to 
furnish IRF services. The effects on total costs resulting from changes 
in the mix of goods and services purchased after the base period are 
not measured. For example, an IRF hiring more nurses after the base 
period to accommodate the needs of patients would increase the volume 
of goods and services purchased by the IRF, but would not be factored 
into the price change measured by a fixed-weight IRF market basket. 
Only when the index is rebased would changes in the quantity and 
intensity be captured, with those changes being reflected in the cost 
weights. Therefore, we rebase the market basket periodically so that 
the cost weights reflect recent changes in the mix of goods and 
services that IRFs purchase (hospital inputs) to furnish inpatient care 
between base periods.

C. Proposed Rebasing and Revising of the IRF PPS Market Basket

    As discussed in the FY 2016 IRF PPS final rule (80 FR 47050), the 
2012-based IRF market basket reflects the Medicare cost reports for 
both freestanding and hospital-based facilities.
    Beginning with FY 2020, we are proposing to rebase and revise the 
2012-based IRF market basket to a 2016 base year reflecting both 
freestanding and hospital-based IRFs. Below we provide a detailed 
description of our methodology used to develop the proposed 2016-based 
IRF market basket. This proposed methodology is generally similar to 
the methodology used to develop the 2012-based IRF market basket with 
the exception of the proposed derivation of the Home Office Contract 
Labor cost weight using the Medicare cost report data as described in 
section V.C.a.(6) of this proposed rule.
1. Development of Cost Categories and Weights for the Proposed 2016-
Based IRF Market Basket
a. Use of Medicare Cost Report Data
    We are proposing a 2016-based IRF market basket that consists of 
seven major cost categories and a residual derived from the 2016 
Medicare cost reports (CMS Form 2552-10) for freestanding and hospital-
based IRFs. The seven cost categories are Wages and Salaries, Employee 
Benefits, Contract Labor, Pharmaceuticals, Professional Liability 
Insurance (PLI), Home Office Contract Labor, and Capital. The residual 
category reflects all remaining costs not captured in the seven cost 
categories. The 2016 cost reports include providers whose cost 
reporting period began on or after October 1, 2015, and prior to 
September 30, 2016. We selected 2016 as the base year because we 
believe that the Medicare cost reports for this year represent the most 
recent, complete set of Medicare cost report data available for 
developing the proposed IRF market basket at this time.
    Since our goal is to establish cost weights that were reflective of 
case mix and practice patterns associated with the services IRFs 
provide to Medicare beneficiaries, as we did for the 2012-based IRF 
market basket, we are proposing to limit the cost reports used to 
establish the 2016-based IRF market basket to those from facilities 
that had a Medicare average length of stay (LOS) that was relatively 
similar to their

[[Page 17262]]

facility average LOS. We believe that this requirement eliminates 
statistical outliers and ensures a more accurate market basket that 
reflects the costs generally incurred during a Medicare-covered stay. 
The Medicare average LOS for freestanding IRFs is calculated from data 
reported on line 14 of Worksheet S-3, part I. The Medicare average LOS 
for hospital-based IRFs is calculated from data reported on line 17 of 
Worksheet S-3, part I. We propose to include the cost report data from 
IRFs with a Medicare average LOS within 15 percent (that is, 15 percent 
higher or lower) of the facility average LOS to establish the sample of 
providers used to estimate the 2016-based IRF market basket cost 
weights. We are proposing to apply this LOS edit to the data for IRFs 
to exclude providers that serve a population whose LOS would indicate 
that the patients served are not consistent with a LOS of a typical 
Medicare patient. We note that this is the same LOS edit that we 
applied to develop the 2012-based IRF market basket. This process 
resulted in the exclusion of about eight percent of the freestanding 
and hospital-based IRF Medicare cost reports. Of those excluded, about 
18 percent were freestanding IRFs and 82 percent were hospital-based 
IRFs. This ratio is relatively consistent with the ratio of the 
universe of freestanding to hospital-based IRF providers.
    We then used the cost reports for IRFs that met this requirement to 
calculate the costs for the seven major cost categories (Wages and 
Salaries, Employee Benefits, Contract Labor, Professional Liability 
Insurance, Pharmaceuticals, Home Office Contract Labor, and Capital) 
for the market basket. For comparison, the 2012-based IRF market basket 
utilized the Bureau of Economic Analysis Benchmark Input-Output data 
rather than Medicare cost report data to derive the Home Office 
Contract Labor cost weight. A more detailed discussion of this 
methodological change is provided in section V.C.1.a.(6). of this 
proposed rule.
    Similar to the 2012-based IRF market basket major cost weights, the 
proposed 2016-based IRF market basket cost weights reflect Medicare 
allowable costs (routine, ancillary and capital)--costs that are 
eligible for reimbursement through the IRF PPS. We propose to define 
Medicare allowable costs for freestanding facilities as the following 
lines on Worksheet A and Worksheet, part I (CMS Form 2552-10): 30 
through 35, 50 through 76 (excluding 52 and 75), 90 through 91 and 93. 
We propose to define Medicare allowable costs for hospital-based 
facilities as the following lines on Worksheet A and Worksheet B, part 
I (CMS Form 2552-10): 41, 50 through 76 (excluding 52 and 75), 90 
through 91, and 93.
    For freestanding IRFs, total Medicare allowable costs would be 
equal to the total costs as reported on Worksheet B, part I, column 26 
for the lines listed above. For hospital-based IRFs, total Medicare 
allowable costs would be equal to total costs for the IRF inpatient 
unit after the allocation of overhead costs (Worksheet B, part I, 
column 26, line 41) and a proportion of total ancillary costs. We 
propose to calculate the portion of ancillary costs attributable to the 
hospital-based IRF for a given ancillary cost center by multiplying 
total facility ancillary costs for the specific cost center (as 
reported on Worksheet B, part I, column 26) by the ratio of IRF 
Medicare ancillary costs for the cost center (as reported on Worksheet 
D-3, column 3 for hospital-based IRFs) to total Medicare ancillary 
costs for the cost center (equal to the sum of Worksheet D-3, column 3 
for all relevant PPS [that is, IPPS, IRF, IPF and skilled nursing 
facility (SNF)]). We propose to use these methods to derive levels of 
total costs for IRF providers. This is the same methodology used for 
the 2012-based IRF market basket. With this work complete, we then set 
about deriving cost levels for the seven major cost categories and then 
derive a residual cost weight reflecting all other costs not 
classified.
(1) Wages and Salaries Costs
    For freestanding IRFs, we are proposing to derive Wages and 
Salaries costs as the sum of routine inpatient salaries, ancillary 
salaries, and a proportion of overhead (or general service cost centers 
in the Medicare cost reports) salaries as reported on Worksheet A, 
column 1. Since overhead salary costs are attributable to the entire 
IRF, we only include the proportion attributable to the Medicare 
allowable cost centers. We are proposing to estimate the proportion of 
overhead salaries that are attributed to Medicare allowable costs 
centers by multiplying the ratio of Medicare allowable area salaries 
(Worksheet A, column 1, lines 50 through 76 (excluding 52 and 75), 90 
through 91, and 93) to total salaries (Worksheet A, column 1, line 200) 
times total overhead salaries (Worksheet A, column 1, lines 4 through 
18). This is the same methodology used in the 2012-based IRF market 
basket.
    For hospital-based IRFs, we are proposing to derive Wages and 
Salaries costs as the sum of inpatient routine salary costs (Worksheet 
A, column 1, line 41) for the hospital-based IRF and the overhead 
salary costs attributable to this IRF inpatient unit; and ancillary 
salaries plus a portion of overhead salary costs attributable to the 
ancillary departments utilized by the hospital-based IRF.
    We are proposing to calculate hospital-based ancillary salary costs 
for a specific cost center (Worksheet A, column 1, lines 50 through 76 
(excluding 52 and 75), 90 through 91, and 93) using salary costs from 
Worksheet A, column 1, multiplied by the ratio of IRF Medicare 
ancillary costs for the cost center (as reported on Worksheet D-3, 
column 3, for IRF subproviders) to total Medicare ancillary costs for 
the cost center (equal to the sum of Worksheet D-3, column 3, for all 
relevant PPS units [that is, IPPS, IRF, IPF and a SNF]). For example, 
if hospital-based IRF Medicare physical therapy costs represent 30 
percent of the total Medicare physical therapy costs for the entire 
facility, then 30 percent of total facility physical therapy salaries 
(as reported in Worksheet A, column 1, line 66) would be attributable 
to the hospital-based IRF. We believe it is appropriate to use only a 
portion of the ancillary costs in the market basket cost weight 
calculations since the hospital-based IRF only utilizes a portion of 
the facility's ancillary services. We believe the ratio of reported IRF 
Medicare costs to reported total Medicare costs provides a reasonable 
estimate of the ancillary services utilized, and costs incurred, by the 
hospital-based IRF.
    We are proposing to calculate the portion of overhead salary costs 
attributable to hospital-based IRFs by first calculating total 
noncapital overhead costs (Worksheet B, part I, columns 4-18, line 41, 
less Worksheet B, part II, columns 4-18, line 41). We then multiply 
total noncapital overhead costs by an overhead ratio equal to the ratio 
of total facility overhead salaries (as reported on Worksheet A, column 
1, lines 4-18) to total facility noncapital overhead costs (as reported 
on Worksheet A, column 1 and 2, lines 4-18). This methodology assumes 
the proportion of total costs related to salaries for the overhead cost 
center is similar for all inpatient units (that is, acute inpatient or 
inpatient rehabilitation).
    We are proposing to calculate the portion of overhead salaries 
attributable to each ancillary department by first calculating total 
noncapital overhead costs attributable to each specific ancillary 
department (Worksheet B, part I, columns 4-18 less, Worksheet B, part 
II, columns 4-18). We then identify the portion of these noncapital 
overhead

[[Page 17263]]

costs attributable to Wages and Salaries by multiplying these costs by 
the overhead ratio defined as the ratio of total facility overhead 
salaries (as reported on Worksheet A, column 1, lines 4-18) to total 
overhead costs (as reported on Worksheet A, column 1 & 2, lines 4-18). 
Finally, we identified the portion of these overhead salaries for each 
ancillary department that is attributable to the hospital-based IRF by 
multiplying by the ratio of IRF Medicare ancillary costs for the cost 
center (as reported on Worksheet D-3, column 3, for hospital-based 
IRFs) to total Medicare ancillary costs for the cost center (equal to 
the sum of Worksheet D-3, column 3, for all relevant PPS units [that 
is, IPPS, IRF, IPF and SNF]). This is the same methodology used to 
derive the 2012-based IRF market basket.
(2) Employee Benefits Costs
    Effective with the implementation of CMS Form 2552-10, we began 
collecting Employee Benefits and Contract Labor data on Worksheet S-3, 
part V.
    For 2016 Medicare cost report data, the majority of providers did 
not report data on Worksheet S-3, part V; particularly, approximately 
48 percent of freestanding IRFs and 40 percent of hospital-based IRFs 
reported data on Worksheet S-3, part V. However, we believe we have a 
large enough sample to enable us to produce a reasonable Employee 
Benefits cost weight. Again, we continue to encourage all providers to 
report these data on the Medicare cost report.
    For freestanding IRFs, we are proposing Employee Benefits costs 
would be equal to the data reported on Worksheet S-3, part V, column 2, 
line 2. We note that while not required to do so, freestanding IRFs 
also may report Employee Benefits data on Worksheet S-3, part II, which 
is applicable to only IPPS providers. For those freestanding IRFs that 
report Worksheet S-3, part II, data, but not Worksheet S-3, part V, we 
are proposing to use the sum of Worksheet S-3, part II, lines 17, 18, 
20, and 22, to derive Employee Benefits costs. This proposed method 
would allow us to obtain data from about 30 more freestanding IRFs than 
if we were to only use the Worksheet S-3, part V, data as was done for 
the 2012-based IRF market basket.
    For hospital-based IRFs, we are proposing to calculate total 
benefit costs as the sum of inpatient unit benefit costs, a portion of 
ancillary benefits, and a portion of overhead benefits attributable to 
the routine inpatient unit and a portion of overhead benefits 
attributable to the ancillary departments. We are proposing inpatient 
unit benefit costs be equal to Worksheet S-3, part V, column 2, line 4. 
We are proposing that the portion of overhead benefits attributable to 
the routine inpatient unit and ancillary departments be calculated by 
multiplying ancillary salaries for the hospital-based IRF and overhead 
salaries attributable to the hospital-based IRF (determined in the 
derivation of hospital-based IRF Wages and Salaries costs as described 
above) by the ratio of total facility benefits to total facility 
salaries. Total facility benefits is equal to the sum of Worksheet S-3, 
part II, column 4, lines 17-25, and total facility salaries is equal to 
Worksheet S-3, part II, column 4, line 1.
(3) Contract Labor Costs
    Contract Labor costs are primarily associated with direct patient 
care services. Contract labor costs for other services such as 
accounting, billing, and legal are calculated separately using other 
government data sources as described in section V.C.3. of this proposed 
rule. To derive contract labor costs using Worksheet S-3, part V, data, 
for freestanding IRFs, we are proposing Contract Labor costs be equal 
to Worksheet S-3, part V, column 1, line 2. As we noted for Employee 
Benefits, freestanding IRFs also may report Contract Labor data on 
Worksheet S-3, part II, which is applicable to only IPPS providers. For 
those freestanding IRFs that report Worksheet S-3, part II data, but 
not Worksheet S-3, part V, we are proposing to use the sum of Worksheet 
S-3, part II, lines 11 and 13, to derive Contract Labor costs.
    For hospital-based IRFs, we are proposing that Contract Labor costs 
would be equal to Worksheet S-3, part V, column 1, line 4. As 
previously noted, for 2016 Medicare cost report data, while there were 
providers that did report data on Worksheet S-3, part V, many providers 
did not complete this worksheet. However, we believe we have a large 
enough sample to enable us to produce a reasonable Contract Labor cost 
weight. We continue to encourage all providers to report these data on 
the Medicare cost report.
(4) Pharmaceuticals Costs
    For freestanding IRFs, we are proposing to calculate 
pharmaceuticals costs using non-salary costs reported on Worksheet A, 
column 7, less Worksheet A, column 1, for the pharmacy cost center 
(line 15) and drugs charged to patients cost center (line 73).
    For hospital-based IRFs, we are proposing to calculate 
pharmaceuticals costs as the sum of a portion of the non-salary 
pharmacy costs and a portion of the non-salary drugs charged to patient 
costs reported for the total facility. We propose that non-salary 
pharmacy costs attributable to the hospital-based IRF would be 
calculated by multiplying total pharmacy costs attributable to the 
hospital-based IRF (as reported on Worksheet B, part I, column 15, line 
41) by the ratio of total non-salary pharmacy costs (Worksheet A, 
column 2, line 15) to total pharmacy costs (sum of Worksheet A, columns 
1 and 2 for line 15) for the total facility. We propose that non-salary 
drugs charged to patient costs attributable to the hospital-based IRF 
would be calculated by multiplying total non-salary drugs charged to 
patient costs (Worksheet B, part I, column 0, line 73 plus Worksheet B, 
part I, column 15, line 73, less Worksheet A, column 1, line 73) for 
the total facility by the ratio of Medicare drugs charged to patient 
ancillary costs for the IRF unit (as reported on Worksheet D-3 for 
hospital-based IRFs, column 3, line 73) to total Medicare drugs charged 
to patient ancillary costs for the total facility (equal to the sum of 
Worksheet D-3, column 3, line 73 for all relevant PPS [that is, IPPS, 
IRF, IPF and SNF]).
(5) Professional Liability Insurance Costs
    For freestanding IRFs, we are proposing that Professional Liability 
Insurance (PLI) costs (often referred to as malpractice costs) would be 
equal to premiums, paid losses and self-insurance costs reported on 
Worksheet S-2, columns 1 through 3, line 118. For hospital-based IRFs, 
we are proposing to assume that the PLI weight for the total facility 
is similar to the hospital-based IRF unit since the only data reported 
on this worksheet is for the entire facility, as we currently have no 
means to identify the proportion of total PLI costs that are only 
attributable to the hospital-based IRF. Therefore, hospital-based IRF 
PLI costs are equal to total facility PLI (as reported on Worksheet S-
2, columns 1 through 3, line 118) divided by total facility costs (as 
reported on Worksheet A, columns 1 and 2, line 200) times hospital-
based IRF Medicare allowable total costs. Our assumption is that the 
same proportion of expenses are used among each unit of the hospital. 
We welcome comments on this proposed method of deriving the PLI costs 
for hospital-based IRFs.
(6) Home Office/Related Organization Contract Labor Costs
    For the 2016-based IRF market basket, we are proposing to determine 
the home office/related organization contract

[[Page 17264]]

labor costs using Medicare cost report data. The 2012-based IRF market 
basket used the 2007 Benchmark Input-Output (I-O) expense data 
published by the Bureau of Economic Analysis (BEA) to derive these 
costs (80 FR 47057). A more detailed explanation of the general 
methodology using the BEA I-O data is provided in section V.C.3. of 
this proposed rule. For freestanding and hospital-based IRFs, we are 
proposing to calculate the home office contract labor cost weight 
(using data reported on Worksheet S-3, part II, column 4, lines 14, 
1401, 1402, 2550, and 2551) and total facility costs (Worksheet B, part 
1, column 26, line 202). We are proposing to use total facility costs 
as the denominator for calculating the home office contract labor cost 
weight as these expenses reported on Worksheet S-3, part II reflect the 
entire hospital facility. Our assumption is that the same proportion of 
expenses are used among each unit of the hospital. For the 2012-based 
IRF market basket, we calculated the home office cost weight using 
expense data for North American Industry Classification System (NAICS) 
code 55, Management of Companies and Enterprises (80 FR 47067).
(7) Capital Costs
    For freestanding IRFs, we are proposing that capital costs would be 
equal to Medicare allowable capital costs as reported on Worksheet B, 
part II, column 26, lines 30 through 35, 50 through 76 (excluding 52 
and 75), 90 through 91, and 93.
    For hospital-based IRFs, we are proposing that capital costs would 
be equal to IRF inpatient capital costs (as reported on Worksheet B, 
part II, column 26, line 41) and a portion of IRF ancillary capital 
costs. We calculate the portion of ancillary capital costs attributable 
to the hospital-based IRF for a given cost center by multiplying total 
facility ancillary capital costs for the specific ancillary cost center 
(as reported on Worksheet B, part II, column 26) by the ratio of IRF 
Medicare ancillary costs for the cost center (as reported on Worksheet 
D-3, column 3 for hospital-based IRFs) to total Medicare ancillary 
costs for the cost center (equal to the sum of Worksheet D-3, column 3 
for all relevant PPS [that is, IPPS, IRF, IPF and SNF]). For example, 
if hospital-based IRF Medicare physical therapy costs represent 30 
percent of the total Medicare physical therapy costs for the entire 
facility, then 30 percent of total facility physical therapy capital 
costs (as reported in Worksheet B, part II, column 26, line 66) would 
be attributable to the hospital-based IRF.
b. Final Major Cost Category Computation
    After we derive costs for the major cost categories for each 
provider using the Medicare cost report data as previously described, 
we propose to trim the data for outliers. For the Wages and Salaries, 
Employee Benefits, Contract Labor, Pharmaceuticals, Professional 
Liability Insurance, and Capital cost weights, we first divide the 
costs for each of these six categories by total Medicare allowable 
costs calculated for the provider to obtain cost weights for the 
universe of IRF providers. We then remove those providers whose derived 
cost weights fall in the top and bottom 5 percent of provider specific 
derived cost weights to ensure the exclusion of outliers. After the 
outliers have been excluded, we sum the costs for each category across 
all remaining providers. We then divide this by the sum of total 
Medicare allowable costs across all remaining providers to obtain a 
cost weight for the proposed 2016-based IRF market basket for the given 
category.
    The proposed trimming methodology for the Home Office Contract 
Labor cost weight is slightly different than the proposed trimming 
methodology for the other six cost categories as described above. For 
the Home Office Contract Labor cost weight, since we are using total 
facility data rather than Medicare-allowable costs associated with IRF 
services, we are proposing to trim the freestanding and hospital-based 
IRF cost weights separately. For each of the providers, we first divide 
the home office contract labor costs by total facility costs to obtain 
a Home Office Contract Labor cost weight for the universe of IRF 
providers. We are then proposing to trim only the top 1 percent of 
providers to exclude outliers while also allowing providers who have 
reported zero home office costs to remain in the Home Office Contract 
Labor cost weight calculations as not all providers will incur home 
office costs. After removing these outliers, we are left with a trimmed 
data set for both freestanding and hospital-based providers. We are 
then proposing to sum the costs for each category (freestanding and 
hospital-based) across all remaining providers. We next divide this by 
the sum of total facility costs across all remaining providers to 
obtain a freestanding and hospital-based cost weight. Lastly, we are 
proposing to weight these two cost weights together using the Medicare-
allowable costs to derive a Home Office Contract Labor cost weight for 
the proposed 2016-based IRF market basket.
    Finally, we calculate the residual ``All Other'' cost weight that 
reflects all remaining costs that are not captured in the seven cost 
categories listed. See Table 5 for the resulting cost weights for these 
major cost categories that we obtain from the Medicare cost reports.

                      Table 5--Major Cost Categories as Derived From Medicare Cost Reports
----------------------------------------------------------------------------------------------------------------
                                                                               Proposed 2016-    2012-based IRF
                            Major cost categories                             based IRF market    market basket
                                                                              basket (percent)      (percent)
----------------------------------------------------------------------------------------------------------------
Wages and Salaries..........................................................              47.1              47.3
Employee Benefits...........................................................              11.3              11.2
Contract Labor..............................................................               1.0               0.8
Professional Liability Insurance (Malpractice)..............................               0.7               0.9
Pharmaceuticals.............................................................               5.1               5.1
Home Office Contract Labor..................................................               3.7               n/a
Capital.....................................................................               9.0               8.6
All Other...................................................................              22.2              26.1
----------------------------------------------------------------------------------------------------------------
* Total may not sum to 100 due to rounding.

    As we did for the 2012-based IRF market basket, we are proposing to 
allocate the Contract Labor cost weight to the Wages and Salaries and 
Employee Benefits cost weights based on their relative proportions 
under the

[[Page 17265]]

assumption that contract labor costs are comprised of both wages and 
salaries and employee benefits. The Contract Labor allocation 
proportion for Wages and Salaries is equal to the Wages and Salaries 
cost weight as a percent of the sum of the Wages and Salaries cost 
weight and the Employee Benefits cost weight. For this proposed rule, 
this rounded percentage is 81 percent; therefore, we are proposing to 
allocate 81 percent of the Contract Labor cost weight to the Wages and 
Salaries cost weight and 19 percent to the Employee Benefits cost 
weight. The 2012-based IRF market basket percentage was also 81 percent 
(80 FR 47056). Table 6 shows the Wages and Salaries and Employee 
Benefit cost weights after Contract Labor cost weight allocation for 
both the proposed 2016-based IRF market basket and 2012-based IRF 
market basket.

         Table 6--Wages and Salaries and Employee Benefits Cost Weights After Contract Labor Allocation
----------------------------------------------------------------------------------------------------------------
                                                                               Proposed 2016-
                            Major cost categories                             based IRF market   2012-based IRF
                                                                                   basket         market basket
----------------------------------------------------------------------------------------------------------------
Wages and Salaries..........................................................              47.9              47.9
Employee Benefits...........................................................              11.4              11.3
----------------------------------------------------------------------------------------------------------------

c. Derivation of the Detailed Operating Cost Weights
    To further divide the ``All Other'' residual cost weight estimated 
from the 2016 Medicare cost report data into more detailed cost 
categories, we propose to use the 2012 Benchmark Input-Output (I-O) 
``Use Tables/Before Redefinitions/Purchaser Value'' for NAICS 622000, 
Hospitals, published by the Bureau of Economic Analysis (BEA). This 
data is publicly available at https://www.bea.gov/industry/io_annual.htm. For the 2012-based IRF market basket, we used the 2007 
Benchmark I-O data, the most recent data available at the time (80 FR 
47057).
    The BEA Benchmark I-O data are scheduled for publication every 5 
years with the most recent data available for 2012. The 2007 Benchmark 
I-O data are derived from the 2012 Economic Census and are the building 
blocks for BEA's economic accounts. Thus, they represent the most 
comprehensive and complete set of data on the economic processes or 
mechanisms by which output is produced and distributed.\1\ BEA also 
produces Annual I-O estimates; however, while based on a similar 
methodology, these estimates reflect less comprehensive and less 
detailed data sources and are subject to revision when benchmark data 
becomes available. Instead of using the less detailed Annual I-O data, 
we propose to inflate the 2012 Benchmark I-O data forward to 2016 by 
applying the annual price changes from the respective price proxies to 
the appropriate market basket cost categories that are obtained from 
the 2012 Benchmark I-O data. We repeat this practice for each year. We 
then propose to calculate the cost shares that each cost category 
represents of the inflated 2012 data. These resulting 2016 cost shares 
are applied to the All Other residual cost weight to obtain the 
proposed detailed cost weights for the 2016-based IRF market basket. 
For example, the cost for Food: Direct Purchases represents 5.0 percent 
of the sum of the ``All Other'' 2012 Benchmark I-O Hospital 
Expenditures inflated to 2016; therefore, the Food: Direct Purchases 
cost weight represents 5.0 percent of the 2016-based IRF market 
basket's ``All Other'' cost category (22.2 percent), yielding a 
``final'' Food: Direct Purchases cost weight of 1.1 percent in the 
proposed 2016-based IRF market basket (0.05 * 22.2 percent = 1.1 
percent).
---------------------------------------------------------------------------

    \1\ https://www.bea.gov/papers/pdf/IOmanual_092906.pdf.
---------------------------------------------------------------------------

    Using this methodology, we propose to derive seventeen detailed IRF 
market basket cost category weights from the proposed 2016-based IRF 
market basket residual cost weight (22.2 percent). These categories 
are: (1) Electricity, (2) Fuel, Oil, and Gasoline (3) Food: Direct 
Purchases, (4) Food: Contract Services, (5) Chemicals, (6) Medical 
Instruments, (7) Rubber & Plastics, (8) Paper and Printing Products, 
(9) Miscellaneous Products, (10) Professional Fees: Labor-related, (11) 
Administrative and Facilities Support Services, (12) Installation, 
Maintenance, and Repair, (13) All Other Labor-related Services, (14) 
Professional Fees: Nonlabor-related, (15) Financial Services, (16) 
Telephone Services, and (17) All Other Nonlabor-related Services. We 
note that for the 2012-based IRF market basket, we had a Water and 
Sewerage cost weight. For the proposed 2016-based IRF market basket, we 
are proposing to include Water and Sewerage costs in the Electricity 
cost weight due to the small amount of costs in this category.
    For the 2012-based IRF market basket, we used the I-O data for 
NAICS 55 Management of Companies to derive the Home Office Contract 
Labor cost weight, which were classified in the Professional Fees: 
Labor-related and Professional Fees: Nonlabor-related cost weights. As 
previously discussed, we are proposing to use the Medicare cost report 
data to derive the Home Office Contract Labor cost weight, which we 
would further classify into the Professional Fees: Labor-related or 
Professional Fees: Nonlabor-related categories.
d. Derivation of the Detailed Capital Cost Weights
    As described in section V.C.1.a.(6) of this proposed rule, we are 
proposing a Capital-Related cost weight of 9.0 percent as obtained from 
the 2016 Medicare cost reports for freestanding and hospital-based IRF 
providers. We are proposing to then separate this total Capital-Related 
cost weight into more detailed cost categories.
    Using 2016 Medicare cost reports, we are able to group Capital-
Related costs into the following categories: Depreciation, Interest, 
Lease, and Other Capital-Related costs. For each of these categories, 
we are proposing to determine separately for hospital-based IRFs and 
freestanding IRFs what proportion of total capital-related costs the 
category represents.
    For freestanding IRFs, we are proposing to derive the proportions 
for Depreciation, Interest, Lease, and Other Capital-related costs 
using the data reported by the IRF on Worksheet A-7, which is similar 
to the methodology used for the 2012-based IRF market basket.
    For hospital-based IRFs, data for these four categories are not 
reported separately for the hospital-based IRF; therefore, we are 
proposing to derive these proportions using data reported on Worksheet 
A-7 for the total facility. We are assuming the cost shares for the 
overall hospital are representative for the hospital-based IRF unit. 
For example, if depreciation costs make up 60 percent of total capital 
costs for the entire facility, we believe it is

[[Page 17266]]

reasonable to assume that the hospital-based IRF would also have a 60 
percent proportion because it is a unit contained within the total 
facility. This is the same methodology used for the 2012-based IRF 
market basket (80 FR 47057).
    To combine each detailed capital cost weight for freestanding and 
hospital-based IRFs into a single capital cost weight for the proposed 
2016-based IRF market basket, we are proposing to weight together the 
shares for each of the categories (Depreciation, Interest, Lease, and 
Other Capital-related costs) based on the share of total capital costs 
each provider type represents of the total capital costs for all IRFs 
for 2016. Applying this methodology results in proportions of total 
capital-related costs for Depreciation, Interest, Lease and Other 
Capital-related costs that are representative of the universe of IRF 
providers. This is the same methodology used for the 2012-based IRF 
market basket (80 FR 47057 through 47058).
    Lease costs are unique in that they are not broken out as a 
separate cost category in the proposed 2016-based IRF market basket. 
Rather, we are proposing to proportionally distribute these costs among 
the cost categories of Depreciation, Interest, and Other Capital-
Related, reflecting the assumption that the underlying cost structure 
of leases is similar to that of capital-related costs in general. As 
was done under the 2012-based IRF market basket, we are proposing to 
assume that 10 percent of the lease costs as a proportion of total 
capital-related costs represents overhead and assign those costs to the 
Other Capital-Related cost category accordingly. We propose to 
distribute the remaining lease costs proportionally across the three 
cost categories (Depreciation, Interest, and Other Capital-Related) 
based on the proportion that these categories comprise of the sum of 
the Depreciation, Interest, and Other Capital-related cost categories 
(excluding lease expenses). This would result in three primary capital-
related cost categories in the proposed 2016-based IRF market basket: 
Depreciation, Interest, and Other Capital-Related costs. This is the 
same methodology used for the 2012-based IRF market basket (80 FR 
47058). The allocation of these lease expenses are shown in Table 6.
    Finally, we are proposing to further divide the Depreciation and 
Interest cost categories. We are proposing to separate Depreciation 
into the following two categories: (1) Building and Fixed Equipment and 
(2) Movable Equipment. We are proposing to separate Interest into the 
following two categories: (1) Government/Nonprofit and (2) For-profit.
    To disaggregate the Depreciation cost weight, we need to determine 
the percent of total Depreciation costs for IRFs that is attributable 
to Building and Fixed Equipment, which we hereafter refer to as the 
``fixed percentage.'' For the proposed 2016-based IRF market basket, we 
are proposing to use slightly different methods to obtain the fixed 
percentages for hospital-based IRFs compared to freestanding IRFs.
    For freestanding IRFs, we are proposing to use depreciation data 
from Worksheet A-7 of the 2016 Medicare cost reports. However, for 
hospital-based IRFs, we determined that the fixed percentage for the 
entire facility may not be representative of the hospital-based IRF 
unit due to the entire facility likely employing more sophisticated 
movable assets that are not utilized by the hospital-based IRF. 
Therefore, for hospital-based IRFs, we are proposing to calculate a 
fixed percentage using: (1) Building and fixture capital costs 
allocated to the hospital-based IRF unit as reported on Worksheet B, 
part I, line 41, and (2) building and fixture capital costs for the top 
five ancillary cost centers utilized by hospital-based IRFs. We propose 
to weight these two fixed percentages (inpatient and ancillary) using 
the proportion that each capital cost type represents of total capital 
costs in the proposed 2016-based IRF market basket. We are proposing to 
then weight the fixed percentages for hospital-based and freestanding 
IRFs together using the proportion of total capital costs each provider 
type represents. For both freestanding and hospital-based IRFs, this is 
the same methodology used for the 2012-based IRF market basket (80 FR 
47058).
    To disaggregate the Interest cost weight, we determined the percent 
of total interest costs for IRFs that are attributable to government 
and nonprofit facilities, which is hereafter referred to as the 
``nonprofit percentage,'' as price pressures associated with these 
types of interest costs tend to differ from those for for-profit 
facilities. For the 2016-based IRF market basket, we are proposing to 
use interest costs data from Worksheet A-7 of the 2016 Medicare cost 
reports for both freestanding and hospital-based IRFs. We are proposing 
to determine the percent of total interest costs that are attributed to 
government and nonprofit IRFs separately for hospital-based and 
freestanding IRFs. We then are proposing to weight the nonprofit 
percentages for hospital-based and freestanding IRFs together using the 
proportion of total capital costs that each provider type represents.
    Table 7 provides the proposed detailed capital cost share 
composition estimated from the 2016 IRF Medicare cost reports. These 
detailed capital cost share composition percentages are applied to the 
total Capital-Related cost weight of 9.0 percent explained in detail in 
section V.C.1.a.(6) of this proposed rule.

              Table 7--Capital Cost Share Composition for the Proposed 2016-Based IRF Market Basket
----------------------------------------------------------------------------------------------------------------
                                                                                Capital cost      Capital cost
                                                                                    share             share
                                                                                 composition       composition
                                                                                before lease       after lease
                                                                                   expense           expense
                                                                               allocation (%)    allocation (%)
----------------------------------------------------------------------------------------------------------------
Depreciation................................................................                59                73
Building and Fixed Equipment................................................                37                45
Movable Equipment...........................................................                22                28
Interest....................................................................                13                16
Government/Nonprofit........................................................                 8                 9
For Profit..................................................................                 5                 7
Lease.......................................................................                21  ................
Other.......................................................................                 7                11
----------------------------------------------------------------------------------------------------------------
* Detail may not add to total due to rounding.


[[Page 17267]]

e. Proposed 2016-Based IRF Market Basket Cost Categories and Weights
    Table 8 compares the cost categories and weights for the proposed 
2016-based IRF market basket compared to the 2012-based IRF market 
basket.
BILLING CODE 4120-01-P
[GRAPHIC] [TIFF OMITTED] TP24AP19.009

BILLING CODE 4120-01-C

[[Page 17268]]

2. Selection of Price Proxies
    After developing the cost weights for the proposed 2016-based IRF 
market basket, we select the most appropriate wage and price proxies 
currently available to represent the rate of price change for each 
expenditure category. For the majority of the cost weights, we base the 
price proxies on U.S. Bureau of Labor Statistics (BLS) data and group 
them into one of the following BLS categories:
     Employment Cost Indexes. Employment Cost Indexes (ECIs) 
measure the rate of change in employment wage rates and employer costs 
for employee benefits per hour worked. These indexes are fixed-weight 
indexes and strictly measure the change in wage rates and employee 
benefits per hour. ECIs are superior to Average Hourly Earnings (AHE) 
as price proxies for input price indexes because they are not affected 
by shifts in occupation or industry mix, and because they measure pure 
price change and are available by both occupational group and by 
industry. The industry ECIs are based on the NAICS and the occupational 
ECIs are based on the Standard Occupational Classification System 
(SOC).
     Producer Price Indexes. Producer Price Indexes (PPIs) 
measure the average change over time in the selling prices received by 
domestic producers for their output. The prices included in the PPI are 
from the first commercial transaction for many products and some 
services (https://www.bls.gov/ppi/).
     Consumer Price Indexes. Consumer Price Indexes (CPIs) 
measure the average change over time in the prices paid by urban 
consumers for a market basket of consumer goods and services (https://www.bls.gov/cpi/). CPIs are only used when the purchases are similar to 
those of retail consumers rather than purchases at the producer level, 
or if no appropriate PPIs are available.
    We evaluate the price proxies using the criteria of reliability, 
timeliness, availability, and relevance:
     Reliability. Reliability indicates that the index is based 
on valid statistical methods and has low sampling variability. Widely 
accepted statistical methods ensure that the data were collected and 
aggregated in a way that can be replicated. Low sampling variability is 
desirable because it indicates that the sample reflects the typical 
members of the population. (Sampling variability is variation that 
occurs by chance because only a sample was surveyed rather than the 
entire population.)
     Timeliness. Timeliness implies that the proxy is published 
regularly, preferably at least once a quarter. The market baskets are 
updated quarterly, and therefore, it is important for the underlying 
price proxies to be up-to-date, reflecting the most recent data 
available. We believe that using proxies that are published regularly 
(at least quarterly, whenever possible) helps to ensure that we are 
using the most recent data available to update the market basket. We 
strive to use publications that are disseminated frequently, because we 
believe that this is an optimal way to stay abreast of the most current 
data available.
     Availability. Availability means that the proxy is 
publicly available. We prefer that our proxies are publicly available 
because this will help ensure that our market basket updates are as 
transparent to the public as possible. In addition, this enables the 
public to be able to obtain the price proxy data on a regular basis.
     Relevance. Relevance means that the proxy is applicable 
and representative of the cost category weight to which it is applied. 
The CPIs, PPIs, and ECIs that we have selected to propose in this 
regulation meet these criteria. Therefore, we believe that they 
continue to be the best measure of price changes for the cost 
categories to which they would be applied.
    Table 11 lists all price proxies that we propose to use for the 
proposed 2016-based IRF market basket. Below is a detailed explanation 
of the price proxies we are proposing for each cost category weight.
a. Price Proxies for the Operating Portion of the Proposed 2016-Based 
IRF Market Basket
(1) Wages and Salaries
    We are proposing to continue to use the ECI for Wages and Salaries 
for All Civilian workers in Hospitals (BLS series code 
CIU1026220000000I) to measure the wage rate growth of this cost 
category. This is the same price proxy used in the 2012-based IRF 
market basket (80 FR 47060).
(2) Benefits
    We are proposing to continue to use the ECI for Total Benefits for 
All Civilian workers in Hospitals to measure price growth of this 
category. This ECI is calculated using the ECI for Total Compensation 
for All Civilian workers in Hospitals (BLS series code 
CIU1016220000000I) and the relative importance of wages and salaries 
within total compensation. This is the same price proxy used in the 
2012-based IRF market basket (80 FR 47060).
(3) Electricity
    We are proposing to continue to use the PPI Commodity Index for 
Commercial Electric Power (BLS series code WPU0542) to measure the 
price growth of this cost category. This is the same price proxy used 
in the 2012-based IRF market basket (80 FR 47060).
(4) Fuel, Oil, and Gasoline
    Similar to the 2012-based IRF market basket, for the 2016-based IRF 
market basket, we are proposing to use a blend of the PPI for Petroleum 
Refineries and the PPI Commodity for Natural Gas. Our analysis of the 
Bureau of Economic Analysis' 2012 Benchmark Input-Output data (use 
table before redefinitions, purchaser's value for NAICS 622000 
[Hospitals]), shows that Petroleum Refineries expenses account for 
approximately 90 percent and Natural Gas expenses account for 
approximately 10 percent of Hospitals' (NAICS 622000) total Fuel, Oil, 
and Gasoline expenses. Therefore, we propose to use a blend of 90 
percent of the PPI for Petroleum Refineries (BLS series code 
PCU324110324110) and 10 percent of the PPI Commodity Index for Natural 
Gas (BLS series code WPU0531) as the price proxy for this cost 
category. The 2012-based IRF market basket used a 70/30 blend of these 
price proxies, reflecting the 2007 I-O data (80 FR 47060). We believe 
that these two price proxies continue to be the most technically 
appropriate indices available to measure the price growth of the Fuel, 
Oil, and Gasoline cost category in the proposed 2016-based IRF market 
basket.
(5) Professional Liability Insurance
    We are proposing to continue to use the CMS Hospital Professional 
Liability Index to measure changes in PLI premiums. To generate this 
index, we collect commercial insurance premiums for a fixed level of 
coverage while holding non-price factors constant (such as a change in 
the level of coverage). This is the same proxy used in the 2012-based 
IRF market basket (80 FR 47060).
(6) Pharmaceuticals
    We are proposing to continue to use the PPI for Pharmaceuticals for 
Human Use, Prescription (BLS series code WPUSI07003) to measure the 
price growth of this cost category. This is the same proxy used in the 
2012-based IRF market basket (80 FR 47060).
(7) Food: Direct Purchases
    We are proposing to continue to use the PPI for Processed Foods and 
Feeds (BLS series code WPU02) to measure the price growth of this cost 
category. This

[[Page 17269]]

is the same proxy used in the 2012-based IRF market basket (80 FR 
47060).
(8) Food: Contract Purchases
    We are proposing to continue to use the CPI for Food Away From Home 
(BLS series code CUUR0000SEFV) to measure the price growth of this cost 
category. This is the same proxy used in the 2012-based IRF market 
basket (80 FR 47060 through 47061).
(9) Chemicals
    Similar to the 2012-based IRF market basket, we are proposing to 
use a four part blended PPI as the proxy for the chemical cost category 
in the proposed 2016-based IRF market basket. The proposed blend is 
composed of the PPI for Industrial Gas Manufacturing, Primary Products 
(BLS series code PCU325120325120P), the PPI for Other Basic Inorganic 
Chemical Manufacturing (BLS series code PCU32518-32518-), the PPI for 
Other Basic Organic Chemical Manufacturing (BLS series code PCU32519-
32519-), and the PPI for Other Miscellaneous Chemical Product 
Manufacturing (BLS series code PCU325998325998). We note that the four 
part blended PPI used in the 2012-based IRF market basket is composed 
of the PPI for Industrial Gas Manufacturing (BLS series code 
PCU325120325120P), the PPI for Other Basic Inorganic Chemical 
Manufacturing (BLS series code PCU32518-32518-), the PPI for Other 
Basic Organic Chemical Manufacturing (BLS series code PCU32519-32519-), 
and the PPI for Soap and Cleaning Compound Manufacturing (BLS series 
code PCU32561-32561-). For the proposed 2016-based IRF market basket, 
we are proposing to derive the weights for the PPIs using the 2012 
Benchmark I-O data. The 2012-based IRF market basket used the 2007 
Benchmark I-O data to derive the weights for the four PPIs (80 FR 
47061).
    Table 9 shows the weights for each of the four PPIs used to create 
the proposed blended Chemical proxy for the proposed 2016 IRF market 
basket compared to the 2012-based blended Chemical proxy.
[GRAPHIC] [TIFF OMITTED] TP24AP19.010

(10) Medical Instruments
    We are proposing to continue to use a blend of two PPIs for the 
Medical Instruments cost category. The 2012 Benchmark Input-Output data 
shows an approximate 57/43 split between Surgical and Medical 
Instruments and Medical and Surgical Appliances and Supplies for this 
cost category. Therefore, we propose a blend composed of 57 percent of 
the commodity-based PPI for Surgical and Medical Instruments (BLS 
series code WPU1562) and 43 percent of the commodity-based PPI for 
Medical and Surgical Appliances and Supplies (BLS series code WPU1563). 
The 2012-based IRF market basket used a 50/50 blend of these PPIs based 
on the 2007 Benchmark I-O data (80 FR 47061).
(11) Rubber and Plastics
    We are proposing to continue to use the PPI for Rubber and Plastic 
Products (BLS series code WPU07) to measure price growth of this cost 
category. This is the same proxy used in the 2012-based IRF market 
basket (80 FR 47061).
(12) Paper and Printing Products
    We are proposing to continue to use the PPI for Converted Paper and 
Paperboard Products (BLS series code WPU0915) to measure the price 
growth of this cost category. This is the same proxy used in the 2012-
based IRF market basket (80 FR 47061).
(13) Miscellaneous Products
    We are proposing to continue to use the PPI for Finished Goods Less 
Food and Energy (BLS series code WPUFD4131) to measure the price growth 
of this cost category. This is the same proxy used in the 2012-based 
IRF market basket (80 FR 47061).
(14) Professional Fees: Labor-Related
    We are proposing to continue to use the ECI for Total Compensation 
for Private Industry workers in Professional and Related (BLS series 
code CIU2010000120000I) to measure the price growth of this category. 
This is the same proxy used in the 2012-based IRF market basket (80 FR 
47061).
(15) Administrative and Facilities Support Services
    We are proposing to continue to use the ECI for Total Compensation 
for Private Industry workers in Office and Administrative Support (BLS 
series code CIU2010000220000I) to measure the price growth of this 
category. This is the same proxy used in the 2012-based IRF market 
basket (80 FR 47061).
(16) Installation, Maintenance, and Repair
    We are proposing to continue to use the ECI for Total Compensation 
for Civilian workers in Installation, Maintenance, and Repair (BLS 
series code CIU1010000430000I) to measure the price growth of this cost 
category. This is the same proxy used in the 2012-based IRF market 
basket (80 FR 47061).
(17) All Other: Labor-Related Services
    We are proposing to continue to use the ECI for Total Compensation 
for Private Industry workers in Service Occupations (BLS series code 
CIU2010000300000I) to measure the price growth of this cost category. 
This is the same proxy used in the 2012-based IRF market basket (80 FR 
47061).
(18) Professional Fees: Nonlabor-Related
    We are proposing to continue to use the ECI for Total Compensation 
for Private Industry workers in Professional and Related (BLS series 
code CIU2010000120000I) to measure the price growth of this category. 
This is the same proxy used in the 2012-based IRF market basket (80 FR 
47061).
(19) Financial Services
    We are proposing to continue to use the ECI for Total Compensation 
for Private Industry workers in Financial

[[Page 17270]]

Activities (BLS series code CIU201520A000000I) to measure the price 
growth of this cost category. This is the same proxy used in the 2012-
based IRF market basket (80 FR 47061).
(20) Telephone Services
    We are proposing to continue to use the CPI for Telephone Services 
(BLS series code CUUR0000SEED) to measure the price growth of this cost 
category. This is the same proxy used in the 2012-based IRF market 
basket (80 FR 47061).
(21) All Other: Nonlabor-Related Services
    We are proposing to continue to use the CPI for All Items Less Food 
and Energy (BLS series code CUUR0000SA0L1E) to measure the price growth 
of this cost category. This is the same proxy used in the 2012-based 
IRF market basket (80 FR 47061).
b. Price Proxies for the Capital Portion of the Proposed 2016-Based IRF 
Market Basket
(1) Capital Price Proxies Prior to Vintage Weighting
    We are proposing to continue to use the same price proxies for the 
capital-related cost categories in the proposed 2016-based IRF market 
basket as were used in the 2012-based IRF market basket (80 FR 47062), 
which are provided in Table 10 and described below. Specifically, we 
are proposing to proxy:
     Depreciation: Building and Fixed Equipment cost category 
by BEA's Chained Price Index for Nonresidential Construction for 
Hospitals and Special Care Facilities (BEA Table 5.4.4. Price Indexes 
for Private Fixed Investment in Structures by Type).
     Depreciation: Movable Equipment cost category by the PPI 
for Machinery and Equipment (BLS series code WPU11).
     Nonprofit Interest cost category by the average yield on 
domestic municipal bonds (Bond Buyer 20-bond index).
     For-profit Interest cost category by the average yield on 
Moody's Aaa bonds (Federal Reserve).
     Other Capital-Related cost category by the CPI-U for Rent 
of Primary Residence (BLS series code CUUS0000SEHA).
    We believe these are the most appropriate proxies for IRF capital-
related costs that meet our selection criteria of relevance, 
timeliness, availability, and reliability. We are also proposing to 
continue to vintage weight the capital price proxies for Depreciation 
and Interest to capture the long-term consumption of capital. This 
vintage weighting method is similar to the method used for the 2012-
based IRF market basket (80 FR 47062) and is described below.
(2) Vintage Weights for Price Proxies
    Because capital is acquired and paid for over time, capital-related 
expenses in any given year are determined by both past and present 
purchases of physical and financial capital. The vintage-weighted 
capital-related portion of the proposed 2016-based IRF market basket is 
intended to capture the long-term consumption of capital, using vintage 
weights for depreciation (physical capital) and interest (financial 
capital). These vintage weights reflect the proportion of capital-
related purchases attributable to each year of the expected life of 
building and fixed equipment, movable equipment, and interest. We are 
proposing to use vintage weights to compute vintage-weighted price 
changes associated with depreciation and interest expenses.
    Capital-related costs are inherently complicated and are determined 
by complex capital-related purchasing decisions, over time, based on 
such factors as interest rates and debt financing. In addition, capital 
is depreciated over time instead of being consumed in the same period 
it is purchased. By accounting for the vintage nature of capital, we 
are able to provide an accurate and stable annual measure of price 
changes. Annual non-vintage price changes for capital are unstable due 
to the volatility of interest rate changes, and therefore, do not 
reflect the actual annual price changes for IRF capital-related costs. 
The capital-related component of the proposed 2016-based IRF market 
basket reflects the underlying stability of the capital-related 
acquisition process.
    The methodology used to calculate the vintage weights for the 
proposed 2016-based IRF market basket is the same as that used for the 
2012-based IRF market basket (80 FR 47062 through 47063) with the only 
difference being the inclusion of more recent data. To calculate the 
vintage weights for depreciation and interest expenses, we first need a 
time series of capital-related purchases for building and fixed 
equipment and movable equipment. We found no single source that 
provides an appropriate time series of capital-related purchases by 
hospitals for all of the above components of capital purchases. The 
early Medicare cost reports did not have sufficient capital-related 
data to meet this need. Data we obtained from the American Hospital 
Association (AHA) do not include annual capital-related purchases. 
However, we are able to obtain data on total expenses back to 1963 from 
the AHA. Consequently, we are proposing to use data from the AHA Panel 
Survey and the AHA Annual Survey to obtain a time series of total 
expenses for hospitals. We are then proposing to use data from the AHA 
Panel Survey supplemented with the ratio of depreciation to total 
hospital expenses obtained from the Medicare cost reports to derive a 
trend of annual depreciation expenses for 1963 through 2016. We propose 
to separate these depreciation expenses into annual amounts of building 
and fixed equipment depreciation and movable equipment depreciation as 
determined earlier. From these annual depreciation amounts, we derive 
annual end-of-year book values for building and fixed equipment and 
movable equipment using the expected life for each type of asset 
category. While data is not available that is specific to IRFs, we 
believe this information for all hospitals serves as a reasonable 
alternative for the pattern of depreciation for IRFs.
    To continue to calculate the vintage weights for depreciation and 
interest expenses, we also need to account for the expected lives for 
Building and Fixed Equipment, Movable Equipment, and Interest for the 
proposed 2016-based IRF market basket. We are proposing to calculate 
the expected lives using Medicare cost report data from freestanding 
and hospital-based IRFs. The expected life of any asset can be 
determined by dividing the value of the asset (excluding fully 
depreciated assets) by its current year depreciation amount. This 
calculation yields the estimated expected life of an asset if the rates 
of depreciation were to continue at current year levels, assuming 
straight-line depreciation. We are proposing to determine the expected 
life of building and fixed equipment separately for hospital-based IRFs 
and freestanding IRFs, and then weight these expected lives using the 
percent of total capital costs each provider type represents. We are 
proposing to apply a similar method for movable equipment. Using these 
proposed methods, we determined the average expected life of building 
and fixed equipment to be equal to 22 years, and the average expected 
life of movable equipment to be equal to 11 years. For the expected 
life of interest, we believe vintage weights for interest should 
represent the average expected life of building and fixed equipment 
because, based on previous research described in the FY 1997 IPPS final 
rule (61 FR 46198), the expected life of hospital debt instruments and 
the expected life of buildings and fixed equipment are similar. We note 
that for the 2012-based

[[Page 17271]]

IRF market basket, the expected life of building and fixed equipment is 
23 years, and the expected life of movable equipment is 11 years (80 FR 
47062).
    Multiplying these expected lives by the annual depreciation amounts 
results in annual year-end asset costs for building and fixed equipment 
and movable equipment. We then calculate a time series, beginning in 
1964, of annual capital purchases by subtracting the previous year's 
asset costs from the current year's asset costs.
    For the building and fixed equipment and movable equipment vintage 
weights, we are proposing to use the real annual capital-related 
purchase amounts for each asset type to capture the actual amount of 
the physical acquisition, net of the effect of price inflation. These 
real annual capital-related purchase amounts are produced by deflating 
the nominal annual purchase amount by the associated price proxy as 
provided earlier in this proposed rule. For the interest vintage 
weights, we are proposing to use the total nominal annual capital-
related purchase amounts to capture the value of the debt instrument 
(including, but not limited to, mortgages and bonds). Using these 
capital-related purchase time series specific to each asset type, we 
are proposing to calculate the vintage weights for building and fixed 
equipment, for movable equipment, and for interest.
    The vintage weights for each asset type are deemed to represent the 
average purchase pattern of the asset over its expected life (in the 
case of building and fixed equipment and interest, 22 years, and in the 
case of movable equipment, 11 years). For each asset type, we used the 
time series of annual capital-related purchase amounts available from 
2016 back to 1964. These data allow us to derive thirty-two 22-year 
periods of capital-related purchases for building and fixed equipment 
and interest, and forty-three 11-year periods of capital-related 
purchases for movable equipment. For each 22-year period for building 
and fixed equipment and interest, or 11-year period for movable 
equipment, we calculate annual vintage weights by dividing the capital-
related purchase amount in any given year by the total amount of 
purchases over the entire 22-year or 11-year period. This calculation 
is done for each year in the 22-year or 11-year period and for each of 
the periods for which we have data. We then calculate the average 
vintage weight for a given year of the expected life by taking the 
average of these vintage weights across the multiple periods of data. 
The vintage weights for the capital-related portion of the proposed 
2016-based IRF market basket and the 2012-based IRF market basket are 
presented in Table 10.
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BILLING CODE 4120-01-C
    The process of creating vintage-weighted price proxies requires 
applying the vintage weights to the price proxy index where the last 
applied vintage weight in Table 8 is applied to the most recent data 
point. We have provided on the CMS website an example of how the 
vintage weighting price proxies are calculated, using

[[Page 17272]]

example vintage weights and example price indices. The example can be 
found at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch.html in the zip file titled ``Weight Calculations 
as described in the IPPS FY 2010 Proposed Rule.''
c. Summary of Price Proxies of the Proposed 2016-Based IRF Market 
Basket
    Table 11 shows both the operating and capital price proxies for the 
proposed 2016-based IRF market basket.

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[[Page 17274]]

D. Proposed FY 2020 Market Basket Update and Productivity Adjustment

1. Proposed FY 2020 Market Basket Update
    For FY 2020 (that is, beginning October 1, 2019 and ending 
September 30, 2020), we are proposing to use the proposed 2016-based 
IRF market basket increase factor described in section V.C. of this 
proposed rule to update the IRF PPS base payment rate. Consistent with 
historical practice, we are proposing to estimate the market basket 
update for the IRF PPS based on IHS Global Inc.'s (IGI's) forecast 
using the most recent available data. IGI is a nationally recognized 
economic and financial forecasting firm with which we contract to 
forecast the components of the market baskets and multifactor 
productivity (MFP).
    Based on IGI's first quarter 2019 forecast with historical data 
through the fourth quarter of 2018, the projected proposed 2016-based 
IRF market basket increase factor for FY 2020 is 3.0 percent. 
Therefore, consistent with our historical practice of estimating market 
basket increases based on the best available data, we are proposing a 
market basket increase factor of 3.0 percent for FY 2020. We are also 
proposing that if more recent data are subsequently available (for 
example, a more recent estimate of the market basket) we would use such 
data to determine the FY 2020 update in the final rule. For comparison, 
the current 2012-based IRF market basket is also projected to increase 
by 3.0 percent in FY 2020 based on IGI's first quarter 2019 forecast. 
Table 12 compares the proposed 2016-based IRF market basket and the 
2012-based IRF market basket percent changes. On average, the two 
indexes produce similar updates to one another, with the 5-year average 
historical and forecasted growth rates for both IRF market baskets 
equal to 2.1 percent and 3.0 percent, respectively.
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2. Proposed Productivity Adjustment
    According to section 1886(j)(3)(C)(i) of the Act, the Secretary 
shall establish an increase factor based on an appropriate percentage 
increase in a market basket of goods and services. As described in 
sections V.C and V.D.1. of this proposed rule, we are proposing to 
estimate the IRF PPS increase factor for FY 2020 based on the proposed 
2016-based IRF market basket. Section 1886(j)(3)(C)(ii) of the Act then 
requires that, after establishing the increase factor for a FY, the 
Secretary shall reduce such increase factor for FY 2012 and each 
subsequent FY, by the productivity adjustment described in section 
1886(b)(3)(B)(xi)(II) of the Act. Section 1886(b)(3)(B)(xi)(II) of the 
Act sets forth the definition of this productivity adjustment. The 
statute defines the productivity adjustment to be equal to the 10-year 
moving average of changes in annual economy-wide private nonfarm 
business MFP (as projected by the Secretary for the 10-year period 
ending with the applicable FY, year, cost reporting period, or other 
annual period) (the ``MFP adjustment''). The BLS publishes the official 
measure of private nonfarm business MFP. Please see https://www.bls.gov/mfp for the BLS historical published MFP data.
    MFP is derived by subtracting the contribution of labor and capital 
input growth from output growth. The projections of the components of 
MFP are currently produced by IGI, a nationally recognized economic 
forecasting firm with which CMS contracts to forecast the components of 
the market basket and MFP. For more information on the productivity 
adjustment, we refer reader to the discussion in the FY 2016 IRF PPS 
final rule (80 FR 47065).
    Using IGI's first quarter 2019 forecast, the MFP adjustment for FY 
2020 (the 10-year moving average of MFP for the period ending FY 2020) 
is projected to be 0.5 percent. Thus, in accordance with section 
1886(j)(3)(C) of the Act, we propose to base the FY 2020 market basket 
update, which is used to determine the applicable percentage increase 
for the IRF payments, on the most recent estimate of the proposed 2016-
based IRF market basket (currently estimated to be 3.0 percent based on 
IGI's first quarter 2019 forecast). We propose to then reduce this 
percentage increase by the current estimate of the MFP adjustment for 
FY 2020 of 0.5 percentage point (the 10-year moving average of MFP for 
the period ending FY 2020 based on IGI's first quarter 2019 forecast). 
Therefore, the current estimate of the FY 2020 IRF update is 2.5 
percent (3.0 percent market basket update, less 0.5 percentage point 
MFP adjustment). Furthermore, we propose that if more recent data are 
subsequently available (for example, a more recent estimate of the 
market basket and MFP adjustment), we would use such data to determine 
the FY 2020 market basket update and MFP adjustment in the final rule.

[[Page 17275]]

    For FY 2020, the Medicare Payment Advisory Commission (MedPAC) 
recommends that a decrease of 5 percent be applied to IRF PPS payment 
rates. As discussed, and in accordance with section 1886(j)(3)(C) of 
the Act, the Secretary proposes to update IRF PPS payment rates for FY 
2020 by an adjusted market basket increase factor of 2.5 percent, as 
section 1886(j)(3)(C) of the Act does not provide the Secretary with 
the authority to apply a different update factor to IRF PPS payment 
rates for FY 2020.
    We invite public comment on these proposals.

E. Proposed Labor-Related Share for FY 2020

    Section 1886(j)(6) of the Act specifies that the Secretary is to 
adjust the proportion (as estimated by the Secretary from time to time) 
of rehabilitation facilities' costs which are attributable to wages and 
wage-related costs, of the prospective payment rates computed under 
section 1886(j)(3) of the Act for area differences in wage levels by a 
factor (established by the Secretary) reflecting the relative hospital 
wage level in the geographic area of the rehabilitation facility 
compared to the national average wage level for such facilities. The 
labor-related share is determined by identifying the national average 
proportion of total costs that are related to, influenced by, or vary 
with the local labor market. We propose to continue to classify a cost 
category as labor-related if the costs are labor-intensive and vary 
with the local labor market. As stated in the FY 2016 IRF PPS final 
rule (80 FR 47068), the labor-related share was defined as the sum of 
the relative importance of Wages and Salaries, Employee Benefits, 
Professional Fees: Labor-related Services, Administrative and 
Facilities Support Services, Installation, Maintenance, and Repair, All 
Other: Labor-related Services, and a portion of the Capital Costs from 
the 2012-based IRF market basket.
    Based on our definition of the labor-related share and the cost 
categories in the proposed 2016-based IRF market basket, we are 
proposing to include in the labor-related share for FY 2020 the sum of 
the FY 2020 relative importance of Wages and Salaries, Employee 
Benefits, Professional Fees: Labor-related, Administrative and 
Facilities Support Services, Installation, Maintenance, and Repair, All 
Other: Labor-related Services, and a portion of the Capital-Related 
cost weight from the proposed 2016-based IRF market basket.
    Similar to the 2012-based IRF market basket (80 FR 47067), the 
proposed 2016-based IRF market basket includes two cost categories for 
nonmedical Professional Fees (including, but not limited to, expenses 
for legal, accounting, and engineering services). These are 
Professional Fees: Labor-related and Professional Fees: Nonlabor-
related. For the proposed 2016-based IRF market basket, we propose to 
estimate the labor-related percentage of non-medical professional fees 
(and assign these expenses to the Professional Fees: Labor-related 
services cost category) based on the same method that was used to 
determine the labor-related percentage of professional fees in the 
2012-based IRF market basket.
    As was done in the 2012-based IRF market basket (80 FR 47067), we 
propose to determine the proportion of legal, accounting and auditing, 
engineering, and management consulting services that meet our 
definition of labor-related services based on a survey of hospitals 
conducted by us in 2008, a discussion of which can be found in the FY 
2010 IPPS/LTCH PPS final rule (74 FR 43850 through 43856). Based on the 
weighted results of the survey, we determined that hospitals purchase, 
on average, the following portions of contracted professional services 
outside of their local labor market:
     34 percent of accounting and auditing services.
     30 percent of engineering services.
     33 percent of legal services.
     42 percent of management consulting services.
    We are proposing to apply each of these percentages to the 
respective Benchmark I-O cost category underlying the professional fees 
cost category to determine the Professional Fees: Nonlabor-related 
costs. The Professional Fees: Labor-related costs were determined to be 
the difference between the total costs for each Benchmark I-O category 
and the Professional Fees: Nonlabor-related costs. This is the same 
methodology that we used to separate the 2012-based IRF market basket 
professional fees category into Professional Fees: Labor-related and 
Professional Fees: Nonlabor-related cost categories (80 FR 47067).
    In the proposed 2016-based IRF market basket, nonmedical 
professional fees that are subject to allocation based on these survey 
results represent 4.4 percent of total costs (and are limited to those 
fees related to Accounting & Auditing, Legal, Engineering, and 
Management Consulting services). Based on our survey results, we 
propose to apportion 2.8 percentage points of the 4.4 percentage point 
figure into the Professional Fees: Labor-related share cost category 
and designate the remaining 1.6 percentage point into the Professional 
Fees: Nonlabor-related cost category.
    In addition to the professional services listed, for the 2016-based 
IRF market basket, we are proposing to allocate a proportion of the 
Home Office Contract Labor cost weight, calculated using the Medicare 
cost reports as stated above, into the Professional Fees: Labor-related 
and Professional Fees: Nonlabor-related cost categories. We are 
proposing to classify these expenses as labor-related and nonlabor-
related as many facilities are not located in the same geographic area 
as their home office, and therefore, do not meet our definition for the 
labor-related share that requires the services to be purchased in the 
local labor market. For the 2012-based IRF market basket, we used the 
BEA I-O expense data for NAICS 55, Management of Companies and 
Enterprises, to estimate the Home Office Contract Labor cost weight (80 
FR 47067). We then allocated these expenses into the Professional Fess: 
Labor-related and Professional Fees: Nonlabor-related cost categories.
    Similar to the 2012-based IRF market basket, we are proposing for 
the 2016-based IRF market basket to use the Medicare cost reports for 
both freestanding IRF providers and hospital-based IRF providers to 
determine the home office labor-related percentages. The Medicare cost 
report requires a hospital to report information regarding their home 
office provider. For the 2016-based IRF market basket, we are proposing 
to start with the sample of IRF providers that passed the top 1 percent 
trim used to derive the Home Office Contract Labor cost weight as 
described in section V.B. of this proposed rule. For both freestanding 
and hospital-based providers, we are proposing to multiply each 
provider's Home Office Contract Labor cost weight (calculated using 
data from the total facility) by Medicare allowable total costs. This 
results in an amount of Medicare allowable home office compensation 
costs for each IRF. Using information on the Medicare cost report, we 
then compare the location of the IRF with the location of the IRF's 
home office. We are proposing to classify an IRF with a home office 
located in their respective local labor market if the IRF and its home 
office are located in the same Metropolitan Statistical Area. We then 
calculate the proportion of Medicare allowable home office compensation 
costs that these IRFs represent of total Medicare allowable home office 
compensation costs. We

[[Page 17276]]

propose to multiply this percentage (42 percent) by the Home Office 
Contract Labor cost weight (3.7 percent) to determine the proportion of 
costs that should be allocated to the labor-related share. Therefore, 
we are allocating 1.6 percentage points of the Home Office Contract 
Labor cost weight (3.7 percent times 42 percent) to the Professional 
Fees: Labor-related cost weight and 2.1 percentage points of the Home 
Office Contract Labor cost weight to the Professional Fees: Nonlabor-
related cost weight (3.7 percent times 58 percent). For the 2012-based 
IRF market basket, we used a similar methodology but we relied on 
provider counts rather than home office/related organization contract 
labor compensation costs to determine the labor-related percentage (80 
FR 47067).
    In summary, we apportioned 2.8 percentage points of the non-medical 
professional fees and 1.6 percentage points of the home office/related 
organization contract labor cost weights into the Professional Fees: 
Labor-related cost category. This amount was added to the portion of 
professional fees that was identified to be labor-related using the I-O 
data such as contracted advertising and marketing costs (approximately 
0.6 percentage point of total costs) resulting in a Professional Fees: 
Labor-related cost weight of 5.0 percent.
    As stated previously, we are proposing to include in the labor-
related share the sum of the relative importance of Wages and Salaries, 
Employee Benefits, Professional Fees: Labor- Related, Administrative 
and Facilities Support Services, Installation, Maintenance, and Repair, 
All Other: Labor-related Services, and a portion of the Capital-Related 
cost weight from the proposed 2016-based IRF market basket. The 
relative importance reflects the different rates of price change for 
these cost categories between the base year (2016) and FY 2020. Based 
on IGI's 1st quarter 2019 forecast for the proposed 2016-based IRF 
market basket, the sum of the FY 2020 relative importance for Wages and 
Salaries, Employee Benefits, Professional Fees: Labor-related, 
Administrative and Facilities Support Services, Installation 
Maintenance & Repair Services, and All Other: Labor-related Services is 
68.7 percent. The portion of Capital costs that is influenced by the 
local labor market is estimated to be 46 percent, which is the same 
percentage applied to the 2012-based IRF market basket (80 FR 47068). 
Since the relative importance for Capital is 8.5 percent of the 
proposed 2016-based IRF market basket in FY 2020, we took 46 percent of 
8.5 percent to determine the proposed labor-related share of Capital 
for FY 2020 of 3.9 percent. Therefore, we are proposing a total labor-
related share for FY 2020 of 72.6 percent (the sum of 68.7 percent for 
the operating costs and 3.9 percent for the labor-related share of 
Capital). Table 13 shows the FY 2020 labor-related share using the 
proposed 2016-based IRF market basket relative importance and the FY 
2019 labor-related share using the 2012-based IRF market basket 
relative importance.

             Table 13--Proposed FY 2020 IRF Labor-Related Share and FY 2019 IRF Labor-Related Share
----------------------------------------------------------------------------------------------------------------
                                                                              FY 2020 proposed    FY 2019 final
                                                                                labor-related     labor related
                                                                                  share \1\         share \2\
----------------------------------------------------------------------------------------------------------------
Wages and Salaries..........................................................              48.1              47.7
Employee Benefits...........................................................              11.4              11.1
Professional Fees: Labor-related \3\........................................               5.0               3.4
Administrative and Facilities Support Services..............................               0.8               0.8
Installation, Maintenance, and Repair.......................................               1.6               1.9
All Other: Labor-related Services...........................................               1.8               1.8
                                                                             -----------------------------------
    Subtotal................................................................              68.7              66.7
----------------------------------------------------------------------------------------------------------------
Labor-related portion of capital (46%)......................................               3.9               3.8
                                                                             -----------------------------------
        Total Labor-Related Share...........................................              72.6              70.5
----------------------------------------------------------------------------------------------------------------
\1\ Based on the proposed 2016-based IRF Market Basket, IHS Global Insight, Inc. 1st quarter 2019 forecast.
\2\ Based on the 2012-based IRF market basket as published in the Federal Register (83 FR 38526).
\3\ Includes all contract advertising and marketing costs and a portion of accounting, architectural,
  engineering, legal, management consulting, and home office contract labor costs.

    We invite public comment on the proposed labor-related share for FY 
2020.

F. Proposed Update to the IRF Wage Index To Use Concurrent FY IPPS Wage 
Index Beginning With FY 2020

1. Background
    Section 1886(j)(6) of the Act requires the Secretary to adjust the 
proportion of rehabilitation facilities' costs attributable to wages 
and wage-related costs (as estimated by the Secretary from time to 
time) by a factor (established by the Secretary) reflecting the 
relative hospital wage level in the geographic area of the 
rehabilitation facility compared to the national average wage level for 
those facilities. The Secretary is required to update the IRF PPS wage 
index on the basis of information available to the Secretary on the 
wages and wage-related costs to furnish rehabilitation services. Any 
adjustment or updates made under section 1886(j)(6) of the Act for a FY 
are made in a budget-neutral manner.
2. Proposed Update to the IRF Wage Index To Use Concurrent FY IPPS Wage 
Index Beginning With FY 2020
    When the IRF PPS was implemented in the FY 2002 IRF PPS final rule 
(66 FR 41358), we finalized the use of the IPPS wage data in the 
creation of an IRF wage index. We believed that a wage index based on 
IPPS wage data was the best proxy and most appropriate wage index to 
use in adjusting payments to IRFs, since both IPPS hospitals and IRFs 
compete in the same labor markets. For this reason, we believed, and 
continue to believe, that the wage data of IPPS hospitals accurately 
captures the relationship of wages and wage-related costs of IRFs in an 
area as compared with the national average. Therefore, in the FY 2002 
IRF PPS final rule, we finalized use of the FY 1997 IPPS wage data to 
develop the wage index for the IRF PPS, as that was the most recent 
final data available.

[[Page 17277]]

    For all subsequent years in which the IRF PPS wage index has been 
updated, we have continued to use the most recent final IPPS data 
available, which has led us to use the pre-floor, pre-reclassified IPPS 
wage index values from the prior fiscal year.
    In the FY 2018 IRF PPS proposed rule (82 FR 20742 through 20743), 
we included a request for information (RFI) to solicit comments from 
stakeholders requesting information on CMS flexibilities and 
efficiencies. The purpose of the RFI was to receive feedback regarding 
ways in which we could reduce burden for hospitals and physicians, 
improve quality of care, decrease costs and ensure that patients 
receive the best care. We received comments from IRF industry 
associations, state and national hospital associations, industry 
groups, representing hospitals, and individual IRF providers in 
response to the solicitation. One of the responses we received to the 
RFI suggested that there is concern among IRF stakeholders about the 
different wage index data used in the different post-acute care 
settings. For the IRF PPS, we use a one-year lag of the pre-floor, pre-
reclassified IPPS wage index, meaning that for the IRF PPS for FY 2019, 
we finalized use of the FY 2018 IPPS wage index (83 FR 38527). However, 
we base the wage indexes for the SNF PPS and the LTCH PPS on the 
concurrent year's IPPS wage index ((83 FR 39172 through 39178) and (83 
FR 41731), respectively).
    As we look towards a more unified post-acute care payment system, 
we believe that standardizing the wage index data across post-acute 
care settings is necessary. Therefore, we are proposing to change the 
IRF wage index methodology to align with other post-acute care 
settings. Specifically, we are proposing to change from our established 
policy of using the pre-floor, pre-reclassified IPPS wage index from 
the prior fiscal year as the basis for the IRF wage index to using, 
instead, the pre-floor, pre-reclassified IPPS wage index from the 
current fiscal year. This proposed change would use the concurrent 
fiscal year's pre-floor, pre-reclassified IPPS wage index for the IRF 
wage index beginning with FY 2020 and continuing for all subsequent 
years. Thus, for the FY 2020 IRF wage index, we would propose to use 
the FY 2020 pre-floor, pre-reclassified IPPS wage index. We are 
proposing to implement these revisions in a budget neutral manner. For 
more information on the impacts of this proposal, we refer readers to 
Table 14. Table 14 shows the estimated effects of maintaining the 
existing wage index methodology for FY 2020 compared to the effects of 
implementing the proposed change to the wage index methodology as 
described above. For a provider specific impact analysis of this 
proposed change, we refer readers to the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
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BILLING CODE 4120-01-C
    Using the current pre-floor, pre-reclassified IPPS wage index would 
result in the most up-to-date wage data being the basis for the IRF 
wage index.

[[Page 17279]]

It would also result in more consistency and equity in the wage index 
methodology used by Medicare.
    We invite comments on this proposal to align the data timeframes 
with that of the IPPS by using the FY 2020 pre-floor, pre-reclassified 
IPPS wage index as the basis for the FY 2020 IRF wage index.
3. Proposed Wage Adjustment for FY 2020 Using Concurrent IPPS Wage 
Index
    Due to our proposal to use the concurrent IPPS wage index beginning 
with FY 2020, for FY 2020, we are proposing to use the policy and 
methodologies described in section V. of this proposed rule related to 
the labor market area definitions and the wage index methodology for 
areas with wage data. Thus, we propose to use the CBSA labor market 
area definitions and the FY 2020 pre-reclassification and pre-floor 
IPPS wage index data. In accordance with section 1886(d)(3)(E) of the 
Act, the FY 2020 pre-reclassification and pre-floor IPPS wage index is 
based on data submitted for hospital cost reporting periods beginning 
on or after October 1, 2015 and before October 1, 2016 (that is, FY 
2016 cost report data).
    The labor market designations made by the OMB include some 
geographic areas where there are no hospitals and, thus, no hospital 
wage index data on which to base the calculation of the IRF PPS wage 
index. We propose to continue to use the same methodology discussed in 
the FY 2008 IRF PPS final rule (72 FR 44299) to address those 
geographic areas where there are no hospitals and, thus, no hospital 
wage index data on which to base the calculation for the FY 2020 IRF 
PPS wage index.
    We invite public comment on this proposal.
4. Core-Based Statistical Areas (CBSAs) for the Proposed FY 2020 IRF 
Wage Index
    The wage index used for the IRF PPS is calculated using the pre-
reclassification and pre-floor IPPS wage index data and is assigned to 
the IRF on the basis of the labor market area in which the IRF is 
geographically located. IRF labor market areas are delineated based on 
the CBSAs established by the OMB. The current CBSA delineations (which 
were implemented for the IRF PPS beginning with FY 2016) are based on 
revised OMB delineations issued on February 28, 2013, in OMB Bulletin 
No. 13-01. OMB Bulletin No. 13-01 established revised delineations for 
Metropolitan Statistical Areas, Micropolitan Statistical Areas, and 
Combined Statistical Areas in the United States and Puerto Rico based 
on the 2010 Census, and provided guidance on the use of the 
delineations of these statistical areas using standards published in 
the June 28, 2010 Federal Register (75 FR 37246 through 37252). We 
refer readers to the FY 2016 IRF PPS final rule (80 FR 47068 through 
47076) for a full discussion of our implementation of the OMB labor 
market area delineations beginning with the FY 2016 wage index.
    Generally, OMB issues major revisions to statistical areas every 10 
years, based on the results of the decennial census. However, OMB 
occasionally issues minor updates and revisions to statistical areas in 
the years between the decennial censuses. On July 15, 2015, OMB issued 
OMB Bulletin No. 15-01, which provides minor updates to and supersedes 
OMB Bulletin No. 13-01 that was issued on February 28, 2013. The 
attachment to OMB Bulletin No. 15-01 provides detailed information on 
the update to statistical areas since February 28, 2013. The updates 
provided in OMB Bulletin No. 15-01 are based on the application of the 
2010 Standards for Delineating Metropolitan and Micropolitan 
Statistical Areas to Census Bureau population estimates for July 1, 
2012 and July 1, 2013.
    In the FY 2018 IRF PPS final rule (82 FR 36250 through 36251), we 
adopted the updates set forth in OMB Bulletin No. 15-01 effective 
October 1, 2017, beginning with the FY 2018 IRF wage index. For a 
complete discussion of the adoption of the updates set forth in OMB 
Bulletin No. 15-01, we refer readers to the FY 2018 IRF PPS final rule. 
In the FY 2019 IRF PPS final rule (83 FR 38527), we continued to use 
the OMB delineations that were adopted beginning with FY 2016 to 
calculate the area wage indexes, with updates set forth in OMB Bulletin 
No. 15-01 that we adopted beginning with the FY 2018 wage index.
    On August 15, 2017, OMB issued OMB Bulletin No. 17-01, which 
provided updates to and superseded OMB Bulletin No. 15-01 that was 
issued on July 15, 2015. The attachments to OMB Bulletin No. 17-01 
provide detailed information on the update to statistical areas since 
July 15, 2015, and are based on the application of the 2010 Standards 
for Delineating Metropolitan and Micropolitan Statistical Areas to 
Census Bureau population estimates for July 1, 2014 and July 1, 2015. 
In OMB Bulletin No. 17-01, OMB announced that one Micropolitan 
Statistical Area now qualifies as a Metropolitan Statistical Area. The 
new urban CBSA is as follows:
     Twin Falls, Idaho (CBSA 46300). This CBSA is comprised of 
the principal city of Twin Falls, Idaho in Jerome County, Idaho and 
Twin Falls County, Idaho.
    The OMB bulletin is available on the OMB website at https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/bulletins/2017/b-17-01.pdf.
    As we indicated in the FY 2019 IRF PPS final rule (83 FR 38528), we 
believe that it is important for the IRF PPS to use the latest labor 
market area delineations available as soon as is reasonably possible to 
maintain a more accurate and up-to-date payment system that reflects 
the reality of population shifts and labor market conditions. As 
discussed in the FY 2019 IPPS and LTCH PPS final rule (83 FR 20591), 
these updated labor market area definitions were implemented under the 
IPPS beginning on October 1, 2018. Therefore, we are proposing to 
implement these revisions for the IRF PPS beginning October 1, 2019, 
consistent with our historical practice of modeling IRF PPS adoption of 
the labor market area delineations after IPPS adoption of these 
delineations.
    We invite public comments on these proposals.
5. Wage Adjustment
    The proposed FY 2020 wage index tables (which, as discussed in 
section V.F above, we propose to base on the FY 2020 pre-reclassified, 
pre-floor FY 2020 IPPS wage index) are available on the CMS website at 
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html. Table A is for 
urban areas, and Table B is for rural areas.
    To calculate the wage-adjusted facility payment for the payment 
rates set forth in this proposed rule, we would multiply the unadjusted 
federal payment rate for IRFs by the FY 2020 labor-related share based 
on the 2016-based IRF market basket (72.6 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 V.E 
of this proposed rule. We would then multiply the labor-related portion 
by the applicable IRF wage index from the tables in the addendum to 
this proposed rule. These tables are available on the CMS website at 
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html. Adjustments or 
updates to the IRF wage index made under section 1886(j)(6) of the Act 
must be made in a

[[Page 17280]]

budget-neutral manner. We propose 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 propose to use the listed steps to ensure that the proposed 
FY 2020 IRF standard payment conversion factor reflects the proposed 
updates to the IRF wage index (based on the FY 2020 IPPS wage index) 
and the labor-related share in a budget-neutral manner:
    Step 1. Determine the total amount of the estimated FY 2019 IRF PPS 
payments, using the FY 2019 standard payment conversion factor and the 
labor-related share and the wage indexes from FY 2019 (as published in 
the FY 2019 IRF PPS final rule (83 FR 38514)).
    Step 2. Calculate the total amount of estimated IRF PPS payments 
using the proposed FY 2020 standard payment conversion factor and the 
proposed FY 2020 labor-related share and CBSA urban and rural wage 
indexes.
    Step 3. Divide the amount calculated in step 1 by the amount 
calculated in step 2. The resulting quotient is the proposed FY 2020 
budget-neutral wage adjustment factor of 1.0076.
    Step 4. Apply the proposed FY 2020 budget-neutral wage adjustment 
factor from step 3 to the FY 2020 IRF PPS standard payment conversion 
factor after the application of the increase factor to determine the FY 
2020 proposed standard payment conversion factor.
    We discuss the calculation of the proposed standard payment 
conversion factor for FY 2020 in section V.H. of this proposed rule.
    We invite public comment on the proposed IRF wage adjustment for FY 
2020.

G. Wage Index Comment Solicitation

    Historically, we have calculated the IRF wage index values using 
unadjusted wage index values from another provider setting. 
Stakeholders have frequently commented on certain aspects of the IRF 
wage index values and their impact on payments. We are soliciting 
comments on concerns stakeholders may have regarding the wage index 
used to adjust IRF payments and suggestions for possible updates and 
improvements to the geographic adjustment of IRF payments.

H. Description of the Proposed IRF Standard Payment Conversion Factor 
and Payment Rates for FY 2020

    To calculate the proposed standard payment conversion factor for FY 
2020, as illustrated in Table 15, we begin by applying the proposed 
increase factor for FY 2020, as adjusted in accordance with sections 
1886(j)(3)(C) of the Act, to the standard payment conversion factor for 
FY 2019 ($16,021). Applying the proposed 2.5 percent increase factor 
for FY 2020 to the standard payment conversion factor for FY 2019 of 
$16,021 yields a standard payment amount of $16,422. Then, we apply the 
proposed budget neutrality factor for the FY 2020 wage index and labor-
related share of 1.0076, which results in a proposed standard payment 
amount of $16,546. We next apply the proposed budget neutrality factor 
for the revised CMGs and CMG relative weights of 1.0016, which results 
in the proposed standard payment conversion factor of $16,573 for FY 
2020.
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    We invite public comment on the proposed FY 2020 standard payment 
conversion factor.
    After the application of the proposed CMG relative weights 
described in section III. of this proposed rule to the proposed FY 2020 
standard payment conversion factor ($16,573), the resulting unadjusted 
IRF prospective payment rates for FY 2020 are shown in Table 16.

[[Page 17281]]

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[[Page 17282]]


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I. Example of the Methodology for Adjusting the Proposed Prospective 
Payment Rates

    Table 17 illustrates the methodology for adjusting the proposed 
prospective payments (as described in section V. of this proposed 
rule). The following examples are based on two hypothetical Medicare 
beneficiaries, both classified into CMG 0107 (without comorbidities). 
The proposed unadjusted prospective payment rate for CMG 0107 (without 
comorbidities) appears in Table 16.
    Example: One beneficiary is in Facility A, an IRF located in rural 
Spencer County, Indiana, and another beneficiary is in Facility B, an 
IRF located in urban Harrison County, Indiana. Facility A, a rural non-
teaching hospital has a Disproportionate Share Hospital (DSH) 
percentage of 5 percent (which would result in a LIP adjustment of 
1.0156), a wage index of 0.8281, and a rural adjustment of 14.9 
percent.

[[Page 17283]]

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 0.8809, and a teaching status adjustment of 0.0784.
    To calculate each IRF's labor and non-labor portion of the proposed 
prospective payment, we begin by taking the unadjusted prospective 
payment rate for CMG 0107 (without comorbidities) from Table 16. Then, 
we multiply the proposed labor-related share for FY 2020 (72.6 percent) 
described in section V.E. of this proposed rule by the proposed 
unadjusted prospective payment rate. To determine the non-labor portion 
of the proposed prospective payment rate, we subtract the labor portion 
of the federal payment from the proposed unadjusted prospective 
payment.
    To compute the proposed wage-adjusted prospective payment, we 
multiply the labor portion of the proposed federal payment by the 
appropriate wage index located in Tables A and B. These tables are 
available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
    The resulting figure is the wage-adjusted labor amount. Next, we 
compute the proposed wage-adjusted federal payment by adding the wage-
adjusted labor amount to the non-labor portion of the proposed federal 
payment.
    Adjusting the proposed wage-adjusted federal payment by the 
facility-level adjustments involves several steps. First, we take the 
wage-adjusted prospective payment and multiply it by the appropriate 
rural and LIP adjustments (if applicable). Second, to determine the 
appropriate amount of additional payment for the teaching status 
adjustment (if applicable), we multiply the teaching status adjustment 
(0.0784, in this example) by the wage-adjusted and rural-adjusted 
amount (if applicable). Finally, we add the additional teaching status 
payments (if applicable) to the wage, rural, and LIP-adjusted 
prospective payment rates. Table 17 illustrates the components of the 
adjusted payment calculation.
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    Thus, the proposed adjusted payment for Facility A would be 
$36,906.90, and the adjusted payment for Facility B would be 
$37,099.73.

VI. Proposed Update to Payments for High-Cost Outliers Under the IRF 
PPS for FY 2020

A. Proposed Update to the Outlier Threshold Amount for FY 2020

    Section 1886(j)(4) of the Act provides the Secretary with the 
authority to make payments in addition to the basic IRF prospective 
payments for cases incurring extraordinarily high costs. A case 
qualifies for an outlier payment if the estimated cost of the case 
exceeds the adjusted outlier threshold. We calculate the adjusted 
outlier threshold by adding the IRF PPS payment for the case (that is, 
the CMG payment adjusted by all of the relevant facility-level 
adjustments) and the adjusted threshold amount (also adjusted by all of 
the relevant facility-level adjustments). Then, we calculate the 
estimated cost of a case by multiplying the IRF's overall CCR by the 
Medicare allowable covered charge. If the estimated cost of the case is 
higher than the adjusted outlier threshold, we make an outlier payment 
for the case equal to 80 percent of the difference between the 
estimated cost of the case and the outlier threshold.
    In the FY 2002 IRF PPS final rule (66 FR 41362 through 41363), we 
discussed our rationale for setting the outlier threshold amount for 
the IRF PPS so that estimated outlier payments would equal 3 percent of 
total estimated payments. For the 2002 IRF PPS final rule, we analyzed 
various outlier policies using 3, 4, and 5 percent of the total 
estimated payments, and we concluded that an outlier policy set at 3 
percent of total estimated payments would optimize the extent to which 
we could reduce the financial risk to IRFs of caring for high-cost 
patients, while still providing for adequate payments for all other 
(non-high cost outlier) cases.
    Subsequently, we updated the IRF outlier threshold amount in the 
FYs 2006 through 2019 IRF PPS final rules and the FY 2011 and FY 2013 
notices (70 FR 47880, 71 FR 48354, 72 FR 44284, 73 FR 46370, 74 FR 
39762, 75 FR 42836, 76 FR 47836, 76 FR 59256, 77 FR

[[Page 17284]]

44618, 78 FR 47860, 79 FR 45872, 80 FR 47036, 81 FR 52056, 82 FR 36238, 
and 83 FR 38514, respectively) to maintain estimated outlier payments 
at 3 percent of total estimated payments. We also stated in the FY 2009 
final rule (73 FR 46370 at 46385) that we would continue to analyze the 
estimated outlier payments for subsequent years and adjust the outlier 
threshold amount as appropriate to maintain the 3 percent target.
    To update the IRF outlier threshold amount for FY 2020, we propose 
to use FY 2018 claims data and the same methodology that we used to set 
the initial outlier threshold amount in the FY 2002 IRF PPS final rule 
(66 FR 41316 and 41362 through 41363), which is also the same 
methodology that we used to update the outlier threshold amounts for 
FYs 2006 through 2019. The outlier threshold is calculated by 
simulating aggregate payments and using an iterative process to 
determine a threshold that results in outlier payments being equal to 3 
percent of total payments under the simulation. To determine the 
outlier threshold for FY 2020, we estimate the amount of FY 2020 IRF 
PPS aggregate and outlier payments using the most recent claims 
available (FY 2018) and the proposed FY 2020 standard payment 
conversion factor, labor-related share, and wage indexes, incorporating 
any applicable budget-neutrality adjustment factors. The outlier 
threshold is adjusted either up or down in this simulation until the 
estimated outlier payments equal 3 percent of the estimated aggregate 
payments. Based on an analysis of the preliminary data used for the 
proposed rule, we estimated that IRF outlier payments as a percentage 
of total estimated payments would be approximately 3.2 percent in FY 
2019. Therefore, we propose to update the outlier threshold amount from 
$9,402 for FY 2019 to $9,935 for FY 2020 to maintain estimated outlier 
payments at approximately 3 percent of total estimated aggregate IRF 
payments for FY 2020.
    We invite public comment on the proposed update to the FY 2020 
outlier threshold amount to maintain estimated outlier payments at 
approximately 3 percent of total estimated IRF payments.

B. Proposed Update to the IRF Cost-to-Charge Ratio Ceiling and Urban/
Rural Averages for FY 2020

    Cost-to-charge ratios are used to adjust charges from Medicare 
claims to costs and are computed annually from facility-specific data 
obtained from Medicare cost reports. IRF specific cost-to-charge ratios 
are used in the development of the CMG relative weights and the 
calculation of outlier payments under the IRF prospective payment 
system. In accordance with the methodology stated in the FY 2004 IRF 
PPS final rule (68 FR 45674, 45692 through 45694), we propose to apply 
a ceiling to IRFs' CCRs. Using the methodology described in that final 
rule, we propose to update the national urban and rural CCRs for IRFs, 
as well as the national CCR ceiling for FY 2020, based on analysis of 
the most recent data that is available. We apply the national urban and 
rural CCRs in the following situations:
     New IRFs that have not yet submitted their first Medicare 
cost report.
     IRFs whose overall CCR is in excess of the national CCR 
ceiling for FY 2020, as discussed below in this section.
     Other IRFs for which accurate data to calculate an overall 
CCR are not available.
    Specifically, for FY 2020, we propose to estimate a national 
average CCR of 0.500 for rural IRFs, which we calculated by taking an 
average of the CCRs for all rural IRFs using their most recently 
submitted cost report data. Similarly, we propose to estimate a 
national average CCR of 0.406 for urban IRFs, which we calculated by 
taking an average of the CCRs for all urban IRFs using their most 
recently submitted cost report data. We apply weights to both of these 
averages using the IRFs' estimated costs, meaning that the CCRs of IRFs 
with higher total costs factor more heavily into the averages than the 
CCRs of IRFs with lower total costs. For this proposed rule, we have 
used the most recent available cost report data (FY 2017). This 
includes all IRFs whose cost reporting periods begin on or after 
October 1, 2016, and before October 1, 2017. If, for any IRF, the FY 
2017 cost report was missing or had an ``as submitted'' status, we used 
data from a previous fiscal year's (that is, FY 2004 through FY 2016) 
settled cost report for that IRF. We do not use cost report data from 
before FY 2004 for any IRF because changes in IRF utilization since FY 
2004 resulting from the 60 percent rule and IRF medical review 
activities suggest that these older data do not adequately reflect the 
current cost of care.
    In accordance with past practice, we propose to set the national 
CCR ceiling at 3 standard deviations above the mean CCR. Using this 
method, we propose a national CCR ceiling of 1.31 for FY 2020. This 
means that, if an individual IRF's CCR were to exceed this ceiling of 
1.31 for FY 2020, we would replace the IRF's CCR with the appropriate 
proposed national average CCR (either rural or urban, depending on the 
geographic location of the IRF). We calculated the proposed national 
CCR ceiling by:
    Step 1. Taking the national average CCR (weighted by each IRF's 
total costs, as previously discussed) of all IRFs for which we have 
sufficient cost report data (both rural and urban IRFs combined).
    Step 2. Estimating the standard deviation of the national average 
CCR computed in step 1.
    Step 3. Multiplying the standard deviation of the national average 
CCR computed in step 2 by a factor of 3 to compute a statistically 
significant reliable ceiling.
    Step 4. Adding the result from step 3 to the national average CCR 
of all IRFs for which we have sufficient cost report data, from step 1.
    The proposed national average rural and urban CCRs and the proposed 
national CCR ceiling in this section will be updated in the final rule 
if more recent data becomes available to use in these analyses.
    We invite public comment on the proposed update to the IRF CCR 
ceiling and the urban/rural averages for FY 2020.

VII. Proposed Amendments to Sec.  412.622 To Clarify the Definition of 
a Rehabilitation Physician

    Under Sec.  412.622(a)(3)(iv), a rehabilitation physician is 
defined as ``a licensed physician with specialized training and 
experience in inpatient rehabilitation.'' The term rehabilitation 
physician is used in several other places in Sec.  412.622, with 
corresponding references to Sec.  412.622(a)(3)(iv). The definition at 
Sec.  412.622(a)(3)(iv) does not specify the level or type of training 
and experience required for a licensed physician to be designated as a 
rehabilitation physician because we believe that the IRFs are in the 
best position to make this determination for purposes of Sec.  412.622.
    Therefore, we propose to amend the definition of a rehabilitation 
physician to clarify that the determination as to whether a physician 
qualifies as a rehabilitation physician (that is, a licensed physician 
with specialized training and experience in inpatient rehabilitation) 
is made by the IRF. For clarity, we also propose to remove this 
definition from Sec.  412.622(a)(3)(iv) and move it to a new paragraph 
(Sec.  412.622(c)). We also propose to make corresponding technical 
corrections elsewhere in Sec.  412.622(a)(3)(iv), (a)(4)(i)(A), 
(a)(4)(iii)(A), and (a)(5)(i) to remove the references to Sec.  
412.622(a)(3)(iv) in those paragraphs,

[[Page 17285]]

so as to reflect the new location of the definition.
    We invite public comment on the proposal to clarify the definition 
of a rehabilitation physician, to move the definition from Sec.  
412.622(a)(3)(iv) to Sec.  412.622(c), and to make corresponding 
technical corrections elsewhere in Sec.  412.622 to remove references 
to the current location of the definition in Sec.  412.622(a)(3)(iv).

VIII. Proposed Revisions and Updates to the IRF Quality Reporting 
Program (QRP)

A. Background

    The Inpatient Rehabilitation Facility Quality Reporting Program 
(IRF QRP) is authorized by section 1886(j)(7) of the Act, and it 
applies to freestanding IRFs, as well as inpatient rehabilitation units 
of hospitals or critical access hospitals (CAHs) paid by Medicare under 
the IRF PPS. Under the IRF QRP, the Secretary must reduce the annual 
increase factor for discharges occurring during such fiscal year by 2 
percentage points for any IRF that does not submit data in accordance 
with the requirements established by the Secretary. For more 
information on the background and statutory authority for the IRF QRP, 
we refer readers to the FY 2012 IRF PPS final rule (76 FR 47873 through 
47874), the CY 2013 Hospital Outpatient Prospective Payment System/
Ambulatory Surgical Center (OPPS/ASC) Payment Systems and Quality 
Reporting Programs final rule (77 FR 68500 through 68503), the FY 2014 
IRF PPS final rule (78 FR 47902), the FY 2015 IRF PPS final rule (79 FR 
45908), the FY 2016 IRF PPS final rule (80 FR 47080 through 47083), the 
FY 2017 IRF PPS final rule (81 FR 52080 through 52081), the FY 2018 IRF 
PPS final rule (82 FR 36269 through 36270), and the FY 2019 IRF PPS 
final rule (83 FR 38555 through 38556).

B. General Considerations Used for the Selection of Measures for the 
IRF QRP

    For a detailed discussion of the considerations we historically 
used for the selection of IRF QRP quality, resource use, and other 
measures, we refer readers to the FY 2016 IRF PPS final rule (80 FR 
47083 through 47084).

C. Quality Measures Currently Adopted for the FY 2021 IRF QRP

    The IRF QRP currently has 15 measures for the FY 2020 program year, 
which are set out in Table 18.
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BILLING CODE 4120-01-C

D. IRF QRP Quality Measure Proposals Beginning With the FY 2022 IRF QRP

    In this proposed rule, we are proposing to adopt two process 
measures for the IRF QRP that would satisfy section 1899B(c)(1)(E)(ii) 
of the Act, which requires that the quality measures specified by the 
Secretary include measures with respect to the

[[Page 17286]]

quality measure domain titled ``Accurately communicating the existence 
of and providing for the transfer of health information and care 
preferences of an individual to the individual, family caregiver of the 
individual, and providers of services furnishing items and services to 
the individual when the individual transitions from a post-acute care 
(PAC) provider to another applicable setting, including a different PAC 
provider, a hospital, a critical access hospital, or the home of the 
individual.'' Given the length of this domain title, hereafter, we will 
refer to this quality measure domain as ``Transfer of Health 
Information.''
    The two measures we are proposing to adopt are: (1) Transfer of 
Health Information to the Provider-Post-Acute Care (PAC); and (2) 
Transfer of Health Information to the Patient-Post-Acute Care (PAC). 
Both of these proposed measures support our Meaningful Measures 
priority of promoting effective communication and coordination of care, 
specifically the Meaningful Measure area of the transfer of health 
information and interoperability.
    In addition to the two measure proposals, we are proposing to 
update the specifications for the Discharge to Community-Post Acute 
Care (PAC) IRF QRP measure to exclude baseline nursing facility (NF) 
residents from the measure.
    We are seeking public comment on each of these proposals.
1. Proposed Transfer of Health Information to the Provider-Post-Acute 
Care (PAC) Measure
    The proposed Transfer of Health Information to the Provider-Post-
Acute Care (PAC) Measure is a process-based measure that assesses 
whether or not a current reconciled medication list is given to the 
subsequent provider when a patient is discharged or transferred from 
his or her current PAC setting.
a. Background
    In 2013, 22.3 percent of all acute hospital discharges were 
discharged to PAC settings, including 11 percent who were discharged to 
home under the care of a home health agency, and nine percent who were 
discharged to SNFs.\2\ The proportion of patients being discharged from 
an acute care hospital to a PAC setting was greater among beneficiaries 
enrolled in Medicare fee-for-service (FFS). Among Medicare FFS patients 
discharged from an acute hospital, 42 percent went directly to PAC 
settings. Of that 42 percent, 20 percent were discharged to a SNF, 18 
percent were discharged to a home health agency (HHA), 3 percent were 
discharged to an IRF, and one percent were discharged to an LTCH.\3\ Of 
the Medicare FFS beneficiaries with an IRF stay in FYs 2016 and 2017, 
an estimated 10 percent were discharged or transferred to an acute care 
hospital, 51 percent discharged home with home health services, 16 
percent discharged or transferred to a SNF, and one percent discharged 
or transferred to another PAC setting (for example, another IRF, a 
hospice, or an LTCH).\4\
---------------------------------------------------------------------------

    \2\ Tian, W. ``An all-payer view of hospital discharge to post-
acute care,'' May 2016. Available at https://www.hcup-us.ahrq.gov/reports/statbriefs/sb205-Hospital-Discharge-Postacute-Care.jsp.
    \3\ Ibid.
    \4\ RTI International analysis of Medicare claims data for index 
stays in IRF 2016/2017. (RTI program reference: MM150).
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    The transfer and/or exchange of health information from one 
provider to another can be done verbally (for example, clinician-to-
clinician communication in-person or by telephone), paper-based (for 
example, faxed or printed copies of records), and via electronic 
communication (for example, through a health information exchange 
network using an electronic health/medical record, and/or secure 
messaging). Health information, such as medication information, that is 
incomplete or missing increases the likelihood of a patient or resident 
safety risk, and is often life-threatening.5 6 7 8 9 10 Poor 
communication and coordination across health care settings contributes 
to patient complications, hospital readmissions, emergency department 
visits, and medication errors.11 12 13 14 15 16 17 18 19 20 
Communication has been cited as the third most frequent root cause in 
sentinel events, which The Joint Commission defines \21\ as a patient 
safety event that results in death, permanent harm, or severe temporary 
harm. Failed or ineffective patient handoffs are estimated to play a 
role in 20 percent of serious preventable adverse events.\22\ When care 
transitions are enhanced through care coordination activities, such as 
expedited patient information flow, these activities can reduce 
duplication of care services and costs of care, resolve conflicting 
care plans, and prevent medical errors. 23 24 25 26 27
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    \5\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G., 
``Medication reconciliation during transitions of care as a patient 
safety strategy: A systematic review,'' Annals of Internal Medicine, 
2013, Vol. 158(5), pp. 397-403.
    \6\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E., 
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission 
medication reconciliation on adverse drug events from admission 
medication changes,'' Archives of Internal Medicine, 2011, Vol. 
171(9), pp. 860-861.
    \7\ Bell, C.M., Brener, S. S., Gunraj, N., Huo, C., Bierman, 
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital 
admission with unintentional discontinuation of medications for 
chronic diseases,'' JAMA, 2011, Vol. 306(8), pp. 840-847.
    \8\ Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J., 
``Prescribing errors on admission to hospital and their potential 
impact: A mixed-methods study,'' BMJ Quality & Safety, 2014, Vol. 
23(1), pp. 17-25.
    \9\ Desai, R., Williams, C.E., Greene, S.B., Pierson, S., & 
Hansen, R.A., ``Medication errors during patient transitions into 
nursing homes: Characteristics and association with patient harm,'' 
The American Journal of Geriatric Pharmacotherapy, 2011, Vol. 9(6), 
pp. 413-422.
    \10\ Boling, P.A., ``Care transitions and home health care,'' 
Clinical Geriatric Medicine, 2009, Vol.25(1), pp. 135-48.
    \11\ Barnsteiner, J.H., ``Medication Reconciliation: Transfer of 
medication information across settings--keeping it free from 
error,'' The American Journal of Nursing, 2005, Vol. 105(3), pp. 31-
36.
    \12\ Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander, 
H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A., ``Regardless 
of age: Incorporating principles from geriatric medicine to improve 
care transitions for patients with complex needs,'' Journal of 
General Internal Medicine, 2014, Vol. 29(6), pp. 932-939.
    \13\ Jencks, S.F., Williams, M.V., & Coleman, E.A., 
``Rehospitalizations among patients in the Medicare fee-for-service 
program,'' New England Journal of Medicine, 2009, Vol. 360(14), pp. 
1418-1428.
    \14\ Institute of Medicine. ``Preventing medication errors: 
quality chasm series,'' Washington, DC: The National Academies Press 
2007. Available at https://www.nap.edu/read/11623/chapter/1.
    \15\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G., 
``Developing a medication communication framework across continuums 
of care using the Circle of Care Modeling approach,'' BMC Health 
Services Research, 2013, Vol. 13(1), pp. 1-10.
    \16\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C., ``The 
revolving door of rehospitalization from skilled nursing 
facilities,'' Health Affairs, 2010, Vol. 29(1), pp. 57-64.
    \17\ Institute of Medicine. ``Preventing medication errors: 
quality chasm series,'' Washington, DC: The National Academies Press 
2007. Available at https://www.nap.edu/read/11623/chapter/1.
    \18\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G., 
``Developing a medication communication framework across continuums 
of care using the Circle of Care Modeling approach,'' BMC Health 
Services Research, 2013, Vol. 13(1), pp. 1-10.
    \19\ Forster, A.J., Murff, H.J., Peterson, J.F., Gandhi, T.K., & 
Bates, D.W., ``The incidence and severity of adverse events 
affecting patients after discharge from the hospital.'' Annals of 
Internal Medicine, 2003,138(3), pp. 161-167.
    \20\ King, B.J., Gilmore[hyphen]Bykovskyi, A.L., Roiland, R.A., 
Polnaszek, B.E., Bowers, B.J., & Kind, A.J. ``The consequences of 
poor communication during transitions from hospital to skilled 
nursing facility: A qualitative study,'' Journal of the American 
Geriatrics Society, 2013, Vol. 61(7), 1095-1102.
    \21\ The Joint Commission, ``Sentinel Event Policy'' available 
at https://www.jointcommission.org/sentinel_event_policy_and_procedures/.
    \22\ The Joint Commission. ``Sentinel Event Data Root Causes by 
Event Type 2004 -2015.'' 2016. Available at https://www.jointcommission.org/assets/1/23/jconline_Mar_2_2016.pdf.
    \23\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C., ``The 
revolving door of rehospitalization from skilled nursing 
facilities,'' Health Affairs, 2010, Vol. 29(1), pp. 57-64.
    \24\ Institute of Medicine, ``Preventing medication errors: 
Quality chasm series,'' Washington, DC: The National Academies 
Press, 2007. Available at https://www.nap.edu/read/11623/chapter/1.
    \25\ Starmer, A.J., Sectish, T. C., Simon, D.W., Keohane, C., 
McSweeney, M.E., Chung, E.Y., Yoon, C.S., Lipsitz, S.R., Wassner, 
A.J., Harper, M.B., & Landrigan, C.P., ``Rates of medical errors and 
preventable adverse events among hospitalized children following 
implementation of a resident handoff bundle,'' JAMA, 2013, Vol. 
310(21), pp. 2262-2270.
    \26\ Pronovost, P., M.M.E. Johns, S. Palmer, R.C. Bono, D.B. 
Fridsma, A. Gettinger, J. Goldman, W. Johnson, M. Karney, C. Samitt, 
R.D. Sriram, A. Zenooz, and Y.C. Wang, Editors. Procuring 
Interoperability: Achieving High-Quality, Connected, and Person-
Centered Care. Washington, DC, 2018 National Academy of Medicine. 
Available at https://nam.edu/wp-content/uploads/2018/10/Procuring-Interoperability_web.pdf.
    \27\ Balaban RB, Weissman JS, Samuel PA, & Woolhandler, S., 
``Redefining and redesigning hospital discharge to enhance patient 
care: A randomized controlled study,'' J Gen Intern Med, 2008, Vol. 
23(8), pp. 1228-33.

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[[Page 17287]]

    Care transitions across health care settings have been 
characterized as complex, costly, and potentially hazardous, and may 
increase the risk for multiple adverse outcomes. 28 29 The 
rising incidence of preventable adverse events, complications, and 
hospital readmissions have drawn attention to the importance of the 
timely transfer of health information and care preferences at the time 
of transition. Failures of care coordination, including poor 
communication of information, were estimated to cost the U.S. health 
care system between $25 billion and $45 billion in wasteful spending in 
2011.\30\ The communication of health information and patient care 
preferences is critical to ensuring safe and effective transitions from 
one health care setting to another.31 32
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    \28\ Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander, 
H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A., ``Regardless 
of age: Incorporating principles from geriatric medicine to improve 
care transitions for patients with complex needs,'' Journal of 
General Internal Medicine, 2014, Vol 29(6), pp. 932-939.
    \29\ Simmons, S., Schnelle, J., Slagle, J., Sathe, N.A., 
Stevenson, D., Carlo, M., & McPheeters, M.L., ``Resident safety 
practices in nursing home settings.'' Technical Brief No. 24 
(Prepared by the Vanderbilt Evidence-based Practice Center under 
Contract No. 290-2015-00003-I.) AHRQ Publication No. 16-EHC022-EF. 
Rockville, MD: Agency for Healthcare Research and Quality. May 2016. 
Available at https://www.ncbi.nlm.nih.gov/books/NBK384624/.
    \30\ Berwick, D.M. & Hackbarth, A.D. ``Eliminating Waste in US 
Health Care,'' JAMA, 2012, Vol. 307(14), pp.1513-1516.
    \31\ McDonald, K.M., Sundaram, V., Bravata, D.M., Lewis, R., 
Lin, N., Kraft, S.A. & Owens, D.K. Care Coordination. Vol. 7 of: 
Shojania K.G., McDonald K.M., Wachter R.M., Owens D.K., editors. 
``Closing the quality gap: A critical analysis of quality 
improvement strategies.'' Technical Review 9 (Prepared by the 
Stanford University-UCSF Evidence-based Practice Center under 
contract 290-02-0017). AHRQ Publication No. 04(07)-0051-7. 
Rockville, MD: Agency for Healthcare Research and Quality. June 
2006. Available at https://www.ncbi.nlm.nih.gov/books/NBK44015/.
    \32\ Lattimer, C., ``When it comes to transitions in patient 
care, effective communication can make all the difference,'' 
Generations, 2011, Vol. 35(1), pp. 69-72.
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    Patients in PAC settings often have complicated medication regimens 
and require efficient and effective communication and coordination of 
care between settings, including detailed transfer of medication 
information.33 34 35 Individuals in PAC settings may be 
vulnerable to adverse health outcomes due to insufficient medication 
information on the part of their health care providers, and the higher 
likelihood for multiple comorbid chronic conditions, polypharmacy, and 
complicated transitions between care settings.36 37 
Preventable adverse drug events (ADEs) may occur after hospital 
discharge in a variety of settings including PAC.\38\ A 2014 Office of 
Inspector General report found that 10 percent of Medicare patients in 
IRFs experienced adverse events, with most of those events being 
medication related. Over 45 percent of the adverse events and temporary 
harm events were clearly or likely preventable.\39\ Medication errors 
and one-fifth of ADEs occur during transitions between settings, 
including admission to or discharge from a hospital to home or a PAC 
setting, or transfer between hospitals.40 41
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    \33\ Starmer A.J., Spector N.D., Srivastava R., West, D.C., 
Rosenbluth, G., Allen, A.D., Noble, E.L., & Landrigen, C.P., 
``Changes in medical errors after implementation of a handoff 
program,'' N Engl J Med, 2014, Vol. 37(1), pp. 1803-1812.
    \34\ Kruse, C.S. Marquez, G., Nelson, D., & Polomares, O., ``The 
use of health information exchange to augment patient handoff in 
long-term care: a systematic review,'' Applied Clinical Informatics, 
2018, Vol. 9(4), pp. 752-771.
    \35\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H., 
Thraen, I., Coarr, M.E., & Rupper, R., ``High prevalence of 
medication discrepancies between home health referrals and Centers 
for Medicare and Medicaid Services home health certification and 
plan of care and their potential to affect safety of vulnerable 
elderly adults,'' Journal of the American Geriatrics Society, 2016, 
Vol. 64(11), pp. e166-e170.
    \36\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E., 
Parsons, K.L., & Zuckerman, I.H., ``Medication reconciliation during 
the transition to and from long-term care settings: a systematic 
review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-75.
    \37\ Levinson, D.R., & General, I., ``Adverse events in skilled 
nursing facilities: national incidence among Medicare 
beneficiaries.'' Washington, DC: U.S. Department of Health and Human 
Services, Office of the Inspector General, February 2014. Available 
at https://oig.hhs.gov/oei/reports/oei-06-11-00370.pdf.
    \38\ Battles J., Azam I., Grady M., & Reback K., ``Advances in 
patient safety and medical liability,'' AHRQ Publication No. 17-
0017-EF. Rockville, MD: Agency for Healthcare Research and Quality, 
August 2017. Available at https://www.ahrq.gov/sites/default/files/publications/files/advances-complete_3.pdf.
    \39\ Health and Human Services Office of Inspector General. 
Adverse Events in Rehabilitation Hospitals: National Incidence Among 
Medicare Beneficiaries. (OEI-06-14-00110). 2018. Available at 
https://oig.hhs.gov/oei/reports/oei-06-14-00110.asp.
    \40\ Barnsteiner, J.H., ``Medication Reconciliation: Transfer of 
medication information across settings--keeping it free from 
error,'' The American Journal of Nursing, 2005, Vol. 105(3), pp. 31-
36.
    \41\ Gleason, K.M., Groszek, J.M., Sullivan, C., Rooney, D., 
Barnard, C., Noskin, G.A., ``Reconciliation of discrepancies in 
medication histories and admission orders of newly hospitalized 
patients,'' American Journal of Health System Pharmacy, 2004, Vol. 
61(16), pp. 1689-1694.
---------------------------------------------------------------------------

    Patients in PAC settings are often taking multiple medications. 
Consequently, PAC providers regularly are in the position of starting 
complex new medication regimens with little knowledge of the patients 
or their medication history upon admission. Furthermore, inter-facility 
communication barriers delay resolving medication discrepancies during 
transitions of care.\42\ Medication discrepancies are common,\43\ and 
found to occur in 86 percent of all transitions, increasing the 
likelihood of ADEs.\44\ \45\ \46\ Up to 90 percent of patients 
experience at least one medication discrepancy in the transition from 
hospital to home care, and discrepancies occur within all therapeutic 
classes of medications.\47\ \48\
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    \42\ Patterson M., Foust J.B., Bollinger, S., Coleman, C., 
Nguyen, D., ``Inter-facility communication barriers delay resolving 
medication discrepancies during transitions of care,'' Research in 
Social & Administrative Pharmacy (2018), doi: 10.1016/
j.sapharm.2018.05.124.
    \43\ Manias, E., Annaikis, N., Considine, J., Weerasuriya, R., & 
Kusljic, S. ``Patient-, medication- and environment-related factors 
affecting medication discrepancies in older patients,'' Collegian, 
2017, Vol. 24, pp. 571-577.
    \44\ Tjia, J., Bonner, A., Briesacher, B.A., McGee, S., Terrill, 
E., Miller, K., ``Medication discrepancies upon hospital to skilled 
nursing facility transitions,'' J Gen Intern Med, 2009, Vol. 24(5), 
pp. 630-635.
    \45\ Sinvani, L.D., Beizer, J., Akerman, M., Pekmezaris, R., 
Nouryan, C., Lutsky, L., Cal, C., Dlugacz, Y., Masick, K., Wolf-
Klein, G., ``Medication reconciliation in continuum of care 
transitions: a moving target,'' J Am Med Dir Assoc, 2013, Vol. 
14(9), 668-672.
    \46\ Coleman E.A., Parry C., Chalmers S., & Min, S.J., ``The 
Care Transitions Intervention: results of a randomized controlled 
trial,'' Arch Intern Med, 2006, Vol. 166, pp. 1822-28.
    \47\ Corbett C.L., Setter S. M., Neumiller J.J., & Wood, L.D., 
``Nurse identified hospital to home medication discrepancies: 
implications for improving transitional care,'' Geriatr Nurs, 2011, 
Vol. 31(3), pp. 188-96.
    \48\ Setter S.M., Corbett C.F., Neumiller J.J., Gates, B.J., 
Sclar, D.A., & Sonnett, T.E., ``Effectiveness of a pharmacist-nurse 
intervention on resolving medication discrepancies in older patients 
transitioning from hospital to home care: impact of a pharmacy/
nursing intervention,'' Am J Health Syst Pharm, 2009, Vol. 66, pp. 
2027-31.
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    Transfer of a medication list between providers is necessary for 
medication reconciliation interventions, which have been shown to be a 
cost-effective way to avoid ADEs by reducing errors,49 50 51

[[Page 17288]]

especially when medications are reviewed by a pharmacist using 
electronic medical records.\52\
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    \49\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E., 
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission 
medication reconciliation on adverse drug events from admission 
medication changes,'' Archives of Internal Medicine, 2011, Vol. 
171(9), pp. 860-861.
    \50\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G., 
``Medication reconciliation during transitions of care as a patient 
safety strategy: a systematic review,'' Annals of Internal Medicine, 
2013, Vol. 158(5), pp. 397-403.
    \51\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E., 
Parsons, K.L., & Zuckerman, I.H., ``Medication reconciliation during 
the transition to and from long-term care settings: a systematic 
review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-75.
    \52\ Agrawal A., Wu WY. ``Reducing medication errors and 
improving systems reliability using an electronic medication 
reconciliation system,'' The Joint Commission Journal on Quality and 
Patient Safety, 2009, Vol. 35(2), pp. 106-114.
---------------------------------------------------------------------------

b. Stakeholder and Technical Expert Panel (TEP) Input
    The proposed measure was developed after consideration of feedback 
we received from stakeholders and four TEPs convened by our 
contractors. Further, the proposed measure was developed after 
evaluation of data collected during two pilot tests we conducted in 
accordance with the CMS Measures Management System Blueprint.
    Our measure development contractors constituted a TEP which met on 
September 27, 2016 \53\, January 27, 2017, and August 3, 2017 \54\ to 
provide input on a prior version of this measure. Based on this input, 
we updated the measure concept in late 2017 to include the transfer of 
a specific component of health information--medication information. Our 
measure development contractors reconvened this TEP on April 20, 2018 
for the purpose of obtaining expert input on the proposed measure, 
including the measure's reliability, components of face validity, and 
feasibility of being implemented across PAC settings. Overall, the TEP 
was supportive of the proposed measure, affirming that the measure 
provides an opportunity to improve the transfer of medication 
information. A summary of the April 20, 2018 TEP proceedings titled 
``Transfer of Health Information TEP Meeting 4--June 2018'' is 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------

    \53\ Technical Expert Panel Summary Report: Development of two 
quality measures to satisfy the Improving Medicare Post-Acute Care 
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health 
Information and Care Preferences When an Individual Transitions to 
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation 
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health 
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP_Summary_Report_Final-June-2017.pdf.
    \54\ Technical Expert Panel Summary Report: Development of two 
quality measures to satisfy the Improving Medicare Post-Acute Care 
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health 
Information and Care Preferences When an Individual Transitions to 
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation 
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health 
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP-Meetings-2-3-Summary-Report_Final_Feb2018.pdf.
---------------------------------------------------------------------------

    Our measure development contractors solicited stakeholder feedback 
on the proposed measure by requesting comment on the CMS Measures 
Management System Blueprint website, and accepted comments that were 
submitted from March 19, 2018 to May 3, 2018. The comments received 
expressed overall support for the measure. Several commenters suggested 
ways to improve the measure, primarily related to what types of 
information should be included at transfer. We incorporated this input 
into development of the proposed measure. The summary report for the 
March 19 to May 3, 2018 public comment period titled ``IMPACT 
Medication Profile Transferred Public Comment Summary Report'' is 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
c. Pilot Testing
    The proposed measure was tested between June and August 2018 in a 
pilot test that involved 24 PAC facilities/agencies, including five 
IRFs, six SNFs, six LTCHs, and seven HHAs. The 24 pilot sites submitted 
a total of 801 records. Analysis of agreement between coders within 
each participating facility (266 qualifying pairs) indicated a 93 
percent agreement for this measure. Overall, pilot testing enabled us 
to verify its reliability, components of face validity, and feasibility 
of being implemented across PAC settings. Further, more than half of 
the sites that participated in the pilot test stated during the 
debriefing interviews that the measure could distinguish facilities or 
agencies with higher quality medication information transfer from those 
with lower quality medication information transfer at discharge. The 
pilot test summary report titled ``Transfer of Health Information 2018 
Pilot Test Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
d. Measure Applications Partnership (MAP) Review and Related Measures
    We included the proposed measure in the IRF QRP section of the 2018 
Measures Under Consideration (MUC) list. The MAP conditionally 
supported this measure pending NQF endorsement, noting that the measure 
can promote the transfer of important medication information. The MAP 
also suggested that CMS consider a measure that can be adapted to 
capture bi-directional information exchange, and recommended that the 
medication information transferred include important information about 
supplements and opioids. More information about the MAP's 
recommendations for this measure is available at https://www.qualityforum.org/Publications/2019/02/MAP_2019_Considerations_for_Implementing_Measures_Final_Report_-_PAC-LTC.aspx.
    As part of the measure development and selection process, we also 
identified one NQF-endorsed quality measure similar to the proposed 
measure, titled Documentation of Current Medications in the Medical 
Record (NQF #0419, CMS eCQM ID: CMS68v8). This measure was adopted as 
one of the recommended adult core clinical quality measures for 
eligible professionals for the EHR Incentive Program beginning in 2014 
and was also adopted under the Merit-based Incentive Payment System 
(MIPS) quality performance category beginning in 2017. The measure is 
calculated based on the percentage of visits for patients aged 18 years 
and older for which the eligible professional or eligible clinician 
attests to documenting a list of current medications using all 
resources immediately available on the date of the encounter.
    The proposed Transfer of Health Information to the Provider-Post-
Acute Care (PAC) measure addresses the transfer of information whereas 
the NQF-endorsed measure #0419 assesses the documentation of 
medications, but not the transfer of such information. This is 
important as the proposed measure assesses for the transfer of 
medication information for the proposed measure calculation. Further, 
the proposed measure utilizes standardized patient assessment data 
elements (SPADEs), which is a

[[Page 17289]]

requirement for measures specified under the Transfer of Health 
Information measure domain under section 1899B(c)(1)(E) of the Act, 
whereas NQF #0419 does not.
    After review of the NQF-endorsed measure, we determined that the 
proposed Transfer of Health Information to the Provider-Post-Acute Care 
(PAC) measure better addresses the Transfer of Health Information 
measure domain, which requires that at least some of the data used to 
calculate the measure be collected as standardized patient assessment 
data through the post-acute care assessment instruments. Section 
1886(j)(7)(D)(i) of the Act requires that any measure specified by the 
Secretary be endorsed by the entity with a contract under section 
1890(a) of the Act, which is currently the National Quality Form (NQF). 
However, when a feasible and practical measure has not been NQF 
endorsed for a specified area or medical topic determined appropriate 
by the Secretary, section 1886(j)(7)(D)(ii) of the Act allows the 
Secretary to specify a measure that is not NQF endorsed as long as due 
consideration is given to the measures that have been endorsed or 
adopted by a consensus organization identified by the Secretary. For 
the reasons discussed previously, we believe that there is currently no 
feasible NQF-endorsed measure that we could adopt under section 
1886(j)(7)(D)(ii) of the Act. However, we note that we intend to submit 
the proposed measure to the NQF for consideration of endorsement when 
feasible.
e. Quality Measure Calculation
    The proposed Transfer of Health Information to the Provider-Post-
Acute Care (PAC) quality measure is calculated as the proportion of 
patient stays with a discharge assessment indicating that a current 
reconciled medication list was provided to the subsequent provider at 
the time of discharge. The proposed measure denominator is the total 
number of IRF patient stays ending in discharge to a subsequent 
provider, which is defined as a short-term general acute-care hospital, 
intermediate care (intellectual and developmental disabilities 
providers), home under care of an organized home health service 
organization or hospice, hospice in an institutional facility, a SNF, 
an LTCH, another IRF, an inpatient psychiatric facility, or a CAH. 
These health care providers were selected for inclusion in the 
denominator because they are identified as subsequent providers on the 
discharge destination item that is currently included on the IRF 
patient assessment instrument (IRF-PAI). The proposed measure numerator 
is the number of IRF patient stays with an IRF-PAI discharge assessment 
indicating a current reconciled medication list was provided to the 
subsequent provider at the time of discharge. For additional technical 
information about this proposed measure, we refer readers to the 
document titled, ``Proposed Specifications for IRF QRP Quality Measures 
and Standardized Patient Assessment Data Elements,'' available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. The data source for the proposed 
quality measure is the IRF-PAI assessment instrument for IRF patients.
    For more information about the data submission requirements we are 
proposing for this measure, we refer readers to section VIII.G.3. of 
this proposed rule.
2. Proposed Transfer of Health Information to the Patient-Post-Acute 
Care (PAC) Measure
    Beginning with the FY 2022 IRF QRP, we are proposing to adopt the 
Transfer of Health Information to the Patient--Post Acute Care (PAC) 
measure, a measure that satisfies the IMPACT Act domain of Transfer of 
Health Information, with data collection for discharges beginning 
October 1, 2020. This process-based measure assesses whether or not a 
current reconciled medication list was provided to the patient, family, 
or caregiver when the patient was discharged from a PAC setting to a 
private home/apartment, a board and care home, assisted living, a group 
home, transitional living or home under care of an organized home 
health service organization, or a hospice.
a. Background
    In 2013, 22.3 percent of all acute hospital discharges were 
discharged to PAC settings, including 11 percent who were discharged to 
home under the care of a home health agency.\55\ Of the Medicare FFS 
beneficiaries with an IRF stay in fiscal years 2016 and 2017, an 
estimated 51 percent were discharged home with home health services, 21 
percent were discharged home with self-care, and .5 percent were 
discharged with home hospice services.\56\
---------------------------------------------------------------------------

    \55\ Tian, W. ``An all-payer view of hospital discharge to 
postacute care,'' May 2016. Available at https://www.hcup-us.ahrq.gov/reports/statbriefs/sb205-Hospital-Discharge-Postacute-Care.jsp.
    \56\ RTI International analysis of Medicare claims data for 
index stays in IRF 2016/2017. (RTI program reference: MM150).
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    The communication of health information, such as a reconciled 
medication list, is critical to ensuring safe and effective patient 
transitions from health care settings to home and/or other community 
settings. Incomplete or missing health information, such as medication 
information, increases the likelihood of a patient safety risk, often 
life-threatening.57 58 59 60 61 Individuals who use PAC care 
services are particularly vulnerable to adverse health outcomes due to 
their higher likelihood of having multiple comorbid chronic conditions, 
polypharmacy, and complicated transitions between care 
settings.62 63 Upon discharge to home, individuals in PAC 
settings may be faced with numerous medication changes, new medication 
regimes, and follow-up details.64 65 66 The efficient

[[Page 17290]]

and effective communication and coordination of medication information 
may be critical to prevent potentially deadly adverse effects. When 
care coordination activities enhance care transitions, these activities 
can reduce duplication of care services and costs of care, resolve 
conflicting care plans, and prevent medical errors.67 68
---------------------------------------------------------------------------

    \57\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G., 
``Medication reconciliation during transitions of care as a patient 
safety strategy: a systematic review,'' Annals of Internal Medicine, 
2013, Vol. 158(5), pp. 397-403.
    \58\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E., 
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission 
medication reconciliation on adverse drug events from admission 
medication changes,'' Archives of Internal Medicine, 2011, Vol. 
171(9), pp. 860-861.
    \59\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman, 
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital 
admission with unintentional discontinuation of medications for 
chronic diseases,'' JAMA, 2011, Vol. 306(8), pp. 840-847.
    \60\ Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J., 
``Prescribing errors on admission to hospital and their potential 
impact: a mixed-methods study,'' BMJ Quality & Safety, 2014, Vol. 
23(1), pp. 17-25.
    \61\ Desai, R., Williams, C.E., Greene, S.B., Pierson, S., & 
Hansen, R.A., ``Medication errors during patient transitions into 
nursing homes: characteristics and association with patient harm,'' 
The American Journal of Geriatric Pharmacotherapy, 2011, Vol. 9(6), 
pp. 413-422.
    \62\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H., 
Thraen, I., Coarr, M.E., & Rupper, R. ``High prevalence of 
medication discrepancies between home health referrals and Centers 
for Medicare and Medicaid Services home health certification and 
plan of care and their potential to affect safety of vulnerable 
elderly adults,'' Journal of the American Geriatrics Society, 2016, 
Vol. 64(11), pp. e166-e170.
    \63\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E., 
Parsons, K.L., & Zuckerman, I.H., ``Medication reconciliation during 
the transition to and from long-term care settings: a systematic 
review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-75.
    \64\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H., 
Thraen, I., Coarr, M.E., & Rupper, R. ``High prevalence of 
medication discrepancies between home health referrals and Centers 
for Medicare and Medicaid Services home health certification and 
plan of care and their potential to affect safety of vulnerable 
elderly adults,'' Journal of the American Geriatrics Society, 2016, 
Vol. 64(11), pp. e166-e170.
    \65\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman, 
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital 
admission with unintentional discontinuation of medications for 
chronic diseases,'' JAMA, 2011, Vol. 306(8), pp. 840-847.
    \66\ Sheehan, O.C., Kharrazi, H., Carl, K.J., Leff, B., Wolff, 
J.L., Roth, D.L., Gabbard, J., & Boyd, C.M., ``Helping older adults 
improve their medication experience (HOME) by addressing medication 
regimen complexity in home healthcare,'' Home Healthcare Now. 2018, 
Vol. 36(1) pp. 10-19.
    \67\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C., ``The 
revolving door of rehospitalization from skilled nursing 
facilities,'' Health Affairs, 2010, Vol. 29(1), pp. 57-64.
    \68\ Starmer, A.J., Sectish, T.C., Simon, D.W., Keohane, C., 
McSweeney, M.E., Chung, E.Y., Yoon, C.S., Lipsitz, S.R., Wassner, 
A.J., Harper, M.B., & Landrigan, C.P., ``Rates of medical errors and 
preventable adverse events among hospitalized children following 
implementation of a resident handoff bundle,'' JAMA, 2013, Vol. 
310(21), pp. 2262-2270.
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    Finally, the transfer of a patient's discharge medication 
information to the patient, family, or caregiver is common practice and 
supported by discharge planning requirements for participation in 
Medicare and Medicaid programs.69 70 Most PAC EHR systems 
generate a discharge medication list to promote patient participation 
in medication management, which has been shown to be potentially useful 
for improving patient outcomes and transitional care.\71\
---------------------------------------------------------------------------

    \69\ CMS, ``Revision to state operations manual (SOM), Hospital 
Appendix A--Interpretive Guidelines for 42 CFR 482.43, Discharge 
Planning'' May 17, 2013. Available at https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Downloads/Survey-and-Cert-Letter-13-32.pdf.
    \70\ The State Operations Manual Guidance to Surveyors for Long 
Term Care Facilities (Guidance Sec.  483.21(c)(1) Rev. 11-22-17) for 
discharge planning process. Available at https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/downloads/som107ap_pp_guidelines_ltcf.pdf.
    \71\ Toles, M., Colon-Emeric, C., Naylor, M.D., Asafu-Adjei, J., 
Hanson, L.C., ``Connect-home: transitional care of skilled nursing 
facility patients and their caregivers,'' Am Geriatr Soc., 2017, 
Vol. 65(10), pp. 2322-2328.
---------------------------------------------------------------------------

b. Stakeholder and Technical Expert Panel (TEP) Input
    The proposed measure was developed after consideration of feedback 
we received from stakeholders and four TEPs convened by our 
contractors. Further, the proposed measure was developed after 
evaluation of data collected during two pilot tests we conducted in 
accordance with the CMS Measures Management System Blueprint.
    Our measure development contractors constituted a TEP which met on 
September 27, 2016,\72\ January 27, 2017, and August 3, 2017 \73\ to 
provide input on a prior version of this measure. Based on this input, 
we updated the measure concept in late 2017 to include the transfer of 
a specific component of health information--medication information. Our 
measure development contractors reconvened this TEP on April 20, 2018 
to seek expert input on the measure. Overall, the TEP members supported 
the proposed measure, affirming that the measure provides an 
opportunity to improve the transfer of medication information. Most of 
the TEP members believed that the measure could improve the transfer of 
medication information to patients, families, and caregivers. Several 
TEP members emphasized the importance of transferring information to 
patients and their caregivers in a clear manner using plain language. A 
summary of the April 20, 2018 TEP proceedings titled ``Transfer of 
Health Information TEP Meeting 4--June 2018'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------

    \72\ Technical Expert Panel Summary Report: Development of two 
quality measures to satisfy the Improving Medicare Post-Acute Care 
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health 
Information and Care Preferences When an Individual Transitions to 
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation 
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health 
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP_Summary_Report_Final-June-2017.pdf.
    \73\ Technical Expert Panel Summary Report: Development of two 
quality measures to satisfy the Improving Medicare Post-Acute Care 
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health 
Information and Care Preferences When an Individual Transitions to 
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation 
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health 
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP-Meetings-2-3-Summary-Report_Final_Feb2018.pdf.
---------------------------------------------------------------------------

    Our measure development contractors solicited stakeholder feedback 
on the proposed measure by requesting comment on the CMS Measures 
Management System Blueprint website, and accepted comments that were 
submitted from March 19, 2018 to May 3, 2018. Several commenters noted 
the importance of ensuring that the instruction provided to patients 
and caregivers is clear and understandable to promote transparent 
access to medical record information and meet the goals of the IMPACT 
Act. The summary report for the March 19 to May 3, 2018 public comment 
period titled ``IMPACT-Medication Profile Transferred Public Comment 
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
c. Pilot Testing
    Between June and August 2018, we held a pilot test involving 24 PAC 
facilities/agencies, including five IRFs, six SNFs, six LTCHs, and 
seven HHAs. The 24 pilot sites submitted a total of 801 assessments. 
Analysis of agreement between coders within each participating facility 
(241 qualifying pairs) indicated an 87 percent agreement for this 
measure. Overall, pilot testing enabled us to verify its reliability, 
components of face validity, and feasibility of being implemented 
across PAC settings. Further, more than half of the sites that 
participated in the pilot test stated, during debriefing interviews, 
that the measure could distinguish facilities or agencies with higher 
quality medication information transfer from those with lower quality 
medication information transfer at discharge. The pilot test summary 
report titled ``Transfer of Health Information 2018 Pilot Test Summary 
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
d. Measure Applications Partnership (MAP) Review and Related Measures
    We included the proposed measure in the IRF QRP section of the 2018 
MUC list. The MAP conditionally supported this measure pending NQF 
endorsement, noting that the measure can promote the transfer of 
important medication information to the patient. The MAP recommended 
that providers transmit medication information to patients that is easy 
to understand because health literacy can impact a person's ability to 
take medication as directed. More information about the MAP's 
recommendations for this measure is available at https://
www.qualityforum.org/Publications/

[[Page 17291]]

2019/02/
MAP_2019_Considerations_for_Implementing_Measures_Final_Report_-_PAC-
LTC.aspx.
    Section 1886(j)(7)(D)(i) of the Act, requires that any measure 
specified by the Secretary be endorsed by the entity with a contract 
under section 1890(a) of the Act, which is currently the NQF. However, 
when a feasible and practical measure has not been NQF endorsed for a 
specified area or medical topic determined appropriate by the 
Secretary, section 1886(j)(7)(D)(ii) of the Act allows the Secretary to 
specify a measure that is not NQF endorsed as long as due consideration 
is given to the measures that have been endorsed or adopted by a 
consensus organization identified by the Secretary. Therefore, in the 
absence of any NQF-endorsed measures that address the proposed Transfer 
of Health Information to the Patient -Post-Acute Care (PAC), which 
requires that at least some of the data used to calculate the measure 
be collected as standardized patient assessment data through post-acute 
care assessment instruments, we believe that there is currently no 
feasible NQF-endorsed measure that we could adopt under section 
1886(j)(7)(D)(ii) of the Act. However, we note that we intend to submit 
the proposed measure to the NQF for consideration of endorsement when 
feasible.
e. Quality Measure Calculation
    The calculation of the proposed Transfer of Health Information to 
the Patient-Post-Acute Care (PAC) measure would be based on the 
proportion of patient stays with a discharge assessment indicating that 
a current reconciled medication list was provided to the patient, 
family, or caregiver at the time of discharge.
    The proposed measure denominator is the total number of IRF patient 
stays ending in discharge to a private home/apartment, a board and care 
home, assisted living, a group home, transitional living or home under 
care of an organized home health service organization, or a hospice. 
These locations were selected for inclusion in the denominator because 
they are identified as home locations on the discharge destination item 
that is currently included on the IRF-PAI. The proposed measure 
numerator is the number of IRF patient stays with an IRF-PAI discharge 
assessment indicating a current reconciled medication list was provided 
to the patient, family, or caregiver at the time of discharge. For 
technical information about this proposed measure, we refer readers to 
the document titled ``Proposed Specifications for IRF QRP Quality 
Measures and Standardized Patient Assessment Data Elements,'' available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Data for the proposed quality 
measure would be calculated using data from the IRF-PAI assessment 
instrument for IRF patients.
    For more information about the data submission requirements we are 
proposing for this measure, we refer readers to section VIII.G.3. of 
this proposed rule.
3. Proposed Update to the Discharge to Community-Post Acute Care (PAC) 
Inpatient Rehabilitation Facility (IRF) Quality Reporting Program (QRP) 
Measure
    We are proposing to update the specifications for the Discharge to 
Community-PAC IRF QRP measure to exclude baseline nursing facility (NF) 
residents from the measure. This measure reports an IRF's risk-
standardized rate of Medicare FFS patients who are discharged to the 
community following an IRF stay, do not have an unplanned readmission 
to an acute care hospital or LTCH in the 31 days following discharge to 
community, and who remain alive during the 31 days following discharge 
to community. We adopted this measure in the FY 2017 IRF PPS final rule 
(81 FR 52095 through 52103).
    In the FY 2017 IRF PPS final rule (81 FR 52099), we addressed 
public comments recommending exclusion of IRF patients who were 
baseline NF residents, as these patients lived in a NF prior to their 
IRF stay, as these patients may not be expected to return to the 
community following their IRF stay. In the FY 2018 IRF PPS final rule 
(82 FR 36285), we addressed public comments expressing support for a 
potential future modification of the measure that would exclude 
baseline NF residents; commenters stated that the exclusion would 
result in the measure more accurately portraying quality of care 
provided by IRFs, while controlling for factors outside of IRF control.
    We assessed the impact of excluding baseline NF residents from the 
measure using CY 2015 and Cy 2016 data, and found that this exclusion 
impacted both patient- and facility-level discharge to community rates. 
We defined baseline NF residents as IRF patients who had a long-term NF 
stay in the 180 days preceding their hospitalization and IRF stay, with 
no intervening community discharge between the NF stay and qualifying 
hospitalization for measure inclusion. Baseline NF residents 
represented 0.3 percent of the measure population after all measure 
exclusions were applied. Observed patient-level discharge to community 
rates were significantly lower for baseline NF residents (20.82 
percent) compared with non-NF residents (64.52 percent). The national 
observed patient-level discharge to community rate was 64.41 percent 
when baseline NF residents were included in the measure, increasing to 
64.52 percent when they were excluded from the measure. After excluding 
baseline NF residents, 26.9 percent of IRFs had an increase in their 
risk-standardized discharge to community rate that exceeded the 
increase in the national observed patient-level discharge to community 
rate.
    Based on public comments received and our impact analysis, we are 
proposing to exclude baseline NF residents from the Discharge to 
Community-PAC IRF QRP measure beginning with the FY 2020 IRF QRP, with 
baseline NF residents defined as IRF patients who had a long-term NF 
stay in the 180 days preceding their hospitalization and IRF stay, with 
no intervening community discharge between the NF stay and 
hospitalization.
    For additional technical information regarding the Discharge to 
Community-PAC IRF QRP measure, including technical information about 
the proposed exclusion, we refer readers to the document titled 
``Proposed Specifications for IRF QRP Quality Measures and Standardized 
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We invite public comment on this proposal.

E. IRF QRP Quality Measures, Measure Concepts, and Standardized Patient 
Assessment Data Elements Under Consideration for Future Years: Request 
for Information

    We are seeking input on the importance, relevance, appropriateness, 
and applicability of each of the measures, standardized patient 
assessment data elements (SPADEs), and concepts under consideration 
listed in the Table 19 for future years in the IRF QRP.

[[Page 17292]]



  Table 19--Future Measures, Measure Concepts, and Standardized Patient
  Assessment Data Elements (SPADEs) Under Consideration for the IRF QRP
------------------------------------------------------------------------
 
-------------------------------------------------------------------------
                  Quality Measures and Measure Concepts
------------------------------------------------------------------------
Opioid use and frequency.
Exchange of Electronic Health Information and Interoperability.
------------------------------------------------------------------------
         Standardized Patient Assessment Data Elements (SPADEs)
------------------------------------------------------------------------
Cognitive complexity, such as executive function and memory.
Dementia.
Bladder and bowel continence including appliance use and episodes of
 incontinence.
Care preferences, advance care directives, and goals of care.
Caregiver Status.
Veteran Status.
Health disparities and risk factors, including education, sex and gender
 identity, and sexual orientation.
------------------------------------------------------------------------

    While we will not be responding to specific comments submitted in 
response to this Request for Information in the FY 2020 IRF PPS final 
rule, we intend to use this input to inform our future measure and 
SPADE development efforts.

F. Proposed Standardized Patient Assessment Data Reporting Beginning 
With the FY 2022 IRF QRP

    Section 1886(j)(7)(F)(ii) of the Act requires that, for fiscal 
years 2019 and each subsequent year, IRFs must report standardized 
patient assessment data (SPADE), required under section 1899B(b)(1) of 
the Act. Section 1899B(a)(1)(C) of the Act requires, in part, the 
Secretary to modify the PAC assessment instruments in order for PAC 
providers, including IRFs, to submit SPADEs under the Medicare program. 
Section 1899B(b)(1)(A) of the Act requires PAC providers to submit 
SPADEs under applicable reporting provisions (which, for IRFs, is the 
IRF QRP) with respect to the admission and discharge of an individual 
(and more frequently as the Secretary deems appropriate), and section 
1899B(b)(1)(B) of the Act defines standardized patient assessment data 
as data required for at least the quality measures described in section 
1899B(c)(1) and that is with respect to the following categories: (1) 
Functional status, such as mobility and self-care at admission to a PAC 
provider and before discharge from a PAC provider; (2) cognitive 
function, such as ability to express ideas and to understand, and 
mental status, such as depression and dementia; (3) special services, 
treatments, and interventions, such as need for ventilator use, 
dialysis, chemotherapy, central line placement, and total parenteral 
nutrition; (4) medical conditions and comorbidities, such as diabetes, 
congestive heart failure, and pressure ulcers; (5) impairments, such as 
incontinence and an impaired ability to hear, see, or swallow, and (6) 
other categories deemed necessary and appropriate by the Secretary.
    In the FY 2018 IRF PPS proposed rule (82 FR 20722 through 20739), 
we proposed to adopt SPADEs that would satisfy the first five 
categories. In the FY 2018 IRF PPS final rule (82 FR 36287 through 
36289), we summarized comments that supported our adoption of SPADEs, 
including support for our broader standardization goal and support for 
the clinical usefulness of specific proposed SPADEs. However, we did 
not finalize the majority of our SPADE proposals in recognition of the 
concern raised by many commenters that we were moving too fast to adopt 
the SPADEs and modify our assessment instruments in light of all of the 
other requirements we were also adopting under the IMPACT Act at that 
time (82 FR 36292 through 36294). In addition, commenters expressed 
that we should conduct further testing of the data elements we have 
proposed (82 FR 36288).
    However, we finalized the adoption of SPADEs for two of the 
categories described in section 1899B(b)(1)(B) of the Act: (1) 
Functional status: Data elements currently reported by IRFs to 
calculate the measure Application of Percent of Long-Term Care Hospital 
Patients with an Admission and Discharge Functional Assessment and a 
Care Plan That Addresses Function (NQF #2631); and (2) Medical 
conditions and comorbidities: The data elements used to calculate the 
pressure ulcer measures, Percent of Residents or Patients with Pressure 
Ulcers That Are New or Worsened (Short Stay) (NQF #0678) and the 
replacement measure, Changes in Skin Integrity Post-Acute Care: 
Pressure Ulcer/Injury. We stated that these data elements were 
important for care planning, known to be valid and reliable, and 
already being reported by IRFs for the calculation of quality measures.
    Since we issued the FY 2018 IRF PPS final rule, IRFs have had an 
opportunity to familiarize themselves with other new reporting 
requirements that we have adopted under the IMPACT Act. We have also 
conducted further testing of the SPADEs, as described more fully below, 
and believe that this testing supports the use of the SPADEs in our PAC 
assessment instruments. Therefore, we are now proposing to adopt many 
of the same SPADEs that we previously proposed to adopt, along with 
other SPADEs.
    We are proposing that IRFs would be required to report these SPADEs 
beginning with the FY 2022 IRF QRP. If finalized as proposed, IRFs 
would be required to report these data with respect to admission and 
discharge for patients discharged between October 1, 2020, and December 
31, 2020 for the FY 2022 IRF QRP. Beginning with the FY 2023 IRF QRP, 
we propose that IRFs must report data with respect to admissions and 
discharges that occur during the subsequent calendar year (for example, 
CY 2021 for the FY 2023 IRF QRP, CY 2022 for the FY 2024 IRF QRP).
    We are also proposing that IRFs that submit the Hearing, Vision, 
Race, and Ethnicity SPADEs with respect to admission only will be 
deemed to have submitted those SPADEs with respect to both admission 
and discharge, because it is unlikely that the assessment of those 
SPADEs at admission will differ from the assessment of the same SPADEs 
at discharge.
    In selecting the proposed SPADEs below, we considered the burden of 
assessment-based data collection and aimed to minimize additional 
burden by evaluating whether any data that is currently collected 
through one or more PAC assessment instruments could be collected as 
SPADE. In selecting the

[[Page 17293]]

proposed SPADEs below, we also took into consideration the following 
factors with respect to each data element:
    (1) Overall clinical relevance;
    (2) Interoperable exchange to facilitate care coordination during 
transitions in care;
    (3) Ability to capture medical complexity and risk factors that can 
inform both payment and quality; and
    (4) Scientific reliability and validity, general consensus 
agreement for its usability.
    In identifying the SPADEs proposed below, we additionally drew on 
input from several sources, including TEPs held by our data element 
contractor, public input, and the results of a recent National Beta 
Test of candidate data elements conducted by our data element 
contractor (hereafter ``National Beta Test'').
    The National Beta Test collected data from 3,121 patients and 
residents across 143 LTCHs, SNFs, IRFs, and HHAs from November 2017 to 
August 2018 to evaluate the feasibility, reliability, and validity of 
the candidate data elements across PAC settings. The National Beta Test 
also gathered feedback on the candidate data elements from staff who 
administered the test protocol in order to understand usability and 
workflow of the candidate data elements. More information on the 
methods, analysis plan, and results for the National Beta Test can be 
found in the document titled, ``Development and Evaluation of Candidate 
Standardized Patient Assessment Data Elements: Findings from the 
National Beta Test (Volume 2),'' available in the document titled, 
``Development and Evaluation of Candidate Standardized Patient 
Assessment Data Elements: Findings from the National Beta Test (Volume 
2),'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Further, to inform the proposed SPADEs, we took into account 
feedback from stakeholders, as well as from technical and clinical 
experts, including feedback on whether the candidate data elements 
would support the factors described above. Where relevant, we also took 
into account the results of the Post-Acute Care Payment Reform 
Demonstration (PAC PRD) that took place from 2006 to 2012.

G. Proposed Standardized Patient Assessment Data by Category

1. Cognitive Function and Mental Status Data
    A number of underlying conditions, including dementia, stroke, 
traumatic brain injury, side effects of medication, metabolic and/or 
endocrine imbalances, delirium, and depression, can affect cognitive 
function and mental status in PAC patient and resident populations.\74\ 
The assessment of cognitive function and mental status by PAC providers 
is important because of the high percentage of patients and residents 
with these conditions,\75\ and because these assessments provide 
opportunity for improving quality of care.
---------------------------------------------------------------------------

    \74\ National Institute on Aging. (2014). Assessing Cognitive 
Impairment in Older Patients. A Quick Guide for Primary Care 
Physicians. Retrieved from: https://www.nia.nih.gov/alzheimers/publication/assessing-cognitive-impairment-older-patients.
    \75\ Gage B., Morley M., Smith L., et al. (2012). Post-Acute 
Care Payment Reform Demonstration (Final report, Volume 4 of 4). 
Research Triangle Park, NC: RTI International.
---------------------------------------------------------------------------

    Symptoms of dementia may improve with pharmacotherapy, occupational 
therapy, or physical activity,76 77 78 and promising 
treatments for severe traumatic brain injury are currently being 
tested.\79\ For older patients and residents diagnosed with depression, 
treatment options to reduce symptoms and improve quality of life 
include antidepressant medication and 
psychotherapy,80 81 82 83 and targeted services, such as 
therapeutic recreation, exercise, and restorative nursing, to increase 
opportunities for psychosocial interaction.\84\
---------------------------------------------------------------------------

    \76\ Casey D.A., Antimisiaris D., O'Brien J. (2010). Drugs for 
Alzheimer's Disease: Are They Effective? Pharmacology & 
Therapeutics, 35, 208-11.
    \77\ Graff M.J., Vernooij-Dassen M.J., Thijssen M., Dekker J., 
Hoefnagels W.H., Rikkert M.G.O. (2006). Community Based Occupational 
Therapy for Patients with Dementia and their Care Givers: Randomised 
Controlled Trial. BMJ, 333(7580): 1196.
    \78\ Bherer L., Erickson K.I., Liu-Ambrose T. (2013). A Review 
of the Effects of Physical Activity and Exercise on Cognitive and 
Brain Functions in Older Adults. Journal of Aging Research, 657508.
    \79\ Giacino J.T., Whyte J., Bagiella E., et al. (2012). 
Placebo-controlled trial of amantadine for severe traumatic brain 
injury. New England Journal of Medicine, 366(9), 819-826.
    \80\ Alexopoulos G.S., Katz I.R., Reynolds C.F. 3rd, Carpenter 
D., Docherty J.P., Ross R.W. (2001). Pharmacotherapy of depression 
in older patients: a summary of the expert consensus guidelines. 
Journal of Psychiatric Practice, 7(6), 361-376.
    \81\ Arean P.A., Cook B.L. (2002). Psychotherapy and combined 
psychotherapy/pharmacotherapy for late life depression. Biological 
Psychiatry, 52(3), 293-303.
    \82\ Hollon S.D., Jarrett R.B., Nierenberg A.A., Thase M.E., 
Trivedi M., Rush A.J. (2005). Psychotherapy and medication in the 
treatment of adult and geriatric depression: which monotherapy or 
combined treatment? Journal of Clinical Psychiatry, 66(4), 455-468.
    \83\ Wagenaar D, Colenda CC, Kreft M, Sawade J, Gardiner J, 
Poverejan E. (2003). Treating depression in nursing homes: practice 
guidelines in the real world. J Am Osteopath Assoc. 103(10), 465-
469.
    \84\ Crespy SD, Van Haitsma K, Kleban M, Hann CJ. Reducing 
Depressive Symptoms in Nursing Home Residents: Evaluation of the 
Pennsylvania Depression Collaborative Quality Improvement Program. J 
Healthc Qual. 2016. Vol. 38, No. 6, pp. e76-e88.
---------------------------------------------------------------------------

    In alignment with our Meaningful Measures Initiative, accurate 
assessment of cognitive function and mental status of patients and 
residents in PAC is expected to make care safer by reducing harm caused 
in the delivery of care; promote effective prevention and treatment of 
chronic disease; strengthen person and family engagement as partners in 
their care; and promote effective communication and coordination of 
care. For example, standardized assessment of cognitive function and 
mental status of patients and residents in PAC will support 
establishing a baseline for identifying changes in cognitive function 
and mental status (for example, delirium), anticipating the patient's 
or resident's ability to understand and participate in treatments 
during a PAC stay, ensuring patient and resident safety (for example, 
risk of falls), and identifying appropriate support needs at the time 
of discharge or transfer. Standardized patient assessment data elements 
will enable or support clinical decision-making and early clinical 
intervention; person-centered, high quality care through facilitating 
better care continuity and coordination; better data exchange and 
interoperability between settings; and longitudinal outcome analysis. 
Therefore, reliable standardized patient assessment data elements 
assessing cognitive function and mental status are needed to initiate a 
management program that can optimize a patient's or resident's 
prognosis and reduce the possibility of adverse events.
    The data elements related to cognitive function and mental status 
were first proposed as standardized patient assessment data elements in 
the FY 2018 IRF PPS proposed rule (82 FR 20723 through 20726). In 
response to our proposals, a few commenters noted that the proposed 
data elements did not capture some dimensions of cognitive function and 
mental status, such as functional cognition, communication, attention, 
concentration, and agitation. One commenter also suggested that other 
cognitive assessments should be considered for standardization. Another 
commenter stated support for the standardized assessment of cognitive 
function and mental status, because it could support appropriate use of 
skilled therapy for beneficiaries with

[[Page 17294]]

degenerative conditions, such as dementia, and appropriate use of 
medications for behavioral and psychological symptoms of dementia.
    We are inviting comment on our proposals to collect as standardized 
patient assessment data the following data with respect to cognitive 
function and mental status.
 Brief Interview for Mental Status (BIMS)
    We are proposing that the data elements that comprise the BIMS meet 
the definition of standardized patient assessment data with respect to 
cognitive function and mental status under section 1899B(b)(1)(B)(ii) 
of the Act.
    As described in the FY 2018 IRF PPS Proposed Rule (82 FR 20723 
through 20724), dementia and cognitive impairment are associated with 
long-term functional dependence and, consequently, poor quality of life 
and increased healthcare costs and mortality.\85\ This makes assessment 
of mental status and early detection of cognitive decline or impairment 
critical in the PAC setting. The intensity of routine nursing care is 
higher for patients and residents with cognitive impairment than those 
without, and dementia is a significant variable in predicting 
readmission after discharge to the community from PAC providers.\86\
---------------------------------------------------------------------------

    \85\ Ag[uuml]ero-Torres, H., Fratiglioni, L., Guo, Z., Viitanen, 
M., von Strauss, E., & Winblad, B. (1998). ``Dementia is the major 
cause of functional dependence in the elderly: 3-year follow-up data 
from a population-based study.'' Am J of Public Health 88(10): 1452-
1456.
    \86\ RTI International. Proposed Measure Specifications for 
Measures Proposed in the FY 2017 IRF QRP NPRM. Research Triangle 
Park, NC. 2016.
---------------------------------------------------------------------------

    The BIMS is a performance-based cognitive assessment screening tool 
that assesses repetition, recall with and without prompting, and 
temporal orientation. The data elements that make up the BIMS are seven 
questions on the repetition of three words, temporal orientation, and 
recall that result in a cognitive function score. The BIMS was 
developed to be a brief, objective screening tool, with a focus on 
learning and memory. As a brief screener, the BIMS was not designed to 
diagnose dementia or cognitive impairment, but rather to be a 
relatively quick and easy to score assessment that could identify 
cognitively impaired patients as well as those who may be at risk for 
cognitive decline and require further assessment. It is currently in 
use in two of the PAC assessments: The MDS used by SNFs and the IRF-PAI 
used by IRFs. For more information on the BIMS, we refer readers to the 
document titled ``Proposed Specifications for IRF QRP Quality Measures 
and Standardized Patient Assessment Data Elements,'' available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The data elements that comprise the BIMS were first proposed as 
standardized patient assessment data elements in the FY 2018 IRF PPS 
proposed rule (82 FR 20723 through 20724). In that proposed rule, we 
stated that the proposal was informed by input we received through a 
call for input published on the CMS Measures Management System 
Blueprint website. Input submitted from August 12 to September 12, 
2016, expressed support for use of the BIMS, noting that it is 
reliable, feasible to use across settings, and will provide useful 
information about patients and residents. We also stated that the data 
collected through the BIMS will provide a clearer picture of patient or 
resident complexity, help with the care planning process, and be useful 
during care transitions and when coordinating across providers. A 
summary report for the August 12 to September 12, 2016 public comment 
period titled ``SPADE August 2016 Public Comment Summary Report'' is 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of the use of the BIMS, 
especially in its capacity to inform care transitions, but other 
commenters were critical, noting the limitations of the BIMS to assess 
mild cognitive impairment and ``functional'' cognition, and that the 
BIMS cannot be completed by patients and residents who are unable to 
communicate. They also stated that other cognitive assessments 
available in the public domain should be considered for 
standardization. One commenter suggested that CMS require use of the 
BIMS with respect to discharge as well as admission.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
BIMS was included in the National Beta Test of candidate data elements 
conducted by our data element contractor from November 2017 to August 
2018. Results of this test found the BIMS to be feasible and reliable 
for use with PAC patients and residents. More information about the 
performance of the BIMS in the National Beta Test can be found in the 
document titled ``Proposed Specifications for IRF QRP Quality Measures 
and Standardized Patient Assessment Data Elements,'' available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements and the TEP supported the 
assessment of patient or resident cognitive status with respect to both 
admission and discharge. A summary of the September 17, 2018 TEP 
meeting titled ``SPADE Technical Expert Panel Summary (Third 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. Some commenters also expressed concern that the BIMS, if used 
alone, may not be sensitive enough to capture the range of cognitive 
impairments, including mild cognitive impairment. A summary of the 
public input received from the November 27, 2018 stakeholder meeting 
titled ``Input on Standardized Patient Assessment Data Elements 
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We understand the concerns raised by stakeholders that BIMS, if 
used alone, may not be sensitive enough to capture the range of 
cognitive impairments, including functional cognition and MCI, but note 
that the purpose of the BIMS

[[Page 17295]]

data elements as SPADEs is to screen for cognitive impairment in a 
broad population. We also acknowledge that further cognitive tests may 
be required based on a patient's condition and will take this feedback 
into consideration in the development of future standardized assessment 
data elements. However, taking together the importance of assessing for 
cognitive status, stakeholder input, and strong test results, we are 
proposing that the BIMS data elements meet the definition of 
standardized patient assessment data with respect to cognitive function 
and mental status under section 1899B(b)(1)(B)(ii) of the Act and to 
adopt the BIMS data elements as standardized patient assessment data 
for use in the IRF QRP.
 Confusion Assessment Method (CAM)
    In this proposed rule, we are proposing that the data elements that 
comprise the Confusion Assessment Method (CAM) meet the definition of 
standardized patient assessment data with respect to cognitive function 
and mental status under section 1899B(b)(1)(B)(ii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20724), 
the CAM was developed to identify the signs and symptoms of delirium. 
It results in a score that suggests whether a patient or resident 
should be assigned a diagnosis of delirium. Because patients and 
residents with multiple comorbidities receive services from PAC 
providers, it is important to assess delirium, which is associated with 
a high mortality rate and prolonged duration of stay in hospitalized 
older adults.\87\ Assessing these signs and symptoms of delirium is 
clinically relevant for care planning by PAC providers.
---------------------------------------------------------------------------

    \87\ Fick, D.M., Steis, M.R., Waller, J.L., & Inouye, S.K. 
(2013). ``Delirium superimposed on dementia is associated with 
prolonged length of stay and poor outcomes in hospitalized older 
adults.'' J of Hospital Med 8(9): 500-505.
---------------------------------------------------------------------------

    The CAM is a patient assessment that screens for overall cognitive 
impairment, as well as distinguishes delirium or reversible confusion 
from other types of cognitive impairment. The CAM is currently in use 
in two of the PAC assessments: A four-item version of the CAM is used 
in the MDS in SNFs; and a six-item version of the CAM is used in the 
LTCH CARE Data Set (LCDS) in LTCHs. We are proposing the four-item 
version of the CAM that assesses acute change in mental status, 
inattention, disorganized thinking, and altered level of consciousness. 
For more information on the CAM, we refer readers to the document 
titled ``Proposed Specifications for IRF QRP Quality Measures and 
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The data elements that comprise the CAM were first proposed as 
standardized patient assessment data elements in the FY 2018 IRF PPS 
proposed rule (82 FR 20724). In that proposed rule, we stated that the 
proposal was informed by public input we received on the CAM through a 
call for input published on the CMS Measures Management System 
Blueprint website. Input submitted from August 12 to September 12, 2016 
expressed support for use of the CAM, noting that it would provide 
important information for care planning and care coordination, and 
therefore, contribute to quality improvement. We also stated that those 
commenters had noted the CAM is particularly helpful in distinguishing 
delirium and reversible confusion from other types of cognitive 
impairment. A summary report for the August 12 to September 12, 2016 
public comment period titled ``SPADE August 2016 Public Comment Summary 
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
one commenter supported use of the CAM for standardized patient 
assessment data. However, some commenters expressed concerns that the 
CAM data elements assess: The presence of behavioral symptoms, but not 
the cause; the possibility of a false positive for delirium due to 
patient cognitive or communication impairments; and the lack of 
specificity of the assessment specifications. In addition, other 
commenters noted that the CAM is not necessary because: Delirium is 
easily diagnosed without a tool; the CAM and BIMS assessments are 
redundant; and some CAM response options are not meaningful.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
CAM was included in the National Beta Test of candidate data elements 
conducted by our data element contractor from November 2017 to August 
2018. Results of this test found the CAM to be feasible and reliable 
for use with PAC patients and residents. More information about the 
performance of the CAM in the National Beta Test can be found in the 
document titled ``Proposed Specifications for IRF QRP Quality Measures 
and Standardized Patient Assessment Data Elements,'' available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although they did not 
specifically discuss the CAM data elements, the TEP supported the 
assessment of patient or resident cognitive status with respect to both 
admission and discharge. A summary of the September 17, 2018 TEP 
meeting titled ``SPADE Technical Expert Panel Summary (Third 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for delirium, 
stakeholder input, and strong test results, we are proposing that the 
CAM data elements meet the definition of standardized patient 
assessment data with respect to cognitive function and mental status 
under section 1899B(b)(1)(B)(ii) of the Act and to adopt the CAM data 
elements as standardized patient assessment data for use in the IRF 
QRP.

[[Page 17296]]

 Patient Health Questionnaire--2 to 9 (PHQ-2 to 9)
    In this proposed rule, we are proposing that the Patient Health 
Questionnaire-2 to 9 (PHQ-2 to 9) data elements meet the definition of 
standardized patient assessment data with respect to cognitive function 
and mental status under section 1899B(b)(1)(B)(ii) of the Act. The 
proposed data elements are based on the PHQ-2 mood interview, which 
focuses on only the two cardinal symptoms of depression, and the longer 
PHQ-9 mood interview, which assesses presence and frequency of nine 
signs and symptoms of depression. The name of the data element, the 
PHQ-2 to 9, refers to an embedded skip pattern that transitions 
patients with a threshold level of symptoms in the PHQ-2 to the longer 
assessment of the PHQ-9. The skip pattern is described further below. 
As described in the FY 2018 IRF PPS proposed rule (82 FR 20725 through 
20726), depression is a common and under-recognized mental health 
condition. Assessments of depression help PAC providers better 
understand the needs of their patients and residents by: Prompting 
further evaluation after establishing a diagnosis of depression; 
elucidating the patient's or resident's ability to participate in 
therapies for conditions other than depression during their stay; and 
identifying appropriate ongoing treatment and support needs at the time 
of discharge.
    The proposed PHQ-2 to 9 is based on the PHQ-9 mood interview. The 
PHQ-2 consists of questions about only the first two symptoms addressed 
in the PHQ-9: depressed mood and anhedonia (inability to feel 
pleasure), which are the cardinal symptoms of depression. The PHQ-2 has 
performed well as both a screening tool for identifying depression, to 
assess depression severity, and to monitor patient mood over 
time.88 89 If a patient demonstrates signs of 
depressed mood and anhedonia under the PHQ-2, then the patient is 
administered the lengthier PHQ-9. This skip pattern (also referred to 
as a gateway) is designed to reduce the length of the interview 
assessment for patients who fail to report the cardinal symptoms of 
depression. The design of the PHQ-2 to 9 reduces the burden that would 
be associated with requiring the full PHQ-9, while ensuring that 
patients and residents with indications of depressive symptoms based on 
the PHQ-2 receive the longer assessment.
---------------------------------------------------------------------------

    \88\ Li, C., Friedman, B., Conwell, Y., & Fiscella, K. (2007). 
``Validity of the Patient Health Questionnaire 2 (PHQ[hyphen]2) in 
identifying major depression in older people.'' J of the A 
Geriatrics Society, 55(4): 596-602.
    \89\ L[ouml]we, B., Kroenke, K., & Gr[auml]fe, K. (2005). 
``Detecting and monitoring depression with a two-item questionnaire 
(PHQ-2).'' J of Psychosomatic Research, 58(2): 163-171.
---------------------------------------------------------------------------

    Components of the proposed data elements are currently used in the 
OASIS for HHAs (PHQ-2) and the MDS for SNFs (PHQ-9). For more 
information on the PHQ-2 to 9, we refer readers to the document titled 
``Proposed Specifications for IRF QRP Quality Measures and Standardized 
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We proposed the PHQ-2 data elements as SPADEs in the FY 2018 IRF 
proposed rule (82 FR 20725 through 20726). In that proposed rule, we 
stated that the proposal was informed by input we received from the TEP 
convened by our data element contractor on April 6 and 7, 2016. The TEP 
members particularly noted that the brevity of the PHQ-2 made it 
feasible to administer with low burden for both assessors and PAC 
patients or residents. A summary of the April 6 and 7, 2016 TEP meeting 
titled ``SPADE Technical Expert Panel Summary (First Convening)'' is 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    That rule proposal was also informed by public input that we 
received through a call for input published on the CMS Measures 
Management System Blueprint website. Input was submitted from August 12 
to September 12, 2016 on three versions of the PHQ depression screener: 
The PHQ-2; the PHQ-9; and the PHQ-2 to 9 with the skip pattern design. 
Many commenters were supportive of the standardized assessment of mood 
in PAC settings, given the role that depression plays in well-being. 
Several commenters expressed support for an approach that would use 
PHQ-2 as a gateway to the longer PHQ-9 while still potentially reducing 
burden on most patients and residents, as well as test administrators, 
and ensuring the administration of the PHQ-9, which exhibits higher 
specificity,\90\ for patients and residents who showed signs and 
symptoms of depression on the PHQ-2. A summary report for the August 12 
to September 12, 2016 public comment period titled ``SPADE August 2016 
Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------

    \90\ Arroll B, Goodyear-Smith F, Crengle S, Gunn J, Kerse N, 
Fishman T, et al. Validation of PHQ-2 and PHQ-9 to screen for major 
depression in the primary care population. Annals of family 
medicine. 2010;8(4):348-53. doi: 10.1370/afm.1139 pmid:20644190; 
PubMed Central PMCID: PMC2906530.
---------------------------------------------------------------------------

    In response to our proposal to use the PHQ-2 in the FY 2018 IRF PPS 
proposed rule (82 FR 20725 through 20726), we received comments 
agreeing to the importance of a standardized assessment of depression 
in patients and residents receiving PAC services. Commenters also 
raised concerns about the ability of the PHQ-2 to correctly identify 
all patients and residents with signs and symptoms of depression. One 
commenter supported using the PHQ-2 as a gateway assessment and 
conducting a more thorough evaluation of depression symptoms with the 
PHQ-9 if the PHQ-2 is positive. Another commenter expressed concern 
that standardized assessment of signs and symptoms of depression via 
the PHQ-2 is not appropriate in the IRF setting, as patients may have 
recently experienced acute illness or injury, and routine screening may 
lead to overprescribing of antidepressant medications. Another 
commenter expressed concern about potential conflicts between the 
results of screening assessments and documented diagnoses based on the 
expertise of physicians and other clinicians. In response to these 
comments, we carried out additional testing, and we provide our 
findings below.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
PHQ-2 to 9 was included in the National Beta Test of candidate data 
elements conducted by our data element contractor from November 2017 to 
August 2018. Results of this test found the PHQ-2 to 9 to be feasible 
and reliable for use with PAC patients and residents. More information 
about the performance of the PHQ-2 to 9 in the National Beta Test can 
be found in the document titled ``Proposed Specifications for IRF QRP 
Quality Measures and Standardized Patient Assessment Data Elements,'' 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of

[[Page 17297]]

soliciting input on the PHQ-2 to 9. The TEP was supportive of the PHQ-2 
to 9 data element set as a screener for signs and symptoms of 
depression. The TEP's discussion noted that symptoms evaluated by the 
full PHQ-9 (for example, concentration, sleep, appetite) had relevance 
to care planning and the overall well-being of the patient or resident, 
but that the gateway approach of the PHQ-2 to 9 would be appropriate as 
a depression screening assessment, as it depends on the well-validated 
PHQ-2 and focuses on the cardinal symptoms of depression. A summary of 
the September 17, 2018 TEP meeting titled ``SPADE Technical Expert 
Panel Summary (Third Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our on-going SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for depression, 
stakeholder input, and test results, we are proposing that the PHQ-2 to 
9 data elements meet the definition of standardized patient assessment 
data with respect to cognitive function and mental status under section 
1899B(b)(1)(B)(ii) of the Act and to adopt the PHQ-2 to 9 data elements 
as standardized patient assessment data for use in the IRF QRP.
2. Special Services, Treatments, and Interventions Data
    Special services, treatments, and interventions performed in PAC 
can have a major effect on an individual's health status, self-image, 
and quality of life. The assessment of these special services, 
treatments, and interventions in PAC is important to ensure the 
continuing appropriateness of care for the patients and residents 
receiving them, and to support care transitions from one PAC provider 
to another, an acute care hospital, or discharge. In alignment with our 
Meaningful Measures Initiative, accurate assessment of special 
services, treatments, and interventions of patients and residents 
served by PAC providers is expected to make care safer by reducing harm 
caused in the delivery of care; promote effective prevention and 
treatment of chronic disease; strengthen person and family engagement 
as partners in their care; and promote effective communication and 
coordination of care.
    For example, standardized assessment of special services, 
treatments, and interventions used in PAC can promote patient and 
resident safety through appropriate care planning (for example, 
mitigating risks such as infection or pulmonary embolism associated 
with central intravenous access), and identifying life-sustaining 
treatments that must be continued, such as mechanical ventilation, 
dialysis, suctioning, and chemotherapy, at the time of discharge or 
transfer. Standardized assessment of these data elements will enable or 
support: Clinical decision-making and early clinical intervention; 
person-centered, high quality care through, for example, facilitating 
better care continuity and coordination; better data exchange and 
interoperability between settings; and longitudinal outcome analysis. 
Therefore, reliable data elements assessing special services, 
treatments, and interventions are needed to initiate a management 
program that can optimize a patient's or resident's prognosis and 
reduce the possibility of adverse events.
    A TEP convened by our data element contractor provided input on the 
proposed data elements for special services, treatments, and 
interventions. In a meeting held on January 5 and 6, 2017, this TEP 
found that these data elements are appropriate for standardization 
because they would provide useful clinical information to inform care 
planning and care coordination. The TEP affirmed that assessment of 
these services and interventions is standard clinical practice, and 
that the collection of these data by means of a list and checkbox 
format would conform with common workflow for PAC providers. A summary 
of the January 5 and 6, 2017 TEP meeting titled ``SPADE Technical 
Expert Panel Summary (Second Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Comments on the category of special services, treatments, and 
interventions were also submitted by stakeholders during the FY 2018 
IRF PPS proposed rule (82 FR 20726 through 20736) public comment 
period. One commenter supported adding the SPADEs for special services, 
treatments and interventions. Others stated labor costs and staff 
burden would increase for data collection. The Medicare Payment 
Advisory Commission (MedPAC) suggested that a few other high-cost 
services, such as cardiac monitoring and specialty bed/surfaces, may 
warrant consideration for inclusion in future collection efforts. One 
commenter believed that the low frequency of the special services, 
treatments, and interventions in the IRF setting makes them not worth 
assessing for patients given the cost of data collection and reporting. 
A few commenters noted that that many of these data elements should be 
obtainable from administrative data (that is, coding and Medicare 
claims), and therefore, assessing them through patient record review 
would be duplicated effort.
    Information on data element performance in the National Beta Test, 
which collected data between November 2017 and August 2018, is reported 
within each data element proposal below. Clinical staff who 
participated in the National Beta Test supported these data elements 
because of their importance in conveying patient or resident 
significant health care needs, complexity, and progress. However, 
clinical staff also noted that, despite the simple ``check box'' format 
of these data element, they sometimes needed to consult multiple 
information sources to determine a patient's or resident's treatments.
    We are inviting comment on our proposals to collect as standardized 
patient assessment data the following data with respect to special 
services, treatments, and interventions.
 Cancer Treatment: Chemotherapy (IV, Oral, Other)
    We are proposing that the Chemotherapy (IV, Oral, Other) data 
element meets the definition of standardized patient assessment data 
with respect to special services, treatments, and interventions under 
section 1899B(b)(1)(B)(iii) of the Act.

[[Page 17298]]

    As described in the FY 2018 IRF PPS proposed rule (82 FR 20726 
through 20727), chemotherapy is a type of cancer treatment that uses 
drugs to destroy cancer cells. It is sometimes used when a patient has 
a malignancy (cancer), which is a serious, often life-threatening or 
life-limiting condition. Both intravenous (IV) and oral chemotherapy 
have serious side effects, including nausea/vomiting, extreme fatigue, 
risk of infection due to a suppressed immune system, anemia, and an 
increased risk of bleeding due to low platelet counts. Oral 
chemotherapy can be as potent as chemotherapy given by IV and can be 
significantly more convenient and less resource-intensive to 
administer. Because of the toxicity of these agents, special care must 
be exercised in handling and transporting chemotherapy drugs. IV 
chemotherapy is administered either peripherally, or more commonly, 
given via an indwelling central line, which raises the risk of 
bloodstream infections. Given the significant burden of malignancy, the 
resource intensity of administering chemotherapy, and the side effects 
and potential complications of these highly-toxic medications, 
assessing the receipt of chemotherapy is important in the PAC setting 
for care planning and determining resource use. The need for 
chemotherapy predicts resource intensity, both because of the 
complexity of administering these potent, toxic drug combinations under 
specific protocols, and because of what the need for chemotherapy 
signals about the patient's underlying medical condition. Furthermore, 
the resource intensity of IV chemotherapy is higher than for oral 
chemotherapy, as the protocols for administration and the care of the 
central line (if present) for IV chemotherapy require significant 
resources.
    The Chemotherapy (IV, Oral, Other) data element consists of a 
principal data element (Chemotherapy) and three response option sub-
elements: IV chemotherapy, which is generally resource-intensive; Oral 
chemotherapy, which is less invasive and generally requires less 
intensive administration protocols; and a third category, Other, 
provided to enable the capture of other less common chemotherapeutic 
approaches. This third category is potentially associated with higher 
risks and is more resource intensive due to delivery by other routes 
(for example, intraventricular or intrathecal). If the assessor 
indicates that the patient is receiving chemotherapy on the principal 
Chemotherapy data element, the assessor would then indicate by which 
route or routes (for example, IV, Oral, Other) the chemotherapy is 
administered.
    A single Chemotherapy data element that does not include the 
proposed three sub-elements is currently in use in the MDS in SNFs. For 
more information on the Chemotherapy (IV, Oral, Other) data element, we 
refer readers to the document titled ``Proposed Specifications for IRF 
QRP Quality Measures and Standardized Patient Assessment Data 
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Chemotherapy data element was first proposed as a standardized 
patient assessment data element in the FY 2018 IRF PPS proposed rule 
(82 FR 20726 through 20727). In that proposed rule, we stated that the 
proposal was informed by input we received through a call for input 
published on the CMS Measures Management System Blueprint website. 
Input submitted from August 12 to September 12, 2016 expressed support 
for the IV Chemotherapy data element and suggested it be included as 
standardized patient assessment data. We also stated that those 
commenters had noted that assessing the use of chemotherapy services is 
relevant to share across the care continuum to facilitate care 
coordination and care transitions and noted the validity of the data 
element. Commenters also noted the importance of capturing all types of 
chemotherapy, regardless of route, and stated that collecting data only 
on patients and residents who received chemotherapy by IV would limit 
the usefulness of this standardized data element. A summary report for 
the August 12 to September 12, 2016 public comment period titled 
``SPADE August 2016 Public Comment Summary Report'' is available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of the special services, 
treatments, and interventions data elements in general; no additional 
comments were received that were specific to the Chemotherapy data 
element.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Chemotherapy data element was included in the National Beta Test of 
candidate data elements conducted by our data element contractor from 
November 2017 to August 2018. Results of this test found the 
Chemotherapy data element to be feasible and reliable for use with PAC 
patients and residents. More information about the performance of the 
Chemotherapy data element in the National Beta Test can be found in the 
document titled ``Proposed Specifications for IRF QRP Quality Measures 
and Standardized Patient Assessment Data Elements,'' available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP members 
did not specifically discuss the Chemotherapy data element, the TEP 
members supported the assessment of the special services, treatments, 
and interventions included in the National Beta Test with respect to 
both admission and discharge. A summary of the September 17, 2018 TEP 
meeting titled ``SPADE Technical Expert Panel Summary (Third 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.

[[Page 17299]]

    Taking together the importance of assessing for chemotherapy, 
stakeholder input, and strong test results, we are proposing that the 
Chemotherapy (IV, Oral, Other) data element with a principal data 
element and three sub-elements meet the definition of standardized 
patient assessment data with respect to special services, treatments, 
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to 
adopt the Chemotherapy (IV, Oral, Other) data element as standardized 
patient assessment data for use in the IRF QRP.
 Cancer Treatment: Radiation
    We are proposing that the Radiation data element meets the 
definition of standardized patient assessment data with respect to 
special services, treatments, and interventions under section 
1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20727 
through 20728), radiation is a type of cancer treatment that uses high-
energy radioactivity to stop cancer by damaging cancer cell DNA, but it 
can also damage normal cells. Radiation is an important therapy for 
particular types of cancer, and the resource utilization is high, with 
frequent radiation sessions required, often daily for a period of 
several weeks. Assessing whether a patient or resident is receiving 
radiation therapy is important to determine resource utilization 
because PAC patients and residents will need to be transported to and 
from radiation treatments, and monitored and treated for side effects 
after receiving this intervention. Therefore, assessing the receipt of 
radiation therapy, which would compete with other care processes given 
the time burden, would be important for care planning and care 
coordination by PAC providers.
    The proposed data element consists of the single Radiation data 
element. The Radiation data element is currently in use in the MDS in 
SNFs. For more information on the Radiation data element, we refer 
readers to the document titled ``Proposed Specifications for IRF QRP 
Quality Measures and Standardized Patient Assessment Data Elements,'' 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Radiation data element was first proposed as a standardized 
patient assessment data element in the FY 2018 IRF PPS proposed rule 
(82 FR 20727 through 20728). In that proposed rule, we stated that the 
proposal was informed by input we received through a call for input 
published on the CMS Measures Management System Blueprint website. 
Input submitted from August 12 to September 12, 2016 expressed support 
for the Radiation data element, noting its importance and clinical 
usefulness for patients and residents in PAC settings, due to the side 
effects and consequences of radiation treatment on patients and 
residents that need to be considered in care planning and care 
transitions, the feasibility of the item, and the potential for it to 
improve quality. A summary report for the August 12 to September 12, 
2016 public comment period titled ``SPADE August 2016 Public Comment 
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of the special services, 
treatments, and interventions data elements in general; no additional 
comments were received that were specific to the Radiation data 
element.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Radiation data element was included in the National Beta Test of 
candidate data elements conducted by our data element contractor from 
November 2017 to August 2018. Results of this test found the Radiation 
data element to be feasible and reliable for use with PAC patients and 
residents. More information about the performance of the Radiation data 
element in the National Beta Test can be found in the document titled 
``Proposed Specifications for IRF QRP Quality Measures and Standardized 
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP members 
did not specifically discuss the Radiation data element, the TEP 
members supported the assessment of the special services, treatments, 
and interventions included in the National Beta Test with respect to 
both admission and discharge. A summary of the September 17, 2018 TEP 
meeting titled ``SPADE Technical Expert Panel Summary (Third 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present results of the National Beta 
Test and solicit additional comments. General input on the testing and 
item development process and concerns about burden were received from 
stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for radiation, 
stakeholder input, and strong test results, we are proposing that the 
Radiation data element meets the definition of standardized patient 
assessment data with respect to special services, treatments, and 
interventions under section 1899B(b)(1)(B)(iii) of the Act and to adopt 
the Radiation data element as standardized patient assessment data for 
use in the IRF QRP.
 Respiratory Treatment: Oxygen Therapy (Intermittent, 
Continuous, High-concentration Oxygen Delivery System)
    We are proposing that the Oxygen Therapy (Intermittent, Continuous, 
High-concentration Oxygen Delivery System) data element meets the 
definition of standardized patient assessment data with respect to 
special services, treatments, and interventions under section 
1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20728), we 
proposed a similar data element related to oxygen therapy. Oxygen 
therapy provides a patient or resident with extra oxygen when medical 
conditions such as chronic obstructive pulmonary

[[Page 17300]]

disease, pneumonia, or severe asthma prevent the patient or resident 
from getting enough oxygen from breathing. Oxygen administration is a 
resource-intensive intervention, as it requires specialized equipment 
such as a source of oxygen, delivery systems (for example, oxygen 
concentrator, liquid oxygen containers, and high-pressure systems), the 
patient interface (for example, nasal cannula or mask), and other 
accessories (for example, regulators, filters, tubing). The data 
element proposed here captures patient or resident use of three types 
of oxygen therapy (intermittent, continuous, and high-concentration 
oxygen delivery system), which reflects the intensity of care needed, 
including the level of monitoring and bedside care required. Assessing 
the receipt of this service is important for care planning and resource 
use for PAC providers.
    The proposed data element, Oxygen Therapy, consists of the 
principal Oxygen Therapy data element and three response option sub-
elements: Continuous (whether the oxygen was delivered continuously, 
typically defined as > =14 hours per day); Intermittent; or High-
concentration Oxygen Delivery System. Based on public comments and 
input from expert advisors about the importance and clinical usefulness 
of documenting the extent of oxygen use, we added a third sub-element, 
high-concentration oxygen delivery system, to the sub-elements, which 
previously included only intermittent and continuous. If the assessor 
indicates that the patient is receiving oxygen therapy on the principal 
oxygen therapy data element, the assessor then would indicate the type 
of oxygen the patient receives (for example, Intermittent, Continuous, 
High-concentration oxygen delivery system).
    These three proposed sub-elements were developed based on similar 
data elements that assess oxygen therapy, currently in use in the MDS 
in SNFs (``Oxygen Therapy''), previously used in the OASIS (``Oxygen 
(intermittent or continuous)''), and a data element tested in the PAC 
PRD that focused on intensive oxygen therapy (``High O2 Concentration 
Delivery System with FiO2 > 40 percent''). For more information on the 
proposed Oxygen Therapy (Continuous, Intermittent, High-concentration 
oxygen delivery system) data element, we refer readers to the document 
titled ``Proposed Specifications for IRF QRP Quality Measures and 
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Oxygen Therapy (Intermittent, Continuous) data element was 
first proposed as standardized patient assessment data in the FY 2018 
IRF PPS proposed rule (82 FR 20728). In that proposed rule, we stated 
that the proposal was informed by input we received on the single data 
element, Oxygen (inclusive of intermittent and continuous oxygen use), 
through a call for input published on the CMS Measures Management 
System Blueprint website. Input submitted from August 12 to September 
12, 2016, expressed the importance of the Oxygen data element, noting 
feasibility of this item in PAC, and the relevance of it to 
facilitating care coordination and supporting care transitions, but 
suggesting that the extent of oxygen use be documented. A summary 
report for the August 12 to September 12, 2016 public comment period 
titled ``SPADE August 2016 Public Comment Summary Report'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of the special services, 
treatments, and interventions data elements in general; no additional 
comments were received that were specific to the Oxygen Therapy 
(Intermittent, Continuous) data element.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Oxygen Therapy data element was included in the National Beta Test of 
candidate data elements conducted by our data element contractor from 
November 2017 to August 2018. Results of this test found the Oxygen 
Therapy data element to be feasible and reliable for use with PAC 
patients and residents. More information about the performance of the 
Oxygen Therapy data element in the National Beta Test can be found in 
the document titled ``Proposed Specifications for IRF QRP Quality 
Measures and Standardized Patient Assessment Data Elements,'' available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP did not 
specifically discuss the Oxygen Therapy data element, the TEP supported 
the assessment of the special services, treatments, and interventions 
included in the National Beta Test with respect to both admission and 
discharge. A summary of the September 17, 2018 TEP meeting titled 
``SPADE Technical Expert Panel Summary (Third Convening)'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing oxygen therapy, 
stakeholder input, and strong test results, we are proposing that the 
Oxygen Therapy (Intermittent, Continuous, High-concentration Oxygen 
Delivery System) data element with a principal data element and three 
sub-elements meets the definition of standardized patient assessment 
data with respect to special services, treatments, and interventions 
under section 1899B(b)(1)(B)(iii) of the Act and to adopt the Oxygen 
Therapy (Intermittent, Continuous, High-concentration Oxygen Delivery 
System) data element as standardized patient assessment data for use in 
the IRF QRP.
 Respiratory Treatment: Suctioning (Scheduled, as Needed)
    We are proposing that the Suctioning (Scheduled, As needed) data 
element meets the definition of standardized

[[Page 17301]]

patient assessment data with respect to special services, treatments, 
and interventions under section 1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20728 
through 20729), suctioning is a process used to clear secretions from 
the airway when a person cannot clear those secretions on his or her 
own. It is done by aspirating secretions through a catheter connected 
to a suction source. Types of suctioning include oropharyngeal and 
nasopharyngeal suctioning, nasotracheal suctioning, and suctioning 
through an artificial airway such as a tracheostomy tube. Oropharyngeal 
and nasopharyngeal suctioning are a key part of many patients' or 
residents' care plans, both to prevent the accumulation of secretions 
than can lead to aspiration pneumonias (a common condition in patients 
and residents with inadequate gag reflexes), and to relieve 
obstructions from mucus plugging during an acute or chronic respiratory 
infection, which often lead to desaturations and increased respiratory 
effort. Suctioning can be done on a scheduled basis if the patient is 
judged to clinically benefit from regular interventions, or can be done 
as needed when secretions become so prominent that gurgling or choking 
is noted, or a sudden desaturation occurs from a mucus plug. As 
suctioning is generally performed by a care provider rather than 
independently, this intervention can be quite resource intensive if it 
occurs every hour, for example, rather than once a shift. It also 
signifies an underlying medical condition that prevents the patient 
from clearing his/her secretions effectively (such as after a stroke, 
or during an acute respiratory infection). Generally, suctioning is 
necessary to ensure that the airway is clear of secretions which can 
inhibit successful oxygenation of the individual. The intent of 
suctioning is to maintain a patent airway, the loss of which can lead 
to death or complications associated with hypoxia.
    The Suctioning (Scheduled, As needed) data element consists of a 
principal data element, and two sub-elements: Scheduled and As needed. 
These sub-elements capture two types of suctioning. Scheduled indicates 
suctioning based on a specific frequency, such as every hour. As needed 
means suctioning only when indicated. If the assessor indicates that 
the patient is receiving suctioning on the principal Suctioning data 
element, the assessor would then indicate the frequency (for example, 
Scheduled, As needed). The proposed data element is based on an item 
currently in use in the MDS in SNFs which does not include our proposed 
two sub-elements, as well as data elements tested in the PAC PRD that 
focused on the frequency of suctioning required for patients and 
residents with tracheostomies (``Trach Tube with Suctioning: Specify 
most intensive frequency of suctioning during stay [Every __hours]''). 
For more information on the Suctioning data element, we refer readers 
to the document titled ``Proposed Specifications for IRF QRP Quality 
Measures and Standardized Patient Assessment Data Elements,'' available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Suctioning data element was first proposed as standardized 
patient assessment data elements in the FY 2018 IRF PPS proposed rule 
(82 FR 20728 through 20729). In that proposed rule, we stated that the 
proposal was informed by input we received through a call for input 
published on the CMS Measures Management System Blueprint website. 
Input submitted from August 12 to September 12, 2016 expressed support 
for the Suctioning data element. The input noted the feasibility of 
this item in PAC, and the relevance of this data element to 
facilitating care coordination and supporting care transitions.
    We also stated that those commenters had suggested that we examine 
the frequency of suctioning to better understand the use of staff time, 
the impact on a patient or resident's capacity to speak and swallow, 
and intensity of care required. Based on these comments, we decided to 
add two sub-elements (Scheduled and As needed) to the suctioning 
element. The proposed Suctioning data element includes both the 
principal Suctioning data element that is included on the MDS in SNFs 
and two sub-elements, Scheduled and As needed. A summary report for the 
August 12 to September 12, 2016 public comment period titled ``SPADE 
August 2016 Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of the special services, 
treatments, and interventions data elements in general; no additional 
comments were received that were specific to the Suctioning data 
element.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Suctioning data element was included in the National Beta Test of 
candidate data elements conducted by our data element contractor from 
November 2017 to August 2018. Results of this test found the Suctioning 
data element to be feasible and reliable for use with PAC patients and 
residents. More information about the performance of the Suctioning 
data element in the National Beta Test can be found in the document 
titled ``Proposed Specifications for IRF QRP Quality Measures and 
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP did not 
specifically discuss the Suctioning data element, the TEP supported the 
assessment of the special services, treatments, and interventions 
included in the National Beta Test with respect to both admission and 
discharge. A summary of the September 17, 2018 TEP meeting titled 
``SPADE Technical Expert Panel Summary (Third Convening)'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicited additional comments. General input on the 
testing and item development process and concerns about burden were 
received from stakeholders during this meeting and via email through 
February 1, 2019. A summary of the public input received from the 
November 27, 2018 stakeholder meeting titled ``Input on Standardized 
Patient Assessment Data Elements (SPADEs) Received After November 27, 
2018 Stakeholder Meeting'' is available at https://www.cms.gov/
Medicare/Quality-

[[Page 17302]]

Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-
Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for suctioning, 
stakeholder input, and strong test results, we are proposing that the 
Suctioning (Scheduled, As needed) data element with a principal data 
element and two sub-elements meets the definition of standardized 
patient assessment data with respect to special services, treatments, 
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to 
adopt the Suctioning (Scheduled, As needed) data element as 
standardized patient assessment data for use in the IRF QRP.
 Respiratory Treatment: Tracheostomy Care
    We are proposing that the Tracheostomy Care data element meets the 
definition of standardized patient assessment data with respect to 
special services, treatments, and interventions under section 
1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20729 
through 20730), a tracheostomy provides an air passage to help a 
patient or resident breathe when the usual route for breathing is 
obstructed or impaired. Generally, in all of these cases, suctioning is 
necessary to ensure that the tracheostomy is clear of secretions, which 
can inhibit successful oxygenation of the individual. Often, 
individuals with tracheostomies are also receiving supplemental 
oxygenation. The presence of a tracheostomy, albeit permanent or 
temporary, warrants careful monitoring and immediate intervention if 
the tracheostomy becomes occluded or if the device used becomes 
dislodged. While in rare cases the presence of a tracheostomy is not 
associated with increased care demands (and in some of those instances, 
the care of the ostomy is performed by the patient) in general the 
presence of such as device is associated with increased patient risk, 
and clinical care services will necessarily include close monitoring to 
ensure that no life-threatening events occur as a result of the 
tracheostomy. In addition, tracheostomy care, which primarily consists 
of cleansing, dressing changes, and replacement of the tracheostomy 
cannula (tube), is a critical part of the care plan. Regular cleansing 
is important to prevent infection, such as pneumonia, and to prevent 
any occlusions with which there are risks for inadequate oxygenation.
    The proposed data element consists of the single Tracheostomy Care 
data element. The proposed data element is currently in use in the MDS 
in SNFs (``Tracheostomy care''). For more information on the 
Tracheostomy Care data element, we refer readers to the document titled 
``Proposed Specifications for IRF QRP Quality Measures and Standardized 
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Tracheostomy Care data element was first proposed as a 
standardized patient assessment data element in the FY 2018 IRF PPS 
proposed rule (82 FR 20729 through 20730). In that proposed rule, we 
stated that the proposal was informed by input we received on the 
Tracheostomy Care data element through a call for input published on 
the CMS Measures Management System Blueprint website. Input submitted 
from August 12 to September 12, 2016 expressed support for this data 
element, noting the feasibility of this item in PAC, and the relevance 
of this data element to facilitating care coordination and supporting 
care transitions. A summary report for the August 12 to September 12, 
2016 public comment period titled ``SPADE August 2016 Public Comment 
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of the special services, 
treatments, and interventions data elements in general; no additional 
comments were received that were specific to the Tracheostomy Care data 
element.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Tracheostomy Care data element was included in the National Beta Test 
of candidate data elements conducted by our data element contractor 
from November 2017 to August 2018. Results of this test found the 
Tracheostomy Care data element to be feasible and reliable for use with 
PAC patients and residents. More information about the performance of 
the Tracheostomy Care data element in the National Beta Test can be 
found in the document titled ``Proposed Specifications for IRF QRP 
Quality Measures and Standardized Patient Assessment Data Elements,'' 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP did not 
specifically discuss the Tracheostomy Care data element, the TEP 
supported the assessment of the special services, treatments, and 
interventions included in the National Beta Test with respect to both 
admission and discharge. A summary of the September 17, 2018 TEP 
meeting titled ``SPADE Technical Expert Panel Summary (Third 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for tracheostomy care, 
stakeholder input, and strong test results, we are proposing that the 
Tracheostomy Care data element meets the definition of standardized 
patient assessment data with respect to special services, treatments, 
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to 
adopt the Tracheostomy Care data element as standardized patient 
assessment data for use in the IRF QRP.

[[Page 17303]]

 Respiratory Treatment: Non-Invasive Mechanical Ventilator 
(BiPAP, CPAP)
    We are proposing that the Non-invasive Mechanical Ventilator 
(Bilevel Positive Airway Pressure [BiPAP], Continuous Positive Airway 
Pressure [CPAP]) data element meets the definition of standardized 
patient assessment data with respect to special services, treatments, 
and interventions under section 1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20730), 
BiPAP and CPAP are respiratory support devices that prevent the airways 
from closing by delivering slightly pressurized air via electronic 
cycling throughout the breathing cycle (BiPAP) or through a mask 
continuously (CPAP). Assessment of non-invasive mechanical ventilation 
is important in care planning, as both CPAP and BiPAP are resource-
intensive (although less so than invasive mechanical ventilation) and 
signify underlying medical conditions about the patient or resident who 
requires the use of this intervention. Particularly when used in 
settings of acute illness or progressive respiratory decline, 
additional staff (for example, respiratory therapists) are required to 
monitor and adjust the CPAP and BiPAP settings and the patient or 
resident may require more nursing resources.
    The proposed data element, Non-invasive Mechanical Ventilator 
(BIPAP, CPAP), consists of the principal Non-invasive Mechanical 
Ventilator data element and two response option sub-elements: BiPAP and 
CPAP. If the assessor indicates that the patient is receiving non-
invasive mechanical ventilation on the principal Non-invasive 
Mechanical Ventilator data element, the assessor would then indicate 
which type (for example, BIPAP, CPAP). Data elements that assess non-
invasive mechanical ventilation are currently included on LCDS for the 
LTCH setting (``Non-invasive Ventilator (BIPAP, CPAP)''), and the MDS 
for the SNF setting (``Non-invasive Mechanical Ventilator (BiPAP/
CPAP)''). For more information on the Non-invasive Mechanical 
Ventilator (BIPAP, CPAP) data element, we refer readers to the document 
titled ``Proposed Specifications for IRF QRP Quality Measures and 
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Non-invasive Mechanical Ventilator data element was first 
proposed as standardized patient assessment data elements in the FY 
2018 IRF PPS proposed rule (82 FR 20730). In that proposed rule, we 
stated that the proposal was informed by input we received through a 
call for input published on the CMS Measures Management System 
Blueprint website. Input submitted from August 12 to September 12, 2016 
on a single data element, BiPAP/CPAP, that captures equivalent clinical 
information but uses a different label than the data element currently 
used in the MDS in SNFs and LCDS, expressed support for this data 
element, noting the feasibility of these items in PAC, and the 
relevance of this data element for facilitating care coordination and 
supporting care transitions. In addition, we also stated that some 
commenters supported separating out BiPAP and CPAP as distinct sub-
elements, as they are therapies used for different types of patients 
and residents. A summary report for the August 12 to September 12, 2016 
public comment period titled ``SPADE August 2016 Public Comment Summary 
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of the special services, 
treatments, and interventions data elements in general. One commenter 
noted appreciation of the revisions to the Non-invasive Mechanical 
Ventilator data element in response to comments submitted during a 
public input period held from August 12 to September 12, 2016.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Non-invasive Mechanical Ventilator data element was included in the 
National Beta Test of candidate data elements conducted by our data 
element contractor from November 2017 to August 2018. Results of this 
test found the Non-invasive Mechanical Ventilator data element to be 
feasible and reliable for use with PAC patients and residents. More 
information about the performance of the Non-invasive Mechanical 
Ventilator data element in the National Beta Test can be found in the 
document titled ``Proposed Specifications for IRF QRP Quality Measures 
and Standardized Patient Assessment Data Elements,'' available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP did not 
specifically discuss the Non-invasive Mechanical Ventilator data 
element, the TEP supported the assessment of the special services, 
treatments, and interventions included in the National Beta Test with 
respect to both admission and discharge. A summary of the September 17, 
2018 TEP meeting titled ``SPADE Technical Expert Panel Summary (Third 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for non-invasive 
mechanical ventilation, stakeholder input, and strong test results, we 
are proposing that the Non-invasive Mechanical Ventilator (BiPAP, CPAP) 
data element with a principal data element and two sub-elements meets 
the definition of standardized patient assessment data with respect to 
special services, treatments, and interventions under section 
1899B(b)(1)(B)(iii) of the Act and to adopt the Non-invasive Mechanical 
Ventilator (BiPAP, CPAP) data element as standardized patient 
assessment data for use in the IRF QRP.

[[Page 17304]]

 Respiratory Treatment: Invasive Mechanical Ventilator
    We are proposing that the Invasive Mechanical Ventilator data 
element meets the definition of standardized patient assessment data 
with respect to special services, treatments, and interventions under 
section 1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20730 
through 20731), invasive mechanical ventilation includes ventilators 
and respirators that ventilate the patient through a tube that extends 
via the oral airway into the pulmonary region or through a surgical 
opening directly into the trachea. Thus, assessment of invasive 
mechanical ventilation is important in care planning and risk 
mitigation. Ventilation in this manner is a resource-intensive therapy 
associated with life-threatening conditions without which the patient 
or resident would not survive. However, ventilator use has inherent 
risks requiring close monitoring. Failure to adequately care for the 
patient or resident who is ventilator dependent can lead to iatrogenic 
events such as death, pneumonia, and sepsis. Mechanical ventilation 
further signifies the complexity of the patient's underlying medical or 
surgical condition. Of note, invasive mechanical ventilation is 
associated with high daily and aggregate costs.\91\
---------------------------------------------------------------------------

    \91\ Wunsch, H., Linde-Zwirble, W.T., Angus, D.C., Hartman, 
M.E., Milbrandt, E.B., & Kahn, J.M. (2010). ``The epidemiology of 
mechanical ventilation use in the United States.'' Critical Care Med 
38(10): 1947-1953.
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    The proposed data element, Invasive Mechanical Ventilator, consists 
of a single data element. Data elements that capture invasive 
mechanical ventilation are currently in use in the MDS in SNFs and LCDS 
in LTCHs. For more information on the Invasive Mechanical Ventilator 
data element, we refer readers to the document titled ``Proposed 
Specifications for IRF QRP Quality Measures and Standardized Patient 
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Invasive Mechanical Ventilator data element was first proposed 
as a standardized patient assessment data element in the FY 2018 IRF 
PPS proposed rule (82 FR 20730 through 20731). In that proposed rule, 
we stated that the proposal was informed by input we received on data 
elements that assess invasive ventilator use and weaning status that 
were tested in the PAC PRD (``Ventilator--Weaning'' and ``Ventilator--
Non-Weaning'') through a call for input published on the CMS Measures 
Management System Blueprint website. Input submitted from August 12 to 
September 12, 2016, expressed support for this data element, 
highlighting the importance of this information in supporting care 
coordination and care transitions. We also stated that some commenters 
had expressed concern about the appropriateness for standardization 
given: The prevalence of ventilator weaning across PAC providers; the 
timing of administration; how weaning is defined; and how weaning 
status in particular relates to quality of care. These public comments 
guided our decision to propose a single data element focused on current 
use of invasive mechanical ventilation only, which does not attempt to 
capture weaning status. A summary report for the August 12 to September 
12, 2016 public comment period titled ``SPADE August 2016 Public 
Comment Summary Report'' we received is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of the special services, 
treatments, and interventions data elements in general. Two commenters 
noted their appreciation of the revisions to the Invasive Mechanical 
Ventilator data element in response to comments submitted during a 
public input period held from August 12 to September 12, 2016.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Invasive Mechanical Ventilator data element was included in the 
National Beta Test of candidate data elements conducted by our data 
element contractor from November 2017 to August 2018. Results of this 
test found the Invasive Mechanical Ventilator data element to be 
feasible and reliable for use with PAC patients and residents. More 
information about the performance of the Invasive Mechanical Ventilator 
data element in the National Beta Test can be found in the document 
titled ``Proposed Specifications for IRF QRP Quality Measures and 
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data element. Although the TEP did not 
specifically discuss the Invasive Mechanical Ventilator data element, 
the TEP supported the assessment of the special services, treatments, 
and interventions included in the National Beta Test with respect to 
both admission and discharge. A summary of the September 17, 2018 TEP 
meeting titled ``SPADE Technical Expert Panel Summary (Third 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present results of the National Beta 
Test and solicit additional comments. General input on the testing and 
item development process and concerns about burden were received from 
stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for invasive mechanical 
ventilation, stakeholder input, and strong test results, we are 
proposing that the Invasive Mechanical Ventilator data element that 
assesses the use of an invasive mechanical ventilator meets the 
definition of standardized patient assessment data with respect to 
special services, treatments, and interventions under section 
1899B(b)(1)(B)(iii) of the Act and to adopt the Invasive Mechanical 
Ventilator data element as standardized patient assessment data for use 
in the IRF QRP.

[[Page 17305]]

 Intravenous (IV) Medications (Antibiotics, Anticoagulants, 
Vasoactive Medications, Other)
    We are proposing that the IV Medications (Antibiotics, 
Anticoagulants, Vasoactive Medications, Other) data element meets the 
definition of standardized patient assessment data with respect to 
special services, treatments, and interventions under section 
1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20731 
through 20732), when we proposed a similar data element related to IV 
medications, IV medications are solutions of a specific medication (for 
example, antibiotics, anticoagulants) administered directly into the 
venous circulation via a syringe or intravenous catheter. IV 
medications are administered via intravenous push, single, 
intermittent, or continuous infusion through a catheter placed into the 
vein. Further, IV medications are more resource intensive to administer 
than oral medications, and signify a higher patient complexity (and 
often higher severity of illness).
    The clinical indications for each of the sub-elements of the IV 
Medications data element (Antibiotics, Anticoagulants, Vasoactive 
Medications, and Other) are very different. IV antibiotics are used for 
severe infections when the bioavailability of the oral form of the 
medication would be inadequate to kill the pathogen or an oral form of 
the medication does not exist. IV anticoagulants refer to anti-clotting 
medications (that is, ``blood thinners''). IV anticoagulants are 
commonly used for hospitalized patients who have deep venous 
thrombosis, pulmonary embolism, or myocardial infarction, as well as 
those undergoing interventional cardiac procedures. Vasoactive 
medications refer to the IV administration of vasoactive drugs, 
including vasopressors, vasodilators, and continuous medication for 
pulmonary edema, which increase or decrease blood pressure or heart 
rate. The indications, risks, and benefits of each of these classes of 
IV medications are distinct, making it important to assess each 
separately in PAC. Knowing whether or not patients and residents are 
receiving IV medication and the type of medication provided by each PAC 
provider will improve quality of care.
    The IV Medications (Antibiotics, Anticoagulants, Vasoactive 
Medications, and Other) data element we are proposing consists of a 
principal data element (IV Medications) and four response option sub-
elements: Antibiotics, Anticoagulants, Vasoactive Medications, and 
Other. The Vasoactive Medications sub-element was not proposed in the 
FY 2018 IRF PPS proposed rule (82 FR 20731 through 20732). We added the 
Vasoactive Medications sub-element to our proposal in order to 
harmonize the proposed IV Mediciations element with the data currently 
collected in the LCDS.
    If the assessor indicates that the patient is receiving IV 
medications on the principal IV Medications data element, the assessor 
would then indicate which types of medications (for example, 
Antibiotics, Anticoagulants, Vasoactive Medications, Other). An IV 
Medications data element is currently in use on the MDS in SNFs and 
there is a related data element in OASIS that collects information on 
Intravenous and Infusion Therapies. For more information on the IV 
Medications (Antibiotics, Anticoagulants, Vasoactive Medications, 
Other) data element, we refer readers to the document titled ``Proposed 
Specifications for IRF QRP Quality Measures and Standardized Patient 
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    An IV Medications data element was first proposed as standardized 
patient assessment data element in the FY 2018 IRF PPS proposed rule 
(82 FR 20731 through 20732). In that proposed rule, we stated that the 
proposal was informed by input we received on Vasoactive Medications 
through a call for input published on the CMS Measures Management 
System Blueprint website. Input submitted from August 12 to September 
12, 2016 supported this data element with one noting the importance of 
this data element in supporting care transitions. We also stated that 
those commenters had criticized the need for collecting specifically 
Vasoactive Medications, giving feedback that the data element was too 
narrowly focused. In addition, public comment received indicated that 
the clinical significance of vasoactive medications administration 
alone was not high enough in PAC to merit mandated assessment, noting 
that related and more useful information could be captured in an item 
that assessed all IV medication use. A summary report for the August 12 
to September 12, 2016 public comment period titled ``SPADE August 2016 
Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of the special services, 
treatments, and interventions data elements in general; no additional 
comments were received that were specific to the IV Medications data 
element.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
IV Medications data element was included in the National Beta Test of 
candidate data elements conducted by our data element contractor from 
November 2017 to August 2018. Results of this test found the IV 
Medications data element to be feasible and reliable for use with PAC 
patients and residents. More information about the performance of the 
IV Medications data element in the National Beta Test can be found in 
the document titled ``Proposed Specifications for IRF QRP Quality 
Measures and Standardized Patient Assessment Data Elements,'' available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP did not 
specifically discuss the IV Medications data element, the TEP supported 
the assessment of the special services, treatments, and interventions 
included in the National Beta Test with respect to both admission and 
discharge. A summary of the September 17, 2018 TEP meeting titled 
``SPADE Technical Expert Panel Summary (Third Convening)'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received

[[Page 17306]]

from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for IV medications, 
stakeholder input, and strong test results, we are proposing that the 
IV Medications (Antibiotics, Anticoagulants, Vasoactive Medications, 
Other) data element with a principal data element and four sub-elements 
meets the definition of standardized patient assessment data with 
respect to special services, treatments, and interventions under 
section 1899B(b)(1)(B)(iii) of the Act and to adopt the IV Medications 
(Antibiotics, Anticoagulants, Vasoactive Medications, Other) data 
element as standardized patient assessment data for use in the IRF QRP.
 Transfusions
    We are proposing that the Transfusions data element meets the 
definition of standardized patient assessment data with respect to 
special services, treatments, and interventions under section 
1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20732), 
transfusion refers to introducing blood or blood products into the 
circulatory system of a person. Blood transfusions are based on 
specific protocols, with multiple safety checks and monitoring required 
during and after the infusion in case of adverse events. Coordination 
with the provider's blood bank is necessary, as well as documentation 
by clinical staff to ensure compliance with regulatory requirements. In 
addition, the need for transfusions signifies underlying patient 
complexity that is likely to require care coordination and patient 
monitoring, and impacts planning for transitions of care, as 
transfusions are not performed by all PAC providers.
    The proposed data element consists of the single Transfusions data 
element. A data element on transfusion is currently in use in the MDS 
in SNFs (``Transfusions'') and a data element tested in the PAC PRD 
(``Blood Transfusions'') was found feasible for use in each of the four 
PAC settings. For more information on the Transfusions data element, we 
refer readers to the document titled ``Proposed Specifications for IRF 
QRP Quality Measures and Standardized Patient Assessment Data 
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Transfusions data element was first proposed as a standardized 
patient assessment data element in the FY 2018 IRF PPS proposed rule 
(82 FR 20732). In response to our proposal in the FY 2018 IRF PPS 
proposed rule, we received public comments in support of the special 
services, treatments, and interventions data elements in general; no 
additional comments were received that were specific to the 
Transfusions data element.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Transfusions data element was included in the National Beta Test of 
candidate data elements conducted by our data element contractor from 
November 2017 to August 2018. Results of this test found the 
Transfusions data element to be feasible and reliable for use with PAC 
patients and residents. More information about the performance of the 
Transfusions data element in the National Beta Test can be found in the 
document titled ``Proposed Specifications for IRF QRP Quality Measures 
and Standardized Patient Assessment Data Elements,'' available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP did not 
specifically discuss the Transfusions data element, the TEP supported 
the assessment of the special services, treatments, and interventions 
included in the National Beta Test with respect to both admission and 
discharge. A summary of the September 17, 2018 TEP meeting titled 
``SPADE Technical Expert Panel Summary (Third Convening)'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for transfusions, 
stakeholder input, and strong test results, we are proposing that the 
Transfusions data element meets the definition of standardized patient 
assessment data with respect to special services, treatments, and 
interventions under section 1899B(b)(1)(B)(iii) of the Act and to adopt 
the Transfusions data element as standardized patient assessment data 
for use in the IRF QRP.
 Dialysis (Hemodialysis, Peritoneal Dialysis)
    We are proposing that the Dialysis (Hemodialysis, Peritoneal 
dialysis) data element meets the definition of standardized patient 
assessment data with respect to special services, treatments, and 
interventions under section 1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20732 
through 20733), dialysis is a treatment primarily used to provide 
replacement for lost kidney function. Both forms of dialysis 
(hemodialysis and peritoneal dialysis) are resource intensive, not only 
during the actual dialysis process but before, during, and following. 
Patients and residents who need and undergo dialysis procedures are at 
high risk for physiologic and hemodynamic instability from fluid shifts 
and electrolyte disturbances, as well as infections that can lead to 
sepsis. Further, patients or residents receiving hemodialysis are often 
transported to a different facility, or at a minimum, to a different 
location in the same facility for treatment. Close monitoring for fluid

[[Page 17307]]

shifts, blood pressure abnormalities, and other adverse effects is 
required prior to, during, and following each dialysis session. Nursing 
staff typically perform peritoneal dialysis at the bedside, and as with 
hemodialysis, close monitoring is required.
    The proposed data element, Dialysis (Hemodialysis, Peritoneal 
dialysis) consists of the principal Dialysis data element and two 
response option sub-elements: Hemodialysis and Peritoneal dialysis. If 
the assessor indicates that the patient is receiving dialysis on the 
principal Dialysis data element, the assessor would then indicate which 
type (Hemodialysis or Peritoneal dialysis). The principal Dialysis data 
element is currently included on the MDS in SNFs and the LCDS for LTCHs 
and assesses the overall use of dialysis.
    As the result public feedback described below, in this proposed 
rule, we are proposing a data element that includes the principal 
Dialysis data element and two sub-elements (Hemodialysis and Peritoneal 
dialysis). For more information on the Dialysis data element, we refer 
readers to the document titled ``Proposed Specifications for IRF QRP 
Quality Measures and Standardized Patient Assessment Data Elements,'' 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Dialysis data element was first proposed as standardized 
patient assessment data in the FY 2018 IRF PPS proposed rule (82 FR 
20732 through 20733). In that proposed rule, we stated that the 
proposal was informed by input we received on a singular Hemodialysis 
data element through a call for input published on the CMS Measures 
Management System Blueprint website. Input submitted from August 12 to 
September 12, 2016 supported the assessment of hemodialysis and 
recommended that the data element be expanded to include peritoneal 
dialysis. We also stated that those commenters had supported the 
singular Hemodialysis data element, noting the relevance of this 
information for sharing across the care continuum to facilitate care 
coordination and care transitions, the potential for this data element 
to be used to improve quality, and the feasibility for use in PAC. In 
addition, we received comments that the item would be useful in 
improving patient and resident transitions of care. We also noted that 
several commenters had stated that peritoneal dialysis should be 
included in a standardized data element on dialysis and recommended 
collecting information on peritoneal dialysis in addition to 
hemodialysis. The rationale for including peritoneal dialysis from 
commenters included the fact that patients and residents receiving 
peritoneal dialysis will have different needs at post-acute discharge 
compared to those receiving hemodialysis or not having any dialysis. 
Based on these comments, the Hemodialysis data element was expanded to 
include a principal Dialysis data element and two sub-elements, 
Hemodialysis and Peritoneal dialysis. We are proposing the version of 
the Dialysis element that includes two types of dialysis. A summary 
report for the August 12 to September 12, 2016 public comment period 
titled ``SPADE August 2016 Public Comment Summary Report'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received comments in support of the special services, treatments, 
and interventions data elements in general. One commenter noted that 
they appreciated the revisions to the Dialysis data element in response 
to comments submitted during a public input period held from August 12 
to September 12, 2016.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Dialysis data element was included in the National Beta Test of 
candidate data elements conducted by our data element contractor from 
November 2017 to August 2018. Results of this test found the Dialysis 
data element to be feasible and reliable for use with PAC patients and 
residents. More information about the performance of the Dialysis data 
element in the National Beta Test can be found in the document titled 
``Proposed Specifications for IRF QRP Quality Measures and Standardized 
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although they did not 
specifically discuss the Dialysis data element, the TEP supported the 
assessment of the special services, treatments, and interventions 
included in the National Beta Test with respect to both admission and 
discharge. A summary of the September 17, 2018 TEP meeting titled 
``SPADE Technical Expert Panel Summary (Third Convening)'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for dialysis, 
stakeholder input, and strong test results, we are proposing that the 
Dialysis (Hemodialysis, Peritoneal dialysis) data element with a 
principal data element and two sub-elements meets the definition of 
standardized patient assessment data with respect to special services, 
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the 
Act and to adopt the Dialysis (Hemodialysis, Peritoneal dialysis) data 
element as standardized patient assessment data for use in the IRF QRP.
 Intravenous (IV) Access (Peripheral IV, Midline, Central line)
    We are proposing that the IV Access (Peripheral IV, Midline, 
Central line) data element meets the definition of standardized patient 
assessment data with respect to special services, treatments, and 
interventions under section 1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20733 
through 20734), patients or residents with central lines, including 
those peripherally inserted or who have

[[Page 17308]]

subcutaneous central line ``port'' access, always require vigilant 
nursing care to keep patency of the lines and ensure that such invasive 
lines remain free from any potentially life-threatening events such as 
infection, air embolism, or bleeding from an open lumen. Clinically 
complex patients and residents are likely to be receiving medications 
or nutrition intravenously. The sub-elements included in the IV Access 
data elements distinguish between peripheral access and different types 
of central access. The rationale for distinguishing between a 
peripheral IV and central IV access is that central lines confer higher 
risks associated with life-threatening events such as pulmonary 
embolism, infection, and bleeding.
    The proposed data element, IV Access (Peripheral IV, Midline, 
Central line), consists of the principal IV Access data element and 
three response option sub-elements: Peripheral IV, Midline, and Central 
line. The proposed IV Access data element is not currently included on 
any of the PAC assessment instruments. For more information on the IV 
Access data element, we refer readers to the document titled ``Proposed 
Specifications for IRF QRP Quality Measures and Standardized Patient 
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The IV Access data element was first proposed as standardized 
patient assessment data elements in the FY 2018 IRF PPS proposed rule 
(82 FR 20733 through 20734). In that proposed rule, we stated that the 
proposal was informed by input we received on one of the PAC PRD data 
elements, Central Line Management, through a call for input published 
on the CMS Measures Management System Blueprint website. A central line 
is a type of IV access. Input submitted from August 12 to September 12, 
2016 supported the assessment of central line management and 
recommended that the data element be broadened to also include other 
types of IV access. Several commenters noted feasibility and importance 
for facilitating care coordination and care transitions. However, a few 
commenters recommended that the definition of this data element be 
broadened to include peripherally inserted central catheters (``PICC 
lines'') and midline IVs. Based on public comment feedback and in 
consultation with expert input, described below, we created an 
overarching IV Access data element with sub-elements for other types of 
IV access in addition to central lines (that is, peripheral IV and 
midline). This expanded version of IV Access is the data element being 
proposed. A summary report for the August 12 to September 12, 2016 
public comment period titled ``SPADE August 2016 Public Comment Summary 
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of the special services, 
treatments, and interventions data elements in general. One commenter 
noted appreciation of the revisions to the IV Access data element in 
response to comments submitted during a public input period held from 
August 12 to September 12, 2016.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
IV Access data element was included in the National Beta Test of 
candidate data elements conducted by our data element contractor from 
November 2017 to August 2018. Results of this test found the IV Access 
data element to be feasible and reliable for use with PAC patients and 
residents. More information about the performance of the IV Access data 
element in the National Beta Test can be found in the document titled 
``Proposed Specifications for IRF QRP Quality Measures and Standardized 
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP did not 
specifically discuss the IV Access data element, the TEP supported the 
assessment of the special services, treatments, and interventions 
included in the National Beta Test with respect to both admission and 
discharge. A summary of the September 17, 2018 TEP meeting titled 
``SPADE Technical Expert Panel Summary (Third Convening)'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present results of the National Beta 
Test and solicit additional comments. General input on the testing and 
item development process and concerns about burden were received from 
stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for IV access, 
stakeholder input, and strong test results, we are proposing that the 
IV access (Peripheral IV, Midline, Central line) data element with a 
principal data element and three sub-elements meets the definition of 
standardized patient assessment data with respect to special services, 
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the 
Act and to adopt the IV Access (Peripheral IV, Midline, Central line) 
data element as standardized patient assessment data for use in the IRF 
QRP.
 Nutritional Approach: Parenteral/IV Feeding
    We are proposing that the Parenteral/IV Feeding data element meets 
the definition of standardized patient assessment data with respect to 
special services, treatments, and interventions under section 
1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20734), 
parenteral nutrition/IV feeding refers to a patient or resident being 
fed intravenously using an infusion pump, bypassing the usual process 
of eating and digestion. The need for IV/parenteral feeding indicates a 
clinical complexity that prevents the patient or resident from meeting 
his or her nutritional needs enterally, and is more resource intensive 
than other forms of nutrition, as it often requires monitoring of blood 
chemistries and the maintenance of a central line. Therefore, assessing 
a patient's or resident's need for parenteral feeding is important for 
care

[[Page 17309]]

planning and resource use. In addition to the risks associated with 
central and peripheral intravenous access, total parenteral nutrition 
is associated with significant risks, such as air embolism and sepsis.
    The proposed data element consists of the single Parenteral/IV 
Feeding data element. The proposed Parenteral/IV Feeding data element 
is currently in use in the MDS in SNFs, and equivalent or related data 
elements are in use in the LCDS, IRF-PAI, and OASIS. We are proposing 
to rename the existing Tube/Parenteral feeding item in the IRF-PAI to 
be the Parenteral/IV Feeding data element. For more information on the 
Parenteral/IV Feeding data element, we refer readers to the document 
titled ``Proposed Specifications for IRF QRP Quality Measures and 
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Parenteral/IV Feeding data element was first proposed as a 
standardized patient assessment data element in the FY 2018 IRF PPS 
proposed rule (82 FR 20734). In that proposed rule, we stated that the 
proposal was informed by input we received on Total Parenteral 
Nutrition (an item with nearly the same meaning as the proposed data 
element, but with the label used in the PAC PRD), through a call for 
input published on the CMS Measures Management System Blueprint 
website. Input submitted from August 12 to September 12, 2016 supported 
this data element, noting its relevance to facilitating care 
coordination and supporting care transitions. After the public comment 
period, the Total Parenteral Nutrition data element was renamed 
Parenteral/IV Feeding, to be consistent with how this data element is 
referred to in the MDS in SNFs. A summary report for the August 12 to 
September 12, 2016 public comment period titled ``SPADE August 2016 
Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received comments in support of the special services, treatments, 
and interventions data elements in general; no additional comments were 
received that were specific to the Parenteral/IV Feeding data element.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Parenteral/IV Feeding data element was included in the National Beta 
Test of candidate data elements conducted by our data element 
contractor from November 2017 to August 2018. Results of this test 
found the Parenteral/IV Feeding data element to be feasible and 
reliable for use with PAC patients and residents. More information 
about the performance of the Parenteral/IV Feeding data element in the 
National Beta Test can be found in the document titled ``Proposed 
Specifications for IRF QRP Quality Measures and Standardized Patient 
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP did not 
specifically discuss the Parenteral/IV Feeding data element, the TEP 
supported the assessment of the special services, treatments, and 
interventions included in the National Beta Test with respect to both 
admission and discharge. A summary of the September 17, 2018 TEP 
meeting titled ``SPADE Technical Expert Panel Summary (Third 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for parenteral/IV 
feeding, stakeholder input, and strong test results, we are proposing 
that the Parenteral/IV Feeding data element meets the definition of 
standardized patient assessment data with respect to special services, 
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the 
Act and to adopt the Parenteral/IV Feeding data element as standardized 
patient assessment data for use in the IRF QRP.
 Nutritional Approach: Feeding Tube
    We are proposing that the Feeding Tube data element meets the 
definition of standardized patient assessment data with respect to 
special services, treatments, and interventions under section 
1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20734 
through 20735), the majority of patients admitted to acute care 
hospitals experience deterioration of their nutritional status during 
their hospital stay, making assessment of nutritional status and method 
of feeding if unable to eat orally very important in PAC. A feeding 
tube can be inserted through the nose or the skin on the abdomen to 
deliver liquid nutrition into the stomach or small intestine. Feeding 
tubes are resource intensive, and therefore, are important to assess 
for care planning and resource use. Patients with severe malnutrition 
are at higher risk for a variety of complications.\92\ In PAC settings, 
there are a variety of reasons that patients and residents may not be 
able to eat orally (including clinical or cognitive status).
---------------------------------------------------------------------------

    \92\ Dempsey, D.T., Mullen, J.L., & Buzby, G.P. (1988). ``The 
link between nutritional status and clinical outcome: Can 
nutritional intervention modify it?'' Am J of Clinical Nutrition, 
47(2): 352-356.
---------------------------------------------------------------------------

    The proposed data element consists of the single Feeding Tube data 
element. The Feeding Tube data element is currently included in the MDS 
for SNFs, and in the OASIS for HHAs, where it is labeled Enteral 
Nutrition. A related data element, collected in the IRF-PAI for IRFs 
(Tube/Parenteral Feeding), assesses use of both feeding tubes and 
parenteral nutrition. We are proposing to rename the existing Tube/
Parenteral feeding item in the IRF-PAI to the Feeding Tube data 
element. For more information on the Feeding Tube data element, we 
refer readers to the document titled

[[Page 17310]]

``Proposed Specifications for IRF QRP Quality Measures and Standardized 
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Feeding Tube data element was first proposed as a standardized 
patient assessment data element in the FY 2018 IRF PPS proposed rule 
(82 FR 20734 through 20735). In that proposed rule, we stated that the 
proposal was informed by input we received on an Enteral Nutrition data 
element (the Enteral Nutrition data item is the same as the data 
element we are proposing in this proposed rule, but is used in the 
OASIS under a different name) through a call for input published on the 
CMS Measures Management System Blueprint website. Input submitted from 
August 12 to September 12, 2016 supported the data element, noting the 
importance of assessing enteral nutrition status for facilitating care 
coordination and care transitions. After the public comment period, the 
Enteral Nutrition data element used in public comment was renamed 
Feeding Tube, indicating the presence of an assistive device. A summary 
report for the August 12 to September 12, 2016 public comment period 
titled ``SPADE August 2016 Public Comment Summary Report'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of the special services, 
treatments, and interventions data elements in general. In addition, a 
commenter recommended that the term ``enteral feeding'' be used instead 
of ``feeding tube''.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Feeding Tube data element was included in the National Beta Test of 
candidate data elements conducted by our data element contractor from 
November 2017 to August 2018. Results of this test found the Feeding 
Tube data element to be feasible and reliable for use with PAC patients 
and residents. More information about the performance of the Feeding 
Tube data element in the National Beta Test can be found in the 
document titled ``Proposed Specifications for IRF QRP Quality Measures 
and Standardized Patient Assessment Data Elements,'' available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP did not 
specifically discuss the Feeding Tube data element, the TEP supported 
the assessment of the special services, treatments, and interventions 
included in the National Beta Test with respect to both admission and 
discharge. A summary of the September 17, 2018 TEP meeting titled 
``SPADE Technical Expert Panel Summary (Third Convening)'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for feeding tubes, 
stakeholder input, and strong test results, we are proposing that the 
Feeding Tube data element meets the definition of standardized patient 
assessment data with respect to special services, treatments, and 
interventions under section 1899B(b)(1)(B)(iii) of the Act and to adopt 
the Feeding Tube data element as standardized patient assessment data 
for use in the IRF QRP.
 Nutritional Approach: Mechanically Altered Diet
    We are proposing that the Mechanically Altered Diet data element 
meets the definition of standardized patient assessment data with 
respect to special services, treatments, and interventions under 
section 1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20735 
through 20736), the Mechanically Altered Diet data element refers to 
food that has been altered to make it easier for the patient or 
resident to chew and swallow, and this type of diet is used for 
patients and residents who have difficulty performing these functions. 
Patients with severe malnutrition are at higher risk for a variety of 
complications.\93\
---------------------------------------------------------------------------

    \93\ Dempsey, D.T., Mullen, J.L., & Buzby, G.P. (1988). ``The 
link between nutritional status and clinical outcome: Can 
nutritional intervention modify it? '' Am J of Clinical Nutrition, 
47(2): 352-356.
---------------------------------------------------------------------------

    In PAC settings, there are a variety of reasons that patients and 
residents may have impairments related to oral feedings, including 
clinical or cognitive status. The provision of a mechanically altered 
diet may be resource intensive, and can signal difficulties associated 
with swallowing/eating safety, including dysphagia. In other cases, it 
signifies the type of altered food source, such as ground or puree that 
will enable the safe and thorough ingestion of nutritional substances 
and ensure safe and adequate delivery of nourishment to the patient. 
Often, patients and residents on mechanically altered diets also 
require additional nursing support, such as individual feeding or 
direct observation, to ensure the safe consumption of the food product. 
Therefore, assessing whether a patient or resident requires a 
mechanically altered diet is important for care planning and resource 
identification.
    The proposed data element consists of the single Mechanically 
Altered Diet data element. The proposed data element is currently 
included on the MDS for SNFs. A related data element (``Modified food 
consistency/supervision'') is currently included on the IRF-PAI for 
IRFs. Another related data element is included in the OASIS for HHAs 
that collects information about independent eating that requires ``a 
liquid, pureed or ground meat diet.'' We are proposing to replace the 
existing Modified food consistency/supervision data element in the IRF-
PAI to the Mechanically Altered Diet data element. For more information 
on the Mechanically Altered Diet data element, we refer readers to the 
document titled

[[Page 17311]]

``Proposed Specifications for IRF QRP Quality Measures and Standardized 
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Mechanically Altered Diet data element was first proposed as a 
standardized patient assessment data element in the FY 2018 IRF PPS 
proposed rule (82 FR 20735 through 20736). In response to our proposal 
in the FY 2018 IRF PPS proposed rule, we received public comments in 
support of the special services, treatments, and interventions data 
elements in general; no additional comments were received that were 
specific to the Mechanically Altered Diet data element.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Mechanically Altered Diet data element was included in the National 
Beta Test of candidate data elements conducted by our data element 
contractor from November 2017 to August 2018. Results of this test 
found the Mechanically Altered Diet data element to be feasible and 
reliable for use with PAC patients and residents. More information 
about the performance of the Mechanically Altered Diet data element in 
the National Beta Test can be found in the document titled ``Proposed 
Specifications for IRF QRP Quality Measures and Standardized Patient 
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP did not 
specifically discuss the Mechanically Altered Diet data element, the 
TEP supported the assessment of the special services, treatments, and 
interventions included in the National Beta Test with respect to both 
admission and discharge. A summary of the September 17, 2018 TEP 
meeting titled ``SPADE Technical Expert Panel Summary (Third 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for mechanically 
altered diet, stakeholder input, and strong test results, we are 
proposing that the Mechanically Altered Diet data element meets the 
definition of standardized patient assessment data with respect to 
special services, treatments, and interventions under section 
1899B(b)(1)(B)(iii) of the Act and to adopt the Mechanically Altered 
Diet data element as standardized patient assessment data for use in 
the IRF QRP.
 Nutritional Approach: Therapeutic Diet
    We are proposing that the Therapeutic Diet data element meets the 
definition of standardized patient assessment data with respect to 
special services, treatments, and interventions under section 
1899B(b)(1)(B)(iii) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20736), a 
therapeutic diet refers to meals planned to increase, decrease, or 
eliminate specific foods or nutrients in a patient's or resident's 
diet, such as a low-salt diet, for the purpose of treating a medical 
condition. The use of therapeutic diets among patients and residents in 
PAC provides insight on the clinical complexity of these patients and 
residents and their multiple comorbidities. Therapeutic diets are less 
resource intensive from the bedside nursing perspective, but do signify 
one or more underlying clinical conditions that preclude the patient 
from eating a regular diet. The communication among PAC providers about 
whether a patient is receiving a particular therapeutic diet is 
critical to ensure safe transitions of care.
    The proposed data element consists of the single Therapeutic Diet 
data element. This data element is currently in use in the MDS in SNFs. 
For more information on the Therapeutic Diet data element, we refer 
readers to the document titled ``Proposed Specifications for IRF QRP 
Quality Measures and Standardized Patient Assessment Data Elements,'' 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Therapeutic Diet data element was first proposed as a 
standardized patient assessment data element in the FY 2018 IRF PPS 
proposed rule (82 FR 20736). In response to our proposal in the FY 2018 
IRF PPS proposed rule, we received public comments in support of the 
special services, treatments, and interventions data elements in 
general. One commenter recommended that the definition of Therapeutic 
Diet be aligned with the Academy of Nutrition and Dietetics' definition 
and that ``medically altered diet'' be added to the list of nutritional 
approaches.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Therapeutic Diet data element was included in the National Beta Test of 
candidate data elements conducted by our data element contractor from 
November 2017 to August 2018. Results of this test found the 
Therapeutic Diet data element to be feasible and reliable for use with 
PAC patients and residents. More information about the performance of 
the Therapeutic Diet data element in the National Beta Test can be 
found in the document titled ``Proposed Specifications for IRF QRP 
Quality Measures and Standardized Patient Assessment Data Elements,'' 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. Although the TEP did not 
specifically discuss the Therapeutic Diet data element, the TEP 
supported the assessment of the special services, treatments, and 
interventions included in the National Beta Test with respect to both 
admission and discharge. A summary of the September 17, 2018 TEP

[[Page 17312]]

meeting titled ``SPADE Technical Expert Panel Summary (Third 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for therapeutic diet, 
stakeholder input, and strong test results, we are proposing that the 
Therapeutic Diet data element meets the definition of standardized 
patient assessment data with respect to special services, treatments, 
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to 
adopt the Therapeutic Diet data element as standardized patient 
assessment data for use in the IRF QRP.
 High-Risk Drug Classes: Use and Indication
    We are proposing that the High-Risk Drug Classes: Use and 
Indication data element meets the definition of standardized patient 
assessment data with respect to special services, treatments, and 
interventions under section 1899B(b)(1)(B)(iii) of the Act.
    Most patients and residents receiving PAC services depend on short- 
and long-term medications to manage their medical conditions. However, 
as a treatment, medications are not without risk; medications are, in 
fact, a leading cause of adverse events. A study by the U.S. Department 
of Health and Human Services found that 31 percent of adverse events 
that occurred in 2008 among hospitalized Medicare beneficiaries were 
related to medication.\94\ Moreover, changes in a patient's condition, 
medications, and transitions between care settings put patients at risk 
of medication errors and adverse drug events (ADEs). ADEs may be caused 
by medication errors such as drug omissions, errors in dosage, and 
errors in dosing frequency.\95\
---------------------------------------------------------------------------

    \94\ U.S. Department of Health and Human Services. Office of 
Inspector General. Daniel R. Levinson. Adverse Events in Hospitals: 
National Incidence Among Medicare Beneficiaries. OEI-06-09-00090. 
November 2010.
    \95\ Boockvar KS, Liu S, Goldstein N, Nebeker J, Siu A, Fried T. 
Prescribing discrepancies likely to cause adverse drug events after 
patient transfer. Qual Saf Health Care. 2009;18(1):32-6.
---------------------------------------------------------------------------

    ADEs are known to occur across different types of healthcare 
settings. For example, the incidence of ADEs in the outpatient setting 
has been estimated at 1.15 ADEs per 100 person-months,\96\ while the 
rate of ADEs in the long-term care setting is approximately 9.80 ADEs 
per 100 resident-months.\97\ In the hospital setting, the incidence has 
been estimated at 15 ADEs per 100 admissions.\98\ In addition, 
approximately half of all hospital-related medication errors and 20 
percent of ADEs occur during transitions within, admission to, transfer 
to, or discharge from a hospital.99 100 101 ADEs are more 
common among older adults, who make up most patients receiving PAC 
services. The rate of emergency department visits for ADEs is three 
times higher among adults 65 years of age and older compared to that 
among those younger than age 65.\102\
---------------------------------------------------------------------------

    \96\ Gandhi TK, Seger AC, Overhage JM, et al. Outpatient adverse 
drug events identified by screening electronic health records. J 
Patient Saf 2010;6:91-6.doi:10.1097/PTS.0b013e3181dcae06.
    \97\ Gurwitz JH, Field TS, Judge J, Rochon P, Harrold LR, 
Cadoret C, et al. The incidence of adverse drug events in two large 
academic long-term care facilities. Am J Med. 2005; 118(3):2518. Epub 2005/03/05. https://doi.org/10.1016/j.amjmed.2004.09.018 PMID: 15745723.
    \98\ Hug BL, Witkowski DJ, Sox CM, Keohane CA, Seger DL, Yoon C, 
Matheny ME, Bates DW. Occurrence of adverse, often preventable, 
events in community hospitals involving nephrotoxic drugs or those 
excreted by the kidney. Kidney Int. 2009; 76:1192-1198. [PubMed: 
19759525].
    \99\ Barnsteiner JH. Medication reconciliation: transfer of 
medication information across settings-keeping it free from error. J 
Infus Nurs. 2005;28(2 Suppl):31-36.
    \100\ Rozich J, Roger, R. Medication safety: one organization's 
approach to the challenge. Journal of Clinical Outcomes Management. 
2001(8):27-34.
    \101\ Gleason KM, Groszek JM, Sullivan C, Rooney D, Barnard C, 
Noskin GA. Reconciliation of discrepancies in medication histories 
and admission orders of newly hospitalized patients. Am J Health 
Syst Pharm. 2004;61(16):1689-1695.
    \102\ Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ, 
Budnitz DS. US emergency department visits for outpatient adverse 
drug events, 2013-2014. JAMA. doi: 10.1001/jama.2016.16201.
---------------------------------------------------------------------------

    Understanding the types of medication a patient is taking, and the 
reason for its use, are key facets of a patient's treatment with 
respect to medication. Some classes of drugs are associated with more 
risk than others.\103\ We are proposing one High-Risk Drug Class data 
element with six sub-elements. The six medication classes response 
options are: Anticoagulants, antiplatelets, hypoglycemics (including 
insulin), opioids, antipsychotics, and antibiotics. These drug classes 
are high-risk due to the adverse effects that may result from use. In 
particular, bleeding risk is associated with anticoagulants and 
antiplatelets; 104 105 fluid retention, heart failure, and 
lactic acidosis are associated with hypoglycemics; \106\ misuse is 
associated with opioids; \107\ fractures and strokes are associated 
with antipsychotics; 108 109 and various adverse events, 
such as central nervous systems effects and gastrointestinal 
intolerance, are associated with antimicrobials,\110\ the larger 
category of medications that include antibiotics. Moreover, some 
medications in five of the six drug classes included in this data 
element are included in the 2019 Updated Beers Criteria[supreg] list as 
potentially inappropriate medications for use in older adults.\111\ 
Finally, although a complete medication list should record several 
important attributes of each medication (for example, dosage, route, 
stop date),

[[Page 17313]]

recording an indication for the drug is of crucial importance.\112\
---------------------------------------------------------------------------

    \103\ Ibid.
    \104\ Shoeb M, Fang MC. Assessing bleeding risk in patients 
taking anticoagulants. J Thromb Thrombolysis. 2013;35(3):312-319. 
doi: 10.1007/s11239-013-0899-7.
    \105\ Melkonian M, Jarzebowski W, Pautas E. Bleeding risk of 
antiplatelet drugs compared with oral anticoagulants in older 
patients with atrial fibrillation: a systematic review and 
meta[hyphen]analysis. J Thromb Haemost. 2017;15:1500-1510. DOI: 
10.1111/jth.13697.
    \106\ Hamnvik OP, McMahon GT. Balancing Risk and Benefit with 
Oral Hypoglycemic Drugs. The Mount Sinai journal of medicine, New 
York. 2009; 76:234-243.
    \107\ Naples JG, Gellad WF, Hanlon JT. The Role of Opioid 
Analgesics in Geriatric Pain Management. Clin Geriatr Med. 
2016;32(4):725-735.
    \108\ Rigler SK, Shireman TI, Cook-Wiens GJ, Ellerbeck EF, 
Whittle JC, Mehr DR, Mahnken JD. Fracture risk in nursing home 
residents initiating antipsychotic medications. J Am Geriatr Soc. 
2013; 61(5):715-722. [PubMed: 23590366].
    \109\ Wang S, Linkletter C, Dore D et al. Age, antipsychotics, 
and the risk of ischemic stroke in the Veterans Health 
Administration. Stroke 2012;43:28-31. doi:10.1161/
STROKEAHA.111.617191.
    \110\ Faulkner CM, Cox HL, Williamson JC. Unique aspects of 
antimicrobial use in older adults. Clin Infect Dis. 2005;40(7):997-
1004.
    \111\ American Geriatrics Society 2019 Beers Criteria Update 
Expert Panel. American Geriatrics Society 2019 Updated Beers 
Criteria for Potentially Inappropriate Medication Use in Older 
Adults. J Am Geriatr Soc 2019; 00:1-21.
    \112\ Li Y, Salmasian H, Harpaz R, Chase H, Friedman C. 
Determining the reasons for medication prescriptions in the EHR 
using knowledge and natural language processing. AMIA Annu Symp 
Proc. 2011;2011:768-76.
---------------------------------------------------------------------------

    The High-Risk Drug Classes: Use and Indication data element 
requires an assessor to record whether or not a patient is taking any 
medications within six the drug classes. The six response options for 
this data element are high-risk drug classes with particular relevance 
to PAC patients and residents, as identified by our data element 
contractor. The six data element response options are Anticoagulants, 
Antiplatelets, Hypoglycemics, Opioids, Antipsychotics, and Antibiotics. 
For each drug class, the assessor is asked to indicate if the patient 
is taking any medications within the class, and, for drug classes in 
which medications were being taken, whether indications for all drugs 
in the class are noted in the medical record. For example, for the 
response option Anticoagulants, if the assessor indicates that the 
patient has received anticoagulant medication, the assessor would then 
indicate if an indication is recorded in the medication record for the 
anticoagulant(s).
    The High-Risk Drug Classes: Use and Indication data element that is 
being proposed as a SPADE was developed as part of a larger set of data 
elements to assess medication reconciliation, the process of obtaining 
a patient's multiple medication lists and reconciling any 
discrepancies. For more information on the High-Risk Drug Classes: Use 
and Indication data element, we refer readers to the document titled 
``Proposed Specifications for IRF QRP Quality Measures and Standardized 
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We sought public input on the relevance of conducting assessments 
on medication reconciliation and specifically on the proposed High-Risk 
Drug Classes: Use and Indication data element. Our data element 
contractor presented data elements related to medication reconciliation 
to the TEP convened on April 6 and 7, 2016. The TEP supported a focus 
on high-risk drugs, because of higher potential for harm to patients 
and residents, and were in favor of a data element to capture whether 
or not indications for medications were recorded in the medical record. 
A summary of the April 6 and 7, 2016 TEP meeting titled ``SPADE 
Technical Expert Panel Summary (First Convening)'' is available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Medication reconciliation data 
elements were also discussed at a second TEP meeting on January 5 and 
6, 2017, convened by our data element contractor. At this meeting, the 
TEP agreed about the importance of evaluating the medication 
reconciliation process, but disagreed about how this could be 
accomplished through standardized assessment. The TEP also disagreed 
about the usability and appropriateness of using the Beers Criteria to 
identify high-risk medications.\113\ A summary of the January 5 and 6, 
2017 TEP meeting titled ``SPADE Technical Expert Panel Summary (Second 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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    \113\ American Geriatrics Society 2015 Beers Criteria Update 
Expert Panel. American Geriatrics Society. Updated Beers Criteria 
for Potentially Inappropriate Medication Use in Older Adults. J Am 
Geriatr Soc 2015; 63:2227-2246.
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    We also solicited public input on data elements related to 
medication reconciliation during a public input period from April 26 to 
June 26, 2017. Several commenters expressed support for the medication 
reconciliation data elements that were put on display, noting the 
importance of medication reconciliation in preventing medication errors 
and stated that the items seemed feasible and clinically useful. A few 
commenters were critical of the choice of 10 drug classes posted during 
that comment period, arguing that ADEs are not limited to high-risk 
drugs, and raised issues related to training assessors to correctly 
complete a valid assessment of medication reconciliation. A summary 
report for the April 26 to June 26, 2017 public comment period titled 
``SPADE May-June 2017 Public Comment Summary Report'' is available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The High-Risk Drug Classes: Use and Indication data element was 
included in the National Beta Test of candidate data elements conducted 
by our data element contractor from November 2017 to August 2018. 
Results of this test found the High-Risk Drug Classes: Use and 
Indication data element to be feasible and reliable for use with PAC 
patients and residents. More information about the performance of the 
High-Risk Drug Classes: Use and Indication data element in the National 
Beta Test can be found in the document titled ``Proposed Specifications 
for IRF QRP Quality Measures and Standardized Patient Assessment Data 
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018, for the purpose of soliciting input on the proposed 
standardized patient assessment data elements. The TEP acknowledged the 
challenges of assessing medication safety, but were supportive of some 
of the data elements focused on medication reconciliation that were 
tested in the National Beta Test. The TEP was especially supportive of 
the focus on the six high-risk drug classes and using these classes to 
assess whether the indication for a drug is recorded. A summary of the 
September 17, 2018 TEP meeting titled ``SPADE Technical Expert Panel 
Summary (Third Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. These 
activities provided updates on the field-testing work and solicited 
feedback on data elements considered for standardization, including the 
High-Risk Drug Classes: Use and Indication data element. One 
stakeholder group was critical of the six drug classes included as 
response options in the High-Risk Drug Classes: Use and Indication data 
element, noting that potentially risky medications (for example, muscle 
relaxants) are not included in this list; that there may be important 
differences between drugs within classes (for example, more recent 
versus older style antidepressants); and that drug allergy information 
is not captured. Finally, on November 27, 2018, our data element 
contractor hosted a public meeting of stakeholders

[[Page 17314]]

to present the results of the National Beta Test and solicit additional 
comments. General input on the testing and item development process and 
concerns about burden were received from stakeholders during this 
meeting and via email through February 1, 2019. Additionally, one 
commenter questioned whether the time to complete the High-Risk Drug 
Classes: Use and Indication data element would differ across settings. 
A summary of the public input received from the November 27, 2018 
stakeholder meeting titled ``Input on Standardized Patient Assessment 
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder 
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing high-risk drugs and for 
whether or not indications are noted for high-risk drugs, stakeholder 
input, and strong test results, we are proposing that the High-Risk 
Drug Classes: Use and Indication data element meets the definition of 
standardized patient assessment data with respect to special services, 
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the 
Act and to adopt the High-Risk Drug Classes: Use and Indication data 
element as standardized patient assessment data for use in the IRF QRP.
3. Medical Condition and Comorbidity Data
    Assessing medical conditions and comorbidities is critically 
important for care planning and safety for patients and residents 
receiving PAC services, and the standardized assessment of selected 
medical conditions and comorbidities across PAC providers is important 
for managing care transitions and understanding medical complexity.
    Below we discuss our proposals for data elements related to the 
medical condition of pain as standardized patient assessment data. 
Appropriate pain management begins with a standardized assessment, and 
thereafter establishing and implementing an overall plan of care that 
is person-centered, multi-modal, and includes the treatment team and 
the patient. Assessing and documenting the effect of pain on sleep, 
participation in therapy, and other activities may provide information 
on undiagnosed conditions and comorbidities and the level of care 
required, and do so more objectively than subjective numerical scores. 
With that, we assess that taken separately and together, these proposed 
data elements are essential for care planning, consistency across 
transitions of care, and identifying medical complexities including 
undiagnosed conditions. We also conclude that it is the standard of 
care to always consider the risks and benefits associated with a 
personalized care plan, including the risks of any pharmacological 
therapy, especially opioids.\114\ We also conclude that in addition to 
assessing and appropriately treating pain through the optimum mix of 
pharmacologic, non-pharmacologic, and alternative therapies, while 
being cognizant of current prescribing guidelines, clinicians in 
partnership with patients are best able to mitigate factors that 
contribute to the current opioid crisis.115 116 117
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    \114\ Department of Health and Human Services: Pain Management 
Best Practices Inter-Agency Task Force. Draft Report on Pain 
Management Best Practices: Updates, Gaps, Inconsistencies, and 
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
    \115\ Department of Health and Human Services: Pain Management 
Best Practices Inter-Agency Task Force. Draft Report on Pain 
Management Best Practices: Updates, Gaps, Inconsistencies, and 
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
    \116\ Fishman SM, Carr DB, Hogans B, et al. Scope and Nature of 
Pain- and Analgesia-Related Content of the United States Medical 
Licensing Examination (USMLE). Pain Med Malden Mass. 2018;19(3):449-
459. doi:10.1093/pm/pnx336.
    \117\ Fishman SM, Young HM, Lucas Arwood E, et al. Core 
competencies for pain management: results of an interprofessional 
consensus summit. Pain Med Malden Mass. 2013;14(7):971-981. 
doi:10.1111/pme.12107.
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    In alignment with our Meaningful Measures Initiative, accurate 
assessment of medical conditions and comorbidities of patients and 
residents in PAC is expected to make care safer by reducing harm caused 
in the delivery of care; promote effective prevention and treatment of 
chronic disease; strengthen person and family engagement as partners in 
their care; and promote effective communication and coordination of 
care. The SPADEs will enable or support: Clinical decision-making and 
early clinical intervention; person-centered, high quality care 
through: Facilitating better care continuity and coordination; better 
data exchange and interoperability between settings; and longitudinal 
outcome analysis. Therefore, reliable data elements assessing medical 
conditions and comorbidities are needed to initiate a management 
program that can optimize a patient's or resident's prognosis and 
reduce the possibility of adverse events.
    We are inviting comment that applies specifically to the 
standardized patient assessment data for the category of medical 
conditions and co-morbidities, specifically on:
 Pain Interference (Pain Effect on Sleep, Pain Interference 
With Therapy Activities, and Pain Interference With Day-to-Day 
Activities)
    In acknowledgement of the opioid crisis, we specifically are 
seeking comment on whether or not we should add these pain items in 
light of those concerns. Commenters should address to what extent the 
collection of the SPADES described below through patient queries might 
encourage providers to prescribe opioids.
    We are proposing that a set of three data elements on the topic of 
Pain Interference (Pain Effect on Sleep, Pain Interference with Therapy 
Activities, and Pain Interference with Day-to-Day Activities) meet the 
definition of standardized patient assessment data with respect to 
medical condition and comorbidity data under section 1899B(b)(1)(B)(iv) 
of the Act.
    The practice of pain management began to undergo significant 
changes in the 1990s because the inadequate, non-standardized, non-
evidence-based assessment and treatment of pain became a public health 
issue.\118\ In pain management, a critical part of providing 
comprehensive care is performance of a thorough initial evaluation, 
including assessment of both the medical and any biopsychosocial 
factors causing or contributing to the pain, with a treatment plan to 
address the causes of pain and to manage pain that persists over 
time.\119\ Quality pain management, based on current guidelines and 
evidence-based practices, can minimize unnecessary opioid prescribing 
both by offering alternatives or supplemental treatment to opioids and 
by clearly stating when they may be appropriate, and how to utilize 
risk-benefit analysis for opioid and non-opioid treatment 
modalities.\120\
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    \118\ Institute of Medicine. Relieving Pain in America: A 
Blueprint for Transforming Prevention, Care, Education, and 
Research. Washington (DC): National Academies Press (U.S.); 2011. 
https://www.ncbi.nlm.nih.gov/books/NBK91497/.
    \119\ Department of Health and Human Services: Pain Management 
Best Practices Inter-Agency Task Force. Draft Report on Pain 
Management Best Practices: Updates, Gaps, Inconsistencies, and 
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
    \120\ National Academies. Pain Management and the Opioid 
Epidemic: Balancing Societal and Individual Benefits and Risks of 
Prescription Opioid Use. Washington, DC National Academies of 
Sciences, Engineering, and Medicine,; 2017.

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[[Page 17315]]

    Pain is not a surprising symptom in PAC patients and residents, 
where healing, recovery, and rehabilitation often require regaining 
mobility and other functions after an acute event. Standardized 
assessment of pain that interferes with function is an important first 
step towards appropriate pain management in PAC settings. The National 
Pain Strategy called for refined assessment items on the topic of pain, 
and describes the need for these improved measures to be implemented in 
PAC assessments.\121\ Further, the focus on pain interference, as 
opposed to pain intensity or pain frequency, was supported by the TEP 
convened by our data element contractor as an appropriate and 
actionable metric for assessing pain. A summary of the September 17, 
2018 TEP meeting titled ``SPADE Technical Expert Panel Summary (Third 
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------

    \121\ National Pain Strategy: A Comprehensive Population-Health 
Level Strategy for Pain. https://iprcc.nih.gov/sites/default/files/HHSNational_Pain_Strategy_508C.pdf.
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    We appreciate the important concerns related to the misuse and 
overuse of opioids in the treatment of pain and to that end we note 
that in this proposed rule we have also proposed a SPADE that assess 
for the use of, as well as importantly the indication for that use of, 
high risk drugs, including opioids. Further, in the FY 2017 IRF PPS 
final rule (81 FR 52111) we adopted the Drug Regimen Review Conducted 
With Follow-Up for Identified Issues--Post Acute Care (PAC) IRF QRP 
measure which assesses whether PAC providers were responsive to 
potential or actual clinically significant medication issue(s), which 
includes issues associated with use and misuse of opioids for pain 
management, when such issues were identified.
    We also note that the proposed SPADE related to pain assessment are 
not associated with any particular approach to management. Since the 
use of opioids is associated with serious complications, particularly 
in the elderly,122 123 124 an array of successful non-
pharmacologic and non-opioid approaches to pain management may be 
considered. PAC providers have historically used a range of pain 
management strategies, including non-steroidal anti-inflammatory drugs, 
ice, transcutaneous electrical nerve stimulation (TENS) therapy, 
supportive devices, acupuncture, and the like. In addition, non-
pharmacological interventions for pain management include, but are not 
limited to, biofeedback, application of heat/cold, massage, physical 
therapy, nerve block, stretching and strengthening exercises, 
chiropractic, electrical stimulation, radiotherapy, and 
ultrasound.125 126 127
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    \122\ Chau, D.L., Walker, V., Pai, L., & Cho, L.M. (2008). 
Opiates and elderly: use and side effects. Clinical interventions in 
aging, 3(2), 273-8.
    \123\ Fine, P.G. (2009). Chronic Pain Management in Older 
Adults: Special Considerations. Journal of Pain and Symptom 
Management, 38(2): S4-S14.
    \124\ Solomon, D.H., Rassen, J.A., Glynn, R.J., Garneau, K., 
Levin, R., Lee, J., & Schneeweiss, S. (2010). Archives Internal 
Medicine, 170(22):1979-1986.
    \125\ Byrd L. Managing chronic pain in older adults: a long-term 
care perspective. Annals of Long-Term Care: Clinical Care and Aging. 
2013;21(12):34-40.
    \126\ Kligler, B., Bair, M.J., Banerjea, R. et al. (2018). 
Clinical Policy Recommendations from the VHA State-of-the-Art 
Conference on Non-Pharmacological Approaches to Chronic 
Musculoskeletal Pain. Journal of General Internal Medicine, 33(Suppl 
1): 16. https://doi.org/10.1007/s11606-018-4323-z.
    \127\ Chou, R., Deyo, R., Friedly, J., et al. (2017). 
Nonpharmacologic Therapies for Low Back Pain: A Systematic Review 
for an American College of Physicians Clinical Practice Guideline. 
Annals of Internal Medicine, 166(7):493-505.
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    We believe that standardized assessment of pain interference will 
support PAC clinicians in applying best-practices in pain management 
for chronic and acute pain, consistent with current clinical 
guidelines. For example, the standardized assessment of both opioids 
and pain interference would support providers in successfully tapering 
patients/residents who arrive in the PAC setting with long-term opioid 
use off of opioids onto non-pharmacologic treatments and non-opioid 
medications, as recommended by the Society for Post-Acute and Long-Term 
Care Medicine,\128\ and consistent with HHS's 5-Point Strategy To 
Combat the Opioid Crisis \129\ which includes ``Better Pain 
Management.''
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    \128\ Society for Post-Acute and Long-Term Care Medicine (AMDA). 
(2018). Opioids in Nursing Homes: Position Statement. https://paltc.org/opioids%20in%20nursing%20homes.
    \129\ https://www.hhs.gov/opioids/about-the-epidemic/hhs-response/.
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    The Pain Interference data elements consist of three data elements: 
Pain Effect on Sleep, Pain Interference with Therapy Activities, and 
Pain Interference with Day-to-Day Activities. Pain Effect on Sleep 
assesses the frequency with which pain effects a resident's sleep. Pain 
Interference with Therapy Activities assesses the frequency with which 
pain interferes with a resident's ability to participate in therapies. 
The Pain Interference with Day-to-Day Activities assesses the extent to 
which pain interferes with a resident's ability to participate in day-
to-day activities excluding therapy.
    A similar data element on the effect of pain on activities is 
currently included in the OASIS. A similar data element on the effect 
on sleep is currently included in the MDS instrument. For more 
information on the Pain Interference data elements, we refer readers to 
the document titled ``Proposed Specifications for IRF QRP Quality 
Measures and Standardized Patient Assessment Data Elements,'' available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We sought public input on the relevance of conducting assessments 
on pain and specifically on the larger set of Pain Interview data 
elements included in the National Beta Test. The proposed data elements 
were supported by comments from the TEP meeting held by our data 
element contractor on April 7 to 8, 2016. The TEP affirmed the 
feasibility and clinical utility of pain as a concept in a standardized 
assessment. The TEP agreed that data elements on pain interference with 
ability to participate in therapies versus other activities should be 
addressed. Further, during a more recent convening of the same TEP on 
September 17, 2018, the TEP supported the interview-based pain data 
elements included in the National Beta Test. The TEP members were 
particularly supportive of the items that focused on how pain 
interferes with activities (that is, Pain Interference data elements), 
because understanding the extent to which pain interferes with function 
would enable clinicians to determine the need for appropriate pain 
treatment. A summary of the September 17, 2018 TEP meeting titled 
``SPADE Technical Expert Panel Summary (Third Convening)'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We held a public input period in 2016 to solicit feedback on the 
standardization of pain and several other items that were under 
development in prior efforts. From the prior public comment period, we 
included several pain data elements (Pain Effect on Sleep; Pain 
Interference--Therapy Activities; Pain Interference--Other Activities) 
in a second call for public input, open from April 26 to June 26, 2017. 
The items we sought comment on were modified from

[[Page 17316]]

all stakeholder and test efforts. Commenters provided general comments 
about pain assessment in general in addition to feedback on the 
specific pain items. A few commenters shared their support for 
assessing pain, the potential for pain assessment to improve the 
quality of care, and for the validity and reliability of the data 
elements. Commenters affirmed that the item of pain and the effect on 
sleep would be suitable for PAC settings. Commenters' main concerns 
included redundancy with existing data elements, feasibility and 
utility for cross-setting use, and the applicability of interview-based 
items to patients and residents with cognitive or communication 
impairments, and deficits. A summary report for the April 26 to June 
26, 2017 public comment period titled ``SPADE May-June 2017 Public 
Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Pain Interference data elements were included in the National 
Beta Test of candidate data elements conducted by our data element 
contractor from November 2017 to August 2018. Results of this test 
found the Pain Interference data elements to be feasible and reliable 
for use with PAC patients and residents. More information about the 
performance of the Pain Interference data elements in the National Beta 
Test can be found in the document titled ``Proposed Specifications for 
SNF QRP Quality Measures and Standardized Patient Assessment Data 
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on 
September 17, 2018 for the purpose of soliciting input on the 
standardized patient assessment data elements. The TEP supported the 
interview-based pain data elements included in the National Beta Test. 
The TEP members were particularly supportive of the items that focused 
on how pain interferes with activities (that is, Pain Interference data 
elements), because understanding the extent to which pain interferes 
with function would enable clinicians to determine the need for pain 
treatment. A summary of the September 17, 2018 TEP meeting titled 
``SPADE Technical Expert Panel Summary (Third Convening)'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our on-going SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. Additionally, one commenter expressed strong support for the Pain 
data elements and was encouraged by the fact that this portion of the 
assessment goes beyond merely measuring the presence of pain. A summary 
of the public input received from the November 27, 2018 stakeholder 
meeting titled ``Input on Standardized Patient Assessment Data Elements 
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Taking together the importance of assessing for the effect of pain 
on function, stakeholder input, and strong test results, we are 
proposing that the three Pain Interference data elements (Pain Effect 
on Sleep, Pain Interference with Therapy Activities, and Pain 
Interference with Day-to-Day Activities) meet the definition of 
standardized patient assessment data with respect to medical conditions 
and comorbidities under section 1899B(b)(1)(B)(iv) of the Act and to 
adopt the Pain Interference data elements (Pain Effect on Sleep; Pain 
Interference with Therapy Activities; and Pain Interference with Day-
to-Day Activities) as standardized patient assessment data for use in 
the IRF QRP.
4. Impairment Data
    Hearing and vision impairments are conditions that, if unaddressed, 
affect activities of daily living, communication, physical functioning, 
rehabilitation outcomes, and overall quality of life. Sensory 
limitations can lead to confusion in new settings, increase isolation, 
contribute to mood disorders, and impede accurate assessment of other 
medical conditions. Failure to appropriately assess, accommodate, and 
treat these conditions increases the likelihood that patients and 
residents will require more intensive and prolonged treatment. Onset of 
these conditions can be gradual, so individualized assessment with 
accurate screening tools and follow-up evaluations are essential to 
determining which patients and residents need hearing- or vision-
specific medical attention or assistive devices and accommodations, 
including auxiliary aids and/or services, and to ensure that person-
directed care plans are developed to accommodate a patient's or 
resident's needs. Accurate diagnosis and management of hearing or 
vision impairment would likely improve rehabilitation outcomes and care 
transitions, including transition from institutional-based care to the 
community. Accurate assessment of hearing and vision impairment would 
be expected to lead to appropriate treatment, accommodations, including 
the provision of auxiliary aids and services during the stay, and 
ensure that patients and residents continue to have their vision and 
hearing needs met when they leave the facility.
    In alignment with our Meaningful Measures Initiative, we expect 
accurate and individualized assessment, treatment, and accommodation of 
hearing and vision impairments of patients and residents in PAC to make 
care safer by reducing harm caused in the delivery of care; promote 
effective prevention and treatment of chronic disease; strengthen 
person and family engagement as partners in their care; and promote 
effective communication and coordination of care. For example, 
standardized assessment of hearing and vision impairments used in PAC 
will support ensuring patient safety (for example, risk of falls), 
identifying accommodations needed during the stay, and appropriate 
support needs at the time of discharge or transfer. Standardized 
assessment of these data elements will: Enable or support clinical 
decision-making and early clinical intervention; person-centered, high 
quality care (for example, facilitating better care continuity and 
coordination); better data exchange and interoperability between 
settings; and longitudinal outcome analysis. Therefore, reliable data 
elements assessing hearing and vision impairments are needed to 
initiate a management program that can optimize a patient's or 
resident's prognosis and reduce the possibility of adverse events.
    Comments on the category of impairments were also submitted by

[[Page 17317]]

stakeholders during the FY 2018 IRF PPS proposed rule (82 FR 20737 
through 20739) public comment period. A commenter stated hearing and 
vision assessments should be administered at the beginning of the 
assessment process to provide evidence about any sensory deficits that 
may affect the patient's ability to participate in the assessment and 
to allow the assessor to offer an assistive device.
    We are inviting comment on our proposals to collect as standardized 
patient assessment data the following data with respect to impairments.
 Hearing
    We are proposing that the Hearing data element meets the definition 
of standardized patient assessment data with respect to impairments 
under section 1899B(b)(1)(B)(v) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20737 
through 20738), accurate assessment of hearing impairment is important 
in the PAC setting for care planning and resource use. Hearing 
impairment has been associated with lower quality of life, including 
poorer physical, mental, social functioning, and emotional 
health.130 131 Treatment and accommodation of hearing 
impairment led to improved health outcomes including, but not limited 
to, quality of life.\132\ For example, hearing loss in elderly 
individuals has been associated with depression and cognitive 
impairment,133 134 135 higher rates of incident cognitive 
impairment and cognitive decline,\136\ and less time in occupational 
therapy.\137\ Accurate assessment of hearing impairment is important in 
the PAC setting for care planning and defining resource use.
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    \130\ Dalton DS, Cruickshanks KJ, Klein BE, Klein R, Wiley TL, 
Nondahl DM. The impact of hearing loss on quality of life in older 
adults. Gerontologist. 2003;43(5):661-668.
    \131\ Hawkins K, Bottone FG, Jr., Ozminkowski RJ, et al. The 
prevalence of hearing impairment and its burden on the quality of 
life among adults with Medicare Supplement Insurance. Qual Life Res. 
2012;21(7):1135-1147.
    \132\ Horn KL, McMahon NB, McMahon DC, Lewis JS, Barker M, 
Gherini S. Functional use of the Nucleus 22-channel cochlear implant 
in the elderly. The Laryngoscope. 1991;101(3):284-288.
    \133\ Sprinzl GM, Riechelmann H. Current trends in treating 
hearing loss in elderly people: a review of the technology and 
treatment options--a mini-review. Gerontology. 2010;56(3):351-358.
    \134\ Lin FR, Thorpe R, Gordon-Salant S, Ferrucci L. Hearing 
Loss Prevalence and Risk Factors Among Older Adults in the United 
States. The Journals of Gerontology Series A: Biological Sciences 
and Medical Sciences. 2011;66A(5):582-590.
    \135\ Hawkins K, Bottone FG, Jr., Ozminkowski RJ, et al. The 
prevalence of hearing impairment and its burden on the quality of 
life among adults with Medicare Supplement Insurance. Qual Life Res. 
2012;21(7):1135-1147.
    \136\ Lin FR, Metter EJ, O'Brien RJ, Resnick SM, Zonderman AB, 
Ferrucci L. Hearing Loss and Incident Dementia. Arch Neurol. 
2011;68(2):214-220.
    \137\ Cimarolli VR, Jung S. Intensity of Occupational Therapy 
Utilization in Nursing Home Residents: The Role of Sensory 
Impairments. J Am Med Dir Assoc. 2016;17(10):939-942.
---------------------------------------------------------------------------

    The proposed data element consists of the single Hearing data 
element. This data consists of one question that assesses level of 
hearing impairment. This data element is currently in use in the MDS in 
SNFs. For more information on the Hearing data element, we refer 
readers to the document titled ``Proposed Specifications for IRF QRP 
Quality Measures and Standardized Patient Assessment Data Elements,'' 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Hearing data element was first proposed as a standardized 
patient assessment data element in the FY 2018 IRF PPS proposed rule 
(82 FR 20737 through 20738). In that proposed rule, we stated that the 
proposal was informed by input we received on the PAC PRD form of the 
data element (``Ability to Hear'') through a call for input published 
on the CMS Measures Management System Blueprint website. Input 
submitted from August 12 to September 12, 2016 recommended that 
hearing, vision, and communication assessments be administered at the 
beginning of patient assessment process. A summary report for the 
August 12 to September 12, 2016 public comment period titled ``SPADE 
August 2016 Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received public comments in support of adopting the Hearing data 
element for standardized cross-setting use, noting that it would help 
address the needs of patient and residents with disabilities and that 
failing to identify impairments during the initial assessment can 
result in inaccurate diagnoses of impaired language or cognition and 
can invalidate other information obtained from patient assessment.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Hearing data element was included in the National Beta Test of 
candidate data elements conducted by our data element contractor from 
November 2017 to August 2018. Results of this test found the Hearing 
data element to be feasible and reliable for use with PAC patients and 
residents. More information about the performance of the Hearing data 
element in the National Beta Test can be found in the document titled 
``Proposed Specifications for IRF QRP Quality Measures and Standardized 
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on January 
5 and 6, 2017 for the purpose of soliciting input on all the SPADEs, 
including the Hearing data element. The TEP affirmed the importance of 
standardized assessment of hearing impairment in PAC patients and 
residents. A summary of the January 5 and 6, 2017 TEP meeting titled 
``SPADE Technical Expert Panel Summary (Second Convening)'' is 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. Additionally, a commenter expressed support for the Hearing data 
element and suggested administration at the beginning of the patient 
assessment to maximize utility. A summary of the public input received 
from the November 27, 2018 stakeholder meeting titled ``Input on 
Standardized Patient Assessment Data Elements (SPADEs) Received After 
November 27, 2018 Stakeholder Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Due to the relatively stable nature of hearing impairment, it is 
unlikely that a

[[Page 17318]]

patient's score on this assessment would change between the start and 
end of the IRF stay. Therefore, we are proposing that IRFs that submit 
the Hearing data element with respect to admission will be considered 
to have submitted with respect to discharge as well.
    Taking together the importance of assessing for hearing, 
stakeholder input, and strong test results, we are proposing that the 
Hearing data element meets the definition of standardized patient 
assessment data with respect to impairments under section 
1899B(b)(1)(B)(v) of the Act and to adopt the Hearing data element as 
standardized patient assessment data for use in the IRF QRP.
 Vision
    We are proposing that the Vision data element meets the definition 
of standardized patient assessment data with respect to impairments 
under section 1899B(b)(1)(B)(v) of the Act.
    As described in the FY 2018 IRF PPS proposed rule (82 FR 20738 
through 20739), evaluation of an individual's ability to see is 
important for assessing for risks such as falls and provides 
opportunities for improvement through treatment and the provision of 
accommodations, including auxiliary aids and services, which can 
safeguard patients and residents and improve their overall quality of 
life. Further, vision impairment is often a treatable risk factor 
associated with adverse events and poor quality of life. For example, 
individuals with visual impairment are more likely to experience falls 
and hip fracture, have less mobility, and report depressive 
symptoms.138 139 140 141 142 143 144 Individualized initial 
screening can lead to life-improving interventions such as 
accommodations, including the provision of auxiliary aids and services, 
during the stay and/or treatments that can improve vision and prevent 
or slow further vision loss. In addition, vision impairment is often a 
treatable risk factor associated with adverse events which can be 
prevented and accommodated during the stay. Accurate assessment of 
vision impairment is important in the IRF setting for care planning and 
defining resource use.
---------------------------------------------------------------------------

    \138\ Colon-Emeric CS, Biggs DP, Schenck AP, Lyles KW. Risk 
factors for hip fracture in skilled nursing facilities: Who should 
be evaluated? Osteoporos Int. 2003;14(6):484-489.
    \139\ Freeman EE, Munoz B, Rubin G, West SK. Visual field loss 
increases the risk of falls in older adults: The Salisbury eye 
evaluation. Invest Ophthalmol Vis Sci. 2007;48(10):4445-4450.
    \140\ Keepnews D, Capitman JA, Rosati RJ. Measuring patient-
level clinical outcomes of home health care. J Nurs Scholarsh. 
2004;36(1):79-85.
    \141\ Nguyen HT, Black SA, Ray LA, Espino DV, Markides KS. 
Predictors of decline in MMSE scores among older Mexican Americans. 
J Gerontol A Biol Sci Med Sci. 2002;57(3):M181-185.
    \142\ Prager AJ, Liebmann JM, Cioffi GA, Blumberg DM. Self-
reported Function, Health Resource Use, and Total Health Care Costs 
Among Medicare Beneficiaries With Glaucoma. JAMA ophthalmology. 
2016;134(4):357-365.
    \143\ Rovner BW, Ganguli M. Depression and disability associated 
with impaired vision: The MoVies Project. J Am Geriatr Soc. 
1998;46(5):617-619.
    \144\ Tinetti ME, Ginter SF. The nursing home life-space 
diameter. A measure of extent and frequency of mobility among 
nursing home residents. J Am Geriatr Soc. 1990;38(12):1311-1315.
---------------------------------------------------------------------------

    The proposed data element consists of the single Vision data 
element (Ability To See in Adequate Light) that consists of one 
question with five response categories. The Vision data element that we 
are proposing for standardization was tested as part of the development 
of the MDS and is currently in use in that assessment in SNFs. Similar 
data elements, but with different wording and fewer response option 
categories, are in use in the OASIS. For more information on the Vision 
data element, we refer readers to the document titled ``Proposed 
Specifications for IRF QRP Quality Measures and Standardized Patient 
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    The Vision data element was first proposed as a standardized 
patient assessment data element in the FY 2018 IRF PPS proposed rule 
(82 FR 20738 through 20739).
    In that proposed rule, we stated that the proposal was informed by 
input we received on the Ability to See in Adequate Light data element 
(version tested in the PAC PRD with three response categories) through 
a call for input published on the CMS Measures Management System 
Blueprint website. Although the data element in public comment differed 
from the proposed data element, input submitted from August 12 to 
September 12, 2016 supported assessing vision in PAC settings and the 
useful information a vision data element would provide. We also stated 
that commenters had noted that the Ability to See item would provide 
important information that would facilitate care coordination and care 
planning, and consequently improve the quality of care. Other 
commenters suggested it would be helpful as an indicator of resource 
use and noted that the item would provide useful information about the 
abilities of patients and residents to care for themselves. Additional 
commenters noted that the item could feasibly be implemented across PAC 
providers and that its kappa scores from the PAC PRD support its 
validity. Some commenters noted a preference for MDS version of the 
Vision data element in SNFs over the form put forward in public 
comment, citing the widespread use of this data element. A summary 
report for the August 12 to September 12, 2016 public comment period 
titled ``SPADE August 2016 Public Comment Summary Report'' is available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In response to our proposal in the FY 2018 IRF PPS proposed rule, 
we received a comment supporting having a standardized patient 
assessment data element for vision across PAC settings, but it stated 
the proposed data element captures only basic information for risk 
adjustment, and more detailed information would need to be collected to 
use it as an outcome measure.
    Subsequent to receiving comments on the FY 2018 IRF PPS rule, the 
Vision data element was included in the National Beta Test of candidate 
data elements conducted by our data element contractor from November 
2017 to August 2018. Results of this test found the Vision data element 
to be feasible and reliable for use with PAC patients and residents. 
More information about the performance of the Vision data element in 
the National Beta Test can be found in the document titled ``Proposed 
Specifications for IRF QRP Quality Measures and Standardized Patient 
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In addition, our data element contractor convened a TEP on January 
5 and 6, 2017 for the purpose of soliciting input on all the SPADEs 
including the Vision data element. The TEP affirmed the importance of 
standardized assessment of vision impairment in PAC patients and 
residents. A summary of the January 5 and 6, 2017 TEP meeting titled 
``SPADE Technical Expert Panel Summary (Second Convening)'' is 
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.

[[Page 17319]]

    We also held Special Open Door Forums and small-group discussions 
with PAC providers and other stakeholders in 2018 for the purpose of 
updating the public about our ongoing SPADE development efforts. 
Finally, on November 27, 2018, our data element contractor hosted a 
public meeting of stakeholders to present the results of the National 
Beta Test and solicit additional comments. General input on the testing 
and item development process and concerns about burden were received 
from stakeholders during this meeting and via email through February 1, 
2019. Additionally, a commenter expressed support for the Vision data 
element and suggested administration at the beginning of the patient 
assessment to maximize utility. A summary of the public input received 
from the November 27, 2018 stakeholder meeting titled ``Input on 
Standardized Patient Assessment Data Elements (SPADEs) Received After 
November 27, 2018 Stakeholder Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    Due to the relatively stable nature of vision impairment, it is 
unlikely that a patient's score on this assessment would change between 
the start and end of the IRF stay. Therefore, we are proposing that 
IRFs that submit the Vision data element with respect to admission will 
be considered to have submitted with respect to discharge as well.
    Taking together the importance of assessing for vision, stakeholder 
input, and strong test results, we are proposing that the Vision data 
element meets the definition of standardized patient assessment data 
with respect to impairments under section 1899B(b)(1)(B)(v) of the Act 
and to adopt the Vision data element as standardized patient assessment 
data for use in the IRF QRP.
4. Proposed New Category: Social Determinants of Health
a. Proposed Social Determinants of Health Data Collection To Inform 
Measures and Other Purposes
    Subparagraph (A) of section 2(d)(2) of the IMPACT Act requires CMS 
to assess appropriate adjustments to quality measures, resource 
measures and other measures, and to assess and implement appropriate 
adjustments to payment under Medicare, based on those measures, after 
taking into account studies conducted by ASPE on social risk factors 
(described below) and other information, and based on an individual's 
health status and other factors. Subparagraph (C) of section 2(d)(2) of 
the IMPACT Act further requires the Secretary to carry out periodic 
analyses, at least every three years, based on the factors referred to 
in subparagraph (A) so as to monitor changes in possible relationships. 
Subparagraph (B) of section 2(d)(2) of the IMPACT Act requires CMS to 
collect or otherwise obtain access to data necessary to carry out the 
requirement of the paragraph (both assessing adjustments described 
above in such subparagraph (A) and for periodic analyses in such 
subparagraph (C)). Accordingly we are proposing to use our authority 
under subparagraph (B) of section 2(d)(2) of the IMPACT Act to 
establish a new data source for information to meet the requirements of 
subparagraphs (A) and (C) of section 2(d)(2) of the IMPACT Act. In this 
rule, we are proposing to collect and access data about social 
determinants of health (SDOH) in order to perform CMS' responsibilities 
under subparagraphs (A) and (C) of section 2(d)(2) of the IMPACT Act, 
as explained in more detail below. Social determinants of health, also 
known as social risk factors, or health-related social needs, are the 
socioeconomic, cultural and environmental circumstances in which 
individuals live that impact their health. We are proposing to collect 
information on seven proposed SDOH SPADE data elements relating to 
race, ethnicity, preferred language, interpreter services, health 
literacy, transportation, and social isolation; a detailed discussion 
of each of the proposed SDOH data elements is found in section 
VII.G.5.b. of this proposed rule.
    We are also proposing to use the assessment instrument for the IRF 
QRP, the IRF-PAI, described as a PAC assessment instrument under 
section 1899B(a)(2)(B) of the Act, to collect these data via an 
existing data collection mechanism. We believe this approach will 
provide CMS with access to data with respect to the requirements of 
section 2(d)(2) of the IMPACT Act, while minimizing the reporting 
burden on PAC health care providers by relying on a data reporting 
mechanism already used and an existing system to which PAC health care 
providers are already accustomed.
    The IMPACT Act includes several requirements applicable to the 
Secretary, in addition to those imposing new data reporting obligations 
on certain PAC providers as discussed in VII.G.5.b. of this proposed 
rule. Subparagraphs (A) and (B) of sections 2(d)(1) of the IMPACT Act 
require the Secretary, acting through the Office of the Assistant 
Secretary for Planning and Evaluation (ASPE), to conduct two studies 
that examine the effect of risk factors, including individuals' 
socioeconomic status, on quality, resource use and other measures under 
the Medicare program. The first ASPE study was completed in December 
2016 and is discussed below, and the second study is to be completed in 
the fall of 2019. We recognize that ASPE, in its studies, is 
considering a broader range of social risk factors than the SDOH data 
elements in this proposal, and address both PAC and non-PAC settings. 
We acknowledge that other data elements may be useful to understand, 
and that some of those elements may be of particular interest in non-
PAC settings. For example, for beneficiaries receiving care in the 
community, as opposed to an in-patient facility, housing stability and 
food insecurity may be more relevant. We will continue to take into 
account the findings from both of ASPE's reports in future policy 
making.
    One of the ASPE's first actions under the IMPACT Act was to 
commission the National Academies of Sciences, Engineering, and 
Medicine (NASEM) to define and conceptualize socioeconomic status for 
the purposes of ASPE's two studies under section 2(d)(1) of the IMPACT 
Act. The NASEM convened a panel of experts in the field and conducted 
an extensive literature review. Based on the information collected, the 
2016 NASEM panel report titled, ``Accounting for Social Risk Factors in 
Medicare Payment: Identifying Social Risk Factors'', concluded that the 
best way to assess how social processes and social relationships 
influence key health-related outcomes in Medicare beneficiaries is 
through a framework of social risk factors instead of socioeconomic 
status. Social risk factors discussed in the NASEM report include 
socioeconomic position, race, ethnicity, gender, social context, and 
community context. These factors are discussed at length in chapter 2 
of the NASEM report, titled ``Social Risk Factors.'' \145\ Consequently 
NASEM framed the results of its report in terms of ``social risk 
factors'' rather than ``socioeconomic status'' or ``sociodemographic 
status.'' The full text of the ``Social Risk Factors'' NASEM report is 
available for reading on the website at https://www.nap.edu/read/21858/chapter/1.
---------------------------------------------------------------------------

    \145\ National Academies of Sciences, Engineering, and Medicine. 
2016. Accounting for social risk factors in Medicare payment: 
Identifying social risk factors. Chapter 2. Washington, DC: The 
National Academies Press.

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[[Page 17320]]

    Each of the data elements we are proposing to collect and access 
under our authority under section 2(d)(2)(B) of the IMPACT Act is 
identified in the 2016 NASEM report as a social risk factor that has 
been shown to impact care use, cost and outcomes for Medicare 
beneficiaries. CMS uses the term social determinants of health (SDOH) 
to denote social risk factors, which is consistent with the objectives 
of Healthy People 2020.\146\
---------------------------------------------------------------------------

    \146\ Social Determinants of Health. Healthy People 2020. 
https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health. (February 2019).
---------------------------------------------------------------------------

    ASPE issued its first Report to Congress, titled ``Social Risk 
Factors and Performance Under Medicare's Value-Based Purchasing 
Programs,'' under section 2(d)(1)(A) of the IMPACT Act on December 21, 
2016.\147\ Using NASEM's social risk factors framework, ASPE focused on 
the following social risk factors, in addition to disability: (1) Dual 
enrollment in Medicare and Medicaid as a marker for low income, (2) 
residence in a low-income area, (3) Black race, (4) Hispanic ethnicity; 
and (5) residence in a rural area. ASPE acknowledged that the social 
risk factors examined in its report were limited due to data 
availability. The report also noted that the data necessary to 
meaningfully attempt to reduce disparities and identify and reward 
improved outcomes for beneficiaries with social risk factors have not 
been collected consistently on a national level in post-acute care 
settings. Where these data have been collected, the collection 
frequently involves lengthy questionnaires. More information on the 
Report to Congress on Social Risk Factors and Performance under 
Medicare's Value-Based Purchasing Programs, including the full report, 
is available on the website at https://aspe.hhs.gov/social-risk-factors-and-medicares-value-based-purchasing-programs-reports.
---------------------------------------------------------------------------

    \147\ U.S. Department of Health and Human Services, Office of 
the Assistant Secretary for Planning and Evaluation. 2016. Report to 
Congress: Social Risk Factors and Performance Under Medicare's 
Value-Based Payment Programs. Washington, DC.
---------------------------------------------------------------------------

    Section 2(d)(2) of the IMPACT Act relates to CMS activities and 
imposes several responsibilities on the Secretary relating to quality, 
resource use, and other measures under Medicare. As mentioned 
previously, under subparagraph (A) of section 2(d)(2) of the IMPACT 
Act, the Secretary is required, on an ongoing basis, taking into 
account the ASPE studies and other information, and based on an 
individual's health status and other factors, to assess appropriate 
adjustments to quality, resource use, and other measures, and to assess 
and implement appropriate adjustments to Medicare payments based on 
those measures. Section 2(d)(2)(A)(i) of the IMPACT Act applies to 
measures adopted under subsections (c) and (d) of section 1899B of the 
Act and to other measures under Medicare. However, CMS' ability to 
perform these analyses, and assess and make appropriate adjustments is 
hindered by limits of existing data collections on SDOH data elements 
for Medicare beneficiaries. In its first study in 2016, in discussing 
the second study, ASPE noted that information relating to many of the 
specific factors listed in the IMPACT Act, such as health literacy, 
limited English proficiency, and Medicare beneficiary activation, are 
not available in Medicare data.
    Subparagraph 2(d)(2)(A) of the IMPACT Act specifically requires the 
Secretary to take the studies and considerations from ASPE's reports to 
Congress, as well as other information as appropriate, into account in 
assessing and implementing adjustments to measures and related payments 
based on measures in Medicare. The results of the ASPE's first study 
demonstrated that Medicare beneficiaries with social risk factors 
tended to have worse outcomes on many quality measures, and providers 
who treated a disproportionate share of beneficiaries with social risk 
factors tended to have worse performance on quality measures. As a 
result of these findings, ASPE suggested a three-pronged strategy to 
guide the development of value-based payment programs under which all 
Medicare beneficiaries receive the highest quality healthcare services 
possible. The three components of this strategy are to: (1) Measure and 
report quality of care for beneficiaries with social risk factors; (2) 
set high, fair quality standards for care provided to all 
beneficiaries; and (3) reward and support better outcomes for 
beneficiaries with social risk factors. In discussing how measuring and 
reporting quality for beneficiaries with social risk factors can be 
applied to Medicare quality payment programs, the report offered nine 
considerations across the three-pronged strategy, including enhancing 
data collection and developing statistical techniques to allow 
measurement and reporting of performance for beneficiaries with social 
risk factors on key quality and resource use measures.
    Congress, in section 2(d)(2)(B) of the IMPACT Act, required the 
Secretary to collect or otherwise obtain access to the data necessary 
to carry out the provisions of paragraph (2) of section 2(d) of the 
IMPACT Act through both new and existing data sources. Taking into 
consideration NASEM's conceptual framework for social risk factors 
discussed above, ASPE's study, and considerations under section 
2(d)(1)(A) of the IMPACT Act, as well as the current data constraints 
of ASPE's first study and its suggested considerations, we are 
proposing to collect and access data about SDOH under section 2(d)(2) 
of the IMPACT Act. Our collection and use of the SDOH data described in 
section VII.G.5.b.(1) of this proposed rule, under section 2(d)(2) of 
the IMPACT Act would be independent of our proposal below (in section 
VII.G.5.b.(2) of this proposed rule) and our authority to require 
submission of that data for use as SPADE under section 1899B(a)(1)(B) 
of the Act.
    Accessing standardized data relating to the SDOH data elements on a 
national level is necessary to permit CMS to conduct periodic analyses, 
to assess appropriate adjustments to quality measures, resource use 
measures, and other measures, and to assess and implement appropriate 
adjustments to Medicare payments based on those measures. We agree with 
ASPE's observations, in the value-based purchasing context, that the 
ability to measure and track quality, outcomes, and costs for 
beneficiaries with social risk factors over time is critical as 
policymakers and providers seek to reduce disparities and improve care 
for these groups. Collecting the data as proposed will provide the 
basis for our periodic analyses of the relationship between an 
individual's health status and other factors and quality, resource use, 
and other measures, as required by section 2(d)(2) of the IMPACT Act, 
and to assess appropriate adjustments. These data will also permit us 
to develop the statistical tools necessary to maximize the value of 
Medicare data, reduce costs and improve the quality of care for all 
beneficiaries. Collecting and accessing SDOH data in this way also 
supports the three-part strategy put forth in the first ASPE report, 
specifically ASPE's consideration to enhance data collection and 
develop statistical techniques to allow measurement and reporting of 
performance for beneficiaries with social risk factors on key quality 
and resource use measures.
    For the reasons discussed above, we are proposing under section 
2(d)(2) of the IMPACT Act, to collect the data on the following SDOH: 
(1) Race, as described in section VII.G.5.b.(1) of this proposed rule; 
(2) Ethnicity, as described in section VII.G5.b.(1) of this

[[Page 17321]]

proposed rule; (3) Preferred Language, as described in section 
VII.G.5.b.(2) of this proposed rule; (4) Interpreter Services, as 
described in section VII.G.5.b.(2) of this proposed rule; (5) Health 
Literacy, as described in section VII.G.5.b.(3) of this proposed rule; 
(6) Transportation, as described in section VII.G.5.b.(4) of this 
proposed rule; and (7) Social Isolation, as described in section 
VII.G.5.b.(5) of this proposed rule. These data elements are discussed 
in more detail below in section VII.G.5.b of this proposed rule. We 
welcome comment on this proposal.
b. Standardized Patient Assessment Data
    Section 1899B(b)(1)(B)(vi) of the Act authorizes the Secretary to 
collect SPADEs with respect to other categories deemed necessary and 
appropriate. Below we are proposing to create a Social Determinants of 
Health SPADE category under section 1899B(b)(1)(B)(vi) of the Act. In 
addition to collecting SDOH data for the purposes outlined above under 
section 2(d)(2)(B), we are also proposing to collect as SPADE these 
same data elements (race, ethnicity, preferred language, interpreter 
services, health literacy, transportation, and social isolation) under 
section 1899B(b)(1)(B)(vi) of the Act. We believe that this proposed 
new category of Social Determinants of Health will inform provider 
understanding of individual patient risk factors and treatment 
preferences, facilitate coordinated care and care planning, and improve 
patient outcomes. We are proposing to deem this category necessary and 
appropriate, for the purposes of SPADE, because using common standards 
and definitions for PAC data elements is important in ensuring 
interoperable exchange of longitudinal information between PAC 
providers and other providers to facilitate coordinated care, 
continuity in care planning, and the discharge planning process from 
post-acute care settings.
    All of the Social Determinants of Health data elements we are 
proposing under section 1899B(b)(1)(B)(vi) of the Act have the capacity 
to take into account treatment preferences and care goals of patients, 
and to inform our understanding of patient complexity and risk factors 
that may affect care outcomes. While acknowledging the existence and 
importance of additional social determinants of health, we are 
proposing to assess some of the factors relevant for patients receiving 
post-acute care that PAC settings are in a position to impact through 
the provision of services and supports, such as connecting patients 
with identified needs with transportation programs, certified 
interpreters, or social support programs.
    As previously mentioned, and described in more detail below, we are 
proposing to adopt the following seven data elements as SPADE under the 
proposed Social Determinants of Health category: Race, ethnicity, 
preferred language, interpreter services, health literacy, 
transportation, and social isolation. To select these data elements, we 
reviewed the research literature, a number of validated assessment 
tools and frameworks for addressing SDOH currently in use (for example, 
Health Leads, NASEM, Protocol for Responding to and Assessing Patients' 
Assets, Risks, and Experiences (PRAPARE), and ICD-10), and we engaged 
in discussions with stakeholders. We also prioritized balancing the 
reporting burden for PAC providers with our policy objective to collect 
SPADEs that will inform care planning and coordination and quality 
improvement across care settings. Furthermore, incorporating SDOH data 
elements into care planning has the potential to reduce readmissions 
and help beneficiaries achieve and maintain their health goals.
    We also considered feedback received during a listening session 
that we held on December 13, 2018. The purpose of the listening session 
was to solicit feedback from health systems, research organizations, 
advocacy organizations and state agencies and other members of the 
public on collecting patient-level data on SDOH across care settings, 
including consideration of race, ethnicity, spoken language, health 
literacy, social isolation, transportation, sex, gender identity, and 
sexual orientation. We also gave participants an option to submit 
written comments. A full summary of the listening session, titled 
``Listening Session on Social Determinants of Health Data Elements: 
Summary of Findings,'' includes a list of participating stakeholders 
and their affiliations, and is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
(1) Race and Ethnicity
    The persistence of racial and ethnic disparities in health and 
health care is widely documented including in PAC settings.\148\ \149\ 
\150\ \151\ \152\ Despite the trend toward overall improvements in 
quality of care and health outcomes, the Agency for Healthcare Research 
and Quality, in its National Healthcare Quality and Disparities 
Reports, consistently indicates that racial and ethnic disparities 
persist, even after controlling for factors such as income, geography, 
and insurance.\153\ For example, racial and ethnic minorities tend to 
have higher rates of infant mortality, diabetes and other chronic 
conditions, and visits to the emergency department, and lower rates of 
having a usual source of care and receiving immunizations such as the 
flu vaccine.\154\ Studies have also shown that African Americans are 
significantly more likely than white Americans to die prematurely from 
heart disease and stroke.\155\ However, our ability to identify and 
address racial and ethnic health disparities has historically been 
constrained by data limitations, particularly for smaller populations 
groups such as Asians, American Indians and Alaska Natives, and Native 
Hawaiians and other Pacific Islanders.\156\
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    \148\ 2017 National Healthcare Quality and Disparities Report. 
Rockville, MD: Agency for Healthcare Research and Quality; September 
2018. AHRQ Pub. No. 18-0033-EF.
    \149\ Fiscella, K. and Sanders, M.R. Racial and Ethnic 
Disparities in the Quality of Health Care. (2016). Annual Review of 
Public Health. 37:375-394.
    \150\ 2018 National Impact Assessment of the Centers for 
Medicare & Medicaid Services (CMS) Quality Measures Reports. 
Baltimore, MD: U.S. Department of Health and Human Services, Centers 
for Medicare and Medicaid Services; February 28, 2018.
    \151\ Smedley, B.D., Stith, A.Y., & Nelson, A.R. (2003). Unequal 
treatment: confronting racial and ethnic disparities in health care. 
Washington, DC, National Academy Press.
    \152\ Chase, J., Huang, L. and Russell, D. (2017). Racial/ethnic 
disparities in disability outcomes among post-acute home care 
patients. J of Aging and Health. 30(9):1406-1426.
    \153\ National Healthcare Quality and Disparities Reports. 
(December 2018). Agency for Healthcare Research and Quality, 
Rockville, MD. https://www.ahrq.gov/research/findings/nhqrdr/.
    \154\ National Center for Health Statistics. Health, United 
States, 2017: With special feature on mortality. Hyattsville, 
Maryland. 2018.
    \155\ HHS. Heart disease and African Americans. 2016b. (October 
24, 2016). https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=19.
    \156\ National Academies of Sciences, Engineering, and Medicine; 
Health and Medicine Division; Board on Population Health and Public 
Health Practice; Committee on Community-Based Solutions to Promote 
Health Equity in the United States; Baciu A, Negussie Y, Geller A, 
et al., editors. Communities in Action: Pathways to Health Equity. 
Washington (DC): National Academies Press (US); 2017 Jan 11. 2, The 
State of Health Disparities in the United States. Available from: 
https://www.ncbi.nlm.nih.gov/books/NBK425844/.
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    The ability to improve understanding of and address racial and 
ethnic disparities in PAC outcomes requires

[[Page 17322]]

the availability of better data. There is currently a Race and 
Ethnicity data element, collected in the MDS, LCDS, IRF-PAI, and OASIS, 
that consists of a single question, which aligns with the 1997 Office 
of Management and Budget (OMB) minimum data standards for federal data 
collection efforts.\157\ The 1997 OMB Standard lists five minimum 
categories of race: (1) American Indian or Alaska Native; (2) Asian; 
(3) Black or African American; (4) Native Hawaiian or Other Pacific 
Islander; (5) and White. The 1997 OMB Standard also lists two minimum 
categories of ethnicity: (1) Hispanic or Latino and (2) Not Hispanic or 
Latino. The 2011 HHS Data Standards requires a two-question format when 
self-identification is used to collect data on race and ethnicity. 
Large federal surveys such as the National Health Interview Survey, 
Behavioral Risk Factor Surveillance System, and the National Survey on 
Drug Use and Health, have implemented the 2011 HHS race and ethnicity 
data standards. CMS has similarly updated the Medicare Current 
Beneficiary Survey, Medicare Health Outcomes Survey, and the Health 
Insurance Marketplace Application for Health Coverage with the 2011 HHS 
data standards. More information about the HHS Race and Ethnicity Data 
Standards are available on the website at https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=54.
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    \157\ ``Revisions to the Standards for the Classification of 
Federal Data on Race and Ethnicity (Notice of Decision)''. Federal 
Register 62:210 (October 30, 1997) pp. 58782-58790. Available from: 
https://www.govinfo.gov/content/pkg/FR-1997-10-30/pdf/97-28653.pdf.
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    We are proposing to revise the current Race and Ethnicity data 
element for purposes of this proposal to conform to the 2011 HHS Data 
Standards for person-level data collection, while also meeting the 1997 
OMB minimum data standards for race and ethnicity. Rather than one data 
element that assesses both race and ethnicity, we are proposing two 
separate data elements: One for Race and one for Ethnicity, that would 
conform with the 2011 HHS Data Standards and the 1997 OMB Standard. In 
accordance with the 2011 HHS Data Standards a two-question format would 
be used for the proposed race and ethnicity data elements.
    The proposed Race data element asks, ``What is your race? We are 
proposing to include fourteen response options under the race data 
element: (1) White; (2) Black or African American; (3) American Indian 
or Alaska Native; (4) Asian Indian; (5) Chinese; (6) Filipino; (7) 
Japanese; (8) Korean; (9) Vietnamese; (10) Other Asian; (11) Native 
Hawaiian; (12) Guamanian or Chamorro; (13) Samoan; and, (14) Other 
Pacific Islander.
    The proposed Ethnicity data element asks, ``Are you Hispanic, 
Latino/a, or Spanish origin?'' We are proposing to include five 
response options under the ethnicity data element: (1) Not of Hispanic, 
Latino/a, or Spanish origin; (2) Mexican, Mexican American, Chicano/a; 
(3) Puerto Rican; (4) Cuban; and, (5) Another Hispanic, Latino, or 
Spanish Origin.
    We believe that the two proposed data elements for race and 
ethnicity conform to the 2011 HHS Data Standards for person-level data 
collection, while also meeting the 1997 OMB minimum data standards for 
race and ethnicity, because under those standards, more detailed 
information on population groups can be collected if those additional 
categories can be aggregated into the OMB minimum standard set of 
categories.
    In addition, we received stakeholder feedback during the December 
13, 2018 SDOH listening session on the importance of improving response 
options for race and ethnicity as a component of health care 
assessments and for monitoring disparities. Some stakeholders 
emphasized the importance of allowing for self-identification of race 
and ethnicity for more categories than are included in the 2011 HHS 
Standard to better reflect state and local diversity, while 
acknowledging the burden of coding an open-ended health care assessment 
question across different settings.
    We believe that the proposed modified race and ethnicity data 
elements more accurately reflect the diversity of the U.S. population 
than the current race/ethnicity data element included in MDS, LCDS, 
IRF-PAI, and OASIS.\158\ \159\ \160\ \161\ We believe, and research 
consistently shows, that improving how race and ethnicity data are 
collected is an important first step in improving quality of care and 
health outcomes. Addressing disparities in access to care, quality of 
care, and health outcomes for Medicare beneficiaries begins with 
identifying and analyzing how SDOH, such as race and ethnicity, align 
with disparities in these areas.\162\ Standardizing self-reported data 
collection for race and ethnicity allows for the equal comparison of 
data across multiple healthcare entities.\163\ By collecting and 
analyzing these data, CMS and other healthcare entities will be able to 
identify challenges and monitor progress. The growing diversity of the 
US population and knowledge of racial and ethnic disparities within and 
across population groups supports the collection of more granular data 
beyond the 1997 OMB minimum standard for reporting categories. The 2011 
HHS race and ethnicity data standard includes additional detail that 
may be used by PAC providers to target quality improvement efforts for 
racial and ethnic groups experiencing disparate outcomes. For more 
information on the Race and Ethnicity data elements, we refer readers 
to the document titled ``Proposed Specifications for IRF QRP Measures 
and Standardized Patient Assessment Data Elements,'' available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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    \158\ Penman-Aguilar, A., Talih, M., Huang, D., Moonesinghe, R., 
Bouye, K., Beckles, G. (2016). Measurement of Health Disparities, 
Health Inequities, and Social Determinants of Health to Support the 
Advancement of Health Equity. J Public Health Manag Pract. 22 Suppl 
1: S33-42.
    \159\ Ramos, R., Davis, J.L., Ross, T., Grant, C.G., Green, B.L. 
(2012). Measuring health disparities and health inequities: do you 
have REGAL data? Qual Manag Health Care. 21(3):176-87.
    \160\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and 
Language Data: Standardization for Health Care Quality Improvement. 
Washington, DC: The National Academies Press.
    \161\ ``Revision of Standards for Maintaining, Collecting, and 
Presenting Federal Data on Race and Ethnicity: Proposals From 
Federal Interagency Working Group (Notice and Request for 
Comments).'' Federal Register 82: 39 (March 1, 2017) p. 12242.
    \162\ National Academies of Sciences, Engineering, and Medicine; 
Health and Medicine Division; Board on Population Health and Public 
Health Practice; Committee on Community-Based Solutions to Promote 
Health Equity in the United States; Baciu A, Negussie Y, Geller A, 
et al., editors. Communities in Action: Pathways to Health Equity. 
Washington (DC): National Academies Press (US); 2017 Jan 11. 2, The 
State of Health Disparities in the United States. Available from: 
https://www.ncbi.nlm.nih.gov/books/NBK425844/.
    \163\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and 
Language Data: Standardization for Health Care Quality Improvement. 
Washington, DC: The National Academies Press.
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    In an effort to standardize the submission of race and ethnicity 
data among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in 
section 1899B(a)(1)(B) of the Act, while minimizing the reporting 
burden, we are proposing to adopt the Race and Ethnicity data elements 
described above as SPADEs with respect to the proposed Social 
Determinants of Health category.
    Specifically, we are proposing to replace the current Race/
Ethnicity data element with the proposed Race and Ethnicity data 
elements on the IRF-PAI. We are also proposing that IRFs that submit 
the Race and Ethnicity data

[[Page 17323]]

elements with respect to admission will be considered to have submitted 
with respect to discharge as well, because it is unlikely that the 
results of these assessment findings will change between the start and 
end of the IRF stay, making the information submitted with respect to a 
patient's admission the same with respect to a patient's discharge.
(2) Preferred Language and Interpreter Services
    More than 64 million Americans speak a language other than English 
at home, and nearly 40 million of those individuals have limited 
English proficiency (LEP).\164\ Individuals with LEP have been shown to 
receive worse care and have poorer health outcomes, including higher 
readmission rates.\165\ \166\ \167\ Communication with individuals with 
LEP is an important component of high quality health care, which starts 
by understanding the population in need of language services. 
Unaddressed language barriers between a patient and provider care team 
negatively affects the ability to identify and address individual 
medical and non-medical care needs, to convey and understand clinical 
information, as well as discharge and follow up instructions, all of 
which are necessary for providing high quality care. Understanding the 
communication assistance needs of patients with LEP, including 
individuals who are Deaf or hard of hearing, is critical for ensuring 
good outcomes.
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    \164\ U.S. Census Bureau, 2013-2017 American Community Survey 5-
Year Estimates.
    \165\ Karliner LS, Kim SE, Meltzer DO, Auerbach AD. Influence of 
language barriers on outcomes of hospital care for general medicine 
inpatients. J Hosp Med. 2010 May-Jun;5(5):276-82. doi: 10.1002/
jhm.658.
    \166\ Kim EJ, Kim T, Paasche-Orlow MK, et al. Disparities in 
Hypertension Associated with Limited English Proficiency. J Gen 
Intern Med. 2017 Jun;32(6):632-639. doi: 10.1007/s11606-017-3999-9.
    \167\ National Academies of Sciences, Engineering, and Medicine. 
2016. Accounting for social risk factors in Medicare payment: 
Identifying social risk factors. Washington, DC: The National 
Academies Press.
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    Presently, the preferred language of patients and residents and 
need for interpreter services are assessed in two PAC assessment tools. 
The LCDS and the MDS use the same two data elements to assess preferred 
language and whether a patient or resident needs or wants an 
interpreter to communicate with health care staff. The MDS initially 
implemented preferred language and interpreter services data elements 
to assess the needs of SNF residents and patients and inform care 
planning. For alignment purposes, the LCDS later adopted the same data 
elements for LTCHs. The 2009 NASEM (formerly Institute of Medicine) 
report on standardizing data for health care quality improvement 
emphasizes that language and communication needs should be assessed as 
a standard part of health care delivery and quality improvement 
strategies.\168\
---------------------------------------------------------------------------

    \168\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and 
Language Data: Standardization for Health Care Quality Improvement. 
Washington, DC: The National Academies Press.
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    In developing our proposal for a standardized language data element 
across PAC settings, we considered the current preferred language and 
interpreter services data elements that are in LCDS and MDS. We also 
considered the 2011 HHS Primary Language Data Standard and peer-
reviewed research. The current preferred language data element in LCDS 
and MDS asks, ``What is your preferred language?'' Because the 
preferred language data element is open-ended, the patient or resident 
is able to identify their preferred language, including American Sign 
Language (ASL). Finally, we considered the recommendations from the 
2009 NASEM (formerly Institute of Medicine) report, ``Race, Ethnicity, 
and Language Data: Standardization for Health Care Quality 
Improvement.'' In it, the committee recommended that organizations 
evaluating a patient's language and communication needs for health care 
purposes, should collect data on the preferred spoken language and on 
an individual's assessment of his/her level of English proficiency.
    A second language data element in LCDS and MDS asks, ``Do you want 
or need an interpreter to communicate with a doctor or health care 
staff?'' and includes yes or no response options. In contrast, the 2011 
HHS Primary Language Data Standard recommends either a single question 
to assess how well someone speaks English or, if more granular 
information is needed, a two-part question to assess whether a language 
other than English is spoken at home and if so, identify that language. 
However, neither option allows for a direct assessment of a patient's 
or resident's preferred spoken or written language nor whether they 
want or need interpreter services for communication with a doctor or 
care team, both of which are an important part of assessing patient/
resident needs and the care planning process. More information about 
the HHS Data Standard for Primary Language is available on the website 
at https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=54.
    Research consistently recommends collecting information about an 
individual's preferred spoken language and evaluating those responses 
for purposes of determining language access needs in health care.\169\ 
However, using ``preferred spoken language'' as the metric does not 
adequately account for people whose preferred language is ASL, which 
would necessitate adopting an additional data element to identify 
visual language. The need to improve the assessment of language 
preferences and communication needs across PAC settings should be 
balanced with the burden associated with data collection on the 
provider and patient. Therefore we are proposing to retain the 
Preferred Language and Interpreter Services data elements currently in 
use on the MDS and LCDS on the IRF-PAI.
---------------------------------------------------------------------------

    \169\ Guerino, P. and James, C. Race, Ethnicity, and Language 
Preference in the Health Insurance Marketplaces 2017 Open Enrollment 
Period. Centers for Medicare & Medicaid Services, Office of Minority 
Health. Data Highlight: Volume 7--April 2017. Available at https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Highlight-Race-Ethnicity-and-Language-Preference-Marketplace.pdf.
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    In addition, we received feedback during the December 13, 2018 
listening session on the importance of evaluating and acting on 
language preferences early to facilitate communication and allowing for 
patient self-identification of preferred language. Although the 
discussion about language was focused on preferred spoken language, 
there was general consensus among participants that stated language 
preferences may or may not accurately indicate the need for interpreter 
services, which supports collecting and evaluating data to determine 
language preference, as well as the need for interpreter services. An 
alternate suggestion was made to inquire about preferred language 
specifically for discussing health or health care needs. While this 
suggestion does allow for ASL as a response option, we do not have data 
indicating how useful this question might be for assessing the desired 
information and thus we are not including this question in our 
proposal.
    Improving how preferred language and need for interpreter services 
data are collected is an important component of improving quality by 
helping PAC providers and other providers understand patient needs and 
develop plans to address them. For more information on the Preferred 
Language and Interpreter Services data elements, we refer readers to 
the document titled ``Proposed Specifications for IRF QRP

[[Page 17324]]

Measures and Standardized Patient Assessment Data Elements,'' available 
on the website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In an effort to standardize the submission of language data among 
IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section 
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we 
are proposing to adopt the Preferred Language and Interpreter Services 
data elements currently used on the MDS and LCDS, and described above, 
as SPADEs with respect to the Social Determinants of Health category. 
We are proposing to add the current Preferred Language and Interpreter 
Services data elements from the MDS and LCDS to the IRF-PAI.
(3) Health Literacy
    The Department of Health and Human Services defines health literacy 
as ``the degree to which individuals have the capacity to obtain, 
process, and understand basic health information and services needed to 
make appropriate health decisions.'' \170\ Similar to language 
barriers, low health literacy can interfere with communication between 
the provider and patient and the ability for patients or their 
caregivers to understand and follow treatment plans, including 
medication management. Poor health literacy is linked to lower levels 
of knowledge about health, worse health outcomes, and the receipt of 
fewer preventive services, but higher medical costs and rates of 
emergency department use.\171\
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    \170\ U.S. Department of Health and Human Services, Office of 
Disease Prevention and Health Promotion. National action plan to 
improve health literacy. Washington (DC): Author; 2010.
    \171\ National Academies of Sciences, Engineering, and Medicine. 
2016. Accounting for social risk factors in Medicare payment: 
Identifying social risk factors. Washington, DC: The National 
Academies Press.
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    Health literacy is prioritized by Healthy People 2020 as an 
SDOH.\172\ Healthy People 2020 is a long-term, evidence-based effort 
led by the Department of Health and Human Services that aims to 
identify nationwide health improvement priorities and improve the 
health of all Americans. Although not designated as a social risk 
factor in NASEM's 2016 report on accounting for social risk factors in 
Medicare payment, the NASEM noted that health literacy is impacted by 
other social risk factors and can affect access to care as well as 
quality of care and health outcomes.\173\ Assessing for health literacy 
across PAC settings would facilitate better care coordination and 
discharge planning. A significant challenge in assessing the health 
literacy of individuals is avoiding excessive burden on patients and 
health care providers. The majority of existing, validated health 
literacy assessment tools use multiple screening items, generally with 
no fewer than four, which would make them burdensome if adopted in MDS, 
LCDS, IRF-PAI, and OASIS. The Single Item Literacy Screener (SILS) 
question asks, ``How often do you need to have someone help you when 
you read instructions, pamphlets, or other written material from your 
doctor or pharmacy?'' Possible response options are: (1) Never; (2) 
Rarely; (3) Sometimes; (4) Often; and (5) Always. The SILS question, 
which assesses reading ability, (a primary component of health 
literacy), tested reasonably well against the 36 item Short Test of 
Functional Health Literacy in Adults (S-TOFHLA), a thoroughly vetted 
and widely adopted health literacy test, in assessing the likelihood of 
low health literacy in an adult sample from primary care practices 
participating in the Vermont Diabetes Information 
System.174 175 The S-TOFHLA is a more complex assessment 
instrument developed using actual hospital related materials such as 
prescription bottle labels and appointment slips, and often considered 
the instrument of choice for a detailed evaluation of health 
literacy.\176\ Furthermore, the S-TOFHLA instrument is proprietary and 
subject to purchase for individual entities or users.\177\ Given that 
SILS is publicly available, shorter and easier to administer than the 
full health literacy screen, and research found that a positive result 
on the SILS demonstrates an increased likelihood that an individual has 
low health literacy, we are proposing to use the single-item reading 
question for health literacy in the standardized data collection across 
PAC settings. We believe that use of this data element will provide 
sufficient information about the health literacy of IRF patients to 
facilitate appropriate care planning, care coordination, and 
interoperable data exchange across PAC settings.
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    \172\ Social Determinants of Health. Healthy People 2020. 
https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health. (February 2019).
    \173\ U.S. Department of Health & Human Services, Office of the 
Assistant Secretary for Planning and Evaluation. Report to Congress: 
Social Risk Factors and Performance Under Medicare's Value-Based 
Purchasing Programs. Available at https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs. Washington, DC: 2016.
    \174\ Morris, N.S., MacLean, C.D., Chew, L.D., & Littenberg, B. 
(2006). The Single Item Literacy Screener: evaluation of a brief 
instrument to identify limited reading ability. BMC family practice, 
7, 21. doi:10.1186/1471-2296-7-21.
    \175\ Brice, J.H., Foster, M.B., Principe, S., Moss, C., Shofer, 
F.S., Falk, R.J., Ferris, M.E., DeWalt, D.A. (2013). Single-item or 
two-item literacy screener to predict the S-TOFHLA among adult 
hemodialysis patients. Patient Educ Couns. 94(1):71-5.
    \176\ University of Miami, School of Nursing & Health Studies, 
Center of Excellence for Health Disparities Research. Test of 
Functional Health Literacy in Adults (TOFHLA). (March 2019). 
Available from: https://elcentro.sonhs.miami.edu/research/measures-library/tofhla/.
    \177\ Nurss, J.R., Parker, R.M., Williams, M.V., &Baker, D.W. 
David W. (2001). TOFHLA. Peppercorn Books & Press. Available from: 
https://www.peppercornbooks.com/catalog/information.php?info_id=5.
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    In addition, we received feedback during the December 13, 2018 SDOH 
listening session on the importance of recognizing health literacy as 
more than understanding written materials and filling out forms, as it 
is also important to evaluate whether patients understand their 
conditions. However, the NASEM recently recommended that health care 
providers implement health literacy universal precautions instead of 
taking steps to ensure care is provided at an appropriate literacy 
level based on individualized assessment of health literacy.\178\ Given 
the dearth of Medicare data on health literacy and gaps in addressing 
health literacy in practice, we recommend the addition of a health 
literacy data element.
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    \178\ Hudson, S., Rikard, R.V., Staiculescu, I. & Edison, K. 
(2017). Improving health and the bottom line: The case for health 
literacy. In Building the case for health literacy: Proceedings of a 
workshop. Washington, DC: The National Academies Press.
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    The proposed Health Literacy data element is consistent with 
considerations raised by NASEM and other stakeholders and research on 
health literacy, which demonstrates an impact on health care use, cost, 
and outcomes.\179\ For more information on the proposed Health Literacy 
data element, we refer readers to the document titled ``Proposed 
Specifications for IRF QRP Measures and Standardized Patient Assessment 
Data Elements,'' available on the website at https://www.cms.gov/
Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-
Care-Quality-Initiatives/IMPACT-Act-of-

[[Page 17325]]

2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------

    \179\ National Academies of Sciences, Engineering, and Medicine. 
2016. Accounting for Social Risk Factors in Medicare Payment: 
Identifying Social Risk Factors. Washington, DC: The National 
Academies Press.
---------------------------------------------------------------------------

    In an effort to standardize the submission of health literacy data 
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section 
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we 
are proposing to adopt SILS question described above for the Health 
Literacy data element as SPADE under the Social Determinants of Health 
Category. We are proposing to add the Health Literacy data element to 
the IRF-PAI.
(4) Transportation
    Transportation barriers commonly affect access to necessary health 
care, causing missed appointments, delayed care, and unfilled 
prescriptions, all of which can have a negative impact on health 
outcomes.\180\ Access to transportation for ongoing health care and 
medication access needs, particularly for those with chronic diseases, 
is essential to successful chronic disease management. Adopting a data 
element to collect and analyze information regarding transportation 
needs across PAC settings would facilitate the connection to programs 
that can address identified needs. We are therefore proposing to adopt 
as SPADE a single transportation data element that is from the Protocol 
for Responding to and Assessing Patients' Assets, Risks, and 
Experiences (PRAPARE) assessment tool and currently part of the 
Accountable Health Communities (AHC) Screening Tool.
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    \180\ Syed, S.T., Gerber, B.S., and Sharp, L.K. (2013). 
Traveling Towards Disease: Transportation Barriers to Health Care 
Access. J Community Health. 38(5): 976-993.
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    The proposed Transportation data element from the PRAPARE tool 
asks, ``Has lack of transportation kept you from medical appointments, 
meetings, work, or from getting things needed for daily living?'' The 
three response options are: (1) Yes, it has kept me from medical 
appointments or from getting my medications; (2) Yes, it has kept me 
from non-medical meetings, appointments, work, or from getting things 
that I need; and (3) No. The patient would be given the option to 
select all responses that apply. We are proposing to use the 
transportation data element from the PRAPARE Tool, with permission from 
National Association of Community Health Centers (NACHC), after 
considering research on the importance of addressing transportation 
needs as a critical SDOH.\181\
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    \181\ Health Research & Educational Trust. (2017, November). 
Social determinants of health series: Transportation and the role of 
hospitals. Chicago, IL. Available at www.aha.org/transportation.www.aha.org/transportation.
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    The proposed data element is responsive to research on the 
importance of addressing transportation needs as a critical SDOH and 
would adopt the Transportation item from the PRAPARE tool.\182\ This 
data element comes from the national PRAPARE social determinants of 
health assessment protocol, developed and owned by NACHC, in 
partnership with the Association of Asian Pacific Community Health 
Organization, the Oregon Primary Care Association, and the Institute 
for Alternative Futures. Similarly the Transportation data element used 
in the AHC Screening Tool was adapted from the PRAPARE tool. The AHC 
screening tool was implemented by the Center for Medicare and Medicaid 
Innovation's AHC Model and developed by a panel of interdisciplinary 
experts that looked at evidence-based ways to measure SDOH, including 
transportation. While the transportation access data element in the AHC 
screening tool serves the same purposes as our proposed SPADE 
collection about transportation barriers, the AHC tool has binary yes 
or no response options that do not differentiate between challenges for 
medical versus non-medical appointments and activities. We believe that 
this is an important nuance for informing PAC discharge planning to a 
community setting, as transportation needs for non-medical activities 
may differ than for medical activities and should be taken into 
account.\183\ We believe that use of this data element will provide 
sufficient information about transportation barriers to medical and 
non-medical care for IRF patients to facilitate appropriate discharge 
planning and care coordination across PAC settings. As such, we are 
proposing to adopt the Transportation data element from PRAPARE. More 
information about development of the PRAPARE tool is available on the 
website at https://protect2.fireeye.com/url?k=7cb6eb44-20e2f238-7cb6da7b-0cc47adc5fa2-1751cb986c8c2f8c&u=https://www.nachc.org/prapare.
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    \182\ Health Research & Educational Trust. (2017, November). 
Social determinants of health series: Transportation and the role of 
hospitals. Chicago, IL. Available at www.aha.org/transportation.
    \183\ Northwestern University. (2017). PROMIS Item Bank v. 1.0--
Emotional Distress--Anger--Short Form 1.
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    In addition, we received stakeholder feedback during the December 
13, 2018 SDOH listening session on the impact of transportation 
barriers on unmet care needs. While recognizing that there is no 
consensus in the field about whether providers should have 
responsibility for resolving patient transportation needs, discussion 
focused on the importance of assessing transportation barriers to 
facilitate connections with available community resources.
    Adding a Transportation data element to the collection of SPADE 
would be an important step to identifying and addressing SDOH that 
impact health outcomes and patient experience for Medicare 
beneficiaries. For more information on the Transportation data element, 
we refer readers to the document titled ``Proposed Specifications for 
IRF QRP Measures and Standardized Patient Assessment Data Elements,'' 
available on the website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In an effort to standardize the submission of transportation data 
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section 
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we 
are proposing to adopt the Transportation data element described above 
as SPADE with respect to the proposed Social Determinants of Health 
category. If finalized as proposed, we would add the Transportation 
data element to the IRF-PAI.
(5) Social Isolation
    Distinct from loneliness, social isolation refers to an actual or 
perceived lack of contact with other people, such as living alone or 
residing in a remote area.184 185 Social isolation tends to 
increase with age, is a risk factor for physical and mental illness, 
and a predictor of mortality.186 187 188 Post-

[[Page 17326]]

acute care providers are well-suited to design and implement programs 
to increase social engagement of patients, while also taking into 
account individual needs and preferences. Adopting a data element to 
collect and analyze information about social isolation in IRFs and 
across PAC settings would facilitate the identification of patients who 
are socially isolated and who may benefit from engagement efforts.
---------------------------------------------------------------------------

    \184\ Tomaka, J., Thompson, S., and Palacios, R. (2006). The 
Relation of Social Isolation, Loneliness, and Social Support to 
Disease Outcomes Among the Elderly. J of Aging and Health. 18(3): 
359-384.
    \185\ Social Connectedness and Engagement Technology for Long-
Term and Post-Acute Care: A Primer and Provider Selection Guide. 
(2019). Leading Age. Available at https://www.leadingage.org/white-papers/social-connectedness-and-engagement-technology-long-term-and-post-acute-care-primer-and#1.1.
    \186\ Landeiro, F., Barrows, P., Nuttall Musson, E., Gray, A.M., 
and Leal, J. (2017). Reducing Social Loneliness in Older People: A 
Systematic Review Protocol. BMJ Open. 7(5): e013778.
    \187\ Ong, A.D., Uchino, B.N., and Wethington, E. (2016). 
Loneliness and Health in Older Adults: A Mini-Review and Synthesis. 
Gerontology. 62:443-449.
    \188\ Leigh-Hunt, N., Bagguley, D., Bash, K., Turner, V., 
Turnbull, S., Valtorta, N., and Caan, W. (2017). An overview of 
systematic reviews on the public health consequences of social 
isolation and loneliness. Public Health. 152:157-171.
---------------------------------------------------------------------------

    We are proposing to adopt as SPADE a single social isolation data 
element that is currently part of the AHC Screening Tool. The AHC item 
was selected from the Patient-Reported Outcomes Measurement Information 
System (PROMIS[supreg]) Item Bank on Emotional Distress and asks, ``How 
often do you feel lonely or isolated from those around you?'' The five 
response options are: (1) Never; (2) Rarely; (3) Sometimes; (4) Often; 
and (5) Always.\189\ The AHC Screening Tool was developed by a panel of 
interdisciplinary experts that looked at evidence-based ways to measure 
SDOH, including social isolation. More information about the AHC 
Screening Tool is available on the website at https://innovation.cms.gov/Files/worksheets/ahcm-screeningtool.pdf.
---------------------------------------------------------------------------

    \189\ Northwestern University. (2017). PROMIS Item Bank v. 1.0--
Emotional Distress--Anger--Short Form 1.
---------------------------------------------------------------------------

    In addition, we received stakeholder feedback during the December 
13, 2018 SDOH listening session on the value of receiving information 
on social isolation for purposes of care planning. Some stakeholders 
also recommended assessing social isolation as an SDOH as opposed to 
social support.
    The proposed Social Isolation data element is consistent with NASEM 
considerations about social isolation as a function of social 
relationships that impacts health outcomes and increases mortality 
risk, as well as the current work of a NASEM committee examining how 
social isolation and loneliness impact health outcomes in adults 50 
years and older. We believe that adding a Social Isolation data element 
would be an important component of better understanding patient 
complexity and the care goals of patients, thereby facilitating care 
coordination and continuity in care planning across PAC settings. For 
more information on the Social Isolation data element, we refer readers 
to the document titled ``Proposed Specifications for IRF QRP Measures 
and Standardized Patient Assessment Data Elements,'' available on the 
website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
    In an effort to standardize the submission of social isolation data 
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section 
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we 
are proposing to adopt the Social Isolation data element described 
above as SPADE with respect to the proposed Social Determinants of 
Health category. We are proposing to add the Social Isolation data 
element to the IRF-PAI.
    We are soliciting comment on this proposal.

H. Form, Manner, and Timing of Data Submission Under the IRF QRP

1. Background
    We refer readers to Sec.  412.634(b) for information regarding the 
current policies for reporting IRF QRP data.
2. Update to the CMS System for Reporting Quality Measures and 
Standardized Patient Assessment Data and Associated Procedural 
Proposals
    IRFs are currently required to submit IRF-PAI data to CMS using the 
Quality Improvement and Evaluation System (QIES) Assessment and 
Submission Processing (ASAP) system. We will be migrating to a new 
internet Quality Improvement and Evaluation System (iQIES) that will 
enable real-time upgrades, and we are proposing to designate that 
system as the data submission system for the IRF QRP beginning October 
1, 2019. We are proposing to revise Sec.  412.634(a)(1) by replacing 
``Certification and Survey Provider Enhanced Reports (CASPER)'' with 
``CMS designated data submission''. We are proposing to revise Sec.  
412.634(d)(1) by replacing the reference to ``Quality Improvement and 
Evaluation System Assessment Submission and Processing (QIES ASAP) 
system'' with ``CMS designated data submission system''. We are 
proposing to revise Sec.  412.634(d)(5) by replacing reference to the 
``QIES ASAP'' with ``CMS designated data submission''. We are also 
proposing to revise Sec.  412.634(f)(1) by replacing ``QIES'' with 
``CMS designated data submission system''. In addition, we are 
proposing to notify the public of any future changes to the CMS 
designated system using subregulatory mechanisms, such as website 
postings, listserv messaging, and webinars.
    We invite public comment on our proposals.
3. Proposed Schedule for Reporting the Transfer of Health Information 
Quality Measures Beginning With the FY 2022 IRF QRP
    As discussed in section VIII.D. of this proposed rule, we are 
proposing to adopt the Transfer of Health Information to the Provider-
Post-Acute Care (PAC) and Transfer of Health Information to the 
Patient-Post-Acute Care (PAC) quality measures beginning with the FY 
2022 IRF QRP. We also are proposing that IRFs would report the data on 
those measures using the IRF-PAI. IRFs would be required to collect 
data on both measures for patients beginning with patients discharged 
on or after October 1, 2020. We refer readers to the FY 2018 IRF PPS 
final rule (82 FR 36291 through 36292) for the data collection and 
submission timeframes that we finalized for the IRF QRP.
    We invite public comment on this proposal.
4. Proposed Schedule for Reporting Standardized Patient Assessment Data 
Elements Beginning With the FY 2022 IRF QRP
    As discussed in section IV.F. of this proposed rule, we are 
proposing to adopt SPADEs beginning with the FY 2022 IRF QRP. We are 
proposing that IRFs would report the data using the IRF-PAI. Similar to 
the proposed schedule for reporting the Transfer of Health Information 
to the Provider-Post-Acute Care (PAC) and Transfer of Health 
Information to the Patient-Post-Acute Care (PAC) quality measures, IRFs 
would be required to collect the SPADEs for all patients discharged on 
or after October 1, 2020, at both admission and discharge. IRFs that 
submit data with respect to admission for the Hearing, Vision, Race, 
and Ethnicity SPADEs would be considered to have submitted data with 
respect to discharges. We refer readers to the FY 2018 IRF PPS final 
rule (82 FR 36291 through 36292) for the data collection and submission 
timeframes that we finalized for the IRF QRP.
    We invite public comment on this proposal.
5. Proposed Data Reporting on Patients for the IRF Quality Reporting 
Program Beginning With the FY 2022 IRF QRP
    We received public input suggesting that the quality measures used 
in the IRF QRP should be calculated using data collected from all IRF 
patients, regardless of the patients' payer. This input was provided to 
us via comments requested about quality measure development on the CMS 
Measures Management System Blueprint

[[Page 17327]]

website,\190\ as well as through comments we received from stakeholders 
via our IRF QRP mailbox, and feedback received from the NQF-convened 
MAP as part of their recommendations on Coordination Strategy for Post-
Acute Care and Long-Term Care Performance Measurement.\191\ Further, in 
the FY 2018 IRF PPS proposed rule (82 FR 20740), we sought input on 
expanding the reporting of quality measures to include all patients, 
regardless of payer, so as to ensure that the IRF QRP makes publicly 
available information regarding the quality of the services furnished 
to the IRF population as a whole, rather than just those patients who 
have Medicare.
---------------------------------------------------------------------------

    \190\ Public Comment Summary Report Posting for Transfer of 
Health Information and Care Preferences. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Development-of-Cross-Setting-Transfer-of-Health-Information-Quality-Meas.pdf.
    \191\ MAP Coordination Strategy for Post-Acute Care and Long-
Term Care Performance Measurement. Feb 2012. https://www.qualityforum.org/Publications/2012/02/MAP_Coordination_Strategy_for_Post-Acute_Care_and_Long-Term_Care_Performance_Measurement.aspx.
---------------------------------------------------------------------------

    In response to that request for public input, several commenters, 
including MedPAC, submitted comments stating that they would be 
supportive of an effort to collect data specified under the IRF QRP 
from all IRF patients regardless of their payer. Many commenters noted 
that this would not be overly burdensome, as most of their 
organizations' members currently complete the IRF-PAI on all patients, 
regardless of their payer. A few commenters had concerns, including 
recommending that CMS continue to align the patient assessment 
instruments across PAC settings and whether the use of the data would 
outweigh any additional reporting burden. For a more detailed 
discussion, we refer readers to the FY 2018 IRF final rule (82 FR 
36292). We have taken these concerns under consideration in proposing 
this policy.
    Further, given that we do not have access to other payer claims, we 
believe that the most accurate representation of the quality provided 
in IRFs would be best conveyed using data collected via the IRF-PAI on 
all IRF patients, regardless of payer, for the purposes of the IRF QRP. 
Medicare is the primary payer for approximately 60 percent of IRF 
patients.\192\
---------------------------------------------------------------------------

    \192\ National Academies of Sciences, Engineering, and Medicine. 
2016. Accounting for social risk factors in Medicare payment: 
Identifying social risk factors. Washington, DC: The National 
Academies Press.
---------------------------------------------------------------------------

    We also believe that data reporting on standardized patient 
assessment data elements using IRF-PAI should include all IRF patients 
for the same reasons for collecting data on all residents for the IRF 
QRP's quality measures: To promote higher quality and more efficient 
health care for Medicare beneficiaries and all patients receiving IRF 
services, for example through the exchange of information and 
longitudinal analysis of the data. With that, we believe that 
collecting quality measure and standardized patient assessment data via 
the IRF-PAI on all IRF patients ensures that quality care is provided 
for Medicare beneficiaries, and patients receiving IRF services as a 
whole. While we appreciate that collecting quality data on all patients 
regardless of payer may create additional burden, we also note that the 
effort to separate out Medicare beneficiaries from other patients is 
also burdensome. We are aware that it is common practice for IRFs to 
collect IRF-PAI data on all patients, regardless of their payer.
    Further, we believe that patients may utilize various payer sources 
for services received during their stay, for example being admitted 
under one payer source including Medicare, and the payer source may 
change during the patient stay which would require the restart of the 
data collection and reporting in the midst of services rather than at 
the actual admission. Collecting data on all IRF patients will provide 
us with the most robust, accurate reflection of the quality of care 
delivered to Medicare beneficiaries as compared with non-Medicare 
patients and residents, and we intend to display the calculation of 
this data on IRF Compare in the future. Accordingly, we are proposing 
that IRFs collect data on all IRF patients to ensure that all patients, 
regardless of their payer, are receiving the same care and that 
provider metrics measure performance across the spectrum of patients.
    Therefore, to meet the quality reporting requirements for IRFs for 
the FY 2022 payment determination and each subsequent year, we propose 
to expand the reporting of IRF-PAI data used for the IRF QRP to include 
data on all patients, regardless of their payer, beginning with 
patients discharged on or after October 1, 2020 for the FY 2022 IRF QRP 
and the IRF-PAI V4.0, effective October 1, 2020.
    We invite public comment on this proposal.

I. Proposed Policies Regarding Public Display of Measure Data for the 
IRF QRP

    Section 1886(j)(7)(E) of the Act requires the Secretary to 
establish procedures for making the IRF QRP data available to the 
public after ensuring that IRFs have the opportunity to review their 
data prior to public display. Measure data are currently displayed on 
the Inpatient Rehabilitation Facility Compare website, an interactive 
web tool that assists individuals by providing information on IRF 
quality of care. For more information on IRF Compare, we refer readers 
to the website at https://www.medicare.gov/inpatientrehabilitationfacilitycompare/. For a more detailed discussion 
about our policies regarding public display of IRF QRP measure data and 
procedures for the opportunity to review and correct data and 
information, we refer readers to the FY 2017 IRF PPS final rule (81 FR 
52125 through 52131).
    In this proposed rule, we are proposing to begin publicly 
displaying data for the Drug Regimen Review Conducted With Follow-Up 
for Identified Issues--PAC IRF QRP measure beginning CY 2020 or as soon 
as technically feasible. We finalized the Drug Regimen Review Conducted 
With Follow-Up for Identified Issues--PAC IRF QRP measure in the FY 
2017 IRF PPS final rule (81 FR 52111 through 52116).
    Data collection for this assessment-based measure began with 
patients discharged on or after October 1, 2018. We are proposing to 
display data based on four rolling quarters, initially using discharges 
from January 1, 2019 through December 31, 2019 (Quarter 1 2019 through 
Quarter 4 2019). To ensure the statistical reliability of the data, we 
are proposing that we would not publicly report an IRF's performance on 
the measure if the IRF had fewer than 20 eligible cases in any four 
consecutive rolling quarters. IRFs that have fewer than 20 eligible 
cases would be distinguished with a footnote that states, ``The number 
of cases/patient stays is too small to publicly report.''
    We invite public comment on these proposals.

J. Proposed Removal of the List of Compliant IRFs

    In the FY 2016 IRF PPS final rule (80 FR 47125 through 47127), we 
finalized that we would publish a list of IRFs that successfully met 
the reporting requirements for the applicable payment determination on 
the IRF QRP website and update the list on an annual basis.
    We have received feedback from stakeholders that this list offers 
minimal benefit. Although the posting of successful providers was the 
final step in the applicable payment determination process, it does not 
provide new information or clarification to the providers regarding 
their annual

[[Page 17328]]

payment update status. Therefore, in this proposed rule, we are 
proposing that we will no longer publish a list of compliant IRFs on 
the IRF QRP website, effective beginning with the FY 2020 payment 
determination.
    We invite public comment on this proposal.

K. Method for Applying the Reduction to the FY 2020 IRF Increase Factor 
for IRFs That Fail To Meet the Quality Reporting Requirements

    As previously noted, section 1886(j)(7)(A)(i) of the Act requires 
the application of a 2-percentage point reduction of the applicable 
market basket increase factor for payments for discharges occurring 
during such fiscal year for IRFs that fail to comply with the quality 
data submission requirements. We propose to apply a 2-percentage point 
reduction to the applicable FY 2020 proposed market basket increase 
factor in calculating an adjusted FY 2020 proposed standard payment 
conversion factor to apply to payments for only those IRFs that failed 
to comply with the data submission requirements. As previously noted, 
application of the 2-percentage point reduction may result in an update 
that is less than 0.0 for a fiscal year and in payment rates for a 
fiscal year being less than such payment rates for the preceding fiscal 
year. Also, reporting-based reductions to the market basket increase 
factor will not be cumulative; they will only apply for the FY 
involved.
    We invite public comment on the proposed method for applying the 
reduction to the FY 2020 IRF increase factor for IRFs that fail to meet 
the quality reporting requirements.
    Table 20 shows the calculation of the proposed adjusted FY 2020 
standard payment conversion factor that will be used to compute IRF PPS 
payment rates for any IRF that failed to meet the quality reporting 
requirements for the applicable reporting period.
[GRAPHIC] [TIFF OMITTED] TP24AP19.020

IX. 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; and
     Recommendations to minimize the information collection 
burden on the affected public, including automated collection 
techniques.
    This proposed rule makes reference to associated information 
collections that are not discussed in the regulation text contained in 
this document.

B. Collection of Information Requirements for Updates Related to the 
IRF QRP

    An IRF that does not meet the requirements of the IRF QRP for a 
fiscal year will receive a 2 percentage point reduction to its 
otherwise applicable annual increase factor for that fiscal year. 
Information is not currently available to determine the precise number 
of IRFs that will receive less than the full annual increase factor for 
FY 2020 due to non-compliance with the requirements of the IRF QRP.
    We believe that the burden associated with the IRF QRP is the time 
and effort associated with complying with the requirements of the IRF 
QRP. As of February 1, 2019, there are approximately 1,119 IRFs 
reporting quality data to CMS. For the purposes of calculating the 
costs associated with the collection of information requirements, we 
obtained mean hourly wages for these staff from the U.S. Bureau of 
Labor Statistics' May 2017 National Occupational Employment and Wage 
Estimates (https://www.bls.gov/oes/current/oes_nat.htm). To account for 
overhead and fringe benefits, we have doubled the hourly wage. These 
amounts are detailed in Table 21.
[GRAPHIC] [TIFF OMITTED] TP24AP19.021


[[Page 17329]]


    As discussed in section VIII.D. of this proposed rule, we are 
proposing to adopt two new measures, (1) Transfer of Health Information 
to the Provider-Post-Acute Care (PAC); and (2) Transfer of Health 
Information to the Patient-Post-Acute Care (PAC), beginning with the FY 
2022 IRF QRP. As a result, the estimated burden and cost for IRFs for 
complying with requirements of the FY 2022 IRF QRP will increase. 
Specifically, we believe that there will be a 0.9 minute addition in 
clinical staff time to report data per patient stay. We estimate 
409,982 discharges from 1,119 IRFs annually. This equates to an 
increase of 8,200 hours in burden for all IRFs (0.02 hours per 
assessment x 409,982 discharges). Given 0.5 minutes of RN time at 
$70.72 per hour and 0.4 minutes of LVN time at $43.96 per hour, we 
estimate that the total cost will be increased by $330 per IRF 
annually, or $369,082 for all IRFs annually. This increase in burden 
will be accounted for in the information collection under OMB control 
number (0938-0842), which expires December 31, 2021.
    In addition, we are proposing to add the standardized patient 
assessment data elements described in section VIII.F beginning with the 
FY 2022 IRF QRP. As a result, the estimated burden and cost for IRFs 
for complying with requirements of the FY 2022 IRF QRP will be 
increased. Specifically, we believe that there will be an addition of 
7.4 minutes on admission, and 11.1 minutes on discharge, for a total of 
8.9 minutes of additional clinical staff time to report data per 
patient stay. We estimate 409,982 discharges from 1,119 IRFs annually. 
This equates to an increase of 131,194 hours in burden for all IRFs 
(0.32 hours per assessment x 409,982 discharges). Given 11.3 minutes of 
RN time at $70.72 per hour and 7.6 minutes of LVN time at $43.96 per 
hour, we estimate that the total cost will be increased by $6,926 per 
IRF annually, or $7,750,194 for all IRFs annually. This increase in 
burden will be accounted for in the information collection under OMB 
control number (0938-0842), which expires December 31, 2021.
    In summary, the proposed IRF QRP quality measures and standardized 
patient assessment data elements will result in a burden addition of 
$7,256 per IRF annually, and $8,119,276 for all IRFs annually.

C. Submission of PRA-Related Comments

    We have submitted a copy of this rule's information collection and 
recordkeeping requirements to OMB for review and approval. These 
requirements are not effective until they have been approved by the 
OMB.
    To obtain copies of the supporting statement and any related forms 
for the proposed collections discussed above, please visit CMS's 
website at www.cms.hhs.gov/PaperworkReductionActof1995, or call the 
Reports Clearance Office at 410-786-1326.
    We invite public comments on these potential information collection 
requirements. If you wish to comment, please refer to the DATES and 
ADDRESSES sections of this rulemaking for instructions. We will 
consider all ICR-related comments received by the date and time 
specified in the DATES section, and, when we proceed with a subsequent 
document, we will respond to the comments in the preamble to that 
document.

X. Response to Comments

    Because of the large number of public comments we normally receive 
on Federal Register documents, we are not able to acknowledge or 
respond to them individually. We will consider all comments we receive 
by the date and time specified in the DATES section of this preamble, 
and, when we proceed with a subsequent document, we will respond to the 
comments in the preamble to that document.

XI. Regulatory Impact Analysis

A. Statement of Need

    This proposed rule updates the IRF prospective payment rates for FY 
2020 as required under section 1886(j)(3)(C) of the Act. It responds to 
section 1886(j)(5) of the Act, which requires the Secretary to publish 
in the Federal Register on or before the August 1 that precedes the 
start of each fiscal year, the classification and weighting factors for 
the IRF PPS's case-mix groups, and a description of the methodology and 
data used in computing the prospective payment rates for that fiscal 
year.
    This proposed rule also implements sections 1886(j)(3)(C) of the 
Act. Section 1886(j)(3)(C)(ii)(I) of the Act requires the Secretary to 
apply a multifactor productivity adjustment to the market basket 
increase factor. The productivity adjustment applies to FYs from 2012 
forward.
    Furthermore, this proposed rule also adopts policy changes under 
the statutory discretion afforded to the Secretary under section 
1886(j)(7) of the Act. Specifically, we are proposing to rebase and 
revise the IRF market basket to reflect a 2016 base year rather than 
the current 2012 base year, revise the CMGs, make a technical 
correction to the regulatory language to indicate that that the 
determination of whether a treating physician has specialized training 
and experience in inpatient rehabilitation is made by the IRF and 
update regulatory language related to IRF QRP data collection.

B. Overall Impact

    We have examined the impacts of this rule as required by Executive 
Order 12866 on Regulatory Planning and Review (September 30, 1993), 
Executive Order 13563 on Improving Regulation and Regulatory Review 
(January 18, 2011), the Regulatory Flexibility Act (RFA) (September 19, 
1980, Pub. L. 96-354), section 1102(b) of the Act, section 202 of the 
Unfunded Mandates Reform Act of 1995 (March 22, 1995; Pub. L. 104-4), 
Executive Order 13132 on Federalism (August 4, 1999), the Congressional 
Review Act (5 U.S.C. 804(2) and Executive Order 13771 on Reducing 
Regulation and Controlling Regulatory Costs (January 30, 2017).
    Executive Orders 12866 and 13563 direct agencies to assess all 
costs and benefits of available regulatory alternatives and, if 
regulation is necessary, to select regulatory approaches that maximize 
net benefits (including potential economic, environmental, public 
health and safety effects, distributive impacts, and equity). Section 
3(f) of Executive Order 12866 defines a ``significant regulatory 
action'' as an action that is likely to result in a rule: (1) Having an 
annual effect on the economy of $100 million or more in any 1 year, or 
adversely and materially affecting a sector of the economy, 
productivity, competition, jobs, the environment, public health or 
safety, or state, local or tribal governments or communities (also 
referred to as ``economically significant''); (2) creating a serious 
inconsistency or otherwise interfering with an action taken or planned 
by another agency; (3) materially altering the budgetary impacts of 
entitlement grants, user fees, or loan programs or the rights and 
obligations of recipients thereof; or (4) raising novel legal or policy 
issues arising out of legal mandates, the President's priorities, or 
the principles set forth in the Executive Order.
    A regulatory impact analysis (RIA) must be prepared for major rules 
with economically significant effects ($100 million or more in any 1 
year). We estimate the total impact of the policy updates described in 
this proposed rule by comparing the estimated payments in FY 2020 with 
those in FY 2019. This analysis results in an estimated $195 million 
increase for FY 2020 IRF PPS payments. Additionally we estimate that

[[Page 17330]]

costs associated with the proposals to update the reporting 
requirements under the IRF quality reporting program result in an 
estimated $8.1 million addition in costs in FY 2020 for IRFs. We 
estimate that this rulemaking is ``economically significant'' as 
measured by the $100 million threshold, and hence also a major rule 
under the Congressional Review Act. Also, the rule has been reviewed by 
OMB. Accordingly, we have prepared a Regulatory Impact Analysis that, 
to the best of our ability, presents the costs and benefits of the 
rulemaking.

C. Anticipated Effects

1. Effects on IRFs
    The RFA requires agencies to analyze options for regulatory relief 
of small entities, if a rule has a significant impact on a substantial 
number of small entities. For purposes of the RFA, small entities 
include small businesses, nonprofit organizations, and small 
governmental jurisdictions. Most IRFs and most other providers and 
suppliers are small entities, either by having revenues of $7.5 million 
to $38.5 million or less in any 1 year depending on industry 
classification, or by being nonprofit organizations that are not 
dominant in their markets. (For details, see the Small Business 
Administration's final rule that set forth size standards for health 
care industries, at 65 FR 69432 at https://www.sba.gov/sites/default/files/files/Size_Standards_Table.pdf, effective March 26, 2012 and 
updated on February 26, 2016.) Because we lack data on individual 
hospital receipts, we cannot determine the number of small proprietary 
IRFs or the proportion of IRFs' revenue that is derived from Medicare 
payments. Therefore, we assume that all IRFs (an approximate total of 
1,120 IRFs, of which approximately 55 percent are nonprofit facilities) 
are considered small entities and that Medicare payment constitutes the 
majority of their revenues. The HHS generally uses a revenue impact of 
3 to 5 percent as a significance threshold under the RFA. As shown in 
Table 22, we estimate that the net revenue impact of this proposed rule 
on all IRFs is to increase estimated payments by approximately 2.3 
percent. The rates and policies set forth in this proposed 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 603 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 
proposed rule will not have a significant impact (not greater than 3 
percent) on a substantial number of rural hospitals based on the data 
of the 136 rural units and 11 rural hospitals in our database of 1,119 
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) (UMRA) also requires that agencies 
assess anticipated costs and benefits before issuing any rule whose 
mandates require spending in any 1 year of $100 million in 1995 
dollars, updated annually for inflation. In 2019, that threshold is 
approximately $154 million. This proposed rule does not mandate any 
requirements for State, local, or tribal governments, or for the 
private sector.
    Executive Order 13132 establishes certain requirements that an 
agency must meet when it issues a proposed rule (and subsequent final 
rule) that imposes substantial direct requirement costs on state and 
local governments, preempts state law, or otherwise has federalism 
implications. As stated, this proposed rule will not have a substantial 
effect on state and local governments, preempt state law, or otherwise 
have a federalism implication.
    Executive Order 13771, titled Reducing Regulation and Controlling 
Regulatory Costs, was issued on January 30, 2017 and requires that the 
costs associated with significant new regulations ``shall, to the 
extent permitted by law, be offset by the elimination of existing costs 
associated with at least two prior regulations.'' This proposed rule is 
considered an E.O. 13771 deregulatory action. We estimate that this 
rule would generate $6.18 million in annualized cost, discounted at 7 
percent relative to year 2016, over a perpetual time horizon. Details 
on the estimated costs of this rule can be found in the preceding 
analyses.
2. Detailed Economic Analysis
    This proposed rule updates to the IRF PPS rates contained in the FY 
2019 IRF PPS final rule (83 FR 38514). Specifically, this proposed rule 
updates the CMG relative weights and average length of stay values, the 
wage index, and the outlier threshold for high-cost cases. This 
proposed rule applies a MFP adjustment to the FY 2020 IRF market basket 
increase factor in accordance with section 1886(j)(3)(C)(ii)(I) of the 
Act. Further, this proposed rule proposes to rebase and revise the IRF 
market basket to reflect a 2016 base year rather than the current 2012 
base year, revise the CMGs based on FY 2017 and 2018 data and to make a 
technical correction to the regulatory language to indicate that the 
determination of whether a treating physician has specialized training 
and experience in inpatient rehabilitation is made by the IRF.
    We estimate that the impact of the changes and updates described in 
this proposed rule would be a net estimated increase of $195 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 VIII.J. of this 
proposed rule). The impact analysis in Table 22 of this proposed rule 
represents the projected effects of the updates to IRF PPS payments for 
FY 2020 compared with the estimated IRF PPS payments in FY 2019. We 
determine the effects by estimating payments while holding all other 
payment variables constant. We use the best data available, but we do 
not attempt to predict behavioral responses to these changes, and we do 
not make adjustments for future changes in such variables as number of 
discharges or case-mix.
    We note that certain events may combine to limit the scope or 
accuracy of our impact analysis, because such an analysis is future-
oriented and, thus, susceptible to forecasting errors because of other 
changes in the forecasted impact time period. Some examples could be 
legislative changes made by the Congress to the Medicare program that 
would impact program funding, or changes specifically related to IRFs. 
Although some of these changes may not necessarily be specific to the 
IRF PPS, the nature of the Medicare program is such that the changes 
may interact, and the complexity of the interaction of these changes 
could make it difficult to predict accurately the full scope of the 
impact upon IRFs.
    In updating the rates for FY 2020, we are proposing standard annual 
revisions described in this proposed rule (for example, the update to 
the wage and market basket indexes used to adjust the

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federal rates). We are also implementing a productivity adjustment to 
the FY 2020 IRF market basket increase factor in accordance with 
section 1886(j)(3)(C)(ii)(I) of the Act. We estimate the total increase 
in payments to IRFs in FY 2020, relative to FY 2019, will be 
approximately $195 million.
    This estimate is derived from the application of the FY 2020 IRF 
market basket increase factor, as reduced by a productivity adjustment 
in accordance with section 1886(j)(3)(C)(ii)(I) of the Act, which 
yields an estimated increase in aggregate payments to IRFs of $210 
million. Furthermore, there is an additional estimated $15 million 
decrease in aggregate payments to IRFs due to the proposed update to 
the outlier threshold amount. Outlier payments are estimated to 
decrease from approximately 3.2 percent in FY 2019 to 3.0 percent in FY 
2020. Therefore, summed together, we estimate that these updates will 
result in a net increase in estimated payments of $195 million from FY 
2019 to FY 2020.
    The effects of the proposed updates that impact IRF PPS payment 
rates are shown in Table 22. The following proposed updates that affect 
the IRF PPS payment rates are discussed separately below:
     The effects of the proposed update to the outlier 
threshold amount, from approximately 3.2 percent to 3.0 percent of 
total estimated payments for FY 2020, consistent with section 
1886(j)(4) of the Act.
     The effects of the proposed annual market basket update 
(using the IRF market basket) to IRF PPS payment rates, as required by 
section 1886(j)(3)(A)(i) and section 1886(j)(3)(C) of the Act, 
including a productivity adjustment in accordance with section 
1886(j)(3)(C)(i)(I) of the Act.
     The effects of applying the proposed budget-neutral labor-
related share and wage index adjustment, as required under section 
1886(j)(6) of the Act.
     The effects of the proposed budget-neutral changes to the 
CMGs, 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 
proposed FY 2020 payment changes relative to the estimated FY 2019 
payments.
3. 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 DSH PP. The top row of Table 22 shows the overall impact on the 
1,119 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 972 IRFs located in 
urban areas included in our analysis. Among these, there are 696 IRF 
units of hospitals located in urban areas and 276 freestanding IRF 
hospitals located in urban areas. There are 147 IRFs located in rural 
areas included in our analysis. Among these, there are 136 IRF units of 
hospitals located in rural areas and 11 freestanding IRF hospitals 
located in rural areas. There are 393 for-profit IRFs. Among these, 
there are 357 IRFs in urban areas and 36 IRFs in rural areas. There are 
612 non-profit IRFs. Among these, there are 522 urban IRFs and 90 rural 
IRFs. There are 114 government-owned IRFs. Among these, there are 93 
urban IRFs and 21 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 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 2020 analysis file.
     Column (3) shows the number of cases in each category in 
our FY 2020 analysis file.
     Column (4) shows the estimated effect of the proposed 
adjustment to the outlier threshold amount.
     Column (5) shows the estimated effect of the proposed 
update to the IRF labor-related share and wage index, in a budget-
neutral manner.
     Column (6) shows the estimated effect of the proposed 
update to the CMGs, 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 proposed 
rule for FY 2020 to our estimates of payments per discharge in FY 2019.
    The average estimated increase for all IRFs is approximately 2.3 
percent. This estimated net increase includes the effects of the 
proposed IRF market basket increase factor for FY 2020 of 3.0 percent, 
reduced by a productivity adjustment of 0.5 percentage point in 
accordance with section 1886(j)(3)(C)(ii)(I) of the Act. It also 
includes the approximate 0.2 percent overall decrease in estimated IRF 
outlier payments from the proposed update to the outlier threshold 
amount. Since we are making the updates to the IRF wage index and the 
CMG relative weights in a budget-neutral manner, they will not be 
expected to affect total estimated IRF payments in the aggregate. 
However, as described in more detail in each section, they will be 
expected to affect the estimated distribution of payments among 
providers.
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4. Impact of the Proposed Update to the Outlier Threshold Amount
    The estimated effects of the proposed update to the outlier 
threshold adjustment are presented in column 4 of Table 22. In the FY 
2019 IRF PPS final rule (83 FR 38531 through 38532), we used FY 2017 
IRF claims data (the best, most complete data available at that time) 
to set the outlier threshold amount for FY 2019 so that estimated 
outlier payments would equal 3 percent of total estimated payments for 
FY 2019.
    For this proposed rule, we are using preliminary FY 2018 IRF claims 
data, and, based on that preliminary analysis, we estimated that IRF 
outlier payments as a percentage of total estimated IRF payments would 
be 3.2 percent in FY 2019. Thus, we propose to adjust the outlier 
threshold amount in this proposed rule to set total estimated outlier 
payments equal to 3 percent of total estimated payments in FY 2020.The 
estimated change in total IRF payments for FY 2020, therefore, includes 
an approximate 0.2 percent decrease in payments because the estimated 
outlier portion of total payments is estimated to decrease from 
approximately 3.2 percent to 3 percent.
    The impact of this proposed outlier adjustment update (as shown in 
column 4 of Table 22) is to decrease estimated overall payments to IRFs 
by about 0.2 percent. We estimate the largest decrease in payments from 
the update to the outlier threshold amount to be 0.6 percent for rural 
IRFs in the Pacific region.
5. Impact of the Proposed CBSA Wage Index and Labor-Related Share
    In column 5 of Table 22, we present the effects of the proposed 
budget-neutral update of the wage index and labor-related share. The 
proposed 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 proposed changes in the two 
have a combined effect on payments to providers. As discussed in 
section V.E. of this proposed rule, we are proposing to update the 
labor-related share from 70.5 percent in FY 2019 to 72.6 percent in FY 
2020.
6. Impact of the Proposed Update to the CMG Relative Weights and 
Average Length of Stay Values.
    In column 6 of Table 22, we present the effects of the proposed 
budget-neutral update of the CMGs, relative weights and average length 
of stay values. In the aggregate, we do not estimate that these 
proposed updates will affect overall estimated payments of IRFs. 
However, we do expect these updates to have small distributional 
effects.
7. Effects of the Requirements for the IRF QRP for FY 2020
    In accordance with section 1886(j)(7)(A) of the Act, the Secretary 
must reduce by 2 percentage points the market basket increase factor 
otherwise applicable to an IRF for a fiscal year if the IRF does not 
comply with the requirements of the IRF QRP for that fiscal year. In 
section VIII.J of this proposed rule, we discuss the proposed method 
for applying the 2 percentage point reduction to IRFs that fail to meet 
the IRF QRP requirements.
    As discussed in section VIII.D. of this proposed rule, we are 
proposing to add two measures to the IRF QRP (1) Transfer of Health 
Information to the Provider--Post-Acute Care (PAC); and (2) Transfer of 
Health Information to the Patient--Post-Acute Care (PAC), beginning 
with the FY 2022 IRF QRP. We are also proposing to add standardized 
patient assessment data elements, as discussed in section IV.G of this 
proposed rule. We describe the estimated burden and cost reductions for 
both of these measures in section VIII.C of this proposed rule. In 
summary, the proposed changes to the IRF QRP will result in a burden 
addition of $7,806 per IRF annually, and $8,119,276 for all IRFs 
annually.
    We intend to continue to closely monitor the effects of the IRF QRP 
on IRFs and to help perpetuate successful reporting outcomes through 
ongoing stakeholder education, national trainings, IRF announcements, 
website postings, CMS Open Door Forums, and general and technical help 
desks.

D. Alternatives Considered

    The following is a discussion of the alternatives considered for 
the IRF PPS updates contained in this proposed rule.
    Section 1886(j)(3)(C) of the Act requires the Secretary to update 
the IRF PPS payment rates by an increase factor that reflects changes 
over time in the prices of an appropriate mix of goods and services 
included in the covered IRF services.
    We are proposing a market basket increase factor for FY 2020 that 
is based on a proposed rebased market basket reflecting a 2016 base 
year. We considered the alternative of continuing to use the IRF market 
basket without rebasing to determine the market basket increase factor 
for FY 2020. However, we typically rebase and revise the market baskets 
for the various PPS every 4 to 5 years so that the cost weights and 
price proxies reflect more recent data. Therefore, we believe it is 
more technically appropriate to use a 2016-based IRF market basket 
since it allows for the FY 2020 market basket increase factor to 
reflect a more up-to-date cost structure experienced by IRFs.
    As noted previously in this proposed rule, section 
1886(j)(3)(C)(ii)(I) of the Act requires the Secretary to apply a 
productivity adjustment to the market basket increase factor for FY 
2020. Thus, in accordance with section 1886(j)(3)(C) of the Act, we 
propose to update the IRF prospective payments in this proposed rule by 
2.5 percent (which equals the

[[Page 17334]]

proposed 3.0 percent estimated IRF market basket increase factor for FY 
2020 reduced by a 0.5 percentage point proposed productivity adjustment 
as determined under section 1886(b)(3)(B)(xi)(II) of the Act (as 
required by section 1886(j)(3)(C)(ii)(I) of the Act)).
    As we finalized in the FY 2019 IRF PPS final rule (83 FR 38514) use 
of the Quality Indicators items in determining payment and the 
associated CMG and CMG relative weight revisions using two years of 
data (FY 2017 and FY 2018) beginning with FY 2020, we did not consider 
any alternative to proposing these changes.
    However, we did consider whether or not to apply a weighting 
methodology to the IRF motor score that was finalized in the FY 2019 
IRF PPS final rule (83 FR 38514) to assign patients to CMGs beginning 
in FY 2020. In light of recent analysis that indicates that weighting 
the motor score would improve the accuracy of payments under the IRF 
PPS, we believe that it is appropriate to propose to weight the motor 
score that would be effective on October 1, 2019.
    We considered not removing the item GG0170A1 Roll left and right 
from the composition of the motor score. However, this item did not 
behave as expected in the models considered to develop the weights. 
Therefore, we believe it is appropriate to propose to remove this item 
from the construction of the weighted motor score.
    We considered updating facility-level adjustment factors for FY 
2020. However, as discussed in more detail in the FY 2015 final rule 
(79 FR 45872), we believe that freezing the facility-level adjustments 
at FY 2014 levels for FY 2015 and all subsequent years (unless and 
until the data indicate that they need to be further updated) will 
allow us an opportunity to monitor the effects of the substantial 
changes to the adjustment factors for FY 2014, and will allow IRFs time 
to adjust to the previous changes.
    We considered not updating the IRF wage index to use the concurrent 
fiscal year's IPPS wage index and instead continuing to use a one-year 
lag of the IPPS wage index. However, we believe that updating the IRF 
wage index based on the concurrent year's IPPS wage index will better 
align the data across acute and post-acute care settings in support of 
our efforts to move toward more unified Medicare payments across post-
acute care settings.
    We considered maintaining the existing outlier threshold amount for 
FY 2020. However, analysis of updated FY 2020 data indicates that 
estimated outlier payments would be higher than 3 percent of total 
estimated payments for FY 2020, by approximately 0.2 percent, unless we 
updated the outlier threshold amount. Consequently, we propose 
adjusting the outlier threshold amount in this proposed rule to reflect 
a 0.2 percent decrease thereby setting the total outlier payments equal 
to 3 percent, instead of 3.2 percent, of aggregate estimated payments 
in FY 2020.
    We considered not amending Sec.  412.622(a)(3)(iv) to clarify that 
the determination as to whether a physician qualifies as a 
rehabilitation physician (that is, a licensed physician with 
specialized training and experience in inpatient rehabilitation is made 
by the IRF. However, we believe that it is important to clarify this 
definition to ensure that IRF providers and Medicare contractors have a 
shared understanding of these regulatory requirements.

E. Regulatory Review Costs

    If regulations impose administrative costs on private entities, 
such as the time needed to read and interpret this proposed rule, we 
should estimate the cost associated with regulatory review. Due to the 
uncertainty involved with accurately quantifying the number of entities 
that will review the rule, we assume that the total number of unique 
commenters on the FY 2019 IRF PPS proposed rule will be the number of 
reviewers of this proposed rule. We acknowledge that this assumption 
may understate or overstate the costs of reviewing this proposed rule. 
It is possible that not all commenters reviewed the FY 2019 IRF PPS 
proposed rule in detail, and it is also possible that some reviewers 
chose not to comment on the proposed rule. For these reasons we thought 
that the number of past commenters would be a fair estimate of the 
number of reviewers of this proposed rule.
    We also recognize that different types of entities are in many 
cases affected by mutually exclusive sections of this proposed rule, 
and therefore for the purposes of our estimate we assume that each 
reviewer reads approximately 50 percent of the rule. We sought comments 
on this assumption.
    Using the wage information from the BLS for medical and health 
service managers (Code 11-9111), we estimate that the cost of reviewing 
this rule is $107.38 per hour, including overhead and fringe benefits 
(https://www.bls.gov/oes/current/oes_nat.htm). Assuming an average 
reading speed, we estimate that it would take approximately 2 hours for 
the staff to review half of this proposed rule. For each IRF that 
reviews the rule, the estimated cost is $214.76 (2 hours x $107.38). 
Therefore, we estimate that the total cost of reviewing this regulation 
is $23,194.08 ($214.76 x 108 reviewers).

F. Accounting Statement and Table

    As required by OMB Circular A-4 (available at https://www.whitehouse.gov/sites/default/files/omb/assets/omb/circulars/a004/a-4.pdf), in Table 23, we have prepared an accounting statement showing 
the classification of the expenditures associated with the provisions 
of this proposed rule. Table 23 provides our best estimate of the 
increase in Medicare payments under the IRF PPS as a result of the 
proposed updates presented in this proposed rule based on the data for 
1,119 IRFs in our database. In addition, Table 23 presents the costs 
associated with the new IRF quality reporting program requirements for 
FY 2020.
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G. Conclusion

    Overall, the estimated payments per discharge for IRFs in FY 2020 
are projected to increase by 2.3 percent, compared with the estimated 
payments in FY 2019, as reflected in column 7 of Table 22.
    IRF payments per discharge are estimated to increase by 2.2 percent 
in urban areas and 4.3 percent in rural areas, compared with estimated 
FY 2019 payments. Payments per discharge to rehabilitation units are 
estimated to increase 4.8 percent in urban areas and 5.6 percent in 
rural areas. Payments per discharge to freestanding rehabilitation 
hospitals are estimated to increase 0.0 percent in urban areas and 
decrease 2.0 percent in rural areas.
    Overall, IRFs are estimated to experience a net increase in 
payments as a result of the proposed policies in this proposed rule. 
The largest payment increase is estimated to be a 6.9 percent increase 
for rural government IRFs. The analysis above, together with the 
remainder of this preamble, provides a Regulatory Impact Analysis.
    In accordance with the provisions of Executive Order 12866, this 
regulation was reviewed by the Office of Management and Budget.

List of Subjects in 42 CFR Part 412

    Administrative practice and procedure, Health facilities, Medicare, 
Puerto Rico, Reporting and recordkeeping requirements.

    For the reasons set forth in the preamble, the Centers for Medicare 
& Medicaid Services proposes to amend 42 CFR chapter IV as follows:

PART 412--PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL 
SERVICES

0
1. The authority citation for part 412 is revised to read as follows:

    Authority:  42 U.S.C. 1302 and 1395hh.

0
2. Section 412.622 is amended by--
0
a. Revising paragraphs (a)(3)(iv), (a)(4)(i)(A), (a)(4)(iii)(A), and 
(a)(5)(i); and
0
b. Adding paragraph (c).
    The revisions and addition read as follows:


Sec.  412.622  Basis of payment.

    (a) * * *
    (3) * * *
    (iv) Requires physician supervision by a rehabilitation physician. 
The requirement for medical supervision means that the rehabilitation 
physician must conduct face-to-face visits with the patient at least 3 
days per week throughout the patient's stay in the IRF to assess the 
patient both medically and functionally, as well as to modify the 
course of treatment as needed to maximize the patient's capacity to 
benefit from the rehabilitation process. The post-admission physician 
evaluation described in paragraph (a)(4)(ii) of this section may count 
as one of the face-to-face visits.
    (4) * * *
    (i) * * *
    (A) It is conducted by a licensed or certified clinician(s) 
designated by a rehabilitation physician within the 48 hours 
immediately preceding the IRF admission. A preadmission screening that 
includes all of the required elements, but that is conducted more than 
48 hours immediately preceding the IRF admission, will be accepted as 
long as an update is conducted in person or by telephone to update the 
patient's medical and functional status within the 48 hours immediately 
preceding the IRF admission and is documented in the patient's medical 
record.
* * * * *
    (iii) * * *
    (A) It is developed by a rehabilitation physician with input from 
the interdisciplinary team within 4 days of the patient's admission to 
the IRF.
* * * * *
    (5) * * *
    (i) The team meetings are led by a rehabilitation physician and 
further consist of a registered nurse with specialized training or 
experience in rehabilitation; a social worker or case manager (or 
both); and a licensed or certified therapist from each therapy 
discipline involved in treating the patient. All team members must have 
current knowledge of the patient's medical and functional status. The 
rehabilitation physician may lead the interdisciplinary team meeting 
remotely via a mode of communication such as video or telephone 
conferencing.
* * * * *
    (c) Definitions. As used in this section--
    Rehabilitation physician means a licensed physician who is 
determined by the IRF to have specialized training and experience in 
inpatient rehabilitation.
0
3. Section 412.634 is amended by revising paragraphs (a)(1), (d)(1) and 
(5), and (f)(1) to read as follows:


Sec.  412.634  Requirements under the Inpatient Rehabilitation Facility 
(IRF) Quality Reporting Program (QRP).

    (a) * * *
    (1) For the FY 2018 payment determination and subsequent years, an 
IRF must begin reporting data under the IRF QRP requirements no later 
than the first day of the calendar quarter subsequent to 30 days after 
the date on its CMS Certification Number (CCN) notification letter, 
which designates the IRF as operating in the CMS designated data 
submission system.
* * * * *
    (d) * * *
    (1) IRFs that do not meet the requirement in paragraph (b) of this 
section for a program year will receive a written notification of non-
compliance through at least one of the following methods: The CMS 
designated data submission system, the United States Postal Service, or 
via an email from the Medicare Administrative Contractor (MAC).
* * * * *
    (5) CMS will notify IRFs, in writing, of its final decision 
regarding any reconsideration request through at least one of the 
following methods: The CMS designated data submission system, the 
United States Postal Service, or via an email from the Medicare 
Administrative Contractor (MAC).
* * * * *
    (f) * * *
    (1) IRFs must meet or exceed two separate data completeness 
thresholds: One threshold set at 95 percent for completion of required 
quality measures data and standardized patient assessment data 
collected using the IRF-PAI submitted through the CMS designated data 
submission system; and a second threshold set at 100 percent for 
measures data collected and submitted using the CDC NHSN.
* * * * *

    Dated: March 26, 2019.
Seema Verma,
Administrator, Centers for Medicare & Medicaid Services.
    Dated: March 28, 2019.
Alex M. Azar II,
Secretary, Department of Health and Human Services.
[FR Doc. 2019-07885 Filed 4-17-19; 4:15 pm]
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