[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 [www.gpo.gov]
[FR Doc No: 2022-07019]



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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  

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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 http://www.regulations.gov. Follow the ``Submit a 
comment'' instructions.
    2. By regular mail. You may mail written comments to the following 
address ONLY:

Centers for Medicare & Medicaid Services, Department of Health and 
Human Services, Attention: CMS-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: http://www.regulations.gov. Follow the search instructions on that website to 
view public comments. CMS will not post on Regulations.gov 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 https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS.
    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 
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS.

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

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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 https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS.
    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 
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS.
    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 http://dx.doi.org/10.15585/mmwr.mm6905e1.
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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 
https://www.cms.gov/files/document/summary-covid-19-emergency-declaration-waivers.pdf).
    \3\ CMS, ``COVID-19 Frequently Asked Questions (FAQs) on 
Medicare Fee-for-Service (FFS) Billing,'' (updated March 5, 2021) 
(available at https://www.cms.gov/files/document/03092020-covid-19-faqs-508.pdf).
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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 http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Software.html. The Grouper software is also embedded in the internet 
Quality Improvement and Evaluation System (iQIES) User tool available 
in iQIES at https://www.cms.gov/medicare/quality-safety-oversight-general-information/iqies.
    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 http://www.cms.gov/manuals/downloads/clm104c25.pdf.
    Section 3 of the ASCA operates in the context of the administrative 
simplification provisions of HIPAA, which include, among others, the 
requirements for transaction standards and code sets codified in 45 CFR 
part 160 and part 162, subparts A and I through R (generally known as 
the Transactions Rule). The Transactions Rule requires covered 
entities, including covered healthcare providers, to conduct covered 
electronic transactions according to the applicable transaction 
standards. (See the CMS program claim memoranda at http://www.cms.gov/ElectronicBillingEDITrans/ and listed in the addenda to the Medicare 
Intermediary Manual, Part 3, section 3600).
    The MAC processes the claim through its software system. This 
software system includes pricing programming called the ``Pricer'' 
software. The Pricer software uses the CMG number, along with other 
specific claim data elements and provider-specific data, to adjust the 
IRF's prospective payment for interrupted stays, transfers, short 
stays, and deaths, and then applies the applicable adjustments to 
account for the IRF's wage index, percentage of low-income patients, 
rural location, and outlier payments. For discharges occurring on or 
after October 1, 2005, the IRF PPS payment also reflects the teaching 
status adjustment that became effective as of FY 2006, as discussed in 
the FY 2006 IRF PPS final rule (70 FR 47880).

D. Advancing Health Information Exchange

    The Department of Health and Human Services (HHS) has a number of 
initiatives designed to encourage and support the adoption of 
interoperable health information technology and to promote nationwide 
health information exchange to improve health care 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 https://www.hl7.org/fhir/.
    \5\ HL7 FHIR. PACIO Functional Status Implementation Guide. 
Available at https://paciowg.github.io/functional-status-ig/.
    \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 (https://del.cms.gov/DELWeb/pubHome) and 
in the ONC Interoperability Standards Advisory (ISA). The 2022 ISA is 
available at https://www.healthit.gov/isa/sites/isa/files/inline-files/2022-ISA-Reference-Edition.pdf.
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    \7\ Centers for Medicare & Medicaid Services. Newsroom. Fact 
sheet: CMS Data Element Library Fact Sheet. June 21, 2018. Available 
at https://www.cms.gov/newsroom/fact-sheets/cms-data-element-library-fact-sheet.
    \8\ Centers for Medicare & Medicaid Services. Health Informatics 
and Interoperability Group. Policies and Technology for 
Interoperability and Burden Reduction. Available at https://www.cms.gov/Regulations-and-Guidance/Guidance/Interoperability/index.
    \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 https://www.healthit.gov/topic/interoperability/trusted-exchange-framework-and-common-agreement.
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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), https://www.healthit.gov/sites/default/files/page/2022-01/Common_Agreement_for_Nationwide_Health_Information_Interoperability_Version_1.pdf.
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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:
     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.
     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.
     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.
     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.
     Describe the calculation of the IRF standard payment 
conversion factor for FY 2023, as discussed in section V. of this 
proposed rule.
     Update the outlier threshold amount for FY 2023, as 
discussed in section VI. of this proposed rule.
     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.
     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.
     Solicit comments on the methodology used to update the 
facility-level adjustment factors, as discussed in section VIII. of 
this proposed rule.
     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:
     Update data reporting requirements under the IRF QRP 
beginning with FY 2025.
     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 www.bls.gov for 
the BLS historical published TFP data. A complete description of IGI's 
TFP projection methodology is available on the CMS website at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch. 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\ https://www.medpac.gov/wp-content/uploads/2022/03/Mar22_MedPAC_ReportToCongress_SEC.pdf.
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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 https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf.
    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 https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf) 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 
https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf). 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

[[Page 20231]]

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 
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
    Adjustments or updates to the IRF wage index made under section 
1886(j)(6) of the Act must be made in a budget-neutral manner. We 
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.
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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.

[[Page 20233]]

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[[Page 20234]]


[GRAPHIC] [TIFF OMITTED] TP06AP22.018

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 https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
    The resulting figure is the wage-adjusted labor amount. Next, we 
compute the proposed wage-adjusted Federal payment by adding the wage-
adjusted labor amount to the non-labor portion of the proposed Federal 
payment.
    Adjusting the proposed wage-adjusted Federal payment by the 
facility-level adjustments involves several steps. First, we take the 
wage-adjusted prospective payment and multiply it by the appropriate 
rural and LIP adjustments (if applicable). Second, to determine the 
appropriate amount of additional payment for the teaching status 
adjustment (if applicable), we multiply the teaching status adjustment 
(0.0784, in this example) by the wage-adjusted and rural-adjusted 
amount (if applicable). Finally, we add the additional teaching status 
payments (if applicable) to the wage, rural, and LIP-adjusted 
prospective payment rates. Table 7 illustrates the components of the 
adjusted payment calculation.
[GRAPHIC] [TIFF OMITTED] TP06AP22.019

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:
     New IRFs that have not yet submitted their first MCR.
     IRFs whose overall CCR is in excess of the national CCR 
ceiling for FY 2023, as discussed below in this section.
     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:
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    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 https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research. 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.
---------------------------------------------------------------------------

    \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] https://oig.hhs.gov.
---------------------------------------------------------------------------

    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:
     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?
     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?
     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.
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[[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.
---------------------------------------------------------------------------

    \13\ Meaningful Measures 2.0: Moving from Measure Reduction to 
Modernization. Available at https://www.cms.gov/meaningful-measures-20-moving-measure-reduction-modernization.
---------------------------------------------------------------------------

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.
---------------------------------------------------------------------------

    \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 https://www.nejm.org/doi/full/10.1056/NEJMoa1801550. 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 https://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf. 
Accessed February 3, 2022.
---------------------------------------------------------------------------

    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.
---------------------------------------------------------------------------

    \16\ Centers for Medicare and Medicaid Services. (2021) Quality 
Measurement Action Plan. Available at https://www.cms.gov/files/document/2021-cms-quality-conference-cms-quality-measurement-action-plan-march-2021.pdf.
---------------------------------------------------------------------------

    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.
---------------------------------------------------------------------------

    \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) (idsociety.org).
---------------------------------------------------------------------------

    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\
---------------------------------------------------------------------------

    \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 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3925409/.
    \19\ Hoover, R. Benefits of using an electronic health record. 
Nursing Critical Care: January 2017--Volume 12--Issue 1--p 9-10. 
Available at https://journals.lww.com/nursingcriticalcare/fulltext/2017/01000/benefits_of_using_an_electronic_health_record.3.aspx.
    \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 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5510649/.
    \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 https://www.jmir.org/2021/12/e26323.
    \22\ U.S. bureau of Labor Statistics. Occupational Employment 
and Wage Statistics. May 2020 National Occupational Employment and 
Wage Estimates. United States. Available at https://www.bls.gov/oes/current/oes_nat.htm#43-0000. 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.
---------------------------------------------------------------------------

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\
---------------------------------------------------------------------------

    \24\ More information on how ARM and SIR compare can be found at 
https://www.cdc.gov/nhsn/ps-analysis-resources/arm/index.html.
---------------------------------------------------------------------------

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.
---------------------------------------------------------------------------

    \25\ Centers for Medicare & Medicaid Services. List of Measures 
Under Consideration for December 1, 2021. Available at https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf. Accessed February 7, 2022.
---------------------------------------------------------------------------

    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 https://www.qualityforum.org/Publications/2022/03/MAP_2021-2022_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx.
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    \26\ 2021-2022 MAP Final Recommendations. Available at https://www.qualityforum.org/map/. Accessed February 3, 2021.
---------------------------------------------------------------------------

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:
     Would you support utilizing IRF EHRs as the mechanism of 
data collection and submission for IRF QRP measures?
     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?
     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?
     Would vendors, including those that service IRFs, be 
interested in or willing to participate in pilots or voluntary 
electronic submission of quality data?
     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\
---------------------------------------------------------------------------

    \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 https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf. 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 https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf. 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 https://www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm?s_cid=mm7005a1_w. 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 
https://www.cms.gov/pillar/health-equity. Accessed February 9, 2022.
---------------------------------------------------------------------------

    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.
---------------------------------------------------------------------------

    \37\ CMS Quality Strategy. 2016. Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Qualityinitiativesgeninfo/downloads/cms-quality-strategy.pdf. Accessed February 3, 2022.
---------------------------------------------------------------------------

    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\
---------------------------------------------------------------------------

    \38\ Centers for Medicare & Medicaid Services (CMS), HHS. 
Disparity Methods Confidential Reporting. Available at https://qualitynet.cms.gov/inpatient/measures/disparity-methods. Accessed 
February 3, 2022.
---------------------------------------------------------------------------

    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
---------------------------------------------------------------------------

    \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 https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf. 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 https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf. 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.
---------------------------------------------------------------------------

    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:
     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.
---------------------------------------------------------------------------

    \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.
---------------------------------------------------------------------------

     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.
---------------------------------------------------------------------------

    \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 https://www.aspe.hhs.gov/reports/report-congress-social-risk-factors-performance-under-medicares-value-based-purchasing-programs. Accessed February 3, 2022.
---------------------------------------------------------------------------

     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.
---------------------------------------------------------------------------

    \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 https://archive.ahrq.gov/research/findings/final-reports/medicareindicators/medicareindicators1.html. 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 https://www.atsdr.cdc.gov/placeandhealth/svi/img/pdf/Flanagan_2011_SVIforDisasterManagement-508.pdf. Accessed 
February 3, 2022.
    \51\ Center for Health Disparities Research. University of 
Wisconsin School of Medicine and Public health. Neighborhood Atlas. 
Available at https://www.neighborhoodatlas.medicine.wisc.edu/. 
Accessed February 3, 2022.
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     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\
---------------------------------------------------------------------------

    \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 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338295/pdf/HESR-54-13.pdf. 
Accessed February 3, 2022.
---------------------------------------------------------------------------

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:
     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.
     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.
     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.
     Benchmarking, or comparing individual results to state or 
national average, is another potential reporting strategy.
     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.

[[Page 20250]]

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).
---------------------------------------------------------------------------

    \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. https://doi.org/10.1186/s12982-021-00100-9. 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

[[Page 20251]]

outcomes (that is, facilities may choose to focus efforts on the 
largest drivers of a disparity).
BILLING CODE 4120-01-P
[GRAPHIC] [TIFF OMITTED] TP06AP22.026


[[Page 20252]]


[GRAPHIC] [TIFF OMITTED] TP06AP22.027

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:
     Measures should be actionable in terms of quality 
improvement;
     Measures should help beneficiaries and their caregivers 
make informed healthcare decisions;
     Measures should not create incentives to lower the quality 
of care; and
     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.

[[Page 20253]]

(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 https://link.springer.com/content/pdf/10.1007/s11606-019-05473-x.pdf. 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 https://s3.amazonaws.com/bizzabo.file.upload/83kO1DYXTs6mKHjVtuk8_1%20-%20Session%2023%20Health%20Equity%20New%20Normal%20FINAL_508.pdf. 
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 https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf. 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 https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf. 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 https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Quality-Measure-and-Quality-Improvement-. 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 https://www.ahrq.gov/talkingquality/measures/types.html. 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:
 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
 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.
 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
 Identification of Meaningful Performance Differences
++ Ways that meaningful difference in disparity results should be 
considered.
 Guiding Principles for Reporting Disparity Measures
++ Guiding principles for the use and application of the results of 
disparity measurement.
 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 https://www.qualityforum.org/Publications/2012/02/MAP_Coordination_Strategy_for_Post-Acute_Care_and_Long-Term_Care_Performance_Measurement.aspx. Accessed January 31, 2022.
    \62\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/MMS-Blueprint. 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 https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Research-Reports-2017-Report-to-Congress-IMPACT-ACT-of-2014.pdf.
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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 effort to 
separate out Medicare beneficiaries from other patients, which is also 
burdensome. We also acknowledge the concerns raised by some 
stakeholders in the past with respect to the administrative challenges 
of implementing all payer data collection and the need to account for 
the burden related to this proposal. In section XI.B. of this proposed 
rule, we have provided an estimate of additional burden related to this 
proposal.
c. Proposal To Require Quality Data Reporting on All IRF Patients
    In order to facilitate and ensure that high quality care is 
delivered to all patients, including Medicare beneficiaries, in the IRF 
setting, we are proposing to require that the IRF-PAI assessment be 
collected on each patient receiving care in an IRF, regardless of 
payer, beginning with the FY 2025 IRF QRP. If finalized as proposed, 
IRFs would be required to report these data with respect to admission 
and discharge for all patients, regardless of payer, discharged between 
October 1, 2023 and December 31, 2023. This data would be used (in 
addition to the data collected January 1, 2023 through September 30, 
2023) to calculate an IRF's data completion threshold for the FY 2025 
IRF QRP.
    If finalized as proposed, we would revise the IRF-PAI in order for 
IRFs to submit data pursuant to the finalized policy. A new item would 
replace the current item identifying payment source on the IRF-PAI 
admission assessment to collect additional payer(s) information. The 
collection of this item would align with the LTCH setting. A draft IRF 
PAI containing this new item will be available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/IRF-Quality-Reporting. We will notify stakeholders when the draft IRF PAI 
is available.
    We invite public comments on this proposal.
3. Proposed Revisions to the Regulation Text To Require IRFs To Submit 
Patient Assessments on All Patients Beginning With the FY 2025 IRF QRP
    As discussed in section X.F.2 of this proposed rule, we are 
proposing to require that the IRF-PAI assessment be collected on each 
patient receiving care in an IRF, regardless of payer. Therefore, we 
also propose, subject to the aforementioned proposal becoming final, to 
revise the regulation text in

[[Page 20256]]

Sec. Sec.  412.604, 412.606, 412.610, 412.614, and 412.618 so that the 
requirements that IRFs must currently satisfy with respect to 
collection and submission of IRF-PAI data for Medicare Part A and 
Medicare Part C patients would also apply to data on all other IRF 
patients, regardless of payer.
    In addition, we note that CMS' regulations at Sec.  412.610(f) 
currently require IRFs to maintain all PAIs completed on Medicare Part 
A fee-for-service patients within the previous 5 years and Medicare 
Part C (Medicare Advantage) patients within the previous 10 years 
either in a paper format in the patient's clinical record or in an 
electronic computer file format that the IRF can easily obtain and 
produce upon request to CMS or its contractors. Subject to the 
aforementioned all-payer proposal becoming final, we are therefore also 
proposing to revise the regulation text at Sec.  410.610(f) to require 
that IRFs maintain PAIs completed on patients receiving care under all 
other payer sources (that is, other than Medicare Part A and Medicare 
Part C) for 5 years. We are proposing a 5-year period for the same 
reasons we proposed a 5-year requirement for Medicare Part A patients 
in the original Medicare Program; Prospective Payment System for 
Inpatient Rehabilitation Facilities final rule that appeared in the 
August 7, 2001 Federal Register (66 FR 41329). Specifically, the 
assessments may be needed as part of a retrospective review conduced at 
the IRF for various purposes, including the fact that the completed 
patient assessments could be beneficial to other entities that 
appropriately have access to these records (for example, a State or 
Federal agency conducting an investigation due to a complaint of 
patient abuse).
    The proposed revisions are outlined in Sec. Sec.  412.604, 412.606, 
412.610, 412.614, and 412.618 in the regulation text of this proposed 
rule. We invite public comments on this proposal.
4. Proposed Revisions to Sec.  412.614(d)(2) To Correct an Error to the 
Regulatory Text
    In accordance with the Administrative Procedure Act, 5 U.S.C. 553, 
it is the Secretary's practice to offer interested parties the 
opportunity to comment on proposed regulations. However, the regulatory 
changes in this proposal are necessary to correct an error and do not 
establish any new substantive rules.
    We are proposing to revise the regulatory text at Sec.  
412.614(d)(2) to correct a reference to another part of the 
regulations. Specifically, we are replacing a reference to Sec.  
412.23(b)(2) with the correct reference to Sec.  412.29(b)(1). The 
proposed revisions are outlined in the regulation text of this proposed 
rule.
    We invite public comments on this proposal.

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

    We are not proposing any new policies regarding the public display 
of measure data at this time.

XI. Collection of Information Requirements

A. Statutory Requirement for Solicitation of Comments

    Under the Paperwork Reduction Act of 1995, we are required to 
provide 60-day notice in the Federal Register and solicit public 
comment before a collection of information requirement is submitted to 
the Office of Management and Budget (OMB) for review and approval. In 
order to fairly evaluate whether an information collection should be 
approved by OMB, section 3506(c)(2)(A) of the Paperwork Reduction Act 
of 1995 requires that we solicit comment on the following issues:
     The need for the information collection and its usefulness 
in carrying out the proper functions of our agency.
     The accuracy of our estimate of the information collection 
burden.
     The quality, utility, and clarity of the information to be 
collected.
     Recommendations to minimize the information collection 
burden on the affected public, including automated collection 
techniques.
    This proposed rule makes reference to associated information 
collections that are not discussed in the regulation text contained in 
this document.

B. Collection of Information Requirements for Updates Related to the 
IRF QRP Beginning With the FY 2025 IRF QRP

    An IRF that does not meet the requirements of the IRF QRP for a 
fiscal year will receive a 2-percentage point reduction to its 
otherwise applicable annual increase factor for that fiscal year.
    We believe that the burden associated with the IRF QRP is the time 
and effort associated with complying with the requirements of the IRF 
QRP. In section X.F.2. of this proposed rule, we are proposing to 
update the data reporting requirements for the IRF QRP beginning with 
the FY 2025 IRF QRP. We are proposing to require IRFs to collect IRF-
PAI assessment information on each patient receiving care in an IRF, 
regardless of payer. We believe the IRF-PAI items are completed by 
Registered Nurses (RN), Licensed Practical and Licensed Vocational 
Nurses (LVN), Respiratory Therapists (RT), Speech-Language Pathologists 
(SLP), Occupational Therapists (OT), Physical Therapists (PT), and/or 
Psychologists (Psy), depending on the item. We identified the staff 
type per item based on past IRF burden calculations in conjunction with 
expert opinion. Our assumptions for staff type were based on the 
categories generally necessary to perform an assessment. Individual 
providers determine the staffing resources necessary; therefore, we 
averaged the national average for these labor types and established a 
composite cost estimate. This composite estimate was calculated by 
weighting each salary based on the following breakdown regarding 
provider types most likely to collect this data: RN 50 percent; LVN 
31.7 percent; RT 7 percent; SLP 6 percent; PT 2.5 percent; OT 2.5 
percent; Psy 2 percent. For the purposes of calculating the costs 
associated with the collection of information requirements, we obtained 
mean hourly wages for these staff from the U.S. Bureau of Labor 
Statistics' May 2020 National Occupational Employment and Wage 
Estimates.\66\ To account for overhead and fringe benefits, we have 
doubled the hourly wage. These amounts are detailed in Table 13.
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    \66\ https://www.bls.gov/oes/current/oes_nat.htm.

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[[Page 20257]]

[GRAPHIC] [TIFF OMITTED] TP06AP22.028

    As a result of this proposal, the estimated burden and cost for 
IRFs for complying with requirements of the FY 2025 IRF QRP will 
increase. Specifically, we believe that there will be a 1.8 hours 
addition in clinical staff time to report data for each additional IRF-
PAI completed. We estimate the collection of an additional 263,988 IRF-
PAIs from 1,115 IRFs annually. This equates to an increase of 475,178 
hours in burden for all IRFs (1.8 hours x 263,988 discharges). Given 
the clinician times estimated in the previous paragraph and the wages 
in Table 13, we calculated a blended hourly rate of $66.82. We estimate 
that each IRF will complete an average of 237 additional IRF-PAIs per 
year, the total cost related to the additional reporting requirements 
is estimated at $28,505.41 per IRF annually [(237 assessment x 1.8 
hours) x $66.82], or $31,783,532.15 for all IRFs annually ($28.505.41 x 
1,115). The increase in burden will be accounted for in a revised 
information collection request under OMB control number (0938-0842). 
The required 60-day and 30-day notices will publish in the Federal 
Register and the comment periods will be separate from those associated 
with this rulemaking. A 60-day Federal Register notice was published on 
February 3, 2022 (87 FR 6175) to extend the information collection 
request. The 60-day comment period for the extension ends April 4, 
2022. The revision will be submitted at the conclusion of the extension 
process.
    As described in section X.F.2.c. of this proposed rule, a new item 
would replace Item 20 on the IRF-PAI V4.0. However, since this item is 
replacing another item already accounted for in the PRA, we do not 
believe this would add any additional burden to the estimate described 
above.

C. Submission of PRA-Related Comments

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

XII. Response to Comments

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

XIII. Regulatory Impact Analysis

A. Statement of Need

    This proposed rule would update the IRF prospective payment rates 
for FY 2023 as required under section 1886(j)(3)(C) of the Act and in 
accordance with section 1886(j)(5) of the Act, which requires the 
Secretary to publish in the Federal Register on or before August 1 
before each FY, the classification and weighting factors for CMGs used 
under the IRF PPS for such FY and a description of the methodology and 
data used in computing the prospective payment rates under the IRF PPS 
for that FY. This proposed rule would also implement section 
1886(j)(3)(C) of the Act, which requires the Secretary to apply a 
productivity adjustment to the market basket increase factor for FY 
2012 and subsequent years.
    Furthermore, this proposed rule would adopt policy changes under 
the statutory discretion afforded to the Secretary under section 
1886(j) of the Act. We are proposing to update the data reporting 
requirements for the IRF QRP and to amend the regulations consistent 
with the proposed requirements. We are also proposing 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) the potential future inclusion of 
the National Healthcare Safety Network (NHSN) Healthcare associated 
Clostridioides difficile Infection Outcome measure in the IRF QRP; and 
(3) overarching principles for measuring equity and health disparities 
across

[[Page 20258]]

CMS Quality Programs, including the IRF QRP.

B. Overall Impact

    We have examined the impacts of this rule as required by Executive 
Order 12866 on Regulatory Planning and Review (September 30, 1993), 
Executive Order 13563 on Improving Regulation and Regulatory Review 
(January 18, 2011), the Regulatory Flexibility Act (RFA) (September 19, 
1980, Pub. L. 96-354), section 1102(b) of the Social Security Act, 
section 202 of the Unfunded Mandates Reform Act of 1995 (March 22, 
1995; Pub. L. 104-4), Executive Order 13132 on Federalism (August 4, 
1999), and the Congressional Review Act (5 U.S.C. 804(2)).
    Executive Orders 12866 and 13563 direct agencies to assess all 
costs and benefits of available regulatory alternatives and, if 
regulation is necessary, to select regulatory approaches that maximize 
net benefits (including potential economic, environmental, public 
health and safety effects, distributive impacts, and equity). Section 
3(f) of Executive Order 12866 defines a ``significant regulatory 
action'' as an action that is likely to result in a rule: (1) Having an 
annual effect on the economy of $100 million or more in any 1 year, or 
adversely and materially affecting a sector of the economy, 
productivity, competition, jobs, the environment, public health or 
safety, or State, local or tribal governments or communities (also 
referred to as ``economically significant''); (2) creating a serious 
inconsistency or otherwise interfering with an action taken or planned 
by another agency; (3) materially altering the budgetary impacts of 
entitlement grants, user fees, or loan programs or the rights and 
obligations of recipients thereof; or (4) raising novel legal or policy 
issues arising out of legal mandates, the President's priorities, or 
the principles set forth in Executive Order 12866.
    Section (6)(a) of Executive Order 12866 provides that a regulatory 
impact analysis (RIA) must be prepared for major rules with 
economically significant effects ($100 million or more in any 1 year). 
We estimate the total impact of the policy updates described in this 
proposed rule by comparing the estimated payments in FY 2023 with those 
in FY 2022. This analysis results in an estimated $170 million increase 
for FY 2023 IRF PPS payments. Additionally, we estimate that costs 
associated with the proposal to update the reporting requirements under 
the IRF QRP result in an estimated $31,783,532.15 additional cost in FY 
2025 for IRFs. Based on our estimates OMB's Office of Information and 
Regulatory Affairs has determined that this rulemaking is 
``economically significant'' as measured by the $100 million threshold. 
Also, the rule has been reviewed by OMB. Accordingly, we have prepared 
an RIA that, to the best of our ability, presents the costs and 
benefits of the rulemaking.

C. Anticipated Effects

1. Effects on IRFs
    The RFA requires agencies to analyze options for regulatory relief 
of small entities, if a rule has a significant impact on a substantial 
number of small entities. For purposes of the RFA, small entities 
include small businesses, nonprofit organizations, and small 
governmental jurisdictions. Most IRFs and most other providers and 
suppliers are small entities, either by having revenues of $8.0 million 
to $41.5 million or less in any 1 year depending on industry 
classification, or by being nonprofit organizations that are not 
dominant in their markets. (For details, see the Small Business 
Administration's final rule that set forth size standards for health 
care industries, at 65 FR 69432 at https://www.sba.gov/sites/default/files/2019-08/SBA%20Table%20of%20Size%20Standards_Effective%20Aug%2019%2C%202019_Rev.pdf, effective January 1, 2017 and updated on August 19, 2019.) Because 
we lack data on individual hospital receipts, we cannot determine the 
number of small proprietary IRFs or the proportion of IRFs' revenue 
that is derived from Medicare payments. Therefore, we assume that all 
IRFs (an approximate total of 1,115 IRFs, of which approximately 52 
percent are nonprofit facilities) are considered small entities and 
that Medicare payment constitutes the majority of their revenues. HHS 
generally uses a revenue impact of 3 to 5 percent as a significance 
threshold under the RFA. As shown in Table 14, we estimate that the net 
revenue impact of this proposed rule on all IRFs is to increase 
estimated payments by approximately 2.0 percent. The rates and policies 
set forth in this proposed rule would not have a significant impact 
(not greater than 3 percent) on a substantial number of small entities. 
The estimated impact on small entities is shown in Table 14. MACs are 
not considered to be small entities. Individuals and States are not 
included in the definition of a small entity.
    In addition, section 1102(b) of the Act requires us to prepare an 
RIA if a rule may have a significant impact on the operations of a 
substantial number of small rural hospitals. This analysis must conform 
to the provisions of section 603 of the RFA. For purposes of section 
1102(b) of the Act, we define a small rural hospital as a hospital that 
is located outside of a Metropolitan Statistical Area and has fewer 
than 100 beds. As shown in Table 14, we estimate that the net revenue 
impact of this proposed rule on rural IRFs is to increase estimated 
payments by approximately 1.8 percent based on the data of the 133 
rural units and 12 rural hospitals in our database of 1,115 IRFs for 
which data were available. We estimate an overall impact for rural IRFs 
in all areas between -1.8 percent and 2.9 percent. As a result, we 
anticipate that this proposed rule would not have a significant impact 
on a substantial number of small entities.
    Section 202 of the Unfunded Mandates Reform Act of 1995 (Pub. L. 
104-04, enacted on March 22, 1995) (UMRA) also requires that agencies 
assess anticipated costs and benefits before issuing any rule whose 
mandates require spending in any 1 year of $100 million in 1995 
dollars, updated annually for inflation. In 2022, that threshold is 
approximately $165 million. This proposed rule does not mandate any 
requirements for State, local, or tribal governments, or for the 
private sector.
    Executive Order 13132 establishes certain requirements that an 
agency must meet when it issues a proposed rule (and subsequent final 
rule) that imposes substantial direct requirement costs on State and 
local governments, preempts State law, or otherwise has federalism 
implications. As stated, this proposed rule would not have a 
substantial effect on State and local governments, preempt State law, 
or otherwise have a federalism implication.
2. Detailed Economic Analysis
    This proposed rule would update the IRF PPS rates contained in the 
FY 2022 IRF PPS final rule (86 FR 42362). Specifically, this proposed 
rule would update the CMG relative weights and ALOS values, the wage 
index, and the outlier threshold for high-cost cases. This proposed 
rule would apply a productivity adjustment to the FY 2023 IRF market 
basket increase factor in accordance with section 1886(j)(3)(C)(ii)(I) 
of the Act. Further, this proposed rule would codify CMS' existing 
teaching status adjustment policy through proposed amendments to the 
regulation text and would update and clarify the IRF teaching policy 
with respect to IRF hospital closures and

[[Page 20259]]

displaced residents. Additionally, this proposed rule would 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.
    We estimate that the impact of the changes and updates described in 
this proposed rule would be a net estimated increase of $170 million in 
payments to IRF providers. The impact analysis in Table 14 of this 
proposed rule represents the projected effects of the updates to IRF 
PPS payments for FY 2023 compared with the estimated IRF PPS payments 
in FY 2022. We determine the effects by estimating payments while 
holding all other payment variables constant. We use the best data 
available, but we do not attempt to predict behavioral responses to 
these changes, and we do not make adjustments for future changes in 
such variables as number of discharges or case-mix.
    We note that certain events may combine to limit the scope or 
accuracy of our impact analysis, because such an analysis is future-
oriented and, thus, susceptible to forecasting errors because of other 
changes in the forecasted impact time period. Some examples could be 
legislative changes made by the Congress to the Medicare program that 
would impact program funding, or changes specifically related to IRFs. 
Although some of these changes may not necessarily be specific to the 
IRF PPS, the nature of the Medicare program is such that the changes 
may interact, and the complexity of the interaction of these changes 
could make it difficult to predict accurately the full scope of the 
impact upon IRFs.
    In updating the rates for FY 2023, we are proposing standard annual 
revisions described in this proposed rule (for example, the update to 
the wage index and market basket increase factor used to adjust the 
Federal rates). We are also reducing the FY 2023 IRF market basket 
increase factor by a productivity adjustment in accordance with section 
1886(j)(3)(C)(ii)(I) of the Act. We estimate the total increase in 
payments to IRFs in FY 2023, relative to FY 2022, would be 
approximately $170 million.
    This estimate is derived from the application of the FY 2023 IRF 
market basket increase factor, as reduced by a productivity adjustment 
in accordance with section 1886(j)(3)(C)(ii)(I) of the Act, which 
yields an estimated increase in aggregate payments to IRFs of $240 
million. However, there is an estimated $70 million decrease in 
aggregate payments to IRFs due to the proposed update to the outlier 
threshold amount. Therefore, we estimate that these updates would 
result in a net increase in estimated payments of $170 million from FY 
2022 to FY 2023.
    The effects of the proposed updates that impact IRF PPS payment 
rates are shown in Table 14. The following proposed updates that affect 
the IRF PPS payment rates are discussed separately below:
     The effects of the proposed update to the outlier 
threshold amount, from approximately 3.8 percent to 3.0 percent of 
total estimated payments for FY 2023, consistent with section 
1886(j)(4) of the Act.
     The effects of the proposed annual market basket update 
(using the IRF market basket) to IRF PPS payment rates, as required by 
sections 1886(j)(3)(A)(i) and (j)(3)(C) of the Act, including a 
productivity adjustment in accordance with section 1886(j)(3)(C)(i)(I) 
of the Act.
     The effects of applying the proposed budget-neutral labor-
related share and wage index adjustment, as required under section 
1886(j)(6) of the Act.
     The effects of applying the proposed budget-neutral 
permanent cap on wage index decreases policy.
     The effects of the proposed budget-neutral changes to the 
CMG relative weights and ALOS values under the authority of section 
1886(j)(2)(C)(i) of the Act.
     The total change in estimated payments based on the FY 
2023 payment changes relative to the estimated FY 2022 payments.
3. Description of Table 14
    Table 14 shows the overall impact on the 1,115 IRFs included in the 
analysis.
    The next 12 rows of Table 14 contain IRFs categorized according to 
their geographic location, designation as either a freestanding 
hospital or a unit of a hospital, and by type of ownership; all urban, 
which is further divided into urban units of a hospital, urban 
freestanding hospitals, and by type of ownership; and all rural, which 
is further divided into rural units of a hospital, rural freestanding 
hospitals, and by type of ownership. There are 970 IRFs located in 
urban areas included in our analysis. Among these, there are 653 IRF 
units of hospitals located in urban areas and 317 freestanding IRF 
hospitals located in urban areas. There are 145 IRFs located in rural 
areas included in our analysis. Among these, there are 133 IRF units of 
hospitals located in rural areas and 12 freestanding IRF hospitals 
located in rural areas. There are 431 for-profit IRFs. Among these, 
there are 396 IRFs in urban areas and 35 IRFs in rural areas. There are 
577 non-profit IRFs. Among these, there are 489 urban IRFs and 88 rural 
IRFs. There are 107 government-owned IRFs. Among these, there are 85 
urban IRFs and 22 rural IRFs.
    The remaining four parts of Table 14 show IRFs grouped by their 
geographic location within a region, by teaching status, and by DSH 
patient percentage (PP). First, IRFs located in urban areas are 
categorized for their location within a particular one of the nine 
Census geographic regions. Second, IRFs located in rural areas are 
categorized for their location within a particular one of the nine 
Census geographic regions. In some cases, especially for rural IRFs 
located in the New England, Mountain, and Pacific regions, the number 
of IRFs represented is small. IRFs are then grouped by teaching status, 
including non-teaching IRFs, IRFs with an intern and resident to 
average daily census (ADC) ratio less than 10 percent, IRFs with an 
intern and resident to ADC ratio greater than or equal to 10 percent 
and less than or equal to 19 percent, and IRFs with an intern and 
resident to ADC ratio greater than 19 percent. Finally, IRFs are 
grouped by DSH PP, including IRFs with zero DSH PP, IRFs with a DSH PP 
less than 5 percent, IRFs with a DSH PP between 5 and less than 10 
percent, IRFs with a DSH PP between 10 and 20 percent, and IRFs with a 
DSH PP greater than 20 percent.
    The estimated impacts of each policy described in this rule to the 
facility categories listed are shown in the columns of Table 14. The 
description of each column is as follows:
     Column (1) shows the facility classification categories.
     Column (2) shows the number of IRFs in each category in 
our FY 2023 analysis file.
     Column (3) shows the number of cases in each category in 
our FY 2023 analysis file.
     Column (4) shows the estimated effect of the proposed 
adjustment to the outlier threshold amount.
     Column (5) shows the estimated effect of the proposed 
update to the IRF labor-related share and wage index, in a budget-
neutral manner.
     Column (6) shows the estimated effect of the proposed 
permanent cap on wage index decreases policy, in a budget-neutral 
manner.
     Column (7) shows the estimated effect of the proposed 
update to the CMG relative weights and ALOS values, in a budget-neutral 
manner.
     Column (8) compares our estimates of the payments per 
discharge, incorporating all of the policies reflected in this proposed 
rule for FY

[[Page 20260]]

2023 to our estimates of payments per discharge in FY 2022.
    The average estimated increase for all IRFs is approximately 2.0 
percent. This estimated net increase includes the effects of the 
proposed IRF market basket increase factor for FY 2023 of 2.8 percent, 
which is based on a proposed IRF market basket update of 3.2 percent, 
less a 0.4 percentage point productivity adjustment, as required by 
section 1886(j)(3)(C)(ii)(I) of the Act. It also includes the 
approximate 0.8 percent overall decrease in estimated IRF outlier 
payments from the proposed update to the outlier threshold amount. 
Since we are making the updates to the IRF wage index, labor-related 
share and the CMG relative weights in a budget-neutral manner, they 
will not be expected to affect total estimated IRF payments in the 
aggregate. However, as described in more detail in each section, they 
will be expected to affect the estimated distribution of payments among 
providers.
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[GRAPHIC] [TIFF OMITTED] TP06AP22.029


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[GRAPHIC] [TIFF OMITTED] TP06AP22.030

BILLING CODE 4120-01-C
4. Impact of the Proposed Update to the Outlier Threshold Amount
    The estimated effects of the proposed update to the outlier 
threshold adjustment are presented in column 4 of Table 14.
    For this proposed rule, we are using preliminary FY 2021 IRF claims 
data, and, based on that preliminary analysis, we estimated that IRF 
outlier payments as a percentage of total estimated IRF payments would 
be 3.8 percent in FY 2023. Thus, we propose to adjust the outlier 
threshold amount in this proposed rule to maintain total estimated 
outlier payments equal to 3 percent of total estimated payments in FY 
2023. The estimated change in total IRF payments for FY 2023, 
therefore, includes an approximate 0.8 percentage point decrease in 
payments because the estimated outlier portion of total payments is 
estimated to decrease from approximately 3.8 percent to 3 percent.
    The impact of this proposed outlier adjustment update (as shown in 
column 4 of Table 14) is to decrease estimated overall payments to IRFs 
by 0.8 percentage point.
5. Impact of the Proposed Wage Index and Labor-Related Share
    In column 5 of Table 14, we present the effects of the proposed 
budget-neutral update of the wage index and labor-related share. The 
proposed changes to the wage index and the

[[Page 20263]]

labor-related share are discussed together because the wage index is 
applied to the labor-related share portion of payments, so the proposed 
changes in the two have a combined effect on payments to providers. As 
discussed in section V.C. of this proposed rule, we are proposing to 
update the labor-related share from 72.9 percent in FY 2022 to 73.2 
percent in FY 2023. In aggregate, we do not estimate that these 
proposed updates will affect overall estimated payments to IRFs. 
However, we do expect these updates to have small distributional 
effects.
6. Impact of the Proposed Wage Index Policy
    In column 6 of Table 14, we present the effects of the budget-
neutral proposed permanent cap on wage index decreases policy. As 
discussed in section V.D.3 of this proposed rule, we are proposing to 
apply a permanent 5-percent cap on any decrease to a provider's wage 
index from its wage index in the prior year to smooth the impact of 
year-to-year changes in IRF payments related to changes in the IRF wage 
index. We are required by section 1886(j)(6) of the Act, to implement 
changes to the wage index in a budget-neutral manner. Thus, there will 
not be an impact on aggregate Medicare payments to IRFs.
7. Impact of the Proposed Update to the CMG Relative Weights and ALOS 
Values
    In column 7 of Table 14, we present the effects of the proposed 
budget-neutral update of the CMG relative weights and ALOS values. In 
the aggregate, we do not estimate that these proposed updates will 
affect overall estimated payments of IRFs. However, we do expect these 
updates to have small distributional effects.
8. Effects of Proposed Codification and Clarifications of IRF Teaching 
Status Adjustment Policy
    As discussed in section VII. of this proposed rule, we are 
proposing to codify the longstanding teaching status adjustment policy 
through the proposed amendments to the regulation text at Sec.  412.602 
and Sec.  412.624(e)(4) provided in this proposed rule.
    We do not anticipate a financial impact associated with the 
proposed codification of the IRF teaching status adjustment policies. 
However, the clarification of certain teaching status adjustment 
policies and proposed codification of these policies will enable us to 
align the IRF policies with recent updates to the IPPS and IPF teaching 
status adjustment policies. Aligning the policy guidance with other 
post-acute care setting regulations will also assist stakeholders in 
providing care for Medicare beneficiaries.
9. Effects of Requirements for the IRF QRP for FY 2025
    In accordance with section1886(j)(7)(A) of the Act, the Secretary 
must reduce by 2 percentage points the annual market basket increase 
factor otherwise applicable to an IRF for a fiscal year if the IRF does 
not comply with the requirements of the IRF QRP for that fiscal year. 
In section X.A. of this proposed rule, we discuss the method for 
applying the 2 percentage point reduction to IRFs that fail to meet the 
IRF QRP requirements.
    As discussed in section X.F.2. of this proposed rule, we are 
proposing to require the reporting of quality data on all patients 
discharged from the IRF beginning with the FY 2025 IRF QRP. We describe 
the estimated burden for this proposal in section XI.B. of this 
proposed rule. In summary, the proposed changes to the IRF QRP will 
result in a burden addition of $28,505.41 per IRF annually, or 
$31,783,532.15 for all IRFs annually beginning with the FY 2025 IRF 
QRP. We note, however, that this estimate may be partially offset by 
eliminating the effort to separate out Medicare beneficiaries from 
other patients.

D. Alternatives Considered

    The following is a discussion of the alternatives considered for 
the IRF PPS updates contained in this proposed rule.
    Section 1886(j)(3)(C) of the Act requires the Secretary to update 
the IRF PPS payment rates by an increase factor that reflects changes 
over time in the prices of an appropriate mix of goods and services 
included in the covered IRF services.
    As noted previously in this proposed rule, section 
1886(j)(3)(C)(ii)(I) of the Act requires the Secretary to apply a 
productivity adjustment to the market basket increase factor for FY 
2023. Thus, in accordance with section 1886(j)(3)(C) of the Act, we 
propose to update the IRF prospective payments in this proposed rule by 
2.8 percent (which equals the 3.2 percent estimated IRF market basket 
increase factor for FY 2023 reduced by a 0.4 percentage point 
productivity adjustment as determined under section 
1886(b)(3)(B)(xi)(II) of the Act (as required by section 
1886(j)(3)(C)(ii)(I) of the Act)).
    We considered maintaining the existing CMG relative weights and 
average length of stay values for FY 2023. However, in light of 
recently available data and our desire to ensure that the CMG relative 
weights and average length of stay values are as reflective as possible 
of recent changes in IRF utilization and case mix, we believe that it 
is appropriate to propose to update the CMG relative weights and 
average length of stay values at this time to ensure that IRF PPS 
payments continue to reflect as accurately as possible the current 
costs of care in IRFs.
    We considered maintaining the existing outlier threshold amount for 
FY 2022. However, analysis of updated FY 2021 data indicates that 
estimated outlier payments would be more than 3 percent of total 
estimated payments for FY 2023, by approximately 0.8 percent, unless we 
updated the outlier threshold amount. Consequently, we propose 
adjusting the outlier threshold amount in this proposed rule to reflect 
a 0.8 percent decrease thereby setting the total outlier payments equal 
to 3 percent, instead of 3.8 percent, of aggregate estimated payments 
in FY 2023.
    We considered not amending Sec.  412.602 and Sec.  412.624(e)(4) to 
codify our longstanding guidance on the teaching status adjustment 
policies and update the IRF teaching policy on IRF program closures and 
displaced residents. However, we believe that codifying these 
longstanding policies into regulation text would improve clarity and 
reduce administrative burden on IRF providers trying to locate all 
relevant information regarding the teaching status adjustment. 
Additionally, we believe that we should streamline all teaching status 
adjustment policy information in the same place for ease of reference.

E. Regulatory Review Costs

    If regulations impose administrative costs on private entities, 
such as the time needed to read and interpret this proposed rule, we 
should estimate the cost associated with regulatory review. Due to the 
uncertainty involved with accurately quantifying the number of entities 
that will review the rule, we assume that the total number of unique 
commenters on the FY 2023 IRF PPS proposed rule will be the number of 
reviewers of last year's proposed rule. We acknowledge that this 
assumption may understate or overstate the costs of reviewing this 
proposed. It is possible that not all commenters reviewed the FY 2022 
IRF PPS proposed rule in detail, and it is also possible that some 
reviewers chose not to comment on the FY 2022 proposed rule. For these 
reasons, we thought that the number of commenters would be a fair 
estimate of

[[Page 20264]]

the number of reviewers of this proposed rule.
    We also recognize that different types of entities are in many 
cases affected by mutually exclusive sections of this proposed rule, 
and therefore, for the purposes of our estimate we assume that each 
reviewer reads approximately 50 percent of the rule. We seek comments 
on this assumption.
    Using the national mean hourly wage data from the May 2020 BLS for 
Occupational Employment Statistics (OES) for medical and health service 
managers (SOC 11-9111), we estimate that the cost of reviewing this 
rule is $114.24 per hour, including overhead and fringe benefits 
(https://www.bls.gov/oes/current/oes_nat.htm). Assuming an average 
reading speed, we estimate that it would take approximately 3 hours for 
the staff to review half of this proposed rule. For each reviewer of 
the rule, the estimated cost is $342.72 (3 hours x $114.24). Therefore, 
we estimate that the total cost of reviewing this regulation is 
$17,478.72 ($342.72 x (50 reviewers).

F. Accounting Statement and Table

    As required by OMB Circular A-4 (available at https://www.whitehouse.gov/wp-content/uploads/legacy_drupal_files/omb/circulars/A4/a-4.pdf), in Table 15 we have prepared an accounting 
statement showing the classification of the expenditures associated 
with the provisions of this proposed rule. Table 15 provides our best 
estimate of the increase in Medicare payments under the IRF PPS as a 
result of the proposed updates presented in this proposed rule based on 
the data for 1,115 IRFs in our database.
[GRAPHIC] [TIFF OMITTED] TP06AP22.031

G. Conclusion

    Overall, the estimated payments per discharge for IRFs in FY 2023 
are projected to increase by 2.0 percent, compared with the estimated 
payments in FY 2022, as reflected in column 8 of Table 14.
    IRF payments per discharge are estimated to increase by 2.0 percent 
in urban areas and 1.8 percent in rural areas, compared with estimated 
FY 2022 payments. Payments per discharge to rehabilitation units are 
estimated to increase 1.2 percent in urban areas and 1.5 percent in 
rural areas. Payments per discharge to freestanding rehabilitation 
hospitals are estimated to increase 2.6 percent in urban areas and 
increase 2.5 percent in rural areas.
    Overall, IRFs are estimated to experience a net increase in 
payments as a result of the proposed policies in this proposed rule. 
The largest payment increase is estimated to be a 2.9 percent increase 
for rural IRFs located in the rural New England region. The analysis 
above, together with the remainder of this preamble, provides an RIA.
    In accordance with the provisions of Executive Order 12866, this 
regulation was reviewed by OMB.
    Chiquita Brooks-LaSure, Administrator of the Centers for Medicare & 
Medicaid Services, approved this document on March 22, 2022.

List of Subjects in 42 CFR Part 412

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

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

PART 412--PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL 
SERVICES

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

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

0
2. Amend Sec.  412.602 by adding the definitions of ``Closure of an 
IRF'', ``Closure of an IRF's residency training program'', and 
``Displaced resident'' in alphabetical order to read as follows:


Sec.  412.602   Definitions.

* * * * *
    Closure of an IRF has the same meaning as ``closure of a hospital'' 
as defined in Sec.  413.79(h)(1)(i) as applied to an IRF meeting the 
requirements of Sec.  412.604(b) for the purposes of accounting for 
indirect teaching costs.
    Closure of an IRF's residency training program has the same meaning 
as ``closure of a hospital residency training program'' as defined in 
Sec.  413.79(h)(1)(ii) as applied to an IRF meeting the requirements of 
Sec.  412.604(b) for the purposes of accounting for indirect teaching 
costs.
* * * * *
    Displaced resident has the same meaning as a ``displaced resident'' 
as defined in Sec.  413.79(h)(1)(iii) as applied to an IRF, for 
purposes of accounting for indirect teaching costs.
* * * * *
0
3. Amend Sec.  412.604 by revising paragraph (c) to read as follows:


Sec.  412.604   Conditions for payment under the prospective payment 
system for inpatient rehabilitation facilities.

* * * * *
    (c) Completion of patient assessment instrument. For each Medicare 
part A fee-for-service patient admitted to or discharged from an IRF on 
or after January 1, 2002, the inpatient rehabilitation facility must 
complete a patient assessment instrument in accordance with Sec.  
412.606. IRFs must also complete a patient assessment instrument in 
accordance with Sec.  412.606 for each Medicare Part C (Medicare 
Advantage) patient admitted to or discharged from an IRF on or after 
October 1, 2009. In addition, IRFs must complete a patient assessment 
instrument in accordance with Sec.  412.606 for all other patients, 
regardless of payer, admitted to or discharged from an IRF on or after 
October 1, 2023.
* * * * *

[[Page 20265]]

0
4. Amend Sec.  412.606 by--
0
a. Revising paragraphs (a) and (b)(1) to read as follows:


Sec.  412.606   Patient assessments.

    (a) Patient assessment instrument. An inpatient rehabilitation 
facility must use the CMS inpatient rehabilitation facility patient 
assessment instrument to assess Medicare Part A fee-for-service and 
Medicare Part C (Medicare Advantage) inpatients who are admitted on or 
after January 1, 2002, or were admitted before January 1, 2002, and are 
still inpatients as of January 1, 2002.
    (1) Starting on October 1, 2023, inpatient rehabilitation 
facilities must use the CMS inpatient rehabilitation facility patient 
assessment instrument to assess all inpatients, regardless of payer, 
who are admitted on or after October 1, 2023, or who were admitted 
before October 1, 2023 and are still inpatients as of October 1, 2023.
    (2) [Reserved]
    (b) * * * (1) A clinician of the inpatient rehabilitation facility 
must perform a comprehensive, accurate, standardized, and reproducible 
assessment of each Medicare Part A fee-for-service inpatient using the 
inpatient rehabilitation facility patient assessment instrument 
specified in paragraph (b) of this section as part of his or her 
patient assessment in accordance with the schedule described in Sec.  
412.610. IRFs must also complete a patient assessment instrument in 
accordance with Sec.  412.606 for each Medicare Part C (Medicare 
Advantage) patient admitted to or discharged from an IRF on or after 
October 1, 2009. In addition, IRFs must complete a patient assessment 
instrument in accordance with Sec.  412.606 for all other patients, 
regardless of payer, admitted to or discharged from an IRF on or after 
October 1, 2023.
* * * * *
0
5. Amend Sec.  412.610 by revising paragraphs (a), (b), (c) 
introductory text, (c)(1)(i)(A), (c)(2)(ii)(B) and (f) to read as 
follows:


Sec.  412.610   Assessment schedule.

    (a) General. For each inpatient, an inpatient rehabilitation 
facility must complete a patient assessment instrument as specified in 
Sec.  412.606 that covers a time period that is in accordance with the 
assessment schedule specified in paragraph (c) of this section.
    (b) Starting the assessment schedule day count. The first day that 
the inpatient is furnished services during his or her current inpatient 
rehabilitation facility hospital stay is counted as day one of the 
patient assessment schedule.
    (c) Assessment schedules and references dates. The inpatient 
rehabilitation facility must complete a patient assessment instrument 
upon the patient's admission and discharge as specified in paragraphs 
(c)(1) and (2) of this section.
    (1) * * *
    (i) * * *
    (A) General. Time period is a span of time that covers calendar 
days 1 through 3 of the patient's current hospitalization.
* * * * *
    (2) * * *
    (ii) * * *
    (B) The patient stops being furnished inpatient rehabilitation 
services.
* * * * *
    (f) Patient assessment instrument record retention. An inpatient 
rehabilitation facility must maintain all patient assessment data sets 
completed on all Medicare Part A fee-for-service patients within the 
previous 5 years, on Medicare Part C (Medicare Advantage) patients 
within the previous 10 years, and all other patients within the 
previous 5 years either in a paper format in the patient's clinical 
record or in an electronic computer file format that the inpatient 
rehabilitation facility can easily obtain and produce upon request to 
CMS or its contractors.
0
6. Amend Sec.  412.614 by--
0
a. Revising paragraphs (a) introductory text and (b)(1);
0
b. Revising paragraph (d)(2)
0
c. Adding paragraph (d)(3).
0
d. Revising paragraph (e).
    The revisions and additions read as follows:


Sec.  412.614   Transmission of patient assessment data.

    (a) Data format. General Rule. The inpatient rehabilitation 
facility must encode and transmit data for each inpatient--
* * * * *
    (b) * * *
    (1) Electronically transmit complete, accurate, and encoded data 
from the patient assessment instrument for each inpatient to our 
patient data system in accordance with the data format specified in 
paragraph (a) of this section; and
* * * * *
    (d) * * *
    (2) Medicare Part C (Medicare Advantage) data. Failure of the 
inpatient rehabilitation facility to transmit all of the required 
patient assessment instrument data for its Medicare Part C (Medicare 
Advantage) patients to our patient data system in accordance with the 
transmission timeline in paragraph (c) of this section will result in a 
forfeiture of the facility's ability to have any of its Medicare Part C 
(Medicare Advantage) data used in the calculations for determining the 
facility's compliance with the regulations in Sec.  412.29(b)(1).
    (3) All other payer data. Failure of the inpatient rehabilitation 
facility to transmit all of the required patient assessment instrument 
data for all other patients, regardless of payer, to our patient data 
system in accordance with the transmission timeline in paragraph (c) of 
this section will result in a forfeiture of the facility's ability to 
have any of its other payer data used in the calculations for 
determining the facility's compliance with the regulations in Sec.  
412.29(b)(1).
    (e) Exemption to the consequences for transmitting the IRF-PAI data 
late for Medicare Part C (Medicare Advantage) patients and all other 
patients, regardless of payer. CMS may waive the consequences of 
failure to submit complete and timely IRF-PAI data specified in 
paragraph (d) of this section when, due to an extraordinary situation 
that is beyond the control of an inpatient rehabilitation facility, the 
inpatient rehabilitation facility is unable to transmit the patient 
assessment data in accordance with paragraph (c) of this section. Only 
CMS can determine if a situation encountered by an inpatient 
rehabilitation facility is extraordinary and qualifies as a situation 
for waiver of the forfeiture specified in paragraphs (d)(2) or (3) of 
this section. An extraordinary situation may be due to, but is not 
limited to, fires, floods, earthquakes, or similar unusual events that 
inflect extensive damage to an inpatient facility. An extraordinary 
situation may be one that produces a data transmission problem that is 
beyond the control of the inpatient rehabilitation facility, as well as 
other situations determined by CMS to be beyond the control of the 
inpatient rehabilitation facility. An extraordinary situation must be 
fully documented by the inpatient rehabilitation facility.
0
7. Amend Sec.  412.618 by amending the introductory text to read as 
follows:


Sec.  412.618   Assessment process for interrupted stays.

    For purposes of the patient assessment process, if any patient has 
an interrupted stay, as defined under Sec.  412.602, the following 
applies:
* * * * *
0
8. Amend Sec.  412.624 by revising paragraphs (e)(1) and (4) to read as 
follows:

[[Page 20266]]

Sec.  412.624   Methodology for calculating the Federal prospective 
payment rates.

* * * * *
    (e) * * *
    (1) Adjustment for area wage levels. The labor portion of a 
facility's Federal prospective payment is adjusted to account for 
geographical differences in the area wage levels using an appropriate 
wage index.
    (i) The application of the wage index is made on the basis of the 
location of the facility in an urban or rural area as defined in Sec.  
412.602.
    (ii) Starting on October 1, 2022, CMS applies a cap on decreases to 
the wage index such that the wage index applied to an IRF is not less 
than 95 percent of the wage index applied to that IRF in the prior FY.
    (iii) Adjustments or updates to the wage data used to adjust a 
facility's Federal prospective payment rate under paragraph (e)(1) of 
this section will be made in a budget neutral manner. CMS determines a 
budget neutral wage adjustment factor, based on any adjustment or 
update to the wage data, to apply to the standard payment conversion 
factor.
* * * * *
    (4) Adjustments for teaching hospitals.(i) General. 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.
    (A) An IRF's teaching adjustment is based on the ratio of the 
number of full-time equivalent residents training in the IRF divided by 
the facility's average daily census.
    (B) As described in Sec.  412.105(f)(1)(iii)(A), residents with 
less than full-time status are counted as partial full time equivalent 
based on the proportion of time assigned to the inpatient 
rehabilitation facility compared to the total time necessary to fill a 
residency slot. Residents rotating to more than one hospital or non-
hospital setting will be counted in proportion to the time they are 
assigned to inpatient rehabilitation facility compared to the total 
time worked in all locations. An inpatient rehabilitation facility 
cannot claim time spent by the resident at another inpatient 
rehabilitation facility or hospital.
    (C) Except as described in paragraph (e)(4)(i)(D) of this section, 
the actual number of current year full-time equivalent residents used 
in calculating the teaching adjustment is limited to the number of 
full-time equivalent residents in the IRF's final settled cost report 
for the most recent cost reporting period ending on or before November 
15, 2004 (base year).
    (D) If the inpatient rehabilitation facility first begins training 
residents in a new approved graduate medical education program after 
November 15, 2004, the number of full-time equivalent residents 
determined under paragraph (e)(4)(i)(C) of this section may be adjusted 
using the method described in Sec.  413.79(e)(1)(i).
    (E) The teaching 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.
    (ii) Closure of an IRF or IRF residency training program. (A) 
Closure of an IRF. For cost reporting periods beginning on or after 
October 1, 2011, an IRF may receive a temporary adjustment to its FTE 
cap to reflect displaced residents added because of another IRFs 
closure if the IRF meets the following criteria:
    (1) The IRF is training additional displaced residents from an IRF 
that closed on or after October 1, 2011.
    (2) No later than 60 days after the IRF begins to train the 
displaced residents, the IRF submits a request to its Medicare 
contractor for a temporary adjustment by identifying the displaced 
residents who have come from the closed IRF and have caused the IRF to 
exceed its cap, and specifies the length of time the adjustment is 
needed.
    (B) Closure of an IRF's residency training program. If an IRF that 
closes its residency training program on or after October 1, 2011, 
agrees to temporarily reduce its FTE cap according to the criteria 
specified in paragraph (e)(4)(ii)(A)(2) of this section, another IRF(s) 
may receive a temporary adjustment to its FTE cap to reflect displaced 
residents added because of the closure of the residency training 
program if the criteria specified in paragraph (e)(4)(ii)(A)(1) of this 
section are met.
    (1) Receiving IRF(s). For cost reporting periods beginning on or 
after October 1, 2011, an IRF may receive a temporary adjustment to its 
FTE cap to reflect displaced residents added because of the closure of 
another IRF's residency training program if the IRF is training 
additional displaced residents from the residency training program of 
an IRF that closed a program; and if no later than 60 days after the 
IRF begins to train the displaced residents the IRF submits to its 
Medicare Contractor a request for a temporary adjustment to its FTE 
cap, documents that it is eligible for this temporary adjustment by 
identifying the displaced residents who have come from another IRF's 
closed program and have caused the IRF to exceed its cap, specifies the 
length of time the adjustment is needed, and submits to its Medicare 
Contractor a copy of the FTE reduction statement by the hospital that 
closed its program, as specified in paragraph (ii)(A)(2) of this 
section.
    (2) IRF that closed its program. An IRF that agrees to train 
displaced residents who have been displaced by the closure of another 
IRF's program may receive a temporary FTE cap adjustment only if the 
hospital with the closed program temporarily reduces its FTE cap based 
on the FTE of displaced residents in each program year training in the 
program at the time of the programs closure. This yearly reduction in 
the FTE cap will be determined based on the number of those displaced 
residents who would have been training in the program during that year 
had the program not closed. No later than 60 days after the displaced 
residents who were in the hospital that closed its program(s) begin 
training at another hospital must submit to its Medicare Contractor a 
statement signed and dated by its representative that specifies that it 
agrees to the temporary reduction in its FTE cap to allow the IRF 
training the displaced residents to obtain a temporary adjustment to 
its cap; identifies the displaced residents who were in the training at 
the time of the program's closure; identifies the IRFs to which the 
displaced residents are transferring once the program closes; and 
specifies the reduction for the applicable program years.
* * * * *

    Dated: March 30, 2022.
Xavier Becerra,
Secretary, Department of Health and Human Services.
[FR Doc. 2022-07019 Filed 3-31-22; 4:15 pm]
BILLING CODE 4120-01-P