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Rule2021-16336

Medicare Program; FY 2022 Inpatient Psychiatric Facilities Prospective Payment System and Quality Reporting Updates for Fiscal Year Beginning October 1, 2021 (FY 2022)

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Published

August 4, 2021

Effective

October 1, 2021

Issuing agencies

Health and Human Services DepartmentCenters for Medicare & Medicaid Services

Abstract

This final rule updates 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 rule also updates and clarifies the IPF teaching policy with respect to IPF hospital closures and displaced residents and finalizes a technical change to one of the 2016-based IPF market basket price proxies. In addition, this final rule finalizes proposals on quality measures and reporting requirements under the Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program. We note that this final rule does not finalize two proposals to remove quality measures. The changes finalized in this rule for the IPFQR Program are effective for IPF discharges occurring during the Fiscal Year (FY) beginning October 1, 2021 through September 30, 2022 (FY 2022).

Full Text

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[Federal Register Volume 86, Number 147 (Wednesday, August 4, 2021)]
[Rules and Regulations]
[Pages 42608-42679]
From the Federal Register Online via the Government Publishing Office [<a href="http://www.gpo.gov">www.gpo.gov</a>]
[FR Doc No: 2021-16336]

[[Page 42607]]

Vol. 86

Wednesday,

No. 147

August 4, 2021

Part VI

Department of Health and Human Services

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Centers for Medicare & Medicaid Services

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42 CFR Part 412

Medicare Program; FY 2022 Inpatient Psychiatric Facilities Prospective
Payment System and Quality Reporting Updates for Fiscal Year Beginning
October 1, 2021 (FY 2022); Final Rule

Federal Register / Vol. 86 , No. 147 / Wednesday, August 4, 2021 /
Rules and Regulations

[[Page 42608]]

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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Medicare & Medicaid Services

42 CFR Part 412

[CMS-1750-F]
RIN 0938-AU40

Medicare Program; FY 2022 Inpatient Psychiatric Facilities
Prospective Payment System and Quality Reporting Updates for Fiscal
Year Beginning October 1, 2021 (FY 2022)

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

ACTION: Final rule.

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SUMMARY: This final rule updates 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 rule also updates
and clarifies the IPF teaching policy with respect to IPF hospital
closures and displaced residents and finalizes a technical change to
one of the 2016-based IPF market basket price proxies. In addition,
this final rule finalizes proposals on quality measures and reporting
requirements under the Inpatient Psychiatric Facilities Quality
Reporting (IPFQR) Program. We note that this final rule does not
finalize two proposals to remove quality measures. The changes
finalized in this rule for the IPFQR Program are effective for IPF
discharges occurring during the Fiscal Year (FY) beginning October 1,
2021 through September 30, 2022 (FY 2022).

DATES: These regulations are effective on October 1, 2021.

FOR FURTHER INFORMATION CONTACT:
The IPF Payment Policy mailbox at <a href="/cdn-cgi/l/email-protection#9ad3cadccafbe3f7fff4eecaf5f6f3f9e3daf9f7e9b4f2f2e9b4fdf5ec">[email&#160;protected]</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:

Availability of Certain Tables Exclusively Through the Internet on the
CMS Website

Addendum A to this final rule summarizes the FY 2022 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
final rule shows the complete listing of ICD-10 Clinical Modification
(CM) and Procedure Coding System (PCS) codes, the FY 2022 IPF PPS
comorbidity adjustment, and electroconvulsive therapy (ECT) procedure
codes. The A and B Addenda are available online at: <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
Tables setting forth the FY 2022 Wage Index for Urban Areas Based
on Core-Based Statistical Area (CBSA) Labor Market Areas and the FY
2022 Wage Index Based on CBSA Labor Market Areas for Rural Areas are
available exclusively through the internet, on the CMS website at
<a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/IPFPPS/WageIndex.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/IPFPPS/WageIndex.html</a>.

I. Executive Summary

A. Purpose

This final rule updates 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 FY 2022 beginning October 1, 2021 through September
30, 2022. This rule also updates and clarifies the IPF teaching policy
with respect to IPF hospital closures and displaced residents and
finalizes a technical change to one of the 2016-based IPF market basket
price proxies. In addition, the final rule finalizes proposals to adopt
quality measures and reporting requirements under the Inpatient
Psychiatric Facilities Quality Reporting (IPFQR) Program.

B. Summary of the Major Provisions

1. Inpatient Psychiatric Facilities Prospective Payment System (IPF
PPS)
For the IPF PPS, we are finalizing our proposal to--
<bullet> Update IPF PPS teaching policy with respect to IPF
hospital closures and displaced residents.
<bullet> Replace one of the price proxies currently used for the
For-profit Interest cost category in the 2016-based IPF market basket
with a similar price proxy.
<bullet> Adjust the 2016-based IPF market basket update (2.7
percent) for economy-wide productivity (0.7 percentage point) as
required by section 1886(s)(2)(A)(i) of the Social Security Act (the
Act), resulting in a final IPF payment rate update of 2.0 percent for
FY 2022.
<bullet> Make technical rate setting changes: The IPF PPS payment
rates will be adjusted annually for inflation, as well as statutory and
other policy factors. This final rule updates:
++ The IPF PPS Federal per diem base rate from $815.22 to $832.94.
++ The IPF PPS Federal per diem base rate for providers who failed
to report quality data to $816.61.
++ The Electroconvulsive therapy (ECT) payment per treatment from
$350.97 to $358.60.
++ The ECT payment per treatment for providers who failed to report
quality data to $351.57.
++ The labor-related share from 77.3 percent to 77.2 percent.
++ The wage index budget-neutrality factor from 0.9989 to 1.0017.
++ The fixed dollar loss threshold amount from $14,630 to $14,470
to maintain estimated outlier payments at 2 percent of total estimated
aggregate IPF PPS payments.
2. Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program
In this final rule, we are:
<bullet> Adopting voluntary patient-level data reporting for chart-
abstracted measures for data submitted for the FY 2023 payment
determination and mandatory patient-level data reporting for chart-
abstracted measures for the FY 2024 payment determination and
subsequent years;
<bullet> Revising our regulations at 42 CFR 412.434(b)(3) by
replacing the term ``QualityNet system administrator'' with
``QualityNet security official'';
<bullet> Adopting the Coronavirus disease 2019 (COVID-19)
Vaccination Coverage Among Health Care Personnel (HCP) measure for the
FY 2023 payment determination and subsequent years;
<bullet> Adopting the Follow-up After Psychiatric Hospitalization
(FAPH) measure for the FY 2024 payment determination and subsequent
years; and
<bullet> Removing the following two measures for FY 2024 payment
determination and subsequent years:
++ Timely Transmission of Transition Record (Discharges from an
Inpatient Facility to Home/Self Care or Any Other Site of Care) measure
and
++ Follow-up After Hospitalization for Mental Illness (FUH)
measure.
<bullet> Not finalizing our proposals to remove the following two
measures for

[[Page 42609]]

FY 2024 payment determination and subsequent years:
++ Alcohol Use Brief Intervention Provided or Offered and Alcohol
Use Brief Intervention Provided (SUB-2/2a) measure; and
++ Tobacco Use Treatment Provided or Offered and Tobacco Use
Treatment (TOB-2/2a) measure.

C. Summary of Impacts
[GRAPHIC] [TIFF OMITTED] TR04AU21.169

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 Prospective Payment System (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
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 take into account such reduction in computing the
payment amount for a subsequent RY. More 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 Fiscal Year 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
Center for Medicare & Medicaid (CMS) website at <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html</a>?redirect=/InpatientPsychFacilPPS/.

B. Overview of the IPF PPS

The November 2004 IPF PPS final rule (69 FR 66922) 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

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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; additionally, there are
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.
In November 2004, we implemented the IPF PPS in a final rule that
published on November 15, 2004 in the Federal Register (69 FR 66922).
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 15, 2004 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 1. When
proposing changes in IPF payment policy, a proposed rule would be
issued in the spring, and the final rule in the summer to be effective
on October 1. 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, 2020 in the Federal Register titled, ``Medicare Program;
FY 2021 Inpatient Psychiatric Facilities Prospective Payment System and
Special Requirements for Psychiatric Hospitals for Fiscal Year
Beginning October 1, 2020 (FY 2021)'' (85 FR 47042), which updated the
IPF PPS payment rates for FY 2021. That final rule updated the IPF PPS
Federal per diem base rates that were published in the FY 2020 IPF PPS
Rate Update final rule (84 FR 38424) in accordance with our established
policies.

III. Provisions of the FY 2022 IPF PPS Final Rule and Responses to
Comments

A. Final Update to the FY 2021 Market Basket 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. Final FY 2022 IPF Market Basket Update
For FY 2022 (that is, beginning October 1, 2021 and ending
September 30, 2022), we proposed to update the IPF PPS payments by a
market basket

[[Page 42611]]

increase factor with a productivity adjustment as required by section
1886(s)(2)(A)(i) of the Act. In the FY 2022 IPF proposed rule (86 FR
19483), we proposed to use the same methodology described in the FY
2021 IPF PPS final rule (85 FR 47045 through 47046), with one proposed
modification to the 2016-based IPF market basket.
For the price proxy for the For-profit Interest cost category of
the 2016-based IPF market basket, we proposed to use the iBoxx AAA
Corporate Bond Yield index instead of the Moody's AAA Corporate Bond
Yield index. Effective for December 2020, the Moody's AAA Corporate
Bond series is no longer available for use under license to IHS Global
Inc. (IGI), the nationally recognized economic and financial
forecasting firm with which we contract to forecast the components of
the market baskets and multi-factor productivity (MFP). Since IGI is no
longer licensed to use and publish the Moody's series, IGI was required
to discontinue the publication of the associated historical data and
forecasts of this series. Therefore, IGI constructed a bond yield index
(iBoxx) that closely replicates the Moody's corporate bond yield
indices currently used in the market baskets.
In the FY 2022 IPF PPS proposed rule, we stated that because the
iBoxx AAA Corporate Bond Yield index captures the same technical
concept as the current corporate bond proxy and tracks similarly to the
current measure that is no longer available, we believed that the iBoxx
AAA Corporate Bond Yield index is technically appropriate to use in the
2016-based IPF market basket.
Based on IGI's fourth quarter 2020 forecast with historical data
through the third quarter of 2020, the proposed 2016-based IPF market
basket increase factor for FY 2022 was projected to be 2.3 percent. We
also proposed that if more recent data became available after the
publication of the proposed rule and before the publication of this
final rule (for example, a more recent estimate of the market basket
update or MFP), we would use such data, if appropriate, to determine
the FY 2022 market basket update in this final rule.
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 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 U.S. Department of Labor's
Bureau of Labor Statistics (BLS) publishes the official measure of
private nonfarm business MFP. Please see <a href="http://www.bls.gov/mfp">http://www.bls.gov/mfp</a> for the
BLS historical published MFP data. A complete description of the MFP
projection methodology is available on the CMS website at <a href="https://www.cms.gov/Research-Statistics-Dataand-Systems/Statistics-Trends-andReports/MedicareProgramRatesStats/MarketBasketResearch.html">https://www.cms.gov/Research-Statistics-Dataand-Systems/Statistics-Trends-andReports/MedicareProgramRatesStats/MarketBasketResearch.html</a>. We note
that effective with FY 2022 and forward, CMS is changing the name of
this adjustment to refer to it as the productivity adjustment rather
than the MFP adjustment. We note that the adjustment relies on the same
underlying data and methodology. This new terminology is more
consistent with the statutory language described in section
1886(s)(2)(A)(i) of the Act.
Using IGI's fourth quarter 2020 forecast, the productivity
adjustment for FY 2022 was projected to be 0.2 percent. We proposed to
then reduce the proposed 2.3 percent IPF market basket update by the
estimated productivity adjustment for FY 2022 of 0.2 percentage point.
Therefore, the proposed FY 2022 IPF update was equal to 2.1 percent
(2.3 percent market basket update reduced by the 0.2 percentage point
productivity adjustment). Furthermore, we proposed that if more recent
data became available after the publication of the proposed rule and
before the publication of this final rule (for example, a more recent
estimate of the market basket or MFP), we would use such data, if
appropriate, to determine the FY 2022 market basket update and
productivity adjustment in this final rule.
Based on the more recent data available for this FY 2022 IPF final
rule (that is, IGI's second quarter 2021 forecast of the 2016-based IPF
market basket with historical data through the first quarter of 2021),
we estimate that the IPF FY 2022 market basket update is 2.7 percent.
The current estimate of the productivity adjustment for FY 2022 is 0.7
percentage point. Therefore, the current estimate of the FY 2022 IPF
increase factor is equal to 2.0 percent (2.7 percent market basket
update reduced by 0.7 percentage point productivity adjustment).
We invited public comment on our proposals for the FY 2022 market
basket update and productivity adjustment. The following is a summary
of the public comments received on the proposed FY 2022 market basket
update and productivity adjustment and our responses:
Comment: One commenter supported the update to the IPF payment
rates of 2.1 percent.
Response: We thank the commenter for their support.
Comment: One commenter stated that given the growing behavioral
health and substance abuse crisis made worse by the COVID-19 Public
Health Emergency (PHE), that CMS should provide additional payment for
IPFs in the future.
Response: We understand the commenter's concern. We acknowledge
that the COVID-19 PHE has amplified the growing need for behavioral
health services in this country and remain committed to trying to find
ways to mitigate its impact on IPFs. Our goal is to ensure that the IPF
payment rates accurately reflect the best available data. For example,
as discussed in section VI.C.3 of this final rule, in comparing and
analyzing FY 2019 and FY 2020 claims, we determined that the COVID-19
PHE appears to have significantly impacted the FY 2020 IPF claims such
that the FY 2019 claims are the best available data to set the outlier
fixed dollar loss threshold for FY 2022. Therefore, we deviated from
our longstanding practice of using the most recent available year of
claims, that is, FY 2020 claims, for estimating IPF PPS payments in FY
2022. We will continue to analyze more recent available IPF claims data
to better understand both the short- and long-term effects of the
COVID-19 PHE on the IPF PPS.
Final Decision: After consideration of the comments we received, we
are finalizing a FY 2022 IPF update equal to 2.0 percent based on the
more recent data available.
3. Final FY 2022 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 proposed to continue to classify a cost category as labor-
related if the costs are labor-intensive and vary with the local labor
market.

[[Page 42612]]

Based on our definition of the labor-related share and the cost
categories in the 2016-based IPF market basket, we proposed to
calculate the labor-related share for FY 2022 as the sum of the FY 2022
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
2022. Based on IGI's fourth quarter 2020 forecast of the 2016-based IPF
market basket, the sum of the FY 2022 relative importance for Wages and
Salaries; Employee Benefits; Professional Fees: Labor-related;
Administrative and Facilities Support Services; Installation
Maintenance & Repair Services; and All Other: Labor related Services
was 74.0 percent. We proposed 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 was 6.7 percent of the
2016-based IPF market basket for FY 2022, we proposed to take 46
percent of 6.7 percent to determine the labor-related share of Capital-
Related costs for FY 2022 of 3.1 percent. Therefore, we proposed a
total labor-related share for FY 2022 of 77.1 percent (the sum of 74.0
percent for the labor-related share of operating costs and 3.1 percent
for the labor-related share of Capital-Related costs). We also proposed
that if more recent data became available after publication of the
proposed rule and before the publication of this final rule (for
example, a more recent estimate of the labor-related share), we would
use such data, if appropriate, to determine the FY 2022 IPF labor-
related share in the final rule.
Based on IGI's second quarter 2021 forecast of the 2016-based IPF
market basket, the sum of the FY 2022 relative importance for Wages and
Salaries; Employee Benefits; Professional Fees: Labor-related;
Administrative and Facilities Support Services; Installation
Maintenance & Repair Services; and All Other: Labor-related Services is
74.1 percent. Since the relative importance for Capital-Related costs
is 6.7 percent of the 2016-based IPF market basket for FY 2022, we take
46 percent of 6.7 percent to determine the labor-related share of
Capital-Related costs for FY 2022 of 3.1 percent. Therefore, the
current estimate of the total labor-related share for FY 2022 is equal
to 77.2 percent (the sum of 74.1 percent for the labor-related share of
operating costs and 3.1 percent for the labor-related share of Capital-
Related costs). Table 1 shows the final FY 2022 labor-related share and
the final FY 2021 labor-related share using the 2016-based IPF market
basket relative importance.
[GRAPHIC] [TIFF OMITTED] TR04AU21.170

We invited public comments on the proposed labor-related share for
FY 2022.
Comment: Several commenters supported the decrease in the labor-
related share from 77.3 percent in FY 2021 to 77.1 percent in FY 2022
noting that it will help any facility that has a wage index less than
1.0. The commenters stated that, across this country there is a growing
disparity between high-wage and low-wage states. Recognizing this
disparity and slightly lowering the labor-related share provides some
aid to hospitals in many rural and underserved communities.
Response: We thank the commenter for their support. We agree with
the commenters that the labor-related share should reflect the
proportion of costs that are attributable to labor and vary
geographically to account for differences in labor-related costs across
geographic areas. More recent data became available; therefore, based
on IGI's second quarter 2021 forecast with historical data through the
first quarter 2021 the FY 2022 labor-related share for the final rule
is 77.2 percent as shown in Table 1.
After consideration of comments received, we are finalizing the use
of the sum of the FY 2022 relative importance

[[Page 42613]]

for the labor-related cost categories based on the most recent forecast
(IGI's second quarter 2021 forecast) of the 2016-based IPF market
basket labor-related share cost weights, as proposed.

B. Final Updates to the IPF PPS Rates for FY Beginning October 1, 2021

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. In addition,
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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html</a>.
IPFs must include a valid procedure code for ECT services provided
to IPF beneficiaries in order to bill for ECT services, as described in
our Medicare Claims Processing Manual, Chapter 3, Section 190.7.3
(available at <a href="https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/Downloads/clm104c03.pdf">https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/Downloads/clm104c03.pdf</a>.) 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 2022. Addendum B to this final rule
shows the ECT procedure codes for FY 2022 and is available on our
website at <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
2. Final Update of the Federal Per Diem Base Rate and Electroconvulsive
Therapy Payment per Treatment
The current (FY 2021) Federal per diem base rate is $815.22 and the
ECT payment per treatment is $350.97. For the final FY 2022 Federal per
diem base rate, we applied the payment rate update of 2.0 percent--that
is, the 2016-based IPF market basket increase for FY 2022 of 2.7
percent less the productivity adjustment of 0.7 percentage point--and
the wage index budget-neutrality factor of 1.0017 (as discussed in
section III.D.1 of this final rule) to the FY 2021 Federal per diem
base rate of $815.22, yielding a final Federal per diem base rate of
$832.94 for FY 2022. Similarly, we applied the 2.0 percent payment rate
update and the 1.0017 wage index budget-neutrality factor to the FY
2021 ECT payment per treatment of $350.97, yielding a final ECT payment
per treatment of $358.60 for FY 2022.
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 requirements under the IPFQR Program,
we applied a 0.0 percent payment rate update--that is, the IPF market
basket increase for FY 2022 of 2.7 percent less the productivity
adjustment of 0.7 percentage point for an update of 2.0 percent, and
further reduced by 2 percentage points in accordance with section
1886(s)(4)(A)(i) of the Act--and the wage index budget-neutrality
factor of 1.0017 to the FY 2021 Federal per diem base rate of $815.22,
yielding a Federal per diem base rate of $816.61 for FY 2022.
<bullet> For IPFs that fail to meet requirements under the IPFQR
Program, we applied the 0.0 percent annual payment rate update and the
1.0017 wage index budget-neutrality factor to the FY 2021 ECT payment
per treatment of $350.97, yielding an ECT payment per treatment of
$351.57 for FY 2022.

C. Final 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 finalizing our proposal to continue to use the existing
regression-derived adjustment factors established in 2005 for FY 2022.
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.

[[Page 42614]]

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. Final 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 2022, we did not propose any changes to the IPF MSDRG adjustment
factors. Therefore, we are finalizing our proposal to maintain 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 <a href="https://www.cms.gov/Medicare/Coding/ICD10/ICD-10-MS-DRG-Conversion-Project.html">https://www.cms.gov/Medicare/Coding/ICD10/ICD-10-MS-DRG-Conversion-Project.html</a>.
For FY 2022, we are finalizing our proposal 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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>. 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, but the payment will not include an MS-DRG
adjustment.
The diagnoses for each IPF MS-DRG will be updated as of October 1,
2021, using the final IPPS FY 2022 ICD-10-CM/PCS code sets. The FY 2022
IPPS/LTCH PPS final rule includes tables of the changes to the ICD-10-
CM/PCS code sets, which underlie the FY 2022 IPF MS-DRGs. Both the FY
2022 IPPS final rule and the tables of final changes to the ICD-10-CM/
PCS code sets, which underlie the FY 2022 MS-DRGs, are available on the
CMS IPPS website at <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/index.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/index.html</a>.
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, the 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 our 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 <a href="https://www.cdc.gov/nchs/data/icd/10cmguidelines-FY2020_final.pdf">https://www.cdc.gov/nchs/data/icd/10cmguidelines-FY2020_final.pdf</a>.
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 2018, FY 2019 and FY 2020, there were no
changes to the final ICD-10-CM codes in the IPF Code First table. For
FY 2021, there were 18 ICD-10-CM codes deleted from the final IPF Code
First table. For FY 2022 there are 18 codes finalized for deletion from
the ICD-10-CM codes in the IPF Code First table. The final FY 2022 Code
First table is shown in Addendum B on our website at <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
b. Final 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.

[[Page 42615]]

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 2022, we are
finalizing our proposal to continue to use the same comorbidity
adjustment factors in effect in FY 2021, which are found in Addendum A,
available on our website at <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
We have updated the ICD-10-CM/PCS codes, which are associated with
the existing IPF PPS comorbidity categories, based upon the final FY
2022 update to the ICD-10-CM/PCS code set. The final FY 2022 ICD-10-CM/
PCS updates include: 8 ICD-10-CM diagnosis codes added to the Poisoning
comorbidity category, 4 codes deleted, and 4 changes to Poisoning
comorbidity long descriptions; 2 ICD-10-CM diagnosis codes added to the
Developmental Disabilities comorbidity category and 1 code deleted; and
3 ICD-10-PCS codes added to the Oncology Procedures comorbidity
category. These updates are detailed in Addenda B of this final rule,
which are available on our website at <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
In accordance with the policy established in the FY 2015 IPF PPS
final rule (79 FR 45949 through 45952), we reviewed all new FY 2022
ICD-10-CM codes to remove codes that were site ``unspecified'' in terms
of laterality from the FY 2022 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 specific
diagnosis based on clinical assessment at the medical encounter. None
of the finalized additions to the FY 2022 ICD-10-CM/PCS codes were site
``unspecified'' by laterality, therefore, we are not removing any of
the new codes.
Comment: A commenter requested that CMS add 13 ICD-10-CM codes for
infectious diseases to the list of codes that qualify for the IPF PPS
comorbidity adjustment.
Response: As noted previously, 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.
Also, the comorbidity adjustments were derived through a regression
analysis, which also includes other IPF PPS adjustments (for example,
the age adjustment). Our established policy is to annually update the
ICD-10-CM/PCS codes, which are associated with the existing IPF PPS
comorbidity categories. Adding or removing codes to the existing
comorbidity categories that are not part of the annual coding update
would occur as part of a larger IPF PPS refinement. We did not propose
to refine the IPF PPS in the FY 2022 IPF PPS proposed rule, and
therefore, are not changing the policy in this final rule. However, we
will consider the comment to potentially inform future refinements.
c. Final 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 2022, we are finalizing our proposal
to continue to use the patient age adjustments currently in effect in
FY 2021, as shown in Addendum A of this rule (see <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>).
d. Final 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 rule.
For FY 2022, we are finalizing our proposal to continue to use the
variable per diem adjustment factors currently in effect, as shown in
Addendum A of this rule (available at <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>). A
complete discussion of the variable per diem adjustments appears in the
November 2004 IPF PPS final rule (69 FR 66946).

[[Page 42616]]

D. Final 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 15, 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 15, 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.
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. It will also result in more consistency and parity in
the wage index methodology used by other Medicare payment systems. The
Medicare SNF PPS already used the concurrent IPPS hospital wage index
data as the basis for the SNF PPS wage index. Thus, the wage adjusted
Medicare payments of various provider types will be based upon wage
index data from the same timeframe. CMS proposed 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. For FY 2022, we proposed to continue to use
the concurrent pre-floor, pre-reclassified IPPS hospital wage index as
the basis for the IPF wage index.
Comment: Several commenters expressed concerns with our proposal to
continue using the concurrent pre-floor, pre-reclassified IPPS hospital
wage index as the basis for the IPF wage index. Three commenters
recommended CMS extend the transition for the reductions in payment for
certain IPFs resulting from the wage index changes adopted in the FY
2021 IPF PPS final rule. Another commenter also recommended that CMS
apply a non-budget neutral 5 percent cap on decreases to a hospital's
wage index value to help mitigate wide annual swings that are beyond a
hospital's ability to control.
Response: We did not propose to modify the transition policy that
was finalized in the FY 2021 IPF PPS final rule; therefore, we are not
changing the previously adopted policy in this final rule. As we
discussed in the FY 2021 IPF PPS final rule (85 FR 47058 through
47059), the transition policy caps the estimated reduction in an IPF's
wage index to 5 percent in FY 2021, with no cap applied in FY 2022. We
stated our belief that implementing updated wage index values along
with the revised OMB delineations will result in wage index values
being more representative of the actual costs of labor in a given area.
As evidenced by the detailed economic analysis (85 FR 47065 through
47068), we estimated that implementing these wage index changes would
have distributional effects, both positive and negative, among IPF
providers. We continue to believe that applying the 5-percent cap
transition policy in year one provided an adequate safeguard against
any significant payment reductions, has 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.

[[Page 42617]]

We note that certain changes to wage index policy may significantly
affect Medicare payments. These changes may arise from revisions to the
OMB delineations of statistical areas resulting from the decennial
census data, periodic updates to the OMB delineations in the years
between the decennial censuses, or other wage index policy changes.
While we consider how best to address these potential scenarios in a
consistent and thoughtful manner, we reiterate 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.
Comment: Two commenters recommended that CMS incorporate a frontier
state floor into the IPF wage index. Another commenter requested that
CMS implement policies to address the disparity in payments between
rural and urban IPFs, similar to policies that have been adopted for
IPPS hospitals.
Response: We appreciate commenters' suggestions regarding
opportunities to improve the accuracy of the IPF wage index. We did not
propose the specific policies that commenters have suggested, but we
will take them into consideration to potentially inform future
rulemaking.
Final Decision: For FY 2022, we are finalizing the proposal to
continue to use the concurrent pre-floor, pre-reclassified IPPS
hospital wage index as the basis for the IPF wage index. Since we did
not propose any changes to the 2-year transition that was finalized in
the FY 2021 IPF PPS final rule, there will be no cap applied to the
reduction in the wage index for the second year (that is, FY 2022).
We will apply the IPF wage index adjustment to the labor-related
share of the national base rate and ECT payment per treatment. The
labor-related share of the national rate and ECT payment per treatment
will change from 77.3 percent in FY 2021 to 77.2 percent in FY 2022.
This percentage reflects the labor-related share of the 2016-based IPF
market basket for FY 2022 (see section III.A.4 of this rule).
b. Office of Management and Budget (OMB) Bulletins
(i.) 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
<a href="https://www.whitehouse.gov/omb/information-for-agencies/bulletins/">https://www.whitehouse.gov/omb/information-for-agencies/bulletins/</a>. 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
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 will 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 <a href="https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf">https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf</a>). 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, 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.
(ii.) 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 the reader to the FY 2007 IPF PPS final rule (71 FR
27064 through 27065) for a complete discussion regarding treating
Micropolitan Areas as rural.
c. Final 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

[[Page 42618]]

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 2022, we proposed
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).
Comment: We received one comment in favor of the proposed extension
of the 17 percent payment adjustment for rural IPFs. The commenter
acknowledged CMS' efforts to avoid disparities in payments to
facilities in rural and underserved communities.
Response: We appreciate this comment of support. Since the
inception of the IPF PPS, we have applied a 17 percent adjustment for
IPFs located in rural areas. As stated in the previous paragraph, this
adjustment was derived from the results of our regression analysis and
was incorporated into the payment system in order to ensure the
accuracy of payments to rural IPFs. CMS continues to look for ways to
ensure accuracy of payments to rural IPFs.
Final Decision: For FY 2022, we are finalizing our proposal 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).
d. Final 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 2022, we
are finalizing our proposal 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 2022 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 2022 update to the wage
indexes (based on the FY 2018 hospital cost report data) and the labor-
related share in a budget-neutral manner:
Step 1: Simulate estimated IPF PPS payments, using the FY 2021 IPF
wage index values (available on the CMS website) and labor-related
share (as published in the FY 2021 IPF PPS final rule (85 FR 47043)).
Step 2: Simulate estimated IPF PPS payments using the final FY 2022
IPF wage index values (available on the CMS website) and final FY 2022
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 FY 2022 budget-
neutral wage adjustment factor of 1.0017.
Step 4: Apply the FY 2022 budget-neutral wage adjustment factor
from step 3 to the FY 2021 IPF PPS Federal per diem base rate after the
application of the market basket update described in section III.A of
this rule, to determine the FY 2022 IPF PPS Federal per diem base rate.
2. Final Teaching Adjustment
a. Background
In the November 2004 IPF PPS final rule, we implemented regulations
at sect; 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).
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. The 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 this 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 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
section III.D.2.b of this final rule, we discuss finalized updates to
the IPF policy on temporary adjustment to the FTE cap.
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 2022 final rule, we are finalizing our proposal
to continue to retain the coefficient value of 0.5150 for the teaching
adjustment to the Federal per diem base rate.
b. Final Update to IPF Teaching Policy on IPF Program Closures and
Displaced Residents
For FY 2022, we proposed to change the IPF policy regarding
displaced residents from IPF closures and closures of IPF teaching
programs. Specifically, we proposed to adopt conforming changes to the
IPF PPS teaching policy

[[Page 42619]]

to align with the policy changes that the IPPS finalized in the FY 2021
IPPS final rule (85 FR 58865 through 58870). We believe that the IPF
IME policy relating to hospital closure and displaced students is
susceptible to the same vulnerabilities as IPPS GME policy. Hence, if
an IPF with a large number of residents training in its residency
program announces that it is closing, these residents will become
displaced and will need to find alternative positions at other IPF
hospitals or risk being unable to become Board-certified. Although we
proposed to adopt a policy under the IPF PPS that is consistent with an
applicable policy under the IPPS, the actual caps under the two payment
systems may not be commingled. In other words, the resident cap
applicable under the IPPS is separate from the resident cap applicable
under the IPF PPS; moreover, a provider cannot add its IPF resident cap
to its IPPS resident cap in order to increase the number of residents
it receives payment for under either payment system.
As stated in the November 2004 IPF PPS final rule (69 FR 66922), 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 facility-level adjustment we are providing for teaching
hospitals under IPF PPS parallels the IME payments paid under the IPPS.
Both payments are add on adjustments to the amount per case and both
are based in part on the number of full-time equivalent (FTE) residents
training at the facility.
The regulation at 42 CFR 412.424(d)(1)(iii)(F) permits an IPF to
temporarily adjust its FTE cap to reflect residents added because of
another hospital or program's closure. We first implemented regulations
regarding residents displaced by teaching hospital and program closures
in the May 6, 2011 IPF PPS final rule (76 FR 26431). In that final
rule, we adopted the IPPS definition of ``closure of a hospital'' at 42
CFR 413.79(h)(1)(i) to apply to IPF closures as well, and to mean that
the IPF terminates its Medicare provider agreement as specified in 42
CFR 489.52. In the proposed rule, we proposed to codify this
definition, as well as, the definition of an IPF program closure, at
Sec. 412.402.
Although not explicitly stated in regulatory text, our current
policy is that a displaced resident is one that is physically present
at the hospital training on the day prior to or the day of hospital or
program closure. This longstanding policy derived from the fact that in
the regulations text, there are requirements that the receiving
hospital identifies the residents ``who have come from the closed IPF''
(Sec. 412.424(d)(1)(iii)(F)(1)(ii)) or identifies the residents ``who
have come from another IPF's closed program'' (Sec.
412.424(d)(1)(iii)(F)(2)(i)), and that the IPF that closed its program
identifies ``the residents who were in training at the time of the
program's closure'' (Sec. 412.424(d)(1)(iii)(F)(2)(ii)). We considered
the residents who were physically present at the IPF to be those
residents who were ``training at the time of the program's closure,''
thereby granting them the status of ``displaced residents.'' Although
we did not want to limit the ``displaced residents'' to only those
physically present at the time of closure, it becomes much more
administratively challenging for the following groups of residents at
closing IPFs/programs to continue their training: (1) Residents who
leave the program after the closure is publicly announced to continue
training at another IPF, but before the actual closure; (2) residents
assigned to and training at planned rotations at other IPFs who will be
unable to return to their rotations at the closing IPF or program; and
(3) individuals (such as medical students or would-be fellows) who
matched into resident programs at the closing IPF or program but have
not yet started training at the closing IPF or program. Other groups of
residents who, under current policy, are already considered ``displaced
residents'' include--(1) residents who are physically training in the
IPF on the day prior to or day of program or IPF closure; and (2)
residents who would have been at the closing IPF or IPF program on the
day prior to or of closure but were on approved leave at that time, and
are unable to return to their training at the closing IPF or IPF
program.
We proposed to amend the IPF policy with regard to closing teaching
IPFs and closing residency programs to address the needs of residents
attempting to find alternative IPFs in which to complete their
training. Additionally, this proposal addresses the incentives of
originating and receiving IPFs with regard to ensuring we appropriately
account for their indirect teaching costs by way of an appropriate IPF
teaching adjustment based on each program's resident FTEs. We proposed
to change two aspects of the current IPF policy, which are discussed in
the following section.
First, rather than link the status of displaced residents, for the
purpose of the receiving IPF's request to increase their FTE cap, to
the resident's presence at the closing IPF or program on the day prior
to or the day of program or IPF closure, we proposed that the ideal day
will be the day that the closure was publicly announced, (for example,
via a press release or a formal notice to the Accreditation Council on
Graduate Medical Education (ACGME)). This will provide greater
flexibility for the residents to transfer while the IPF operations or
residency programs were winding down, rather than waiting until the
last day of IPF or program operation. This will address the needs of
the first group of residents as previously described: Residents who
leave the IPF program after the closure was publicly announced to
continue training at another IPF, but before the day of actual closure.
Second, by removing the link between the status of displaced
residents and their presence at the closing IPF or program on the day
prior to or the day of program or IPF closure, we proposed to also
allow the second and third group of residents who are not physically at
the closing IPF/closing program, but had intended to train at (or
return to training at, in the case of residents on rotation) to be
considered displaced residents. Thus, we proposed to revise our
teaching policy with regard to which residents can be considered
``displaced'' for the purpose of the receiving IPF's request to
increase their FTE cap in the situation where an IPF announces publicly
that it is closing or that it is closing an IPF residency program(s).
Specifically, we are adopting the definitions of ``closure of a
hospital'', ``closure of a hospital residency training program'', and
``displaced resident'' as defined at 42 CFR 413.79(h) but with respect
to IPFs and for the purposes of accounting for indirect teaching costs.
In addition, we proposed to change another detail of the IPF
teaching policy specific to the requirements for the receiving IPF. To
apply for the temporary increase in the FTE resident cap, the receiving
IPF will have to submit a letter to its Medicare Administrative
Contractor (MAC) within 60 days of beginning the training of the
displaced residents. As established under existing regulation at Sec.
412.424(d)(1)(iii)(F)(1)(ii) and Sec. 412.424(d)(1)(iii)(F)(2)(i),
this letter must identify the residents who have come from the closed
IPF or program that have caused the receiving IPF to exceed its cap,
and the receiving IPF must specify the length of time the adjustment is
needed. Moreover, we want to propose clarifications on how the
information will be delivered in this letter. Consistent with IPPS
teaching policy, we proposed that the letter from the receiving IPF
will have to include:

[[Page 42620]]

(1) The name of each displaced resident; (2) the last four digits of
each displaced resident's social security number; (3) the IPF and
program in which each resident was training previously; and (4) the
amount of the cap increase needed for each resident (based on how much
the receiving IPF is in excess of its cap and the length of time for
which the adjustments are needed). We proposed to require the receiving
hospital to only supply the last four digits of each displaced
resident's social security number to reduce the amount of personally
identifiable information (PII) included in these agreements.
We also clarified, as previously discussed in the May 6, 2011 IPF
PPS final rule (76 FR 26455), the maximum number of FTE resident cap
slots that could be transferred to all receiving IPFs is the number of
FTE resident cap slots belonging to the IPF that has the closed program
or that is closing. Therefore, if the originating IPF is training
residents in excess of its cap, then being a displaced resident does
not guarantee that a cap slot will be transferred along with that
resident. Therefore, if there are more IPF displaced residents than
available cap slots, the slots may be apportioned according to the
closing IPF's discretion. The decision to transfer a cap slot if one is
available will be voluntary and made at the sole discretion of the
originating IPF. However, if the originating IPF decides to do so, then
it will be the originating IPF's responsibility to determine how much
of an available cap slot will go with a particular resident (if any).
We also note, as we previously discussed in the May 6, 2011 IPF PPS
final rule (76 FR 25455), only to the extent a receiving IPF would
exceed its FTE cap by training displaced residents would it be eligible
for a temporary adjustment to its resident FTE cap. Displaced residents
are factored into the receiving IPF's ratio of resident FTEs to the
facility's average daily census.
Comment: We received 3 comments on our proposed updates to IPF
teaching policy. All commenters appreciate the alignment of IPF
teaching policy with IPPS. They believe it is important to protect
medical education. Therefore, decreasing confusion and streamlining the
process gives residents and program directors more time to find a new
program or rotation site, which can only help the transfer process.
Response: We thank these commenters for their support.
Final Decision: For FY 2022, we are finalizing the closure policy
as proposed. Section 124 of the BBRA gives the Secretary broad
discretion to determine the appropriate adjustment factors for the IPF
PPS. We are finalizing our proposal to implement the policy regarding
IPF resident caps and closures to remain consistent with the way that
the IPPS teaching policy calculates FTE resident caps in the case of a
receiving hospital that obtains a temporary IME and direct GME cap
adjustment for assuming the training of displaced residents due to
another hospital or residency program's closure. We are also finalizing
our proposal that in the future, we will deviate from IPPS teaching
policy as it pertains to counting displaced residents for the purposes
of the IPF teaching adjustment only when it is necessary and
appropriate for the IPF PPS.
In addition, we are finalizing our proposal to amend the IPF policy
with regard to closing teaching IPFs and closing residency programs to
address the needs of residents attempting to find alternative IPFs in
which to complete their training. This proposal addresses the
incentives of originating and receiving IPFs with regard to ensuring we
appropriately account for their indirect teaching costs by way of an
appropriate IPF teaching adjustment based on each program's resident
FTEs. We are also finalizing our proposal to change two aspects of the
current IPF policy, which are discussed in the following section.
First, rather than link the status of displaced residents for the
purpose of the receiving IPF's request to increase their FTE cap to the
resident's presence at the closing IPF or program on the day prior to
or the day of program or IPF closure, we are finalizing our proposal
that the ideal day will be the day that the closure was publicly
announced, (for example, via a press release or a formal notice to the
Accreditation Council on Graduate Medical Education (ACGME)). This will
provide greater flexibility for the residents to transfer while the IPF
operations or residency programs were winding down, rather than waiting
until the last day of IPF or program operation. This will address the
needs of the first group of residents as previously described:
Residents who leave the IPF program after the closure was publicly
announced to continue training at another IPF, but before the day of
actual closure.
Second, by removing the link between the status of displaced
residents and their presence at the closing IPF or program on the day
prior to or the day of program or IPF closure, we are finalizing to
also allow the second and third group of residents who are not
physically at the closing IPF/closing program, but had intended to
train at (or return to training at, in the case of residents on
rotation) to be considered a displaced resident. Thus, we are
finalizing our proposal to revise our teaching policy with regard to
which residents can be considered ``displaced'' for the purpose of the
receiving IPF's request to increase their FTE cap in the situation
where an IPF announces publicly that it is closing or that it is
closing an IPF residency program(s). Specifically, we are adopting the
definitions of ``closure of a hospital'', ``closure of a hospital
residency training program'', and ``displaced resident'' as defined at
42 CFR 413.79(h) but with respect to IPFs and for the purposes of
accounting for indirect teaching costs.
In addition, we are finalizing our proposal to change another
detail of the IPF teaching policy specific to the requirements for the
receiving IPF. To apply for the temporary increase in the FTE resident
cap, the receiving IPF will have to submit a letter to its Medicare
Administrative Contractor (MAC) within 60 days of beginning the
training of the displaced residents. As established under existing
regulation at Sec. 412.424(d)(1)(iii)(F)(1)(ii) and Sec.
412.424(d)(1)(iii)(F)(2)(i), this letter must identify the residents
who have come from the closed IPF or program that have caused the
receiving IPF to exceed its cap, and the receiving IPF must specify the
length of time the adjustment is needed. Moreover, we are finalizing
the clarifications on how the information will be delivered in this
letter. Consistent with IPPS teaching policy, the letter from the
receiving IPF will have to include: (1) The name of each displaced
resident; (2) the last four digits of each displaced resident's social
security number; (3) the IPF and program in which each resident was
training previously; and (4) the amount of the cap increase needed for
each resident (based on how much the receiving IPF is in excess of its
cap and the length of time for which the adjustments are needed). We
are also finalizing our proposal to require the receiving hospital to
only supply the last four digits of each displaced resident's social
security number to reduce the amount of personally identifiable
information (PII) included in these agreements.
We are also finalizing the clarification that the maximum number of
FTE resident cap slots that could be transferred to all receiving IPFs
is the number of FTE resident cap slots belonging to the IPF that has
the closed program or that is closing. Therefore, if the originating
IPF is training residents in excess of its cap, then being a displaced
resident does not guarantee that a cap slot will be transferred along

[[Page 42621]]

with that resident. Therefore, if there are more IPF displaced
residents than available cap slots, the slots may be apportioned
according to the closing IPF's discretion. The decision to transfer a
cap slot if one is available will be voluntary and made at the sole
discretion of the originating IPF. However, if the originating IPF
decides to do so, then it will be the originating IPF's responsibility
to determine how much of an available cap slot will go with a
particular resident (if any). We also note that, as we previously
discussed in the May 6, 2011 IPF PPS final rule (76 FR 25455), only to
the extent a receiving IPF would exceed its FTE cap by training
displaced residents would it be eligible for a temporary adjustment to
its resident FTE cap. Displaced residents are factored into the
receiving IPF's ratio of resident FTEs to the facility's average daily
census.
3. Final 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 <a href="https://www.chcoc.gov/content/nonforeign-area-retirement-equity-assurance-act">https://www.chcoc.gov/content/nonforeign-area-retirement-equity-assurance-act</a>.
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 think 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 updated for FY 2018, the COLA factors were
updated in FY 2018 IPPS/LTCH rulemaking (82 FR 38529). As such, we also
updated the IPF PPS COLA factors for FY 2018 (82 FR 36780 through
36782) to reflect the updated COLA factors finalized in the FY 2018
IPPS/LTCH rulemaking.
For FY 2022, we are finalizing our proposal to update the COLA
factors published by OPM for 2009 (as these are the last COLA factors
OPM published prior to transitioning from COLAs to locality pay) using
the methodology that we finalized in the FY 2013 IPPS/LTCH PPS final
rule and adopted for the IPF PPS in the FY 2015 IPF final rule.
Specifically, we are finalizing our proposal to update the 2009 OPM
COLA factors by a comparison of the growth in the Consumer Price
Indices (CPIs) for the areas of Urban Alaska and Urban Hawaii, relative
to the growth in the CPI for the average U.S. city as published by the
Bureau of Labor Statistics (BLS). We note that for the prior update to
the COLA factors, we used the growth in the CPI for Anchorage and the
CPI for Honolulu. Beginning in 2018, these indexes were renamed to the
CPI for Urban Alaska and the CPI for Urban Hawaii due to the BLS
updating its sample to reflect the data from the 2010 Decennial Census
on the distribution of the urban population (<a href="https://www.bls.gov/regions/west/factsheet/2018cpirevisionwest.pdf">https://www.bls.gov/regions/west/factsheet/2018cpirevisionwest.pdf</a>, accessed January 22,
2021). The CPI for Urban Alaska area covers Anchorage and Matanuska-
Susitna Borough in the State of Alaska and the CPI for Urban Hawaii
covers Honolulu in the State of Hawaii. BLS notes that the indexes are
considered continuous over time, regardless of name or composition
changes.
Because BLS publishes CPI data for only Urban Alaska and Urban
Hawaii, using the methodology we finalized in the FY 2013 IPPS/LTCH PPS
final rule and adopted for the IPF PPS in the FY 2015 IPF final rule,
we are finalizing our proposal to use the comparison of the growth in
the overall CPI relative to the growth in the CPI for those areas to
update the COLA factors for all areas in Alaska and Hawaii,
respectively. We believe that the relative price differences between
these urban areas and the U.S. (as measured by the CPIs) are
appropriate proxies for the relative price differences between the
``other areas'' of Alaska and Hawaii and the U.S.
BLS publishes the CPI for All Items for Urban Alaska, Urban Hawaii,
and for the average U.S. city. However, consistent with our methodology
finalized in the FY 2013 IPPS/LTCH PPS final rule and adopted for the
IPF PPS in the FY 2015 IPF final rule, we are finalizing our proposal
to create reweighted CPIs for each of the respective areas to reflect
the underlying

[[Page 42622]]

composition of the IPPS market basket nonlabor-related share. The
current composition of the CPI for All Items for all of the respective
areas is approximately 40 percent commodities and 60 percent services.
However, the IPPS nonlabor-related share is comprised of a different
mix of commodities and services. Therefore, we are finalizing our
proposal to create reweighted indexes for Urban Alaska, Urban Hawaii,
and the average U.S. city using the respective CPI commodities index
and CPI services index and proposed shares of 57 percent commodities/43
percent. We created reweighted indexes using BLS data for 2009 through
2020--the most recent data available at the time of this final
rulemaking. In the FY 2018 IPPS/LTCH PPS final rule (82 FR 38530), we
created reweighted indexes based on the 2014-based IPPS market basket
(which was adopted for the FY 2018 IPPS update) and BLS data for 2009
through 2016 (the most recent BLS data at the time of the FY 2018 IPPS/
LTCH PPS rulemaking), and we updated the IPF PPS COLA factors
accordingly for FY 2018.
We continue to believe this methodology is appropriate because we
continue to make a COLA for hospitals located in Alaska and Hawaii by
multiplying the nonlabor-related portion of the standardized amount by
a COLA factor. We note that OPM's COLA factors were calculated with a
statutorily mandated cap of 25 percent. As stated in the FY 2018 IPPS/
LTCH PPS final rule (82 FR 38530), under the COLA update methodology we
finalized in the FY 2013 IPPS/LTCH PPS final rule, we exercised our
discretionary authority to adjust payments to hospitals in Alaska and
Hawaii by incorporating this cap. In applying this finalized
methodology for updating the COLA factors, for FY 2022, we are
finalizing our proposal to continue to use such a cap, as our policy is
based on OPM's COLA factors (updated by the methodology described
above).
Applying this methodology, the COLA factors that we are finalizing
our proposal to establish for FY 2022 to adjust the nonlabor-related
portion of the standardized amount for IPFs located in Alaska and
Hawaii are shown in Table 2. For comparison purposes, we also are
showing the COLA factors effective for FY 2018 through FY 2021.
[GRAPHIC] [TIFF OMITTED] TR04AU21.171

The final IPF PPS COLA factors for FY 2022 are also shown in
Addendum A to this final rule, and is available at <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
4. Final 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

[[Page 42623]]

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 2022, we are finalizing our proposal 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).

F. 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. Final Update to the Outlier Fixed Dollar Loss Threshold Amount
In accordance with the update methodology described in Sec.
412.428(d), we are finalizing our proposal 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. For this final rulemaking,
the most recent available data are the FY 2020 claims. However, during
FY 2020, the U.S. healthcare system undertook an unprecedented response
to the PHE declared by the Health and Human Services Secretary on
January 31, 2020 in response to the outbreak of respiratory disease
caused by a novel (new) coronavirus that has been named ``SARS CoV 2''
and the disease it causes, which has been named ``coronavirus disease
2019'' (abbreviated ``COVID-19''). Therefore, as discussed in section
VI.C.3 of the FY 2022 IPF PPS proposed rule (86 FR 19524 through
195266), we considered whether the most recent available year of
claims, FY 2020, or the prior year, FY 2019, would be the best for
estimating IPF PPS payments in FY 2021 and FY 2022. We compared the two
years' claims distributions as well as the impact results, and based on
that analysis determined that the FY 2019 claims appeared to be the
best available data at this time. We refer the reader to section VI.C.3
of the FY 2022 IPF PPS proposed rule (86 FR 19524 through 195266 FR)
for a detailed discussion of that analysis.
Comment: We received 2 comments on our analysis of the FY 2019 and
FY 2020 claims in determining the best available data for estimating
IPF PPS payments in FY 2021 and FY 2022. Both comments were supportive
of our proposal to use the FY 2019 claims for this purpose. One of
these commenters expressed appreciation for the proposed reduction in
the outlier fixed dollar loss threshold. Another commenter agreed with
our assessment that FY 2020 claims were heavily impacted by the
intensity of the COVID-19 pandemic.
Response: We appreciate these commenters' support. Based on the
revised impact analysis discussed in section VI.C.3 of this final rule,
we continue to believe that the FY 2019 claims are the best available
data for estimating FY 2021 and FY 2022 payments.
Final Decision: We are finalizing as proposed to use the June 2020
update of the FY 2019 IPF claims for updating the outlier fixed dollar
loss threshold.
Based on an analysis of the June 2020 update of FY 2019 IPF claims
and the FY 2021 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 finalizing our proposal to update the IPF outlier threshold
amount for FY 2022 using FY 2019 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 2021. Based on an analysis of these updated data, we
estimate that IPF outlier payments as a percentage of total estimated
payments are approximately 1.9 percent in FY 2021. Therefore, we are
finalizing our proposal to update the outlier threshold amount to
$14,470 to maintain estimated outlier payments at 2 percent of total
estimated aggregate IPF payments for FY 2022. This final update is a
decrease from the FY 2021 threshold of $14,630. In contrast, using the
FY 2020 claims to estimate payments, the final outlier fixed dollar
loss threshold for FY 2022 would be $22,720, which would have been an
increase from the FY 2021 threshold of $14,630. We refer the reader to
section VI.C.3 of this final rule for a detailed discussion of the
estimated impacts of the final update to the outlier fixed dollar loss
threshold.
We note that our use of the FY 2019 claims to set the final outlier
fixed dollar loss threshold for FY 2022 deviates from what has been our
longstanding practice of using the most recent available year of
claims, which is FY 2020 data. However, we are finalizing this policy
in a way that remains otherwise consistent with the

[[Page 42624]]

established outlier update methodology. As discussed in this section
and in section VI.C.3 of this final rule, we are finalizing our
proposal to update the outlier fixed dollar loss threshold based on FY
2019 IPF claims in order to maintain the appropriate outlier percentage
in FY 2022. We are finalizing 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 COVID-19 PHE appears to have
significantly impacted the FY 2020 IPF claims. As discussed in section
VI.C.3 of this final rule, we have analyzed more recent available IPF
claims data and continue to believe that using FY 2019 IPF claims is
appropriate for the FY 2022 update. We intend 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.
3. Final 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 2022, we are finalizing our proposal 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 2022 is 2.0261 for rural IPFs, and 1.6879 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 finalizing our proposal to continue to update the FY 2022
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
2022, to be used in each of the three situations listed previously,
using the most recent CCRs entered in the CY 2021 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. Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program

A. Background and Statutory Authority

We refer readers to the FY 2019 IPF PPS final rule (83 FR 38589)
for a discussion of the background and statutory authority \1\ of the
IPFQR Program.
---------------------------------------------------------------------------

\1\ We note that the statute uses the term ``rate year'' (RY).
However, beginning with the annual update of the inpatient
psychiatric facility prospective payment system (IPF PPS) that took
effect on July 1, 2011 (RY 2012), we aligned the IPF PPS update with
the annual update of the ICD codes, effective on October 1 of each
year. This change allowed for annual payment updates and the ICD
coding update to occur on the same schedule and appear in the same
Federal Register document, promoting administrative efficiency. To
reflect the change to the annual payment rate update cycle, we
revised the regulations at 42 CFR 412.402 to specify that, beginning
October 1, 2012, the IPF PPS RY means the 12-month period from
October 1 through September 30, which we refer to as a ``fiscal
year'' (FY) (76 FR 26435). Therefore, with respect to the IPFQR
Program, the terms ``rate year,'' as used in the statute, and
``fiscal year'' as used in the regulation, both refer to the period
from October 1 through September 30. For more information regarding
this terminology change, we refer readers to section III. of the RY
2012 IPF PPS final rule (76 FR 26434 through 26435).
---------------------------------------------------------------------------

B. Covered Entities

In the FY 2013 IPPS/LTCH PPS final rule (77 FR 53645), we
established that the IPFQR Program's quality reporting requirements
cover those psychiatric hospitals and psychiatric units paid under
Medicare's IPF PPS (Sec. 412.404(b)). Generally, psychiatric hospitals
and psychiatric units within acute care and critical access hospitals
that treat Medicare patients are paid under the IPF PPS. Consistent
with previous regulations, we continue to use the terms ``facility'' or
IPF to refer to both inpatient psychiatric hospitals and psychiatric
units. This usage follows the terminology in our IPF PPS regulations at
Sec. 412.402. For more information on covered entities, we refer
readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR 53645).

C. Previously Finalized Measures and Administrative Procedures

The current IPFQR Program includes 14 measures. For more
information on these measures, we refer readers to Table 5 of this
final rule and the following final rules:
<bullet> The FY 2013 IPPS/LTCH PPS final rule (77 FR 53646 through
53652);
<bullet> The FY 2014 IPPS/LTCH PPS final rule (78 FR 50889 through
50897);
<bullet> The FY 2015 IPF PPS final rule (79 FR 45963 through
45975);
<bullet> The FY 2016 IPF PPS final rule (80 FR 46695 through
46714);
<bullet> The FY 2017 IPPS/LTCH PPS final rule (81 FR 57238 through
57247);
<bullet> The FY 2019 IPF PPS final rule (83 FR 38590 through
38606); and
<bullet> The FY 2020 IPF PPS final rule (84 FR 38459 through
38467).
For more information on previously adopted procedural requirements,
we refer readers to the following rules:
<bullet> The FY 2013 IPPS/LTCH PPS final rule (77 FR 53653 through
53660);
<bullet> The FY 2014 IPPS/LTCH PPS final rule (78 FR 50897 through
50903);
<bullet> The FY 2015 IPF PPS final rule (79 FR 45975 through
45978);
<bullet> The FY 2016 IPF PPS final rule (80 FR 46715 through
46719);

[[Page 42625]]

<bullet> The FY 2017 IPPS/LTCH PPS final rule (81 FR 57248 through
57249);
<bullet> The FY 2018 IPPS/LTCH PPS final rule (82 FR 38471 through
38474);
<bullet> The FY 2019 IPF PPS final rule (83 FR 38606 through
38608); and
<bullet> The FY 2020 IPF PPS final rule (84 FR 38467 through
38468).

D. Closing the Health Equity Gap in CMS Quality Programs--Request for
Information (RFI)

Persistent inequities in health care outcomes exist in the U.S.,
including among Medicare patients. In recognition of persistent health
disparities and the importance of closing the health equity gap, we
requested information on revising several CMS programs to make
reporting of health disparities based on social risk factors and race
and ethnicity more comprehensive and actionable for facilities,
providers, and patients. The RFI that was included in the proposed rule
is part of an ongoing effort across CMS to evaluate appropriate
initiatives to reduce health disparities. Feedback will be used to
inform the creation of a future, comprehensive, RFI focused on closing
the health equity gap in CMS programs and policies.
The RFI contained four parts:
<bullet> Background: This section provided information describing
our commitment to health equity, and existing initiatives with an
emphasis on reducing health disparities.
<bullet> Current CMS Disparity Methods: This section described the
methods, measures, and indicators of social risk currently used with
the CMS Disparity Methods.
<bullet> Future potential stratification of quality measure
results: This section described four potential future expansions of the
CMS Disparity Methods, including (1) Stratification of Quality Measure
Results--Dual Eligibility; (2) Stratification of Quality Measure
Results--Race and Ethnicity; (3) Improving Demographic Data Collection;
and (4) Potential Creation of a Facility Equity Score to Synthesize
Results Across Multiple Social Risk Factors.
<bullet> Solicitation of public comment: This section specified 12
requests for feedback on these topics. We reviewed feedback on these
topics and note our intention for an additional RFI or rulemaking on
this topic in the future.
1. Background
Significant and persistent inequities in health care outcomes exist
in the U.S. Belonging to a racial or ethnic minority group; living with
a disability; being a member of the lesbian, gay, bisexual,
transgender, and queer (LGBTQ+) community; living in a rural area; or
being near or below the poverty level, is often associated with worse
health outcomes.2 3 4 5 6 7 8 9 Such disparities in health
outcomes are the result of number of factors, but importantly for CMS
programs, although not the sole determinant, poor access and provision
of lower quality health care contribute to health disparities. For
instance, numerous studies have shown that among Medicare
beneficiaries, racial and ethnic minority individuals often receive
lower quality of care, report lower experiences of care, and experience
more frequent hospital readmissions and operative
complications.10 11 12 13 14 15 Readmission rates for common
conditions in the Hospital Readmissions Reduction Program are higher
for Black Medicare beneficiaries and higher for Hispanic Medicare
beneficiaries with Congestive Heart Failure and Acute Myocardial
Infarction.16 17 18 19 20 Studies have also shown that
African Americans are significantly more likely than white Americans to
die prematurely from heart disease, and stroke.\21\ The COVID-19
pandemic has further illustrated many of these longstanding health
inequities with higher rates of infection, hospitalization, and
mortality among Black, Latino, and Indigenous and Native American
persons relative to White persons.22 23 As noted by the
Centers for Disease Control ``long-standing systemic health and social
inequities have put many people from racial and ethnic minority groups
at increased risk of getting sick and dying from COVID-19.'' \24\ One
important strategy for addressing these important inequities is
improving data collection to allow for better measurement and reporting
on equity across our programs and policies.
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\2\ Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for
Medicare Beneficiaries by Race and Site of Care. JAMA.
2011;305(7):675-681.
\3\ Lindenauer PK, Lagu T, Rothberg MB, et al. Income Inequality
and 30 Day Outcomes After Acute Myocardial Infarction, Heart
Failure, and Pneumonia: Retrospective Cohort Study. British Medical
Journal. 2013;346.
\4\ Trivedi AN, Nsa W, Hausmann LRM, et al. Quality and Equity
of Care in U.S. Hospitals. New England Journal of Medicine.
2014;371(24):2298-2308.
\5\ Polyakova, M., et al. Racial Disparities In Excess All-Cause
Mortality During The Early COVID-19 Pandemic Varied Substantially
Across States. Health Affairs. 2021; 40(2): 307-316.
\6\ Rural Health Research Gateway. Rural Communities: Age,
Income, and Health Status. Rural Health Research Recap. November
2018.
\7\ <a href="https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf">https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf</a>.
\8\ <a href="http://www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm">www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm</a>.
\9\ Poteat TC, Reisner SL, Miller M, Wirtz AL. 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. Published 2020 Jul 24. doi:10.1101/
2020.07.21.20159327.
\10\ Martino, SC, Elliott, MN, Dembosky, JW, Hambarsoomian, K,
Burkhart, Q, Klein, DJ, Gildner, J, and Haviland, AM. Racial,
Ethnic, and Gender Disparities in Health Care in Medicare Advantage.
Baltimore, MD: CMS Office of Minority Health. 2020.
\11\ Guide to Reducing Disparities in Readmissions. CMS Office
of Minority Health. Revised August 2018. Available at: <a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf">https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf</a>.
\12\ Singh JA, Lu X, Rosenthal GE, Ibrahim S, Cram P. Racial
disparities in knee and hip total joint arthroplasty: an 18-year
analysis of national Medicare data. Ann Rheum Dis. 2014
Dec;73(12):2107-15.
\13\ Rivera-Hernandez M, Rahman M, Mor V, Trivedi AN. Racial
Disparities in Readmission Rates among Patients Discharged to
Skilled Nursing Facilities. J Am Geriatr Soc. 2019 Aug;67(8):1672-
1679.
\14\ Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for
Medicare Beneficiaries by Race and Site of Care. JAMA.
2011;305(7):675-681.
\15\ Tsai TC, Orav EJ, Joynt KE. Disparities in surgical 30-day
readmission rates for Medicare beneficiaries by race and site of
care. Ann Surg. Jun 2014;259(6):1086-1090.
\16\ Rodriguez F, Joynt KE, Lopez L, Saldana F, Jha AK.
Readmission rates for Hispanic Medicare beneficiaries with heart
failure and acute myocardial infarction. Am Heart J. Aug
2011;162(2):254-261 e253.
\17\ Centers for Medicare and Medicaid Services. Medicare
Hospital Quality Chartbook: Performance Report on Outcome Measures;
2014.
\18\ Guide to Reducing Disparities in Readmissions. CMS Office
of Minority Health. Revised August 2018. Available at: <a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf">https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf</a>.
\19\ Prieto-Centurion V, Gussin HA, Rolle AJ, Krishnan JA.
Chronic obstructive pulmonary disease readmissions at minority-
serving institutions. Ann Am Thorac Soc. Dec 2013;10(6):680-684.
\20\ Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for
Medicare Beneficiaries by Race and Site of Care. JAMA.
2011;305(7):675-681.
\21\ HHS. Heart disease and African Americans. (March 29, 2021).
<a href="https://www.minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=19">https://www.minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=19</a>.
\22\ <a href="https://www.cms.gov/files/document/medicare-covid-19-data-snapshot-fact-sheet.pdf">https://www.cms.gov/files/document/medicare-covid-19-data-snapshot-fact-sheet.pdf</a>.
\23\ Ochieng N, Cubanski J, Neuman T, Artiga S, and Damico A.
Racial and Ethnic Health Inequities and Medicare. Kaiser Family
Foundation. February 2021. Available at: <a href="https://www.kff.org/medicare/report/racial-and-ethnic-health-inequities-and-medicare/">https://www.kff.org/medicare/report/racial-and-ethnic-health-inequities-and-medicare/</a>.
\24\ <a href="https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html">https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html</a>.
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We are committed to achieving equity in health care outcomes for
our beneficiaries by supporting providers in quality improvement
activities to reduce health inequities, enabling them to make more
informed decisions, and promoting provider accountability for health
care disparities.\25\ For the purposes of this final rule, we are using
a definition of equity established in

[[Page 42626]]

Executive Order 13985, as ``the consistent and systematic fair, just,
and impartial treatment of all individuals, including individuals who
belong to underserved communities that have been denied such treatment,
such as Black, Latino, and Indigenous and Native American persons,
Asian Americans and Pacific Islanders and other persons of color;
members of religious minorities; lesbian, gay, bisexual, transgender,
and queer (LGBTQ+) persons; persons with disabilities; persons who live
in rural areas; and persons otherwise adversely affected by persistent
poverty or inequality.'' \26\ We note that this definition was recently
established by the current administration, and provides a useful,
common definition for equity across different areas of government,
although numerous other definitions of equity exist.
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\25\ <a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf</a>.
\26\ <a href="https://www.federalregister.gov/documents/2021/01/25/2021-01753/advancing-racial-equity-and-support-for-underserved-communities-through-the-Federal-government">https://www.federalregister.gov/documents/2021/01/25/2021-01753/advancing-racial-equity-and-support-for-underserved-communities-through-the-Federal-government</a>.
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Our ongoing commitment to closing the equity gap in CMS quality
programs is demonstrated by a portfolio of programs aimed at making
information on the quality of health care providers and services,
including disparities, more transparent to consumers and providers. The
CMS Equity Plan for Improving Quality in Medicare outlines a path to
equity which aims to support Quality Improvement Networks and Quality
Improvement Organizations (QIN-QIOs) in their efforts to engage with
and assist providers that care for vulnerable populations; Federal,
state, local, and tribal organizations; providers; researchers;
policymakers; beneficiaries and their families; and other stakeholders
in activities to achieve health equity.\27\ The CMS Equity Plan for
Improving Quality in Medicare focuses on three core priority areas
which inform our policies and programs: (1) Increasing understanding
and awareness of health disparities; (2) developing and disseminating
solutions to achieve health equity; and (3) implementing sustainable
actions to achieve health equity.\28\ The CMS Quality Strategy \29\ and
Meaningful Measures Framework \30\ include elimination of racial and
ethnic disparities as a central principle. Our efforts aimed at closing
the health equity gap to date have included providing transparency
about health disparities, supporting providers with evidence-informed
solutions to achieve health equity, and reporting to providers on gaps
in quality through the following reports and programs:
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\27\ Centers for Medicare and Medicaid Services Office of
Minority Health. The CMS Equity Plan for Improving Quality in
Medicare. 2015. <a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_090615.pdf">https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_090615.pdf</a>.
\28\ Centers for Medicare and Medicaid Services Office of
Minority Health. The CMS Equity Plan for Improving Quality in
Medicare. 2015. <a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_090615.pdf">https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_090615.pdf</a>.
\29\ Centers for Medicare Services. CMS Quality Strategy. 2016.
<a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf</a>.
\30\ <a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/MMF/General-info-Sub-Page">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/MMF/General-info-Sub-Page</a>.
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<bullet> The CMS Mapping Medicare Disparities Tool, which is an
interactive map that identifies areas of disparities and a starting
point to understand and investigate geographical, racial and ethnic
differences in health outcomes for Medicare patients.\31\
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\31\ <a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH-Mapping-Medicare-Disparities">https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH-Mapping-Medicare-Disparities</a>.
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<bullet> The Racial, Ethnic, and Gender Disparities in Health Care
in Medicare Advantage Stratified Report, which highlights racial and
ethnic differences in health care experiences and clinical care,
compares quality of care for women and men, and looks at racial and
ethnic differences in quality of care among women and men separately
for Medicare Advantage plans.\32\
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\32\ <a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting">https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting</a>.
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<bullet> The Rural-Urban Disparities in Health Care in Medicare
Report, which details rural-urban differences in health care
experiences and clinical care.\33\
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\33\ Centers for Medicare and Medicaid Services. Rural-Urban
Disparities in Health Care in Medicare. 2019. <a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Rural-Urban-Disparities-in-Health-Care-in-Medicare-Report.pdf">https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Rural-Urban-Disparities-in-Health-Care-in-Medicare-Report.pdf</a>.
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<bullet> The Standardized Patient Assessment Data Elements for
certain post-acute care Quality Reporting Programs, which now includes
data reporting for race and ethnicity and preferred language, in
addition to screening questions for social needs (84 FR 42536 through
42588).
<bullet> The CMS Innovation Center's Accountable Health Communities
Model, which include standardized data collection of health-related
social needs data.
<bullet> The Guide to Reducing Disparities which provides an
overview of key issues related to disparities in readmissions and
reviews sets of activities that can help hospital leaders reduce
readmissions in diverse populations.\34\
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\34\ Guide to Reducing Disparities in Readmissions. CMS Office
of Minority Health. Revised August 2018. Available at: <a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf">https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf</a>.
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<bullet> The CMS Disparity Methods, which provide hospital-level
confidential results stratified by dual eligibility for condition-
specific readmission measures currently included in the Hospital
Readmission Reduction Program (84 FR 42496 through 42500).
These programs are informed by reports by the National Academies of
Science, Engineering and Medicine (NASEM) \35\ and the Office of the
Assistant Secretary for Planning and Evaluation (ASPE) \36\ which have
examined the influence of social risk factors on several of our quality
programs. In this RFI, we addressed only the seventh initiative listed,
the CMS Disparity Methods, which we have implemented for measures in
the Hospital Readmissions Reduction Program and are considering in
other programs, including the IPFQR Program. We discussed the
implementation of these methods to date and present considerations for
continuing to improve and expand these methods to provide providers and
ultimately consumers with actionable information on disparities in
health care quality to support efforts at closing the equity gap.
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\35\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for Social Risk Factors in Medicare Payment:
Identifying Social Risk Factors. Washington, DC: The National
Academies Press. <a href="https://doi.org/10.17226/21858">https://doi.org/10.17226/21858</a>.
\36\ <a href="https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs">https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs</a>.
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2. Current CMS Disparity Methods
We first sought public comment on potential confidential and public
reporting of IPFQR program measure data stratified by social risk
factors in the FY 2018 IPPS/LTCH PPS proposed rule (82 FR 20121). We
initially focused on stratification by dual eligibility, which is
consistent with recommendations from ASPE's First Report to Congress
which was required by the Improving Medicare Post-Acute Care
Transformation (IMPACT) Act of 2014 (Pub. L. 113-185).\37\ This report
found that in the context of value-based purchasing (VBP) programs,
dual eligibility was among the most powerful predictors of poor health
outcomes

[[Page 42627]]

among those social risk factors that ASPE examined and tested.
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\37\ <a href="https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs">https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs</a>.
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In the FY 2018 IPPS/LTCH PPS final rule we also solicited feedback
on two potential methods for illuminating differences in outcomes rates
among patient groups within a provider's patient population that would
also allow for a comparison of those differences, or disparities,
across providers for the Hospital IQR Program (82 FR 38403 through
38409). The first method (the Within-Hospital disparity method)
promotes quality improvement by calculating differences in outcome
rates among patient groups within a hospital while accounting for their
clinical risk factors. This method also allows for a comparison of the
magnitude of disparity across hospitals, permitting hospitals to assess
how well they are closing disparity gaps compared to other hospitals.
The second methodological approach (the Across-Hospital method) is
complementary and assesses hospitals' outcome rates for dual-eligible
patients only, across hospitals, allowing for a comparison among
hospitals on their performance caring for their patients with social
risk factors. In the FY 2018 IPPS/LTCH PPS proposed rule under the
IPFQR Program (82 FR 20121), we also specifically solicited feedback on
which social risk factors provide the most valuable information to
stakeholders. Overall, comments supported the use of dual eligibility
as a proxy for social risk, although commenters also suggested
investigation of additional social risk factors, and we continue to
consider which risk factors provide the most valuable information to
stakeholders.
Concurrent with our comment solicitation on stratification in the
IPFQR Program, we have considered methods for stratifying measure
results for other quality reporting programs. For example, in the FY
2019 IPPS/LTCH PPS final rule (82 FR 41597 through 41601), we finalized
plans to provide confidential hospital-specific reports (HSRs)
containing stratified results of the Pneumonia Readmission (NQF #0506)
and Pneumonia Mortality (NQF #0468) measures including both the Across-
Hospital Disparity Method and the Within-Hospital Disparity Method
(disparity methods), stratified by dual eligibility. In the FY 2019
IPPS/LTCH PPS final rule (83 FR 41554 through 41556), we also removed
six condition/procedure specific readmissions measures, including the
Pneumonia Readmission measure (NQF #0506) and five mortality measures,
including the Pneumonia Mortality measure (NQF #0468) (83 FR 41556
through 41558) from the Hospital IQR Program. However, the Pneumonia
Readmission (NQF #0506) and the other condition/procedure readmissions
measures remained in the Hospital Readmissions Reduction Program. In
2019, we provided hospitals with results of the Pneumonia Readmission
measure (NQF#0506) stratified using dual eligibility. We provided this
information in annual confidential HSRs for claims-based measures.
We then, in the FY 2020 IPPS/LTCH PPS Final Rule (84 FR 42388
through 42390), finalized the proposal to provide confidential hospital
specific reports (HSRs) containing data stratified by dual-eligible
status for all six readmission measures included in the Hospital
Readmission Reduction Program.
3. Potential Expansion of the CMS Disparity Methods
We are committed to advancing health equity by improving data
collection to better measure and analyze disparities across programs
and policies.\38\ As we previously noted, we have been considering,
among other things, expanding our efforts to provide stratified data
for additional social risk factors and measures, optimizing the ease-
of-use of the results, enhancing public transparency of equity results,
and building towards provider accountability for health equity. We
sought public comment on the potential stratification of quality
measures in the IPFQR Program across two social risk factors: Dual
eligibility and race/ethnicity.
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\38\ Centers for Medicare Services. CMS Quality Strategy. 2016.
<a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf</a>.
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a. Stratification of Quality Measure Results--Dual Eligibility
As described previously in this section, landmark reports by the
National Academies of Science, Engineering and Medicine (NASEM) \39\
and the Office of the Assistant Secretary for Planning and Evaluation
(ASPE),\40\ which have examined the influence of social risk factors on
several of our quality programs, have shown that in the context of
value-based purchasing (VBP) programs, dual eligibility, as an
indicator of social risk, is a powerful predictor of poor health
outcomes. We noted that the patient population of IPFs has a higher
percentage of dually eligible patients than the general Medicare
population. Specifically, over half (56 percent) of Medicare patients
in IPFs are dually eligible \41\ while approximately 20 percent of all
Medicare patients are dually eligible.\42\ We are considering
stratification of quality measure results in the IPFQR Program and are
considering which measures would be most appropriate for stratification
and if dual eligibility would be a meaningful social risk factor for
stratification.
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\39\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for Social Risk Factors in Medicare Payment:
Identifying Social Risk Factors. Washington, DC: The National
Academies Press. <a href="https://doi.org/10.17226/21858">https://doi.org/10.17226/21858</a>.
\40\ <a href="https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs">https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs</a>.
\41\ <a href="https://aspe.hhs.gov/basic-report/transitions-care-and-service-use-among-medicare-beneficiaries-inpatient-psychiatric-facilities-issue-brief">https://aspe.hhs.gov/basic-report/transitions-care-and-service-use-among-medicare-beneficiaries-inpatient-psychiatric-facilities-issue-brief</a>.
\42\ <a href="https://www.cms.gov/Medicare-Medicaid-Coordination/Medicare-and-Medicaid-Coordination/Medicare-Medicaid-Coordination-Office/DataStatisticalResources/Downloads/MedicareMedicaidDualEnrollmentEverEnrolledTrendsDataBrief2006-2018.pdf">https://www.cms.gov/Medicare-Medicaid-Coordination/Medicare-and-Medicaid-Coordination/Medicare-Medicaid-Coordination-Office/DataStatisticalResources/Downloads/MedicareMedicaidDualEnrollmentEverEnrolledTrendsDataBrief2006-2018.pdf</a>.
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For the IPFQR Program, we would consider disparity reporting using
two disparity methods derived from the Within-Hospital and Across-
Hospital methods, described in section IV.D.2 of this final rule. The
first method (based on the Within-Facility disparity method) would aim
to promote quality improvement by calculating differences in outcome
rates between dual and non-dual eligible patient groups within a
facility while accounting for their clinical risk factors. This method
would allow for a comparison of those differences, or disparities,
across facilities, so facilities could assess how well they are closing
disparity gaps compared to other facilities. The second approach (based
on the Across-Facility method) would be complementary and assesses
facilities' outcome rates for subgroups of patients, such as dual
eligible patients, across facilities, allowing for a comparison among
facilities on their performance caring for their patients with social
risk factors.
b. Stratification of Quality Measure Results--Race and Ethnicity
The Administration's Executive Order on Advancing Racial Equity and
Support for Underserved Communities Through the Federal Government
directs agencies to assess potential barriers that underserved
communities and individuals may face to enrollment in and access to
benefits and services in Federal Programs. As summarized in section
IV.D of this final rule, studies have shown that among Medicare
beneficiaries, racial and ethnic minority persons often experience
worse health outcomes, including more frequent hospital readmissions
and operative

[[Page 42628]]

complications. An important part of identifying and addressing
inequities in health care is improving data collection to allow us to
better measure and report on equity across our programs and policies.
We are considering stratification of quality measure results in the
IPFQR Program by race and ethnicity and are considering which measures
would be most appropriate for stratification.
As outlined in the 1997 Office of Management and Budget (OMB)
Revisions to the Standards for the Collection of Federal Data on Race
and Ethnicity, the racial and ethnic categories, which may be used for
reporting the disparity methods are considered to be social and
cultural, not biological or genetic.\43\ The 1997 OMB Standard lists
five minimum categories of race: (1) American Indian or Alaska Native;
(2) Asian; (3) Black or African American; (4) Native Hawaiian or Other
Pacific Islander; (5) and White. In the OMB standards, Hispanic or
Latino is the only ethnicity category included, and since race and
ethnicity are two separate and distinct concepts, persons who report
themselves as Hispanic or Latino can be of any race.\44\ Another
example, the ``Race & Ethnicity--CDC'' code system in Public Health
Information Network (PHIN) Vocabulary Access and Distribution System
(VADS) \45\ permits a much more granular structured recording of a
patient's race and ethnicity with its inclusion of over 900 concepts
for race and ethnicity. The recording and exchange of patient race and
ethnicity at such a granular level can facilitate the accurate
identification and analysis of health disparities based on race and
ethnicity. Further, the ``Race & Ethnicity--CDC'' code system has a
hierarchy that rolls up to the OMB minimum categories for race and
ethnicity and, thus, supports aggregation and reporting using the OMB
standard. ONC includes both the CDC and OMB standards in its criterion
for certified health IT products.\46\ For race and ethnicity, a
certified health IT product must be able to express both detailed races
and ethnicities using any of the 900 plus concepts in the ``Race &
Ethnicity--CDC'' code system in the PHIN VADS, as well as aggregate
each one of a patient's races and ethnicities to the categories in the
OMB standard for race and ethnicity. This approach can reduce burden on
providers recording demographics using certified products.
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\43\ Executive Office of the President Office of Management and
Budget, Office of Information and Regulatory Affairs. Revisions to
the standards for the classification of Federal data on race and
ethnicity. Vol 62. Federal Register. 1997:58782-58790
\44\ <a href="https://www.census.gov/topics/population/hispanic-origin/about.html">https://www.census.gov/topics/population/hispanic-origin/about.html</a>.
\45\ <a href="https://phinvads.cdc.gov/vads/ViewValueSet.action?id=67D34BBC-617F-DD11-B38D-00188B398520">https://phinvads.cdc.gov/vads/ViewValueSet.action?id=67D34BBC-617F-DD11-B38D-00188B398520</a>.
\46\ ONC criteria for certified health IT products: <a href="https://www.healthit.gov/isa/representing-patient-race-and-ethnicity">https://www.healthit.gov/isa/representing-patient-race-and-ethnicity</a>.
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Self-reported race and ethnicity data remain the gold standard for
classifying an individual according to race or ethnicity. However, CMS
does not consistently collect self-reported race and ethnicity for the
Medicare program, but instead gets the data from the Social Security
Administration (SSA) and the data accuracy and comprehensiveness have
proven challenging despite capabilities in the marketplace via
certified health IT products. Historical inaccuracies in Federal data
systems and limited collection classifications have contributed to the
limited quality of race and ethnicity information in Medicare's
administrative data systems.\47\ In recent decades, to address these
data quality issues, we have undertaken numerous initiatives, including
updating data taxonomies and conducting direct mailings to some
beneficiaries to enable more comprehensive race and ethnic
identification.48 49 Despite those efforts, studies reveal
varying data accuracy in identification of racial and ethnic groups in
Medicare administrative data, with higher sensitivity for correctly
identifying White and Black individuals, and lower sensitivity for
correctly identifying individuals of Hispanic ethnicity or of Asian/
Pacific Islander and American Indian/Alaskan Native race.\50\
Incorrectly classified race or ethnicity may result in overestimation
or underestimation in the quality of care received by certain groups of
beneficiaries.
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\47\ Eicheldinger, C., & Bonito, A. (2008). More accurate racial
and ethnic codes for Medicare administrative data. Health Care
Financing Review, 29(3), 27-42.
\48\ Filice CE, Joynt KE. Examining Race and Ethnicity
Information in Medicare Administrative Data. Med Care.
2017;55(12):e170-e176. doi:10.1097/MLR.0000000000000608.
\49\ Eicheldinger, C., & Bonito, A. (2008). More accurate racial
and ethnic codes for Medicare administrative data. Health Care
Financing Review, 29(3), 27-42.
\50\ Centers for Medicare and Medicaid Services. Building an
Organizational Response to Health Disparities Inventory of Resources
for Standardized Demographic and Language Data Collection. 2020.
<a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf">https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf</a>.
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We continue to work with Federal and private partners to better
collect and leverage data on social risk to improve our understanding
of how these factors can be better measured in order to close the
health equity gap. Among other things, we have developed an Inventory
of Resources for Standardized Demographic and Language Data Collection
\51\ and supported collection of specialized International
Classification of Disease, 10th Revision, Clinical Modification (ICD-
10-CM) codes for describing the socioeconomic, cultural, and
environmental determinants of health, and sponsored several initiatives
to statistically estimate race and ethnicity information when it is
absent.\52\ The Office of the National Coordinator for Health
Information Technology (ONC) included social, psychological, and
behavioral standards in the 2015 Edition health information technology
(IT) certification criteria (2015 Edition), providing interoperability
standards (LOINC (Logical Observation Identifiers Names and Codes) and
SNOMED CT (Systematized Nomenclature of Medicine--Clinical Terms)) for
financial strain, education, social connection and isolation, and
others. Additional stakeholder efforts underway to expand capabilities
to capture additional social determinants of health data elements
include the Gravity Project to identify and harmonize social risk
factor data for interoperable electronic health information exchange
for EHR fields, as well as proposals to expand the ICD-10
(International Classification of Diseases, Tenth Revision) Z codes, the
alphanumeric codes used worldwide to represent diagnoses.\53\
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\51\ Centers for Medicare and Medicaid Services. Building an
Organizational Response to Health Disparities Inventory of Resources
for Standardized Demographic and Language Data Collection. 2020.
<a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf">https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf</a>.
\52\ <a href="https://pubmed.ncbi.nlm.nih.gov/18567241/">https://pubmed.ncbi.nlm.nih.gov/18567241/</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/30506674/">https://pubmed.ncbi.nlm.nih.gov/30506674/</a>, Eicheldinger C, Bonito A. More
accurate racial and ethnic codes for Medicare administrative data.
Health Care Finance Rev. 2008;29(3):27-42. Haas A, Elliott MN,
Dembosky JW, et al. Imputation of race/ethnicity to enable
measurement of HEDIS performance by race/ethnicity. Health Serv Res.
2019;54(1):13-23. doi:10.1111/1475-6773.13099.
\53\ <a href="https://aspe.hhs.gov/pdf-report/second-impact-report-to-congress">https://aspe.hhs.gov/pdf-report/second-impact-report-to-congress</a>.
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While development of sustainable and consistent programs to collect
data on social determinants of health can be considerable undertakings,
we recognize that another method to identify better race and ethnicity
data is needed in the short term to address the need for reporting on
health equity. In working with our contractors, two algorithms have
been developed to indirectly estimate the race and ethnicity of
Medicare beneficiaries (as described further in the following
paragraphs). We feel that using indirect estimation can

[[Page 42629]]

help to overcome the current limitations of demographic information and
enable timelier reporting of equity results until longer term
collaborations to improve demographic data quality across the health
care sector materialize. The use of indirectly estimated race and
ethnicity for conducting stratified reporting does not place any
additional collection or reporting burdens on facilities as these data
are derived using existing administrative and census-linked data.
Indirect estimation relies on a statistical imputation method for
inferring a missing variable or improving an imperfect administrative
variable using a related set of information that is more readily
available.\54\ Indirectly estimated data are most commonly used at the
population level (such as the facility or health plan-level), where
aggregated results form a more accurate description of the population
than existing, imperfect data sets. These methods often estimate race
and ethnicity using a combination of other data sources which are
predictive of self-identified race and ethnicity, such as language
preference, information about race and ethnicity in our administrative
records, first and last names matched to validated lists of names
correlated to specific national origin groups, and the racial and
ethnic composition of the surrounding neighborhood. Indirect estimation
has been used in other settings to support population-based equity
measurement when self-identified data are not available.\55\
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\54\ IOM. 2009. Race, Ethnicity, and Language Data:
Standardization for Health Care Quality Improvement. Washington, DC:
The National Academies Press.
\55\ IOM. 2009. Race, Ethnicity, and Language Data:
Standardization for Health Care Quality Improvement. Washington, DC:
The National Academies Press.
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As described in section IV.D.2, we have previously supported the
development of two such methods of indirect estimation of race and
ethnicity of Medicare beneficiaries. One indirect estimation approach,
developed by our contractor, uses Medicare administrative data, first
name and surname matching, derived from the U.S. Census and other
sources, with beneficiary language preference, state of residence, and
the source of the race and ethnicity code in Medicare administrative
data to reclassify some beneficiaries as Hispanic or Asian/Pacific
Islander (API).\56\ In recent years, we have also worked with another
contractor to develop a new approach, the Medicare Bayesian Improved
Surname Geocoding (MBISG), which combines Medicare administrative data,
first and surname matching, geocoded residential address linked to the
2010 U.S. Census, and uses both Bayesian updating and multinomial
logistic regression to estimate the probability of belonging to each of
six racial/ethnic groups.\57\
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\56\ Bonito AJ, 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.
\57\ Haas, A., Elliott, M. et al (2018). Imputation of race/
ethnicity to enable measurement of HEDIS performance by race/
ethnicity. Health Services Research, 54:13-23.
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The MBISG model is currently used to conduct the national,
contract-level, stratified reporting of Medicare Part C & D performance
data for Medicare Advantage Plans by race and ethnicity.\58\ Validation
testing reveals concordances with self-reported race and ethnicity of
0.96 through 0.99 for API, Black, Hispanic, and White beneficiaries for
MBISG version 2.1.\59\ The algorithms under consideration are
considerably less accurate for individuals who self-identify as
American Indian/Alaskan Native or multiracial.\60\ Indirect estimation
can be a statistically reliable approach for calculating population-
level equity results for groups of individuals (such as the facility-
level) and is not intended, nor being considered, as an approach for
inferring the race and ethnicity of an individual.
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\58\ The Office of Minority Health (2020). Racial, Ethnic, and
Gender Disparities in Health Care in Medicare Advantage, The Centers
for Medicare and Medicaid Services, (pg vii). <a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting">https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting</a>.
\59\ MBISG 2.1 validation results performed under contract #GS-
10F-0012Y/HHSM-500-2016-00097G). Pending public release of the 2021
Part C and D Performance Data Stratified by Race, Ethnicity, and
Gender Report, available at: <a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting">https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting</a>.
\60\ Haas, A., Elliott, M. et al (2018). Imputation of race/
ethnicity to enable measurement of HEDIS performance by race/
ethnicity. Health Services Research, 54:13-23 and Bonito AJ, 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.
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However, despite the high degree of statistical accuracy of the
indirect estimation algorithms under consideration there remains the
small risk of unintentionally introducing bias. For example, if the
indirect estimation is not as accurate in correctly estimating race and
ethnicity in certain geographies or populations it could lead to some
bias in the method results. Such bias might result in slight
overestimation or underestimation of the quality of care received by a
given group. We feel this amount of bias is considerably less than
would be expected if stratified reporting was conducted using the race
and ethnicity currently contained in our administrative data. Indirect
estimation of race and ethnicity is envisioned as an intermediate step,
filling the pressing need for more accurate demographic information for
the purposes of exploring inequities in service delivery, while
allowing newer approaches, as described in the next section, for
improving demographic data collection to progress. We expressed
interest in learning more about, and solicited comments about, the
potential benefits and challenges associated with measuring facility
equity using an imputation algorithm to enhance existing administrative
data quality for race and ethnicity until self-reported information is
sufficiently available.
c. Improving Demographic Data Collection
Stratified facility-level reporting using dual eligibility and
indirectly estimated race and ethnicity would represent an important
advance in our ability to provide equity reports to facilities.
However, self-reported race and ethnicity data remain the gold standard
for classifying an individual according to race or ethnicity. The CMS
Quality Strategy outlines our commitment to strengthening
infrastructure and data systems by ensuring that standardized
demographic information is collected to identify disparities in health
care delivery outcomes.\61\ Collection and sharing of a standardized
set of social, psychological, and behavioral data by facilities,
including race and ethnicity, using electronic data definitions which
permit nationwide, interoperable health information exchange, can
significantly enhance the accuracy and robustness of our equity
reporting.\62\ This could potentially include expansion to

[[Page 42630]]

additional social risk factors, such as disability status, where
accuracy of administrative data is currently limited. We are mindful
that additional resources, including data collection and staff training
may be necessary to ensure that conditions are created whereby all
patients are comfortable answering all demographic questions, and that
individual preferences for non-response are maintained.
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\61\ The Centers for Medicare & Medicaid Services. CMS Quality
Strategy. 2016. <a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf</a>.
\62\ The Office of the National Coordinator for Health
Information Technology. United State Core Data for Interoperability
Draft Version 2. 2021. <a href="https://www.healthit.gov/isa/sites/isa/files/2021-01/Draft-USCDI-Version-2-January-2021-Final.pdf">https://www.healthit.gov/isa/sites/isa/files/2021-01/Draft-USCDI-Version-2-January-2021-Final.pdf</a>.
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We are also interested in learning about and solicited comments on
current data collection practices by facilities to capture demographic
data elements (such as race, ethnicity, sex, sexual orientation and
gender identity (SOGI), primary language, and disability status).
Further, we are interested in potential challenges facing facility
collection, at the time of admission, of a minimum set of demographic
data elements in alignment with national data collection standards
(such as the standards finalized by the Affordable Care Act) \63\ and
standards for interoperable exchange (such as the U.S. Core Data for
Interoperability incorporated into certified health IT products as part
of the 2015 Edition of health IT certification criteria).\64\ Advancing
data interoperability through collection of a minimum set of
demographic data collection, and incorporation of this demographic
information into quality measure specifications, has the potential for
improving the robustness of the disparity method results, potentially
permitting reporting using more accurate, self-reported information,
such as race and ethnicity, and expanding reporting to additional
dimensions of equity, including stratified reporting by disability
status.
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\63\ <a href="https://minorityhealth.hhs.gov/assets/pdf/checked/1/Fact_Sheet_Section_4302.pdf">https://minorityhealth.hhs.gov/assets/pdf/checked/1/Fact_Sheet_Section_4302.pdf</a>.
\64\ <a href="https://www.healthit.gov/sites/default/files/2020-08/2015EdCures_Update_CCG_USCDI.pdf">https://www.healthit.gov/sites/default/files/2020-08/2015EdCures_Update_CCG_USCDI.pdf</a>.
---------------------------------------------------------------------------

d. Potential Creation of a Facility Equity Score To Synthesize Results
Across Multiple Social Risk Factors
As we describe in section IV.D.3.a of this final rule, we are
considering expanding the disparity methods to IPFs and to include two
social risk factors (dual eligibility and race/ethnicity). This
approach would improve the comprehensiveness of health equity
information provided to facilities. Aggregated results from multiple
measures and multiple social risk factors, from the CMS Disparity
Methods, in the format of a summary score, can improve the usefulness
of the equity results. In working with our contractors, we recently
developed an equity summary score for Medicare Advantage contract/
plans, the Health Equity Summary Score (HESS), with application to
stratified reporting using two social risk factors: Dual eligibility
and race and as described in Incentivizing Excellent Care to At-Risk
Groups with a Health Equity Summary Score.\65\
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\65\ Agniel D, Martino SC, Burkhart Q, et al. Incentivizing
Excellent Care to At-Risk Groups with a Health Equity Summary Score.
J Gen Intern Med. Published online November 11, 2019 Nov 11. doi:
10.1007/s11606-019-05473-x.
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The HESS calculates standardized and combined performance scores
blended across the two social risk factors. The HESS also combines
results of the within-plan (similar to the Within-Facility method) and
across-plan method (similar to the Across-Facility method) across
multiple performance measures.
We are considering building a ``Facility Equity Score,'' not yet
developed, which would be modeled off the HESS but adapted to the
context of risk-adjusted facility outcome measures and potentially
other IPF quality measures. We envision that the Facility Equity Score
would synthesize results for a range of measures and using multiple
social risk factors, using measures and social risk factors, which
would be reported to facilities as part of the CMS Disparity Methods.
We believe that creation of the Facility Equity Score has the potential
to supplement the overall measure data already reported on the Care
Compare or successor website, by providing easy to interpret
information regarding disparities measured within individual facilities
and across facilities nationally. A summary score would decrease burden
by minimizing the number of measure results provided and providing an
overall indicator of equity.
The Facility Equity Score under consideration would potentially:
<bullet> Summarize facility performance across multiple social
determinants of health (initially dual eligibility and indirectly
estimated race and ethnicity); and
<bullet> Summarize facility performance across the two disparity
methods (that is, the Within-Facility Disparity Method and the Across-
Facility Disparity Method) and potentially for multiple measures.
Prior to any future public reporting, if we determine that a
Facility Equity Score can be feasibly and accurately calculated, we
would provide results of the Facility Equity Score, in confidential
facility specific reports, which facilities and their QIN-QIOs would be
able to download. Any potential future proposal to display the Facility
Equity Score on the Care Compare or successor website would be made
through future RFI or rulemaking.
c. Solicitation of Public Comment
We solicited public comments on the possibility of stratifying
IPFQR Program measures by dual eligibility and race and ethnicity. We
also solicited public comments on mechanisms of incorporating co-
occurring disability status into such stratification as well. We sought
public comments on the application of the within-facility or across-
facility disparities methods IPFQR Program measures if we were to
stratify IPFQR Program measures. We also solicited comment on the
possibility of facility collection of standardized demographic
information for the purposes of potential future quality reporting and
measure stratification. In addition, we solicited public comments on
the potential design of a facility equity score for calculating results
across multiple social risk factors and measures, including race and
disability. Any data pertaining to these areas that are recommended for
collection for measure reporting for a CMS program and any potential
public disclosure on Care Compare or successor website would be
addressed through a separate and future notice- and-comment rulemaking.
We plan to continue working with ASPE, facilities, the public, and
other key stakeholders on this important issue to identify policy
solutions that achieve the goals of attaining health equity for all
patients and minimizing unintended consequences. We also noted our
intention for additional RFIs or rulemaking on this topic in the
future.
Specifically, we solicited public comment on the following:
Future Potential Stratification of Quality Measure Results
<bullet> The possible stratification of facility-specific reports
for IPFQR program measure data by dual-eligibility status given that
over half of the patient population in IPFs are dually eligible,
including, which measures would be most appropriate for stratification;
<bullet> The potential future application of indirect estimation of
race and ethnicity to permit stratification of measure data for
reporting facility-level disparity results until more accurate forms of
self-identified demographic information are available;
<bullet> Appropriate privacy safeguards with respect to data
produced from the indirect estimation of race and ethnicity to ensure
that such data are properly

[[Page 42631]]

identified if/when they are shared with providers;
<bullet> Ways to address the challenges of defining and collecting
accurate and standardized self-identified demographic information,
including information on race and ethnicity and disability, for the
purposes of reporting, measure stratification and other data collection
efforts relating to quality.
<bullet> Recommendations for other types of readily available data
elements for measuring disadvantage and discrimination for the purposes
of reporting, measure stratification and other data collection efforts
relating to quality, in addition, or in combination with race and
ethnicity.
<bullet> Recommendations for types of quality measures or
measurement domains to prioritize for stratified reporting by dual
eligibility, race and ethnicity, and disability.
<bullet> Examples of approaches, methods, research, and
considerations or any combination of these for use of data-driven
technologies that do not facilitate exacerbation of health inequities,
recognizing that biases may occur in methodology or be encoded in
datasets.
We received comments on these topics.
Comments: Many commenters expressed support for the collection of
data to support stratifying or otherwise measuring disparities in care
related to dual-eligibility, race and ethnicity, and disability. Some
commenters specifically supported the confidential reporting of
stratified results to facilities. Several commenters urged CMS to
expand data collection and measure stratification to include factors
such as language preference, veteran status, health literacy, gender
identity, and sexual orientation to provide a more comprehensive
assessment of health equity. One commenter urged CMS to collect data on
race and ethnicity specifically for patients suffering from psychiatric
disorders, while another noted that for the IPF patient population risk
factors, such as substance abuse, may be of more importance. One
commenter also provided examples of how their health system has
successfully collected and begun to analyze patient-level demographic
data. Another commenter referred to an existing effort by the National
Committee for Quality Assurance to improve the collection of race and
ethnicity data as a possible model for improving data collection. This
commenter also supported the use of indirect estimation of race and
ethnicity for Medicare beneficiaries, noting some concern about the
lack of granularity, especially with respect to Native American and
Asian populations. One commenter urged CMS to explore how to best
identify social determinants of health using current claims data.
While many commenters expressed support for stratification of
claims-based measures, many commenters expressed concern that the
existing chart-abstracted measures would face limitations when
stratified and thus felt the burden of collecting stratification data
for these measures significantly outweighed any potential benefit of
doing so. Specifically, commenters noted that stratifying the IPF
patient population is more vulnerable to statistical concerns during
the stratification process than other patient populations (for example,
numbers of patients in one or more strata may be insufficient for
reliable sampling and calculations) due to low patient volume in some
facilities. One commenter suggested that for this and other reasons CMS
should develop disparities reporting specifically for the IPF program
rather than adopt an approach developed for a different program. A few
commenters also questioned the value of stratification of these
measures given the current high levels of performance by many IPFs.
One commenter noted that stratified claims-based measures would
exclude all privately insured care and thus be less useful. Several
commenters stated that interoperability issues such as a lack of EHRs,
particularly for IPFs that are smaller or not part of a large hospital
or health system, further add to the burden of stratifying chart-
abstracted measures and may contribute to bias in the data.
Several commenters also noted that stratification may be
challenging due to differences in the patient population served by IPFs
compared to other Medicare programs such as acute and long-term care
hospitals, for example, age, proportion and reason for dual-eligibility
(income versus disability), and substance abuse disorder prevalence.
However, several commenters noted many of these same characteristics,
as well as the mental and behavioral health needs of patients cared for
by IPFs, are evidence of the need to improve data collection and
measurement in IPFs. A commenter also recommended further analysis on
the predictive power of social risk factors on mental and behavioral
health patient outcomes compared to that of the diagnosis requiring
treatment. Several commenters recommended CMS further address issues
related to the potential stratification of data such as: Patient
privacy and the collection and sharing of social risk factors from
patient records or through indirect estimation, differing requirements
for collection of race and ethnicity data, transparency regarding
indirect estimation methods, and differing Medicaid eligibility
requirements by state. One commenter related these concerns to public
reporting, suggesting support for confidential reporting until these
issues are addressed.
We appreciate all of the comments and interest in this topic. We
believe that this input is very valuable in the continuing development
of the CMS health equity quality measurement efforts. We will continue
to take all concerns, comments, and suggestions into account for future
development and expansion of our health equity quality measurement
efforts.
Improving Demographic Data Collection
<bullet> Experiences of users of certified health IT regarding
local adoption of practices for collection of social, psychological,
and behavioral data elements, the perceived value of using these data
for improving decision-making and care delivery, and the potential
challenges and benefits of collecting more granular, structured
demographic information, such as the ``Race & Ethnicity--CDC'' code
system.
<bullet> The possible collection of a minimum set of social,
psychological, and behavioral data elements by hospitals at the time of
admission using structured, interoperable data standards, for the
purposes of reporting, measure stratification and other data collection
efforts relating to quality.
We received comments on these topics.
Comments: We received mixed feedback regarding demographic data
collection. Many commenters supported the need for and use of such
data, noting that structured, interoperable electronic health data are
the gold standard. They also noted that many barriers exist to adopting
electronic health information technology systems necessary for capture
of these data, particularly in freestanding psychiatric facilities. A
commenter stated that the commenter's organization cannot support
demographic data collection due to the workload burden it would place
on both the IPF and patients and their families. This commenter also
noted that the likelihood of patients and families comfortably
answering multiple sensitive demographic questions is low, especially
upon admission. Another commenter expressed concerns with the current
capabilities of the industry to collect these data, specifying a lack
of standardization in screening and data collection and need for staff
training.

[[Page 42632]]

Multiple commenters expressed concern about the patient and family's
perception of the organization if given a data collection questionnaire
upon admission, noting that they may think the organization is more
focused on data collection rather than care.
Other commenters noted the importance of closing the health equity
gap through measurement of demographic characteristics. A commenter
suggested that agencies leverage the role of nurses in identifying
sociodemographic factors and barriers to health equity. Another
commenter supported this method, noting that although this may add
another step to data collection processes, it would be valuable in
addressing health equity gaps. To reduce possible workload burden on
organizations that are new to this process, a commenter recommended a
staggered approach to data collection, suggesting CMS require providers
and facilities to collect data on age and sex by the end of 2022, race
and ethnicity by the end of 2023, etc., with the goal of at least 80
percent data completeness with 80 percent accuracy. In addition,
commenters suggested reducing burden by adopting standardized screening
tools to collect this information, such as ICD-Z-codes, which in
practice would allow patients to be referred to resources and
initiatives when appropriate. Several commenters encouraged collection
of comprehensive social determinants of health and demographic
information in addition to race and ethnicity, such as disability,
sexual orientation, and primary language. Several commenters provided
feedback on the potential use of an indirect estimation algorithm when
race and ethnicity are missing/incorrect, and emphasized the
sensitivity of demographic information and recommended that CMS use
caution when using estimates from the algorithm, including assessing
for potential bias, reporting the results of indirect estimation
alongside direct self-report at the organizational level for
comparison, and establishing a timeline to transition to entirely
directly collected data. Commenters also advised that CMS be
transparent with beneficiaries and explain why data are being collected
and the plans to use these data. A commenter noted that information
technology infrastructure should be established in advance to ensure
that this information is being used and exchanged appropriately.
We appreciate all of the comments and interest in this topic. We
believe that this input is very valuable in the continuing development
of the CMS health equity quality measurement efforts. We will continue
to take all concerns, comments, and suggestions into account for future
development and expansion of our health equity quality measurement
efforts.
Potential Creation of a Facility Equity Score To Synthesize Results
Across Multiple Social Risk Factors
<bullet> The possible creation and confidential reporting of a
Facility Equity Score to synthesize results across multiple social risk
factors and disparity measures.
<bullet> Interventions facilities could institute to improve a low
facility equity score and how improved demographic data could assist
with these efforts.
We received comments on these topics.
Comments: Commenters generally supported ongoing thoughtful
investigation into best practices for measuring health equity.
Many commenters expressed concerns about the potential Facility
Equity Score. Commenters argued that the current approach used to
generate the composite score may not lead to aggregate results, which
would not be actionable for many facilities. Commenters also raised
concerns about risk adjustment, limitations in stratification
variables, and the appropriateness of the current measure set. A
commenter noted that although they support thoughtful efforts to
categorize performance, the HESS has been established only as a ``proof
of concept'' and will require considerable time and resources to
produce a valid and actionable measure. The same commenter also noted
that HESS scoring was only feasible for less than one-half of Medicare
Advantage (MA) plans and as such, may not be practical for many smaller
facilities, or facilities whose enrolled populations differ in social
risk factor distribution patterns compared to typical MA plans.
Commenters generally did not support use of the Facility Equity
Score in public reporting or payment incentive programs, suggesting
that it is imperative to first understand any unintended consequences
prior to implementation. More specifically, several commenters gave the
example of facilities failing to raise the quality of care for at-risk
patients while appearing to achieve greater equity due to lower quality
of care for patients that are not at risk. A commenter stated the
belief that CMS should begin their initiative to improve health equity
by using structural health equity measures. Commenters also raised
concerns about use of dual-eligibility as a social risk factor due to
variations in state-level eligibility for Medicaid, making national
comparisons, or benchmarking of facility scores unreliable.
Additionally, commenters who expressed data reliability concerns
recommended that CMS focus its resources on improving standardized data
collection and reporting procedures for sociodemographic data before
moving forward with a Facility Equity Score.
We appreciate all of the comments and interest in this topic. We
believe that this input is very valuable in the continuing development
of the CMS health equity quality measurement efforts. We will continue
to take all concerns, comments, and suggestions into account for future
development and expansion of our health equity quality measurement
efforts.
We also received comments on the general topic of health equity in
the IPFQR Program.
Comments: Many commenters expressed overall support of CMS' goals
to advance health equity. There were some comments regarding the need
to further extend and specify the definition of equity provided in the
proposed rule. Commenters also noted that equity initiatives should be
based on existing disparities and population health goals, be mindful
of the needs of the communities served, and work to bridge hospitals
with post-acute and community-based providers. Several commenters
encouraged CMS to be mindful about whether collection of additional
quality measures and standardized patient assessment elements might
increase provider burden. Several commenters noted support for
consideration of a measure of organizational commitment to health
equity, outlining how infrastructure supports delivery of equitable
care. A commenter noted the importance of focusing programming on
inequities in vaccine-preventable illness. Another commenter noted that
CMS may expand their view of equity beyond quality reporting to payment
and coverage policies.
We appreciate all of the comments and interest in this topic. We
believe that this input is very valuable in the continuing development
of the CMS health equity quality measurement efforts. We will continue
to take all concerns, comments, and suggestions into account for future
development and expansion of our health equity quality measurement
efforts.

E. Measure Adoption

We strive to put consumers and caregivers first, ensuring they are
empowered to make decisions about their own healthcare along with their

[[Page 42633]]

clinicians using information from data-driven insights that are
increasingly aligned with meaningful quality measures. We support
technology that reduces burden and allows clinicians to focus on
providing high-quality healthcare for their patients. We also support
innovative approaches to improve quality, accessibility, and
affordability of care while paying particular attention to improving
clinicians' and beneficiaries' experiences when interacting with our
programs. In combination with other efforts across the Department of
Health and Human Services (HHS), we believe the IPFQR Program helps to
incentivize facilities to improve healthcare quality and value while
giving patients and providers the tools and information needed to make
the best decisions for them. Consistent with these goals, our objective
in selecting quality measures is to balance the need for information on
the full spectrum of care delivery and the need to minimize the burden
of data collection and reporting. We have primarily focused on measures
that evaluate critical processes of care that have significant impact
on patient outcomes and support CMS and HHS priorities for improved
quality and efficiency of care provided by IPFs. When possible, we also
propose to incorporate measures that directly evaluate patient outcomes
and experience. We refer readers to section VIII.F.4.a. of the FY 2013
IPPS/LTCH PPS final rule (77 FR 53645 through 53646) for a detailed
discussion of the considerations taken into account in selecting
quality measures.
1. Measure Selection Process
Before being proposed for inclusion in the IPFQR Program, measures
are placed on a list of measures under consideration (MUC), which is
published annually on behalf of CMS by the National Quality Forum
(NQF). Following publication on the MUC list, the Measure Applications
Partnership (MAP), a multi-stakeholder group convened by the NQF,
reviews the measures under consideration for the IPFQR Program, among
other Federal programs, and provides input on those measures to the
Secretary. We consider the input and recommendations provided by the
MAP in selecting all measures for the IPFQR Program. In our evaluation
of the IPFQR Program measure set, we identified two measures that we
believe are appropriate for the IPFQR Program.
2. COVID-19 Vaccination Coverage Among Health Care Personnel (HCP)
66 Measure for the FY 2023 Payment Determination and
Subsequent Years
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\66\ This measure was previously titled, ``SARS-CoV-2
Vaccination Coverage among Healthcare Personnel.''
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a. Background
On January 31, 2020, the Secretary declared a PHE for the U.S. in
response to the global outbreak of SARS-CoV-2, a novel (new)
coronavirus that causes a disease named ``coronavirus disease 2019''
(COVID-19).\67\ COVID-19 is a contagious respiratory illness \68\ that
can cause serious illness and death. Older individuals and those with
underlying medical conditions are considered to be at higher risk for
more serious complications from COVID-19.\69\
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\67\ U.S. Dept of Health and Human Services, Office of the
Assistant Secretary for Preparedness and Response. (2020).
Determination that a Public Health Emergency Exists. Available at:
<a href="https://www.phe.gov/emergency/news/healthactions/phe/Pages/2019-nCoV.aspx">https://www.phe.gov/emergency/news/healthactions/phe/Pages/2019-nCoV.aspx</a>.
\68\ Centers for Disease Control and Prevention. (2020). Your
Health: Symptoms of Coronavirus. Available at: <a href="https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html">https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html</a>.
\69\ Centers for Disease Control and Prevention. (2020). Your
Health: Symptoms of Coronavirus. Available at <a href="https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html">https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html</a>.
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As of April 2, 2021, the U.S. had reported over 30 million cases of
COVID-19 and over 550,000 COVID-19 deaths.\70\ Hospitals and health
systems saw significant surges of COVID-19 patients as community
infection levels increased.\71\ From December 2, 2020 through January
30, 2021, more than 100,000 Americans were in the hospital with COVID-
19 at the same time.\72\
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\70\ Centers for Disease Control and Prevention. (2020). CDC
COVID Data Tracker. Accessed on April 3, 2021 at: <a href="https://covid.cdc.gov/covid-data-tracker/#cases_casesper100klast7days">https://covid.cdc.gov/covid-data-tracker/#cases_casesper100klast7days</a>.
\71\ Associated Press. Tired to the Bone. Hospitals Overwhelmed
with Virus Cases. November 18, 2020. Accessed on December 16, 2020,
at <a href="https://apnews.com/article/hospitals-overwhelmed-coronavirus-cases-74a1f0dc3634917a5dc13408455cd895">https://apnews.com/article/hospit

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