[Federal Register Volume 85, Number 106 (Tuesday, June 2, 2020)]
[Proposed Rules]
[Pages 33595-33617]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2020-11703]


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DEPARTMENT OF HEALTH AND HUMAN SERVICES

45 CFR Part 153

[CMS-9913-P]
RIN 0938-AU23


Amendments to the HHS-Operated Risk Adjustment Data Validation 
Under the Patient Protection and Affordable Care Act's HHS-Operated 
Risk Adjustment Program

AGENCY: Centers for Medicare & Medicaid Services (CMS), Department of 
Health and Human Services (HHS).

ACTION: Proposed rule.

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SUMMARY: This rule proposes to adopt certain changes to the risk 
adjustment data validation error estimation methodology starting with 
the 2019 benefit year and beyond for states where the Department of 
Health and Human Services (HHS) operates the risk adjustment program. 
The Patient Protection and Affordable Care Act (PPACA) established a 
permanent risk adjustment program under which payments are made to 
health insurance issuers that attract higher-than-average risk 
populations funded by payments from health insurance issuers that 
attract lower-than-average risk populations. To ensure the integrity of 
the HHS-operated risk adjustment program, CMS, on behalf of HHS, 
performs risk adjustment data validation, also known as HHS-RADV, to 
validate the accuracy of data submitted by issuers for the purposes of 
risk adjustment transfer calculations. Based on lessons learned from 
the first payment year of HHS-RADV, this rule proposes changes to the 
HHS-RADV error estimation methodology, which is used to calculate 
adjusted risk scores and risk adjustment transfers, beginning with the 
2019 benefit year of HHS-RADV. This rule also proposes to change the 
benefit year to which HHS-RADV adjustments to risk scores and risk 
adjustment transfers would be applied starting with 2021 benefit year 
HHS-RADV. These proposals seek to further the integrity of the HHS-RADV 
program, address stakeholder feedback, promote fairness, and improve 
the predictability of HHS-RADV adjustments.

DATES: To be assured consideration, comments must be received at one of 
the addresses provided below, no later than 5 p.m. on July 2, 2020.

ADDRESSES: In commenting, please refer to file code CMS-9913-P. Because 
of staff and resource limitations, we cannot accept comments by 
facsimile (FAX) transmission.
    Comments, including mass comment submissions, must be submitted in 
one of the following three ways (please choose only one of the ways 
listed):
    1. Electronically. You may submit electronic comments on this 
regulation to http://www.regulations.gov. Follow the ``Submit a 
comment'' instructions.
    2. By regular mail. You may mail written comments to the following 
address ONLY: Centers for Medicare & Medicaid Services, Department of 
Health and Human Services, Attention: CMS-9913-P, P.O. Box 8010, 
Baltimore, MD 21244-8010.
    Please allow sufficient time for mailed comments to be received 
before the close of the comment period.
    3. By express or overnight mail. You may send written comments to 
the following address ONLY: Centers for Medicare & Medicaid Services, 
Department of Health and Human Services, Attention: CMS-9913-P, Mail 
Stop C4-26-05, 7500 Security Boulevard, Baltimore, MD 21244-1850.
    For information on viewing public comments, see the beginning of 
the SUPPLEMENTARY INFORMATION section.

FOR FURTHER INFORMATION CONTACT: Allison Yadsko, (410) 786-1740; Joshua 
Paul, (301) 492-4347; Adrianne Patterson, (410) 786-0686; and Jaya 
Ghildiyal, (301) 492-5149.

SUPPLEMENTARY INFORMATION: 
    Inspection of Public Comments: All comments received before the 
close of the comment period are available for viewing by the public, 
including any personally identifiable or confidential business 
information that is included in a comment. We post all comments 
received before the close of the comment period on the following 
website as soon as possible after they have been received: http://www.regulations.gov. Follow the search instructions on that website to 
view public comments.

I. Background

A. Legislative and Regulatory Overview

    The Patient Protection and Affordable Care Act (Pub. L. 111-148) 
was enacted on March 23, 2010; the Health Care and Education 
Reconciliation Act of 2010 (Pub. L. 111-152) was enacted on March 30, 
2010. These statutes are collectively referred to as ``PPACA'' in this 
proposed rule. Section 1343 of the PPACA \1\ established a permanent 
risk adjustment program to provide payments to health insurance issuers 
that attract higher-than-average risk populations, such as those with 
chronic conditions, funded by payments from those that attract lower-
than-average risk populations, thereby reducing incentives for issuers 
to avoid higher-risk enrollees. The PPACA directs the Secretary, in 
consultation with the states, to establish criteria and methods to be 
used in carrying out risk adjustment activities, such as determining 
the actuarial risk of enrollees in risk adjustment covered plans within 
a state market risk pool.\2\ The statute also provides that the 
Secretary may utilize criteria and methods similar to the ones utilized

[[Page 33596]]

under Medicare Parts C or D.\3\ Consistent with section 1321(c)(1) of 
the PPACA, the Secretary is responsible for operating the risk 
adjustment program on behalf of any state that elected not to do so. 
For the 2014-2016 benefit years, all states and the District of 
Columbia, except Massachusetts, participated in the HHS-operated risk 
adjustment program. Since the 2017 benefit year, all states and the 
District of Columbia have participated in the HHS-operated risk 
adjustment program.
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    \1\ 42 U.S.C. 18063.
    \2\ 42 U.S.C. 18063(a) and (b).
    \3\ 42 U.S.C. 18063(b).
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    Data submission requirements for the HHS-operated risk adjustment 
program are set forth at 45 CFR 153.700 through 153.740. Each issuer is 
required to establish and maintain an External Data Gathering 
Environment (EDGE) server on which the issuer submits masked enrollee 
demographics, claims, and encounter diagnosis-level data in a format 
specified by HHS. Issuers must also execute software provided by HHS on 
their respective EDGE servers to generate summary reports, which HHS 
uses to calculate the enrollee-level risk score to determine the 
average plan liability risk scores for each state market risk pool, the 
individual issuers' plan liability risk scores, and the transfer 
amounts by state market risk pool for the applicable benefit year.
    Pursuant to 45 CFR 153.350, HHS performs risk adjustment data 
validation (also known as HHS-RADV) to validate the accuracy of data 
submitted by issuers for the purposes of risk adjustment transfer 
calculations for states where HHS operates the risk adjustment program. 
This process establishes uniform audit standards to ensure that 
actuarial risk is accurately and consistently measured, thereby 
strengthening the integrity of the risk adjustment program.\4\ HHS-RADV 
also ensures that issuers' actual actuarial risk is reflected in risk 
adjustment transfers and that the HHS-operated program assesses charges 
to issuers with plans with lower-than-average actuarial risk while 
making payments to issuers with plans with higher-than-average 
actuarial risk. Pursuant to 45 CFR 153.350(a), HHS, in states where it 
operates the program, must ensure proper validation of a statistically 
valid sample of risk adjustment data from each issuer that offers at 
least one risk adjustment covered plan \5\ in that state. Under 45 CFR 
153.350, HHS, in states where it operates the program, may adjust the 
plan average actuarial risk for a risk adjustment covered plan based on 
discrepancies discovered as a result of HHS-RADV and use those adjusted 
risk scores to modify charges and payments to all risk adjustment 
covered plan issuers in the same state market risk pool.
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    \4\ HHS also has general authority to audit issuers of risk 
adjustment covered plans pursuant to 45 CFR 153.620(c).
    \5\ See 45 CFR 153.20 for the definition of ``risk adjustment 
covered plan.''
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    For the HHS-operated risk adjustment program, 45 CFR 153.630 
requires an issuer of a risk adjustment covered plan to have an initial 
and second validation audit performed on its risk adjustment data for 
the applicable benefit year. Each issuer must engage one or more 
independent auditors to perform the initial validation audit of a 
sample of risk adjustment data selected by HHS.\6\ After the initial 
validation audit entity has validated the HHS-selected sample, a 
subsample is validated in a second validation audit.\7\ The second 
validation audit is conducted by an entity HHS retains to verify the 
accuracy of the findings of the initial validation audits.
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    \6\ 45 CFR 153.630(b).
    \7\ 45 CFR 153.630(c).
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    HHS conducted two pilot years of HHS-RADV for the 2015 and 2016 
benefit years \8\ to give HHS and issuers experience with HHS-RADV 
prior to applying HHS-RADV findings to adjust issuers' risk scores, as 
well as the risk adjustment transfers in the applicable state market 
risk pool(s). The 2017 benefit year HHS-RADV was the first non-pilot 
year that resulted in adjustments to issuers' risk scores and the risk 
adjustment transfers in the applicable state market risk pool(s) as a 
result of HHS-RADV findings.9 10
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    \8\ HHS-RADV was not conducted for the 2014 benefit year. See 
FAQ ID 11290a (March 7, 2016), available at: https://www.regtap.info/faq_viewu.php?id=11290.
    \9\ The Summary Report of 2017 Benefit Year HHS-RADV Adjustments 
to Risk Adjustment Transfers released on August 1, 2019 is available 
at: https://www.cms.gov/CCIIO/Programs-and-Initiatives/Premium-Stabilization-Programs/Downloads/BY2017-HHSRADV-Adjustments-to-RA-Transfers-Summary-Report.pdf.
    \10\ The one exception is for Massachusetts issuers, who were 
not able to participate in prior HHS-RADV pilot years because the 
state operated risk adjustment for the 2014-2016 benefit years. 
Therefore, HHS made the 2017 benefit year HHS-RADV a pilot year for 
Massachusetts issuers. See 84 FR 17454 at 17508.
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    When initially developing the HHS-RADV process, HHS sought the 
input of issuers, consumer advocates, providers, and other 
stakeholders, and issued the ``Affordable Care Act HHS-Operated Risk 
Adjustment Data Validation Process White Paper'' on June 22, 2013 (the 
2013 RADV White Paper).\11\ The 2013 RADV White Paper discussed and 
sought comment on a number of potential considerations for the 
development and operation of the HHS-RADV program. Based on the 
feedback received, HHS promulgated regulations to implement HHS-RADV 
that we have modified in certain respects based on experience and 
public comments, as follows.
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    \11\ A copy of the Affordable Care Act HHS-Operated Risk 
Adjustment Data Validation Process White Paper (June 22, 2013) is 
available at: https://www.regtap.info/uploads/library/ACA_HHS_OperatedRADVWhitePaper_062213_5CR_050718.pdf.
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    In the July 15, 2011 Federal Register (76 FR 41929), we published a 
proposed rule outlining the framework for the risk adjustment program, 
including standards related to HHS-RADV. We implemented the risk 
adjustment program and adopted standards related to HHS-RADV in a final 
rule, published in the March 23, 2012 Federal Register (77 FR 17219) 
(Premium Stabilization Rule). The HHS-RADV regulations adopted in the 
Premium Stabilization Rule provide for adjustments to risk scores and 
risk adjustment transfers to reflect HHS-RADV errors, including the 
two-sided nature of such adjustments.
    In the December 7, 2012 Federal Register (77 FR 73117), we 
published a proposed rule outlining benefit and payment parameters 
related to the risk adjustment program, including six steps for error 
estimation for HHS-RADV in 45 CFR 153.630 (proposed 2014 Payment 
Notice). We published the 2014 Payment Notice final rule in the March 
11, 2013 Federal Register (78 FR 15436). In addition to finalizing 45 
CFR 153.630, this final rule further clarified HHS-RADV policies, 
including that adjustments would occur when an issuer under-reported 
its risk scores.
    In the December 2, 2013 Federal Register (78 FR 72321), we 
published a proposed rule outlining the benefit and payment parameters 
related to the risk adjustment program (proposed 2015 Payment Notice). 
This rule also included several HHS-RADV proposals. We published the 
2015 Payment Notice final rule, which finalized HHS-RADV requirements 
related to sampling; initial validation audit standards, second 
validation audit processes, and medical record review as the basis of 
enrollee risk score validation; the error estimation process and 
original methodology; and HHS-RADV appeals, oversight, and data 
security standards in the March 11, 2014 Federal Register (79 FR 
13743). Under the original methodology adopted in that final rule, 
almost every failure to validate an Hierarchical Condition Category 
(HCC) during HHS-RADV would have resulted in an adjustment to the 
issuer's risk score and an accompanying adjustment to all transfers in 
the applicable state market risk pool.

[[Page 33597]]

    In the September 6, 2016 Federal Register (81 FR 61455), we 
published a proposed rule outlining benefit and payment parameters 
related to the risk adjustment program (proposed 2018 Payment Notice) 
that included proposals related to HHS-RADV. We published the 2018 
Payment Notice final rule in the December 22, 2016 Federal Register (81 
FR 94058), which included finalizing proposals related to HHS-RADV 
discrepancy reporting, clarifications related to certain aspects of the 
HHS-RADV appeals process, and a materiality threshold for HHS-RADV to 
ease the burden of the annual audit requirements for smaller issuers. 
Under the materiality threshold, issuers with total annual premiums at 
or below $15 million are not subject to annual initial validation audit 
requirements, but would be subject to such audits approximately every 3 
years (barring risk-based triggers that would warrant more frequent 
audits).
    In the November 2, 2017 Federal Register (82 FR 51042), we 
published a proposed rule outlining benefit and payment parameters 
related to the risk adjustment program (proposed 2019 Payment Notice) 
that included proposed provisions related to HHS-RADV. We published the 
2019 Payment Notice final rule in the April 17, 2018 Federal Register 
(83 FR 16930), which included finalizing for 2017 benefit year HHS-RADV 
and beyond, an amended error estimation methodology to only calculate 
and adjust issuers' risk scores when an issuer's failure rate is 
statistically significantly different from other issuers based on three 
HCC groupings (low, medium, and high), that is, when an issuer is 
identified as an outlier. We also finalized an exemption for issuers 
with 500 or fewer billable member months from HHS-RADV; a requirement 
that initial validation audit samples only include enrollees from state 
market risk pools with more than one issuer; clarifications regarding 
civil money penalties for non-compliance with HHS-RADV; and a process 
to handle demographic or enrollment errors discovered during HHS-RADV. 
We finalized an exception to the prospective application of HHS-RADV 
results for exiting issuers,\12\ such that exiting outlier issuers' 
results are used to adjust the benefit year being audited (rather than 
the following transfer year).
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    \12\ To be an exiting issuer, the issuer has to exit all of the 
market risk pools in the state (that is, not sell or offer any new 
plans in the state). If an issuer only exits some market risk pools 
in the state, but continues to sell or offer plans in others, it is 
not an exiting issuer. A small group issuer with off-calendar year 
coverage, who exits the small group market risk pool in a state and 
only has small group carry-over coverage that ends in the next 
benefit year, and is not otherwise selling or offering new plans in 
any market risk pools in the state, would be an exiting issuer. See 
83 FR 16965 through 16966 and 84 FR 17503. The exiting issuer 
exception is discussed in Section II.B.
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    In the July 30, 2018 Federal Register (83 FR 36456), we published a 
final rule that adopted the 2017 benefit year HHS-operated risk 
adjustment methodology set forth in the final rules published in the 
March 23, 2012 and March 8, 2016 editions of the Federal Register (77 
FR 17220 through 17252 and 81 FR 12204 through 12352, respectively). 
This final rule set forth additional explanation of the rationale 
supporting use of statewide average premium in the HHS-operated risk 
adjustment state payment transfer formula for the 2017 benefit year, 
including why the program is operated in a budget-neutral manner. This 
final rule permitted HHS to resume 2017 benefit year program 
operations, including collection of risk adjustment charges and 
distribution of risk adjustment payments. HHS also provided guidance as 
to the operation of the HHS-operated risk adjustment program for the 
2017 benefit year in light of publication of this final rule.\13\
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    \13\ ``Update on the HHS-operated Risk Adjustment Program for 
the 2017 Benefit Year.'' July 27, 2018. Available at https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/2017-RA-Final-Rule-Resumption-RAOps.pdf.
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    In the August 10, 2018 Federal Register (83 FR 39644), we published 
a proposed rule concerning the adoption of the 2018 benefit year HHS-
operated risk adjustment methodology set forth in the final rules 
published in the March 23, 2012 and December 22, 2016 editions of the 
Federal Register (77 FR 17220 through 17252 and 81 FR 94058 through 
94183, respectively). The proposed rule set forth additional 
explanation of the rationale supporting use of statewide average 
premium in the HHS-operated risk adjustment state payment transfer 
formula for the 2018 benefit year, including why the program is 
operated in a budget-neutral manner. In the December 10, 2018 Federal 
Register (83 FR 63419), we issued a final rule adopting the 2018 
benefit year HHS-operated risk adjustment methodology as established in 
the final rules published in the March 23, 2012 and the December 22, 
2016 (77 FR 17220 through 1752 and 81 FR 94058 through 94183, 
respectively) editions of the Federal Register. This final rule 
permitted HHS to resume 2018 benefit year program operations, including 
collection of risk adjustment charges and distribution of risk 
adjustment payments.
    In the January 24, 2019 Federal Register (84 FR 227), we published 
a proposed rule outlining the benefit and payment parameters related to 
the risk adjustment program, including updates to HHS-RADV requirements 
(proposed 2020 Payment Notice). We published the 2020 Payment Notice 
final rule in the April 25, 2019 Federal Register (84 FR 17454). The 
final rule included policies related to incorporating risk adjustment 
prescription drug categories (RXCs) \14\ into HHS-RADV beginning with 
the 2018 benefit year and extending the Neyman allocation to the 10th 
stratum for HHS-RADV sampling. We also finalized using precision 
analysis to determine whether the second validation audit results of 
the full sample or the subsample (of up to 100 enrollees) results 
should be used in place of initial validation audit results when an 
issuer's initial validation audit results have insufficient agreement 
with SVA results following a pairwise means test. We clarified the 
application and distribution of default data validation charges under 
45 CFR 153.630(b)(10) and how CMS will apply error rates for exiting 
issuers and sole issuer markets. We codified the previously established 
materiality threshold and exemption for issuers with 500 or fewer 
billable member months and established a new exemption from HHS-RADV 
for issuers in liquidation who met certain conditions. In response to 
comments, in the final rule, we updated the timeline for collection, 
distribution, and reporting of HHS-RADV adjustments to transfers; 
provided that the 2017 benefit year would be a pilot year for HHS-RADV 
for Massachusetts; and established that the 2018 benefit year would be 
a pilot year for incorporating RXCs into HHS-RADV.
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    \14\ An RXC uses a drug to impute a diagnosis (or indicate the 
severity of diagnosis) otherwise indicated through medical coding in 
a hybrid diagnoses-and-drugs risk adjustment model.
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    In the February 6, 2020 Federal Register (85 FR 7088), we published 
a proposed rule outlining the benefit and payment parameters related to 
the risk adjustment program (proposed 2021 Payment Notice), including 
several HHS-RADV proposals. Among other things, in this rulemaking, we 
proposed updates to the diagnostic classifications and risk factors in 
the HHS risk adjustment models beginning with the 2021 benefit year to 
reflect more recent claims data, as well as proposed amendments to the 
outlier identification process for HHS-RADV in cases where an issuer's 
HCC count is low. We proposed that beginning with 2019 benefit year 
HHS-RADV, any issuer with fewer than 30 HCCs (diagnostic conditions) 
within an HCC failure rate

[[Page 33598]]

group would not be determined an outlier. We also proposed to make 2019 
benefit year HHS-RADV another pilot year for the incorporation of RXCs 
to allow additional time for HHS, issuers, and auditors to gain 
experience with validating RXCs. On May 14, 2020, we published the HHS 
Notice of Benefit and Payment Parameters for 2021 final rule (85 FR 
29164) (2021 Payment Notice) that finalized these HHS-RADV changes as 
proposed. The proposed updates to the diagnostic classifications and 
risk factors in the HHS risk adjustment models were also finalized with 
some modifications.
    As explained in prior notice-and-comment rulemaking,\15\ while the 
PPACA did not include an explicit requirement that the risk adjustment 
program operate in a budget-neutral manner, HHS is constrained by 
appropriations law to devise and implement its risk adjustment program 
in a budget-neutral fashion.\16\ Although the statutory provisions for 
many other PPACA programs appropriated funding, authorized amounts to 
be appropriated, or provided budget authority in advance of 
appropriations,\17\ the PPACA neither authorized nor appropriated 
additional funding for risk adjustment payments beyond the amount of 
charges paid in, and did not authorize HHS to obligate itself for risk 
adjustment payments in excess of charges collected.\18\ Indeed, unlike 
the Medicare Part D statute, which expressly authorized the 
appropriation of funds and provided budget authority in advance of 
appropriations to make Part D risk-adjusted payments, the PPACA's risk 
adjustment statute made no reference to additional appropriations.\19\ 
Congress did not give HHS discretion to implement a risk adjustment 
program that was not budget neutral. Because Congress omitted from the 
PPACA any provision appropriating independent funding or creating 
budget authority in advance of an appropriation for the risk adjustment 
program, we explained that HHS could not--absent another source of 
appropriations--have designed the program in a way that required 
payments in excess of collections consistent with binding 
appropriations law.
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    \15\ See, e.g., 78 FR 15441 and 83 FR 16930.
    \16\ Also see New Mexico Health Connections v. United States 
Department of Health and Human Services, 946 F.3d 1138 (10th Cir. 
2019).
    \17\ For examples of PPACA provisions appropriating funds, see 
PPACA secs. 1101(g)(1), 1311(a)(1), 1322(g), and 1323(c). For 
examples of PPACA provisions authorizing the appropriation of funds, 
see PPACA secs. 1002, 2705(f), 2706(e), 3013(c), 3015, 3504(b), 
3505(a)(5), 3505(b), 3506, 3509(a)(1), 3509(b), 3509(e), 3509(f), 
3509(g), 3511, 4003(a), 4003(b), 4004(j), 4101(b), 4102(a), 4102(c), 
4102(d)(1)(C), 4102(d)(4), 4201(f), 4202(a)(5), 4204(b), 4206, 
4302(a), 4304, 4305(a), 4305(c), 5101(h), 5102(e), 5103(a)(3), 5203, 
5204, 5206(b), 5207, 5208(b), 5210, 5301, 5302, 5303, 5304, 5305(a), 
5306(a), 5307(a), and 5309(b).
    \18\ See 42 U.S.C. 18063.
    \19\ Compare 42 U.S.C. 18063 (failing to specify source of 
funding other than risk adjustment charges), with 42 U.S.C. 1395w-
116(c)(3) (authorizing appropriations for Medicare Part D risk 
adjusted payments); 42 U.S.C. 1395w-115(a) (establishing ``budget 
authority in advance of appropriations Acts'' for Medicare Part D 
risk adjusted payments).
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B. Stakeholder Consultation and Input

    HHS has consulted with stakeholders on policies related to the HHS-
operated risk adjustment program and HHS-RADV. We held a series of 
stakeholder listening sessions to gather input, and received input from 
numerous interested groups, including states, health insurance issuers, 
and trade groups. We also issued a white paper for public comment on 
December 6, 2019 entitled the HHS Risk Adjustment Data Validation (HHS-
RADV) White Paper (2019 RADV White Paper).\20\ We considered comments 
received on the 2019 RADV White Paper and in connection with previous 
rules as we developed the policies in this proposed rule.
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    \20\ The 2019 RADV White Paper is available at: https://www.cms.gov/files/document/2019-hhs-risk-adjustment-data-validation-hhs-radv-white-paper.
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II. Provisions of the Proposed Regulations

    HHS conducts HHS-RADV under 45 CFR 153.630 and 153.350 in any state 
where HHS is operating risk adjustment on a state's behalf. Since the 
2017 benefit year, HHS has been operating risk adjustment and HHS-RADV 
in all 50 states and the District of Columbia. The purpose of HHS-RADV 
is to ensure issuers are providing accurate and complete risk 
adjustment data to HHS, which is crucial to the purpose and proper 
functioning of the HHS-operated risk adjustment program. HHS-RADV 
ensures that issuers' actual actuarial risk is reflected in risk 
adjustment transfers and that the HHS-operated risk adjustment program 
assesses charges to issuers with plans with lower-than-average 
actuarial risk while making payments to issuers with plans with higher-
than-average actuarial risk.
    HHS-RADV consists of an initial validation audit and a second 
validation audit. Under 45 CFR 153.630, each issuer of a risk 
adjustment covered plan must engage an independent initial validation 
auditor. The issuer provides demographic, enrollment, claims data and 
medical record documentation for a sample of enrollees selected by HHS 
to its initial validation auditor for data validation. Each issuer's 
initial validation audit is followed by a second validation audit, 
which is conducted by an entity that HHS retains to verify the accuracy 
of the findings of the initial validation audit.
    This rule proposes changes to two aspects of HHS-RADV: (A) The 
error rate calculation, and (B) the application of HHS-RADV results. 
Beginning with the 2019 benefit year of HHS-RADV,\21\ we propose to: 
(1) Modify the HCC grouping methodology used in the error rate 
calculation; (2) refine the error rate calculation in cases where an 
outlier issuer is only slightly outside of the confidence interval for 
one or more HCC groups; and (3) modify the error rate calculation in 
cases where a negative error rate outlier issuer also has a negative 
failure rate. We also propose, beginning with the 2021 benefit year of 
HHS-RADV, to transition from the current prospective application of 
HHS-RADV results \22\ to an approach that would apply HHS-RADV results 
to the benefit year being audited. We believe these proposals 
specifically address stakeholder feedback received after the first 
payment year of HHS-RADV. These proposals seek to further the integrity 
of the HHS-RADV program, while promoting fairness and improving the 
predictability of HHS-RADV.
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    \21\ As part of the Administration's efforts to combat the 
Coronavirus Disease 2019 (COVID-19), we announced the postponement 
of the 2019 benefit year RADV process. We intend to provide further 
guidance by August 2020 on our plans to begin 2019 benefit year RADV 
in calendar year 2021. See https://www.cms.gov/files/document/2019-HHS-RADV-Postponement-Memo.pdf.
    \22\ The exception to the current prospective application of 
HHS-RADV results is for exiting issuers, whose HHS-RADV results are 
applied to the risk scores and transfer amounts for the benefit year 
being audited. See 83 FR 16930 at 16965.
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    In addition to soliciting comments on the following proposals, we 
also request feedback on the potential impact of the COVID-19 public 
health emergency on the proposed timelines for implementation of the 
proposals in this rulemaking.

A. Error Rate Calculation Methodology

    HHS recognizes that variation in provider documentation of 
enrollees' health status across provider types and groups results in 
natural variation and validation errors. Therefore, in the 2019 Payment 
Notice final rule,\23\ HHS adopted the current error rate calculation 
methodology to evaluate material statistical deviation in failure 
rates. The current methodology was adopted to avoid adjusting issuers' 
risk scores and transfers due to expected

[[Page 33599]]

variation and error. Instead, HHS amends an issuer's risk score only 
when the issuer's failure rate materially deviates from a statistically 
meaningful national value. HHS defines the national statistically 
meaningful value as the weighted mean and standard deviation of the 
failure rate calculated based on all issuers' HHS-RADV results. Each 
issuer's results are compared to these national metrics to determine 
whether the issuer's results are outliers. Based on outlier issuers' 
failure rate results, error rates are calculated and applied to outlier 
issuers' plan liability risk scores.\24\
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    \23\ See 83 FR 16930 at 16961 through 16965.
    \24\ As detailed further below, these risk score changes are 
then used to adjust risk adjustment transfers for the applicable 
state market risk pool.
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    Given comments received on the 2019 RADV White Paper and to help 
put the methodological changes proposed in this rule in context, this 
section outlines how the current error rate calculation methodology 
would apply if no changes were made since the latest policies were 
finalized in the 2021 Payment Notice.\25\ This includes information on 
how HHS uses outlier issuer group failure rates to adjust enrollee risk 
scores, calculates an outlier issuer's error rate, and applies that 
error rate to the outlier issuer's plan liability risk score.
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    \25\ 85 FR 29164.
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    To apply the current error rate calculation methodology, HHS first 
uses the failure rates for each HCC to categorize all HCCs into three 
HCC groupings (a high, medium, or low HCC failure rate grouping). These 
HCC groupings are determined by first ranking all HCC failure rates and 
then dividing the rankings into three groupings, such that the total 
observations of HCCs on EDGE in each grouping are relatively equal 
across all issuers' initial validation audit (IVA) samples (or second 
validation audit (SVA) samples, if applicable), resulting in high, 
medium, and low HCC failure rate groupings. An issuer's HCC group 
failure rate is calculated as follows:
[GRAPHIC] [TIFF OMITTED] TP02JN20.023

Where:

freqEDGEG,i is the number of occurrences of HCCs in group G that are 
recorded on EDGE for all enrollees sampled from issuer i.
freqIVAG,i is the number of occurrences of HCCs in group G that are 
identified by the IVA audit (or SVA audit, as applicable) for all 
enrollees sampled from issuer i.
GFRG,i is issuer i's group failure rate for the HCC group G.

    HHS calculates the weighted mean failure rate and the standard 
deviation of each HCC group as:
[GRAPHIC] [TIFF OMITTED] TP02JN20.024

Where:

    [mu]{GFRG{time}  is the weighted mean of GFRG,i of all issuers 
for the HCC group G weighted by all issuers' sample observations in 
each group.
    Sd{GFRG{time}  is the weighted standard deviation of GFRG,i of 
all issuers for the HCC group G.

    Each issuer's HCC group failure rates are then compared to the 
national metrics for each HCC grouping. All enrollee HCCs identified by 
the IVA (or SVA, as applicable) are used to determine an issuer's 
failure rate for the applicable HCC group. If an issuer's failure rate 
for an HCC group falls outside of the 95 percent confidence interval 
around the weighted mean failure rate for the HCC group, that is, a 
failure rate further than 1.96 standard deviations from the weighted 
mean failure rate when assuming all issuers' group failure rates are 
normally distributed, the failure rate for the issuer's HCCs in that 
group is considered an outlier (if the issuer meets the minimum number 
of HCCs for the HCC group). To calculate the outlier status thresholds, 
HHS calculates the lower and upper limits as:

LBG = [mu]{GFRG{time}  - sigma_cutoff * Sd{GFRG{time} 
UBG = [mu]{GFRG{time}  - sigma_cutoff * Sd{GFRG{time} 

Where:

sigma_cutoff is the parameter used to set the threshold for the 
outlier detection as the number of standard deviations away from the 
mean; 1.96 for a two-tailed 95 percent confidence interval as 
determined by a normal distribution.
LBG, UBG are the lower and upper thresholds to classify issuers as 
outliers or not outliers for group G.

    Outlier status is determined independently for each issuer's HCC 
failure rate group such that an issuer may be considered an outlier in 
one HCC failure rate group but not an outlier in another HCC failure 
rate group. Beginning with the 2019 benefit year, issuers are also not 
considered an outlier for an HCC group in which the issuer has fewer 
than 30 HCCs.26 27 If no issuers' HCC group failure rates in 
a state market risk pool materially deviate from the national mean of 
failure rates or does not meet the minimum HCC requirements (that is, 
no issuers are outliers), HHS does not apply any adjustments to 
issuers' risk scores or to transfers in that state market risk pool.
---------------------------------------------------------------------------

    \26\ See 85 FR 29196-29198.
    \27\ Data from issuers with fewer than 30 HCCs in an HCC group 
will be included in the calculation of national metrics for that HCC 
group, including the national mean failure rate, standard deviation, 
and upper and lower confidence interval bounds. Ibid.
---------------------------------------------------------------------------

    When an issuer's HCC group failure rate is an outlier, we reduce 
(or increase) each of the applicable IVA sample (or SVA sample, if 
applicable) enrollees' HCC risk coefficients for HCCs in that group by 
the difference between the outlier issuer's failure rate for the HCC 
group and the weighted mean failure rate for the HCC group. 
Specifically, this will result in the sample enrollees' applicable HCC 
risk score components being reduced (or increased) by a partial value, 
or percentage, calculated as the difference between the outlier failure 
rate for the HCC group and the weighted mean failure rate for the 
applicable HCC group. Beginning with the 2019 benefit year, when the 
issuer meets the minimum HCC frequency requirement per an HCC group 
(Freq_EDGEG,i this group adjustment factor GAFG,i amount for outliers 
is the distance between issuer i's Group Failure Rate GFRG,i and

[[Page 33600]]

the weighted mean [mu]{GFRG{time} . This is calculated \28\ as:
---------------------------------------------------------------------------

    \28\ This calculation sequence is printed here as it appears in 
the 2021 Payment Notice (85 FR 29164 at 29196-29198). In certain 
later sections of this proposed rule, we revised the order of 
similar sequences to ensure simplicity when demonstrating how the 
proposals in this proposed rule would be combined with the current 
error rate calculation methodology (including the changes finalized 
in the 2021 Payment Notice). The different display of these 
sequences does not modify or otherwise change the amendments to the 
outlier identification process finalized in the 2021 Payment Notice.

If GFRG,i > UBG or GFRG,i < LBG,
And if Freq_EDGEG,i <= 30:
Then FlagG,i = ``outlier'' and GAFG,i - [mu]{GFRG{time} 
If GFRG,i <= UBGand GFRG,i >=, LBG,
Or if Freq_EDGEG,i < 30:
Then FlagG,i = ``not outlier'' and GAFG,i = 0
Where:

FlagG,i is the indicator if the value of issuer i's group failure 
rate for group G is more extreme than a calculated threshold by 
which we classify issuers into ``outliers'' or ``not outliers'' for 
group G.
    GAFG,i is the calculated adjustment factor for issuer i's risk 
score component for all sampled HCCs in group G that are recorded on 
EDGE.

    The enrollee adjustment factor is then calculated by applying the 
group adjustment factor GAFG,i to individual HCCs. For example, if an 
issuer has one enrollee with the HIV/AIDS HCC and the issuer's HCC 
group adjustment rate is 10 percent (the difference between the 
issuer's group failure rate and the weighted mean failure rate) for the 
HCC group that contains the HIV/AIDS HCC, the enrollee's HIV/AIDS 
coefficient would be reduced by 10 percent. This reduction would be 
aggregated with any reductions to other HCCs for that enrollee to 
arrive at the overall enrollee adjustment factor. This value is 
calculated according to the following formula for each enrollee in 
stratum 1 through 9:
[GRAPHIC] [TIFF OMITTED] TP02JN20.025

Where:

RSh,G,i,e is the risk score component of a single HCC h (belonging 
to HCC group G) recorded on EDGE for enrollee e of issuer i.
Adjustmenti,e is the calculated adjustment factor to adjust enrollee 
e of issuer i's EDGE risk scores.
GAFG,i is the calculated adjustment factor for issuer i's risk score 
components for all sampled HCCs in group G that are recorded on 
EDGE.

    The calculation of the enrollee adjustment factor above only 
considers risk score components related to the HCC and ignores any 
other risk score components (such as demographic components and RXC 
components). Newly identified HCCs by the IVA (or SVA as applicable) 
contribute to the calculation of the issuer's group failure rate but do 
not contribute to enrollee risk score adjustments for that enrollee and 
adjusted enrollee risk scores are only computed for sampled enrollees 
with HCCs in strata 1 through 9.
    Next, for each sampled enrollee with HCCs, HHS applies the enrollee 
adjustment factor to each stratum 1 through 9 enrollee's risk score 
(including the non-HCC risk adjustment components, such as demographic 
components and RXC components) as recorded on the EDGE server, 
calculating the total adjusted enrollee risk score for these enrollees 
as:

AdjRSi,e = EdgeRSi,.e * (1 - Adjustmentsi,e)

Where:

    EdgeRSi,e is the risk score as recorded on the EDGE server of 
enrollee e of issuer i.
AdjRSi,e is the amended risk score for sampled enrollee e of issuer 
i.
Adjustmenti,e is the adjustment factor by which we estimate the EDGE 
risk score exceeds or falls short of the initial or second 
validation audit projected total risk score for sampled enrollee e 
of issuer i.

    The calculation of the total adjusted enrollee risk score AdjRSi,e 
for sample enrollees in strata 1-9 is based on the risk score recorded 
on EDGE server EdgeRSi,e that includes all risk score components (that 
is, both HCCs and the non-HCC components). Enrollees with no HCCs do 
not have enrollee adjustment factors or adjusted risk scores; however, 
we note that they contribute to the calculation of the outlier issuer's 
group failure rate in advance of the calculation of adjustments.
    After calculating the adjusted EDGE risk scores for outlier 
issuers' sample enrollees with HCCs, HHS calculates an outlier issuer's 
error rate by extrapolating the difference between the amended risk 
score and EDGE risk score for all enrollees (stratum 1 through 10) in 
the sample. The weight in the extrapolation formula associated with an 
enrollee's amended risk score and EDGE risk score is determined as the 
ratio of (1) the stratum size in the issuer's population for the 
enrollee's stratum, to (2) the number of sampled enrollees in the same 
stratum as the enrollee. Sample enrollees with no HCCs are included in 
the extrapolation of the error rate for outlier issuers with unchanged 
EDGE risk scores where AdjRSi,e = EdgeRSi,e for enrollees with no HCCs. 
The formulas to compute the error rate using the stratum-weighted risk 
score before and after the adjustment are:
[GRAPHIC] [TIFF OMITTED] TP02JN20.026

    Consistent with 45 CFR 153.350(c), HHS then applies the outlier 
issuer's error rate to adjust that issuer's applicable benefit year 
plan liability risk score.\29\ This risk score change, which also 
impacts the state market average

[[Page 33601]]

risk score, is then used to adjust the applicable benefit year's risk 
adjustment transfers for the applicable state market risk pool. Due to 
the budget-neutral nature of the HHS-operated program, adjustments to 
one issuer's risk scores and risk adjustment transfers based on HHS-
RADV findings will affect other issuers in the state market risk pool 
(including those who were not identified as outliers) because the state 
market average risk score is recalculated to reflect the change in the 
outlier issuer's plan liability risk score. This also means that 
issuers that are exempt from HHS-RADV for a given benefit year may have 
their risk adjustment transfers adjusted based on other issuers' HHS-
RADV results.
---------------------------------------------------------------------------

    \29\ Exiting outlier issuer risk score error rates are currently 
applied to the plan liability risk scores and risk adjustment 
transfer amounts for the benefit year being audited. For all other 
outlier issuers, risk score error rates are currently applied to the 
plan liability risk scores and risk adjustment transfer amounts for 
the current transfer year. The exiting issuer exception is discussed 
in Section II.B.
---------------------------------------------------------------------------

    In response to stakeholder concerns, comments to the 2019 RADV 
White Paper, and our analyses of 2017 benefit year HHS-RADV results, 
HHS is proposing to modify the HCC grouping methodology used to 
calculate failure rates by combining certain HCCs with the same risk 
score coefficient for grouping purposes, and to refine the error 
estimation methodology to mitigate the impact of the ``payment cliff'' 
effect, in which some issuers with similar HHS-RADV findings may 
experience different adjustments to their risk scores and transfers. We 
also propose changes to mitigate the impact of HHS-RADV adjustments 
that result from negative error rate outlier issuers with negative 
failure rates.
    The 2019 RADV White Paper discussed several alternatives for 
potential changes to HHS-RADV, and we considered those alternatives and 
the comments we received on them when considering which proposals to 
propose in this rulemaking. This proposed rule addresses only certain 
policies discussed in the 2019 RADV White Paper. We intend to continue 
to analyze HHS-RADV results and consider potential further refinements 
to the HHS-RADV methodology for future benefit years.
1. HCC Grouping for Failure Rate Calculation
    HHS groups medical conditions in multiple distinct ways during the 
risk adjustment and HHS-RADV processes. These grouping processes 
include:
    For risk adjustment model development:
    (1) The hierarchies of Hierarchical Condition Categories (HCCs),
    (2) HCC coefficient estimation groups,\30\
---------------------------------------------------------------------------

    \30\ The current HCC coefficient estimation groups for the adult 
models are identified in Column B of Table 6 in the ``Do It 
Yourself'' Software. The current HCC coefficient estimation groups 
for the child models are identified in Column B of Table 7 in the 
``Do it Yourself'' Software.
---------------------------------------------------------------------------

    (3) A priori stability constraints, and
    (4) Hierarchy violation constraints.
    And, for HHS-RADV:
    (5) HHS-RADV HCC failure rate groups.
    The first four of these grouping processes are related to the 
development and estimation of coefficients in the HHS risk adjustment 
models, while the fifth is related to error estimation during HHS-RADV. 
These grouping processes are not concurrent. The grouping processes 
related to the risk adjustment models are implemented prior to the 
benefit year and interact with HHS-RADV HCC failure rate groups that 
are implemented after the benefit year. Our experience in the initial 
years of HHS-RADV found that differences among the risk adjustment and 
HHS-RADV grouping procedures interact in varying ways and may result in 
greater or lesser HHS-RADV adjustments than may be warranted in certain 
circumstances. Examples of these interactions are discussed later in 
this proposed rule.
    The first grouping of medical conditions --HCCs--is used to 
aggregate thousands of standard disease codes into medically meaningful 
but statistically manageable categories. HCCs in the 2019 benefit year 
HHS risk adjustment models were derived from ICD-9-CM codes \31\ that 
are aggregated into diagnostic groups (DXGs), which are in turn 
aggregated into broader condition categories (CCs). Then, clinical 
hierarchies are applied to the CCs, so that an enrollee receives an 
increase to their risk score for only the most severe manifestation 
among related diseases that may appear in their medical claims data on 
an issuer's EDGE server.\32\ Condition categories become Hierarchical 
Condition Categories (HCCs) once these hierarchies are imposed.
---------------------------------------------------------------------------

    \31\ In the 2021 Payment Notice, we finalized several updates to 
the HHS-HCC clinical classification by using more recent claims data 
to develop updated risk factors that apply beginning with the 2021 
benefit year risk adjustment models. See 85 FR 29164 at 29175 (May 
14, 2020). Also see The Potential Updates to HHS-HCCs for the HHS-
operated Risk Adjustment Program (June 17, 2019) (2019 HHS-HCC 
Potential Updates Paper), available at: https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/Potential-Updates-to-HHS-HCCs-HHS-operated-Risk-Adjustment-Program.pdf.
    \32\ The process for creating hierarchies is an iterative 
process that considers severity, as well as costs of the HCCs in the 
hierarchies and clinical input, among other factors. For information 
on this process, see section 2.3 of the 2019 HHS-HCC Potential 
Updates Paper.
---------------------------------------------------------------------------

    As noted above, for a given hierarchy, if an enrollee has more than 
one HCC recorded in an issuer's EDGE server, only the most severe of 
those HCCs will be applied for the purposes of risk adjustment model 
and plan liability risk score calculation.\33\ For example, respiratory 
distress diagnosis codes are organized in a hierarchy consisting of 
three HCCs arranged in descending order of clinical severity from (1) 
HCC 125 Respirator Dependence/Tracheostomy Status to (2) HCC 126 
Respiratory Arrest to (3) HCC 127 Cardio-Respiratory Failure and Shock, 
Including Respiratory Distress Syndromes. An enrollee may have 
diagnosis codes in two respiratory distress HCCs, but once hierarchies 
are imposed, that enrollee would only be assigned the single highest 
severity HCC in the hierarchy. Thus, an enrollee with diagnosis codes 
in HCC 126 Respiratory Arrest and HCC 127 Cardio-Respiratory Failure 
and Shock, Including Respiratory Distress Syndromes would only be 
assigned the single highest HCC (in this case, HCC 126 Respiratory 
Arrest). Although HCCs reflect hierarchies among related disease 
categories, for unrelated diseases, multiple HCCs can accumulate for 
those enrollees, that is, the model is ``additive.'' For example, an 
enrollee with both diabetes and asthma would have (at least) two 
separate HCCs coded and the predicted cost for that enrollee will 
reflect increments for both conditions.
---------------------------------------------------------------------------

    \33\ Once hierarchies are imposed, CC code groups are referred 
to as HCCs.
---------------------------------------------------------------------------

    In the risk adjustment models, estimated coefficients of the 
various HCCs within a hierarchy will ensure that more severe and 
expensive HCCs within that hierarchy receive higher risk factors than 
less severe and less expensive HCCs. Additionally, as a part of the 
recalibration of the risk adjustment models, HHS has grouped some HCCs 
so that the coefficients of two or more HCCs are equal in the fitted 
risk adjustment models and only one model factor is assigned to an 
enrollee regardless of the number of HCCs from that group present for 
that enrollee on the issuer's EDGE server,\34\ giving rise to the 
second set of condition groupings used in risk adjustment. We impose 
these HCC coefficient estimation groups for a number of reasons, 
including the limitation of diagnostic upcoding by severity within an 
HCC hierarchy and the reduction of additivity within disease groups 
(but not across disease

[[Page 33602]]

groups) in order to decrease the sensitivity of the models to coding 
proliferation.
---------------------------------------------------------------------------

    \34\ As described in the June 17, 2019 document ``Potential 
Updates to HHS-HCCs for the HHS-operated Risk Adjustment Program'', 
available at https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/Potential-Updates-to-HHS-HCCs-HHS-operated-Risk-Adjustment-Program.pdf#page=11.
---------------------------------------------------------------------------

    Some of these HCC coefficient estimation groups occur within 
hierarchies. For example, HCC 126 Respiratory Arrest and HCC 127 
Cardio-Respiratory Failure and Shock, Including Respiratory Distress 
Syndromes within the respiratory distress hierarchy are grouped into a 
single HCC coefficient estimation group. However, some HCC coefficient 
estimation groups include HCCs that do not share a hierarchy. For 
example, another HCC coefficient estimation group consists of HCC 61 
Osteogenesis Imperfecta and Other Osteodystrophies and HCC 62 
Congenital/Developmental Skeletal and Connective Tissue Disorders. 
Within an HCC coefficient estimation group, each HCC will have the same 
coefficient in our risk adjustment models. However, as with 
hierarchies, only one risk marker is triggered by the presence of one 
or more HCCs in the HCC coefficient estimation groups. These HCC 
coefficient estimation groups are identified in DIY Software Table 6 
for the adult models and DIY Software Table 7 for the child models. The 
adult model HCC coefficient estimation groups for the V05 risk 
adjustment models \35\ are displayed in Table 1:
---------------------------------------------------------------------------

    \35\ The shorthand ``V05'' refers to the current HHS-HCC 
classification for the HHS risk adjustment models, which applies 
through the 2020 benefit year.

  Table 1--HCC Coefficient Estimation Groups From Adult Risk Adjustment
                               Models V05
------------------------------------------------------------------------
                                                      Adult model HCC
        HHS HCC             V05 HHS-HCC label     coefficient estimation
                                                           group
------------------------------------------------------------------------
19....................  Diabetes with Acute       G01
                         Complications.
20....................  Diabetes with Chronic     G01
                         Complications.
21....................  Diabetes without          G01
                         Complication.
26....................  Mucopolysaccharidosis...  G02A
27....................  Lipidoses and             G02A
                         Glycogenosis.
29....................  Amyloidosis, Porphyria,   G02A
                         and Other Metabolic
                         Disorders.
30....................  Adrenal, Pituitary, and   G02A
                         Other Significant
                         Endocrine Disorders.
54....................  Necrotizing Fasciitis...  G03
55....................  Bone/Joint/Muscle         G03
                         Infections/Necrosis.
61....................  Osteogenesis Imperfecta   G04
                         and Other
                         Osteodystrophies.
62....................  Congenital/Developmental  G04
                         Skeletal and Connective
                         Tissue Disorders.
67....................  Myelodysplastic           G06
                         Syndromes and
                         Myelofibrosis.
68....................  Aplastic Anemia.........  G06
69....................  Acquired Hemolytic        G07
                         Anemia, Including
                         Hemolytic Disease of
                         Newborn.
70....................  Sickle Cell Anemia (Hb-   G07
                         SS).
71....................  Thalassemia Major.......  G07
73....................  Combined and Other        G08
                         Severe
                         Immunodeficiencies.
74....................  Disorders of the Immune   G08
                         Mechanism.
81....................  Drug Psychosis..........  G09
82....................  Drug Dependence.........  G09
106...................  Traumatic Complete        G10
                         Lesion Cervical Spinal
                         Cord.
107...................  Quadriplegia............  G10
108...................  Traumatic Complete        G11
                         Lesion Dorsal Spinal
                         Cord.
109...................  Paraplegia..............  G11
117...................  Muscular Dystrophy......  G12
119...................  Parkinson's,              G12
                         Huntington's, and
                         Spinocerebellar
                         Disease, and Other
                         Neurodegenerative
                         Disorders.
126...................  Respiratory Arrest......  G13
127...................  Cardio-Respiratory        G13
                         Failure and Shock,
                         Including Respiratory
                         Distress Syndromes.
128...................  Heart Assistive Device/   G14
                         Artificial Heart.
129...................  Heart Transplant........  G14
160...................  Chronic Obstructive       G15
                         Pulmonary Disease,
                         Including
                         Bronchiectasis.
161...................  Asthma..................  G15
187...................  Chronic Kidney Disease,   G16
                         Stage 5.
188...................  Chronic Kidney Disease,   G16
                         Severe (Stage 4).
203...................  Ectopic and Molar         G17
                         Pregnancy, Except with
                         Renal Failure, Shock,
                         or Embolism.
204...................  Miscarriage with          G17
                         Complications.
205...................  Miscarriage with No or    G17
                         Minor Complications.
207...................  Completed Pregnancy With  G18
                         Major Complications.
208...................  Completed Pregnancy With  G18
                         Complications.
209...................  Completed Pregnancy with  G18
                         No or Minor
                         Complications.
------------------------------------------------------------------------

    The HHS-HCC model also incorporates a small number of ``a priori 
stability constraints'' to stabilize estimates that might vary greatly 
due to small sample size.\36\ These a priori stability constraints 
differ from the HCC coefficient estimation groups in how the 
corresponding estimates are counted. In contrast to HCC coefficient 
estimation groups, with a priori stability constraints, a person can 
have more than one indicated condition (each with

[[Page 33603]]

the same coefficient value) as long as the HCCs are not in the same 
hierarchy. As seen in Table 2, prior to the 2021 benefit year 
recalibration,\37\ only one a priori stability constraint was applied 
to the models, and this constraint was only applied to the child 
models.
---------------------------------------------------------------------------

    \36\ For example, we previously finalized a constraint for six 
coefficients associated with seven transplant status HCCs (excluding 
kidney transplants) in the child model, as the sample sizes of 
transplants are smaller in the child than the adult model. Because 
the levels and changes in the child transplant relative coefficients 
appeared to be dominated by random instability at the time, we 
believed the accuracy of the models were improved by constraining 
these coefficients. See the HHS Notice of Benefit and Payment 
Parameters for 2016, Final Rule, 80 FR 10749 at 10761 (February 27, 
2015).
    \37\ In the 2021 Payment Notice (85 FR 29164 at 29178), we 
introduced an additional a priori stability constraint to the child 
risk adjustment models, constraining HCC 218 Extensive Third Degree 
Burns and HCC 223 Severe Head Injury to have the same risk 
adjustment coefficient due to small sample size. We also revised the 
current single transplant stability constraint in the child models 
(shown in Table 2) into two stability constraints to better 
distinguish transplant cost differences.

     Table 2--HCCs Subject to a Priori Stability Constraints in Risk
                       Adjustment Child Models V05
------------------------------------------------------------------------
                                                   Child model a Priori
        HHS HCC             V05 HHS-HCC label      stability constraint
------------------------------------------------------------------------
18....................  Pancreas Transplant       S1
                         Status/Complications.
34....................  Liver Transplant Status/  S1
                         Complications.
41....................  Intestine Transplant      S1
                         Status/Complications.
128...................  Heart Assistive Device/   S1
                         Artificial Heart.
129...................  Heart Transplant........  S1
158...................  Lung Transplant Status/   S1
                         Complications.
251...................  Stem Cell, Including      S1
                         Bone Marrow, Transplant
                         Status/Complications.
------------------------------------------------------------------------

    HCC coefficient estimation group constraints and a priori stability 
constraints are both applied in the initial phase of risk adjustment 
regression modeling. Other constraints may be applied in later stages 
depending on regression results. For example, HCCs may be constrained 
equal to each other if there is a hierarchy violation (a lower severity 
HCC has a higher estimate than a higher severity HCC in the same 
hierarchy).\38\ HCC coefficients may also be constrained to 0 if the 
estimates fitted by the regression model are negative.
---------------------------------------------------------------------------

    \38\ For example, in the 2019 benefit year of risk adjustment 
adult models, HCC 88 (Major Depression and Bipolar Disorders) and 
HCC 89 (Reactive and Unspecified Psychosis, Delusional Disorders) 
were constrained to be equal due to a hierarchy violation occurring. 
Therefore, these HCCs in the 2019 benefit year final adult models 
have the same risk scores; however, these two HCCs are not grouped 
(as shown in Table 6, Column B of 2019 benefit year DIY Software).
---------------------------------------------------------------------------

    The final set of groupings is imposed during the error estimation 
stage of the HHS-RADV process. In this process, HCCs are categorized 
into low, medium, and high HCC failure rate groups. These groupings are 
designed to balance the need to assess the impact of medical coding 
errors of individual HCCs on risk scores and risk adjustment transfers 
and the need to assess failure rates on enough HCCs to provide 
statistically meaningful HHS-RADV results. Furthermore, these groupings 
are intended to reflect the fact that some HCCs are more difficult to 
code accurately than other HCCs and to provide national standards that 
take into account the level of coding difficulty for a given HCC.
    To create the HHS-RADV HCC failure rate groupings, the first step 
is to calculate the national average failure rate for each HCC 
individually. The second step involves ranking HCCs in order of their 
failure rates and then dividing them into three groups--a low, medium, 
and high failure rate group--such that the total counts of HCCs in each 
group nationally as recorded in EDGE data across all IVA samples (or 
SVA samples if applicable) are roughly equal. These HCC failure rate 
groups form the basis of the failure rate outlier determination 
process, with each failure rate group receiving an independent 
assessment of outlier status for each issuer.\39\
---------------------------------------------------------------------------

    \39\ For a table of the HCC failure rate groupings for 2017 
benefit year HHS-RADV, see the 2019 RADV White Paper, Appendix E.
---------------------------------------------------------------------------

    Based on our experience with the initial years of HHS-RADV, HHS 
observed that, in certain situations, the risk adjustment HCC 
hierarchies and HCC coefficient estimation groups can influence and 
interact with the HHS-RADV HCC failure rate groupings in varying ways 
that could result in misalignments.\40\ For example:
---------------------------------------------------------------------------

    \40\ See Section 3.3 of the 2019 RADV White Paper.
---------------------------------------------------------------------------

     Scenario 1: HCCs in the same HCC hierarchy with different 
coefficients are sorted into different HHS-RADV HCC failure rate 
groupings.
    ++ If one HCC is commonly miscoded as another HCC in the same 
hierarchy, but the two HCCs are sorted into different HCC failure rate 
groupings in HHS-RADV, an issuer may be flagged as an outlier in either 
of the HCC failure rate groupings where one HCC is missing or the other 
HCC is newly found.
    ++ For example, HCC 8 Metastatic Cancer and HCC 11 Colorectal, 
Breast (Age <50), Kidney, and Other Cancers are in the same hierarchy 
in risk adjustment, but for the 2017 benefit year of HHS-RADV, HCC 8 
was in the medium HCC failure rate grouping and HCC 11 was in the high 
HCC failure rate grouping. In validating an enrollee with HCC 8 in HHS-
RADV, the IVA or SVA Entity may find that an enrollee with HCC 8 
reported in EDGE is not validated as having HCC 8, which is at the top 
of the HCC hierarchy in risk adjustment, but the enrollee may have been 
found to have HCC 11 in the issuer's HHS-RADV audit data. In this case, 
HCC 8 would be considered missing in the medium HCC failure rate 
grouping, and HCC 11 would be considered found in the high HCC failure 
rate grouping.
    ++ This circumstance would influence the failure rate for that 
issuer, potentially leading to the issuer being classified as an 
outlier in an HCC failure rate grouping. If the issuer is found to be 
an outlier in one of the two failure rate groupings, the issuer's HCC 
failure rate would not represent the actual difference in risk and 
costs between these two coefficients.
     Scenario 2: HCCs in the same HCC hierarchy with different 
coefficients are sorted into the same HHS-RADV HCC failure rate 
grouping.
    ++ If one HCC is commonly miscoded as another HCC in the same 
hierarchy, and the two HCCs are sorted into the same HCC failure rate 
grouping, the issuer may not be flagged as an outlier for that HCC 
grouping. This may occur because the failure to validate an HCC and the 
discovery of a new HCC in that same HCC failure rate grouping have a 
net impact of zero on the total final value of the issuer's failure 
rate. For purposes of the calculation of the failure rate, there would 
appear to be no difference between the two HCCs, even though they have 
different coefficients in risk adjustment.

[[Page 33604]]

    ++ For example, HCC 35 End-Stage Liver Disease and HCC 34 Liver 
Transplant Status/Complications are in the same hierarchy in risk 
adjustment and were both sorted into the medium HCC failure rate 
grouping in the 2017 benefit year HHS-RADV results. In validating an 
enrollee with HCC 35 in HHS-RADV, the IVA or SVA Entity may find that 
an enrollee with HCC 35 reported in EDGE is not validated as having HCC 
35, but the enrollee may have been found to have HCC 34 in issuer's 
HHS-RADV audit data. In this case, not validating HCC 35 and finding 
HCC 34 in the same HCC grouping in HHS-RADV would, when taken together, 
have no net impact on the issuer's HCC group failure rate.
    ++ This situation would influence the failure rate for that issuer, 
potentially leading to the issuer not being classified as an outlier in 
an HCC failure rate grouping even though the two HCCs have different 
risk and costs. If the issuer is not found to be an outlier in the 
applicable failure rate grouping, the issuer's HHS-RADV adjustment 
would not represent the actual difference in risk and costs between 
these two coefficients.
     Scenario 3: HCCs in the same HCC coefficient estimation 
group are sorted into different HCC failure rate groupings.
    ++ In this situation, a miscoding of one HCC for the other may lead 
to the issuer being identified as a positive outlier in one HCC failure 
rate grouping or a negative outlier in another, despite there being no 
difference in risk score due to the coding error.
    ++ For example, HCC 54 Necrotizing Fasciitis and HCC 55 Bone/Joint/
Muscle Infections/Necrosis share a hierarchy and an HCC coefficient 
estimation group in risk adjustment, resulting in risk score 
coefficients constrained to be equal, but for 2017 benefit year HHS-
RADV, HCC 54 was in the high failure rate HCC grouping, while HCC 55 
was in the medium failure rate HCC grouping. In validating an enrollee 
with HCC 54 in HHS-RADV, the IVA or SVA Entity may find that an 
enrollee with HCC 54 reported in EDGE is not validated as having HCC 
54, but the enrollee may have been found to have HCC 55 in issuer's 
HHS-RADV audit data.
    ++ In this case, when taken together with the issuer's other HHS-
RADV results, HCCs in the same HCC coefficient estimation group could 
contribute to an issuer's failure rate in a HCC failure rate grouping, 
even though the HCCs do not have different risk scores and an 
adjustment to risk scores is not conceptually warranted. If the issuer 
is found to be an outlier in one of the two failure rate groupings, the 
issuer's HCC failure rate would not represent actual differences in 
risk or costs between these two coefficients.
    Based on HHS's initial analysis of the occurrence of these 
scenarios in the 2017 benefit year HHS-RADV results,\41\ and in 
response to comments to the 2019 RADV White Paper, HHS is considering 
an option in this proposed rule to address the influence of the HCC 
hierarchies and HCC coefficient estimation groups on the HCC failure 
rate groupings in HHS-RADV. Our intention is to address this issue on 
an interim basis while we continue to assess different longer-term 
options, including potential significant changes to the outlier 
determination process, which require additional analysis and 
consideration before proposing.
---------------------------------------------------------------------------

    \41\ As discussed in the 2019 RADV White Paper, we performed an 
initial review of the occurrence of these scenarios in the 2017 
benefit year HHS-RADV results. Of all the HCCs in EDGE that were not 
validated in the audit data, about 1/8th represented HCCs that IVA 
or SVA auditors coded as different HCCs within the same hierarchy. 
Of the HCCs that were newly found in the audit data--that is, they 
were not recorded in the original EDGE data--around 1/3rd 
represented HCCs that were newly found because they were originally 
reported on EDGE as a different HCC in the same hierarchy. However, 
we note that these occurrences reflect both HCCs sorted into 
different HCC failure rate groups and HCCs sorted into the same HCC 
failure rate groups, including a scenario, discussed in the 
whitepaper wherein HCCs in the same hierarchy and the same HCC 
coefficient estimation group are sorted into the same HCC failure 
rate group, which would have no impact on failure rate and would not 
warrant any adjustment to risk score. Therefore, for many issuers, 
these occurrences would be unlikely to impact whether they were an 
outlier in an HCC failure rate grouping. However, we note that the 
initial review discussed in the white paper did not consider HCCs 
that share an HCC coefficient estimation group, but do not share a 
hierarchy.
---------------------------------------------------------------------------

    To address Scenario 3, we propose to modify the creation of HHS-
RADV HCC failure rate groupings and place all HCCs that share an HCC 
coefficient estimation group in the adult risk adjustment models (see 
Table 1 for the list of the HCC coefficient estimation groups in the 
V05 classification) into the same HCC failure rate grouping. 
Specifically, we propose that when HHS calculates EDGE and IVA 
frequencies for each individual HCC and prior to sorting the HCCs into 
low, medium, and high failure rate groups for HHS-RADV, HCCs that are 
in the same HCC coefficient estimation group in the adult risk 
adjustment models (and, therefore, have coefficients constrained to be 
equal to one another) would be aggregated into one HCC. These new 
frequencies, including the aggregated frequencies of HCC coefficient 
estimation groups and the frequencies of all other unconstrained HCCs, 
treated separately, would be considered frequencies of ``Super HCCs''.
    In the current process,\42\ before sorting into the three HCC 
failure rate groups, failure rates for each HCC are calculated 
individually as:
---------------------------------------------------------------------------

    \42\ See the 2018 HHS-RADV protocols, section 11.3.1, available 
at: https://www.regtap.info/uploads/library/HRADV_2018Protocols_070319_5CR_070519.pdf.
[GRAPHIC] [TIFF OMITTED] TP02JN20.027

---------------------------------------------------------------------------
Where:

h is the index of the hth HCC code;
freqEDGEh is the frequency of an HCC h occurring in EDGE data; that 
is, the number of sampled enrollees recording HCC h in EDGE data 
across all issuers participating in HHS-RADV;
freqIVAh is the frequency of an HCC h occurring in IVA results (or 
SVA results, as applicable); that is, the number of sampled 
enrollees recording HCC h in IVA (or SVA, as applicable) results 
across all issuers participating in HHS-RADV; and
FRh is the national overall (average) failure rate of HCC h across 
all issuers participating in HHS-RADV.

    In the proposed methodology, this step would be modified as:
    [GRAPHIC] [TIFF OMITTED] TP02JN20.028
    
Where:

c is the index of the cth Super HCC;
freqEDGEc is the frequency of a Super HCC c occurring in EDGE data 
across all issuers participating in HHS-RADV; that is, the sum of 
freqEDGEh for all HCCs that share an HCC coefficient estimation 
group in the adult models:
[GRAPHIC] [TIFF OMITTED] TP02JN20.038

When an HCC is not in an HCC coefficient estimation group in the 
adult risk adjustment models, the freqEDGEc for that HCC will be 
equivalent to freqEDGEh;
freqIVAc is the frequency of a Super HCC c occurring in IVA results 
(or SVA results, as applicable) across all issuers participating in 
HHS-RADV; that is, the sum of freqIVAh for all HCCs that share an 
HCC coefficient estimation group in the adult risk adjustment 
models:
[GRAPHIC] [TIFF OMITTED] TP02JN20.039

And;
FRc is the national overall (average) failure rate of Super HCC c 
across all issuers participating in HHS-RADV.

    Then, the failure rates for all Super HCCs, both those composed of 
a single

[[Page 33605]]

HCC and those composed of the aggregate frequencies of HCCs that share 
an HCC coefficient estimation group in the adult risk adjustment 
models, would be grouped according to the current HHS-RADV failure rate 
grouping methodology.
    As an illustrative example, this proposal would mean that, for 
purposes of HHS-RADV groupings, two of the three current respiratory 
distress HCCs in the adult risk adjustment models, HCC 126 Respiratory 
Arrest and HCC 127 Cardio-Respiratory Failure and Shock, Including 
Respiratory Distress Syndromes, would be aggregated into one Super HCC 
because they have the same estimated costs and share an HCC coefficient 
estimation group. That Super HCC would then be sorted into a failure 
rate group according to its overall national failure rate. As such, all 
validations or failures to validate either of the two HCCs composing 
the Super HCC would contribute to the failure rate for the same HCC 
failure rate grouping. However, if an enrollee with one of the two HCCs 
in the Super HCC reported on EDGE was not validated as having the EDGE 
reported HCC but is found to have the other HCC in the Super HCC (e.g., 
an enrollee with HCC 126 reported on EDGE is not validated as having 
HCC 126 but is found to have HCC 127), the issuer's failure rate would 
not be affected. This approach would ensure that HCCs with the same 
estimated costs in the adult risk adjustment models that share an HCC 
coefficient estimation group do not contribute to an issuer's failure 
rate in a HCC failure rate grouping. To promote fairness and ensure the 
integrity of the program, we do not believe that issuers should be 
considered to have an HHS-RADV error for similar conditions from the 
same HCC coefficient estimation group and, as a result, were estimated 
as having the same risk in the adult risk adjustment models. This 
proposal to aggregate the frequencies of HCCs in the same HCC 
coefficient estimation group in the adult risk adjustment models would 
refine the HHS-RADV methodology to better identify and focus outlier 
determinations on actual differences in risk and costs. Based on our 
testing of this proposed policy on 2017 benefit year HHS-RADV results, 
we estimate that by creating the proposed Super HCCs, approximately 
98.1 percent of the occurrences of HCCs on EDGE belong to HCCs that 
would be assigned to the same failure rate groups under the proposed 
methodology as they have been under the current methodology as seen in 
Table 3. Although the impact on individual issuer results may vary 
depending upon the accuracy of their initial data submissions and the 
rate of occurrence of various HCCs in their enrollee population, the 
national metrics used for HHS-RADV would only be slightly affected, as 
seen in Table 4. The stability of these metrics and high proportion of 
EDGE frequencies of HCCs that would be assigned to the same failure 
rate group under the proposed and current sorting methodologies 
reflects that the most common conditions will have similar failure 
rates if this proposal is adopted. However, the failure rate estimates 
of less common conditions may be stabilized with the proposed creation 
of Super HCCs by ensuring these conditions are grouped alongside more 
common, related conditions.
    In testing this proposal to create the Super HCCs in HHS-RADV, we 
grouped HCCs in the same HCC coefficient estimation group in the adult 
risk adjustment models. To do this, we used variables in Column B in 
Table 6 of the HHS-Developed Risk Adjustment Model Algorithm ``Do It 
Yourself'' software \43\ to determine the candidate HCCs that should be 
incorporated into Super HCCs under this policy proposal. If a set of 
candidate HCCs are all from the same HCC coefficient estimation group, 
they would be grouped into one Super HCC in HHS-RADV. Each remaining 
HCC that does not meet these criteria would be assigned to its own 
Super HCC prior to determining the HCC failure rate grouping. We chose 
to use the adult risk adjustment models for testing because the 
majority of the population with HCCs in the HHS-RADV samples are 
subject to the adult models (88.3 percent for the 2017 benefit 
year).\44\ As such, the adult models' HCC coefficient estimation groups 
will be applicable to the vast majority of enrollees and we believe 
that the use of HCC coefficient estimation groups present in the adult 
risk adjustment models sufficiently balances the representativeness and 
precision of HCC failure rate estimates across the entire population in 
aggregate and may be used as the source for the proposed creation of 
Super HCCs for all RADV sample enrollees, regardless of the risk 
adjustment model to which they are subject.
---------------------------------------------------------------------------

    \43\ 2017 Benefit Year Risk Adjustment: HHS-Developed Risk 
Adjustment Model Algorithm ``Do It Yourself (DIY)'' Software. 
Technical Details. July 21, 2017. Assessed at: https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/DIY-Tables-7-12-2017.xlsx.
    \44\ This was calculated after removing issuers in Massachusetts 
and incorporating cases where issuers failed pairwise and the SVA 
sub-sample was used.
---------------------------------------------------------------------------

    In developing this policy, we limited the grouping of risk 
adjustment HCCs into Super HCCs for HHS-RADV to HCC coefficient 
estimation groups alone and have not considered including a priori 
stability constraints or hierarchy violation constraints in the 
aggregation of Super HCCs. A priori stability constraints currently are 
only applied to a limited number of HCCs in the child models and are 
applied differently than HCC hierarchies and HCC coefficient estimation 
groups. Whereas enrollees can only receive one HCC from a hierarchy or 
one model factor from a coefficient estimation group (for example, one 
factor for the presence of either HCC 61 Osteogenesis Imperfecta and 
Other Osteodystrophies or HCC 62 Congenital/Developmental Skeletal and 
Connective Tissue Disorders), enrollees may receive more than one HCC 
when there is an a priori stability constraint (for example, HCC 129 
Heart Transplant and HCC 158 Lung Transplant Status/Complications in 
the child model). Although HCCs subject to a priori stability 
constraints will have the same coefficient value, the possible additive 
nature of these HCCs suggests that a failure to validate one HCC 
subject to an a priori stability constraint paired with the IVA or SVA 
entity identifying a different HCC subject to the same a priori 
stability constraint does not constitute a swapping of HCCs in the same 
way that a similar scenario among HCCs in a common HCC coefficient 
estimation group would. As such, we do not find it necessary or 
appropriate to include a priori stability constraints in the 
aggregation of Super HCCs.
    We also did not consider hierarchy violation constraints as a part 
of the sorting algorithm in order to balance complexity and 
consistency, as hierarchy violation constraints in the risk adjustment 
models can change from year-to-year as a natural result of risk 
adjustment model coefficient annual recalibration updates. These year-
to-year changes would make HCC groupings for these HCCs less stable and 
transparent, and would reduce predictability for issuers.
    For the above mentioned reasons, we propose to combine HCCs in HCC 
coefficient estimation groups in the adult risk adjustment models into 
Super HCCs prior to sorting the HCCs into low, medium and high failure 
rate groups for HHS-RADV, starting with the 2019 benefit year of HHS-
RADV. If finalized as proposed, these Super HCC groupings would apply 
to all RADV sample enrollees, regardless of the risk adjustment models 
to which they are subject. Once sorted into failure rate groups, the 
failure rates for all Super

[[Page 33606]]

HCCs, both those composed of a single HCC and those composed of the 
aggregate frequencies of HCCs that share an HCC coefficient estimation 
group in the adult risk adjustment models, would be grouped according 
to the current HHS-RADV failure rate grouping methodology.
    We solicit comment on all aspects of this proposal. In particular, 
we solicit comments on the proposed use of the HCC coefficient 
estimation groups to identify the HCCs that would be aggregated into 
Super HCCs in HHS-RADV and whether we should also consider 
incorporating a priori stability constraints from the child models, or 
hierarchy violation constraints from the adult risk adjustment models 
as part of HHS-RADV Super HCCs. We also solicit comment on whether, in 
addition to the Super HCCs based on the adult risk adjustment models, 
CMS should create separate infant Super HCCs for each severity type in 
the infant risk adjustment models. As we considered with the adult risk 
adjustment model-based Super HCCs, if we were to adopt separate infant 
model-based Super HCCs, we solicit comments on whether we should 
incorporate only the HCC coefficient groupings inherent in the infant 
severity level determination process, or both these groupings and any 
hierarchy violation constraints that may occur in the infant models. 
The latter option may make the composition of HCC groups less stable 
year-to-year, but may more comprehensively address Scenario 3 when it 
occurs and reflect the full risk structure of HCC hierarchies as 
expressed in infant risk adjustment models.
    Additionally, we solicit comment regarding the impact of COVID-19 
on the proposed changes to the HCC grouping methodology for error rate 
calculation. In particular, we solicit comment on whether the need for 
providers to focus on caring for patients during the COVID-19 pandemic 
could impact the completeness of the data that would be used to 
implement the new HCC grouping methodology for HHS-RADV, such that we 
should consider a later applicability date if we finalize this 
proposal.

 Table 3--Estimated Proposed Changes in the HCC Groupings Using Super HCCs Based on Adult Model HCC Coefficient
                                                Estimation Groups
                                 [Using the 2017 benefit year HHS-RADV results]
----------------------------------------------------------------------------------------------------------------
                                                                    Super HCCs using HCC coefficient estimation
                                                                             groups (proposed option)
       Count of HCC categories in each failure rate group        -----------------------------------------------
                                                                        Low           Medium           High
----------------------------------------------------------------------------------------------------------------
Current Methodology:
    Low.........................................................              31               1               1
    Medium......................................................               2              29               4
    High........................................................               1               5              53
----------------------------------------------------------------------------------------------------------------


 
                                                                    Super HCCs using HCC coefficient estimation
                                                                             groups (proposed option)
               Frequency of HCC occurrence on EDGE               -----------------------------------------------
                                                                                      Medium
                                                                   Low (percent)     (percent)    High (percent)
----------------------------------------------------------------------------------------------------------------
Current Methodology:
    Low.........................................................            32.2             0.0             0.0
    Medium......................................................             0.1            33.0             1.0
    High........................................................             0.3             0.3            32.9
----------------------------------------------------------------------------------------------------------------


   Table 4--Estimated Proposed National Metrics in the HCC Groupings Using Super HCCs Based on Adult Model HCC
                                          Coefficient Estimation Groups
                                 [Using the 2017 benefit year HHS-RADV results]
----------------------------------------------------------------------------------------------------------------
                                                  Weighted  mean  Weighted  std.       Lower           Upper
     HCC grouping options             Group        failure  rate        dev          threshold       threshold
----------------------------------------------------------------------------------------------------------------
Current.......................  Low.............          0.0476          0.0973         -0.1431          0.2382
                                Med.............          0.1549          0.0992         -0.0395          0.3493
                                High............          0.2621          0.1064          0.0536          0.4706
Super HCCs using HCC            Low.............          0.0496          0.0959         -0.1384          0.2376
 Coefficient Estimation Groups
 (Proposed Option).
                                Med.............          0.1557          0.0994         -0.0392          0.3506
                                High............          0.2595          0.1065          0.0508          0.4682
----------------------------------------------------------------------------------------------------------------

2. ``Payment cliff'' Effect
    The HHS-RADV error rate calculation methodology is based on the 
identification of outliers, as determined using certain national 
thresholds. In the case of the current error rate calculation 
methodology, those thresholds are used to determine whether an issuer 
is an outlier, and to determine the error rate that will be used to 
adjust risk scores. As previously discussed, under the current 
methodology, 1.96 standard deviations on both sides of the confidence 
interval around the weighted HCC group means are the thresholds 
currently used to determine whether an issuer is an outlier. In 
practice, these thresholds mean that an issuer with failure rates 
outside the 1.96 standard deviations range for any of the HCC failure 
groups is deemed an outlier and

[[Page 33607]]

receives an adjustment to its risk score, while an issuer with failure 
rates inside the 1.96 standard deviations range for all groups receives 
no adjustment to its risk score.\45\
---------------------------------------------------------------------------

    \45\ An issuer with no error rate would not have its risk score 
adjusted due to HHS-RADV, but that issuer may have its risk 
adjustment transfer impacted if there is another issuer(s) in the 
state market risk pool that is an outlier.
---------------------------------------------------------------------------

    As stated in the 2021 Payment Notice, beginning with the 2019 
benefit year, when the issuers meets the minimum HCC requirement per an 
HCC group (Freq_EDGEG,i, the group adjustment factor for outliers is 
the distance between issuer i's Group Failure Rate GFRG,i and the 
weighted mean [mu]{GFRG{time}  calculated \46\ as:
---------------------------------------------------------------------------

    \46\ This calculation sequence is printed here as it appears in 
the 2021 Payment Notice (85 FR 29164 at 29196-29198). In later 
sections of this rule, we revised the order of similar sequences for 
simplicity when demonstrating how this sequence would be combined 
with proposals in this proposed rule. The different display does not 
modify or otherwise change the amendments to the outlier 
identification process finalized in the 2021 Payment Notice.

If GFRG,i > UBG or GFRG,i < LBG:
And if Freq_EDGEG,i >= 30:
Then FlagG,i = ``outlier'' and GAFG,i = GFRG,i - 
[mu]{GFRG{time} 
If GFRG,i <= UBG amd GFRG,i >= LBG,
Or if Freq_EDGEG,i < 30:
Then FlagG,i = ``not outlier'' and GAFG,i = 0

Where:
FlagG,i is the indicator if issuer i's group failure rate for group 
G is located beyond a calculated threshold that we use to classify 
issuers into ``outliers'' or ``not outliers'' for group G.
    GAFG,i is the calculated adjustment factor to adjust issuer i's 
EDGE risk score components for all sampled HCCs in group G.

    For each sampled enrollee with HCCs, the group adjustment factor 
(GAF) is applied at the individual HCC level to all EDGE HCCs in the 
HCC grouping in which the issuer is an outlier. For example, if an 
issuer's sample has one enrollee with the HIV/AIDS HCC and the issuer's 
HCC GAF \47\ is 10 percent (the difference between the outlier issuer's 
group failure rate and the weighted mean group failure rate) for the 
HCC group that contains the HIV/AIDS HCC, the enrollee's HIV/AIDS HCC 
risk score coefficient would be reduced by 10 percent. This reduction 
would be aggregated with any reductions to other HCCs for that enrollee 
to arrive at the overall enrollee adjustment factor for each sample 
enrollee in stratum 1 through 9. Next, each stratum 1 through 9 sample 
enrollee's enrollee adjustment factor is applied to that enrollee's 
entire EDGE risk score (including the non-HCC risk adjustment 
components) to calculate an adjusted risk score for that sample 
enrollee. These adjusted risk scores are extrapolated to the issuer's 
population strata and aggregated with the unadjusted risk scores of 
stratum 10 enrollees in the calculation of the issuer's error rate.
---------------------------------------------------------------------------

    \47\ To more clearly distinguish between the enrollee adjustment 
factor and the group adjustment factor, for the purposes of this 
proposed rule, we use GAF instead of ``adjustment''.
---------------------------------------------------------------------------

    Some stakeholders have expressed concern that the failure rates of 
issuers that are just outside of the confidence intervals receive an 
adjustment, even though they may not be significantly different from 
the failure rates of issuers just inside the confidence intervals who 
receive no adjustment, creating a ``payment cliff'' or ``leap frog'' 
effect. For example, an issuer with a low HCC group failure rate of 
23.9 percent would be considered a positive error rate outlier for that 
HCC group based on the 2017 benefit year national failure rate 
statistics, because the upper bound confidence interval for the low HCC 
group is 23.8 percent. That issuer's GAF would be calculated based on 
the difference between the weighted low HCC group mean of 4.8 percent 
and the issuer's 23.9 percent failure rate for that HCC group. Under 
this example, the issuer's GAF would be 19.1 percent, and that GAF 
would be applied to the enrollee-level risk score coefficients for 
enrollees in the issuer's sample who have HCCs in the HCC failure rate 
group for which the issuer was determined to be an outlier. At the same 
time, another issuer with a low HCC group failure rate of 23.7 percent 
would receive no adjustment to its risk score as a result of HHS-RADV. 
While this result is due to the nature of establishing and using a 
threshold, some stakeholders have recommended mitigating this effect by 
calculating error rates based on the position of the bounds of the 
confidence interval for the HCC group and not on the position of the 
weighted mean for the HCC group. Others have recommended not adjusting 
issuers' risk scores in the case of negative error rate issuers to 
limit the impact of these adjustments on issuers who are not determined 
to be outliers.\48\
---------------------------------------------------------------------------

    \48\ See Section II.A.3 for proposals intended to mitigate the 
impact of HHS-RADV adjustments for negative error rate issuers with 
negative failure rates.
---------------------------------------------------------------------------

    As we have previously discussed,\49\ we have concerns about only 
adjusting issuers' risk scores for positive error rate outliers. 
However, we recognize that changing the calculation and application of 
an outlier issuer's error rate may be appropriate if the outlier issuer 
is not statistically different from the issuers within the confidence 
intervals. Therefore, to promote fairness, HHS's focus in considering 
potential changes to mitigate the payment cliff in the calculation of 
error rates is on situations where issuers with failure rates that are 
close to the bounds of the confidence intervals are not substantially 
different from issuers with failure rates inside the confidence 
intervals. To address this issue, we are considering potential 
modifications to the error rate calculation that maintain the two-sided 
approach of HHS-RADV through which both positive and negative error 
rate outliers would continue to receive risk score adjustments.
---------------------------------------------------------------------------

    \49\ See, for example, Section 4.4.3 of the 2019 RADV White 
Paper. Also see 84 FR 17504 through 17508.
---------------------------------------------------------------------------

    While HHS considered several possible methods to address the 
payment cliff in the 2019 RADV White Paper, we are proposing to address 
the payment cliff by adding a sliding scale adjustment to the current 
error rate calculation, such that different adjustments would be 
applied to issuers based on their distance from the mean and the 
farthest outlier threshold. This proposed approach would employ 
additional thresholds to create a smoothing of the error rate 
calculation beyond what the current methodology allows and to help 
reduce the disparity of risk score adjustments using a linear 
adjustment.\50\ We are proposing to make this modification beginning 
with 2019 benefit year HHS-RADV.
---------------------------------------------------------------------------

    \50\ In the 2020 Payment Notice final rule, we stated that we 
may consider alternative options for error rate adjustments, such as 
using multiple or smoothed confidence intervals for outlier 
identification and risk score adjustments. See 84 FR at 17507.
---------------------------------------------------------------------------

    To apply the sliding scale adjustment, we propose to modify the 
calculation of the GAF by providing a linear sliding scale adjustment, 
for issuers whose failure rates are near the point at which the payment 
cliff occurs. For those issuers, we propose to add an additional step 
to the calculation of their GAFs to take into consideration these 
issuers' distance from the confidence interval. The present formula for 
an issuers' GAF, GAFG,i = GFRG,i - [mu]{GFRG{time} , would be modified 
by replacing the GFRG,i with a decomposition of this value that uses 
the national weighted mean and national weighted standard deviation for 
the HCC failure rate group, as well as zG,i, the z-score associated 
with the GFRG,i, where:
[GRAPHIC] [TIFF OMITTED] TP02JN20.029


[[Page 33608]]


    And therefore:

GFRG,i = zG,i * Sd{GFRG + [mu]{GFRG{time} 
    So:

GAFG,i = [zG,i * Sd{GFRG{time}  + [mu]{GFRG{time} ] - [mu]{GFRG{time} 
    The z-score would then be discounted using the general formula:, 
where disZG,i,r = a * zG,i + br, Where disZG,i,r is the confidence-
level discounted z-score for that value of zG,i according to the 
parameters of the positive or negative sliding scale range, r. This 
disZG,i,r value would replace the zG,i value in the GAFG,i formula to 
provide the value of the sliding scale adjustment for the positive or 
negative side of the confidence interval:

GAFG,i,r = [disZG,i,r * Sd{GFRG{time}  + [mu]{GFRG{time} ] - 
[mu]{GFRG{time} 
    In the calculation of disZG,i,r, the coefficient a would be the 
slope of the linear adjustment, which shows the adjustment increase 
rate per unit increase of GFRG,i, and br is the intercept of the linear 
adjustment for either the negative or positive sliding scale range. The 
coefficients would be determined based on the standard deviation 
thresholds of the range selected for the application of the sliding 
scale adjustment. Specifically, coefficient a would be defined as:
[GRAPHIC] [TIFF OMITTED] TP02JN20.030


Where:

 a is the slope of the sliding scale adjustment
 r indicates whether the GAF is being calculated for a 
negative or positive outlier
     outerZr is the greater magnitude z-score selected to 
define the edge of a given sliding scale range r (3.00 for positive 
outliers; and -3.00 for negative outliers)
     innerZr is the lower magnitude z-score selected to 
define the edge of a given sliding scale range r (1.645 for positive 
outliers; and -1.645 for negative outliers)

    The value of intercept br would differ based on whether the sliding 
scale were being calculated for a positive or negative outlier and 
would be defined as:

br = outerZr - a * (outerZr) = outerZr * (1 - a)
    In the absence of the constraints on negative failure rates 
described later in this proposed rule, the final formula for the group 
adjustment when an outlier issuer is subject to the sliding scale 
(GAFG,i,r, above) could be simplified to:

GAFG,i,r = disZG,i,r * Sd{GFRG{time} 
    However, for the purposes of aligning formulas between the multiple 
proposals in this proposed rule, we feel that it is helpful to provide 
both the above expanded and simplified versions of the sliding scale 
GAFG,i,r formula in this section.
    This sliding scale GAFG,i,r would be applied to the HCC 
coefficients in the applicable HCC failure rate group when calculating 
each enrollee with an HCCs' risk score adjustment factor for an issuer 
that had a failure rate with a z-score within the range of values 
selected for the sliding scale adjustment (innerZr and outerZr). All 
other enrollee adjustment factors would be calculated using the current 
formula for the GAFG,i. Using this linear sliding scale adjustment 
would provide a smoothing effect in the error rate calculation for 
issuers with failure rates just outside of the confidence interval of 
an HCC group.
    To implement this proposed option, we would need to select the 
thresholds of the range (innerZr and outerZr) to calculate and apply 
the sliding scale adjustment.\51\ Commenters to the 2019 RADV White 
Paper supported a sliding scale option that would calculate and apply 
the sliding scale adjustment from +/-1.96 to 3 standard deviations. 
This option would retain the confidence interval at 1.96 standard 
deviations under the current methodology, meaning that issuers within 
the 95 percent confidence interval would not have their respective risk 
scores adjusted. This option would also retain the full adjustment to 
the mean failure rate for issuers outside of the 99.7 percent 
confidence interval (beyond 3 standard deviations). While some of these 
stakeholders would prefer that the error rate be calculated to the edge 
of the confidence intervals for all outliers, rather than applying a 
sliding scale, some of these same commenters expressed support for this 
option because it would not increase the number of outliers compared to 
the current methodology, promoting stability for issuers. Specifically, 
this option would provide stability by maintaining the current 
thresholds used in the error rate calculation and without changing the 
number of issuers that would be impacted. While we recognize that this 
option would mitigate the payment cliff, we have concerns that it would 
weaken the HHS-RADV program by reducing its overall impact and the 
magnitude of HHS-RADV adjustments to the risk scores of outlier 
issuers.
---------------------------------------------------------------------------

    \51\ In the 2019 RADV White Paper, we considered four different 
options on how to calculate and apply additional thresholds for the 
sliding scale adjustment to the error rate calculation. See section 
4.4.4 and 4.4.5 of the 2019 RADV White Paper.
---------------------------------------------------------------------------

    Instead, in this proposed rule, we propose to calculate and apply a 
sliding scale adjustment between the 90 and 99.7 percent confidence 
interval bounds (from +/-1.645 to 3 standard deviations). Under this 
proposal, the determination of outliers in HHS-RADV for each HCC 
grouping would no longer have a 95 percent confidence interval or 1.96 
standard deviations, and would instead have a 90 percent confidence 
interval or 1.645 standard deviations. Specifically, this approach 
would adjust the upper and lower bounds of the confidence interval to 
be at 1.645 standard deviations, meaning that issuers outside of the 90 
percent confidence interval would have their risk scores adjusted, 
instead of beginning adjustments for issuers at the 95 percent 
confidence interval under the current methodology. This would mean that 
more issuers would be considered outliers under this proposal than the 
current methodology.
    Under this proposed approach, the above formulas would be 
implemented \52\ as follows:
---------------------------------------------------------------------------

    \52\ This calculation sequence is expressed here in a revised 
order compared to how the sequence is published in the 2021 Payment 
Notice (85 FR 29164 at 29196-29198). This change was made for 
simplicity to demonstrate how the current sequence would be combined 
with this proposed approach. The different display does not modify 
or otherwise change the amendments to the outlier identification 
process finalized in the 2021 Payment Notice.

If Freq_EDGEG,i >= 30, then:
If zG,i < -3.00 or zG,i > 3.00
Then FlagG,i = ``outlier'' and GAFG,i = GFRG,i - [mu]{GFRG{time} 
Or if -3 < zG,i < -1.645 or 3 > zG,i > 1.645
Then FlagG,i = ``outlier'' and GAFG,i = disZG,i,r * Sd{GFRG{time} 
If Freq_EDGEG,i <30 or if -1.645 <= zG,i <= 1.645.
Then FlagG,i = ``not outlier'' and GAFG,i = 0
Where disZG,i,r is calculated using 3.00 (or -3.00, for negative 
outliers) as the value of outerZr and 1.645 (or -1.645, for negative 
outliers) as the value of innerZr.
    This proposed approach would retain the current significant 
adjustment to the HCC group weighted mean for issuers beyond three 
standard deviations to ensure that the mitigation of the payment cliff 
for those issuers close to the confidence intervals does not impact 
situations where outlier issuers' failure rates are not close to the 
confidence intervals and a larger adjustment is warranted.
    As discussed in the 2019 RADV White Paper, we tested a sliding 
scale adjustment between the 90 and 99 percent confidence interval 
bounds using 2017 HHS-RADV results.\53\ We found that even though it 
would

[[Page 33609]]

increase the number of outliers by including issuers whose failure 
rates fell between 1.645 and 1.96 standard deviations from the mean, it 
would lower the overall impact of HHS-RADV adjustments to transfers and 
result in the distribution of issuers' error rates moving closer to 
zero compared to the current methodology.\54\ Therefore, this proposal 
preserves a strong incentive for issuers to submit accurate EDGE data 
that can be validated in HHS-RADV because it increases the range in 
which issuers can be flagged as outliers, while lowering the 
calculation of that adjustment amount for those outlier issuers close 
to the confidence intervals and maintaining a larger adjustment for 
those who are not close to the confidence intervals. For these reasons, 
we believe that this proposal for calculating and applying the sliding 
scale adjustment provides a balanced approach to addressing the payment 
cliff. We seek comment on this proposal, including the proposed 
calculation of the sliding scale adjustment and the thresholds used to 
calculate and apply it.
---------------------------------------------------------------------------

    \53\ See section 4.4.5 and Appendix C of the 2019 RADV White 
Paper.
    \54\ Ibid.
---------------------------------------------------------------------------

3. Negative Error Rate Issuers With Negative Failure Rates
    HHS-RADV is intended to promote confidence and stability in the 
budget neutral HHS-operated risk adjustment program by ensuring the 
integrity and quality of data provided by issuers. HHS-RADV also serves 
to ensure that, consistent with the statute, charges are collected from 
issuers with lower-than-average actuarial risk and payments are made to 
issuers with higher-than-average actuarial risk. It uses a two-sided 
outlier identification approach because the long-standing intent of 
HHS-RADV has been to account for identified material risk differences 
between what issuers submitted to their EDGE servers and what was 
validated in medical records through HHS-RADV, regardless of the 
direction of those differences.\55\ In addition, the two-sided 
adjustment policy penalizes issuers who validate HCCs in HHS-RADV at 
much lower rates than the national average and rewards issuers in HHS-
RADV who validate HCCs in HHS-RADV at rates that are much higher than 
the national average, encouraging issuers to ensure that their EDGE-
reported risk scores reflect the true actuarial risk of their 
enrollees. Positive and negative error rate outliers represent these 
two types of adjustments, respectively.
---------------------------------------------------------------------------

    \55\ An exception to this approach was established, beginning 
with the 2018 benefit year of HHS-RADV, for exiting issuers who are 
negative error rate outliers. See 84 FR at 17503-17504.
---------------------------------------------------------------------------

    If an issuer is a positive error rate outlier, its risk score will 
be adjusted downward. Assuming no changes to risk scores for the other 
issuers in the same state market risk pool, this downward adjustment 
increases the issuer's charge or decreases its payment for the 
applicable benefit year, leading to a decrease in charges or an 
increase in payments for the other issuers in the state market risk 
pool. If an issuer is a negative error rate outlier, its risk score 
will be adjusted upward. Assuming no changes to risk scores for the 
other issuers in the same state market risk pool, this upward 
adjustment reduces the issuer's charge or increases its payment for the 
applicable benefit year, leading to an increase in charges or a 
decrease in payments for the other issuers in the state market risk 
pool. The increase to risk score(s) for negative error rate outliers is 
consistent with the upward and downward risk score adjustments 
finalized as part of the original HHS-RADV methodology in the 2015 
Payment Notice \56\ and the HCC failure rate approach to error 
estimation finalized in the 2019 Payment Notice.\57\ As noted above, 
some stakeholders have recommended HHS not adjust issuers' risk scores 
in the case of negative error rate issuers to limit the impact of these 
adjustments on issuers who are not outliers.
---------------------------------------------------------------------------

    \56\ For example, we stated that ``the effect of an issuer's 
risk score error adjustment will depend upon its magnitude and 
direction compared to the average risk score error adjustment and 
direction for the entire market.'' See 79 FR 13743 at 13769.
    \57\ See 83 FR 16930 at 16962. The shorthand ``positive error 
rate outlier'' captures those issuers whose HCC coefficients are 
reduced as a result of being identified as an outlier, while 
``negative error rate outlier'' captures those issuers whose HCC 
coefficients are increased as a result of being identified as an 
outlier.
---------------------------------------------------------------------------

    An issuer can be identified as a negative error rate outlier for a 
number of reasons. However, the current error rate methodology does not 
distinguish between low failure rates due to accurate data submission 
and failure rates that have been depressed through the presence of 
found HCCs (that is, HCCs in the audit data that were not present in 
the EDGE data). If a negative failure rate is due to a large number of 
found HCCs, it does not reflect accurate reporting through the EDGE 
server for risk adjustment. While we believe that any issuer with a 
negative failure rate is likely to review their internal processes to 
better capture missing HCCs in their future EDGE data submissions, we 
are proposing to refine the current error rate calculation to mitigate 
the impact of adjustments that result from negative error rate outliers 
whose low failure rates are driven by newly found HCCs rather than by 
high validation rates. We believe that a constraint in the GAF 
calculation in the current error rate calculation would mitigate 
potential incentives for issuers to use HHS-RADV to identify more HCCs 
than were reported to their EDGE servers. It also would mitigate the 
impact of HHS-RADV adjustments to transfers in the case of negative 
error rate issuers with negative failure rates and improve 
predictability.
    Currently, an outlier issuer's error rate is calculated based on 
the difference between the weighted mean failure rate for the HCC group 
and the issuer's failure rate for that HCC grouping, which may be a 
negative failure rate. Beginning with 2019 benefit year HHS-RADV, we 
propose to adopt an approach that constrains negative error rate 
outlier issuers' error rate calculations in cases when an issuer's 
failure rate is negative. The proposed constraint would be to the GAF 
whereby the error rates of a negative error rate outlier issuer with a 
negative failure rate would be calculated as the difference between the 
weighted mean failure rate for the HCC grouping (if positive) and zero 
(0). This would be calculated by substituting the following 
[bond][bond]double bars[bond][bond] terms into the error rate 
calculation \58\ process:
---------------------------------------------------------------------------

    \58\ This calculation sequence is expressed here in a revised 
order compared to how the sequence is published in the 2021 Payment 
Notice (85 FR 29164 at 29196-29198). This change was made for 
simplicity when demonstrating how this sequence would be combined 
with this proposal. The different display does not modify or 
otherwise change the amendments to the outlier identification 
process finalized in the 2021 Payment Notice.

If Freq_EDGEG,i >= 30, then:
If GFRG,i > UBG or GFRG,i < LBG:
Then FlagG,i = ``outlier'' and GAFG,i = [bond][bond]GFRG,i,constr - 
[mu]{GFRG{time} constr[bond][bond]
If Freq_EDGEG,i < 30 or if GFRG,i <= UBG and GFRG,i >= LBG:
Then FlagG,i = ``not outlier'' and GAFG,i = 0

Where:

GFRG,i is an issuer's failure rate for the HCC failure rate grouping
[bond][bond]GFRG,i,constr is an issuer's failure rate for the HCC 
failure rate grouping, constrained to 0 if is less than 0. Also 
expressed as:
GFRG,i,constr = max{0, GFRG,i{time} [bond][bond]
[mu]{GFRG{time}  is the weighted national mean failure rate for the 
HCC failure rate grouping
[bond][bond][mu]{GFRG{time} constr is the weighted national mean 
failure rate for the HCC failure rate grouping, constrained to 0 if 
[mu]{GFRG{time}  is less than 0. Also expressed as:
[mu]{GFRG{time} constr = max{0,[mu]{GFRG{time} {time} [bond][bond]
UBG and LBG are the upper and lower bounds of the HCC failure rate 
grouping confidence interval, respectively.

[[Page 33610]]

FlagG,i is the indicator if issuer i's group failure rate for group 
G locates beyond a calculated threshold that we are using to 
classify issuers into ``outliers'' or ``not outliers'' for group G.
GAFG,i is the calculated adjustment amount to adjust issuer i's EDGE 
risk score components for all sampled HCCs in group G.

    We would then compute total adjustments and error rates for each 
outlier issuer based on the weighted aggregates of the GAFG,i.\59\
---------------------------------------------------------------------------

    \59\ See, for example, the 2018 Benefit Year Protocols: PPACA 
HHS Risk Adjustment Data Validation, Version 7.0 (June 24, 2019), 
available at: https://www.regtap.info/uploads/library/HRADV_2018Protocols_070319_5CR_070519.pdf.
---------------------------------------------------------------------------

    This approach would limit the financial impact that negative error 
rate outliers with negative failure rates would have on other issuers 
in the same state market risk pool, and would help provide stability to 
issuers in predicting the impact of HHS-RADV adjustments. For example, 
under the current error rate methodology using the 2017 benefit year 
HHS-RADV metrics, a negative outlier issuer with a -15 percent failure 
rate for the low HCC grouping would receive a GAF of the difference 
between -15 percent and the weighted mean for the low HCC grouping of 
4.8 percent of -19.8 percent. However, under the proposal in this 
rulemaking to constrain the negative failure rates for negative outlier 
issuers to zero, the GAF in this example would be the difference 
between 0 percent and the weighted mean for the low HCC grouping of 4.8 
percent, resulting in a -4.8 percent GAF.
    If this proposal is finalized, the constrained values in the 
calculation of the GAF would only impact issuers with negative failure 
rates; therefore, issuers who have been extremely accurate in reporting 
their data to their EDGE server will not be affected. Issuers who 
report accurately to their EDGE servers are likely to have failure 
rates very close to zero, and may have negative error rates, but not 
negative failure rates. As such, these issuers would not have their GAF 
values constrained. In contrast, the issuers found to have negative 
failure rates, indicating that diagnosis data to their EDGE server was 
underreported for a particular benefit year, would have their GAF 
values constrained. As such, the proposed constraints on the GAF 
calculation will not apply or impact adjustments for issuers who are 
extremely accurate in reporting their diagnosis data to their EDGE 
servers.
    We are proposing this option because it could be easily implemented 
under the current error rate methodology, would address stakeholders' 
concerns about the impact of adjustments due to negative error rate 
issuers with negative failure rates, and would reduce incentives that 
may exist for issuers to use HHS-RADV to identify more HCCs than 
existed in EDGE. We seek comment on this proposal.
a. Combining the HCC Grouping Constraint, Negative Failure Rate 
Constraint and the Sliding Scale Proposals
    To help commenters understand the interaction of the above 
proposals to create Super HCCs for grouping purposes, apply a sliding 
scale option, and constrain negative failure rates for negative error 
rate outliers, this section outlines the complete proposed revised 
error rate calculation methodology formulas, integrating all the 
changes proposed to apply beginning with 2019 HHS-RADV in this proposed 
rule.
    First, HHS would use the failure rates for Super HCCs to group each 
HCC into three HCC groupings (a high, medium, or low HCC failure rate 
grouping). Under the above proposed approach, Super HCCs would be 
defined as HCCs that have been aggregated such that HCCs that are in 
the same HCC coefficient estimation group are aggregated together and 
all other HCCs each compose an individual Super HCC. Using the Super 
HCCs, we would calculate the HCC failure rate as follows:
[GRAPHIC] [TIFF OMITTED] TP02JN20.031

Where:

c is the index of the cth Super HCC;
freqEDGEc is the frequency of a Super HCC c occurring in EDGE data; 
that is, the sum of freqEDGEh for all HCCs that share an HCC 
coefficient estimation group in the adult risk adjustment models:

[GRAPHIC] [TIFF OMITTED] TP02JN20.040


When an HCC is not in an HCC coefficient estimation group in the 
adult risk adjustment models, the freqEDGEc for that HCC will be 
equivalent to freqEDGEh;
freqIVAc is the frequency of a Super HCC c occurring in IVA results 
(or SVA results, as applicable); that is, the sum of freqIVAh for 
all HCCs that share an HCC coefficient estimation group in the adult 
risk adjustment models:

[GRAPHIC] [TIFF OMITTED] TP02JN20.041


And;
FRc is the national overall (average) failure rate of Super HCC c 
across all issuers.

    Then, the failure rates for all Super HCCs, both those composed of 
a single HCC and those composed of the aggregate frequencies of HCCs 
that share an HCC coefficient estimation group in the adult models, 
would be grouped according to the current HHS-RADV failure rate 
grouping methodology. These HCC groupings would be determined by first 
ranking all Super HCC failure rates and then dividing the rankings into 
the three groupings weighted by total observations of that Super HCC 
across all issuers' IVA samples, assigning each Super HCC into a high, 
medium, or low HCC grouping. This process ensures that all HCCs in a 
Super HCC are grouped into the same HCC grouping in HHS-RADV.
    Next, an issuer's HCC group failure rate would be calculated as 
follows:
[GRAPHIC] [TIFF OMITTED] TP02JN20.032

Where:

freqEDGEG,i is the number of occurrences of HCCs in group G that are 
recorded on EDGE for all enrollees sampled from issuer i.
freqIVAG,i is the number of occurrences of HCCs in group G that are 
identified by the IVA audit (or SVA audit, as applicable) for all 
enrollees sampled from issuer i.
GFRG,i is issuer i's group failure rate for the HCC group G.

    HHS calculates the weighted mean failure rate and the standard 
deviation of each HCC group as:

[[Page 33611]]

[GRAPHIC] [TIFF OMITTED] TP02JN20.033

Where:

[mu]{GFRG{time}  is the weighted mean of GFRG,i of all issuers for 
the HCC group G weighted by all issuers' sample observations in each 
group.
Sd{GFRG{time}  is the weighted standard deviation of GFRG,i of all 
issuers for the HCC group G.

    Each issuer's HCC group failure rates would then be compared to the 
national metrics for each HCC grouping. If an issuer's failure rate for 
an HCC group falls outside of the two-tailed 90 percent confidence 
interval with a 1.645 standard deviation cutoff based on the weighted 
mean failure rate for the HCC group, the failure rate for the issuer's 
HCCs in that group would be considered an outlier (if the issuer meets 
the minimum number of HCCs for the HCC group). Based on issuers' 
failure rates for each HCC group, outlier status would be determined 
for each issuer independently for each issuer's HCC failure rate group 
such that an issuer may be considered an outlier in one HCC failure 
rate group but not an outlier in another HCC failure rate group. 
Beginning with the 2019 benefit year, issuers will not be considered an 
outlier for an HCC group in which the issuer has fewer than 30 HCCs. If 
no issuers' HCC group failure rates in a state market risk pool 
materially deviate from the national mean of failure rates (that is, no 
issuers are outliers), HHS does not apply any adjustments to issuers' 
risk scores or to transfers in that state market risk pool.
    Then, once the outlier issuers are determined, we would calculate 
the group adjustment factor taking into consideration the outlier 
issuer's distance from the confidence interval and limiting calculation 
of the group adjustment factor when the issuer has a negative failure 
rate. The formula \60\ would apply as follows:
---------------------------------------------------------------------------

    \60\ This calculation sequence is expressed here in a revised 
order compared to how the sequence is published in the 2021 Payment 
Notice (85 FR 29164 at 29196-29198). This change was made for 
simplicity to demonstrate how this sequence would be combined with 
proposals in this proposed rule. The different display does not 
modify or otherwise change the amendments to the outlier 
identification process finalized in the 2021 Payment Notice.

If Freq_EDGEG,i >= 30, then:
If zG,i < -3.00 or zG,i > 3.00
Then FlagG,i = ``outlier'' and
GAFG,i = max{0,GFRG,i{time}  -max{0,[mu]{GFRG{time} {time} 
Or if -3 < zG,i < -1.645 or 3 > zG,i > 1.645
Then FlagG,i = ``outlier'' and
GAFG,i = max{0, (disZG,i,r * Sd{GFRG{time}  + [mu]{GFRG{time} ){time}  
- max{0, [mu]{GFRG{time} {time} 
If Freq_EDGEG,i < 30 or if -1.645 <= zG,i <= 1.645
Then FlagG,i = ``not outlier'' and GAFG,i = 0
Where:

 r indicates whether the GAF is being calculated for a 
negative or positive outlier;
 a is the slope of the sliding scale adjustment, calculated 
as:
[GRAPHIC] [TIFF OMITTED] TP02JN20.034

    With outerZr defined as the greater magnitude z-score 
selected to define the edge of the sliding scale range r (3.00 for 
positive outliers; and -3.00 for negative outliers) and 
innerZr defined as the lower magnitude z-score selected 
to define the edge of the range r (1.645 for positive outliers; and 
-1.645 for negative outliers);

 br is the intercept of the sliding scale 
adjustment for a given sliding scale range r, calculated as:

br = outerZr - a * (outerZr = outerZr * (1 - a)

 disZG,i,r is the z-score of issuer i's 
GFRG,i, for HCC failure rate group G discounted according 
to the sliding scale for range r, calculated as:

disZG,i,r = a * zG,i + br

With zG,i defined as the z-score of i issuers' 
GFRG,i:
[GRAPHIC] [TIFF OMITTED] TP02JN20.035

 GAFG,i is the group adjustment factor for HCC 
failure rate group G for an issuer i;
 Sd{GFRG{time}  is the weighted national standard 
deviation of all issuers' GFRs for HCC failure rate group G;
 [mu]{GFRG{time}  is the weighted national mean 
of all issuers' GFRs for HCC failure rate group G.

    Once an outlier issuer's group adjustment factor is calculated, the 
enrollee adjustment would be calculated by applying the group 
adjustment factor to an enrollee's individual HCCs. For example, if an 
issuer has one enrollee with the HIV/AIDS HCC and the issuer's HCC 
group adjustment rate is 10 percent for the HCC group that contains the 
HIV/AIDS HCC, the enrollee's HIV/AIDS coefficient would be reduced by 
10 percent. This reduction would be aggregated with any reductions to 
other HCCs for that enrollee to arrive at the overall enrollee 
adjustment factor. This value would be calculated according to the 
following formula for each sample enrollee in stratum 1 through 9:
[GRAPHIC] [TIFF OMITTED] TP02JN20.036

Where:

RSh,G,i,e is the risk score component of a single HCC h 
(belonging to HCC group G) recorded on EDGE for enrollee e of issuer 
i.
GAFG,i is the group adjustment factor for HCC failure 
rate group G for an issuer i;
Adjustmenti,e is the calculated adjustment amount to 
adjust enrollee e of issuer i's EDGE risk scores.

    The calculation of the enrollee adjustment factor only considers 
risk score factors related to the HCCs and ignores any other risk score 
factors (such as demographic factors and RXC factors). Furthermore, 
because this formula is concerned exclusively with EDGE HCCs, HCCs 
newly identified by the IVA (or SVA as applicable) would not contribute 
to enrollee risk score

[[Page 33612]]

adjustments for that enrollee and adjusted enrollee risk scores are 
only computed for sampled enrollees with HCCs in strata 1 through 9.
    Next, for each sampled enrollee with HCCs, HHS would calculate the 
total adjusted enrollee risk score as:

AdjRSi,e = EdgeRSi,e * (1-Adjustmenti,e)

Where:

EdgeRSi,e is the risk score as recorded on the EDGE server of 
enrollee e of issuer i.
AdjRSi,e is the amended risk score for sampled enrollee e of issuer 
i.
Adjustmenti,e is the adjustment factor by which we estimate whether 
the EDGE risk score exceeds or falls short of the initial or second 
validation audit projected total risk score for sampled enrollee e 
of issuer i.

    The calculation of the sample enrollee's adjusted risk score 
includes all EDGE server components for sample enrollees in strata 1 
through 9.
    After calculating the outlier issuers' sample enrollees with HCCs' 
adjusted EDGE risk scores, HHS would calculate an outlier issuer's 
error rate by extrapolating the difference between the amended risk 
score and EDGE risk score for all enrollees (stratum 1 through 10) in 
the sample. The extrapolation formula would be weighted by determining 
the ratio of an enrollee's stratum size in the issuer's population to 
the number of sample enrollees in the same stratum as the enrollee. 
Sample enrollees with no HCCs would be included in the extrapolation of 
the error rate for outlier issuers with the EDGE risk score unchanged 
for these sample enrollees. The formulas to compute the error rate 
using the stratum-weighted risk score before and after the adjustment 
would be:
[GRAPHIC] [TIFF OMITTED] TP02JN20.037

    Consistent with 45 CFR 153.350(b), HHS then would apply the outlier 
issuer's error rate to adjust that issuer's applicable benefit year's 
plan liability risk score.\61\ This risk score change, which also would 
impact the state market average risk score, would then be used to 
adjust the applicable benefit year's risk adjustment transfers for the 
applicable state market risk pool.\62\ Due to the budget-neutral nature 
of the HHS-operated program, adjustments to one issuer's risk scores 
and risk adjustment transfers based on HHS-RADV findings affects other 
issuers in the state market risk pool (including those who were not 
identified as outliers) because the state market average risk score 
changes to reflect the outlier issuer's change in its plan liability 
risk score. This also means that issuers that are exempt from HHS-RADV 
for a given benefit year will have their risk adjustment transfers 
adjusted based on other issuers' HHS-RADV results if any issuers in the 
applicable state market risk pool are identified as outliers. We seek 
comments on our modified error rate calculation methodology proposed to 
be applicable starting for the 2019 benefit year of HHS-RADV.
---------------------------------------------------------------------------

    \61\ Exiting outlier issuer risk score error rates are currently 
applied to the plan liability risk scores and risk adjustment 
transfer amounts for the benefit year being audited if they are a 
positive error rate outlier. For all other outlier issuers, risk 
score error rates are currently applied to the plan liability risk 
scores and risk adjustment transfer amounts for the current transfer 
year. The exiting issuer exception is discussed in Section II.B.
    \62\ See 45 CFR 153.350(c).
---------------------------------------------------------------------------

    In drafting this proposed rule, as requested by commenters on the 
2019 RADV White Paper, we estimated the combined impact of applying the 
proposed sliding scale adjustment, the proposed negative failure rate 
constraint and the proposed Super HCC aggregation using 2017 benefit 
year HHS-RADV results. Table 5 provides a comparison of the estimated 
change in error rates between the current methodology for sorting HCCs 
for HHS-RADV grouping and the proposed Super HCC aggregation for 
sorting of HCCs for HHS-RADV grouping, the proposed negative failure 
rate constraint and the proposed sliding scale option in this proposed 
rule. In addition, in response to comments on the 2019 RADV White Paper 
that supported the adoption of a sliding scale adjustment from +/-1.96 
to 3 standard deviations, Table 5 also includes information on the 
estimated change(s) if option 1 from the 2019 RADV White Paper was 
adopted as the sliding scale adjustment.
    As shown in Table 5, we also found through testing the 2017 benefit 
year HHS-RADV results that, although the proposed sliding scale 
adjustment (adjusting from +/-1.645 to 3 standard deviations) increases 
the number of outliers, the mean error rates among positive outliers 
under this proposal are smaller than the mean error rates among 
positive outliers for the 2019 RADV White Paper sliding scale option 1 
(adjusting from +/-1.96 to 3 standard deviations), even when tested in 
combination with the proposed negative failure rate constraint and/or 
the current and proposed sorting methodologies. This suggests that the 
proposed sliding scale option would result in reduced HHS-RADV 
adjustments to risk adjustment transfers relative to both the current 
methodology and the 2019 RADV White Paper sliding scale option 1, and 
reflects the smoother transition between a GAF of zero and a full-value 
GAF that is provided by the proposed sliding scale option when compared 
to 2019 RADV White Paper sliding scale option 1.

[[Page 33613]]



  Table 5--A Comparison of HHS-RADV Error Rate (ER) Estimated Changes Based on 2017 Benefit Year \63\ HHS-RADV
                                                      Data
----------------------------------------------------------------------------------------------------------------
                                                      Current sorting method           Super HCCs using HCC
                                                 --------------------------------  coefficient estimation groups
                    Scenario                                                     -------------------------------
                                                    Mean Neg ER     Mean Pos ER     Mean Neg ER     Mean Pos ER
                                                        (%)             (%)             (%)             (%)
----------------------------------------------------------------------------------------------------------------
Sorting Method Only.............................           -5.68            9.96           -5.98            9.91
Sorting Method with Proposed Negative Constraint           -3.11            9.96           -3.38            9.91
Sorting Method with Proposed Sliding Scale                 -2.27            5.28           -2.49            5.32
 Option \64\....................................
Sorting Method, Proposed Sliding Scale Option &            -1.50            5.28           -1.66            5.32
 Proposed Negative Constraint...................
Sorting Method with 2019 RADV White Paper                  -2.16            6.46           -2.48            6.51
 Sliding Scale Option 1 \65\....................
Sorting Method with 2019 RADV White Paper                  -1.12            6.46           -1.26            6.51
 Sliding Scale Option 1 & Proposed Negative
 Constraint.....................................
----------------------------------------------------------------------------------------------------------------

B. Application of HHS-RADV Results
---------------------------------------------------------------------------

    \63\ These estimates include the exclusion from outlier status 
of issuers with fewer than 30 HCCs in an HCC group, consistent with 
the policy finalized in the 2021 Payment Notice (85 FR 29164), which 
was not in effect for 2017 Benefit Year HHS-RADV. We included the 
fewer than 30 HCC exclusion from outlier status in these estimates 
to provide a sense of the impact of the proposed changes when 
compared to the methodology presently in effect for 2019 benefit 
year HHS-RADV and beyond.
    \64\ The Proposed Sliding Scale Option outlined in Section 
II.A.2. of this rule would create a sliding scale adjustment from +/
-1.645 to 3 standard deviations.
    \65\ The 2019 RADV White Paper Sliding Scale Option 1 would 
create a sliding scale adjustment from +/-1.96 to 3 standard 
deviations.
---------------------------------------------------------------------------

    In the 2014 Payment Notice, HHS finalized a prospective approach 
for making adjustments to risk adjustment transfers based on findings 
from the HHS-RADV process.\66\ Specifically, we finalized using an 
issuer's HHS-RADV error rates from the prior year to adjust the 
issuer's average risk score in the current benefit year. As such, we 
used the 2017 benefit year HHS-RADV results to adjust 2018 benefit year 
risk adjustment plan liability risk scores for non-exiting issuers, 
resulting in adjustments to 2018 benefit year risk adjustment transfer 
amounts.67 68
---------------------------------------------------------------------------

    \66\ See 78 FR 15409 at 15438.
    \67\ See the Summary Report of 2017 Benefit Year HHS-RADV 
Adjustments to Risk Adjustment Transfers released on August 1, 2019, 
available at: https://www.cms.gov/CCIIO/Programs-and-Initiatives/Premium-Stabilization-Programs/Downloads/BY2017-HHSRADV-Adjustments-to-RA-Transfers-Summary-Report.pdf.
    \68\ In the 2019 Payment Notice, we adopted an exception to the 
prospective application of HHS-RADV results for exiting issuers, 
whereby risk score error rates for outlier exiting issuers are 
applied to the plan liability risk scores and transfer amounts for 
the benefit year being audited. Therefore, for exiting issuers, we 
used the 2017 benefit year's HHS-RADV results to adjust 2017 benefit 
year risk adjustment plan liability risk scores, resulting in 
adjustments to 2017 benefit year risk adjustment transfer amounts. 
See 83 FR at 16965 through 16966.
---------------------------------------------------------------------------

    When we finalized the prospective HHS-RADV results application 
policy in the 2014 Payment Notice, we did not anticipate the extent of 
the changes that could occur in the risk profile of enrollees or market 
participation in the individual and small group markets from benefit 
year to benefit year. As a result of experience with these changes over 
the early years of the program, and in light of the changes finalized 
in the 2020 Payment Notice to the timeline for the reporting, 
collection, and disbursement of risk adjustment transfer adjustments 
for HHS-RADV \69\ and the changes to the risk adjustment holdback 
policy,\70\ both of which will lead to reopening of prior year risk 
adjustment transfers, we are now proposing changes to this prospective 
approach for non-exiting issuers.
---------------------------------------------------------------------------

    \69\ See 84 FR at 17504 through 17508.
    \70\ See the Change to Risk Adjustment Holdback Policy for the 
2018 Benefit Year and Beyond Bulletin (May 31, 2019) (May 2019 
Holdback Guidance), available at: https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/Change-to-Risk-Adjustment-Holdback-Policy-for-the-2018-Benefit-Year-and-Beyond.pdf.
---------------------------------------------------------------------------

    Starting with the 2021 benefit year of HHS-RADV, we propose 
applying HHS-RADV results to the benefit year being audited for all 
issuers. This proposal is intended to address stakeholder concerns 
about maintaining actuarial soundness in the application of an issuer's 
HHS-RADV error rate if an issuer's risk profile, enrollment, or market 
participation changes substantially from benefit year to benefit year. 
This proposed change has the potential to provide more stability for 
issuers of risk adjustment covered plans and help them better predict 
the impact of HHS-RADV results. It would also prevent situations where 
an issuer who newly enters a state market risk pool is subject to HHS-
RADV adjustments from the prior benefit year for which they did not 
participate. We seek comment on this proposal.
    If we finalize and implement the policy to adjust the benefit year 
being audited beginning with the 2021 benefit year HHS-RADV, we would 
need to adopt transitional measures to move from the current 
prospective approach to one that applies the HHS-RADV results to the 
benefit year being audited. More specifically, 2021 benefit year risk 
adjustment plan liability risk scores and transfers would need to be 
adjusted first to reflect 2020 benefit year HHS-RADV results, and 
adjusted again based on 2021 benefit year HHS-RADV results. For the 
2022 benefit year of HHS-RADV and beyond, risk adjustment plan 
liability risk scores and transfers would only be adjusted once based 
on the same benefit year's HHS-RADV results (that is, 2022 benefit year 
HHS-RADV results would adjust 2022 benefit year risk adjustment plan 
liability risk scores and transfers).\71\
---------------------------------------------------------------------------

    \71\ As discussed in the May 2019 Holdback Guidance, a 
successful HHS-RADV appeal may require additional adjustments to 
transfers for the applicable benefit year in the impacted state 
market risk pool.
---------------------------------------------------------------------------

    In order to effectuate this transition, we considered and are 
proposing an ``average error rate approach,'' as set forth in the 2019 
RADV White Paper, under which HHS would calculate an average value for 
the 2021 and 2020 benefit years' HHS-RADV error rates and apply this 
average error rate to 2021 risk adjustment plan liability risk scores 
and transfers. This approach would result in one final HHS-RADV 
adjustment to 2021 benefit year risk adjustment plan liability risk 
scores and transfers, reflecting the average value for the 2021 and 
2020 benefit years' HHS-RADV error rates. The adjustments to transfers 
would be collected and paid in accordance with the 2021 benefit year 
HHS-RADV timeline, in 2025.\72\
---------------------------------------------------------------------------

    \72\ For a general description of the current timeline for 
reporting, collection, and disbursement of HHS-RADV adjustments to 
transfers, see 84 FR at 17506 through 17507.
---------------------------------------------------------------------------

    However, in an effort to be consistent with our current risk score 
error rate application and calculation and ensure

[[Page 33614]]

that both years of HHS-RADV results are taken into consideration in 
calculating risk adjustment plan liability risk scores, we also propose 
as an alternative transition strategy from the prospective application 
of HHS-RADV results to a concurrent application approach the ``combined 
plan liability risk score option,'' also set forth in the 2019 RADV 
White Paper. Under the combined plan liability risk score option, we 
would apply 2020 benefit year HHS-RADV risk score adjustments to 2021 
benefit year plan liability risk scores, and then apply 2021 benefit 
year HHS-RADV risk score adjustments to the adjusted 2021 plan 
liability risk scores. We would then use the final adjusted plan 
liability risk scores (reflecting both the 2020 and 2021 HHS-RADV 
adjustments to risk scores) to adjust 2021 benefit year transfers. 
Under this proposal, HHS would calculate risk score adjustments for 
2020 and 2021 benefit year HHS-RADV sequentially and incorporate 2020 
and 2021 benefit year HHS-RADV results in one final adjustment amount 
to 2021 benefit year transfers that would be collected and paid in 
accordance with the 2021 benefit year HHS-RADV timeline, in 2025. We 
seek comment on both of these approaches to transition from the current 
prospective approach to one that applies the HHS-RADV results to the 
benefit year being audited.
    Additionally, the transition to a policy to apply HHS-RADV results 
to the benefit year being audited would remove the need to continue the 
current policy on issuers entering sole issuer markets that was 
finalized in the 2020 Payment Notice.\73\ As finalized in the 2020 
Payment Notice, new issuer(s) that enter a new market or a previously 
sole issuer market have their risk adjustment transfers in the current 
benefit year adjusted if there was an outlier issuer in the applicable 
state market risk pool in the prior benefit year's HHS-RADV.\74\ If the 
proposal to apply HHS-RADV results to the benefit year being audited 
for all issuers is finalized, new issuers, including new issuers in 
previously sole issuer markets, would no longer be prospectively 
impacted by HHS-RADV results from a previous benefit year; rather, the 
new issuer would only have their current benefit year risk scores (and 
subsequently, risk adjustment transfers) impacted. The exception would 
be for the proposed transition benefit years, 2020 and 2021. If a new 
issuer enters a market in 2021, its risk adjustment plan liability risk 
score and transfers could be impacted by the new issuer's own 2021 HHS-
RADV results and the combined 2020 and 2021 HHS-RADV results of other 
issuers in the same state market risk pool(s). In addition, since the 
current prospective approach would continue to apply to the 2019 
benefit year HHS-RADV, if a new issuer enters a sole issuer market in 
2020, this new issuer would see its 2020 risk adjustment plan liability 
risk scores and transfers impacted if there was an outlier issuer as a 
result of 2019 benefit year HHS-RADV in the applicable state market 
risk pool.
---------------------------------------------------------------------------

    \73\ 84 FR at 17504.
    \74\ Ibid.
---------------------------------------------------------------------------

    We solicit comment on all of these proposals. In addition, in light 
of the postponement of the 2019 HHS-RADV process as part of the 
Administration's efforts to combat COVID-19,\75\ we are additionally 
seeking comment on an alternative timeline for the proposed transition 
from the prospective application of HHS-RADV results for non-exiting 
issuers.
---------------------------------------------------------------------------

    \75\ Available at https://www.cms.gov/files/document/2019-HHS-RADV-Postponement-Memo.pdf. As discussed in the memo, our intention 
is to provide guidance by August 2020 on the updated timeline for 
2019 benefit year HHS-RADV activities that we plan to begin in 2021.
---------------------------------------------------------------------------

    Under this alternative timeline, we would apply HHS-RADV results to 
the benefit year being audited for all issuers starting with the 2020 
benefit year of HHS-RADV, rather than the 2021 benefit year. If we 
finalize and implement either of the above transition options using the 
alterative timeline, 2020 benefit year risk adjustment plan liability 
risk scores and transfers would need to be adjusted twice--first to 
reflect 2019 benefit year HHS-RADV results and again based on 2020 
benefit year HHS-RADV results.\76\ To accomplish this, we would either 
(1) implement the ``combined plan liability risk score option,'' 
whereby we would apply 2019 benefit year HHS-RADV risk score 
adjustments to 2020 benefit year plan liability risk scores, and then 
apply 2020 benefit year HHS-RADV risk score adjustments to the already 
adjusted 2020 plan liability risk scores, or (2) implement the 
``average error rate approach,'' whereby we would calculate an average 
value for the 2019 and 2020 benefit years' HHS-RADV error rates and 
apply the averaged error rate to 2020 benefit year plan liability risk 
scores. We would then use the final adjusted plan liability risk scores 
from either of these approaches to adjust 2020 benefit year transfers. 
The adjustments to transfers would be collected and paid in accordance 
with the 2020 benefit year HHS-RADV timeline, in 2024. We also seek 
comment on whether, if we finalize and implement either of the above 
transition options using the alterative timeline, we should also pilot 
RXCs for the 2020 benefit year HHS-RADV to increase consistency between 
the operations of 2019 and 2020 HHS-RADV. We solicit comment on all of 
these proposals.
---------------------------------------------------------------------------

    \76\ If no changes are made to the timeline for 2020 benefit 
year HHS-RADV activities, they would begin with the release of 
enrollee samples in late May 2021. Given the postponement of 2019 
benefit year HHS-RADV activities in response to the COVID-19 
pandemic, it is possible HHS-RADV activities for the 2019 and 2020 
benefit years would be conducted at the same time.
---------------------------------------------------------------------------

III. Collection of Information Requirements

    This document does not impose information collection requirements, 
that is, reporting, recordkeeping, or third-party disclosure 
requirements. Consequently, there is no need for review by the Office 
of Management and Budget under the authority of the Paperwork Reduction 
Act of 1995 (44 U.S.C. 3501 et seq.).
    Under this proposed rule, we propose to amend the calculation of 
error rates to modify the sorting methodology for HCCs that share an 
HCC coefficient estimation group in the adult risk adjustment models; 
to amend the error rate calculation for cases where outlier issuers are 
near the confidence intervals; to constrain the error rate calculation 
for issuers with negative failure rates; and to transition to the 
application of HHS-RADV results to the benefit year being audited. 
These proposed changes are methodological changes to the error 
estimation methodology used in calculating error rates and changes to 
the application of HHS-RADV results to risk scores and transfers. Since 
HHS calculates error rates and applies HHS-RADV results to risk scores 
and transfers, we do not estimate a burden change on issuers to conduct 
and complete HHS-RADV in states where HHS operates the risk adjustment 
program for a given benefit year.\77\
---------------------------------------------------------------------------

    \77\ Since the 2017 benefit year, HHS has been responsible for 
operating risk adjustment in all 50 states and the District of 
Columbia.
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IV. Response to Comments

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

[[Page 33615]]

V. Regulatory Impact Statement

A. Statement of Need

    This rule proposes standards related to the HHS-RADV program, 
including certain refinements to the calculation of error rates and a 
transition from the prospective application of HHS-RADV results. The 
Premium Stabilization Rule and other rulemakings noted above provided 
detail on the implementation of the HHS-RADV program.

B. Overall Impact

    We have examined the impact of this rule as required by Executive 
Order 12866 on Regulatory Planning and Review (September 30, 1993), 
Executive Order 13563 on Improving Regulation and Regulatory Review 
(January 18, 2011), the Regulatory Flexibility Act (RFA) (September 19, 
1980, Pub. L. 96-354), section 1102(b) of the Social Security Act (the 
Act), section 202 of the Unfunded Mandates Reform Act of 1995 (March 
22, 1995; Pub. L. 104-4), Executive Order 13132 on Federalism (August 
4, 1999), the Congressional Review Act (5 U.S.C. 804(2)), and Executive 
Order 13771 on Reducing Regulation and Controlling Regulatory Costs 
(January 30, 2017).
    Executive Orders 12866 and 13563 direct agencies to assess all 
costs and benefits of available regulatory alternatives and, if 
regulation is necessary, to select regulatory approaches that maximize 
net benefits (including potential economic, environmental, public 
health and safety effects, distributive impacts, and equity). A 
Regulatory Impact Analysis (RIA) must be prepared for major rules with 
economically significant effects ($100 million or more in any 1 year). 
This rule does not reach the economic significance threshold, and thus 
is not considered a major rule. For the same reason, it is not a major 
rule under the Congressional Review Act.

C. Regulatory Alternatives Considered

    In developing the policies contained in this proposed rule, we 
considered numerous alternatives to the presented proposals. Below we 
discuss the key regulatory alternatives considered.
    We considered an alternative approach to the proposed sorting of 
all HCCs that share an HCC coefficient estimation group in the adult 
models into the same ``Super HCC'' for HHS-RADV HCC grouping purposes. 
This alternative approach would have combined all HCCs in the same 
hierarchy into the same Super HCC for HHS-RADV HCC grouping purposes 
even if those HCCs had different coefficients in the risk adjustment 
models. While we did analyze this option, we were concerned that it 
would not account for risk differences within the HCC hierarchies, and 
that the proposed approach that focuses on HCCs with the same risk 
scores in the adult models would better ensure that HHS-RADV results 
account for risk differences within HCC hierarchies. Additionally, by 
forcing all HCCs that share a hierarchy into the same HHS-RADV failure 
rate grouping regardless of whether they have different coefficients, 
we would not only diminish our ability to allow for differences among 
various diseases within an HCC hierarchy but would also reduce our 
ability to recognize differences in the difficulty of providing medical 
documentation for them.\78\
---------------------------------------------------------------------------

    \78\ See 83 FR 16961 and 16965.
---------------------------------------------------------------------------

    We considered several other options for addressing the payment 
cliff effect besides the specific sliding scale approach that we 
proposed. One option was returning to the original methodology 
finalized in the 2015 Payment Notice, which would have adjusted almost 
all issuers' risk scores for every error identified as a result of HHS-
RADV.\79\ The adjustments under the original methodology would have 
used the issuer's corrected average risk score to compute an adjustment 
factor, which would have been based on the ratio between the corrected 
and original average risk scores. However, our analysis indicated that 
the original methodology generally resulted in a more severe payment 
cliff effect, since the majority of outlier issuers had their original 
failure rates applied without the benefit of subtracting the weighted 
mean difference.\80\
---------------------------------------------------------------------------

    \79\ See 79 FR 13755-13770.
    \80\ See the 2019 RADV White Paper at pages 78-79 and Appendix 
B.
---------------------------------------------------------------------------

    The second option we considered was to modify the error rate 
calculation by calculating the issuer's GAF using the HCC group 
confidence interval rather than the distance to the weighted HCC group 
mean. As described in the 2019 RADV White Paper and in previous 
rulemaking,\81\ we have concerns that this option would result in 
under-adjustments based on HHS-RADV results for issuers farthest from 
the confidence intervals. Thus, although this option could address the 
payment cliff effect for issuers just outside of the confidence 
interval, it also could create the unintended consequence of mitigating 
the payment impact for situations where issuers are not close to the 
confidence intervals, potentially reducing incentives for issuers to 
submit accurate risk adjustment data to their EDGE servers.
---------------------------------------------------------------------------

    \81\ See 84 FR 17507-17508. See also the 2019 RADV White Paper 
at page 80.
---------------------------------------------------------------------------

    An additional option suggested by some stakeholders that could 
address, at least in part, the payment cliff effect that we considered 
would be to modify the current two-sided approach to HHS-RADV and only 
adjust issuers who are positive error rate outliers. However, moving to 
a one-sided outlier identification methodology would not have addressed 
the payment cliff effect because it would still exist on the positive 
error rate side of the methodology.\82\ In addition, the two-sided 
outlier identification, and the resulting adjustments to outlier issuer 
risk scores that have significantly better-than-average or poorer-than-
average data validation results, ensures that HHS-RADV adjusts for 
identified, material risk differences between what issuers submitted to 
their EDGE servers and what was validated by the issuers' medical 
records. The two-sided outlier identification approach ensures that an 
issuer who is coding well is able to recoup funds that might have been 
lost through risk adjustment because its competitors are coding badly.
---------------------------------------------------------------------------

    \82\ It is important to note the purpose of HHS-RADV approach is 
fundamentally different from the Medicare Advantage risk adjustment 
data validation (MA-RADV) approach. MA-RADV only adjusts for 
positive error rate outliers, as the program's intent is to recoup 
Federal funding that was the result of improper payments under the 
Medicare Part C program.
---------------------------------------------------------------------------

    We also considered various other options for the thresholds under 
the sliding scale option that we are proposing to address the payment 
cliff effect. For example, we considered as an alternative the adoption 
of a sliding scale option that would adjust outlier issuers' error 
rates on a sliding scale between the 95 and 99 percent confidence 
interval bounds (from +/-1.96 to 3 standard deviations). This 
alternative sliding scale option would retain the current methodology's 
confidence interval at 1.96 standard deviations, the full adjustment to 
the mean failure rate for issuers outside of the 99 percent confidence 
interval (beyond three standard deviations), and the current 
significant adjustment to the HCC group weighted mean after three 
standard deviations. In comments on the 2019 RADV White Paper, 
stakeholders expressed support for this sliding-scale option because it 
addressed the payment cliff issue without increasing the number of 
issuers identified as outliers. However, while we recognize that this 
alternative also would address the payment cliff effect, we are 
concerned it would not

[[Page 33616]]

provide the same balanced approach as the proposed sliding scale option 
and would instead weaken the HHS-RADV program by reducing its overall 
impact and the magnitude of HHS-RADV adjustments to outlier issuer's 
risk scores.
    When developing a process for implementing the transition from the 
prospective application of HHS-RADV results to a concurrent application 
approach, we considered three options for the transition year. In 
previous sections of the proposed rule, we described two of those 
options. The third option is the ``RA transfer option.'' The RA 
transfer option would separately calculate 2020 benefit year HHS-RADV 
adjustments to 2021 benefit year transfers and 2021 benefit year HHS-
RADV adjustments to 2021 benefit year transfers.\83\ Under this option, 
we would then calculate the difference between each of these values and 
the unadjusted 2021 benefit year transfers before any HHS-RADV 
adjustments were applied, and add these differences together to arrive 
at the total HHS-RADV adjustment that would be applied to the 2021 
benefit year transfers. That is, HHS would separately calculate 
adjustments for the 2020 and 2021 benefit year HHS-RADV results and 
incorporate 2020 and 2021 benefit year HHS-RADV results in one final 
adjustment to 2021 benefit year transfers that would be collected and 
paid in accordance with the 2021 benefit year HHS-RADV timeline, in 
2025.\84\ However, we believe this alternative is not as consistent 
with our current risk score error rate application and calculation as 
the combined plan liability risk score option, or as simple as the 
average error rate approach discussed above.
---------------------------------------------------------------------------

    \83\ See section 5.2 of the 2019 RADV White Paper.
    \84\ For a general description of the current timeline for 
publication, collection, and distribution of HHS-RADV adjustments to 
transfers, see 84 FR at 17506-17507.
---------------------------------------------------------------------------

VI. Regulatory Flexibility Act

    The Regulatory Flexibility Act (5 U.S.C. 601, et seq.) (RFA), 
requires agencies to prepare an initial regulatory flexibility analysis 
to describe the impact of a proposed rule on small entities, unless the 
head of the agency can certify that the rule will not have a 
significant economic impact on a substantial number of small entities. 
The RFA generally defines a ``small entity'' as (1) a proprietary firm 
meeting the size standards of the Small Business Administration (SBA), 
(2) a not-for-profit organization that is not dominant in its field, or 
(3) a small government jurisdiction with a population of less than 
50,000. States and individuals are not included in the definition of 
``small entity.'' HHS uses a change in revenues of more than 3 to 5 
percent as its measure of significant economic impact on a substantial 
number of small entities.
    In this proposed rule, we propose standards for the HHS-RADV 
program. This program is generally intended to ensure the integrity of 
the HHS-operated risk adjustment program, which stabilizes premiums and 
reduces the incentives for issuers to avoid higher-risk enrollees. 
Because we believe that insurance firms offering comprehensive health 
insurance policies generally exceed the size thresholds for ``small 
entities'' established by the SBA, we do not believe that an initial 
regulatory flexibility analysis is required for such firms.
    We believe that health insurance issuers would be classified under 
the North American Industry Classification System code 524114 (Direct 
Health and Medical Insurance Carriers). According to SBA size 
standards, entities with average annual receipts of $41.5 million or 
less would be considered small entities for these North American 
Industry Classification System codes. Issuers could possibly be 
classified in 621491 (HMO Medical Centers) and, if this is the case, 
the SBA size standard would be $35.0 million or less.\85\ We believe 
that few, if any, insurance companies underwriting comprehensive health 
insurance policies (in contrast, for example, to travel insurance 
policies or dental discount policies) fall below these size thresholds. 
Based on data from MLR annual report \86\ submissions for the 2017 MLR 
reporting year, approximately 90 out of 500 issuers of health insurance 
coverage nationwide had total premium revenue of $41.5 million or less. 
This estimate may overstate the actual number of small health insurance 
companies that may be affected, since over 72 percent of these small 
companies belong to larger holding groups, and many, if not all, of 
these small companies are likely to have non-health lines of business 
that will result in their revenues exceeding $41.5 million.
---------------------------------------------------------------------------

    \85\ https://www.sba.gov/document/support--table-size-standards.
    \86\ Available at https://www.cms.gov/CCIIO/Resources/Data-Resources/mlr.html.
---------------------------------------------------------------------------

    In addition, section 1102(b) of the Act requires us to prepare an 
RIA if a rule may have a significant impact on the operations of a 
substantial number of small rural hospitals. This analysis must conform 
to the provisions of section 603 of the RFA. For purposes of section 
1102(b) of the Act, we define a small rural hospital as a hospital that 
is located outside of a metropolitan statistical area and has fewer 
than 100 beds. This proposed rule would not affect small rural 
hospitals. Therefore, the Secretary has determined that this proposed 
rule will not have a significant impact on the operations of a 
substantial number of small rural hospitals.

VII. Unfunded Mandates

    Section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA) 
requires that agencies assess anticipated costs and benefits and take 
certain other actions before issuing a proposed rule that includes any 
Federal mandate that may result in expenditures in any 1 year by state, 
local, or Tribal governments, in the aggregate, or by the private 
sector, of $100 million in 1995 dollars, updated annually for 
inflation. In 2019, that threshold is approximately $154 million. 
Although we have not been able to quantify all costs, we expect the 
combined impact on state, local, or Tribal governments and the private 
sector to be below the threshold.

VIII. Federalism

    Executive Order 13132 establishes certain requirements that an 
agency must meet when it issues a proposed rule that imposes 
substantial direct costs on state and local governments, preempts state 
law, or otherwise has federalism implications.
    In compliance with the requirement of Executive Order 13132 that 
agencies examine closely any policies that may have federalism 
implications or limit the policymaking discretion of the states, we 
have engaged in efforts to consult with and work cooperatively with 
affected states, including participating in conference calls with and 
attending conferences of the National Association of Insurance 
Commissioners, and consulting with state insurance officials on an 
individual basis.
    While developing this proposed rule, we attempted to balance the 
states' interests in regulating health insurance issuers with the need 
to ensure market stability. By doing so, it is our view that we have 
complied with the requirements of Executive Order 13132.
    Because states have flexibility in designing their Exchange and 
Exchange-related programs, state decisions will ultimately influence 
both administrative expenses and overall premiums. States are not 
required to establish an Exchange or risk adjustment program. HHS 
operates risk adjustment on behalf of any state that does not elect to 
do so. Beginning with the 2017 benefit year,

[[Page 33617]]

HHS has operated risk adjustment for all 50 states and the District of 
Columbia.
    In our view, while this proposed rule would not impose substantial 
direct requirement costs on state and local governments, it has 
federalism implications due to direct effects on the distribution of 
power and responsibilities among the state and Federal Governments 
relating to determining standards about health insurance that is 
offered in the individual and small group markets.

IX. Reducing Regulation and Controlling Regulatory Costs

    Executive Order 13771 requires that the costs associated with 
significant new regulations ``to the extent permitted by law, be offset 
by the elimination of existing costs associated with at least two prior 
regulations.'' This proposed rule is not subject to the requirements of 
Executive Order 13771 because it is expected to result in no more than 
de minimis costs.

X. Conclusion

    In accordance with the provisions of Executive Order 12866, this 
regulation was reviewed by the Office of Management and Budget.

    Dated: February 19, 2020.
Seema Verma,
Administrator, Centers for Medicare & Medicaid Services.

    Dated: May 20, 2020.
Alex M. Azar II,
Secretary, Department of Health and Human Services.
[FR Doc. 2020-11703 Filed 5-29-20; 4:15 pm]
 BILLING CODE 4120-01-P