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Proposed Rule2022-07019

Medicare Program; Inpatient Rehabilitation Facility Prospective Payment System for Federal Fiscal Year 2023 and Updates to the IRF Quality Reporting Program

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Published

April 6, 2022

Issuing agencies

Health and Human Services DepartmentCenters for Medicare & Medicaid Services

Abstract

This rulemaking proposes updating the prospective payment rates for inpatient rehabilitation facilities (IRFs) for Federal fiscal year (FY) 2023. As required by statute, this proposed rule includes the classification and weighting factors for the IRF prospective payment system's case-mix groups and a description of the methodologies and data used in computing the prospective payment rates for FY 2023. In addition, we are proposing to codify CMS' existing teaching status adjustment policy through proposed amendments to the regulation text and proposing to update and clarify the IRF teaching policy with respect to IRF hospital closures and displaced residents. In this proposed rule, we are also soliciting comments on the methodology for updating the facility level adjustment factors. Additionally, we are soliciting comments regarding the IRF transfer payment policy. This rule proposes to establish a permanent cap policy to smooth the impact of year-to-year changes in IRF payments related to changes in the IRF wage index. This proposed rule also includes updates for the IRF Quality Reporting Program (QRP).

Full Text

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[Federal Register Volume 87, Number 66 (Wednesday, April 6, 2022)]
[Proposed Rules]
[Pages 20218-20266]
From the Federal Register Online via the Government Publishing Office [<a href="http://www.gpo.gov">www.gpo.gov</a>]
[FR Doc No: 2022-07019]

[[Page 20217]]

Vol. 87

Wednesday,

No. 66

April 6, 2022

Part III

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 Prospective Payment
System for Federal Fiscal Year 2023 and Updates to the IRF Quality
Reporting Program; Proposed Rule

Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 /
Proposed Rules

[[Page 20218]]

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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Medicare & Medicaid Services

42 CFR Part 412

[CMS-1767-P]
RIN 0938-AU78

Medicare Program; Inpatient Rehabilitation Facility Prospective
Payment System for Federal Fiscal Year 2023 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 rulemaking proposes updating the prospective payment
rates for inpatient rehabilitation facilities (IRFs) for Federal fiscal
year (FY) 2023. As required by statute, this proposed rule includes the
classification and weighting factors for the IRF prospective payment
system's case-mix groups and a description of the methodologies and
data used in computing the prospective payment rates for FY 2023. In
addition, we are proposing to codify CMS' existing teaching status
adjustment policy through proposed amendments to the regulation text
and proposing to update and clarify the IRF teaching policy with
respect to IRF hospital closures and displaced residents. In this
proposed rule, we are also soliciting comments on the methodology for
updating the facility level adjustment factors. Additionally, we are
soliciting comments regarding the IRF transfer payment policy. This
rule proposes to establish a permanent cap policy to smooth the impact
of year-to-year changes in IRF payments related to changes in the IRF
wage index. This proposed rule also includes updates for the IRF
Quality Reporting Program (QRP).

DATES: To be assured consideration, comments must be received at one of
the addresses provided below, no later than 5 p.m. on May 31, 2022.

ADDRESSES: In commenting, please refer to file code CMS-1767-P.
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 <a href="http://www.regulations.gov">http://www.regulations.gov</a>. 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-1767-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-1767-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 Cooksey, (410) 786-0179, for information about the IRF
payment policies and payment rates.
Kim Schwartz, (410) 786-2571 and Gwendolyn Johnson, (410) 786-6954,
for information about the IRF coverage policies.
Ariel Cress, (410) 786-8571, for information about the IRF quality
reporting program.

SUPPLEMENTARY INFORMATION:
Inspection of Public Comments: All comments received before the
close of the comment period are available for viewing by the public,
including any personally identifiable or confidential business
information that is included in a comment. We post all comments
received before the close of the comment period on the following
website as soon as possible after they have been received: <a href="http://www.regulations.gov">http://www.regulations.gov</a>. Follow the search instructions on that website to
view public comments. CMS will not post on <a href="http://Regulations.gov">Regulations.gov</a> public
comments that make threats to individuals or institutions or suggest
that the individual will take actions to harm the individual. CMS
continues to encourage individuals not to submit duplicative comments.
We will post acceptable comments from multiple unique commenters even
if the content is identical or nearly identical to other comments.

Availability of Certain Information Through the Internet on the CMS
Website

The IRF prospective payment system (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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS</a>.
We note that prior to 2020, each rule or notice issued under the
IRF PPS has included a detailed reiteration of the various regulatory
provisions that have affected the IRF PPS over the years. That
discussion, along with detailed background information for various
other aspects of the IRF PPS, is now available on the CMS website at
<a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS</a>.

I. Executive Summary

A. Purpose

This rulemaking proposes updating the prospective payment rates for
IRFs for FY 2023 (that is, for discharges occurring on or after October
1, 2022, and on or before September 30, 2023) as required under section
1886(j)(3)(C) of the Social Security Act (the Act). As required by
section 1886(j)(5) of the Act, this proposed rule includes the
classification and weighting factors for the IRF PPS's case-mix groups
(CMGs) and a description of the methodologies and data used in
computing the prospective payment rates for FY 2023. This proposed rule
proposes to codify CMS' existing teaching status adjustment policy
through proposed amendments to the regulation text and proposes to
update and clarify the IRF teaching policy with respect to IRF hospital
closures and displaced residents. We are also soliciting comments on
the methodology for updating the facility level adjustment factors.
Additionally, we are soliciting comments regarding the IRF transfer
payment policy. We are also proposing to establish a permanent cap
policy to smooth the impact of year-to-year changes in IRF payments
related to changes in the IRF wage index. This rule also proposes to
require quality data reporting on all IRF patients beginning with the
FY 2025 IRF QRP and amend the regulations consistent with the proposed
requirements. This rule also proposes to correct an error in the
regulations text at Sec. 412.614(d)(2). Finally, we are seeking
comment on three issues: (1) Future measure concepts under
consideration for the IRF QRP; (2) a future dQM for the IRF QRP; and
(3) overarching principles for measuring equity and health quality
disparities across CMS Quality Programs, including the IRF QRP.

B. Summary of Major Provisions

In this proposed rule, we use the methods described in the FY 2022
IRF

[[Page 20219]]

PPS final rule (86 FR 42362) to update the prospective payment rates
for FY 2023 using updated FY 2021 IRF claims and the most recent
available IRF cost report data, which is FY 2020 IRF cost report data.
This proposed rule proposes to codify CMS' existing teaching status
adjustment policy through proposed amendments to the regulation text
and proposes to update and clarify the IRF teaching status adjustment
policy with respect to IRF hospital closures and displaced residents.
We are also soliciting comments on the methodology for updating the
facility level adjustment factors. Additionally, we are soliciting
comments regarding the IRF transfer payment policy.
We are also proposing to establish a permanent cap policy to smooth
the impact of year-to-year changes in IRF payments related to changes
in the IRF wage index. This rule also proposes to collect quality
reporting data for all IRF patients beginning with the FY 2025 IRF QRP
and revise the regulations. Finally, we are seeking comment on three
issues: (1) Future measure concepts for the IRF QRP; (2) a future
digital quality measure (dQM) for the IRF QRP; and (3) overarching
principles for measuring equity and health quality disparities across
CMS Quality Programs, including the IRF QRP.

C. Summary of Impact
[GRAPHIC] [TIFF OMITTED] TP06AP22.009

II. Background

A. Statutory Basis and Scope for IRF PPS Provisions

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. 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) and we provided
a general description of the IRF PPS for FYs 2007 through 2019 in the
FY 2020 IRF PPS final rule (84 FR 39055 through 39057). A general
description of the IRF PPS for FYs 2020 through 2022, along with
detailed background information for various other aspects of the IRF
PPS, is now available on the CMS website at <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS</a>.
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's) 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

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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.
The regulatory history previously included in each rule or notice
issued under the IRF PPS, including a general description of the IRF
PPS for FYs 2007 through 2020, is available on the CMS website at
<a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS</a>.
In late 2019,\1\ the United States began responding to an outbreak
of a virus named ``SARS-CoV-2'' and the disease it causes, which is
named ``coronavirus disease 2019'' (abbreviated ``COVID-19''). Due to
our prioritizing efforts in support of containing and combatting the
PHE for COVID-19, and devoting significant resources to that end, we
published two interim final rules with comment period affecting IRF
payment and conditions for participation. The interim final rule with
comment period (IFC) entitled, ``Medicare and Medicaid Programs; Policy
and Regulatory Revisions in Response to the COVID-19 Public Health
Emergency'', published on April 6, 2020 (85 FR 19230) (hereinafter
referred to as the April 6, 2020 IFC), included certain changes to the
IRF PPS medical supervision requirements at 42 CFR 412.622(a)(3)(iv)
and 412.29(e) during the PHE for COVID-19. In addition, in the April 6,
2020 IFC, we removed the post-admission physician evaluation
requirement at Sec. 412.622(a)(4)(ii) for all IRFs during the PHE for
COVID-19. In the FY 2021 IRF PPS final rule, to ease documentation and
administrative burden, we also removed the post-admission physician
evaluation documentation requirement at 42 CFR 412.622(a)(4)(ii)
permanently beginning in FY 2021.
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\1\ Patel A, Jernigan DB. Initial Public Health Response and
Interim Clinical Guidance for the 2019 Novel Coronavirus Outbreak--
United States, December 31, 2019-February 4, 2020. MMWR Morb Mortal
Wkly Rep 2020;69:140-146. DOI <a href="http://dx.doi.org/10.15585/mmwr.mm6905e1">http://dx.doi.org/10.15585/mmwr.mm6905e1</a>.
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A second IFC entitled, ``Medicare and Medicaid Programs, Basic
Health Program, and Exchanges; Additional Policy and Regulatory
Revisions in Response to the COVID-19 Public Health Emergency and Delay
of Certain Reporting Requirements for the Skilled Nursing Facility
Quality Reporting Program'' was published on May 8, 2020 (85 FR 27550)
(hereinafter referred to as the May 8, 2020 IFC). Among other changes,
the May 8, 2020 IFC included a waiver of the ``3-hour rule'' at Sec.
412.622(a)(3)(ii) to reflect the waiver required by section 3711(a) of
the Coronavirus Aid, Relief, and Economic Security Act (CARES Act)
(Pub. L. 116-136, enacted on March 27, 2020). In the May 8, 2020 IFC,
we also modified certain IRF coverage and classification requirements
for freestanding IRF hospitals to relieve acute care hospital capacity
concerns in States (or regions, as applicable) experiencing a surge
during the PHE for COVID-19. In addition to the policies adopted in our
IFCs, we responded to the PHE with numerous blanket waivers \2\ and
other flexibilities,\3\ some of which are applicable to the IRF PPS.
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\2\ CMS, ``COVID-19 Emergency Declaration Blanket Waivers for
Health Care Providers,'' (updated Feb. 19 2021) (available at
<a href="https://www.cms.gov/files/document/summary-covid-19-emergency-declaration-waivers.pdf">https://www.cms.gov/files/document/summary-covid-19-emergency-declaration-waivers.pdf</a>).
\3\ CMS, ``COVID-19 Frequently Asked Questions (FAQs) on
Medicare Fee-for-Service (FFS) Billing,'' (updated March 5, 2021)
(available at <a href="https://www.cms.gov/files/document/03092020-covid-19-faqs-508.pdf">https://www.cms.gov/files/document/03092020-covid-19-faqs-508.pdf</a>).
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B. Provisions of the PPACA and the Medicare Access and CHIP
Reauthorization Act of 2015 (MACRA) Affecting the IRF PPS in FY 2012
and Beyond

The Patient Protection and Affordable Care Act (PPACA) (Pub. L.
111-148) was enacted on March 23, 2010. The Health Care and Education
Reconciliation Act of 2010 (Pub. L. 111-152), which amended and revised
several provisions of the PPACA, was enacted on March 30, 2010. In this
proposed rule, we refer to the two statutes collectively as the
``Patient Protection and Affordable Care Act'' or ``PPACA''.
The PPACA included several provisions that affect the IRF PPS in
FYs 2012 and beyond. In addition to what was previously discussed,
section 3401(d) of the PPACA also added section 1886(j)(3)(C)(ii)(I) of
the Act (providing for a ``productivity adjustment'' for FY 2012 and
each subsequent FY). The productivity adjustment for FY 2023 is
discussed in section V.B. 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 FY and in payment rates for a FY
being less than such payment rates for the preceding FY.
Sections 3004(b) of the PPACA and section 411(b) of the MACRA (Pub.
L. 114-10, enacted on April 16, 2015) also addressed the IRF PPS.
Section 3004(b) of PPACA reassigned the previously designated section
1886(j)(7) of the Act to section 1886(j)(8) of the Act and inserted a
new section 1886(j)(7) of the Act, which contains requirements for the
Secretary to establish a QRP for IRFs. Under that program, data must be
submitted in a form and manner and at a time specified by the
Secretary. Beginning in FY 2014, section 1886(j)(7)(A)(i) of the Act
requires the application of a 2-percentage point reduction to the
market basket increase factor otherwise applicable to an IRF (after
application of paragraphs (C)(iii) and (D) of section 1886(j)(3) of the
Act) for a FY if the IRF does not comply with the requirements of the
IRF QRP for that FY. Application of the 2-percentage point reduction
may result in an update that is less than 0.0 for a FY and in payment
rates for a FY being less than such payment rates for the preceding FY.
Reporting-based reductions to the market basket increase factor are not
cumulative; they only apply for the FY involved. Section 411(b) of the
MACRA amended section 1886(j)(3)(C) of the Act by adding paragraph
(iii), which required us to apply for FY 2018, after the application of
section 1886(j)(3)(C)(ii) of the Act, an increase factor of 1.0 percent
to update the IRF prospective payment rates.

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

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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. A free download of the Grouper
software is available on the CMS website at <a href="http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Software.html">http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Software.html</a>. The Grouper software is also embedded in the internet
Quality Improvement and Evaluation System (iQIES) User tool available
in iQIES at <a href="https://www.cms.gov/medicare/quality-safety-oversight-general-information/iqies">https://www.cms.gov/medicare/quality-safety-oversight-general-information/iqies</a>.
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 (HIPAA) (Pub. L. 104-191, enacted on August 21, 1996) -
compliant electronic claim or, if the Administrative Simplification
Compliance Act of 2002 (ASCA) (Pub. L. 107-105, enacted on December 27,
2002) permits, a paper claim (a UB-04 or a CMS-1450 as appropriate)
using the five-character CMG number and sends it to the appropriate
Medicare Administrative Contractor (MAC). In addition, once a MA
patient is discharged, in accordance with the Medicare Claims
Processing Manual, chapter 3, section 20.3 (Pub. 100-04), hospitals
(including IRFs) must submit an informational-only bill (type of bill
(TOB) 111), which includes Condition Code 04 to their MAC. This will
ensure that the MA days are included in the hospital's Supplemental
Security Income (SSI) ratio (used in calculating the IRF LIP
adjustment) for FY 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 <a href="http://www.cms.gov/manuals/downloads/clm104c25.pdf">http://www.cms.gov/manuals/downloads/clm104c25.pdf</a>.
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 healthcare providers, to conduct covered
electronic transactions according to the applicable transaction
standards. (See the CMS program claim memoranda at <a href="http://www.cms.gov/ElectronicBillingEDITrans/">http://www.cms.gov/ElectronicBillingEDITrans/</a> 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 and patient access
to their electronic health information.
To further interoperability in post-acute care settings, CMS and
the Office of the National Coordinator for Health Information
Technology (ONC) participate in the Post-Acute Care Interoperability
Workgroup (PACIO) to facilitate collaboration with industry
stakeholders to develop Fast Healthcare Interoperability
Resources[supreg] (FHIR) standards. These standards could support the
exchange and reuse of patient assessment data derived from the post-
acute care (PAC) setting assessment tools, such as the Minimum Data Set
(MDS), Inpatient Rehabilitation Facility-Patient Assessment Instrument
(IRF-PAI), Long Term Care Hospital (LTCH) Continuity Assessment Record
and Evaluation (CARE) Data Set (LCDS), Outcome and Assessment
Information Set (OASIS), and other sources.4 5 The PACIO
Project has focused on HL7 FHIR implementation guides for functional
status, cognitive status and new use cases on advance directives, re-
assessment timepoints, and Speech, Language, Swallowing, Cognitive
communication and Hearing (SPLASCH) pathology.\6\ We encourage PAC
provider and health information technology (IT) vendor participation as
the efforts advance.
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\4\ HL7 FHIR Release 4. Available at <a href="https://www.hl7.org/fhir/">https://www.hl7.org/fhir/</a>.
\5\ HL7 FHIR. PACIO Functional Status Implementation Guide.
Available at <a href="https://paciowg.github.io/functional-status-ig/">https://paciowg.github.io/functional-status-ig/</a>.
\6\ The IMPACT Act (Pub. L. 113-185) requires the reporting of
standardized patient assessment data with regard to quality measures
and standardized patient assessment data elements. The Act also
requires the submission of data pertaining to measure domains of
resource use, and other domains. In addition, the IMPACT Act
requires assessment data to be standardized and interoperable to
allow for exchange of the data among post-acute providers and other
providers. The Act intends for standardized post-acute care data to
improve Medicare beneficiary outcomes through shared-decision
making, care coordination, and enhanced discharge planning.
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The CMS Data Element Library (DEL) continues to be updated and
serves as a resource for PAC assessment data elements and their
associated mappings to health IT standards, such as Logical Observation
Identifiers Names and Codes (LOINC) and Systematized Nomenclature of
Medicine Clinical Terms (SNOMED).\7\ The DEL furthers CMS' goal of data
standardization and interoperability. These interoperable data elements
can reduce provider burden by allowing the use and exchange of
healthcare data; supporting provider exchange of electronic health
information for care coordination, person-centered care; and supporting
real-time, data driven, clinical decision-making.8 9
Standards in the DEL can be

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referenced on the CMS website (<a href="https://del.cms.gov/DELWeb/pubHome">https://del.cms.gov/DELWeb/pubHome</a>) and
in the ONC Interoperability Standards Advisory (ISA). The 2022 ISA is
available at <a href="https://www.healthit.gov/isa/sites/isa/files/inline-files/2022-ISA-Reference-Edition.pdf">https://www.healthit.gov/isa/sites/isa/files/inline-files/2022-ISA-Reference-Edition.pdf</a>.
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\7\ Centers for Medicare & Medicaid Services. Newsroom. Fact
sheet: CMS Data Element Library Fact Sheet. June 21, 2018. Available
at <a href="https://www.cms.gov/newsroom/fact-sheets/cms-data-element-library-fact-sheet">https://www.cms.gov/newsroom/fact-sheets/cms-data-element-library-fact-sheet</a>.
\8\ Centers for Medicare & Medicaid Services. Health Informatics
and Interoperability Group. Policies and Technology for
Interoperability and Burden Reduction. Available at <a href="https://www.cms.gov/Regulations-and-Guidance/Guidance/Interoperability/index">https://www.cms.gov/Regulations-and-Guidance/Guidance/Interoperability/index</a>.
\9\ Bates, David W, and Lipika Samal. ``Interoperability: What
Is It, How Can We Make It Work for Clinicians, and How Should We
Measure It in the Future?.'' Health services research vol. 53,5
(2018): 3270-3277. doi:10.1111/1475-6773.12852.
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The 21st Century Cures Act (Cures Act), (Pub L. 114-255, enacted
December 13, 2016) requires HHS to take new steps to enable the
electronic sharing of health information and to further
interoperability for providers and settings across the care continuum.
Section 4003 of the Cures Act required HHS to take steps to advance
interoperability through the development of a trusted exchange
framework and common agreement aimed at establishing a universal floor
of interoperability across the country. On January 18, 2022, ONC
announced a significant milestone by releasing the Trusted Exchange
Framework and Common Agreement Version 1. The Trusted Exchange
Framework is a set of non-binding principles for health information
exchange, and the Common Agreement is a contract that advances those
principles. The Common Agreement and the incorporated by reference
Qualified Health Information Network Technical Framework Version 1
establish the technical infrastructure model and governing approach for
different health information networks and their users to securely share
clinical information with each other, all under commonly agreed to
terms. The Common Agreement follows a network-of-networks structure,
which allows for connection at different levels and is inclusive of
many different types of entities, such as health information networks,
healthcare practices, hospitals, public health agencies, and Individual
Access Services (IAS) Providers.\10\ For more information, we refer
readers to <a href="https://www.healthit.gov/topic/interoperability/trusted-exchange-framework-and-common-agreement">https://www.healthit.gov/topic/interoperability/trusted-exchange-framework-and-common-agreement</a>.
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\10\ The Common Agreement defines Individual Access Services
(IAS) as ``with respect to the Exchange Purposes definition, the
services provided utilizing the Connectivity Services, to the extent
consistent with Applicable Law, to an Individual with whom the QHIN,
Participant, or Subparticipant has a Direct Relationship to satisfy
that Individual's ability to access, inspect, or obtain a copy of
that Individual's Required Information that is then maintained by or
for any QHIN, Participant, or Subparticipant.'' The Common Agreement
defines ``IAS Provider'' as: ``Each QHIN, Participant, and
Subparticipant that offers Individual Access Services.'' See Common
Agreement for Nationwide Health Information Interoperability Version
1, at 7 (Jan. 2022), <a href="https://www.healthit.gov/sites/default/files/page/2022-01/Common_Agreement_for_Nationwide_Health_Information_Interoperability_Version_1.pdf">https://www.healthit.gov/sites/default/files/page/2022-01/Common_Agreement_for_Nationwide_Health_Information_Interoperability_Version_1.pdf</a>.
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We invite providers to learn more about these important
developments and how they are likely to affect IRFs.

III. Summary of Provisions of the Proposed Rule

In this proposed rule, we are proposing to update the IRF PPS for
FY 2023 and the IRF QRP for FY 2025.
The proposed policy changes and updates to the IRF prospective
payment rates for FY 2023 are as follows:
<bullet> Update the CMG relative weights and average length of stay
values for FY 2023, in a budget neutral manner, as discussed in section
IV. of this proposed rule.
<bullet> Update the IRF PPS payment rates for FY 2023 by the market
basket increase factor, based upon the most current data available,
with a productivity adjustment required by section 1886(j)(3)(C)(ii)(I)
of the Act, as described in section V. of this proposed rule.
<bullet> Describe the establishment of a permanent cap policy in
order to smooth the impact of year-to-year changes in IRF payments
related to certain changes to the IRF wage index, as discussed in
section V. of this proposed rule.
<bullet> Update the FY 2023 IRF PPS payment rates by the FY 2023
wage index and the labor-related share in a budget-neutral manner, as
discussed in section V. of this proposed rule.
<bullet> Describe the calculation of the IRF standard payment
conversion factor for FY 2023, as discussed in section V. of this
proposed rule.
<bullet> Update the outlier threshold amount for FY 2023, as
discussed in section VI. of this proposed rule.
<bullet> Update the cost-to-charge ratio (CCR) ceiling and urban/
rural average CCRs for FY 2023, as discussed in section VI. of this
proposed rule.
<bullet> Describe the proposed codification of CMS' existing
teaching status adjustment policy and proposed clarifications and
updates of the IRF teaching status adjustment policy with respect to
IRF hospital closures and displaced residents, as discussed in section
VII. of this proposed rule.
<bullet> Solicit comments on the methodology used to update the
facility-level adjustment factors, as discussed in section VIII. of
this proposed rule.
<bullet> Solicit comments on the IRF transfer payment policy, as
discussed in section IX. of this proposed rule.
We also propose updates to the IRF QRP and request information in
section VII. of this proposed rule as follows:
<bullet> Update data reporting requirements under the IRF QRP
beginning with FY 2025.
<bullet> Request information on (1) future measure concepts under
consideration for the IRF QRP; (2) inclusion of a future dQM for the
IRF QRP; and (3) CMS' overarching principles for measuring healthcare
disparities across CMS Quality Programs, including the IRF QRP.

IV. Proposed Update to the Case-Mix Group (CMG) Relative Weights and
Average Length of Stay (ALOS) Values for FY 2023

As specified in Sec. 412.620(b)(1), we calculate a relative weight
for each CMG that is proportional to the resources needed by an average
inpatient rehabilitation case in that CMG. For example, cases in a CMG
with a relative weight of 2, on average, will cost twice as much as
cases in a CMG with a relative weight of 1. Relative weights account
for the variance in cost per discharge due to the variance in resource
utilization among the payment groups, and their use helps to ensure
that IRF PPS payments support beneficiary access to care, as well as
provider efficiency.
In this proposed rule, we propose to update the CMG relative
weights and ALOS values for FY 2023. Typically, we use the most recent
available data to update the CMG relative weights and average lengths
of stay. For FY 2023, we are proposing to use the FY 2021 IRF claims
and FY 2020 IRF cost report data. These data are the most current and
complete data available at this time. Currently, only a small portion
of the FY 2021 IRF cost report data are available for analysis, but the
majority of the FY 2021 IRF claims data are available for analysis. We
are proposing that if more recent data become available after the
publication of this proposed rule and before the publication of the
final rule, we would use such data to determine the FY 2023 CMG
relative weights and ALOS values in the final rule.
We are proposing to apply these data using the same methodologies
that we have used to update the CMG relative weights and ALOS values
each FY since we implemented an update to the methodology. The detailed
CCR data from the cost reports of IRF provider units of primary acute
care hospitals is used for this methodology, instead of CCR data from
the associated primary care hospitals, to calculate IRFs' average costs
per case, as discussed in the FY 2009 IRF PPS final rule (73 FR 46372).
In calculating the CMG relative weights, we use a hospital-specific
relative value

[[Page 20223]]

method to estimate operating (routine and ancillary services) and
capital costs of IRFs. The process to calculate the CMG relative
weights for this proposed rule is as follows:
Step 1. We estimate the effects that comorbidities have on costs.
Step 2. We adjust the cost of each Medicare discharge (case) to
reflect the effects found in the first step.
Step 3. We use the adjusted costs from the second step to calculate
CMG relative weights, using the hospital-specific relative value
method.
Step 4. We normalize the FY 2023 CMG relative weights to the same
average CMG relative weight from the CMG relative weights implemented
in the FY 2022 IRF PPS final rule (86 FR 42362).
Consistent with the methodology that we have used to update the IRF
classification system in each instance in the past, we propose to
update the CMG relative weights for FY 2023 in such a way that total
estimated aggregate payments to IRFs for FY 2023 are the same with or
without the changes (that is, in a budget-neutral manner) by applying a
budget neutrality factor to the standard payment amount. To calculate
the appropriate budget neutrality factor for use in updating the FY
2023 CMG relative weights, we use the following steps:
Step 1. Calculate the estimated total amount of IRF PPS payments
for FY 2023 (with no changes to the CMG relative weights).
Step 2. Calculate the estimated total amount of IRF PPS payments
for FY 2023 by applying the proposed changes to the CMG relative
weights (as discussed 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 of
0.9979 that would maintain the same total estimated aggregate payments
in FY 2023 with and without the proposed changes to the CMG relative
weights.
Step 4. Apply the budget neutrality factor from step 3 to the FY
2023 IRF PPS standard payment amount after the application of the
budget-neutral wage adjustment factor.
In section V.E. of this proposed rule, we discuss the proposed use
of the existing methodology to calculate the proposed standard payment
conversion factor for FY 2023.
In Table 2, ``Proposed Relative Weights and Average Length of Stay
Values for Case-Mix Groups,'' we present the proposed CMGs, the
comorbidity tiers, the corresponding relative weights, and the ALOS
values for each CMG and tier for FY 2023. The ALOS for each CMG is used
to determine when an IRF discharge meets the definition of a short-stay
transfer, which results in a per diem case level adjustment.
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Generally, updates to the CMG relative weights result in some
increases and some decreases to the CMG relative weight values. Table 2
shows how we estimate that the application of the proposed revisions
for FY 2023 would affect particular CMG relative weight values, which
would affect the overall distribution of payments within CMGs and
tiers. We note that, because we propose to implement the CMG relative
weight revisions in a budget-neutral manner (as previously described),
total estimated aggregate payments to IRFs for FY 2023 would not be
affected as a result of the proposed CMG relative weight revisions.
However, the proposed revisions would affect the distribution of
payments within CMGs and tiers.
[GRAPHIC] [TIFF OMITTED] TP06AP22.014

BILLING CODE 4120-01-C
As shown in Table 3, 99.3 percent of all IRF cases are in CMGs and
tiers that would experience less than a 5 percent change (either
increase or decrease) in the CMG relative weight value as a result of
the proposed revisions for FY 2023. The proposed changes in the ALOS
values for FY 2023, compared with the FY 2022 ALOS values, are small
and do not show any particular trends in IRF length of stay patterns.
We invite public comment on our proposed updates to the CMG
relative weights and ALOS values for FY 2023.

V. Proposed FY 2023 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 for which payment is
made under the IRF PPS. 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 the productivity adjustment described in
section 1886(b)(3)(B)(xi)(II) of the Act. Thus, in this proposed rule,
we are proposing to update the IRF PPS payments for FY 2023 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).
In 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
finalized a rebased and revised IRF market basket to reflect a 2016
base year. The FY 2020 IRF PPS final rule (84 FR 39071 through 39086)
contains a complete discussion of the development of the 2016-based IRF
market basket.

B. Proposed FY 2023 Market Basket Update and Productivity Adjustment

For FY 2023 (that is, beginning October 1, 2022 and ending
September 30, 2023), we are proposing to update the IRF PPS payments by
a market basket increase factor as required by section 1886(j)(3)(C) of
the Act, with a productivity adjustment as required by section
1886(j)(3)(C)(ii)(I) of the Act. For FY 2023, we are proposing to use
the same methodology described in the FY 2022 IRF PPS final rule (86 FR
42373 through 42376).
Consistent with historical practice, we are proposing to estimate
the market basket update for the IRF PPS for FY 2023 based on IHS
Global Inc.'s (IGI's) forecast using the most recent available data.
Based on IGI's fourth quarter 2021 forecast with historical data
through the third quarter of 2021, the proposed 2016-based IRF market
basket increase factor for FY 2023 is projected to be 3.2 percent. We
are also proposing that if more recent data become available after the
publication of the proposed rule and before the publication of the
final rule (for example, a more recent estimate of the market basket
update or productivity adjustment), we would use

[[Page 20228]]

such data, if appropriate, to determine the FY 2023 market basket
update in the final rule.
According to section 1886(j)(3)(C)(i) of the Act, the Secretary
shall establish an increase factor based on an appropriate percentage
increase in a market basket of goods and services. Section
1886(j)(3)(C)(ii) of the Act then requires that, after establishing the
increase factor for a FY, the Secretary shall reduce such increase
factor for FY 2012 and each subsequent FY, by the 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 multifactor productivity
(as projected by the Secretary for the 10-year period ending with the
applicable FY, year, cost reporting period, or other annual period)
(the ``productivity adjustment''). The U.S. Department of Labor's
Bureau of Labor Statistics (BLS) publishes the official measures of
productivity for the U.S. economy. We note that previously the
productivity measure referenced in section 1886(b)(3)(B)(xi)(II) of the
Act, was published by BLS as private nonfarm business multifactor
productivity. Beginning with the November 18, 2021 release of
productivity data, BLS replaced the term multifactor productivity (MFP)
with total factor productivity (TFP). BLS noted that this is a change
in terminology only and will not affect the data or methodology. As a
result of the BLS name change, the productivity measure referenced in
section 1886(b)(3)(B)(xi)(II) is now published by BLS as private
nonfarm business total factor productivity. However, as mentioned
above, the data and methods are unchanged. Please see <a href="http://www.bls.gov">www.bls.gov</a> for
the BLS historical published TFP data. A complete description of IGI's
TFP projection methodology is available on the CMS website at <a href="https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch">https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch</a>. In addition, in
the FY 2022 IRF final rule (86 FR 42374), we noted that effective with
FY 2022 and forward, CMS changed the name of this adjustment to refer
to it as the productivity adjustment rather than the MFP adjustment.
Using IGI's fourth quarter 2021 forecast, the 10-year moving
average growth of TFP for FY 2023 is projected to be 0.4 percent. Thus,
in accordance with section 1886(j)(3)(C) of the Act, we are proposing
to base the FY 2023 market basket update, which is used to determine
the applicable percentage increase for the IRF payments, on IGI's
fourth quarter 2021 forecast of the 2016-based IRF market basket. We
are proposing to then reduce this percentage increase by the estimated
productivity adjustment for FY 2023 of 0.4 percentage point (the 10-
year moving average growth of TFP for the period ending FY 2023 based
on IGI's fourth quarter 2021 forecast). Therefore, the proposed FY 2023
IRF update is equal to 2.8 percent (3.2 percent market basket update
reduced by the 0.4 percentage point productivity adjustment).
Furthermore, we are proposing that if more recent data become available
after the publication of the proposed rule and before the publication
of the final rule (for example, a more recent estimate of the market
basket and/or productivity adjustment), we would use such data, if
appropriate, to determine the FY 2023 market basket update and
productivity adjustment in the final rule.
For FY 2023, the Medicare Payment Advisory Commission (MedPAC)
recommends that we reduce IRF PPS payment rates by 5 percent.\11\ As
discussed, and in accordance with sections 1886(j)(3)(C) and
1886(j)(3)(D) of the Act, the Secretary is proposing to update the IRF
PPS payment rates for FY 2023 by a productivity-adjusted IRF market
basket increase factor of 2.8 percent. 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 2023.
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\11\ <a href="https://www.medpac.gov/wp-content/uploads/2022/03/Mar22_MedPAC_ReportToCongress_SEC.pdf">https://www.medpac.gov/wp-content/uploads/2022/03/Mar22_MedPAC_ReportToCongress_SEC.pdf</a>.
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We invite public comment on our proposals for the FY 2023 market
basket update and productivity adjustment.

C. Proposed Labor-Related Share for FY 2023

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 IRFs' costs that 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 are proposing to continue to classify a cost category as
labor-related if the costs are labor-intensive and vary with the local
labor market.
Based on our definition of the labor-related share and the cost
categories in the 2016-based IRF market basket, we are proposing to
calculate the labor-related share for FY 2023 as the sum of the FY 2023
relative importance of Wages and Salaries, Employee Benefits,
Professional Fees: Labor-related, Administrative and Facilities Support
Services, Installation, Maintenance, and Repair Services, All Other:
Labor-related Services, and a portion of the Capital-Related relative
importance from the 2016-based IRF market basket. For more details
regarding the methodology for determining specific cost categories for
inclusion in the 2016-based IRF labor-related share, see the FY 2020
IRF PPS final rule (84 FR 39087 through 39089).
The relative importance reflects the different rates of price
change for these cost categories between the base year (2016) and FY
2023. Based on IGI's fourth quarter 2021 forecast of the 2016-based IRF
market basket, the sum of the FY 2023 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
69.4 percent. We are proposing that the portion of Capital-Related
costs that are influenced by the local labor market is 46 percent.
Since the relative importance for Capital-Related costs is 8.2 percent
of the 2016-based IRF market basket for FY 2023, we are proposing to
take 46 percent of 8.2 percent to determine the labor-related share of
Capital-Related costs for FY 2022 of 3.8 percent. Therefore, we are
proposing a total labor-related share for FY 2023 of 73.2 percent (the
sum of 69.4 percent for the proposed labor-related share of operating
costs and 3.8 percent for the proposed labor-related share of Capital-
Related costs). We are proposing that if more recent data become
available after publication of the proposed rule and before the
publication of the final rule (for example, a more recent estimate of
the labor-related share), we would use such data, if appropriate, to
determine the FY 2023 IRF labor-related share in the final rule.
Table 4 shows the current estimate of the proposed FY 2023 labor-
related share and the FY 2022 final labor-related share using the 2016-
based IRF market basket relative importance.

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We invite public comments on the proposed labor-related share for
FY 2023.

D. Proposed Wage Adjustment for FY 2023

1. Background
Section 1886(j)(6) of the Act requires the Secretary to adjust the
proportion of rehabilitation facilities' costs attributable to wages
and wage-related costs (as estimated by the Secretary from time to
time) by a factor (established by the Secretary) reflecting the
relative hospital wage level in the geographic area of the
rehabilitation facility compared to the national average wage level for
those facilities. The Secretary is required to update the IRF PPS wage
index on the basis of information available to the Secretary on the
wages and wage-related costs to furnish rehabilitation services. Any
adjustment or updates made under section 1886(j)(6) of the Act for a FY
are made in a budget-neutral manner.
For FY 2023, we propose to maintain the policies and methodologies
described in the FY 2022 IRF PPS final rule (86 FR 42377) related to
the labor market area definitions and the wage index methodology for
areas with wage data. Thus, we propose to use the core based
statistical areas (CBSAs) labor market area definitions and the FY 2023
pre-reclassification and pre-floor hospital wage index data. In
accordance with section 1886(d)(3)(E) of the Act, the FY 2023 pre-
reclassification and pre-floor hospital wage index is based on data
submitted for hospital cost reporting periods beginning on or after
October 1, 2018, and before October 1, 2019 (that is, FY 2019 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 2023 IRF
PPS wage index.
We invite public comment on our proposals regarding the Wage
Adjustment for FY 2023.
2. Core-Based Statistical Areas (CBSAs) for the FY 2023 IRF Wage Index
The wage index used for the IRF PPS is calculated using the pre-
reclassification and pre-floor inpatient PPS (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 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. Additionally, OMB
occasionally issues updates and revisions to the statistical areas in
between decennial censuses to reflect the recognition of new areas or
the addition of counties to existing areas. In some instances, these
updates merge formerly separate areas, transfer components of an area
from one area to another, or drop components from an area. On July 15,
2015, OMB issued OMB Bulletin No. 15-01, which provides minor updates
to and supersedes OMB Bulletin No. 13-01 that was issued on February
28, 2013. The attachment to OMB Bulletin No. 15-01 provides detailed
information on the update to statistical areas since February 28, 2013.
The updates provided in OMB Bulletin No. 15-01 are based on the
application of the 2010 Standards for Delineating Metropolitan and
Micropolitan Statistical Areas to Census Bureau population estimates
for July 1, 2012 and July 1, 2013.
In the FY 2018 IRF PPS final rule (82 FR 36250 through 36251), we
adopted the updates set forth in OMB Bulletin No. 15-01 effective
October 1, 2017, beginning with the FY 2018 IRF wage index. For a
complete discussion of the adoption of the updates set forth in OMB
Bulletin No. 15-01, we refer readers to the FY 2018 IRF PPS final rule.
In the FY 2019 IRF PPS final rule (83 FR 38527), we continued to use
the OMB delineations that were adopted

[[Page 20230]]

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 the FY 2020 IRF PPS final rule (84 FR 39090 through 39091), we
adopted the updates set forth in OMB Bulletin No. 17-01 effective
October 1, 2019, beginning with the FY 2020 IRF wage index.
On April 10, 2018, OMB issued OMB Bulletin No. 18-03, which
superseded the August 15, 2017 OMB Bulletin No. 17-01, and on September
14, 2018, OMB issued OMB Bulletin No. 18-04, which superseded the April
10, 2018 OMB Bulletin No. 18-03. These bulletins established revised
delineations for Metropolitan Statistical Areas, Micropolitan
Statistical Areas, and Combined Statistical Areas, and provided
guidance on the use of the delineations of these statistical areas. A
copy of this bulletin may be obtained at <a href="https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf">https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf</a>.
To this end, as discussed in the FY 2021 IRF PPS proposed (85 FR
22075 through 22079) and final (85 FR 48434 through 48440) rules, we
adopted the revised OMB delineations identified in OMB Bulletin No. 18-
04 (available at <a href="https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf">https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf</a>) beginning October 1, 2020, including a 1-year
transition for FY 2021 under which we applied a 5 percent cap on any
decrease in an IRF's wage index compared to its wage index for the
prior fiscal year (FY 2020). The updated OMB delineations more
accurately reflect the contemporary urban and rural nature of areas
across the country, and the use of such delineations allows us to
determine more accurately the appropriate wage index and rate tables to
apply under the IRF PPS. OMB issued further revised CBSA delineations
in OMB Bulletin No. 20-01, on March 6, 2020 (available on the web at
<a href="https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf">https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf</a>). However, we determined that the changes in OMB Bulletin No.
20-01 do not impact the CBSA-based labor market area delineations
adopted in FY 2021. Therefore, CMS did not propose to adopt the revised
OMB delineations identified in OMB Bulletin No. 20-01 for FY 2022, and
for these reasons CMS is likewise not making such a proposal for FY
2023.
3. Proposed Permanent Cap on Wage Index Decreases
As discussed above in this section of the rule, we have proposed
and finalized temporary transition policies in the past to mitigate
significant changes to payments due to changes to the IRF PPS wage
index. Specifically, for FY 2016 (80 FR 47068), we implemented a 50/50
blend for all geographic areas consisting of the wage index values
computed using the then-current OMB area delineations and the wage
index values computed using new area delineations based on OMB Bulletin
No. 13-01. In FY 2021 (85 FR 48434), we implemented a 1-year transition
to mitigate any negative effects of wage index changes by applying a 5
percent cap on any decrease in an IRF's wage index from the final wage
index from FY 2020. We explained that we believed the 5-percent cap
would provide greater transparency and would be administratively less
complex than the prior methodology of applying a 50/50 blended wage
index. We indicated that no cap would be applied to the reduction in
the wage index for FY 2022, and that this transition approach struck an
appropriate balance by providing a transition period to mitigate the
resulting short-term instability and negative impacts on providers and
time for them to adjust to their new labor market area delineations and
wage index values.
In the FY 2022 final rule (86 FR 42378), commenters recommended CMS
extend the transition period adopted in the FY 2021 IRF PPS final rule
so that wage index values do not change by more than 5 percent from
year-to-year to protect IRFs from large payment volatility. Because we
did not propose to modify the transition policy that was finalized in
the FY 2021 IRF PPS final rule, we did not extend the transition period
for FY 2022. However, we acknowledged that certain changes to wage
index policy may significantly affect Medicare payments. In addition,
we reiterated that our policy principles with regard to the wage index
include generally using the most current data and information available
and providing that data and information, as well as any approaches to
addressing any significant effects on Medicare payments resulting from
these potential scenarios, in notice and comment rulemaking. With these
policy principles in mind, for this FY 2023 proposed rule we considered
how best to address the potential scenarios about which commenters
raised concerns in the FY 2022 final rule around IRF payment
volatility; that is, scenarios in which changes to wage index policy
may significantly affect Medicare payments.
In the past, we have established transition policies of limited
duration to phase in significant changes to labor market areas. In
taking this approach in the past, we sought to mitigate short-term
instability and fluctuations that can negatively impact providers due
to wage index changes. In accordance with the requirements of the IRF
PPS wage index regulations at Sec. 412.624(a)(2), we use an
appropriate wage index based on the best available data, including the
best available labor market area delineations, to adjust IRF PPS
payments for wage differences. We have previously stated that, because
the wage index is a relative measure of the value of labor in
prescribed labor market areas, we believe it is important to implement
new labor market area delineations with as minimal a transition as is
reasonably possible. However, we recognize that changes to the wage
index have the potential to create instability and significant negative
impacts on certain providers even when labor market areas do not
change. In addition, year-to-year fluctuations in an area's wage index
can occur due to external factors beyond a provider's control, such as
the COVID-19 PHE. For an individual provider, these fluctuations can be
difficult to predict. So, we also recognize that predictability in
Medicare payments is important to enable providers to budget and plan
their operations.
In light of these considerations, we are proposing a permanent
approach to smooth year-to-year changes in providers' wage indexes. We
are proposing a policy that we believe increases the predictability of
IRF PPS payments for providers, and mitigates instability and
significant negative impacts to providers resulting from changes to the
wage index.
As previously discussed, we believed applying a 5-percent cap on
wage index decreases for FY 2021 provided greater transparency and was
administratively less complex than prior transition methodologies. In
addition, we believed this methodology mitigated short-term instability
and fluctuations that can negatively impact providers due to wage index
changes. Lastly, we believed the 5-percent cap applied to all wage
index decreases for FY 2021 provided an

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adequate safeguard against significant payment reductions related to
the adoption of the revised CBSAs. However, as discussed earlier in
this section of the proposed rule, we recognize there are circumstances
that a 1-year mitigation policy, like the one adopted for FY 2021,
would not effectively address future years in which providers continue
to be negatively affected by significant wage index decreases.
Typical year-to-year variation in the IRF PPS wage index has
historically been within 5 percent, and we expect this will continue to
be the case in future years. Because providers are usually experienced
with this level of wage index fluctuation, we believe applying a 5-
percent cap on all wage index decreases each year, regardless of the
reason for the decrease, would effectively mitigate instability in IRF
PPS payments due to any significant wage index decreases that may
affect providers in a year. We believe this approach would address
concerns about instability that commenters raised in the FY 2022 IRF
PPS rule. Additionally, we believe that applying a 5-percent cap on all
wage index decreases would support increased predictability about IRF
PPS payments for providers, enabling them to more effectively budget
and plan their operations. Lastly, because applying a 5-percent cap on
all wage index decreases would represent a small overall impact on the
labor market area wage index system we believe it would ensure the wage
index is a relative measure of the value of labor in prescribed labor
market areas. As discussed in further detail in section XIII.C.2. of
this proposed rule, we estimate that applying a 5-percent cap on all
wage index decreases will have a very small effect on the wage index
budget neutrality factor for FY 2023. Because the wage index is a
measure of the value of labor (wage and wage-related costs) in a
prescribed labor market area relative to the national average, we
anticipate that in the absence of proposed policy changes most
providers will not experience year-to-year wage index declines greater
than 5 percent in any given year. We also believe that when the 5-
percent cap would be applied under this proposal, it is likely that it
would be applied similarly to all IRFs in the same labor market area,
as the hospital average hourly wage data in the CBSA (and any relative
decreases compared to the national average hourly wage) would be
similar. While this policy may result in IRFs in a CBSA receiving a
higher wage index than others in the same area (such as situations when
delineations change), we believe the impact would be temporary.
Therefore, we anticipate that the impact to the wage index budget
neutrality factor in future years would continue to be minimal.
The Secretary has broad authority to establish appropriate payment
adjustments under the IRF PPS, including the wage index adjustment. As
discussed earlier in this section, the IRF PPS regulations require us
to use an appropriate wage index based on the best available data. For
the reasons discussed in this section, we believe a 5-percent cap on
wage index decreases would be appropriate for the IRF PPS. Therefore,
for FY 2023 and subsequent years, we are proposing to apply a 5-percent
cap on any decrease to a provider's wage index from its wage index in
the prior year, regardless of the circumstances causing the decline.
That is, we are proposing that an IRF's wage index for FY 2023 would
not be less than 95 percent of its final wage index for FY 2022,
regardless of whether the IRF is part of an updated CBSA, and that for
subsequent years, a provider's wage index would not be less than 95
percent of its wage index calculated in the prior FY. This also means
that if an IRF's prior FY wage index is calculated with the application
of the 5-percent cap, the following year's wage index would not be less
than 95 percent of the IRF's capped wage index in the prior FY. For
example, if an IRF's wage index for FY 2023 is calculated with the
application of the 5-percent cap, then its wage index for FY 2024 would
not be less than 95 percent of its capped wage index in FY 2023.
Lastly, we propose that a new IRF would be paid the wage index for the
area in which it is geographically located for its first full or
partial FY with no cap applied, because a new IRF would not have a wage
index in the prior FY. As we have discussed in this proposed rule, we
believe this proposed methodology would maintain the IRF PPS wage index
as a relative measure of the value of labor in prescribed labor market
areas, increase the predictability of IRF PPS payments for providers,
and mitigate instability and significant negative impacts to providers
resulting from significant changes to the wage index. In section
XIII.C.2. of this proposed rule, we estimate the impact to payments for
providers in FY 2023 based on this proposed policy. We also note that
we would examine the effects of this policy on an ongoing basis in the
future in order to assess its appropriateness.
Subject to the aforementioned proposal becoming final, we are also
proposing to revise the regulation text at Sec. 412.624(e)(1) to
provide that starting October 1, 2022, CMS would apply a cap on
decreases to the wage index such that the wage index applied is not
less than 95 percent of the wage index applied to that IRF in the prior
year.
We invite public comments on this proposal.
4. Proposed Wage Adjustment
To calculate the wage-adjusted facility payment for the proposed
payment rates set forth in this proposed rule, we multiply the proposed
unadjusted Federal payment rate for IRFs by the FY 2023 labor-related
share based on the 2016-based IRF market basket relative importance
(73.2 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.C. of this proposed rule. We
would then multiply the labor-related portion by the applicable IRF
wage index. The wage index tables are available on the CMS website at
<a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html</a>.
Adjustments or updates to the IRF wage index made under section
1886(j)(6) of the Act must be made in a budget-neutral manner. We
propose to calculate a budget-neutral wage adjustment factor as
established in the FY 2004 IRF PPS final rule (68 FR 45689) and
codified at Sec. 412.624(e)(1), as described in the steps below. We
propose to use the listed steps to ensure that the FY 2023 IRF standard
payment conversion factor reflects the proposed update to the wage
indexes (based on the FY 2019 hospital cost report data) and the
proposed update to the labor-related share, in a budget-neutral manner:
Step 1. Calculate the total amount of estimated IRF PPS payments
using the labor-related share and the wage indexes from FY 2022 (as
published in the FY 2022 IRF PPS final rule (86 FR 42362)).
Step 2. Calculate the total amount of estimated IRF PPS payments
using the proposed FY 2023 wage index values (based on updated hospital
wage data and taking into account the proposed permanent cap on wage
index decreases policy) and the FY 2023 labor-related share of 73.2
percent.
Step 3. Divide the amount calculated in step 1 by the amount
calculated in step 2. The resulting quotient is the proposed FY 2023
budget-neutral wage adjustment factor of 1.0007.
Step 4. Apply the budget neutrality factor from step 3 to the FY
2023 IRF PPS standard payment amount after the

[[Page 20232]]

application of the increase factor to determine the proposed FY 2023
standard payment conversion factor.
We discuss the calculation of the proposed standard payment
conversion factor for FY 2023 in section V.E. of this proposed rule.
We invite public comments on the proposed IRF wage adjustment for
FY 2023 (and the proposed permanent cap on wage index decreases
policy).

E. Description of the Proposed IRF Standard Payment Conversion Factor
and Payment Rates for FY 2023

To calculate the proposed standard payment conversion factor for FY
2023, as illustrated in Table 5, we begin by applying the proposed
increase factor for FY 2023, as adjusted in accordance with sections
1886(j)(3)(C) of the Act, to the standard payment conversion factor for
FY 2022 ($17,240). Applying the proposed 2.8 percent increase factor
for FY 2023 to the standard payment conversion factor for FY 2022 of
$17,240 yields a standard payment amount of $17,723. Then, we apply the
proposed budget neutrality factor for the FY 2023 wage index (taking
into account the proposed permanent cap on wage index decreases
policy), and labor-related share of 1.0007, which results in a standard
payment amount of $17,735. We next apply the proposed budget neutrality
factor for the CMG relative weights of 0.9979, which results in the
standard payment conversion factor of $17,698 for FY 2023.
We invite public comments on the proposed FY 2023 standard payment
conversion factor.
BILLING CODE 4120-01-P
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After the application of the proposed CMG relative weights
described in section IV. of this proposed rule to the proposed FY 2023
standard payment conversion factor ($17,698), the resulting unadjusted
IRF prospective payment rates for FY 2023 are shown in Table 6.

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[[Page 20234]]

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

Table 7 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 0104 (without comorbidities).
The proposed unadjusted prospective payment rate for CMG 0104 (without
comorbidities) appears in Table 7.
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 0.8384, and a rural adjustment of 14.9
percent. Facility B, an urban teaching hospital, has a DSH percentage
of 15 percent (which would result in a LIP adjustment of 1.0454
percent), a wage index of 0.8763, 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 0104 (without

[[Page 20235]]

comorbidities) from Table 7. Then, we multiply the proposed labor-
related share for FY 2023 (73.2 percent) described in section V.C. 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 the applicable wage index table. This
table is available on the CMS website at <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html</a>.
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 7 illustrates the components of the
adjusted payment calculation.
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BILLING CODE 4120-01-C
Thus, the proposed adjusted payment for Facility A would be
$28,522.97, and the adjusted payment for Facility B would be
$28,333.19.

VI. Proposed Update to Payments for High-Cost Outliers Under the IRF
PPS for FY 2023

A. Proposed Update to the Outlier Threshold Amount for FY 2023

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 FY 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 2022 IRF PPS final rules and the FY 2011 and FY 2013
notices (70 FR 47880, 71 FR 48354, 72 FR 44284, 73 FR 46370, 74 FR
39762, 75 FR 42836, 76 FR 47836, 76 FR 59256, 77 FR 44618, 78 FR 47860,
79 FR 45872, 80 FR 47036, 81 FR 52056, 82 FR 36238, 83 FR 38514, 84 FR
39054, 85 FR 48444, and 86 FR 42362, 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 2023, we propose
to use FY 2021 claims data and the same methodology that we used to set
the

[[Page 20236]]

initial outlier threshold amount in the FY 2002 IRF PPS final rule (66
FR 41362 through 41363), which is also the same methodology that we
used to update the outlier threshold amounts for FYs 2006 through 2022.
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 2023, we
estimated the amount of FY 2023 IRF PPS aggregate and outlier payments
using the most recent claims available (FY 2021) and the proposed FY
2023 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 estimate that IRF outlier payments
as a percentage of total estimated payments would be approximately 3.8
percent in FY 2022. Therefore, we propose to update the outlier
threshold amount from $9,491 for FY 2022 to $13,038 for FY 2023 to
maintain estimated outlier payments at approximately 3 percent of total
estimated aggregate IRF payments for FY 2023.
Although we believe that updating the outlier threshold for FY 2023
would be appropriate to maintain IRF PPS outlier payments at 3 percent
of total estimated payments, we recognize that the proposed outlier
threshold amount for FY 2023 would result in a significant increase
from the current outlier threshold amount for FY 2022. As we continue
to explore the underlying reasons for the large change in the proposed
outlier threshold amount, we welcome comments from stakeholders on any
observations or information related to the increase in the proposed
update to outlier threshold amount for FY 2023.

B. Proposed Update to the IRF Cost-to-Charge Ratio Ceiling and Urban/
Rural Averages for FY 2023

CCRs are used to adjust charges from Medicare claims to costs and
are computed annually from facility-specific data obtained from MCRs.
IRF specific CCRs are used in the development of the CMG relative
weights and the calculation of outlier payments under the IRF PPS. In
accordance with the methodology stated in the FY 2004 IRF PPS final
rule (68 FR45692 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 2023, based on analysis of the most recent
data available. We apply the national urban and rural CCRs in the
following situations:
<bullet> New IRFs that have not yet submitted their first MCR.
<bullet> IRFs whose overall CCR is in excess of the national CCR
ceiling for FY 2023, as discussed below in this section.
<bullet> Other IRFs for which accurate data to calculate an overall
CCR are not available.
Specifically, for FY 2023, we propose to estimate a national
average CCR of 0.463 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 0.393 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 2020). This
includes all IRFs whose cost reporting periods begin on or after
October 1, 2019, and before October 1, 2020. If, for any IRF, the FY
2020 cost report was missing or had an ``as submitted'' status, we used
data from a previous FY's (that is, FY 2004 through FY 2019) settled
cost report for that IRF. We do not use cost report data from before FY
2004 for any IRF because changes in IRF utilization since FY 2004
resulting from the 60 percent rule and IRF medical review activities
suggest that these older data do not adequately reflect the current
cost of care. Using updated FY 2020 cost report data for this proposed
rule, we estimate a national average CCR of 0.463 for rural IRFs, and a
national average CCR of 0.393 for urban IRFs.
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 1.40 for FY 2023. This
means that, if an individual IRF's CCR were to exceed this ceiling of
1.40 for FY 2023, we will 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.
We are also proposing that if more recent data become available
after the publication of this proposed rule and before the publication
of the final rule, we would use such data to determine the FY 2023
national average rural and urban CCRs and the national CCR ceiling in
the final rule. We invite public comment on the proposed update to the
IRF CCR ceiling and the urban/rural averages for FY 2023.

VII. Proposed Codification and Clarifications of IRF Teaching Status
Adjustment Policy

In the FY 2006 IRF PPS final rule (70 FR 47928 through 47932), we
implemented Sec. 412.624(e)(4) to establish a facility level
adjustment for IRFs that are, teaching hospitals or units of teaching
hospitals. The teaching status adjustment accounts for the higher
indirect operating costs experienced by IRFs that participate in
training residents in graduate medical education (GME) programs. The
teaching status payment adjustment is based on the ratio of the number
of full-time equivalent (FTE) interns and residents training in the IRF
divided by the IRF's average daily census. Section 1886(j)(3)(A)(v) of
the Act requires the Secretary to adjust the prospective payment rates
for the IRF PPS by such factors as the Secretary determines are
necessary to properly reflect the variations in necessary costs of
treatment among rehabilitation facilities.
We established the IRF teaching status adjustment in a manner that
limited the incentives for IRFs to add FTE interns and residents for
the purpose of increasing their teaching status adjustment, as has been
done in the payment systems for Inpatient Psychiatric Facilities (IPF)
and acute care hospitals. That is, we imposed a cap on the number of
FTE interns and

[[Page 20237]]

residents that the IRF can count for the purpose of calculating the
teaching status adjustment. This cap is similar to the cap established
by the Balanced Budget Act of 1997 (Pub. L. 105-33, enacted August 5,
1997) section 4621, that added section 1886(d)(5)(B)(v) of the Act
(indirect medical education (IME) FTE cap for IPPS hospitals. The cap
limits the number of FTE interns and residents that teaching IRFs may
count for the purpose of calculating the IRF PPS teaching status
adjustment, not the number of interns and residents that teaching
institutions care hire or train. The cap is equal to the number of FTE
interns and residents that trained in the IRF during a ``base year,''
that is based on the most recent final settled cost report for a cost
reporting period ending on or before November 15, 2004. A complete
discussion of how the IRF teaching status adjustment was calculated
appears in the FY 2006 IRF PPS final rule (70 FR 47928 through 47932).
In the FY 2012 IRF PPS final rule (76 FR 47846 through 47848)
published on August 5, 2011, we updated the IRF PPS teaching status
adjustment policy in order to maintain consistency, to the extent
feasible, with the indirect medical education (IME) teaching policies
that were finalized in the IPPS FY 1999 final rule (64 FR 41522), the
IPPS FY 2001 final rule (66 FR 39900), and the IPF PPS teaching
adjustment policies finalized in the 2012 IPF PPS final rule (76 FR
26454 through 26456). In that final rule, we adopted a policy which
permits a temporary increase in the FTE intern and resident cap when an
IRF increases the number of FTE residents it trains, in order to accept
displaced residents because another IRF closes or closes a medical
residency training program. We refer to a ``displaced'' resident or
intern as one that is training in an IRF and is unable to complete
training in that IRF, either because the IRF closes or closes a medical
residency training program.
The cap adjustment for IRFs, adopted in the FY 2012 IRF PPS final
rule, is considered temporary because it is resident-specific and will
only apply to the residents until they have completed their training in
the program in which they were training at the time of the IRF closure
or the closure of the program. Similar to the IPPS and IPF policy for
displaced residents, the IRF PPS temporary cap adjustment only applies
to residents that were still training at the IRF at the time the IRF
closed or at the time the IRF ceased training residents in the
residency training program(s). Residents who leave the IRF, for
whatever reason, before the closure of the IRF or the closure of the
medical residency training program are not considered displaced
residents for purposes of the IRF temporary cap adjustment policy.
In the FY 2012 IRF PPS final rule, we also adopted the IPPS
definition of ``closure of a hospital'' at Sec. 413.79(h)(1)(i) to
mean the IRF terminates its Medicare provider agreement as specified in
Sec. 489.52. In this instance, we allow a temporary adjustment to an
IRF's FTE cap to reflect residents added to their medical residency
training program because of an IRF's closure. We allow an adjustment to
an IRF's FTE cap if the IRF meets the criteria outlined in the FY 2012
IRF PPS final rule (76 FR 47847). After the displaced residents leave
the accepting IRF's training program or complete their medical
residency training program, the accepting IRF's cap will revert to its
original level. As such, the temporary adjustment to the FTE cap will
be available to the IRF only for the period of time necessary for the
displaced residents to complete their training.
Additionally, in the FY 2012 IRF PPS final rule, we adopted the
IPPS definition of ``closure of a hospital residency training
program,'' as specified in Sec. 413.79(h)(1)(ii), which means that the
hospital ceases to offer training for interns and residents in a
particular approved medical residency training program. In this
instance, if an IRF ceases training residents in a medical residency
training program(s) and agrees to temporarily reduce its FTE cap,
another IRF may receive a temporary adjustment to its FTE cap to
reflect the addition of the displaced residents. For more discussion
regarding the methodology for adjusting the caps for the ``receiving
IRF'' and the ``IRF that closed its program,'' refer to the FY 2012 IRF
PPS final rule (76 FR 47847).

A. Proposed Codification of Existing Teaching Status Adjustment
Policies

In an effort to streamline the IRF PPS teaching status adjustment
policies that were finalized in the FY 2006 IRF PPS final rule (70 FR
47928 through 47932) and the FY 2012 IRF PPS final rule (76 FR 47846
through 47848), we are proposing to codify the longstanding policy so
that these policies can be easily located by IRF providers and can also
align, to the extent feasible, with the IPPS IME and IPF teaching
adjustment policy regulations.
First, we are proposing to codify policy that was finalized in the
FY 2006 IRF PPS final rule with respect to how CMS adjusts the Federal
prospective payment on a facility basis by a factor to account for
indirect teaching costs. When the teaching status adjustment policy was
finalized in the FY 2006 IRF PPS final rule (70 FR 47928 through
47932), the definition of this ``factor'' and explanations of how it is
computed were not included in the regulations. Rather, the more
detailed definition and the explanation of the teaching status payment
adjustment provided in the FY 2006 IRF PPS final rule, were published
in the Medicare Claims Processing Manual (100-04, chapter 3,
140.2.5.4). Currently, Sec. 412.624(e)(4) states, for discharges on or
after October 1, 2005, CMS adjusts the Federal prospective payment on a
facility basis by a factor as specified by CMS for facilities that are
teaching institutions or units of teaching institutions. This
adjustment is made on a claim basis as an interim payment and the final
payment in full for the claim is made during the final settlement of
the cost report.
Second, we are also proposing to codify the IRF policy that was
adopted in the FY 2012 IRF PPS final rule (76 FR 47846 through 47848)
allowing an IRF to receive a temporary adjustment to its FTE cap to
reflect residents added to its teaching program because of another IRFs
closure or an IRFs medical residency training program closure. We
believe that codifying these longstanding policies would improve
clarity and reduce administrative burden on IRF providers and others
trying to locate all relevant information pertaining to the teaching
hospital adjustment.
Thus, we are proposing to codify CMS' existing IRF PPS' teaching
hospital adjustment policies through proposed amendments to Sec. Sec.
412.602 and 412.624(e)(4) presented in this proposed rule; except as
specifically noted in this proposed rule, our intent is to codify the
existing IRF PPS teaching status adjustment policy.
We invite public comment on our proposal to amend Sec. Sec.
412.602 and 412.624(e)(4) to codify our longstanding policies regarding
the teaching status adjustment.

B. Proposed Update to the IRF Teaching Policy on IRF Program Closures
and Displaced Residents

For FY 2023, we are also proposing to change the IRF policy
pertaining to displaced residents resulting from IRF closures and
closures of IRF residency teaching programs. Specifically, we are
proposing to adopt conforming changes to the IRF PPS teaching status
adjustment policy to align with the policy changes that the IPPS
finalized in the FY 2021 IPPS final rule (85 FR 58865 through 58870)
and that the IPF

[[Page 20238]]

finalized in the FY 2022 IPF PPS final rule (86 FR 42618 through
42621). We believe that the IRF teaching status adjustment policy
relating to hospital closure and displaced residents is susceptible to
the same vulnerabilities as IPPS IME policy. Hence, if an IRF with
residents training in its residency program announces it is closing,
these residents will become displaced and will need to find alternative
positions at other IRFs or risk being unable to become board-certified.
We are proposing to implement the policy discussed in this section
to remain consistent with the IPPS policy for calculating the temporary
IME resident cap adjustment in situations where the receiving hospital
assumes the training of displaced residents due to another hospital or
residency program's closure. We are also proposing that, in the future,
we would deviate from the IPPS IME policy as it pertains to counting
displaced residents for the purposes of the IRF teaching status
adjustment only when it is necessary and appropriate for the IRF PPS.
The policy adopted in the FY 2012 IRF PPS final rule (76 FR 47846
through 47848), published August 5, 2011, permits an IRF to temporarily
adjust its FTE cap to reflect displaced residents added to their
residency program because of another IRF closure or IRF residency
program closure. In that final rule, we adopted the IPPS definition of
``closure of a hospital'' at Sec. 413.79(h)(1)(i) to also apply to
IRF, and to mean that the IRF terminates its Medicare provider
agreement as specified in Sec. 489.52. We also adopted the IPPS
definition of ``closure of a hospital residency training program'' as
it is currently defined at Sec. 413.79(h)(1)(ii) to also apply to IRF
residency training program closures, and to mean that the IRF ceases to
offer training for residents in a particular approved medical residency
training program. In this proposed rule, we are proposing to codify
both of these definitions within the IRF PPS definitions section
provided at Sec. 412.602 so that the IRF teaching policies are more
centrally located and more easily accessible.
Although not explicitly stated in the regulations, our current
policy is that a displaced resident is one that is physically present
at the hospital training on the day prior to or the day of hospital or
residency program closure. This longstanding policy derived from the
fact that there are requirements that the receiving IRF identifies the
residents ``who have come from the closed IRF'' or identifies the
residents ``who have come from another IRF's closed residency
program,'' and that the IRF that closed its program identifies ``the
residents who were in training at the time of the residency program's
closure.'' We considered the residents who were physically present at
the IRF to be those residents who were ``training at the time of the
program's closure,'' thereby granting them the status of ``displaced
residents.'' Although we did not want to limit the ``displaced
residents'' to only those physically present at the time of closure, it
becomes much more administratively challenging for the following groups
of residents at closing IRFs/residency programs to continue their
training:
(1) Residents who leave the program after the closure is publicly
announced to continue training at another IRF, but before the actual
closure;
(2) Residents assigned to and training at planned rotations at
other IRFs who will be unable to return to their rotations at the
closing IPF or program; and
(3) Individuals (such as medical students or would-be fellows) who
matched into resident programs at the closing IRF or residency program,
but have not yet started training at the closing IRF or residency
program.
Other groups of residents who, under current policy, are already
considered ``displaced residents'' include--
(1) Residents who are physically training in the IRF on the day
prior to or day of residency program or IRF closure; and
(2) Residents who would have been at the closing IRF or IRF
residency program on the day prior to or day of closure, but were on
approved leave at that time, and are unable to return to their training
at the closing IRF or IRF residency training program.
We are proposing to amend our IRF policy with regard to closing
teaching IRFs and closing IRF medical residency training programs to
address the needs of interns and residents attempting to find
alternative IRFs in which to complete their training. Additionally,
this proposal addresses the incentives of originating and receiving
IRFs with regard to ensuring we appropriately account for their
indirect teaching costs by way of an appropriate IRF teaching
adjustment based on each program's FTE resident count. We are proposing
to make changes to the current IRF teaching status adjustment policy
related to displaced residents as discussed below.
First, rather than link the status of displaced residents for the
purpose of the receiving IRF's request to increase their FTE cap to the
resident's presence at the closing IRF or program on the day prior to
or the day of the residency program or IRF closure, we are proposing to
link the status of the displaced residents to the day that the closure
was publicly announced (for example, via a press release or a formal
notice to the Accreditation Council on Graduate Medical Education).
This would provide great flexibility for the interns and residents to
transfer while the IRF operations or teaching programs are winding
down, rather than waiting until the last day of IRF or IRF teaching
program operation. This would address the needs of the group of
residents who would leave the program after the closure was publicly
announced to continue training at another hospital, but before the day
of actual closure.
Second, by removing the link between the status of displaced
residents and their presence at the closing IRF or residency program on
the day prior to or the day of the IRF closure or program closure, we
propose to also allow the residents assigned to and training at planned
rotations at other IRFs who will be unable to return to their rotations
at the closing IRF or program and individuals (such as medical students
or would-be fellows) who matched into resident programs at the closing
IRF or residency program, but have not yet started training at the
closing IRF or residency program, to be considered a displaced
resident.
Thus, we are proposing to revise our teaching policy with regard to
which residents can be considered ``displaced'' for the purpose of the
receiving IRF's request to increase their IRF cap in the situation
where an IRF announces publicly that it is closing, and/or that it is
closing an IRF residency program. Specifically, we are proposing to
adopt the FY 2021 IPPS final rule definition of ``displaced resident''
as defined at Sec. 413.79(h)(1)(ii), for the purpose of calculating
the IRF's teaching status adjustment.
In addition, we are proposing to change another detail of the
policy specific to the requirements for the receiving IRF. To apply for
the temporary increase in the FTE resident cap, the receiving IRF would
have to submit a letter to its Medicare Administrative Contractor (MAC)
within 60 days after beginning to train the displaced interns and
residents. As established in the FY 2012 IRF PPS final rule, this
letter must identify the residents who have come from the closed IRF or
closed residency program and caused the receiving IRF to exceed its
cap, and must specify the length of time that the adjustment is needed.

[[Page 20239]]

Furthermore, to maintain consistency with the IPPS IME policy, we are
proposing that the letter must also include:
(1) The name of each displaced resident;
(2) The last four digits of each displaced resident's social
security number; this will reduce the amount of personally identifiable
information (PII);
(3) The name of the IRF and the name of the residency program or
programs in which each resident was training at previously; and
(4) The amount of the cap increase needed for each resident (based
on how much the receiving IRF is in excess of its cap and the length of
time for which the adjustments are needed).
As we previously discussed in the FY 2012 IRF PPS final rule (76 FR
47846 through 47848), we are also clarifying that the maximum number of
FTE resident cap slots that could be transferred to all receiving IRFs
is the number of FTE resident cap slots belonging to the IRF that has
closed the resident training program, or that is closing. Therefore, if
the originating IRF is training residents in excess of its cap, then
being a displaced resident does not guarantee that a cap slot will be
transferred along with the resident. Therefore, we are proposing that
if there are more IRF displaced residents than available cap slots, the
slots may be apportioned according to the closing IRF's discretion. The
decision to transfer a cap slot if one is available would be voluntary
and made at the sole discretion of the originating IRF. However, if the
originating IRF decides to do so, then it would be the originating
IRF's responsibility to determine how much of an available cap slot
would go with a particular resident (if any). We also note that, as we
previously discussed in the FY 2012 IRF PPS final rule (76 FR 47846
through 47848), only to the extent a receiving IRF would exceed its FTE
cap by training displaced residents would it be eligible for a
temporary adjustment to its resident FTE cap. As such, displaced
residents are factored into the receiving IRF's ratio of resident FTEs
to the facility's average daily census.
We invite public comment on the proposed updates to the IRF
teaching policy.

VIII. Solicitation of Comments Regarding the Facility-Level Adjustment
Factor Methodology

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 a facility's percentage of low-income patients
(LIP), teaching status, and location in a rural area, if applicable, as
described in Sec. 412.624(e).
The facility-level adjustment factors are intended to account for
differences in costs attributable to the different types of IRF
providers and to better align payments with the costs of providing IRF
care. The LIP and rural facility-level adjustment factors have been
utilized since the inception of the IRF PPS, while the teaching status
adjustment factor was finalized in the FY 2006 IRF PPS final rule (70
FR 47880) when our regression analysis indicated that it had become
statistically significant in predicting IRF costs. Each of the
facility-level adjustment factors were implemented using the same
statistical approach, that is, utilizing coefficients determined from
regression analysis.
Historically, we have observed relatively large fluctuations in
these factors from year-to-year which lead us to explore a number of
options to provide greater stability and predictability between years
and increase the accuracy of Medicare payments for IRFs. In addition to
holding these factors constant over multiple years to mitigate
fluctuations in payments, we also implemented a number of refinements
to the methodology used to calculate the adjustment factors in efforts
to better align payments with the costs of care. For example, in FY
2010 (74 FR 39762) we implemented a 3-year moving average approach to
updating the facility-level adjustment factors to promote more
consistency in the adjustment factors over time. Additionally, in FY
2014 (78 FR 47859) we added an indicator variable for a facility's
freestanding or hospital-based status to the payment regression to
improve the accuracy of the IRF payment adjustments. This variable was
added to control for differences in cost structure between hospital-
based and freestanding IRFs in the regression analysis, so that these
differences would not inappropriately influence the adjustment factor
estimates. We refer readers to the FY 2015 IRF PPS final rule (79 FR
45882 through 45883) for a full discussion of the refinements that have
been made to the methodology used to determine the facility-level
adjustment factors and other analysis that has been considered over
time. Due to the revisions to the regression analysis and the
substantive changes to the facility-level adjustment factors that were
adopted in the FY 2014 IRF PPS final rule, we finalized a proposal in
the FY 2015 IRF PPS final rule (79 FR 45871) to freeze the facility-
level adjustment factors for FY 2015 and all subsequent years at the FY
2014 levels while we continued to monitor changes in the adjustment
factors over time. Table 8 shows how the IRF facility-level adjustment
factors have changed over time since the start of the IRF PPS:
BILLING CODE 4120-01-P
[GRAPHIC] [TIFF OMITTED] TP06AP22.020

We have continued monitoring the adjustment factors using the same
methodology described in the FY 2014 IRF PPS final rule (78 FR 47869).
That is, we have continued to calculate the facility-level adjustment
factors using the following the steps:

(Steps 1 and 2 are performed independently for each of three years of
IRF claims data)

[[Page 20240]]

Step 1. Calculate the average cost per case for each IRF in the
available IRF claims data.
Step 2. Perform a logarithmic regression analysis on the average
cost per case to compute the coefficients for the rural, LIP, and
teaching status adjustments. This regression analysis incorporates an
indicator variable to account for whether a facility is a freestanding
IRF hospital or a unit of an acute care hospital (or a CAH).
Step 3. Calculate a mean for each of the coefficients across the 3
years of data (using logarithms for the LIP and teaching status
adjustment coefficients (because they are continuous variables), but
not for the rural adjustment coefficient (because the rural variable is
either zero (if not rural) or 1 (if rural)). To compute the LIP and
teaching status adjustment factors, we convert these factors back out
of the logarithmic form.
Additional information on the regression analysis used to calculate
the facility-level adjustment factors can be found on the CMS website
at <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research</a>. We have continued to monitor changes in
the facility-level adjustment factors for each FY since they were
frozen in FY 2015 at the FY 2014 levels. Table 9, contains the rural,
LIP, and teaching status adjustment factors for each FY since they were
frozen at their 2014 levels.
[GRAPHIC] [TIFF OMITTED] TP06AP22.021

[[Page 20241]]

Table 10. Shows the potential estimated impacts of updating the
facility-level adjustments for FY 2023.
[GRAPHIC] [TIFF OMITTED] TP06AP22.022

[[Page 20242]]

[GRAPHIC] [TIFF OMITTED] TP06AP22.023

BILLING CODE 4120-01-C
Table 10 shows how we estimate that the application of the FY 2023
facility-level adjustment factors would affect particular groups if we
were to implement updates to these factors for FY 2023. Table 10
categorizes IRFs by geographic location, including urban or rural
location, and location for CMS' 9 Census divisions of the country. In
addition, Table 10 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).
Note that, because the facility-level adjustment factors are
implemented in a budget-neutral manner, total estimated aggregate
payments to IRFs would not be affected. However, these updates would
affect the distribution of payments across providers.
Typically, the facility-level adjustment factors have been updated
on an intermittent basis to reflect changes in the costs of caring for
patients. However, given the magnitude of the increases we are
consistently seeing in the teaching status adjustment we do not believe
that they are true reflections of the higher costs of teaching IRFs. In
addition, we are concerned with the negative effects that the
inordinately high teaching status adjustments would have on rural IRFs,
given that the updates would be implemented in a budget neutral manner.
Given the changes in the teaching status adjustment and the rural
adjustment from their 2014 levels and the potential payment impacts
associated with these adjustments, we are soliciting comments from
stakeholders on the methodology used to determine the facility-level
adjustment factors and suggestions for possible updates and refinements
to this methodology. Additionally, we welcome ideas and suggestions as
to what could be driving the changes observed in these adjustment
factors from year-to-year.

IX. Solicitation of Comments Regarding the IRF Transfer Payment Policy

In the Medicare Program; Prospective Payment System for Inpatient
Rehabilitation Facilities final rule that appeared in the August 7,
2001 Federal Register (66 FR 41353 through 41355), we finalized a
transfer payment policy under Sec. 412.624(f) to provide for payments
that more accurately reflect facility resources used and services
delivered. This reflected our belief that it is important to minimize
the inherent incentives specifically associated with the early transfer
of patients in a discharge-based payment system. Specifically, we were
concerned that incentives might exist for IRFs to discharge patients
prematurely, as well as to admit patients that may not be able to
endure intense inpatient therapy services. Even if patients were
transferred before receiving the typical, full course of inpatient
rehabilitation, the IRF could still be paid the full CMG payment rate
in the absence of a transfer payment policy. Length of stay has been
shown to be a good proxy measure of costs. Thus, in general, reducing
lengths of stay would be profitable under the IRF prospective payment
system. To address these concerns, we therefore implemented a transfer
payment policy, which took effect beginning January 1, 2002, that,
under certain circumstances, reduced the full CMG payment rate when a
Medicare beneficiary is transferred.
The IRF transfer payment policy applies to IRF stays that are less
than the average length of stay for the applicable CMG and tier and are
transferred directly to another institutional site, including another
IRF, an inpatient hospital, a nursing home that accepts payment under
Medicare and Medicaid, or a long-term care hospital. However, the IRF
transfer payment policy currently does not apply to IRF stays that are
less than the average length of stay for the applicable CMG and tier
and are transferred to home health care.
In the August 7, 2001 final rule (66 FR 41353 through 41355), we
stated that we did not propose to include early discharges to home
health care as part of the transfer payment policy because there were
analytical challenges as a result of the recent implementation of the
new home health prospective payment system. However, to date, the
analytical challenges would not present an issue as we feel the home
health payment system is well established with an adequate supply of
claims data.
A recent Office of Inspector General (OIG) report, ``Early
Discharges From Inpatient Rehabilitation Facilities to Home Health
Services'' \12\ recommends that CMS expand the IRF transfer payment
policy to apply to early discharges to home health. The OIG recommends
that the IRF PPS should update its transfer payment policy, similar to
the IPPS transfer payment policy, to include home health. The OIG
conducted an audit of calendar year 2017 and 2018 Medicare claims data
and determined that if CMS had expanded its IRF transfer payment policy
to include early discharges to home health it could have realized a
significant savings of approximately $993 million over the 2-year
period to Medicare.
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\12\ Office of the Inspector General. December 7, 2021 Early
Discharges From Inpatient Rehabilitation Facilities to Home Health
Services [Report No. A-01-20-00501] <a href="https://oig.hhs.gov">https://oig.hhs.gov</a>.
---------------------------------------------------------------------------

Initially, home health was not added to the IRF transfer policy due
to a lack of home health claims data under the newly-established
prospective payment system that we could analyze to determine the
impact of this policy

[[Page 20243]]

change. However, given the findings from the recent OIG report
mentioned above, we plan to analyze home health claims data to
determine the appropriateness of including home health in the IRF
transfer policy:
<bullet> Beyond the existing Medicare claims data, under what
circumstances, and for what types of patients (in terms of clinical,
demographic, and geographic characteristics) do IRFs currently transfer
patients to home health?
<bullet> Should we consider a policy similar to the IPPS transfer
payment policy (see Sec. 412.4(a), (b) and (c))--such as including as
part of the IRF transfer payment policy a discharge from an IRF to home
health under a written plan for the provision of home health services
from a home health agency and those services to begin within 48 hours
of referral, or within 48 hours of the patient's return home (see Sec.
484.55(a)(1)), or on the provider's start of care date?
<bullet> What impact, if any, do stakeholders believe this proposed
policy change could have on patient access to appropriate post-acute
care services?
While we are not proposing to include home health care as part of
the IRF transfer payment policy at this time, we hope to use this
information from stakeholders in conjunction with our future analysis
for potential rulemaking.

X. Inpatient Rehabilitation Facility (IRF) Quality Reporting Program
(QRP)

A. Background and Statutory Authority

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 by 2
percentage points the annual increase factor for discharges occurring
during a fiscal year for any IRF that does not submit data in
accordance with the IRF QRP 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), the FY 2019 IRF
PPS final rule (83 FR 38555 through 38556), the FY 2020 IRF PPS final
rule (84 FR 39054 through 39165) and the FY 2022 IRF PPS final rule (86
FR 42384 through 42408).

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

For a detailed discussion of the considerations we use for the
selection of IRF QRP quality, resource use, or other measures, we refer
readers to the FY 2016 IRF PPS final rule (80 FR 47083 through 47084).
1. Quality Measures Currently Adopted for the FY 2023 IRF QRP
The IRF QRP currently has 18 measures for the FY 2023 program year,
which are set out in Table 11.
BILLING CODE 4120-01-P

[[Page 20244]]

[GRAPHIC] [TIFF OMITTED] TP06AP22.024

BILLING CODE 4120-01-C

There are no proposals in this proposed rule for new measures for the
IRF QRP.

C. IRF QRP Quality Measure Concepts Under Consideration for Future
Years: Request for Information (RFI)

We are seeking input on the importance, relevance, and
applicability of each of the concepts under consideration listed in
Table 12 for future years in the IRF QRP. More specifically, we are
seeking input on a cross-setting functional measure that would
incorporate the domains of self-care and mobility. Our measure
development contractor for the cross-setting functional outcome measure
convened a Technical Expert Panel (TEP) on June 15 and June 16, 2021 to
obtain expert input on the development of a functional outcome measure
for PAC. During this meeting, the possibility of creating one measure
to capture both self-care and mobility was discussed. We are also
seeking input on measures of health equity, such as structural measures
that assess an organization's leadership in advancing equity goals or
assess progress towards achieving equity priorities. Finally, we seek
input on the value of a COVID-19 Vaccination Coverage measure that
would assess whether IRF patients were up to date on their COVID-19
vaccine.

[[Page 20245]]

[GRAPHIC] [TIFF OMITTED] TP06AP22.025

While we will not be responding to specific comments in response to
this Request for Information in the FY 2023 IRF PPS final rule, we
intend to use this input to inform our future measure development
efforts.

D. Inclusion of the National Healthcare Safety Network (NHSN)
Healthcare-Associated Clostridioides Difficile Infection Outcome
Measure in the IRF QRP--Request for Information

1. Background
The IRF QRP is authorized by section 1886(j)(7) of the Act and
furthers our mission to improve the quality of health care for
beneficiaries through measurement, transparency, and public reporting
of data. The IRF QRP and CMS' other quality programs are foundational
for contributing to improvements in health care, enhancing patient
outcomes, and informing consumer choice. In October 2017, we launched
the Meaningful Measures Framework. This framework captures our vision
to address health care quality priorities and gaps, including
emphasizing digital quality measurement (dQM), reducing measurement
burden, and promoting patient perspectives, while also focusing on
modernization and innovation. The scope of the Meaningful Measures
Framework has evolved to accommodate the changes in the health care
environment, initially focusing on measure and burden reduction to
include the promotion of innovation and modernization of all aspects of
quality.\13\ As a result, we have identified a need to streamline our
approach to data collection, calculation, and reporting to fully
leverage clinical and patient-centered information for measurement,
improvement, and learning.
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\13\ Meaningful Measures 2.0: Moving from Measure Reduction to
Modernization. Available at <a href="https://www.cms.gov/meaningful-measures-20-moving-measure-reduction-modernization">https://www.cms.gov/meaningful-measures-20-moving-measure-reduction-modernization</a>.
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2. Potential Future Inclusion of an Electronic Health Record Driven
Digital National Healthcare Safety Network (NHSN) Measure
In the FY 2015 IRF PPS final rule (79 FR 45913 through 45914), we
finalized the National Healthcare Safety Network (NHSN) Facility-Wide
Inpatient Hospital-onset Clostridium difficile Infection (CDI) Outcome
Measure (NQF #1717) for inclusion in the IRF QRP. Clostridioides
difficile (C. difficile) is responsible for a spectrum of CDIs,
including uncomplicated diarrhea, pseudomembranous colitis, and toxic
megacolon, which can, in some instances, lead to sepsis and even death.
CDIs are one of the most common healthcare-associated infections
(HAIs), as healthcare-associated CDIs affected 0.54 percent of all
hospitalizations in a 2015 survey.\14\ In 2017, the CDC estimated there
were 223,900 CDIs requiring hospitalizations in the United States with
12,800 resulting in deaths.\15\ We have recently identified the NHSN
Healthcare-Associated Clostridioides Difficile Infection (HA-CDI)
Outcome measure as a potential measure which utilizes Electronic Health
Record (EHR)-derived data to help address hospital-based adverse
events, specifically hospital-onset infections.
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\14\ Magil S.M., O'Leary, E., Janelle, S. J. et al. Changes in
Prevalence of Health Care-Associated Infections in U.S. Hospitals. N
Engl J Med 2018; 379:1732-1744. Available at <a href="https://www.nejm.org/doi/full/10.1056/NEJMoa1801550">https://www.nejm.org/doi/full/10.1056/NEJMoa1801550</a>. Accessed February 3, 2022.
\15\ U.S. Department of Health and Human Services. Centers for
Disease Control and Prevention. Antibiotic Resistance Threats in the
United States, 2019. Available at <a href="https://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf">https://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf</a>.
Accessed February 3, 2022.
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CDIs are currently reported to the CDC's NHSN by various
mechanisms, one of which is based on laboratory-identified events
collected in the NHSN. The IRF QRP measure, the NHSN Facility-Wide
Inpatient Hospital CDI Outcome Measure does not utilize EHR-derived
data. Rather IRFs collect data and submit it on a monthly basis to the
CDC's NHSN using the CDC's NHSN Multidrug-Resistant Organism &
Clostridioides difficile Infection (MDRO/CDI) Module. The CDC has now
developed the NHSN HA-CDI Outcome measure that utilizes EHR-derived
data.
The newly-developed version of the measure, the NHSN HA-CDI, would
improve on the original version of the measure in two ways. First, the
new measure would require both microbiologic evidence of C. difficile
in stool and evidence of antimicrobial treatment, whereas the original
measure only requires C. difficile facility-wide Laboratory-Identified
(Lab-ID) event reporting. Second, consistent with the Meaningful
Measures Framework, we specifically believe it would reduce reporting
and regulatory burden on providers and accelerate the move to fully
digital measures.\16\ We discuss each of these improvements below.
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\16\ Centers for Medicare and Medicaid Services. (2021) Quality
Measurement Action Plan. Available at <a href="https://www.cms.gov/files/document/2021-cms-quality-conference-cms-quality-measurement-action-plan-march-2021.pdf">https://www.cms.gov/files/document/2021-cms-quality-conference-cms-quality-measurement-action-plan-march-2021.pdf</a>.
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CDI testing practices have continued to evolve, with recent
guidelines from the Infectious Disease Society of America recommending
a multi-step testing algorithm to better distinguish between C.
difficile colonization and active infection.\17\ However, the growing
number of testing algorithms in use, each with different performance
characteristics, poses a challenge for CDI surveillance. This new CDI
measure defines CDI using both a positive microbiological test for C.
difficile and evidence of treatment, increasing the specificity and
sensitivity of the measure. Adding a requirement of CDI treatment to a
CDI surveillance measure would increase the clinical validity of the
measure, since a record of CDI treatment serves as a proxy for C.
difficile test results that were interpreted as true infections by the
clinician.
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\17\ Clinical Practice Guidelines for Clostridium difficile
Infection in Adults and Children: 2017 Update by the Infectious
Diseases Society of America (IDSA) and Society for Healthcare
Epidemiology of America (SHEA) (<a href="http://idsociety.org">idsociety.org</a>).
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We believe there are important reasons for IRFs to adopt and
utilize EHRs, although we understand that for IRFs who do not yet use
EHRs, there will be initial implementation and training costs. EHRs
facilitate moving to fully digital measures which we believe reduces
reporting and regulatory burden on providers. Additionally, both

[[Page 20246]]

surveys 18 19 and studies 20 21 have demonstrated
that when healthcare providers have access to complete and accurate
information, patients receive better medical care, including timely
identification and treatment of infections. We believe the utilization
of EHRs can improve the ability to diagnose diseases and reduce (even
prevent) medical errors, both of which improve patient outcomes.
Additionally, the use of a fully digital measure using a Measure
Calculation Tool (MCT) that pulls data directly from the EHR via a
standardized FHIR interface would eliminate multiple steps for the
provider, including creating or updating monthly reporting plans, and
completing the data fields required for both numerator and denominator
every month, even when no events were identified. Finally, the locally
installed MCT would be responsible for extracting data, calculating the
measure and submitting the data and would eliminate the need for the
IRF to manually enter the data into the NHSN web-based application or
via file imports. For example, if each IRF executed approximately one
C. difficile event per month (12 events per IRF annually), then using
2020 Bureau of Labor Statistics (BLS) data,\22\ we estimate a potential
cost savings of approximately 3 hours per IRF per year and a total of
$191.38 per IRF per year if a digital version of the measure replaced
the NHSN-based measure.\23\
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\18\ King J., Patel, V., Jamoom, E., & Furukawa, M. (2012,
August). Clinical Benefits of Electronic Health Record Use: National
Findings. Health Serv Res. 2014 Feb; 49(1 pt 2): 392-404. Available
at <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3925409/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3925409/</a>.
\19\ Hoover, R. Benefits of using an electronic health record.
Nursing Critical Care: January 2017--Volume 12--Issue 1--p 9-10.
Available at <a href="https://journals.lww.com/nursingcriticalcare/fulltext/2017/01000/benefits_of_using_an_electronic_health_record.3.aspx">https://journals.lww.com/nursingcriticalcare/fulltext/2017/01000/benefits_of_using_an_electronic_health_record.3.aspx</a>.
\20\ Escobar, G., Turk B., Ragins A., Ha J., et al. Piloting
electronic medical record-based early detection of inpatient
deterioration in community hospitals. J Hosp Med. 2016 Nov; 11 Suppl
1(Suppl 1):S18-S24. Available at <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5510649/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5510649/</a>.
\21\ Uslu A., Stausberg J. Value of the Electronic Medical
Record for Hospital Care: Update from the literature. JMed internet
Res 2021;23(12):e26323. Available at <a href="https://www.jmir.org/2021/12/e26323">https://www.jmir.org/2021/12/e26323</a>.
\22\ U.S. bureau of Labor Statistics. Occupational Employment
and Wage Statistics. May 2020 National Occupational Employment and
Wage Estimates. United States. Available at <a href="https://www.bls.gov/oes/current/oes_nat.htm#43-0000">https://www.bls.gov/oes/current/oes_nat.htm#43-0000</a>. Accessed February 3, 2022.
\23\ Estimated using 10 minutes of clinical nursing time
(Occupation Code 29-1141) and 15 minutes of clerical time
(Occupation Code 43-6013) necessary to enter the data into the NHSN.
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3. Overview of the NHSN Healthcare-Associated Clostridioides difficile
Infection Outcome Measure
The EHR driven digital version of the NHSN HA-CDI Outcome measure
would track the development of new CDI among patients already admitted
to IRFs, using algorithmic determinations from data sources widely
available in EHRs.
The numerator would include those patient records with a qualifying
C. difficile-positive assay on an inpatient encounter on day 4 or later
of an IRF admission and with no previously positive event in <=14 days
before the IRF encounter, and new qualifying antimicrobial therapy for
C. difficile started within the appropriate window period of stool
specimen collection. The denominator would be the number of patients
admitted to IRFs.
The NHSN HA-CDI Outcome measure would use the Standardized
Infection Ratio (SIR) of hospital-onset CDIs among patients to compare
within facility types. SIR is a primary summary statistic used by the
NHSN to track HAIs. The Adjusted Ranking Metric (ARM) is a new
statistic currently available for acute care hospitals that accounts
for differences in the volume of exposure (specifically, in the
denominator) between facilities. ARM provides complementary information
to SIR and was developed for use in acute-care hospitals, but is also
intended for use in post-acute care facilities.\24\
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\24\ More information on how ARM and SIR compare can be found at
<a href="https://www.cdc.gov/nhsn/ps-analysis-resources/arm/index.html">https://www.cdc.gov/nhsn/ps-analysis-resources/arm/index.html</a>.
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4. Measure Application Partnership (MAP) Review
The NHSN HA-CDI Outcome measure (MUC2021-098) was included in the
publicly available ``List of Measures Under Consideration for December
1, 2021'' (MUC List),\25\ a list of measures under consideration for
use in various Medicare programs, including the IRF QRP. This allows
multi-stakeholder groups to provide recommendations to the Secretary on
the measures included on the list.
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\25\ Centers for Medicare & Medicaid Services. List of Measures
Under Consideration for December 1, 2021. Available at <a href="https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf">https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf</a>. Accessed February 7, 2022.
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The NHSN HA-CDI Outcome measure was included under the IRF QRP
Program on the MUC List. The National Quality Forum (NQF)-convened MAP
Post-Acute Care--Long Term Care (PAC-LTC) Workgroup met on January 19,
2022 and provided input on the proposed measure. The MAP offered
conditional support of the NHSN HA-CDI Outcome measure for rulemaking
contingent upon NQF endorsement, noting that the measure has the
potential to mitigate unintended consequences from the current
measure's design, which counts a case based on a positive test only,
which may have led to a historical under-counting of observed HA-CDIs.
The MAP recognized that the measure is consistent with the program's
priority to measure HAIs and the Patient Safety Meaningful Measures 2.0
area.\26\ The final MAP report is available at <a href="https://www.qualityforum.org/Publications/2022/03/MAP_2021-2022_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx">https://www.qualityforum.org/Publications/2022/03/MAP_2021-2022_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx</a>.
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\26\ 2021-2022 MAP Final Recommendations. Available at <a href="https://www.qualityforum.org/map/">https://www.qualityforum.org/map/</a>. Accessed February 3, 2021.
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5. Data Sources
The data source for the NHSN HA-CDI Outcome measure would be the
IRFs' EHR. The primary sources of data for determining numerator events
would include microbiology data (C. difficile infection test),
medication administration data (C. difficile infection antimicrobial
treatment), and patient encounter, demographic, and location
information.
To facilitate rapid, automated, and secure data exchange, the CDC's
NHSN is planning to enable and promote reporting of this measure using
Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR).
However, as HL7 FHIR capabilities are evolving and not uniform across
healthcare systems, CDC is also planning on enabling reporting using
the existing HL7 Clinical Document Architecture (CDA), and potentially
other formats as well in order to provide all facilities with an option
for reporting. Furthermore, this measure would not immediately replace
the current NHSN CDI measure. NHSN would continue to host and support
the current CDI measure until sufficient experience is achieved with
the new measure to phase out the current CDI measure in each applicable
setting.
6. Solicitation of Public Comment
In this proposed rule, we are requesting stakeholder input on the
potential electronic submission of quality data from IRFs via their
EHRs under the IRF QRP. We specifically seek comment on the future
inclusion of the NHSN Healthcare-Associated Clostridioides difficile
Infection Outcome measure (HA-CDI)

[[Page 20247]]

(MUC2021-098) as a digital quality measure in the IRF QRP.
Specifically, we seek comment on the following:
<bullet> Would you support utilizing IRF EHRs as the mechanism of
data collection and submission for IRF QRP measures?
<bullet> Would your EHR support exposing data via HL7 FHIR to a
locally installed MCT? For IRFs using certified health IT systems, how
can existing certification criteria under the Office of the National
Coordinator (ONC) Health Information Technology (IT) Certification
Program support reporting of this data? What updates, if any, to the
Certification Program would be needed to better support capture and
submission of this data?
<bullet> Is a transition period between the current method of data
submission and an electronic submission method necessary? If so, how
long of a transition would be necessary and what specific factors are
relevant in determining the length of any transition?
<bullet> Would vendors, including those that service IRFs, be
interested in or willing to participate in pilots or voluntary
electronic submission of quality data?
<bullet> Do IRFs anticipate challenges, other than the adoption of
EHR to adopting the HA-CDI, and if so, what are potential solutions for
those challenges?
While we will not be responding to specific comments submitted in
response to this RFI in the FY 2023 IRF PPS final rule, we will
actively consider all input as we develop future regulatory proposals.
Any updates to specific program requirements related to quality
measurement and reporting provisions would be addressed through
separate and future notice-and-comment rulemaking, as necessary.

E. Overarching Principles for Measuring Equity and Healthcare Quality
Disparities Across CMS Quality Programs--Request for Information

Significant and persistent disparities in healthcare outcomes exist
in the United States. Belonging to an underserved community is often
associated with worse health
outcomes.27 28 29 30 31 32 33 34 35 With this in mind, CMS
aims to advance health equity, by which we mean the attainment of the
highest level of health for all people, where everyone has a fair and
just opportunity to attain their optimal health regardless of race,
ethnicity, disability, sexual orientation, gender identity,
socioeconomic status, geography, preferred language, or other factors
that affect access to care and health outcomes. CMS is working to
advance health equity by designing, implementing, and operationalizing
policies and programs that support health for all the people served by
our programs, eliminating avoidable differences in health outcomes
experienced by people who are disadvantaged or underserved, and
providing the care and support that our beneficiaries need to
thrive.\36\
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\27\ Joynt KE, Orav E, Jha AK. (2011). Thirty-day readmission
rates for Medicare beneficiaries by race and site of care. JAMA,
305(7):675-681.
\28\ Lindenauer PK, Lagu T, Rothberg MB, et al. (2013). Income
inequality and 30-day outcomes after acute myocardial infarction,
heart failure, and pneumonia: Retrospective cohort study. British
Medical Journal, 346.
\29\ Trivedi AN, Nsa W, Hausmann LRM, et al. (2014). Quality and
equity of care in U.S. hospitals. New England Journal of Medicine,
371(24):2298- 2308.
\30\ Polyakova, M., et al. (2021). Racial disparities in excess
all-cause mortality during the early COVID-19 pandemic varied
substantially across states. Health Affairs, 40(2): 307-316.
\31\ Rural Health Research Gateway. (2018). Rural communities:
Age, Income, and Health status. Rural Health Research Recap.
Available at <a href="https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf">https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf</a>. Accessed
February 3, 2022.
\32\ U.S. Department of Health and Human Services. Office of the
Secretary. Progress Report to Congress. HHS Office of Minority
Health. 2020 Update on the Action Plan to Reduce Racial and Ethnic
Health Disparities. FY 2020. Available at <a href="https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf">https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf</a>. Accessed February 3, 2022.
\33\ Centers for Disease Control and Prevention. Morbidity and
Mortality Weekly Report (MMWR). Heslin, KC, Hall JE. Sexual
Orientation Disparities in Risk Factors for Adverse COVID-19-Related
Outcomes, by Race/Ethnicity--Behavioral Risk Factor Surveillance
System, United States, 2017-2019. February 5, 2021/70(5); 149-154.
Available at <a href="https://www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm?s_cid=mm7005a1_w">https://www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm?s_cid=mm7005a1_w</a>. Accessed February 3, 2022.
\34\ Poteat TC, Reisner SL, Miller M, Wirtz AL. (2020). COVID-19
vulnerability of transgender women with and without HIV infection in
the Eastern and Southern U.S. preprint. medRxiv. 2020;2020.07.21.
20159327. doi:10.1101/2020.07.21.20159327.
\35\ Milkie Vu et al. Predictors of Delayed Healthcare Seeking
Among American Muslim Women, Journal of Women's Health 26(6) (2016)
at 58; S.B. Nadimpalli, et al., The Association between
Discrimination and the Health of Sikh Asian Indians.
\36\ Centers for Medicare and Medicaid Services. Available at
<a href="https://www.cms.gov/pillar/health-equity">https://www.cms.gov/pillar/health-equity</a>. Accessed February 9, 2022.
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We are committed to achieving equity in healthcare outcomes for our
enrollees by supporting healthcare providers' quality improvement
activities to reduce health disparities, enabling them to make more
informed decisions, and promoting healthcare provider accountability
for healthcare disparities.\37\ Measuring healthcare disparities in
quality measures is a cornerstone of our approach to advancing
healthcare equity. Hospital performance results that illustrate
differences in outcomes between patient populations have been reported
to hospitals confidentially since 2015. We provide additional
information about this program in section X.E.1.a. of this proposed
rule.
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\37\ CMS Quality Strategy. 2016. Available at <a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Qualityinitiativesgeninfo/downloads/cms-quality-strategy.pdf">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Qualityinitiativesgeninfo/downloads/cms-quality-strategy.pdf</a>. Accessed February 3, 2022.
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This RFI consists of three sections. The first section discusses a
general framework that could be utilized across CMS quality programs to
assess disparities in healthcare quality. The next section outlines
approaches that could be used in the IRF QRP to assess drivers of
healthcare quality disparities in the IRF QRP. Additionally, this
section discusses measures of health equity that could be adapted for
use in the IRF QRP. Finally, the third section solicits public comment
on the principles and approaches listed in the first two sections as
well as seeking other thoughts about disparity measurement guidelines
for the IRF QRP.
1. Cross-Setting Framework To Assess Healthcare Quality Disparities
CMS has identified five key considerations that we could apply
consistently across CMS programs when advancing the use of measurement
and stratification as tools to address health care disparities and
advance health equity. The remainder of this section describes each of
these considerations.
a. Identification of Goals and Approaches for Measuring Healthcare
Disparities and Using Measures Stratification Across CMS Quality
Programs
By quantifying healthcare disparities through measure
stratification (that is, measuring performance differences among
subgroups of beneficiaries), we aim to provide useful tools for
healthcare providers to drive improvement based on data. We hope that
these results support healthcare providers efforts in examining the
underlying drivers of disparities in their patients' care and to
develop their own innovative and targeted quality improvement
interventions. Quantification of health disparities can also support
communities in prioritizing and engaging with healthcare providers to
execute such interventions, as well as providing additional tools for
accountability and decision-making.
There are several different conceptual approaches to reporting
health

[[Page 20248]]

disparities in the acute care setting, including two complementary
approaches that are already used to confidentially provide disparity
information to hospitals for a subset of existing measures. The first
approach, referred to as the ``within-hospital disparity method,''
compares measure performance results for a single measure between
subgroups of patients with and without a given factor. This type of
comparison directly estimates disparities in outcomes between subgroups
and can be helpful to identify potential disparities in care. This type
of approach can be used with most measures that include patient-level
data. The second approach, referred to as the ``between-hospital
disparity methodology,'' provides performance on measures for only the
subgroup of patients with a particular social risk factor. These
approaches can be used by a healthcare provider to compare their own
measure performance on a particular subgroup of patients against
subgroup-specific state and national benchmarks. Alone, each approach
may provide an incomplete picture of disparities in care for a
particular measure, but when reported together with overall quality
performance, these approaches may provide detailed information about
where differences in care may exist or where additional scrutiny may be
appropriate. For example, the between-provider disparity method may
indicate that an IRF underperformed (when compared to other facilities
on average) for patients with a given social risk factor, which would
signal the need to improve care for this population. However, if the
IRF also underperformed for patients without that social risk factor,
the measured difference, or disparity in care, (the ``within-hospital''
disparity, as described above) could be negligible even though
performance for the group that has been historically marginalized
remains poor. We refer readers to the technical report describing the
CMS Disparity Methods in detail as well as the FY 2018 IPPS/LTCH PPS
final rule (82 FR 38405 through 38407) and the posted Disparity methods
Updates and Specifications Report posted on the QualityNet website.\38\
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\38\ Centers for Medicare & Medicaid Services (CMS), HHS.
Disparity Methods Confidential Reporting. Available at <a href="https://qualitynet.cms.gov/inpatient/measures/disparity-methods">https://qualitynet.cms.gov/inpatient/measures/disparity-methods</a>. Accessed
February 3, 2022.
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CMS is interested in whether similar approaches to the two
discussed in the previous paragraph could be used to produce
confidential stratified measure results for selected IRF QRP measures,
as appropriate and feasible. However, final decisions regarding
disparity reporting will be made at the program-level, as CMS intends
to tailor the approach used in each setting to achieve the greatest
benefit and avoid unintentional consequences or biases in measurement
that may exacerbate disparities in care.
b. Guiding Principles for Selecting and Prioritizing Measures for
Disparity Reporting
We intend to expand our efforts to provide stratified reporting for
additional clinical quality measures, provided they offer meaningful,
actionable, and valid feedback to healthcare providers on their care
for populations that may face social disadvantage or other forms of
discrimination or bias. We are mindful, however, that it may not be
possible to calculate stratified results for all quality measures, and
that there may be situations where stratified reporting is not desired.
To help inform prioritization of the next generation of candidate
measures for stratified reporting, we aim to receive feedback on
several systematic principles under consideration that we believe will
help us prioritize measures for disparity reporting across programs:
(1) Programs may consider stratification among existing clinical
quality measures for further disparity reporting, prioritizing
recognized measures which have met industry standards for measure
reliability and validity.
(2) Programs may consider measures for prioritization that show
evidence that a treatment or outcome being measured is affected by
underlying healthcare disparities for a specific social or demographic
factor. Literature related to the measure or outcome should be reviewed
to identify disparities related to the treatment or outcome, and should
carefully consider both social risk factors and patient demographics.
In addition, analysis of Medicare-specific data should be done in order
to demonstrate evidence of disparity in care for some or most
healthcare providers that treat Medicare patients.
(3) Programs may consider establishing statistical reliability and
representation standards (for example, the percent of patients with a
social risk factor included in reporting facilities) prior to reporting
results. They may also consider prioritizing measures that reflect
performance on greater numbers of patients to ensure that the reported
results of the disparity calculation are reliable and representative.
(4) After completing stratification, programs may consider
prioritizing the reporting of measures that show differences in measure
performance between subgroups across healthcare providers.
c. Principles for Social Risk Factor and Demographic Data Selection and
Use
Social risk factors are the wide array of non-clinical drivers of
health known to negatively impact patient outcomes. These include
factors such as socioeconomic status, housing availability, and
nutrition (among others), often inequitably affecting historically
marginalized communities on the basis of race and ethnicity, rurality,
sexual orientation and gender identity, religion, and
disability.39 40 41 42 43 44 45 46
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\39\ Joynt KE, Orav E, Jha AK. (2011). Thirty-day readmission
rates for Medicare beneficiaries by race and site of care. JAMA,
305(7):675-681.
\40\ Lindenauer PK, Lagu T, Rothberg MB, et al. (2013). Income
inequality and 30-day outcomes after acute myocardial infarction,
heart failure, and pneumonia: Retrospective cohort study. British
Medical Journal, 346.
\41\ Trivedi AN, Nsa W, Hausmann LRM, et al. (2014). Quality and
equity of care in U.S. hospitals. New England Journal of Medicine,
371(24):2298- 2308.
\42\ Polyakova, M., et al. (2021). Racial disparities in excess
all-cause mortality during the early COVID-19 pandemic varied
substantially across States. Health Affairs, 40(2): 307-316.
\43\ Rural Health Research Gateway. (2018). Rural communities:
Age, Income, and Health status. Rural Health Research Recap.
Available at <a href="https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf">https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf</a>. Accessed
February 3, 2022.
\44\ HHS Office of Minority Health (2020). 2020 Update on the
Action Plan to Reduce Racial and Ethnic Health Disparities.
Available at <a href="https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf">https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf</a>. Accessed February 3, 2022.
\45\ Poteat TC, Reisner SL, Miller M, Wirtz AL. 2020. COVID-19
vulnerability of transgender women with and without HIV infection in
the Eastern and Southern U.S. medRxiv [Preprint].
2020.07.21.20159327. doi: 10.1101/2020.07.21.20159327. PMID:
32743608; PMCID: PMC7386532.
\46\ Milkie Vu et al. Predictors of Delayed Healthcare Seeking
Among American Muslim Women, Journal of Women's Health 26(6) (2016)
at 58; S.B. Nadimpalli, et al., The Association between
Discrimination and the Health of Sikh Asian Indians.
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Identifying and prioritizing social risk or demographic variables
to consider for disparity reporting can be challenging. This is due to
the high number of variables that have been identified in the
literature as risk factors for poorer health outcomes and the limited
availability of many self-reported social risk factors and demographic
factors across the healthcare sector. Several proxy data sources, such
as area-based indicators of social risk and imputation methods, may be
used if individual

[[Page 20249]]

patient-level data is not available. Each source of data has advantages
and disadvantages for disparity reporting:
<bullet> Patient-reported data are considered to be the gold
standard for evaluating quality of care for patients with social risk
factors.\47\ While data sources for many social risk factors and
demographic variables are still developing among several CMS settings,
demographic data elements collected through assessments already exist
in IRFs. Beginning October 1, 2022, IRFs (86 FR 62386) will also begin
collecting additional standardized patient data elements about race,
ethnicity, preferred language, transportation, health literacy, and
social isolation.
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\47\ Jarr[iacute]n OF, Nyandege AN, Grafova IB, Dong X, Lin H.
(2020). Validity of race and ethnicity codes in Medicare
administrative data compared with gold-standard self-reported race
collected during routine home health care visits. Med Care,
58(1):e1-e8. doi: 10.1097/MLR.0000000000001216. PMID: 31688554;
PMCID: PMC6904433.
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<bullet> CMS Administrative Claims data have long been used for
quality measurement due to their availability and will continue to be
evaluated for usability in measure development and or stratification.
Using these existing data allows for high impact analyses with
negligible healthcare provider burden. For example, dual eligibility
for Medicare and Medicaid has been found to be an effective indicator
of social risk in beneficiary populations.\48\ There are, however,
limitations in these data's usability for stratification analysis.
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\48\ Office of the Assistant Secretary for Planning and
Evaluation. Report to Congress: Social Risk factors and Performance
Under Medicare's Value-Based Purchasing Program. December 20, 2016.
Available at <a href="https://www.aspe.hhs.gov/reports/report-congress-social-risk-factors-performance-under-medicares-value-based-purchasing-programs">https://www.aspe.hhs.gov/reports/report-congress-social-risk-factors-performance-under-medicares-value-based-purchasing-programs</a>. Accessed February 3, 2022.
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<bullet> Area-based indicators of social risk create approximations
of patient risk based on the neighborhood or context that a patient
resides in. Several indexes, such as Agency for Healthcare Research and
Quality (AHRQ) Socioeconomic Status (SES) Index,\49\ Centers for
Disease Control and Prevention/Agency for Toxic Substances and Disease
Registry (CDC/ATSDR) Social Vulnerability Index (SVI),\50\ and Health
Resources and Services Administration (HRSA) Area Deprivation Index
(ADI),\51\ provide multifaceted contextual information about an area
and may be considered as an efficient way to stratify measures that
include many social risk factors.
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\49\ Bonito A., Bann C., Eicheldinger C., Carpenter L. Creation
of New Race-Ethnicity Codes andSocioeconomic Status (SES) Indicators
for Medicare Beneficiaries. Final Report, Sub-Task 2. (Prepared by
RTI International for the Centers for Medicare and Medicaid Services
through an interagency agreement with the Agency for Healthcare
Research and Policy, under Contract No. 500-00-0024, Task No. 21)
AHRQ Publication No. 08-0029-EF. Rockville, MD, Agency for
Healthcare Research and Quality. January 2008. Available at <a href="https://archive.ahrq.gov/research/findings/final-reports/medicareindicators/medicareindicators1.html">https://archive.ahrq.gov/research/findings/final-reports/medicareindicators/medicareindicators1.html</a>. Accessed February 7, 2022.
\50\ Flanagan, B.E., Gregory, E.W., Hallisey, E.J., Heitgerd,
J.L., Lewis, B. (2011). A social vulnerability index for disaster
management. Journal of Homeland Security and Emergency Management,
8(1). Available at <a href="https://www.atsdr.cdc.gov/placeandhealth/svi/img/pdf/Flanagan_2011_SVIforDisasterManagement-508.pdf">https://www.atsdr.cdc.gov/placeandhealth/svi/img/pdf/Flanagan_2011_SVIforDisasterManagement-508.pdf</a>. Accessed
February 3, 2022.
\51\ Center for Health Disparities Research. University of
Wisconsin School of Medicine and Public health. Neighborhood Atlas.
Available at <a href="https://www.neighborhoodatlas.medicine.wisc.edu/">https://www.neighborhoodatlas.medicine.wisc.edu/</a>.
Accessed February 3, 2022.
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<bullet> Imputed data sources use statistical techniques to
estimate patient-reported factors, including race and ethnicity. One
such tool is the Medicare Bayesian Improved Surname Geocoding (MBISG)
method (currently in version 2.1), which combines information from
administrative data, surname, and residential location to estimate
patient race and ethnicity.\52\
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\52\ Haas A., Elliott M.N., Dembosky J.W., Adams J.L., Wilson-
Frederick S.M., Mallett J.S., Gaillot S, Haffer S.C., Haviland A.M.
(2019). Imputation of race/ethnicity to enable measurement of HEDIS
performance by race/ethnicity. Health Serv Res, 54(1):13-23. doi:
10.1111/1475-6773.13099. Epub 2018 Dec 3. PMID: 30506674; PMCID:
PMC6338295. Imputation of race/ethnicity to enable measurement of
HEDIS performance by race/ethnicity. Available at <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338295/pdf/HESR-54-13.pdf">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338295/pdf/HESR-54-13.pdf</a>.
Accessed February 3, 2022.
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d. Identifying Meaningful Performance Differences
While we aim to use standardized approaches where possible,
identifying differences in performance on stratified results will be
made at the program level due to contextual variations across programs
and settings. We look forward to feedback on the benefits and
limitations of the possible reporting approaches described below:
<bullet> Statistical approaches could be used to reliably group
results, such as using confidence intervals, creating cut points based
on standard deviations, or using a clustering algorithm.
<bullet> Programs could use a ranked ordering and percentile
approach, ordering healthcare providers in a ranked system based on
their performance on disparity measures to quickly allow them to
compare their performance to other similar healthcare providers.
<bullet> Healthcare providers could be categorized into groups
based on their performance using defined thresholds, such as fixed
intervals of results of disparity measures, indicating different levels
of performance.
<bullet> Benchmarking, or comparing individual results to state or
national average, is another potential reporting strategy.
<bullet> Finally, a ranking system may not be appropriate for all
programs and care settings, and some programs may only report disparity
results.
e. Guiding Principles for Reporting Disparity Measures
Reporting of the results discussed above can be employed in several
ways to drive improvements in quality. Confidential reporting, or
reporting results privately to healthcare providers, is generally used
for new programs or new measures recently adopted for programs through
notice and comment rulemaking to give healthcare providers an
opportunity to become more familiar with calculation methods and to
improve before other forms of reporting are used. In addition, many
results are reported publicly, in accordance with the statute. This
method provides all stakeholders with important information on
healthcare provider quality, and in turn, relies on market forces to
incentivize healthcare providers to improve and become more competitive
in their markets without directly influencing payment from CMS. One
important consideration is to assess differential impact on IRFs, such
as those located in rural, or critical access areas, to ensure that
reporting does not disadvantage already resource-limited settings. The
type of reporting chosen by programs will depend on the program
context.
Regardless of the methods used to report results, it is important
to report stratified measure data alongside overall measure results.
Review of both measures results along with stratified results can
illuminate greater levels of detail about quality of care for subgroups
of patients, providing important information to drive quality
improvement. Unstratified quality measure results address general
differences in quality of care between healthcare providers and promote
improvement for all patients, but unless stratified results are
available, it is unclear if there are subgroups of patients that
benefit most from initiatives. Notably, even if overall quality measure
scores improve, without identifying and measuring differences in
outcomes between groups of patients, it is impossible to track progress
in reducing disparity for patients with heightened risk of poor
outcomes.

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2. Approaches To Assessing Drivers of Healthcare Quality Disparities
and Developing Measures of Healthcare Equity in the IRF QRP
This section presents information on two approaches for the IRF
QRP. The first section presents information about a method that could
be used to assist IRFs in identifying potential drivers of healthcare
quality disparities. The second section describes measures of
healthcare equity that might be appropriate for inclusion in the IRF
QRP.
a. Performance Disparity Decomposition
In response to the FY 2022 IRF PPS proposed rule's RFI (86 FR 19110
through 19112), ``Closing the Health Equity Gap in Post-Acute Care
Quality Reporting Programs'', some stakeholders noted that, while
stratified results provide more information about disparities compared
to overall measure scores, they provide limited information towards
understanding the drivers of these disparities. As a result, it is up
to the IRFs to determine which factors are leading to performance gaps
so that they can be addressed. Unfortunately, identifying which factors
are contributing to the performance gaps may not always be
straightforward, especially if the IRF has limited information or
resources to determine the extent to which a patient's social
determinants of health (SDOH) or other mediating factors (for example:
Health histories) explain a given disparity. An additional complicating
factor is the reality that there are likely multiple SDOH and other
mediating factors responsible for a given disparity, and it may not be
obvious to the IRF which of these factors are the primary drivers.
Consequently, CMS may consider methods to use the data already
available in enrollment, claims, and assessment data to estimate the
extent to which various SDOH (for example, transportation, health
literacy) and other mediating factors drive disparities in an effort to
provide more actionable information. Researchers have utilized
decomposition techniques to examine inequality in health care and,
specifically, as a way to understand and explain the underlying causes
of inequality.\53\ At a high level, regression decomposition is a
method that allows one to estimate the extent to which disparities
(that is, differences) in measure performance between subgroups of
patient populations are due to specific factors. These factors can be
either non-clinical (for example, SDOH) or clinical. Similarly, CMS may
utilize regression decomposition to identify and calculate the specific
contribution of SDOHs and other mediating factors to observed
disparities. This approach may better inform our understanding of the
extent to which providers and policy-makers may be able to narrow the
gap in healthcare outcomes. Additionally, provider-specific
decomposition results could be shared through confidential results so
that IRFs can see the disparities within their facility with more
granularity, allowing them to set priority targets in some performance
areas while knowing which areas of their care are already relatively
equitable. Importantly, these results could help IRFs identify reasons
for disparities that might not be obvious without having access to
additional data sources (for example: The ability to link data across
providers).
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\53\ Rahimi E, Hashemi Nazari S. A detailed explanation and
graphical representation of the Blinder-Oaxaca decomposition method
with its application in health inequalities. Emerg Themes Epidemiol.
(2021)18:12. <a href="https://doi.org/10.1186/s12982-021-00100-9">https://doi.org/10.1186/s12982-021-00100-9</a>. Retrieved
2/24/2022.
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To more explicitly demonstrate the types of information that could
be provided through decomposition of a measure disparity, consider the
following example for a given IRF. Figures 1 through 3 depict an
example (using hypothetical data) of how a disparity in a measure of
Medicare Spending Per Beneficiary (MSPB) between dual eligible
beneficiaries (that is, those enrolled in Medicare and Medicaid) and
non-dual eligible beneficiaries (that is, those with Medicare only)
could be decomposed among two mediating factors, one SDOH and one
clinical factor: (1) Low health literacy and (2) high volume of
emergency department (ED) use. These examples were selected because if
they were shown to be drivers of disparity in their IRF, the healthcare
provider could mitigate their effects. Additionally, high volume ED use
is used as a potential mediating factor that could be difficult for
IRFs to determine on their own, as it would require having longitudinal
data for patients across multiple facilities.
In Figure 1, the overall Medicare spending disparity is $1,000:
Spending, on average, is $5,000 per non-dual beneficiary and $6,000 per
dual beneficiary. We can also see from Figure 2 that in this IRF, the
dual population has twice the prevalence of beneficiaries with low
health literacy and high ED use compared to the non-dual population.
Using regression techniques, the difference in overall spending between
non-dual and dual beneficiaries can be divided into three causes: (1) A
difference in the prevalence of mediating factors (for example: Low
health literacy and high ED use) between the two groups, (2) a
difference in how much spending is observed for beneficiaries with
these mediating factors between the two groups, and (3) differences in
baseline spending that are not due to either (1) or (2). In Figure 3,
the `Non-Dual Beneficiaries' column breaks down the overall spending
per non-dual beneficiary, $5,000, into a baseline spending of $4,600
plus the effects of the higher spending for the 10 percent of non-dual
beneficiaries with low health literacy ($300) and the 5 percent with
high ED use ($100). The `Dual Beneficiaries' column similarly
decomposes the overall spending per dual beneficiary ($6,000) into a
baseline spending of $5,000, plus the amounts due to dual
beneficiaries' 20 percent prevalence of low health literacy ($600,
twice as large as the figure for non-dual beneficiaries because the
prevalence is twice as high), and dual beneficiaries' 10 percent
prevalence of high-volume ED use ($200, similarly twice as high as for
non-duals beneficiaries due to higher prevalence). This column also
includes an additional $100 per risk factor because dual beneficiaries
experience a higher cost than non-dual beneficiaries within the low
health literacy risk factor, and similarly within the high ED use risk
factor. Based on this information, an IRF can determine that the
overall $1,000 disparity can be divided into differences simply due to
risk factor prevalence ($300 + $100 = $400 or 40 percent of the total
disparity), disparities in costs for beneficiaries with risk factors
($100 + $100 = $200 or 20 percent) and disparities that remain
unexplained (differences in baseline costs: $400 or 40 percent).
In particular, the IRF can see that simply having more patients
with low health literacy and high ED use accounts for a disparity of
$400. In addition, there is still a $200 disparity stemming from
differences in costs between non-dual and dual patients for a given
risk factor, and another $400 that is not explained by either low
health literacy or high ED use. These differences may instead be
explained by other SDOH that have not yet been included in this
breakdown, or by the distinctive pattern of care decisions made by
providers for dual and non-dual beneficiaries. These cost estimates
would provide additional information that facilities could use when
determining where to devote resources aimed at achieving equitable
health

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outcomes (that is, facilities may choose to focus efforts on the
largest drivers of a disparity).
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BILLING CODE 4120-01-C
b. Measures Related to Health Equity
Beyond identifying disparities in individual health outcomes and by
individual risk factors, there is interest in developing more
comprehensive measures of health equity that reflect organizational
performance. When determining which equity measures could be
prioritized for development for IRF QRP, CMS may consider the
following:
<bullet> Measures should be actionable in terms of quality
improvement;
<bullet> Measures should help beneficiaries and their caregivers
make informed healthcare decisions;
<bullet> Measures should not create incentives to lower the quality
of care; and
<bullet> Measures should adhere to high scientific acceptability
standards.
CMS has developed measures assessing health equity, or designed to
promote health equity, in other settings outside of the IRF. As a
result, there may be measures that could be adapted for use in the IRF
QRP. The remainder of this section discusses two such measures,
beginning with the Health Equity Summary Score (HESS), and then a
structural measure assessing the degree of hospital leadership
engagement in health equity performance data.

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(1) Health Equity Summary Score
The HESS measure was developed by the CMS OMH 54 55 to
identify and to reward healthcare providers (that is, Medicare
Advantage [MA] plans) that perform relatively well on measures of care
provided to beneficiaries with social risk factors (SRFs), as well as
to discourage the non-treatment of patients who are potentially high-
risk, in the context of value-based purchasing. Additionally, a version
of the HESS is under consideration for the Hospital Inpatient Quality
Reporting (HIQR) program.\56\ The HESS composite measure provides a
summary of equity of care delivery by combining performance and
improvement across multiple measures and multiple at-risk groups. The
HESS was developed with the following goals: Allow for ``multiple
grouping variables, not all of which will be measurable for all
plans,'' allow for ``disaggregation by grouping variable for nuanced
insights,'' and allow for the future usage of additional and different
SRFs for grouping.\57\
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\54\ Agniel D., Martino S.C., Burkhart Q., Hambarsoomian K., Orr
N., Beckett M.K., James C., Scholle S.H., WilsonFrederick S., Ng J.,
Elliott M.N. (2021). Incentivizing excellent care to at-risk groups
with a health equity summary score. J Gen Intern Med, 36(7):1847-
1857. doi: 10.1007/s11606-019-05473-x. Epub 2019 Nov 11. PMID:
31713030; PMCID: PMC8298664. Available at <a href="https://link.springer.com/content/pdf/10.1007/s11606-019-05473-x.pdf">https://link.springer.com/content/pdf/10.1007/s11606-019-05473-x.pdf</a>. Accessed February 3,
2022.
\55\ 2021 Quality Conference. Health Equity as a ``New Normal'':
CMS Efforts to Address the Causes of Health Disparities. Available
at <a href="https://s3.amazonaws.com/bizzabo.file.upload/83kO1DYXTs6mKHjVtuk8_1%20-%20Session%2023%20Health%20Equity%20New%20Normal%20FINAL_508.pdf">https://s3.amazonaws.com/bizzabo.file.upload/83kO1DYXTs6mKHjVtuk8_1%20-%20Session%2023%20Health%20Equity%20New%20Normal%20FINAL_508.pdf</a>.
Accessed March 2, 2022.
\56\ Centers for Medicare & Medicaid Services, FY 2022 IPPS/LTCH
PPS Proposed Rule. 88 FR 25560. May 10, 2021.
\57\ Centers for Medicare & Medicaid Services Office of Minority
Health (CMS OMH). 2021b. ``Health Equity as a `New Normal': CMS
Efforts to Address the Causes of Health Disparities.'' Presented at
CMS Quality Conference, March 2-3, 2021.
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The HESS computes across-provider disparity in performance, as well
as within-provider and across-provider disparity improvement in
performance. Calculation starts with a cross-sectional score and an
overall improvement score for each SRF of race/ethnicity and dual
eligibility, for each plan. The overall improvement score is based on
two separate improvement metrics: Within-plan improvement and
nationally benchmarked improvement. Within-plan improvement is defined
as how that plan improves the care of patients with SRFs relative to
higher-performing patients between the baseline period and performance
period, and is targeted at eliminating within-plan disparities.
Nationally benchmarked improvement is improvement of care for
beneficiaries with SRFs served by that MA plan, relative to the
improvement of care for similar beneficiaries across all MA plans, and
is targeted at improving the overall care of populations with SRFs.
Within-plan improvement and nationally benchmarked improvement are then
combined into an overall improvement score. Meanwhile, the cross-
sectional score measures overall measure performance among
beneficiaries with SRFs during the performance period, regardless of
improvement.
To calculate a provider's overall score, the HESS uses a composite
of five clinical quality measures based on HEDIS data and seven MA
Consumer Assessment of Healthcare Providers and Systems (CAHPS) patient
experience measures. A provider's overall HESS score is calculated once
using only CAHPS-based measures and once using only HEDIS-based
measures, due to incompatibility between the two data sources. The HESS
uses a composite of these measures to form a cross-sectional score, a
nationally benchmarked improvement score, and a within-plan improvement
score, one for each SRF. These scores are combined to produce an SRF-
specific blended score, which is then combined with the blended score
for another SRF to produce the overall HESS.
(2) Degree of Hospital Leadership Engagement in Health Equity
Performance Data
We have developed a structural measure for use in acute care
hospitals assessing the degree to which hospital leadership is engaged
in the collection of health equity performance data, with the
motivation that that organizational leadership and culture can play an
essential role in advancing equity goals. This structural measure,
entitled the Hospital Commitment to Health Equity measure (MUC2021-106)
was included on the 2021 CMS List of Measures Under Consideration (MUC
List) \58\ and assesses hospital commitment to health equity using a
suite of equity-focused organizational competencies aimed at achieving
health equity for racial and ethnic minorities, people with
disabilities, sexual and gender minorities, individuals with limited
English proficiency, rural populations, religious minorities, and
people facing socioeconomic challenges. The measure will include five
attestation-based questions, each representing a separate domain of
commitment. A hospital will receive a point for each domain where they
attest to the corresponding statement (for a total of 5 points). At a
high level, the five domains cover the following areas: (1) Strategic
plan to reduce health disparities; (2) approach to collecting valid and
reliable demographic and SDOH data; (3) analyses performed to assess
disparities; (4) engagement in quality improvement activities; \59\ and
(5) leadership involvement in activities designed to reduce
disparities. The specific questions asked within each domain, as well
as the detailed measure specification are found in the CMS List of MUC
for December 2021 at <a href="https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf">https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf</a>. An IRF could receive a point for
each domain where data are submitted through a CMS portal to reflect
actions taken by the IRF for each corresponding domain (for a point
total).
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\58\ Centers for Medicare & Medicaid Services. List of Measures
Under Consideration for December 1, 2021. Available at <a href="https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf">https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf</a>. Accessed 3/1/2022.
\59\ Quality is defined by the National Academy of Medicine as
the degree to which health services for individuals and populations
increase the likelihood of desired health outcomes and are
consistent with current professional knowledge. Quality improvement
is the framework used to systematically improve care. Quality
improvement seeks to standardize processes and structure to reduce
variation, achieve predictable results, and improve outcomes for
patients, healthcare systems, and organizations. Structure includes
things like technology, culture, leadership, and physical capital;
process includes knowledge capital (e.g., standard operating
procedures) or human capital (e.g., education and training).
Available at <a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Quality-Measure-and-Quality-Improvement-">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Quality-Measure-and-Quality-Improvement-</a>. Accessed 3/1/2022.
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CMS believes this type of organizational commitment structural
measure may complement the health disparities approach described in
previous sections, and support IRFs in quality improvement, efficient,
effective use of resources, and leveraging available data. As defined
by AHRQ, structural measures aim to ``give consumers a sense of a
healthcare provider's capacity, systems, and processes to provide high-
quality care.'' \60\ We acknowledge that collection of this structural
measure may impose administrative and/or reporting requirements for
IRFs.
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\60\ Agency for Healthcare Research and Quality. Types of Health
Care Quality Measures. 2015. Available at <a href="https://www.ahrq.gov/talkingquality/measures/types.html">https://www.ahrq.gov/talkingquality/measures/types.html</a>. Accessed February 3, 2022.
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We are interested in obtaining feedback from stakeholders on
conceptual and measurement priorities for the IRF QRP to better
illuminate organizational commitment to health equity.

[[Page 20254]]

3. Solicitation of Public Comment
The goal of this request for information is to describe key
principles and approaches that we will consider when advancing the use
of quality measure development and stratification to address healthcare
disparities and advance health equity across our programs.
We invite general comments on the principles and approaches
described previously in this section of the rule, as well as additional
thoughts about disparity measurement or stratification guidelines
suitable for overarching consideration across CMS' QRP programs.
Specifically, we invite comment on:
<bullet> Identification of Goals and Approaches for Measuring
Healthcare Disparities and Using Measure Stratification Across CMS
Quality Reporting Programs
++ The use of the within- and between-provider disparity methods in
IRFs to present stratified measure results
++ The use of decomposition approaches to explain possible causes of
measure performance disparities
++ Alternative methods to identify disparities and the drivers of
disparities
<bullet> Guiding Principles for Selecting and Prioritizing Measures for
Disparity Reporting
++ Principles to consider for prioritization of health equity measures
and measures for disparity reporting, including prioritizing
stratification for validated clinical quality measures, those measures
with established disparities in care, measures that have adequate
sample size and representation among healthcare providers and outcomes,
and measures of appropriate access and care.
<bullet> Principles for Social Risk Factor and Demographic Data
Selection and Use
++ Principles to be considered for the selection of social risk factors
and demographic data for use in collecting disparity data including the
importance of expanding variables used in measure stratification to
consider a wide range of social risk factors, demographic variables and
other markers of historic disadvantage. In the absence of patient-
reported data we will consider use of administrative data, area-based
indicators and imputed variables as appropriate
<bullet> Identification of Meaningful Performance Differences
++ Ways that meaningful difference in disparity results should be
considered.
<bullet> Guiding Principles for Reporting Disparity Measures
++ Guiding principles for the use and application of the results of
disparity measurement.
<bullet> Measures Related to Health Equity
++ The usefulness of a HESS score for IRFs, both in terms of provider
actionability to improve health equity, and in terms of whether this
information would support Care Compare website users in making informed
healthcare decisions.
++ The potential for a structural measure assessing an IRF's commitment
to health equity, the specific domains that should be captured, and
options for reporting this data in a manner that would minimize burden.
++ Options to collect facility-level information that could be used to
support the calculation of a structural measure of health equity.
++ Other options for measures that address health equity.

While we will not be responding to specific comments submitted in
response to this RFI in the FY 2023 IRF PPS final rule, we will
actively consider all input as we develop future regulatory proposals
or future subregulatory policy guidance. Any updates to specific
program requirements related to quality measurement and reporting
provisions would be addressed through separate and future notice-and-
comment rulemaking, as necessary.

F. Proposals Relating to the Form, Manner, and Timing of Data
Submission Under the IRF QRP

1. Background
We refer readers to the regulatory text at Sec. 412.634(b) for
information regarding the current policies for reporting IRF QRP data.
2. Proposal To Require Quality Data Reporting on All IRF Patients
Beginning With the FY 2025 IRF QRP
a. Background
We have received public input for the past 10 years on the need to
standardize measurement data collection across all payers in the PAC
settings. For example, as part of their recommendations on Coordination
Strategy for Post-Acute Care and Long-term Care Performance
Measurement,\61\ the National Quality Forum (NQF)-convened Measures
Application Partnership (MAP) defined priorities and core measure
concepts for PAC, including IRFs, in order to improve care coordination
for patients. The MAP concluded that standardized measurement data
collection is needed to support the flow of information and data among
PAC providers and recommended CMS collect data across all payers. Since
the implementation of the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) and the development of the
statutorily required quality measures, we have also 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 has been provided to us through
different mechanisms, including comments requested about quality
measure development. Specifically, in response to the call for public
comment on quality measures to satisfy the IMPACT Act domain of
Transfer of Health Information and Care Preferences When an Individual
Transitions,\62\ the majority of comments expressed concern over the
non-standardized populations across the PAC setting and urged CMS to
standardize the patient populations. One commenter stated having an all
payer policy in place in some, but not all PAC settings, limits the
ability of providers and consumers to interpret the information. In the
FY 2018 IRF PPS proposed rule, (82 FR 20740), we sought input on
expanding the quality measures to include all patients regardless of
payer status. In response to the Request for Information (RFI), several
commenters supported expanding the IRF QRP to include all patients
regardless of payer. The Medicare Payment Advisory Committee (MedPAC)
was supportive of the effort to ensure quality care for all patients,
but sensitive to the issue of additional burden, while another
commenter questioned whether the use of additional data would outweigh
the burden of additional reporting. Other commenters were also
supportive, noting that it would not be overly burdensome since most of
their

[[Page 20255]]

organizations' members already complete the IRF-PAI on all patients,
regardless of payer status. One commenter supported the idea since
collecting information on only a subset of patients could be
interpreted as having provided different levels of care based on the
payer.
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\61\ National Quality Forum. MAP Coordination Strategy for Post-
Acute Care and Long-Term Care Performance Measurement. February
2012. Available at <a href="https://www.qualityforum.org/Publications/2012/02/MAP_Coordination_Strategy_for_Post-Acute_Care_and_Long-Term_Care_Performance_Measurement.aspx">https://www.qualityforum.org/Publications/2012/02/MAP_Coordination_Strategy_for_Post-Acute_Care_and_Long-Term_Care_Performance_Measurement.aspx</a>. Accessed January 31, 2022.
\62\ <a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/MMS-Blueprint">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/MMS-Blueprint</a>. Accessed January 31, 2022.
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In the FY 2020 IRF PPS Proposed Rule (84 FR 17326 to 17327), CMS
proposed to expand IRF quality data reporting on all patients
regardless of payer for purposes of the IRF QRP. In the FY 2020 IRF PPS
final rule (84 FR 39161 through 39163), we decided not to finalize the
proposal at the time, but rather use the comments to help inform a
future all payer proposal.
b. Support for Expanding Quality Reporting Data on All IRF Patients
Currently, IRF-PAI assessment data are collected on patients
admitted under the Medicare Part A fee-for-service (FFS) and Medicare
Part C benefits.\63\
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\63\ In the FY 2010 IRF PPS final rule (74 FR 39798 through
39800), CMS revised the regulation text in Sec. Sec. 412.604,
412.606, 412.610, 412.614, and 412.618 to require that all IRFs
submit IRF-PAI data on all of their Medicare Part C patients.
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The concept of requiring quality data reporting on all patients
regardless of payer is not new; as part of the Long-Term Care Hospital
(LTCH) quality reporting program, CMS currently collects quality data
on all patients regardless of payer. CMS also collects quality data on
all Hospice patients for the Hospice Quality Reporting Program (HQRP)
regardless of payer. Eligible clinicians participating in the Merit-
based Incentive Payment System (MIPS) who submit quality measure data
on Qualified Clinical Qualified Data Registry (QCDR) measures, MIPS
clinical quality measures (CQMs) or electronic clinical quality
measures (eCQMs) must submit such data on a specified percentage of
patients regardless of payer. Collecting such quality data on all
patients in the IRF setting would provide the most robust and accurate
representation of quality in the IRFs since CMS does not have access to
other payer claims. Additionally, the data would promote higher quality
and more efficient health care for Medicare beneficiaries and all
patients through the exchange of information and longitudinal analysis
of that data.
We believe that data reporting on standardized patient assessment
data elements using the IRF-PAI should include all IRF patients for the
same reasons we believe that collecting data on Medicare beneficiaries
for the IRF QRP's quality measures is important: To achieve equity in
healthcare outcomes for our beneficiaries by supporting providers in
quality improvement activities, enabling them to make more informed
decisions, and promoting provider accountability for healthcare
disparities.64 65 We believe that we have authority to
collect all payer data for the IRF QRP under section 1886(j)(7) of the
Act. We believe it is necessary to obtain admission and discharge
assessment information on all patients admitted to IRFs in order to
obtain full and complete data regarding the quality of care provided by
the IRF to the Medicare patients receiving care in that facility. We
note, however that this data would not be used by CMS for purposes of
updating the IRF PPS payment rates annually. In addition, we note that
section 1886(j)(7) of the Act does not limit the Secretary to
collecting data only on individuals with Medicare, and therefore this
proposal is not inconsistent with CMS' statutory obligations.
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\64\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-
Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-
Quality-Strategy.pdf.
\65\ Report to Congress: Improving Medicare Post-Acute Care
Transformation (IMPACT) Act of 2014 Strategic Plan for Accessing
Race and Ethnicity Data. January 5, 2017. Available at <a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Research-Reports-2017-Report-to-Congress-IMPACT-ACT-of-2014.pdf">https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Research-Reports-2017-Report-to-Congress-IMPACT-ACT-of-2014.pdf</a>.
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We take the appropriate access to care in IRFs very seriously, and
routinely monitor the QRP measures' performance, including performance
gaps across IRFs. We intend to monitor closely whether any proposed
change to the IRF QRP has unintended consequences on access to care for
high risk patients. Should we find any unintended consequences, we will
take appropriate steps to address these issues in future rulemaking.
Expanding the reporting of quality measures to include all patients,
regardless of payer, would ensure that the IRF QRP makes publicly
available information regarding the quality of services furnished to
the IRF population as a whole, rather than limiting it to only those
patients with Medicare fee-for service or Medicare Advantage benefits.
We also take the privacy and security of protected health
information (PHI) very seriously. Our systems conform to all applicable
Federal laws and regulations as well as Federal government, HHS, and
CMS policies and standards as they relate to information security and
data privacy. The system limits data access to authorized users and
monitors such users to ensure against unauthorized data access or
disclosures.
While we appreciate that collecting quality data on all patients
regardless of payer may create additional burden, we also note that
this burden may be partially offset by eliminating the

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