[Federal Register Volume 87, Number 64 (Monday, April 4, 2022)]
[Proposed Rules]
[Pages 19415-19441]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-06906]


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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Medicare & Medicaid Services

42 CFR Part 412

[CMS-1769-P]
RIN 0938-AU80


Medicare Program; FY 2023 Inpatient Psychiatric Facilities 
Prospective Payment System--Rate Update and Quality Reporting--Request 
for Information

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

ACTION: Proposed rule.

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SUMMARY: This proposed rule would update the prospective payment rates, 
the outlier threshold, and the wage index for Medicare inpatient 
hospital services provided by Inpatient Psychiatric Facilities (IPF), 
which include psychiatric hospitals and excluded psychiatric units of 
an acute care hospital or critical access hospital. This proposed rule 
would also establish a permanent mitigation policy to smooth the impact 
of year-to-year changes in IPF payments related to decreases in the IPF 
wage index. In addition, this proposed rule includes a request for 
comment on the results of the data analysis of the IPF Prospective 
Payment System adjustments. The proposed changes in this rule would be 
effective for IPF discharges occurring during the Fiscal Year (FY) 
beginning October 1, 2022 through September 30, 2023 (FY 2023). Lastly, 
this proposed rule requests information on Measuring Equity and 
Healthcare Quality Disparities Across CMS Quality Programs.

DATES: To be assured consideration, comments must be received at one of 
the addresses provided below by May 31, 2022.

ADDRESSES: In commenting, please refer to file code CMS-1769-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-1769-P, P.O. Box 8010, 
Baltimore, MD 21244-8010.
    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-1769-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: The IPF Payment Policy mailbox at 
<a href="/cdn-cgi/l/email-protection#6c253c2a3c0d15010902183c0300050f152c0f011f4204041f420b031a"><span class="__cf_email__" data-cfemail="f4bda4b2a4958d99919a80a49b989d978db4979987da9c9c87da939b82">[email protected]</span></a> for general information.
    Mollie Knight (410) 786-7948 or Eric Laib (410) 786-9759, for 
information regarding the market basket update or the labor-related 
share.
    Nick Brock (410) 786-5148 or Theresa Bean (410) 786-2287, for 
information regarding the regulatory impact analysis.
    Lauren Lowenstein, (410) 786-4507, for information regarding the 
inpatient psychiatric facilities 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 Tables Exclusively Through the Internet on the 
CMS Website

    Addendum A to this proposed rule summarizes the FY 2023 IPF PPS 
payment rates, outlier threshold, cost of living adjustment factors 
(COLA) for Alaska and Hawaii, national and upper limit cost-to-charge 
ratios, and adjustment factors. In addition, the B Addenda to this 
proposed rule shows

[[Page 19416]]

the complete listing of ICD-10 Clinical Modification (CM) and Procedure 
Coding System (PCS) codes, the FY 2023 IPF PPS comorbidity adjustment, 
and electroconvulsive therapy (ECT) procedure codes. The A and B 
Addenda are available online at: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
    Tables setting forth the FY 2023 Wage Index for Urban Areas Based 
on Core-Based Statistical Area (CBSA) Labor Market Areas and the FY 
2023 Wage Index Based on CBSA Labor Market Areas for Rural Areas are 
available exclusively through the internet, on the CMS website at 
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/IPFPPS/WageIndex.html.

I. Executive Summary

A. Purpose

    This proposed rule would update the prospective payment rates, the 
outlier threshold, and the wage index for Medicare inpatient hospital 
services provided by Inpatient Psychiatric Facilities (IPFs) for 
discharges occurring during Fiscal Year (FY) 2023 beginning October 1, 
2022 through September 30, 2023. This proposed rule would also 
establish a permanent mitigation policy to smooth the impact of year-
to-year changes in IPF payments related to changes in the IPF wage 
index. In addition, this proposed rule includes a request for comment 
on the results of the data analysis of the IPF Prospective Payment 
System (PPS) adjustments. Lastly, this proposed rule requests 
information on Measuring Equity and Healthcare Quality Disparities 
Across CMS Quality Programs.

B. Summary of the Major Provisions

1. Inpatient Psychiatric Facilities Prospective Payment System
    For the IPF PPS, we are proposing to--
    <bullet> Establish a permanent mitigation policy in order to smooth 
the impact of year-to-year changes in IPF payments related to decreases 
to the IPF wage index.
    <bullet> Solicit comments on the results of the data analysis of 
the IPF PPS adjustments, which have been summarized in a technical 
report posted to the Centers for Medicare & Medicaid Services (CMS) 
website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS.
    <bullet> Update the IPF PPS base rate by the 2016-based IPF market 
basket update (3.1 percent) adjusted for economy-wide productivity (0.4 
percentage point) as required by section 1886(s)(2)(A)(i) of the Social 
Security Act (the Act), resulting in a proposed IPF payment rate update 
of 2.7 percent for FY 2023.
    <bullet> Make technical rate setting updates: The IPF PPS payment 
rates would be adjusted annually for inflation, as well as statutory 
and other policy factors. This rule proposes to update:
    ++ The IPF PPS Federal per diem base rate from $832.94 to $856.80.
    ++ The IPF PPS Federal per diem base rate for providers who failed 
to report quality data to $840.11.
    ++ The ECT payment per treatment from $358.60 to $368.87.
    ++ The ECT payment per treatment for providers who failed to report 
quality data to $361.69.
    ++ The labor-related share from 77.2 percent to 77.4 percent.
    ++ The wage index budget-neutrality factor to 1.0016.
    ++ The fixed dollar loss threshold amount from $16,040 to $24,270 
to maintain estimated outlier payments at 2 percent of total estimated 
aggregate IPF PPS payments.
2. Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program
    We are not proposing any changes to the IPFQR Program. However, we 
are including a request for information (RFI) on the Overarching 
Principles for Measuring Healthcare Quality Disparities Across CMS 
Quality Programs. Feedback provided will inform future efforts in all 
CMS Quality programs and, as applicable, may be introduced in the IPFQR 
as future RFIs or proposals.

C. Summary of Impacts

------------------------------------------------------------------------
                                              Total transfers & cost
         Provision description                      reductions
------------------------------------------------------------------------
FY 2023 IPF PPS payment update.........  The overall economic impact of
                                          this proposed rule is an
                                          estimated $50 million in
                                          increased payments to IPFs
                                          during FY 2023.
------------------------------------------------------------------------

II. Background

A. Overview of the Legislative Requirements of the IPF PPS

    Section 124 of the Medicare, Medicaid, and State Children's Health 
Insurance Program Balanced Budget Refinement Act of 1999 (BBRA) (Pub. 
L. 106-113) required the establishment and implementation of an IPF 
PPS. Specifically, section 124 of the BBRA mandated that the Secretary 
of the Department of Health and Human Services (the Secretary) develop 
a per diem PPS for inpatient hospital services furnished in psychiatric 
hospitals and excluded psychiatric units including an adequate patient 
classification system that reflects the differences in patient resource 
use and costs among psychiatric hospitals and excluded psychiatric 
units. ``Excluded psychiatric unit'' means a psychiatric unit of an 
acute care hospital or of a Critical Access Hospital (CAH), which is 
excluded from payment under the Inpatient Prospective Payment System 
(IPPS) or CAH payment system, respectively. These excluded psychiatric 
units will be paid under the IPF PPS.
    Section 405(g)(2) of the Medicare Prescription Drug, Improvement, 
and Modernization Act of 2003 (MMA) (Pub. L. 108-173) extended the IPF 
PPS to psychiatric distinct part units of CAHs. Sections 3401(f) and 
10322 of the Patient Protection and Affordable Care Act (Pub. L. 111-
148) as amended by section 10319(e) of that Act and by section 1105(d) 
of the Health Care and Education Reconciliation Act of 2010 (Pub. L. 
111-152) (hereafter referred to jointly as ``the Affordable Care Act'') 
added subsection (s) to section 1886 of the Act.
    Section 1886(s)(1) of the Act titled ``Reference to Establishment 
and Implementation of System,'' refers to section 124 of the BBRA, 
which relates to the establishment of the IPF PPS. Section 
1886(s)(2)(A)(i) of the Act requires the application of the 
productivity adjustment described in section 1886(b)(3)(B)(xi)(II) of 
the Act to the IPF PPS for the rate year (RY) beginning in 2012 (that 
is, a RY that coincides with a FY) and each subsequent RY.
    Section 1886(s)(2)(A)(ii) of the Act required the application of an 
``other adjustment'' that reduced any update to an IPF PPS base rate by 
a percentage point amount specified in section 1886(s)(3) of the Act 
for the RY beginning in 2010 through the RY beginning in 2019. As noted 
in the FY 2020 IPF PPS final rule, for the RY beginning in 2019, 
section 1886(s)(3)(E) of the Act required that the other adjustment 
reduction be equal to 0.75 percentage point; this was the final year

[[Page 19417]]

the statute required the application of this adjustment. Because FY 
2021 was a RY beginning in 2020, FY 2021 was the first-year section 
1886(s)(2)(A)(ii) did not apply since its enactment.
    Sections 1886(s)(4)(A) through (D) of the Act require that for RY 
2014 and each subsequent RY, IPFs that fail to report required quality 
data with respect to such a RY will have their annual update to a 
standard Federal rate for discharges reduced by 2.0 percentage points. 
This may result in an annual update being less than 0.0 for a RY, and 
may result in payment rates for the upcoming RY being less than such 
payment rates for the preceding RY. Any reduction for failure to report 
required quality data will apply only to the RY involved, and the 
Secretary will not consider such reduction in computing the payment 
amount for a subsequent RY. Additional information about the specifics 
of the current Inpatient Psychiatric Facilities Quality Reporting 
(IPFQR) Program is available in the FY 2020 IPF PPS and Quality 
Reporting Updates for FY Beginning October 1, 2019 final rule (84 FR 
38459 through 38468).
    To implement and periodically update these provisions, we have 
published various proposed and final rules and notices in the Federal 
Register. For more information regarding these documents, see the CMS 
website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html?redirect=/
InpatientPsychFacilPPS/.

B. Overview of the IPF PPS

    On November 15, 2004, we published the IPF PPS final rule in the 
Federal Register (69 FR 66922). The November 2004 IPF PPS final rule 
established the IPF PPS, as required by section 124 of the BBRA and 
codified at 42 CFR part 412, subpart N. The November 2004 IPF PPS final 
rule set forth the Federal per diem base rate for the implementation 
year (the 18-month period from January 1, 2005 through June 30, 2006), 
and provided payment for the inpatient operating and capital costs to 
IPFs for covered psychiatric services they furnish (that is, routine, 
ancillary, and capital costs, but not costs of approved educational 
activities, bad debts, and other services or items that are outside the 
scope of the IPF PPS). Covered psychiatric services include services 
for which benefits are provided under the fee-for-service Part A 
(Hospital Insurance Program) of the Medicare program.
    The IPF PPS established the Federal per diem base rate for each 
patient day in an IPF derived from the national average daily routine 
operating, ancillary, and capital costs in IPFs in FY 2002. The average 
per diem cost was updated to the midpoint of the first year under the 
IPF PPS, standardized to account for the overall positive effects of 
the IPF PPS payment adjustments, and adjusted for budget-neutrality.
    The Federal per diem payment under the IPF PPS is comprised of the 
Federal per diem base rate described previously and certain patient- 
and facility-level payment adjustments for characteristics that were 
found in the regression analysis to be associated with statistically 
significant per diem cost differences; with statistical significance 
defined as p less than 0.05. A complete discussion of the regression 
analysis that established the IPF PPS adjustment factors can be found 
in the November 2004 IPF PPS final rule (69 FR 66933 through 66936).
    The patient-level adjustments include age, Diagnosis-Related Group 
(DRG) assignment, and comorbidities, as well as adjustments to reflect 
higher per diem costs at the beginning of a patient's IPF stay and 
lower costs for later days of the stay. Facility-level adjustments 
include adjustments for the IPF's wage index, rural location, teaching 
status, a cost-of-living adjustment for IPFs located in Alaska and 
Hawaii, and an adjustment for the presence of a qualifying emergency 
department (ED).
    The IPF PPS provides additional payment policies for outlier cases, 
interrupted stays, and a per treatment payment for patients who undergo 
electroconvulsive therapy (ECT). During the IPF PPS mandatory 3-year 
transition period, stop-loss payments were also provided; however, 
since the transition ended as of January 1, 2008, these payments are no 
longer available.

C. Annual Requirements for Updating the IPF PPS

    Section 124 of the BBRA did not specify an annual rate update 
strategy for the IPF PPS and was broadly written to give the Secretary 
discretion in establishing an update methodology. Therefore, in the 
November 2004 IPF PPS final rule, we implemented the IPF PPS using the 
following update strategy:
    <bullet> Calculate the final Federal per diem base rate to be 
budget-neutral for the 18-month period of January 1, 2005 through June 
30, 2006.
    <bullet> Use a July 1 through June 30 annual update cycle.
    <bullet> Allow the IPF PPS first update to be effective for 
discharges on or after July 1, 2006 through June 30, 2007.
    The November 2004 final rule (69 FR 66922) implemented the IPF PPS. 
In developing the IPF PPS, and to ensure that the IPF PPS can account 
adequately for each IPF's case-mix, we performed an extensive 
regression analysis of the relationship between the per diem costs and 
certain patient and facility characteristics to determine those 
characteristics associated with statistically significant cost 
differences on a per diem basis. That regression analysis is described 
in detail in our November 28, 2003 IPF proposed rule (68 FR 66923; 
66928 through 66933) and our November 15, 2004 IPF final rule (69 FR 
66933 through 66960). For characteristics with statistically 
significant cost differences, we used the regression coefficients of 
those variables to determine the size of the corresponding payment 
adjustments.
    In the November 2004 IPF final rule, we explained the reasons for 
delaying an update to the adjustment factors, derived from the 
regression analysis, including waiting until we have IPF PPS data that 
yields as much information as possible regarding the patient-level 
characteristics of the population that each IPF serves. We indicated 
that we did not intend to update the regression analysis and the 
patient-level and facility-level adjustments until we complete that 
analysis. Until that analysis is complete, we stated our intention to 
publish a notice in the Federal Register each spring to update the IPF 
PPS (69 FR 66966).
    On May 6, 2011, we published a final rule in the Federal Register 
titled, ``Inpatient Psychiatric Facilities Prospective Payment System--
Update for Rate Year Beginning July 1, 2011 (RY 2012)'' (76 FR 26432), 
which changed the payment rate update period to a RY that coincides 
with a FY update. Therefore, final rules are now published in the 
Federal Register in the summer to be effective on October 1st. When 
proposing changes in IPF payment policy, a proposed rule is issued in 
the spring, and the final rule in the summer to be effective on October 
1st. For a detailed list of updates to the IPF PPS, we refer readers to 
our regulations at 42 CFR 412.428.
    The most recent IPF PPS annual update was published in a final rule 
on August 4, 2021 in the Federal Register titled, ``Medicare Program; 
FY 2022 Inpatient Psychiatric Facilities Prospective Payment System and 
Quality Reporting Updates for Fiscal Year Beginning October 1, 2021 (FY 
2022)'' (86 FR 42608), which updated the IPF PPS payment rates for FY 
2022. That final rule updated the IPF PPS Federal per diem base rates 
that were

[[Page 19418]]

published in the FY 2021 IPF PPS Rate Update final rule (85 FR 47042) 
in accordance with our established policies.

III. Provisions of the FY 2023 IPF PPS Proposed Rule

A. Proposed FY 2023 Market Basket Update and Productivity Adjustment 
for the IPF PPS

1. Background
    Originally, the input price index that was used to develop the IPF 
PPS was the ``Excluded Hospital with Capital'' market basket. This 
market basket was based on 1997 Medicare cost reports for Medicare 
participating inpatient rehabilitation facilities (IRFs), IPFs, long-
term care hospitals (LTCHs), cancer hospitals, and children's 
hospitals. Although ``market basket'' technically describes the mix of 
goods and services used in providing health care at a given point in 
time, this term is also commonly used to denote the input price index 
(that is, cost category weights and price proxies) derived from that 
market basket. Accordingly, the term market basket as used in this 
document, refers to an input price index.
    Since the IPF PPS inception, the market basket used to update IPF 
PPS payments has been rebased and revised to reflect more recent data 
on IPF cost structures. We last rebased and revised the IPF market 
basket in the FY 2020 IPF PPS rule, where we adopted a 2016-based IPF 
market basket, using Medicare cost report data for both Medicare 
participating freestanding psychiatric hospitals and psychiatric units. 
We refer readers to the FY 2020 IPF PPS final rule for a detailed 
discussion of the 2016-based IPF PPS market basket and its development 
(84 FR 38426 through 38447). References to the historical market 
baskets used to update IPF PPS payments are listed in the FY 2016 IPF 
PPS final rule (80 FR 46656).
2. Proposed FY 2023 IPF Market Basket Update
    For FY 2023 (beginning October 1, 2022 and ending September 30, 
2023), we are proposing to update the IPF PPS payments by a market 
basket increase factor with a productivity adjustment as required by 
section 1886(s)(2)(A)(i) of the Act. Consistent with historical 
practice, we are proposing to estimate the market basket update for the 
IPF PPS based on the most recent forecast available at the time of 
rulemaking from IHS Global Inc. (IGI). IGI is a nationally recognized 
economic and financial forecasting firm with which CMS contracts to 
forecast the components of the market baskets and productivity 
adjustment. For the proposed rule, based on IGI's fourth quarter 2021 
forecast with historical data through the third quarter of 2021, the 
2016-based IPF market basket increase factor for FY 2023 is 3.1 
percent.
    Section 1886(s)(2)(A)(i) of the Act 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 (MFP) (as projected by the Secretary for the 10-year 
period ending with the applicable FY, year, cost reporting period, or 
other annual period) (the ``productivity adjustment''). The United 
States Department of Labor's Bureau of Labor Statistics (BLS) publishes 
the official measures of productivity for the United States 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 MFP. Beginning with the November 18, 2021 release of 
productivity data, BLS replaced the term ``multifactor productivity'' 
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) of the Act is now published by BLS as 
private nonfarm business total factor productivity. However, as 
mentioned previously, the data and methods are unchanged. We refer 
readers to 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 IPF final rule (86 FR 
42611), 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.
    Section 1886(s)(2)(A)(i) of the Act requires the application of the 
productivity adjustment described in section 1886(b)(3)(B)(xi)(II) of 
the Act to the IPF PPS for the RY beginning in 2012 (a RY that 
coincides with a FY) and each subsequent RY. For this FY 2023 IPF PPS 
proposed rule, based on IGI's fourth quarter 2021 forecast, the 
proposed productivity adjustment for FY 2023 (the 10-year moving 
average of TFP for the period ending FY 2023) is projected to be 0.4 
percent. Accordingly, we are proposing to reduce the 3.1 percent IPF 
market basket update by this 0.4 percentage point productivity 
adjustment, as mandated by the Act. This results in a proposed FY 2023 
IPF PPS payment rate update of 2.7 percent (3.1-0.4 = 2.7). We are also 
proposing that if more recent data become available, we would use such 
data, if appropriate, to determine the FY 2023 IPF market basket update 
and productivity adjustment for the final rule.
3. Proposed FY 2023 IPF Labor-Related Share
    Due to variations in geographic wage levels and other labor-related 
costs, we believe that payment rates under the IPF PPS should continue 
to be adjusted by a geographic wage index, which would apply to the 
labor-related portion of the Federal per diem base rate (hereafter 
referred to as the labor-related share). 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 IPF market basket, we are proposing to 
continue to include in the labor-related share the sum of the relative 
importance of Wages and Salaries; Employee Benefits; Professional Fees: 
Labor-related; Administrative and Facilities Support Services; 
Installation, Maintenance, and Repair Services; All Other: Labor-
related Services; and a portion of the Capital-Related relative 
importance from the 2016-based IPF market basket. For more details 
regarding the methodology for determining specific cost categories for 
inclusion in the 2016-based IPF labor-related share, see the FY 2020 
IPF PPS final rule (84 FR 38445 through 38447).
    The relative importance reflects the different rates of price 
change for these cost categories between the base year (FY 2016) and FY 
2023. Based on IGI's fourth quarter 2021 forecast of the 2016-based IPF 
market basket, the sum of the FY 2023 relative importance moving 
average of Wages and Salaries; Employee Benefits; Professional Fees:

[[Page 19419]]

Labor-related; Administrative and Facilities Support Services; 
Installation, Maintenance, and Repair Services; All Other: Labor-
related Services is 74.4 percent. We also propose, consistent with 
prior rulemaking, 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 are 6.6 percent of the 2016-based 
IPF market basket for FY 2023, we propose to take 46 percent of 6.6 
percent to determine a labor-related share of Capital-Related costs for 
FY 2023 of 3.0 percent. Therefore, we propose a total labor-related 
share for FY 2023 of 77.4 percent (the sum of 74.4 percent for the 
labor-related share of operating costs and 3.0 percent for the labor-
related share of Capital-Related costs). We are also proposing that if 
more recent data become available, we would use such data, if 
appropriate, to determine the FY 2023 labor-related share for the final 
rule. For more information on the labor-related share and its 
calculation, we refer readers to the FY 2020 IPF PPS final rule (84 FR 
38445 through 38447).
    Table 1 shows the proposed FY 2023 labor-related share and the 
final FY 2022 labor-related share using the 2016-based IPF market 
basket relative importance.

              Table 1--FY 2023 Proposed IPF Labor-Related Share and FY 2022 IPF Labor-Related Share
----------------------------------------------------------------------------------------------------------------
                                                                  Relative importance,     Relative importance,
                                                                 proposed labor-related    labor-related  share
                                                                   share FY 2023 \1\           FY 2022 \2\
----------------------------------------------------------------------------------------------------------------
Wages and Salaries............................................                     53.3                     52.8
Employee Benefits.............................................                     13.4                     13.6
Professional Fees: Labor-related..............................                      4.3                      4.3
Administrative and Facilities Support Services................                      0.6                      0.6
Installation, Maintenance and Repair..........................                      1.3                      1.3
All Other Labor-related.......................................                      1.5                      1.5
Services......................................................
                                                               -------------------------------------------------
    Subtotal..................................................                     74.4                     74.1
Labor-related portion of Capital-Related (.46)................                      3.0                      3.1
                                                               -------------------------------------------------
    Total Labor-Related Share.................................                     77.4                     77.2
----------------------------------------------------------------------------------------------------------------
\1\ Based on the 4th quarter 2021 IHS Global Inc. forecast of the 2016-based IPF market basket.
\2\ Based on the 2nd quarter 2021 IHS Global Inc. forecast of the 2016-based IPF market basket.

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

B. Proposed Updates to the IPF PPS Rates for FY Beginning October 1, 
2022

    The IPF PPS is based on a standardized Federal per diem base rate 
calculated from the IPF average per diem costs and adjusted for budget-
neutrality in the implementation year. The Federal per diem base rate 
is used as the standard payment per day under the IPF PPS and is 
adjusted by the patient-level and facility-level adjustments that are 
applicable to the IPF stay. A detailed explanation of how we calculated 
the average per diem cost appears in the November 2004 IPF PPS final 
rule (69 FR 66926).
1. Determining the Standardized Budget-Neutral Federal Per Diem Base 
Rate
    Section 124(a)(1) of the BBRA required that we implement the IPF 
PPS in a budget-neutral manner. In other words, the amount of total 
payments under the IPF PPS, including any payment adjustments, must be 
projected to be equal to the amount of total payments that would have 
been made if the IPF PPS were not implemented. Therefore, we calculated 
the budget-neutrality factor by setting the total estimated IPF PPS 
payments to be equal to the total estimated payments that would have 
been made under the Tax Equity and Fiscal Responsibility Act of 1982 
(TEFRA) (Pub. L. 97-248) methodology had the IPF PPS not been 
implemented. A step-by-step description of the methodology used to 
estimate payments under the TEFRA payment system appears in the 
November 2004 IPF PPS final rule (69 FR 66926).
    Under the IPF PPS methodology, we calculated the final Federal per 
diem base rate to be budget-neutral during the IPF PPS implementation 
period (that is, the 18-month period from January 1, 2005 through June 
30, 2006) using a July 1 update cycle. We updated the average cost per 
day to the midpoint of the IPF PPS implementation period (October 1, 
2005), and this amount was used in the payment model to establish the 
budget-neutrality adjustment.
    Next, we standardized the IPF PPS Federal per diem base rate to 
account for the overall positive effects of the IPF PPS payment 
adjustment factors by dividing total estimated payments under the TEFRA 
payment system by estimated payments under the IPF PPS. The information 
concerning this standardization can be found in the November 2004 IPF 
PPS final rule (69 FR 66932) and the RY 2006 IPF PPS final rule (71 FR 
27045). We then reduced the standardized Federal per diem base rate to 
account for the outlier policy, the stop loss provision, and 
anticipated behavioral changes. A complete discussion of how we 
calculated each component of the budget-neutrality adjustment appears 
in the November 2004 IPF PPS final rule (69 FR 66932 through 66933) and 
in the RY 2007 IPF PPS final rule (71 FR 27044 through 27046). The 
final standardized budget-neutral Federal per diem base rate 
established for cost reporting periods beginning on or after January 1, 
2005 was calculated to be $575.95.
    The Federal per diem base rate has been updated in accordance with 
applicable statutory requirements and Sec.  412.428 through publication 
of annual notices or proposed and final rules. A detailed discussion on 
the standardized budget-neutral Federal per diem base rate and the 
electroconvulsive therapy (ECT) payment per treatment appears in the FY 
2014 IPF PPS update notice (78 FR 46738 through 46740). These documents 
are available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html.
    IPFs must include a valid procedure code for ECT services provided 
to IPF beneficiaries in order to bill for ECT

[[Page 19420]]

services, as described in our Medicare Claims Processing Manual, 
Chapter 3, Section 190.7.3 (available at https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/Downloads/clm104c03.pdf.) 
There were no changes to the ECT procedure codes used on IPF claims as 
a result of the final update to the ICD-10-PCS code set for FY 2023. 
Addendum B to this proposed rule shows the ECT procedure codes for FY 
2023 and is available on our website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
2. Proposed Update of the Federal per Diem Base Rate and 
Electroconvulsive Therapy Payment per Treatment
    The current (FY 2022) Federal per diem base rate is $832.94 and the 
ECT payment per treatment is $358.60. For the proposed FY 2023 Federal 
per diem base rate, we applied the payment rate update of 2.7 percent--
that is, the proposed 2016-based IPF market basket increase for FY 2023 
of 3.1 percent less the proposed productivity adjustment of 0.4 
percentage point--and the proposed wage index budget-neutrality factor 
of 1.0016 (as discussed in section III.D.1 of this proposed rule) to 
the FY 2022 Federal per diem base rate of $832.94, yielding a proposed 
Federal per diem base rate of $856.80 for FY 2023. Similarly, we 
applied the proposed 2.7 percent payment rate update and the proposed 
1.0016 wage index budget-neutrality factor to the FY 2022 ECT payment 
per treatment of $358.60, yielding a proposed ECT payment per treatment 
of $368.87 for FY 2023.
    Section 1886(s)(4)(A)(i) of the Act requires that for RY 2014 and 
each subsequent RY, in the case of an IPF that fails to report required 
quality data with respect to such RY, the Secretary will reduce any 
annual update to a standard Federal rate for discharges during the RY 
by 2.0 percentage points. Therefore, we are applying a 2.0 percentage 
point reduction to the Federal per diem base rate and the ECT payment 
per treatment as follows:
    <bullet> For IPFs that fail to report required data under the IPFQR 
Program, we applied a 0.7 percent payment rate update--that is, the 
proposed IPF market basket increase for FY 2023 of 3.1 percent less the 
proposed productivity adjustment of 0.4 percentage point for an update 
of 2.7 percent, and further reduced by 2.0 percentage points in 
accordance with section 1886(s)(4)(A)(i) of the Act--and the proposed 
wage index budget-neutrality factor of 1.0016 to the FY 2022 Federal 
per diem base rate of $832.94, yielding a proposed Federal per diem 
base rate of $840.11 for FY 2023.
    <bullet> For IPFs that fail to report required data under the IPFQR 
Program, we applied the proposed 0.7 percent annual payment rate update 
and the proposed 1.0016 wage index budget-neutrality factor to the FY 
2022 ECT payment per treatment of $358.60, yielding a proposed ECT 
payment per treatment of $361.69 for FY 2023.
    Lastly, we are also proposing that if more recent data become 
available, we would use such data, if appropriate, to determine the FY 
2023 Federal per diem base rate and ECT payment per treatment for the 
final rule.

C. Proposed Updates to the IPF PPS Patient-Level Adjustment Factors

1. Overview of the IPF PPS Adjustment Factors
    The IPF PPS payment adjustments were derived from a regression 
analysis of 100 percent of the FY 2002 Medicare Provider and Analysis 
Review (MedPAR) data file, which contained 483,038 cases. For a more 
detailed description of the data file used for the regression analysis, 
see the November 2004 IPF PPS final rule (69 FR 66935 through 66936). 
We are proposing to continue to use the existing regression-derived 
adjustment factors established in 2005 for FY 2023. However, we have 
used more recent claims data to simulate payments to finalize the 
outlier fixed dollar loss threshold amount and to assess the impact of 
the IPF PPS updates.
2. IPF PPS Patient-Level Adjustments
    The IPF PPS includes payment adjustments for the following patient-
level characteristics: Medicare Severity Diagnosis Related Groups (MS-
DRGs) assignment of the patient's principal diagnosis, selected 
comorbidities, patient age, and the variable per diem adjustments.
a. Proposed Update to MS-DRG Assignment
    We believe it is important to maintain for IPFs the same diagnostic 
coding and Diagnosis Related Group (DRG) classification used under the 
IPPS for providing psychiatric care. For this reason, when the IPF PPS 
was implemented for cost reporting periods beginning on or after 
January 1, 2005, we adopted the same diagnostic code set (ICD-9-CM) and 
DRG patient classification system (MS-DRGs) that were utilized at the 
time under the IPPS. In the RY 2009 IPF PPS notice (73 FR 25709), we 
discussed CMS' effort to better recognize resource use and the severity 
of illness among patients. CMS adopted the new MS-DRGs for the IPPS in 
the FY 2008 IPPS final rule with comment period (72 FR 47130). In the 
RY 2009 IPF PPS notice (73 FR 25716), we provided a crosswalk to 
reflect changes that were made under the IPF PPS to adopt the new MS-
DRGs. For a detailed description of the mapping changes from the 
original DRG adjustment categories to the current MS-DRG adjustment 
categories, we refer readers to the RY 2009 IPF PPS notice (73 FR 
25714).
    The IPF PPS includes payment adjustments for designated psychiatric 
DRGs assigned to the claim based on the patient's principal diagnosis. 
The DRG adjustment factors were expressed relative to the most 
frequently reported psychiatric DRG in FY 2002, that is, DRG 430 
(psychoses). The coefficient values and adjustment factors were derived 
from the regression analysis discussed in detail in the November 28, 
2003 IPF proposed rule (68 FR 66923; 66928 through 66933) and the 
November 15, 2004 IPF final rule (69 FR 66933 through 66960). Mapping 
the DRGs to the MS-DRGs resulted in the current 17 IPF MS-DRGs, instead 
of the original 15 DRGs, for which the IPF PPS provides an adjustment. 
For FY 2023, we are not proposing any changes to the IPF MS-DRG 
adjustment factors. Therefore, we are retaining the existing IPF MS-DRG 
adjustment factors.
    In the FY 2015 IPF PPS final rule published August 6, 2014 in the 
Federal Register titled, ``Inpatient Psychiatric Facilities Prospective 
Payment System--Update for FY Beginning October 1, 2014 (FY 2015)'' (79 
FR 45945 through 45947), we finalized conversions of the ICD-9-CM-based 
MS-DRGs to ICD-10-CM/PCS-based MS-DRGs, which were implemented on 
October 1, 2015. Further information on the ICD-10-CM/PCS MS-DRG 
conversion project can be found on the CMS ICD-10-CM website at https://www.cms.gov/Medicare/Coding/ICD10/ICD-10-MS-DRG-Conversion-Project.html.
    For FY 2023, we are proposing to continue to make the existing 
payment adjustment for psychiatric diagnoses that group to one of the 
existing 17 IPF MS-DRGs listed in Addendum A. Addendum A is available 
on our website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html. Psychiatric 
principal diagnoses that do not group to one of the 17 designated MS-
DRGs will still receive the Federal per diem base rate and all other 
applicable adjustments;

[[Page 19421]]

however, the payment will not include an MS-DRG adjustment. The 
diagnoses for each IPF MS-DRG will be updated as of October 1, 2022, 
using the final IPPS FY 2023 ICD-10-CM/PCS code sets. The FY 2023 IPPS/
LTCH PPS final rule includes tables of the changes to the ICD-10-CM/PCS 
code sets, which underlie the FY 2023 IPF MS-DRGs. Both the FY 2023 
IPPS final rule and the tables of final changes to the ICD-10-CM/PCS 
code sets, which underlie the FY 2023 MS-DRGs, are available on the CMS 
IPPS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/index.html.
Code First
    As discussed in the ICD-10-CM Official Guidelines for Coding and 
Reporting, certain conditions have both an underlying etiology and 
multiple body system manifestations due to the underlying etiology. For 
such conditions, ICD-10-CM has a coding convention that requires the 
underlying condition be sequenced first followed by the manifestation. 
Wherever such a combination exists, there is a ``use additional code'' 
note at the etiology code, and a ``code first'' note at the 
manifestation code. These instructional notes indicate the proper 
sequencing order of the codes (etiology followed by manifestation). In 
accordance with the ICD-10-CM Official Guidelines for Coding and 
Reporting, when a primary (psychiatric) diagnosis code has a ``code 
first'' note, the provider will follow the instructions in the ICD-10-
CM Tabular List. The submitted claim goes through the CMS processing 
system, which will identify the principal diagnosis code as non-
psychiatric and search the secondary codes for a psychiatric code to 
assign a DRG code for adjustment. The system will continue to search 
the secondary codes for those that are appropriate for comorbidity 
adjustment.
    For more information on the code first policy, we refer readers to 
the November 2004 IPF PPS final rule (69 FR 66945) and see sections 
I.A.13 and I.B.7 of the FY 2020 ICD-10-CM Coding Guidelines, available 
at https://www.cdc.gov/nchs/data/icd/10cmguidelines-FY2020_final.pdf. 
In the FY 2015 IPF PPS final rule, we provided a code first table for 
reference that highlights the same or similar manifestation codes where 
the code first instructions apply in ICD-10-CM that were present in 
ICD-9-CM (79 FR 46009). In FY 2022 there were 18 codes finalized for 
deletion from the ICD-10-CM codes in the IPF Code First table. For FY 
2023, we are proposing to delete 2 ICD-10-PCS codes and proposing to 
add 48 ICD-10-PCS codes to the IPF Code First table. The proposed FY 
2023 Code First table is shown in Addendum B on the CMS website at 
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
b. Proposed Payment for Comorbid Conditions
    The intent of the comorbidity adjustments is to recognize the 
increased costs associated with comorbid conditions by providing 
additional payments for certain existing medical or psychiatric 
conditions that are expensive to treat. In our RY 2012 IPF PPS final 
rule (76 FR 26451 through 26452), we explained that the IPF PPS 
includes 17 comorbidity categories and identified the new, revised, and 
deleted ICD-9-CM diagnosis codes that generate a comorbid condition 
payment adjustment under the IPF PPS for RY 2012 (76 FR 26451).
    Comorbidities are specific patient conditions that are secondary to 
the patient's principal diagnosis and that require treatment during the 
stay. Diagnoses that relate to an earlier episode of care and have no 
bearing on the current hospital stay are excluded and must not be 
reported on IPF claims. Comorbid conditions must exist at the time of 
admission or develop subsequently, and affect the treatment received, 
length of stay (LOS), or both treatment and LOS.
    For each claim, an IPF may receive only one comorbidity adjustment 
within a comorbidity category, but it may receive an adjustment for 
more than one comorbidity category. Current billing instructions for 
discharge claims, on or after October 1, 2015, require IPFs to enter 
the complete ICD-10-CM codes for up to 24 additional diagnoses if they 
co-exist at the time of admission, or develop subsequently and impact 
the treatment provided.
    The comorbidity adjustments were determined based on the regression 
analysis using the diagnoses reported by IPFs in FY 2002. The principal 
diagnoses were used to establish the DRG adjustments and were not 
accounted for in establishing the comorbidity category adjustments, 
except where ICD-9-CM code first instructions applied. In a code first 
situation, the submitted claim goes through the CMS processing system, 
which will identify the principal diagnosis code as non-psychiatric and 
search the secondary codes for a psychiatric code to assign an MS-DRG 
code for adjustment. The system will continue to search the secondary 
codes for those that are appropriate for comorbidity adjustment.
    As noted previously, it is our policy to maintain the same 
diagnostic coding set for IPFs that is used under the IPPS for 
providing the same psychiatric care. The 17 comorbidity categories 
formerly defined using ICD-9-CM codes were converted to ICD-10-CM/PCS 
in our FY 2015 IPF PPS final rule (79 FR 45947 through 45955). The goal 
for converting the comorbidity categories is referred to as 
replication, meaning that the payment adjustment for a given patient 
encounter is the same after ICD-10-CM implementation as it will be if 
the same record had been coded in ICD-9-CM and submitted prior to ICD-
10-CM/PCS implementation on October 1, 2015. All conversion efforts 
were made with the intent of achieving this goal. For FY 2023, we are 
proposing to continue to use the same comorbidity adjustment factors in 
effect in FY 2022. The proposed FY 2023 comorbidity adjustment factors 
are found in Addendum A, available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
    For FY 2023, we are proposing to add 10 ICD-10-CM/PCS codes and 
remove 1 ICD-10-CM/PCS code from the Coagulation Factor category; 
proposing to add 3 ICD-10-CM/PCS codes and remove 11 ICD-10-CM/PCS 
codes from the Oncology Treatment comorbidity category; and proposing 
to add 4 ICD-10-CM/PCS codes to the Poisoning comorbidity category. The 
proposed FY 2023 comorbidity codes are shown in Addenda B, available on 
the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
    In accordance with the policy established in the FY 2015 IPF PPS 
final rule (79 FR 45949 through 45952), we reviewed all new FY 2023 
ICD-10-CM codes to remove codes that were site ``unspecified'' in terms 
of laterality from the FY 2023 ICD-10-CM/PCS codes in instances where 
more specific codes are available. As we stated in the FY 2015 IPF PPS 
final rule, we believe that specific diagnosis codes that narrowly 
identify anatomical sites where disease, injury, or a condition exists 
should be used when coding patients' diagnoses whenever these codes are 
available. We finalized in the FY 2015 IPF PPS rule, that we would 
remove site ``unspecified'' codes from the IPF PPS ICD-10-CM/PCS codes 
in instances when laterality codes (site specified codes) are 
available, as the clinician should be able to identify a more

[[Page 19422]]

specific diagnosis based on clinical assessment at the medical 
encounter. There were no proposed changes to the FY 2023 ICD-10-CM/PCS 
codes, therefore, we are not proposing to remove any of the new codes.
c. Proposed Patient Age Adjustments
    As explained in the November 2004 IPF PPS final rule (69 FR 66922), 
we analyzed the impact of age on per diem cost by examining the age 
variable (range of ages) for payment adjustments. In general, we found 
that the cost per day increases with age. The older age groups are 
costlier than the under 45 age group, the differences in per diem cost 
increase for each successive age group, and the differences are 
statistically significant. For FY 2023, we are proposing to continue to 
use the patient age adjustments currently in effect in FY 2022, as 
shown in Addendum A of this rule (see https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html).
d. Proposed Variable Per Diem Adjustments
    We explained in the November 2004 IPF PPS final rule (69 FR 66946) 
that the regression analysis indicated that per diem cost declines as 
the length of stay (LOS) increases. The variable per diem adjustments 
to the Federal per diem base rate account for ancillary and 
administrative costs that occur disproportionately in the first days 
after admission to an IPF. As discussed in the November 2004 IPF PPS 
final rule, we used a regression analysis to estimate the average 
differences in per diem cost among stays of different lengths (69 FR 
66947 through 66950). As a result of this analysis, we established 
variable per diem adjustments that begin on day 1 and decline gradually 
until day 21 of a patient's stay. For day 22 and thereafter, the 
variable per diem adjustment remains the same each day for the 
remainder of the stay. However, the adjustment applied to day 1 depends 
upon whether the IPF has a qualifying ED. If an IPF has a qualifying 
ED, it receives a 1.31 adjustment factor for day 1 of each stay. If an 
IPF does not have a qualifying ED, it receives a 1.19 adjustment factor 
for day 1 of the stay. The ED adjustment is explained in more detail in 
section III.D.4 of this propose rule.
    For FY 2023, we are proposing to continue to use the variable per 
diem adjustment factors currently in effect, as shown in Addendum A to 
this rule, which is available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html. A complete discussion of the 
variable per diem adjustments appears in the November 2004 IPF PPS 
final rule (69 FR 66946).

D. Proposed Updates to the IPF PPS Facility-Level Adjustments

    The IPF PPS includes facility-level adjustments for the wage index, 
IPFs located in rural areas, teaching IPFs, cost of living adjustments 
for IPFs located in Alaska and Hawaii, and IPFs with a qualifying ED.
1. Wage Index Adjustment
a. Background
    As discussed in the RY 2007 IPF PPS final rule (71 FR 27061), RY 
2009 IPF PPS (73 FR 25719) and the RY 2010 IPF PPS notices (74 FR 
20373), in order to provide an adjustment for geographic wage levels, 
the labor-related portion of an IPF's payment is adjusted using an 
appropriate wage index. Currently, an IPF's geographic wage index value 
is determined based on the actual location of the IPF in an urban or 
rural area, as defined in Sec.  412.64(b)(1)(ii)(A) and (C).
    Due to the variation in costs and because of the differences in 
geographic wage levels, in the November 2004 IPF PPS final rule, we 
required that payment rates under the IPF PPS be adjusted by a 
geographic wage index. We proposed and finalized a policy to use the 
unadjusted, pre-floor, pre-reclassified IPPS hospital wage index to 
account for geographic differences in IPF labor costs. We implemented 
use of the pre-floor, pre-reclassified IPPS hospital wage data to 
compute the IPF wage index since there was not an IPF-specific wage 
index available. We believe that IPFs generally compete in the same 
labor market as IPPS hospitals so the pre-floor, pre-reclassified IPPS 
hospital wage data should be reflective of labor costs of IPFs. We 
believe this pre-floor, pre-reclassified IPPS hospital wage index to be 
the best available data to use as proxy for an IPF specific wage index. 
As discussed in the RY 2007 IPF PPS final rule (71 FR 27061 through 
27067), under the IPF PPS, the wage index is calculated using the IPPS 
wage index for the labor market area in which the IPF is located, 
without considering geographic reclassifications, floors, and other 
adjustments made to the wage index under the IPPS. For a complete 
description of these IPPS wage index adjustments, we refer readers to 
the FY 2019 IPPS/LTCH PPS final rule (83 FR 41362 through 41390). Our 
wage index policy at Sec.  412.424(a)(2), requires us to use the best 
Medicare data available to estimate costs per day, including an 
appropriate wage index to adjust for wage differences.
    When the IPF PPS was implemented in the November 2004 IPF PPS final 
rule, with an effective date of January 1, 2005, the pre-floor, pre-
reclassified IPPS hospital wage index that was available at the time 
was the FY 2005 pre-floor, pre-reclassified IPPS hospital wage index. 
Historically, the IPF wage index for a given RY has used the pre-floor, 
pre-reclassified IPPS hospital wage index from the prior FY as its 
basis. This has been due in part to the pre-floor, pre-reclassified 
IPPS hospital wage index data that were available during the IPF 
rulemaking cycle, where an annual IPF notice or IPF final rule was 
usually published in early May. This publication timeframe was 
relatively early compared to other Medicare payment rules because the 
IPF PPS follows a RY, which was defined in the implementation of the 
IPF PPS as the 12-month period from July 1 to June 30 (69 FR 66927). 
Therefore, the best available data at the time the IPF PPS was 
implemented was the pre-floor, pre-reclassified IPPS hospital wage 
index from the prior FY (for example, the RY 2006 IPF wage index was 
based on the FY 2005 pre-floor, pre-reclassified IPPS hospital wage 
index).
    In the RY 2012 IPF PPS final rule, we changed the reporting year 
timeframe for IPFs from a RY to the FY, which begins October 1 and ends 
September 30 (76 FR 26434 through 26435). In that FY 2012 IPF PPS final 
rule, we continued our established policy of using the pre-floor, pre-
reclassified IPPS hospital wage index from the prior year (that is, 
from FY 2011) as the basis for the FY 2012 IPF wage index. This policy 
of basing a wage index on the prior year's pre-floor, pre-reclassified 
IPPS hospital wage index has been followed by other Medicare payment 
systems, such as hospice and inpatient rehabilitation facilities. By 
continuing with our established policy, we remained consistent with 
other Medicare payment systems.
    In FY 2020, we finalized the IPF wage index methodology to align 
the IPF PPS wage index with the same wage data timeframe used by the 
IPPS for FY 2020 and subsequent years. Specifically, we finalized to 
use the pre-floor, pre-reclassified IPPS hospital wage index from the 
FY concurrent with the IPF FY as the basis for the IPF wage index. For 
example, the FY 2020 IPF wage index was based on the FY 2020 pre-floor, 
pre-reclassified IPPS hospital wage index rather than on the FY 2019 
pre-floor, pre-reclassified IPPS hospital wage index.

[[Page 19423]]

    We explained in the FY 2020 proposed rule (84 FR 16973), that using 
the concurrent pre-floor, pre-reclassified IPPS hospital wage index 
will result in the most up-to-date wage data being the basis for the 
IPF wage index. We noted that it would also result in more consistency 
and parity in the wage index methodology used by other Medicare payment 
systems. We indicated that the Medicare SNF PPS already used the 
concurrent IPPS hospital wage index data as the basis for the SNF PPS 
wage index. CMS proposed and finalized similar policies to use the 
concurrent pre-floor, pre-reclassified IPPS hospital wage index data in 
other Medicare payment systems, such as hospice and inpatient 
rehabilitation facilities. Thus, the wage adjusted Medicare payments of 
various provider types are based upon wage index data from the same 
timeframe. For FY 2023, we propose to continue to use the concurrent 
pre-floor, pre-reclassified IPPS hospital wage index as the basis for 
the IPF wage index.
b. Office of Management and Budget (OMB) Bulletins
1. Background
    The wage index used for the IPF PPS is calculated using the 
unadjusted, pre-reclassified and pre-floor IPPS wage index data and is 
assigned to the IPF on the basis of the labor market area in which the 
IPF is geographically located. IPF labor market areas are delineated 
based on the Core-Based Statistical Area (CBSAs) established by the 
OMB.
    Generally, OMB issues major revisions to statistical areas every 10 
years, based on the results of the decennial census. However, OMB 
occasionally issues minor updates and revisions to statistical areas in 
the years between the decennial censuses through OMB Bulletins. These 
bulletins contain information regarding CBSA changes, including changes 
to CBSA numbers and titles. OMB bulletins may be accessed online at 
https://www.whitehouse.gov/omb/information-for-agencies/bulletins/. In 
accordance with our established methodology, the IPF PPS has 
historically adopted any CBSA changes that are published in the OMB 
bulletin that corresponds with the IPPS hospital wage index used to 
determine the IPF wage index and, when necessary and appropriate, has 
proposed and finalized transition policies for these changes.
    In the RY 2007 IPF PPS final rule (71 FR 27061 through 27067), we 
adopted the changes discussed in the OMB Bulletin No. 03-04 (June 6, 
2003), which announced revised definitions for MSAs, and the creation 
of Micropolitan Statistical Areas and Combined Statistical Areas. In 
adopting the OMB CBSA geographic designations in RY 2007, we did not 
provide a separate transition for the CBSA-based wage index since the 
IPF PPS was already in a transition period from TEFRA payments to PPS 
payments.
    In the RY 2009 IPF PPS notice, we incorporated the CBSA 
nomenclature changes published in the most recent OMB bulletin that 
applied to the IPPS hospital wage index used to determine the current 
IPF wage index and stated that we expected to continue to do the same 
for all the OMB CBSA nomenclature changes in future IPF PPS rules and 
notices, as necessary (73 FR 25721).
    Subsequently, CMS adopted the changes that were published in past 
OMB bulletins in the FY 2016 IPF PPS final rule (80 FR 46682 through 
46689), the FY 2018 IPF PPS rate update (82 FR 36778 through 36779), 
the FY 2020 IPF PPS final rule (84 FR 38453 through 38454), and the FY 
2021 IPF PPS final rule (85 FR 47051 through 47059). We direct readers 
to each of these rules for more information about the changes that were 
adopted and any associated transition policies.
    In part due to the scope of changes involved in adopting the CBSA 
delineations for FY 2021, we finalized a 2-year transition policy in 
the FY 2021 IPF PPS final rule consistent with our past practice of 
using transition policies to help mitigate negative impacts on 
hospitals of certain wage index policy changes. We applied a 5-percent 
cap on wage index decreases to all IPF providers that had any decrease 
in their wage indexes, regardless of the circumstance causing the 
decline, so that an IPF's final wage index for FY 2021 would not be 
less than 95 percent of its final wage index for FY 2020, regardless of 
whether the IPF was part of an updated CBSA. We refer readers to the FY 
2021 IPF PPS final rule (85 FR 47058 through 47059) for a more detailed 
discussion about the wage index transition policy for FY 2021.
    On March 6, 2020, OMB issued OMB Bulletin 20-01 (available on the 
web at https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf). In considering whether to adopt this bulletin, we analyzed 
whether the changes in this bulletin would have a material impact on 
the IPF PPS wage index. This bulletin creates only one Micropolitan 
statistical area. As discussed in further detail in section 
III.D.1.b.ii of this proposed rule since Micropolitan areas are 
considered rural for the IPF PPS wage index, this bulletin has no 
material impact on the IPF PPS wage index. That is, the constituent 
county of the new Micropolitan area was considered rural effective as 
of FY 2021 and would continue to be considered rural if we adopted OMB 
Bulletin 20-01. Therefore, we did not propose to adopt OMB Bulletin 20-
01 in the FY 2022 IPF PPS proposed rule.
2. Micropolitan Statistical Areas
    OMB defines a ``Micropolitan Statistical Area'' as a CBSA 
associated with at least one urban cluster that has a population of at 
least 10,000, but less than 50,000 (75 FR 37252). We refer to these as 
Micropolitan Areas. After extensive impact analysis, consistent with 
the treatment of these areas under the IPPS as discussed in the FY 2005 
IPPS final rule (69 FR 49029 through 49032), we determined the best 
course of action would be to treat Micropolitan Areas as ``rural'' and 
include them in the calculation of each state's IPF PPS rural wage 
index. We refer readers to the FY 2007 IPF PPS final rule (71 FR 27064 
through 27065) for a complete discussion regarding treating 
Micropolitan Areas as rural.
c. Proposed Permanent Cap on Wage Index Decreases
    As discussed in section III.D.1.b.(1) of this proposed rule, we 
have proposed and finalized temporary transition policies in the past 
to mitigate significant changes to payments due to changes to the IPF 
PPS wage index. Specifically, for FY 2016 (80 FR 46652), 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 47059), we implemented a 2-year 
transition to mitigate any negative effects of wage index changes by 
applying a 5-percent cap on any decrease in an IPF's wage index from 
the IPF's 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 the second year, that is, 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

[[Page 19424]]

market area delineations and wage index values.
    In FY 2022 (86 FR 42616 through 42617), a couple of commenters 
recommended CMS extend the transition period adopted in the FY 2021 IPF 
PPS final rule. Because we did not propose to modify the transition 
policy that was finalized in the FY 2021 IPF PPS final rule, we did not 
extend the transition period for FY 2022. In the FY 2022 IPF PPS final 
rule, we stated that we continued to believe that applying the 5-
percent cap transition policy in year one provided an adequate 
safeguard against any significant payment reductions associated with 
the adoption of the revised CBSA delineations in FY 2021, allowed for 
sufficient time to make operational changes for future FYs, and 
provided a reasonable balance between mitigating some short-term 
instability in IPF payments and improving the accuracy of the payment 
adjustment for differences in area wage levels. 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, we considered for this FY 2023 proposed rule how 
best to address the potential scenarios about which commenters raised 
concerns; 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 IPF 
PPS wage index regulations at Sec.  412.424(a)(2), we use an 
appropriate wage index based on the best available data, including the 
best available labor market area delineations, to adjust IPF 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, and for an individual provider, these fluctuations 
can be difficult to predict. 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 
IPF 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 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 IPF 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 IPF 
PPS payments due to any significant wage index decreases that may 
affect providers in a year. Therefore, we believe this approach would 
address concerns about instability that commenters raised in the FY 
2022 IPF PPS rule. In addition, we believe that applying a 5-percent 
cap on all wage index decreases would support increased predictability 
about IPF 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 III.D.1.e 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. Therefore, we anticipate that 
the impact to the wage index budget neutrality factor in future years 
would continue to be minimal. 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 IPFs 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 IPFs 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.
    The Secretary has broad authority to establish appropriate payment 
adjustments under the IPF PPS, including the wage index adjustment. As 
discussed earlier in this section, the IPF 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 IPF 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 IPF'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 IPF 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 IPF's

[[Page 19425]]

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 IPF's capped wage index in the prior FY. For example, if an 
IPF'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 IPF 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 IPF would not have a wage index in the prior FY. 
We would reflect the proposed permanent cap on wage index decreases at 
Sec.  412.424(d)(1)(i).
    As previously discussed, we believe this proposed methodology would 
maintain the IPF PPS wage index as a relative measure of the value of 
labor in prescribed labor market areas, increase predictability of IPF 
PPS payments for providers, and mitigate instability and significant 
negative impacts to providers resulting from significant changes to the 
wage index. In section VIII.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.
d. Proposed Adjustment for Rural Location
    In the November 2004 IPF PPS final rule, (69 FR 66954) we provided 
a 17 percent payment adjustment for IPFs located in a rural area. This 
adjustment was based on the regression analysis, which indicated that 
the per diem cost of rural facilities was 17 percent higher than that 
of urban facilities after accounting for the influence of the other 
variables included in the regression. This 17 percent adjustment has 
been part of the IPF PPS each year since the inception of the IPF PPS. 
For FY 2023, we propose to continue to apply a 17 percent payment 
adjustment for IPFs located in a rural area as defined at Sec.  
412.64(b)(1)(ii)(C) (see 69 FR 66954 for a complete discussion of the 
adjustment for rural locations).
e. Proposed Budget Neutrality Adjustment
    Changes to the wage index are made in a budget-neutral manner so 
that updates do not increase expenditures. Therefore, for FY 2023, we 
are proposing to continue to apply a budget-neutrality adjustment in 
accordance with our existing budget-neutrality policy. This policy 
requires us to update the wage index in such a way that total estimated 
payments to IPFs 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 IPF PPS rates. We use the following steps to ensure that 
the rates reflect the FY 2023 update to the wage indexes (based on the 
FY 2019 hospital cost report data) and the labor-related share in a 
budget-neutral manner:
    Step 1: Simulate estimated IPF PPS payments, using the FY 2022 IPF 
wage index values (available on the CMS website) and labor-related 
share (as published in the FY 2022 IPF PPS final rule (86 FR 42608).
    Step 2: Simulate estimated IPF PPS payments using the proposed FY 
2023 IPF wage index values (available on the CMS website), the proposed 
5-percent cap on any decrease to a provider's wage index from its wage 
index in the prior year, and the proposed FY 2023 labor-related share 
(based on the latest available data as discussed previously).
    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 1.0016.
    Step 4: Apply the FY 2023 budget-neutral wage adjustment factor 
from step 3 to the FY 2022 IPF PPS Federal per diem base rate after the 
application of the market basket update described in section III.A of 
this proposed rule, to determine the FY 2023 IPF PPS Federal per diem 
base rate.
    For this proposed rule, we also followed these steps to separately 
calculate the budget neutrality factor associated with the proposed 5-
percent cap on any decrease to a provider's wage index from its wage 
index in the prior year. First, we calculated the budget neutrality 
factor associated with the proposed FY 2023 IPF wage index and proposed 
FY 2023 labor-related share. We divided the amount of simulated 
payments using the FY 2022 IPF wage index and labor-related share by 
the amount of simulated payments using the proposed FY 2023 wage index 
and proposed FY 2023 labor-related share. The resulting quotient is 
1.0017.
    Next, we calculated the budget neutrality factor associated with 
the proposed 5-percent cap on any decrease to a provider's wage index 
from its wage index in the prior year. We divided the amount of 
simulated payments using the proposed FY 2023 wage index and proposed 
FY 2023 labor-related share by the amount of simulated payments using 
the proposed FY 2023 wage index, the proposed 5-percent cap on any 
decrease to a provider's wage index from its wage index in the prior 
year, and the proposed FY 2023 labor-related share. The resulting 
quotient is 0.9999. The combined budget neutrality factor, which is the 
proposed FY 2023 budget-neutral wage adjustment factor as discussed 
earlier in this section, is 1.0016.
2. Proposed Teaching Adjustment
    In the November 2004 IPF PPS final rule, we implemented regulations 
at Sec.  412.424(d)(1)(iii) to establish a facility-level adjustment 
for IPFs that are, or are part of, teaching hospitals. The teaching 
adjustment accounts for the higher indirect operating costs experienced 
by hospitals that participate in graduate medical education (GME) 
programs. The payment adjustments are made based on the ratio of the 
number of full-time equivalent (FTE) interns and residents training in 
the IPF and the IPF's average daily census (ADC).
    Under the Inpatient Prospective Payment System (IPPS), Medicare 
makes direct GME payments (for direct costs such as resident and 
teaching physician salaries, and other direct teaching costs) to all 
teaching hospitals including those paid under a PPS, and those paid 
under the TEFRA rate-of-increase limits. These direct GME payments are 
made separately from payments for hospital operating costs and are not 
part of the IPF PPS. In addition, direct GME payments do not address 
the estimated higher indirect operating costs teaching hospitals may 
face.
    The results of the regression analysis of FY 2002 IPF data 
established the basis for the payment adjustments included in the 
November 2004 IPF PPS final rule. The results showed that the indirect 
teaching cost variable is significant in explaining the higher costs of 
IPFs that have teaching programs. We calculated the teaching adjustment 
based on the IPF's ``teaching variable,'' which is (1 + (the number of 
FTE residents training in the IPF/the IPF's ADC)). The teaching 
variable is then raised to the 0.5150 power to result in the teaching 
adjustment. This formula is subject to the limitations on the number of 
FTE residents, which are described in this section of the proposed 
rule.
    We established the teaching adjustment in a manner that limited the 
incentives for IPFs to add FTE residents for the purpose of increasing 
their teaching adjustment. We imposed a cap on the number of FTE 
residents that may be counted for purposes of calculating the teaching 
adjustment. The cap limits the number of FTE residents that teaching 
IPFs may count for the purpose of calculating the IPF PPS

[[Page 19426]]

teaching adjustment, not the number of residents teaching institutions 
can hire or train. We calculated the number of FTE residents that 
trained in the IPF during a ``base year'' and used that FTE resident 
number as the cap. An IPF's FTE resident cap is ultimately determined 
based on the final settlement of the IPF's most recent cost report 
filed before November 15, 2004 (publication date of the IPF PPS final 
rule). A complete discussion of the temporary adjustment to the FTE cap 
to reflect residents due to hospital closure or residency program 
closure appears in the RY 2012 IPF PPS proposed rule (76 FR 5018 
through 5020) and the RY 2012 IPF PPS final rule (76 FR 26453 through 
26456).
    In the regression analysis, the logarithm of the teaching variable 
had a coefficient value of 0.5150. We converted this cost effect to a 
teaching payment adjustment by treating the regression coefficient as 
an exponent and raising the teaching variable to a power equal to the 
coefficient value. We note that the coefficient value of 0.5150 was 
based on the regression analysis holding all other components of the 
payment system constant. A complete discussion of how the teaching 
adjustment was calculated appears in the November 2004 IPF PPS final 
rule (69 FR 66954 through 66957) and the RY 2009 IPF PPS notice (73 FR 
25721). As with other adjustment factors derived through the regression 
analysis, we do not plan to rerun the teaching adjustment factors in 
the regression analysis until we more fully analyze IPF PPS data. 
Therefore, in this FY 2023 proposed rule, we are proposing to continue 
to retain the coefficient value of 0.5150 for the teaching adjustment 
to the Federal per diem base rate.
3. Proposed Cost of Living Adjustment for IPFs Located in Alaska and 
Hawaii
    The IPF PPS includes a payment adjustment for IPFs located in 
Alaska and Hawaii based upon the area in which the IPF is located. As 
we explained in the November 2004 IPF PPS final rule, the FY 2002 data 
demonstrated that IPFs in Alaska and Hawaii had per diem costs that 
were disproportionately higher than other IPFs. Other Medicare 
prospective payment systems (for example, the IPPS and LTCH PPS) 
adopted a COLA to account for the cost differential of care furnished 
in Alaska and Hawaii.
    We analyzed the effect of applying a COLA to payments for IPFs 
located in Alaska and Hawaii. The results of our analysis demonstrated 
that a COLA for IPFs located in Alaska and Hawaii will improve payment 
equity for these facilities. As a result of this analysis, we provided 
a COLA in the November 2004 IPF PPS final rule.
    A COLA for IPFs located in Alaska and Hawaii is made by multiplying 
the non-labor-related portion of the Federal per diem base rate by the 
applicable COLA factor based on the COLA area in which the IPF is 
located.
    The COLA factors through 2009 were published by the Office of 
Personnel Management (OPM), and the OPM memo showing the 2009 COLA 
factors is available at https://www.chcoc.gov/content/nonforeign-area-retirement-equity-assurance-act.
    We note that the COLA areas for Alaska are not defined by county as 
are the COLA areas for Hawaii. In 5 CFR 591.207, the OPM established 
the following COLA areas:
    <bullet> City of Anchorage, and 80-kilometer (50-mile) radius by 
road, as measured from the Federal courthouse.
    <bullet> City of Fairbanks, and 80-kilometer (50-mile) radius by 
road, as measured from the Federal courthouse.
    <bullet> City of Juneau, and 80-kilometer (50-mile) radius by road, 
as measured from the Federal courthouse.
    <bullet> Rest of the state of Alaska.
    As stated in the November 2004 IPF PPS final rule, we update the 
COLA factors according to updates established by the OPM. However, 
sections 1911 through 1919 of the Non-foreign Area Retirement Equity 
Assurance Act, as contained in subtitle B of title XIX of the National 
Defense Authorization Act (NDAA) for FY 2010 (Pub. L. 111-84, October 
28, 2009), transitions the Alaska and Hawaii COLAs to locality pay. 
Under section 1914 of NDAA, locality pay was phased in over a 3-year 
period beginning in January 2010, with COLA rates frozen as of the date 
of enactment, October 28, 2009, and then proportionately reduced to 
reflect the phase-in of locality pay.
    When we published the proposed COLA factors in the RY 2012 IPF PPS 
proposed rule (76 FR 4998), we inadvertently selected the FY 2010 COLA 
rates, which had been reduced to account for the phase-in of locality 
pay. We did not intend to propose the reduced COLA rates because that 
would have understated the adjustment. Since the 2009 COLA rates did 
not reflect the phase-in of locality pay, we finalized the FY 2009 COLA 
rates for RY 2010 through RY 2014.
    In the FY 2013 IPPS/LTCH final rule (77 FR 53700 through 53701), we 
established a new methodology to update the COLA factors for Alaska and 
Hawaii, and adopted this methodology for the IPF PPS in the FY 2015 IPF 
final rule (79 FR 45958 through 45960). We adopted this new COLA 
methodology for the IPF PPS because IPFs are hospitals with a similar 
mix of commodities and services. We believe it is appropriate to have a 
consistent policy approach with that of other hospitals in Alaska and 
Hawaii. Therefore, the IPF COLAs for FY 2015 through FY 2017 were the 
same as those applied under the IPPS in those years. As finalized in 
the FY 2013 IPPS/LTCH PPS final rule (77 FR 53700 and 53701), the COLA 
updates are determined every 4 years, when the IPPS market basket 
labor-related share is updated. Because the labor-related share of the 
IPPS market basket was most recently updated for FY 2022, the COLA 
factors were updated in FY 2022 IPPS/LTCH rulemaking (86 FR 45547). As 
such, we also updated the IPF PPS COLA factors for FY 2022 (86 FR 42621 
through 42622) to reflect the updated COLA factors finalized in the FY 
2022 IPPS/LTCH rulemaking. Table 2 shows the proposed IPF PPS COLA 
factors effective for FY 2022 through FY 2025.

   Table 2--IPF PPS Cost-of-Living Adjustment Factors: IPFs Located in
                            Alaska and Hawaii
------------------------------------------------------------------------
                                                        FY 2022 through
                         Area                               FY 2025
------------------------------------------------------------------------
Alaska:
    City of Anchorage and 80-kilometer (50-mile)                    1.22
     radius by road..................................
    City of Fairbanks and 80-kilometer (50-mile)                    1.22
     radius by road..................................
    City of Juneau and 80-kilometer (50-mile) radius                1.22
     by road.........................................
    Rest of Alaska...................................               1.24
Hawaii:
    City and County of Honolulu......................               1.25

[[Page 19427]]

 
    County of Hawaii.................................               1.22
    County of Kauai..................................               1.25
    County of Maui and County of Kalawao.............               1.25
------------------------------------------------------------------------

    The proposed IPF PPS COLA factors for FY 2023 are also shown in 
Addendum A to this proposed rule, and is available on the CMS website 
at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
4. Proposed Adjustment for IPFs With a Qualifying Emergency Department 
(ED)
    The IPF PPS includes a facility-level adjustment for IPFs with 
qualifying EDs. We provide an adjustment to the Federal per diem base 
rate to account for the costs associated with maintaining a full-
service ED. The adjustment is intended to account for ED costs incurred 
by a psychiatric hospital with a qualifying ED or an excluded 
psychiatric unit of an IPPS hospital or a CAH, for preadmission 
services otherwise payable under the Medicare Hospital Outpatient 
Prospective Payment System (OPPS), furnished to a beneficiary on the 
date of the beneficiary's admission to the hospital and during the day 
immediately preceding the date of admission to the IPF (see Sec.  
413.40(c)(2)), and the overhead cost of maintaining the ED. This 
payment is a facility-level adjustment that applies to all IPF 
admissions (with one exception which we described), regardless of 
whether a particular patient receives preadmission services in the 
hospital's ED.
    The ED adjustment is incorporated into the variable per diem 
adjustment for the first day of each stay for IPFs with a qualifying 
ED. Those IPFs with a qualifying ED receive an adjustment factor of 
1.31 as the variable per diem adjustment for day 1 of each patient 
stay. If an IPF does not have a qualifying ED, it receives an 
adjustment factor of 1.19 as the variable per diem adjustment for day 1 
of each patient stay.
    The ED adjustment is made on every qualifying claim except as 
described in this section of the proposed rule. As specified in Sec.  
412.424(d)(1)(v)(B), the ED adjustment is not made when a patient is 
discharged from an IPPS hospital or CAH and admitted to the same IPPS 
hospital's or CAH's excluded psychiatric unit. We clarified in the 
November 2004 IPF PPS final rule (69 FR 66960) that an ED adjustment is 
not made in this case because the costs associated with ED services are 
reflected in the DRG payment to the IPPS hospital or through the 
reasonable cost payment made to the CAH.
    Therefore, when patients are discharged from an IPPS hospital or 
CAH and admitted to the same hospital's or CAH's excluded psychiatric 
unit, the IPF receives the 1.19 adjustment factor as the variable per 
diem adjustment for the first day of the patient's stay in the IPF. For 
FY 2023, we are proposing to continue to retain the 1.31 adjustment 
factor for IPFs with qualifying EDs. A complete discussion of the steps 
involved in the calculation of the ED adjustment factors are in the 
November 2004 IPF PPS final rule (69 FR 66959 through 66960) and the RY 
2007 IPF PPS final rule (71 FR 27070 through 27072).

E. Other Final Payment Adjustments and Policies

1. Outlier Payment Overview
    The IPF PPS includes an outlier adjustment to promote access to IPF 
care for those patients who require expensive care and to limit the 
financial risk of IPFs treating unusually costly patients. In the 
November 2004 IPF PPS final rule, we implemented regulations at Sec.  
412.424(d)(3)(i) to provide a per-case payment for IPF stays that are 
extraordinarily costly. Providing additional payments to IPFs for 
extremely costly cases strongly improves the accuracy of the IPF PPS in 
determining resource costs at the patient and facility level. These 
additional payments reduce the financial losses that would otherwise be 
incurred in treating patients who require costlier care, and therefore, 
reduce the incentives for IPFs to under-serve these patients. We make 
outlier payments for discharges in which an IPF's estimated total cost 
for a case exceeds a fixed dollar loss threshold amount (multiplied by 
the IPF's facility-level adjustments) plus the Federal per diem payment 
amount for the case.
    In instances when the case qualifies for an outlier payment, we pay 
80 percent of the difference between the estimated cost for the case 
and the adjusted threshold amount for days 1 through 9 of the stay 
(consistent with the median LOS for IPFs in FY 2002), and 60 percent of 
the difference for day 10 and thereafter. The adjusted threshold amount 
is equal to the outlier threshold amount adjusted for wage area, 
teaching status, rural area, and the COLA adjustment (if applicable), 
plus the amount of the Medicare IPF payment for the case. We 
established the 80 percent and 60 percent loss sharing ratios because 
we were concerned that a single ratio established at 80 percent (like 
other Medicare PPSs) might provide an incentive under the IPF per diem 
payment system to increase LOS in order to receive additional payments.
    After establishing the loss sharing ratios, we determined the 
current fixed dollar loss threshold amount through payment simulations 
designed to compute a dollar loss beyond which payments are estimated 
to meet the 2 percent outlier spending target. Each year when we update 
the IPF PPS, we simulate payments using the latest available data to 
compute the fixed dollar loss threshold so that outlier payments 
represent 2 percent of total estimated IPF PPS payments.
2. Proposed Update to the Outlier Fixed Dollar Loss Threshold Amount
    In accordance with the update methodology described in Sec.  
412.428(d), we are proposing to update the fixed dollar loss threshold 
amount used under the IPF PPS outlier policy. Based on the regression 
analysis and payment simulations used to develop the IPF PPS, we 
established a 2 percent outlier policy, which strikes an appropriate 
balance between protecting IPFs from extraordinarily costly cases while 
ensuring the adequacy of the Federal per diem base rate for all other 
cases that are not outlier cases.
    Our longstanding methodology for updating the outlier fixed dollar 
loss threshold involves using the best available data, which is 
typically the most recent available data. Last year for the FY 2022 IPF 
PPS final rule, we finalized the use of FY 2019 claims rather than the 
more recent FY 2020 claims for updating the outlier fixed dollar loss 
threshold (86 FR 42623). We

[[Page 19428]]

noted that our use of the FY 2019 claims to set the final outlier fixed 
dollar loss threshold for FY 2022 deviated from our longstanding 
practice of using the most recent available year of claims, but 
remained otherwise consistent with the established outlier update 
methodology. We explained that we finalized our proposal to deviate 
from our longstanding practice of using the most recent available year 
of claims only because, and to the extent that, the ``coronavirus 
disease 2019'' (abbreviated ``COVID-19'') Public Health Emergency (PHE) 
appeared to have significantly impacted the FY 2020 IPF claims. We 
further stated that we intended to continue to analyze further data in 
order to better understand both the short-term and long-term effects of 
the COVID-19 PHE on IPFs (86 FR 42624).
    For this FY 2023 IPF PPS proposed rulemaking, consistent with our 
longstanding practice, we analyzed the most recent available data for 
simulating IPF PPS payments in FY 2023. We observed a continuation of 
two main trends that we noted in our analysis of FY 2020 claims for FY 
2022--that is, an overall increase in average cost per day and an 
overall decrease in the number of covered days. However, we also 
identified that some providers had significant increases in their 
charges, resulting in higher than normal estimated cost per day that 
would skew our estimate of outlier payments for FY 2022 and FY 2023.
    Historically, we have applied statistical trims under the IPF PPS 
in order to improve the statistical validity of the data used for 
ratesetting. In the November 2004 final rule, we explained that we 
applied a 3 standard deviation trim on cost per day prior to 
calculating the average per diem cost used to calculate the IPF PPS 
Federal per diem base rate (69 FR 66927). Furthermore, as discussed in 
section III.E.3 of this proposed rule, our longstanding policy applies 
a ceiling on a provider's cost-to-charge ratio when it exceeds 3 
standard deviations from the mean cost-to-charge ratio for urban or 
rural providers. We are proposing a similar approach in order to 
address the skew in estimated cost per day that we observed in the FY 
2021 claims. Specifically, we are proposing for FY 2023 to exclude 
providers from our simulation of IPF PPS payments for FY 2022 and FY 
2023 if their change in estimated average cost per day is outside 3 
standard deviations from the mean.
    Based on an analysis of the December 2021 update of FY 2021 IPF 
claims and the FY 2022 rate increases, we believe it is necessary to 
update the fixed dollar loss threshold amount to maintain an outlier 
percentage that equals 2 percent of total estimated IPF PPS payments. 
We are proposing to update the IPF outlier threshold amount for FY 2023 
using FY 2021 claims data and the same methodology that we used to set 
the initial outlier threshold amount in the RY 2007 IPF PPS final rule 
(71 FR 27072 and 27073), which is also the same methodology that we 
used to update the outlier threshold amounts for years 2008 through 
2022. However, as discussed earlier in this section, we also propose 
for FY 2023 to exclude providers from our impact simulations whose 
change in simulated cost per day is outside 3 standard deviations from 
the mean. Based on an analysis of these updated data, we estimate that 
IPF outlier payments as a percentage of total estimated payments are 
approximately 3.2 percent in FY 2022. Therefore, we are proposing to 
update the outlier threshold amount to $24,270 to maintain estimated 
outlier payments at 2 percent of total estimated aggregate IPF payments 
for FY 2023. This proposed update is an increase from the FY 2022 
threshold of $16,040.
3. Proposed Update to IPF Cost-to-Charge Ratio Ceilings
    Under the IPF PPS, an outlier payment is made if an IPF's cost for 
a stay exceeds a fixed dollar loss threshold amount plus the IPF PPS 
amount. In order to establish an IPF's cost for a particular case, we 
multiply the IPF's reported charges on the discharge bill by its 
overall cost-to-charge ratio (CCR). This approach to determining an 
IPF's cost is consistent with the approach used under the IPPS and 
other PPSs. In the FY 2004 IPPS final rule (68 FR 34494), we 
implemented changes to the IPPS policy used to determine CCRs for IPPS 
hospitals, because we became aware that payment vulnerabilities 
resulted in inappropriate outlier payments. Under the IPPS, we 
established a statistical measure of accuracy for CCRs to ensure that 
aberrant CCR data did not result in inappropriate outlier payments.
    As we indicated in the November 2004 IPF PPS final rule (69 FR 
66961), we believe that the IPF outlier policy is susceptible to the 
same payment vulnerabilities as the IPPS; therefore, we adopted a 
method to ensure the statistical accuracy of CCRs under the IPF PPS. 
Specifically, we adopted the following procedure in the November 2004 
IPF PPS final rule:
    <bullet> Calculated two national ceilings, one for IPFs located in 
rural areas and one for IPFs located in urban areas.
    <bullet> Computed the ceilings by first calculating the national 
average and the standard deviation of the CCR for both urban and rural 
IPFs using the most recent CCRs entered in the most recent Provider 
Specific File (PSF) available.
    For FY 2023, we propose to continue to follow this methodology.
    To determine the rural and urban ceilings, we multiplied each of 
the standard deviations by 3 and added the result to the appropriate 
national CCR average (either rural or urban). The upper threshold CCR 
for IPFs in FY 2023 is 2.0472 for rural IPFs, and 1.7279 for urban 
IPFs, based on CBSA-based geographic designations. If an IPF's CCR is 
above the applicable ceiling, the ratio is considered statistically 
inaccurate, and we assign the appropriate national (either rural or 
urban) median CCR to the IPF.
    We apply the national median CCRs to the following situations:
    <bullet> New IPFs that have not yet submitted their first Medicare 
cost report. We continue to use these national median CCRs until the 
facility's actual CCR can be computed using the first tentatively or 
final settled cost report.
    <bullet> IPFs whose overall CCR is in excess of three standard 
deviations above the corresponding national geometric mean (that is, 
above the ceiling).
    <bullet> Other IPFs for which the MAC obtains inaccurate or 
incomplete data with which to calculate a CCR.
    We are proposing to continue to update the FY 2023 national median 
and ceiling CCRs for urban and rural IPFs based on the CCRs entered in 
the latest available IPF PPS PSF. Specifically, for FY 2023, to be used 
in each of the three situations listed previously, using the most 
recent CCRs entered in the CY 2022 PSF, we provide an estimated 
national median CCR of 0.5720 for rural IPFs and a national median CCR 
of 0.4200 for urban IPFs. These calculations are based on the IPF's 
location (either urban or rural) using the CBSA-based geographic 
designations. A complete discussion regarding the national median CCRs 
appears in the November 2004 IPF PPS final rule (69 FR 66961 through 
66964).

IV. Comment Solicitation on Analysis of IPF PPS Adjustments

A. Background

    As discussed in section III.C.1 of this proposed rule, we are 
proposing to continue to use the existing regression-derived adjustment 
factors for FY 2023. In the November 15, 2004 final rule, we indicated 
that we did not intend to update the regression analysis and the 
patient-level and facility-level adjustments until we complete further

[[Page 19429]]

analysis of IPF costs using IPF PPS data that yields as much 
information as possible regarding the patient-level characteristics of 
the population that each IPF serves.
    Since that time, we undertook analysis to better understand IPF 
industry practices so that we may refine the IPF PPS in the future, as 
appropriate. For RY 2012, we identified several areas of concern for 
future refinement, and we invited comments on these issues in the RY 
2012 IPF PPS proposed and final rules. For further discussion of these 
issues and to review the public comments, we refer readers to the RY 
2012 IPF PPS proposed rule (76 FR 4998) and final rule (76 FR 26432).
    Our preliminary analysis, which we previously discussed in the FY 
2016 IPF PPS final rule (80 FR 46693 through 46694), also revealed 
variation in cost and claim data, particularly related to labor costs, 
drugs costs, and laboratory services. We found that some providers have 
very low labor costs, or very low or missing drug or laboratory costs 
or charges, relative to other providers. As we noted in the FY 2016 IPF 
PPS final rule, our preliminary analysis of 2012 to 2013 IPF data found 
that over 20 percent of IPF stays reported no ancillary costs, such as 
laboratory and drug costs, in their cost reports, or laboratory or drug 
charges on their claims. In the past, we stated that we expect that 
most patients requiring hospitalization for active psychiatric 
treatment would need drugs and laboratory services, and we reminded 
providers that the IPF PPS Federal per diem base rate includes the cost 
of all ancillary services, including drugs and laboratory services.
    On November 17, 2017, we issued Transmittal 12, which made changes 
to the hospital cost report form CMS- 2552-10 (OMB No. 0938-0050), and 
included the requirement that cost reports from psychiatric hospitals 
include certain ancillary costs, or the cost report will be rejected. 
On January 30, 2018, we issued Transmittal 13, which changed the 
implementation date for Transmittal 12 to be for cost reporting periods 
ending on or after September 30, 2017. For details, we refer readers to 
see these Transmittals, which are available on the CMS website at 
https://www.cms.gov/Regulationsand-Guidance/Guidance/Transmittals/index.html. CMS suspended the requirement that cost reports from 
psychiatric hospitals include certain ancillary costs effective April 
27, 2018, in order to consider excluding all-inclusive rate providers 
from this requirement. CMS issued Transmittal 15 on October 19, 2018, 
reinstating the requirement that cost reports from psychiatric 
hospitals, except all-inclusive rate providers, include certain 
ancillary costs.

B. Update and Comment Solicitation on Analysis of IPF PPS Adjustments

    Working in collaboration with a contractor, we have undertaken 
further analysis of more recent IPF cost and claim information. We have 
posted a report on the CMS website, which summarizes the results of the 
latest analysis. For public awareness, this report is available online 
at https://www.cms.gov/medicare/medicare-fee-for-service-payment/
inpatientpsychfacilpps. This updated analysis finds that the existing 
IPF PPS model continues to be generally appropriate in terms of 
effectively aligning IPF PPS payments with the cost of providing IPF 
services, but suggests that certain updates to the codes, categories, 
adjustment factors, and ECT payment amount per treatment could improve 
payment accuracy. We are requesting comments on the results of our 
latest analysis as summarized in the report. In particular, we are 
interested in comments about the following topics, which are discussed 
in detail in the report:
    <bullet> The report summarizes results of the analysis regarding 
patient-level characteristics, about which we are requesting comments:
    ++ The updated regression analysis suggests that certain technical 
changes to the DRG and comorbidity adjustment factors, consolidation of 
the age categories for the patient age adjustment, and changes to the 
adjustment factors for age and length of stay could be appropriate.
    ++ The analysis of ancillary costs for IPF stays with ECT suggests 
that a higher ECT payment amount per treatment could better align IPF 
PPS payments with the costs of furnishing ECT.
    ++ The analysis of the outlier percentage suggests that fewer IPF 
cases qualify for outliers under the current 2 percent outlier target 
than were estimated when the IPF PPS was established. We estimate that 
increasing the outlier percentage would increase the number of IPF 
cases that qualify for outliers, but would have distributional effects 
due to budget neutrality.
    <bullet> The report summarizes the results of analysis regarding 
facility-level characteristics, about which we are requesting comments:
    ++ The updated regression analysis suggests that updating the 
adjustment factors for teaching facilities, rural facilities, and 
facilities with an ED could improve payment accuracy; however, we 
estimate such changes could have positive and negative effects on 
payments for different types of IPFs.
    ++ The analysis of occupancy-related control variables included in 
the regression model indicates that these control variables are 
correlated with the rural adjustment factor, and that removal of these 
control variables from the model could result in an increase to the 
rural adjustment factor in the regression model.
    <bullet> The report summarizes certain areas where we believe 
additional research is needed. We are requesting comments about the 
results summarized in the report. We are also requesting comments about 
additional analyses that we should undertake to better understand how 
these issues affect the cost of providing IPF services, and how the IPF 
PPS could better account for these costs:
    ++ We analyzed the costs associated with social determinants of 
health, but found that our analysis was confounded by a low frequency 
of IPF claims reporting the applicable ICD-10 diagnosis codes. We are 
soliciting public comments about the results of this analysis, and 
whether there are additional patient characteristics that affect the 
cost of providing IPF services that may not be consistently reported on 
claims. Additionally, we are soliciting public comments about how we 
could better identify such patient characteristics and their effects on 
costs.
    ++ We analyzed the costs associated with the percentage of low-
income patients that IPFs treat, based on a construction of the 
Disproportionate Share Hospitals (DSH) percentage that is used in other 
payment systems using the data currently available for IPFs. We are 
soliciting public comments about the results of this analysis, which 
suggest that the addition of an adjustment factor for disproportionate 
share intensity could improve the accuracy of IPF PPS payments.

V. Inpatient Psychiatric Facility Quality Reporting (IPFQR) Program

A. 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

[[Page 19430]]

outcomes.<SUP>1 2 3 4 5 6 7 8 9</SUP> 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.\10\
---------------------------------------------------------------------------

    \1\ 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.
    \2\ 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.
    \3\ 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.
    \4\ 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.
    \5\ 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.
    \6\ 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.
    \7\ 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.
    \8\ 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.
    \9\ 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.
    \10\ 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.\11\ 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 2018.
---------------------------------------------------------------------------

    \11\ 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 IPFQR Program to assess drivers of 
healthcare quality disparities in the IPFQR Program. Additionally, this 
section discusses measures of health equity that could be adapted for 
use in the IPFQR Program. 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 IPFQR Program.
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 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 IPF 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 IPF 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.\12\
---------------------------------------------------------------------------

    \12\ 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.

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[[Page 19431]]

    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 IPF 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.<SUP>13 14 15 16 17 18 19 20</SUP>
---------------------------------------------------------------------------

    \13\ 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.
    \14\ 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.
    \15\ 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.
    \16\ 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.
    \17\ 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.
    \18\ 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.
    \19\ 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.
    \20\ 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 patient-level data is not available. Each source of 
data has advantages and disadvantages for disparity reporting:
    <bullet> Patient-reported data are considered to be the gold 
standard for evaluating quality of care for patients with social risk 
factors.\21\ While data sources for many social risk factors and 
demographic variables are still developing among several CMS settings, 
the IPFQR Program will begin collecting mandatory patient-level data 
for certain chart-abstracted measures the FY 2024 payment determination 
and subsequent years (86 FR 42608).
---------------------------------------------------------------------------

    \21\ 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.
---------------------------------------------------------------------------

    <bullet> CMS Administrative Claims data have long been used for 
quality measurement due to their availability and will continue to be 
evaluated for usability in measure development and or stratification. 
Using these existing data allows for high impact analyses with 
negligible healthcare provider burden. For example, dual eligibility 
for Medicare and Medicaid has been found to be an effective indicator 
of social risk in beneficiary populations.\22\ There are, however, 
limitations in these data's usability for stratification analysis.
---------------------------------------------------------------------------

    \22\ 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.
---------------------------------------------------------------------------

    <bullet> Area-based indicators of social risk create approximations 
of patient risk based on the neighborhood or context that a patient 
resides in. Several indexes, such as Agency for Healthcare Research and 
Quality (AHRQ) Socioeconomic Status (SES) Index,\23\

[[Page 19432]]

Centers for Disease Control and Prevention/Agency for Toxic Substances 
and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI),\24\ 
and Health Resources and Services Administration (HRSA) Area 
Deprivation Index (ADI),\25\ provide multifaceted contextual 
information about an area and may be considered as an efficient way to 
stratify measures that include many social risk factors.
---------------------------------------------------------------------------

    \23\ Bonito A., Bann C., Eicheldinger C., Carpenter L. Creation 
of New Race-Ethnicity Codes and Socioeconomic 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.
    \24\ 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.
    \25\ 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.
---------------------------------------------------------------------------

    <bullet> Imputed data sources use statistical techniques to 
estimate patient-reported factors, including race and ethnicity. One 
such tool is the Medicare Bayesian Improved Surname Geocoding (MBISG) 
method (currently in version 2.1), which combines information from 
administrative data, surname, and residential location to estimate 
patient race and ethnicity.\26\
---------------------------------------------------------------------------

    \26\ 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.
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d. Identifying Meaningful Performance Differences
    While we aim to use standardized approaches where possible, 
identifying differences in performance on stratified results will be 
made at the program level due to contextual variations across programs 
and settings. We look forward to feedback on the benefits and 
limitations of the possible reporting approaches described below:
    <bullet> Statistical approaches could be used to reliably group 
results, such as using confidence intervals, creating cut points based 
on standard deviations, or using a clustering algorithm.
    <bullet<ls-thn-eq> Programs could use a ranked ordering and 
percentile approach, ordering healthcare providers in a ranked system 
based on their performance on disparity measures to quickly allow them 
to compare their performance to other similar healthcare providers.
    <bullet<ls-thn-eq> Healthcare providers could be categorized into 
groups based on their performance using defined thresholds, such as 
fixed intervals of results of disparity measures, indicating different 
levels of performance.
    <bullet> Benchmarking, or comparing individual results to state or 
national average, is another potential reporting strategy.
    <bullet> Finally, a ranking system may not be appropriate for all 
programs and care settings, and some programs may only report disparity 
results.
e. Guiding Principles for Reporting Disparity Measures
    Reporting of the results discussed above can be employed in several 
ways to drive improvements in quality. Confidential reporting, or 
reporting results privately to healthcare providers, is generally used 
for new programs or new measures recently adopted for programs through 
notice and comment rulemaking to give healthcare providers an 
opportunity to become more familiar with calculation methods and to 
improve before other forms of reporting are used. In addition, many 
results are reported publicly, in accordance with the statute. This 
method provides all stakeholders with important information on 
healthcare provider quality, and in turn, relies on market forces to 
incentivize healthcare providers to improve and become more competitive 
in their markets without directly influencing payment from CMS. One 
important consideration is to assess differential impact on IPFs, 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.
2. Approaches to Assessing Drivers of Healthcare Quality Disparities 
and Developing Measures of Healthcare Equity in the IPFQR Program
    This section presents information on two approaches for the IPFQR 
Program. The first section presents information about a method that 
could be used to assist IPFs in identifying potential drivers of 
healthcare quality disparities. The second section describes measures 
of healthcare equity that might be appropriate for inclusion in the 
IPFQR Program.
a. Performance Disparity Decomposition
    In response to the FY 2022 IPF PPS proposed rule's RFI (86 FR 19494 
through 19500), ``Closing the Health Equity Gap in CMS Quality 
Programs'', some stakeholders noted that identifying which factors are 
contributing to the performance gaps may not always be straightforward, 
especially if the IPF 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 IPF 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.\27\ 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

[[Page 19433]]

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 IPFs 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 IPFs 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).
---------------------------------------------------------------------------

    \27\ 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 IPF. 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 
they are factors the healthcare provider could mitigate the effects of, 
if they were shown to be drivers of disparity in their IPF. 
Additionally, high volume ED use is used as a potential mediating 
factor that could be difficult for IPFs 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 IPF, 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 IPF 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 IPF 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 outcomes (for example, facilities may choose to focus efforts on 
the largest drivers of a disparity).
BILLING CODE 4120-01-P

[[Page 19434]]

[GRAPHIC] [TIFF OMITTED] TP04AP22.000


[[Page 19435]]


[GRAPHIC] [TIFF OMITTED] TP04AP22.001

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 the IPFQRP Program, CMS may consider 
the following:
    <bullet> Measures should be actionable in terms of quality 
improvement;
    <bullet> Measures should help beneficiaries and their caregivers 
make informed healthcare decisions;
    <bullet> Measures should not create incentives to lower the quality 
of care; and
    <bullet> Measures should adhere to high scientific acceptability 
standards.
    CMS has developed measures assessing health equity, or designed to 
promote health equity, in other settings outside of the IPF. As a 
result, there may be measures that could be adapted for use in the 
IPFQR Program. 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 19436]]

(1) Health Equity Summary Score
    The HESS measure was developed by the CMS OMH \28\ \29\ 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.\30\ 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.\31\
---------------------------------------------------------------------------

    \28\ 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.
    \29\ 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.
    \30\ Centers for Medicare & Medicaid Services, FY 2022 IPPS/LTCH 
PPS Proposed Rule. 88 FR 25560. May 10, 2021.
    \31\ 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.
---------------------------------------------------------------------------

    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
    CMS has 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) \32\ 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; \33\ 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 IPF could receive a point for 
each domain where data are submitted through a CMS portal to reflect 
actions taken by the IPF for each corresponding domain (for a point 
total).
---------------------------------------------------------------------------

    \32\ 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.
    \33\ 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.
---------------------------------------------------------------------------

    CMS believes this type of organizational commitment structural 
measure may complement the health disparities approach described in 
previous sections, and support IPFs 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.'' \34\ We acknowledge that collection of this structural 
measure may impose administrative and/or reporting requirements for 
IPFs.
---------------------------------------------------------------------------

    \34\ 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.
---------------------------------------------------------------------------

    We are interested in obtaining feedback from stakeholders on 
conceptual and measurement priorities for the IPFQR Program to better 
illuminate organizational commitment to health equity.

[[Page 19437]]

3. Solicitation of Public Comment
    The goal of this request for information is to describe key 
principles and approaches that we will consider when advancing the use 
of quality measure development and stratification to address healthcare 
disparities and advance health equity across our programs.
    We invite general comments on the principles and approaches 
described previously in this section of the rule, as well as additional 
thoughts about disparity measurement or stratification guidelines 
suitable for overarching consideration across CMS' QRP programs. 
Specifically, we invite comment on:
<bullet> Identification of Goals and Approaches for Measuring 
Healthcare Disparities and Using Measure Stratification Across CMS 
Quality Reporting Programs
    ++ The use of the within- and between-provider disparity methods in 
IPFs 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 IPFs, 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 IPF'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 IPF 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.

VI. Collection of Information Requirements

    This rule proposes updates to the prospective payment rates, 
outlier threshold, and wage index for Medicare inpatient hospital 
services provided by IPFs. It also proposes to establish a default 
mitigation policy for providers negatively affected by changes to the 
IPF PPS wage index. While discussed in section IV (Comment Solicitation 
on Analysis of IPF PPS Adjustments) of this preamble, the active 
requirements and burden associated with our hospital cost report form 
CMS-2552-10 (OMB control number 0938-0050) are unaffected by this rule.
    Overall, this rule's proposed changes would not impose any new or 
revised ``collection of information'' requirements or burden as defined 
under 5 CFR 1320.3(c).). Consequently, this rule is not subject to the 
requirements of the Paperwork Reduction Act of 1995 (44 U.S.C. 3501 et 
seq.).

VII. 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.

VIII. Regulatory Impact Analysis

A. Statement of Need

    This rule proposes updates to the prospective payment rates for 
Medicare inpatient hospital services provided by IPFs for discharges 
occurring during FY 2023 (October 1, 2022 through September 30, 2023). 
We are proposing to apply the 2016-based IPF market basket increase of 
3.1 percent, less the productivity adjustment of 0.4 percentage point 
as required by 1886(s)(2)(A)(i) of the Act for a proposed total FY 2023 
payment rate update of 2.7 percent. In this proposed rule, we are 
proposing to update the outlier fixed dollar loss threshold amount, 
update the IPF labor-related share, and update the IPF wage index to 
reflect the FY 2023 hospital inpatient wage index. Lastly, 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.

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

[[Page 19438]]

result in a rule: (1) Having an annual effect on the economy of $100 
million or more in any 1 year, or adversely and materially affecting a 
sector of the economy, productivity, competition, jobs, the 
environment, public health or safety, or state, local or tribal 
governments or communities (also referred to as ``economically 
significant''); (2) creating a serious inconsistency or otherwise 
interfering with an action taken or planned by another agency; (3) 
materially altering the budgetary impacts of entitlement grants, user 
fees, or loan programs or the rights and obligations of recipients 
thereof; or (4) raising novel legal or policy issues arising out of 
legal mandates, the President's priorities, or the principles set forth 
in the Executive Order. In accordance with the provisions of Executive 
Order 12866, this regulation was reviewed by the Office of Management 
and Budget.
    A regulatory impact analysis (RIA) must be prepared for major rules 
with significant regulatory action/s and/or with economically 
significant effects ($100 million or more in any 1 year). We estimate 
that the total impact of these changes for FY 2023 payments compared to 
FY 2022 payments will be a net increase of approximately $50 million. 
This reflects a $90 million increase from the update to the payment 
rates (+$105 million from the 4th quarter 2021 IGI forecast of the 
2016-based IPF market basket of 3.1 percent, and -$15 million for the 
productivity adjustment of 0.4 percentage point), as well as a $40 
million decrease as a result of the update to the outlier threshold 
amount. Outlier payments are estimated to change from 3.2 percent in FY 
2022 to 2.0 percent of total estimated IPF payments in FY 2023.
    Based on our estimates, OMB's Office of Information and Regulatory 
Affairs has determined this rulemaking is ``economically significant'' 
as measured by the $100 million threshold. Accordingly, we have 
prepared a Regulatory Impact Analysis that to the best of our ability 
presents the costs and benefits of the rulemaking. Based on our 
estimates, OMB's Office of Information and Regulatory Affairs has 
determined that this rulemaking is ``significant''. Therefore, OMB has 
reviewed these proposed regulations, and the Departments have provided 
the following assessment of their impact.

C. Detailed Economic Analysis

    In this section, we discuss the historical background of the IPF 
PPS and the impact of this proposed rule on the Federal Medicare budget 
and on IPFs.
1. Budgetary Impact
    As discussed in the November 2004 and RY 2007 IPF PPS final rules, 
we applied a budget neutrality factor to the Federal per diem base rate 
and ECT payment per treatment to ensure that total estimated payments 
under the IPF PPS in the implementation period would equal the amount 
that would have been paid if the IPF PPS had not been implemented. This 
Budget neutrality factor included the following components: Outlier 
adjustment, stop-loss adjustment, and the behavioral offset. As 
discussed in the RY 2009 IPF PPS notice (73 FR 25711), the stop-loss 
adjustment is no longer applicable under the IPF PPS.
    As discussed in section III.D.1 of this proposed rule, we are 
proposing to update the wage index and labor-related share, as well as 
apply the proposed 5-percent cap on any decrease to a provider's wage 
index from its wage index in the prior year, in a budget neutral manner 
by applying a wage index budget neutrality factor to the Federal per 
diem base rate and ECT payment per treatment. Therefore, the budgetary 
impact to the Medicare program of this proposed rule will be due to the 
market basket update for FY 2023 of 3.1 percent (see section III.A.2 of 
this proposed rule) less the productivity adjustment of 0.4 percentage 
point required by section 1886(s)(2)(A)(i) of the Act and the update to 
the outlier fixed dollar loss threshold amount.
    We estimate that the FY 2023 impact will be a net increase of $50 
million in payments to IPF providers. This reflects an estimated $90 
million increase from the update to the payment rates and a $40 million 
decrease due to the update to the outlier threshold amount to set total 
estimated outlier payments at 2.0 percent of total estimated payments 
in FY 2023. This estimate does not include the implementation of the 
required 2.0 percentage point reduction of the productivity-adjusted 
market basket update factor for any IPF that fails to meet the IPF 
quality reporting requirements (as discussed in section III.B.2. of 
this proposed rule).
2. Impact on Providers
    To show the impact on providers of the changes to the IPF PPS 
discussed in this proposed rule, we compare estimated payments under 
the proposed IPF PPS rates and factors for FY 2023 versus those under 
FY 2022. We determined the percent change in the estimated FY 2023 IPF 
PPS payments compared to the estimated FY 2022 IPF PPS payments for 
each category of IPFs. In addition, for each category of IPFs, we have 
included the estimated percent change in payments resulting from the 
proposed update to the outlier fixed dollar loss threshold amount; the 
updated wage index data including the proposed labor-related share and 
the proposed 5-percent cap on any decrease to a provider's wage index 
from its wage index in the prior year; and the proposed market basket 
update for FY 2023, as reduced by the proposed productivity adjustment 
according to section 1886(s)(2)(A)(i) of the Act.
    To illustrate the impacts of the proposed FY 2023 changes in this 
proposed rule, our analysis begins with FY 2021 IPF PPS claims (based 
on the 2021 MedPAR claims, December 2021 update). As discussed in 
section III.E.2 of this proposed rule, we also proposed to exclude 
providers from our impact simulations whose change in estimated cost 
per day is outside 3 standard deviations from the mean. We estimate FY 
2022 IPF PPS payments using these 2021 claims, the finalized FY 2022 
IPF PPS Federal per diem base rates, and the finalized FY 2022 IPF PPS 
patient and facility level adjustment factors (as published in the FY 
2022 IPF PPS final rule (86 FR 42608)). We then estimate the FY 2022 
outlier payments based on these simulated FY 2022 IPF PPS payments 
using the same methodology as finalized in the FY 2022 IPF PPS final 
rule (86 FR 42623 through 42624) where total outlier payments are 
maintained at 2 percent of total estimated FY 2022 IPF PPS payments.
    Each of the following changes is added incrementally to this 
baseline model in order for us to isolate the effects of each change:
    <bullet> The proposed update to the outlier fixed dollar loss 
threshold amount.
    <bullet> The proposed FY 2023 IPF wage index, the proposed 5-
percent cap on any decrease to a provider's wage index from its wage 
index in the prior year, and the proposed FY 2023 labor-related share.
    <bullet> The proposed market basket update for FY 2023 of 3.1 
percent less the proposed productivity adjustment of 0.4 percentage 
point in accordance with section 1886(s)(2)(A)(i) of the Act for a 
payment rate update of 2.7 percent.
    Our proposed column comparison in Table 3 illustrates the percent 
change in payments from FY 2022 (that is, October 1, 2022, to September 
30, 2022) to FY 2023 (that is, October 1, 2022, to September 30, 2023) 
including all the proposed payment policy changes.

[[Page 19439]]



                                Table 3--FY 2023 IPF PPS Proposed Payment Impacts
----------------------------------------------------------------------------------------------------------------
                                                                                   FY 2023  wage
                Facility by type                     Number of        Outlier      index  (with    Total percent
                                                    facilities                     cap)  and LRS    change \1\
----------------------------------------------------------------------------------------------------------------
(1)                                                          (2)             (3)             (4)             (5)
----------------------------------------------------------------------------------------------------------------
All Facilities..................................           1,418            -1.2             0.0             1.5
    Total Urban.................................           1,148            -1.3             0.0             1.4
        Urban unit..............................             677            -1.9             0.0             0.7
        Urban hospital..........................             471            -0.4             0.1             2.4
    Total Rural.................................             270            -0.8            -0.2             1.7
        Rural unit..............................             213            -0.9            -0.2             1.6
        Rural hospital..........................              57            -0.4            -0.3             2.0
By Type of Ownership:
Freestanding IPFs:
    Urban Psychiatric Hospitals:
        Government..............................             119            -1.8             0.1             0.9
        Non-Profit..............................              88            -0.7             0.3             2.3
        For-Profit..............................             264            -0.1             0.0             2.7
    Rural Psychiatric Hospitals:
        Government..............................              30            -0.7            -0.3             1.7
        Non-Profit..............................              12            -1.5            -0.1             1.1
        For-Profit..............................              15            -0.1            -0.3             2.3
IPF Units:
    Urban:
        Government..............................              92            -2.4             0.0             0.3
        Non-Profit..............................             450            -2.2            -0.1             0.4
        For-Profit..............................             135            -1.0             0.1             1.8
    Rural:
        Government..............................              48            -0.8             0.0             1.9
        Non-Profit..............................             123            -0.9            -0.2             1.5
        For-Profit..............................              42            -1.0            -0.2             1.4
By Teaching Status:
    Non-teaching................................           1,234            -0.9             0.1             1.8
    Less than 10% interns and residents to beds.              99            -1.6            -0.2             0.8
    10% to 30% interns and residents to beds....              61            -2.9            -0.4            -0.7
    More than 30% interns and residents to beds.              24            -3.7             0.2            -0.9
By Region:
    New England.................................             102            -1.8            -0.5             0.4
    Mid-Atlantic................................             181            -1.6            -0.1             1.0
    South Atlantic..............................             219            -0.7            -0.1             1.9
    East North Central..........................             233            -1.0            -0.2             1.4
    East South Central..........................             143            -1.0            -0.3             1.4
    West North Central..........................             102            -1.7            -0.3             0.7
    West South Central..........................             211            -0.5             0.3             2.5
    Mountain....................................              99            -0.7             0.1             2.0
    Pacific.....................................             128            -1.7             0.9             1.8
By Bed Size:
    Psychiatric Hospitals:
        Beds: 0-24..............................              82            -0.5             0.2             2.4
        Beds: 25-49.............................              73            -0.1             0.1             2.7
        Beds: 50-75.............................              78            -0.1            -0.1             2.5
        Beds: 76 +..............................             295            -0.5             0.1             2.2
    Psychiatric Units:
        Beds: 0-24..............................             486            -1.5             0.0             1.2
        Beds: 25-49.............................             240            -1.7            -0.1             0.9
        Beds: 50-75.............................             100            -2.2            -0.1             0.3
        Beds: 76 +..............................              64            -2.1            -0.1             0.5
----------------------------------------------------------------------------------------------------------------
\1\ This column includes the impact of the updates in columns (3) through (5) above, and of the proposed IPF
  market basket update factor for FY 2023 (3.1 percent), reduced by 0.4 percentage point for the proposed
  productivity adjustment as required by section 1886(s)(2)(A)(i) of the Act.

3. Impact Results
    Table 3 displays the results of our analysis. The table groups IPFs 
into the categories listed here based on characteristics provided in 
the Provider of Services file, the IPF PSF, and cost report data from 
the Healthcare Cost Report Information System:
    <bullet> Facility Type.
    <bullet> Location.
    <bullet> Teaching Status Adjustment.
    <bullet> Census Region.
    <bullet> Size.
    The top row of the table shows the overall impact on the 1,418 IPFs 
included in the analysis. In column 2, we present the number of 
facilities of each type that had information available in the PSF, had 
claims in the MedPAR dataset for FY 2021, and were not excluded due to 
the proposed trim on providers whose change in estimated cost per day 
is outside 3 standard deviations from the mean.

[[Page 19440]]

    In column 3, we present the effects of the update to the outlier 
fixed dollar loss threshold amount. We estimate that IPF outlier 
payments as a percentage of total IPF payments are 3.2 percent in FY 
2022. Therefore, we propose to adjust the outlier threshold amount to 
set total estimated outlier payments equal to 2.0 percent of total 
payments in FY 2023. The estimated change in total IPF payments for FY 
2023, therefore, includes an approximate 1.2 percent decrease in 
payments because we would expect the outlier portion of total payments 
to decrease from approximately 3.2 percent to 2.0 percent.
    The overall impact of the estimated decrease to payments due to 
updating the outlier fixed dollar loss threshold (as shown in column 3 
of Table 3), across all hospital groups, is a 1.2 percent decrease. The 
largest decrease in payments due to this change is estimated to be 3.7 
percent for teaching IPFs with more than 30 percent interns and 
residents to beds.
    In column 4, we present the effects of the proposed budget-neutral 
update to the IPF wage index, the proposed Labor-Related Share (LRS), 
and the 5-percent cap on any decrease to a provider's wage index from 
its wage index in the prior year discussed in section III.D.2 of this 
proposed rule. This represents the effect of using the concurrent 
hospital wage data as discussed in section III.D.1.a of this proposed 
rule. That is, the impact represented in this column reflects the 
proposed update from the FY 2022 IPF wage index to the proposed FY 2023 
IPF wage index, which includes basing the FY 2023 IPF wage index on the 
FY 2023 pre-floor, pre-reclassified IPPS hospital wage index data, 
applying a 5-percent cap on any decrease to a provider's wage index 
from its wage index in the prior year, and updating the LRS from 77.2 
percent in FY 2022 to 77.4 percent in FY 2023. We note that there is no 
projected change in aggregate payments to IPFs, as indicated in the 
first row of column 4; however, there would be distributional effects 
among different categories of IPFs. For example, we estimate the 
largest increase in payments to be 0.9 percent for Pacific IPFs, and 
the largest decrease in payments to be 0.5 percent for New England 
IPFs.
    IPF payments are therefore estimated to increase by 1.4 percent in 
urban areas and 1.7 percent in rural areas. Overall, IPFs are estimated 
to experience a net increase in payments as a result of the updates in 
this proposed rule. The largest payment increases are estimated at 2.7 
percent for freestanding urban for-profit IPFs and IPF hospitals with 
25-49 beds.
4. Effect on Beneficiaries
    Under the FY 2023 IPF PPS, IPFs will continue to receive payment 
based on the average resources consumed by patients for each day. Our 
longstanding payment methodology reflects the differences in patient 
resource use and costs among IPFs, as required under section 124 of the 
BBRA. We expect that updating IPF PPS rates in this proposed rule will 
improve or maintain beneficiary access to high quality care by ensuring 
that payment rates reflect the best available data on the resources 
involved in inpatient psychiatric care and the costs of these 
resources. We continue to expect that paying prospectively for IPF 
services under the FY 2023 IPF PPS will enhance the efficiency of the 
Medicare program.
5. 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 be directly impacted and will review this proposed rule, we 
assume that the total number of unique commenters on the most recent 
IPF proposed rule will be the number of reviewers of this proposed 
rule. For this FY 2023 IPF PPS proposed rule, the most recent IPF 
proposed rule was the FY 2022 IPF PPS proposed rule, and we received 
898 unique comments on this proposed rule. We acknowledge that this 
assumption may understate or overstate the costs of reviewing this 
proposed rule. It is possible that not all commenters reviewed the FY 
2022 IPF proposed rule in detail, and it is also possible that some 
reviewers chose not to comment on that proposed rule. For these 
reasons, we thought that the number of commenters would be a fair 
estimate of the number of reviewers who are directly impacted by this 
proposed rule. We are soliciting comments on this assumption.
    We also recognize that different types of entities are in many 
cases affected by mutually exclusive sections of this proposed rule; 
therefore, for the purposes of our estimate, we assume that each 
reviewer reads approximately 50 percent of this proposed rule.
    Using the May, 2020 mean (average) wage information from the BLS 
for medical and health service managers (Code 11-9111), we estimate 
that the cost of reviewing this proposed rule is $114.24 per hour, 
including overhead and fringe benefits https://www.bls.gov/oes/current/oes119111.htm. Assuming an average reading speed of 250 words per 
minute, we estimate that it would take approximately 50 minutes (0.833 
hours) for the staff to review half of this proposed rule, which 
contains a total of approximately 25,000 words. For each IPF that 
reviews the proposed rule, the estimated cost is (0.833 x $114.24) or 
$95.16. Therefore, we estimate that the total cost of reviewing this 
proposed rule is $85,453.68 ($95.16 x 898 reviewers).

D. Alternatives Considered

    The statute does not specify an update strategy for the IPF PPS and 
is broadly written to give the Secretary discretion in establishing an 
update methodology. We continue to believe it is appropriate to 
routinely update the IPF PPS so that it reflects the best available 
data about differences in patient resource use and costs among IPFs as 
required by the statute. Therefore, we are proposing to: Update the IPF 
PPS using the methodology published in the November 2004 IPF PPS final 
rule; apply the proposed 2016-based IPF PPS market basket update for FY 
2023 of 3.1 percent, reduced by the statutorily required proposed 
productivity adjustment of 0.4 percentage point along with the proposed 
wage index budget neutrality adjustment to update the payment rates; 
and use a FY 2023 IPF wage index which uses the FY 2023 pre-floor, pre-
reclassified IPPS hospital wage index as its basis. Additionally, 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. Lastly, we are 
proposing for FY 2023 to exclude providers from our simulation of IPF 
PPS payments for FY 2022 and FY 2023 if their change in estimated cost 
per day is outside 3 standard deviations from the mean.

E. Accounting Statement

    As required by OMB Circular A-4 (available at www.whitehouse.gov/sites/whitehouse.gov/files/omb/circulars/A4/a-4.pdf), in Table 4, we 
have prepared an accounting statement showing the classification of the 
expenditures associated with the updates to the IPF wage index and 
payment rates in this proposed rule. Table 4 provides our best estimate 
of the increase in Medicare payments under the IPF PPS as a result of 
the changes presented in this proposed rule and based on the data for 
1,418 IPFs with data available in the PSF, with claims in our FY 2021 
MedPAR claims dataset, and which were not excluded due to the proposed 
trim on providers whose change in

[[Page 19441]]

estimated cost per day is outside 3 standard deviations from the mean. 
Lastly, Table 4 also includes our best estimate of the costs of 
reviewing and understanding this proposed rule.

                                Table 4--Accounting Statement: Classification of Estimated Costs, Savings, and Transfers
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                              Primary                                                          Units
                                                             estimate                                    -----------------------------------------------
                        Category                            ($million/     Low  estimate  High  estimate                                      Period
                                                               year)                                       Year  dollars  Discount  rate      covered
--------------------------------------------------------------------------------------------------------------------------------------------------------
Regulatory Review Costs.................................            0.07  ..............  ..............            2020  ..............         FY 2023
Annualized Monetized Transfers from Federal Government                50  ..............  ..............         FY 2023  ..............         FY 2023
 to IPF Medicare Providers..............................
--------------------------------------------------------------------------------------------------------------------------------------------------------

F. Regulatory Flexibility Act

    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 IPFs and most other providers and 
suppliers are small entities, either by nonprofit status or having 
revenues of $8 million to $41.5 million or less in any 1 year. 
Individuals and states are not included in the definition of a small 
entity.
    Because we lack data on individual hospital receipts, we cannot 
determine the number of small proprietary IPFs or the proportion of 
IPFs' revenue derived from Medicare payments. Therefore, we assume that 
all IPFs are considered small entities.
    The Department of Health and Human Services generally uses a 
revenue impact of 3 to 5 percent as a significance threshold under the 
RFA. As shown in Table 3, we estimate that the overall revenue impact 
of this proposed rule on all IPFs is to increase estimated Medicare 
payments by approximately 1.5 percent. As a result, since the estimated 
impact of this proposed rule is a net increase in revenue across almost 
all categories of IPFs, the Secretary has determined that this proposed 
rule will have a positive revenue impact on a substantial number of 
small entities.
    In addition, section 1102(b) of the Act requires us to prepare a 
regulatory impact analysis if a rule may have a significant impact on 
the operations of a substantial number of small rural hospitals. This 
analysis must conform to the provisions of section 603 of the RFA. For 
purposes of section 1102(b) of the Act, we define a small rural 
hospital as a hospital that is located outside of a metropolitan 
statistical area and has fewer than 100 beds. As discussed in section 
VIII.C.2 of this proposed rule, the rates and policies set forth in 
this proposed rule will not have an adverse impact on the rural 
hospitals based on the data of the 213 rural excluded psychiatric units 
and 57 rural psychiatric hospitals in our database of 1,418 IPFs for 
which data were available. Therefore, the Secretary has determined that 
this proposed rule will not have a significant impact on the operations 
of a substantial number of small rural hospitals.

G. Unfunded Mandate Reform Act (UMRA)

    Section 202 of the Unfunded Mandates Reform Act of 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. This proposed rule would not impose a mandate 
that will result in the expenditure by state, local, and tribal 
governments, in the aggregate, or by the private sector, of more than 
$165 million in any 1 year.

H. Federalism

    Executive Order 13132 establishes certain requirements that an 
agency must meet when it promulgates a proposed rule that imposes 
substantial direct requirement costs on state and local governments, 
preempts state law, or otherwise has Federalism implications. This 
proposed rule does not impose substantial direct costs on state or 
local governments or preempt state law.
    Chiquita Brooks-LaSure, Administrator of the Centers for Medicare & 
Medicaid Services, approved this document on March 24, 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 part 412 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. Section 412.424 is amended by revising paragraph (d)(1)(i) to read 
as follows:


Sec.  412.424   Methodology for calculating the Federal per diem 
payment amount.

* * * * *
    (d) * * *
    (1) * * *
    (i) Adjustment for wages. CMS adjusts the labor portion of the 
Federal per diem base rate to account for geographic differences in the 
area wage levels using an appropriate wage index.
    (A) The application of the wage index is made on the basis of the 
location of the inpatient psychiatric facility in an urban or rural 
area as defined in Sec.  412.402.
    (B) Beginning October 1, 2022, CMS applies a cap on decreases to 
the wage index, such that the wage index applied to an inpatient 
psychiatric facility is not less than 95 percent of the wage index 
applied to that inpatient psychiatric facility in the prior fiscal 
year.
* * * * *

    Dated: March 29, 2022.
Xavier Becerra,
Secretary, Department of Health and Human Services.
[FR Doc. 2022-06906 Filed 3-31-22; 4:15 pm]
BILLING CODE 4120-01-P


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