Federal Register / Vol. 85, No. 231 / Tuesday, December 1, 2020 / Rules and Regulations 76979 

1 42 U.S.C. 18063. 

2 42 U.S.C. 18063(a) and (b). 

3 42 U.S.C. 18063(b). 

4HHS also uses the data issuers submit to their 

EDGE servers for the calculation of the high-cost 

risk pool payments and charges added to the HHS 

risk adjustment methodology beginning with the 

2018 benefit year. 

§ 1.6011–8 Requirement of income tax 

return for taxpayers who claim the premium 

tax credit under section 36B. 

(a) Requirement of return. Except as 

otherwise provided in this paragraph 

(a), a taxpayer who receives the benefit 

of advance payments of the premium 

tax credit (advance credit payments) 

under section 36B must file an income 

tax return for that taxable year on or 

before the due date for the return 

(including extensions of time for filing) 

and reconcile the advance credit 

payments. However, if advance credit 

payments are made for coverage of an 

individual who is not included in any 

taxpayer’s family, as defined in § 1.36B– 

1(d), the taxpayer who attested to the 

Exchange to the intention to include 

such individual in the taxpayer’s family 

as part of the advance credit payment 

eligibility determination for coverage of 

the individual must file a tax return and 

reconcile the advance credit payments. 

(b) Applicability dates—(1) In general. 

Except as provided in paragraph (b)(2) 

of this section, paragraph (a) of this 

section applies for taxable years ending 

on or after December 31, 2020. 

(2) Prior periods. Paragraph (a) of this 

section as contained in 26 CFR part 1 

edition revised as of April 1, 2016, 

applies to taxable years ending after 

December 31, 2013, and beginning 

before January 1, 2017. Paragraph (a) of 

this section as contained in 26 CFR part 

1 edition revised as of April 1, 2020, 

applies to taxable years beginning after 

December 31, 2016, and ending before 

December 31, 2020. 

Sunita Lough, 

Deputy Commissioner for Services and 

Enforcement. 

Approved: September 4, 2020. 

David J. Kautter, 

Assistant Secretary of the Treasury (Tax 

Policy). 

[FR Doc. 2020–26200 Filed 11–27–20; 11:15 am] 

BILLING CODE 4830–01–P 

DEPARTMENT OF JUSTICE 

Office of the Attorney General 

28 CFR Part 26 

[Docket Number OAG 171; AG Order No. 

4911–2020 ] 

RIN 1105–AB63 

Manner of Federal Executions 

Correction 

In rule document 2020–25867 

beginning on page 75846 in the issue of 

Friday, November 27, 2020, make the 

following correction: 

On page 75846, in the third column, 

in the last line, ‘‘December 24, 2020’’ 

should read ‘‘December 28, 2020.’’ 

[FR Doc. C1–2020–25867 Filed 11–30–20; 8:45 am] 

BILLING CODE 1301–00–D 

DEPARTMENT OF HEALTH AND 

HUMAN SERVICES 

Centers for Medicare & Medicaid 

Services 

45 CFR Part 153 

[CMS–9913–F] 

RIN 0938–AU23 

Amendments to the HHS-Operated 

Risk Adjustment Data Validation (HHS– 

RADV) Under the Patient Protection 

and Affordable Care Act’s HHSOperated Risk Adjustment Program 

AGENCY: Centers for Medicare & 

Medicaid Services (CMS), Department 

of Health and Human Services (HHS). 

ACTION: Final rule. 

SUMMARY: This final rule adopts certain 

changes to the risk adjustment data 

validation error estimation methodology 

beginning with the 2019 benefit year for 

states where the Department of Health 

and Human Services (HHS) operates the 

risk adjustment program. This rule is 

finalizing 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 finalizes a change 

to the benefit year to which HHS–RADV 

adjustments to risk scores and risk 

adjustment transfers would be applied 

beginning with the 2020 benefit year of 

HHS–RADV. These policies seek to 

further the integrity of HHS–RADV, 

address stakeholder feedback, promote 

fairness, and improve the predictability 

of HHS–RADV adjustments. 

DATES: These regulations are effective 

on December 31, 2020. 

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: 

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 final 

rule. Section 1343 of the PPACA 1

established a permanent risk adjustment 

program to provide payments to health 

insurance issuers that attract higherthan-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 of the 

Department of Health and Human 

Services (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 

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 through 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 HHSoperated risk adjustment program. 

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 the Department of 

Health and Human Services (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 scores 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.4

Pursuant to 45 CFR 153.350, HHS 

performs HHS–RADV to validate the 

accuracy of data submitted by issuers 


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5HHS also has general authority to audit issuers 

of risk adjustment covered plans pursuant to 45 

CFR 153.620(c). 6See 45 CFR 153.20 for the definition of ‘‘risk 

adjustment covered plan.’’ 

7 45 CFR 153.630(b). 

8 45 CFR 153.630(c). 

9HHS–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. 

10The 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-andInitiatives/Premium-Stabilization-Programs/ 

Downloads/BY2017-HHSRADV-Adjustments-to-RATransfers-Summary-Report.pdf. 

11The 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. 

12A copy of the Affordable Care Act HHSOperated 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. 

for the purposes of risk adjustment 

transfer calculations for states where 

HHS operates the risk adjustment 

program. 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 HHSoperated 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 HHS-operated risk adjustment 

program.5 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-thanaverage 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 6 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 errors 

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. 

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 (IVA) of a sample 

of risk adjustment data selected by 

HHS.7 The issuer provides 

demographic, enrollment, and claims 

data and medical record documentation 

for a sample of enrollees selected by 

HHS to its IVA entity for data 

validation. After the IVA entity has 

validated the HHS-selected sample, a 

subsample is validated in a second 

validation audit (SVA).8 The SVA is 

conducted by an entity HHS retains to 

verify the accuracy of the findings of the 

IVA. 

HHS conducted two pilot years of 

HHS–RADV for the 2015 and 2016 

benefit years 9 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 pools. The 2017 benefit 

year HHS–RADV was the first payment 

year that resulted in adjustments to 

issuers’ risk scores and the risk 

adjustment transfers in the applicable 

state market risk pools as a result of 

HHS–RADV findings.10 11

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).12 The 

2013 RADV White Paper discussed and 

sought comment on a number of 

potential considerations for the 

development and operation of HHS– 

RADV. Based on the feedback received, 

HHS promulgated regulations to 

implement HHS–RADV that we have 

modified in certain respects based on 

experience and public input, as follows. 

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. 

In the March 11, 2014 Federal Register 

(79 FR 13743), we published the 2015 

Payment Notice final rule, which 

finalized HHS–RADV requirements 

related to sampling; IVA standards, SVA 

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. 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. 

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 IVA 

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 


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13To 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 offcalendar 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 through 17504. 

14 ‘‘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-RAFinal-Rule-Resumption-RAOps.pdf. 

15An 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. 

16See, e.g., 78 FR 15441 and 83 FR 16930. 

17Also see New Mexico Health Connections v. 

United States Department of Health and Human 

Services, 946 F.3d 1138 (10th Cir. 2019). 

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 adjust 

issuers’ risk scores when an issuer’s 

failure rate is materially 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 IVA 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,13 such that 

exiting outlier issuers’ results are used 

to adjust the benefit year being audited 

(rather than the following transfer year). 

In the July 30, 2018 Federal Register 

(83 FR 36456), we published a final rule 

that adopted the 2017 benefit year HHSoperated 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 the use of 

statewide average premium in the HHSoperated 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.14

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) (2020 Payment Notice). The 

final rule included policies related to 

incorporating risk adjustment 

prescription drug categories (RXCs) 15

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 SVA results of 

the full sample or the subsample (of up 

to 100 enrollees) results should be used 

in place of IVA results when an issuer’s 

IVA 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 HHS 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. 

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 rule, 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 EDGE HCCs 

(hierarchical condition categories) 

within an HCC failure rate group would 

not be determined to be 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-andcomment rulemaking,16 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.17 Although 

the statutory provisions for many other 

PPACA programs appropriated funding, 

authorized amounts to be appropriated, 

or provided budget authority in advance 


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18For 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). 

19See 42 U.S.C. 18063. 

20Compare 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). 

21The 2019 RADV White Paper is available at: 

https://www.cms.gov/files/document/2019-hhs-riskadjustment-data-validation-hhs-radv-white-paper. 

22The exception to the current prospective 

application of HHS–RADV results is for exiting 

issuers identified as positive error rate outliers, 

whose HHS–RADV results are applied to the risk 

scores and transfer amounts for the benefit year 

being audited. See the 2020 Payment Notice, 84 FR 

at 17503–17504. 

23As part of the Administration’s efforts to 

combat the Coronavirus Disease 2019 (COVID–19), 

we announced the postponement of the 2019 

benefit year HHS–RADV process. See https://

www.cms.gov/files/document/2019-HHS-RADVPostponement-Memo.pdf. Also, we have provided 

further guidance on the updated schedule for the 

2019 benefit year HHS–RADV, which is outlined in 

the 2019 Benefit Year Timeline of Activities: 

https://www.regtap.info/uploads/library/HRADV_

Timeline_091020_5CR_091020.pdf. 24The exception to the current prospective 

application of HHS–RADV results is for exiting 

issuers identified as positive error rate outliers, 

whose HHS–RADV results are applied to the risk 

scores and transfer amounts for the benefit year 

being audited. 

25As detailed in section II.B, to effectuate the 

transition beginning with the 2020 benefit year, we 

will aggregate results from the 2019 and 2020 

benefit years of HHS–RADV for non-exiting issuers 

using the average error rate approach and apply the 

aggregated results to 2020 risk scores and transfers. 

26We note that a correction notice was issued for 

the proposed rule to address the misalignment of 

certain text between the final draft version of the 

proposed rule approved for publication and the 

published version in the Federal Register. See 85 

FR 38107 (June 25, 2020). Since publishing the 

correction notice, an additional error between the 

two versions was identified. When describing the 

current HHS–RADV error methodology in the 

proposed rule at 85 FR 33599, the upper bound of 

the confidence interval was incorrectly published 

as U BG = m{GF RG}¥sigma_cutoff * Sd{GF RG}. 

This formula should have instead been published 

as U BG = m{GF RG} + sigma_cutoff * Sd{GF RG}. 

of appropriations,18 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.19 Indeed, 

unlike the Medicare Prescription Drug, 

Improvement and Modernization Act of 

2003, which expressly authorized the 

appropriation of funds and provided 

budget authority in advance of 

appropriations to make Part D riskadjusted payments, the PPACA’s risk 

adjustment statute made no reference to 

additional appropriations.20 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. 

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. Prior to the proposed rule, 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).21 We 

considered comments received on the 

2019 RADV White Paper and in 

connection with previous rules as we 

developed the policies in the proposed 

rule. For this final rule, we considered 

all public input we received on the 

topics addressed in the proposed rule as 

we developed the finalized policies. 

II. Provisions of the Final Regulations 

and Analyses and Responses to Public 

Comments 

In the June 2, 2020 Federal Register 

(85 FR 33595), we published the 

‘‘Amendments to the HHS-Operated 

Risk Adjustment Data Validation Under 

the Patient Protection and Affordable 

Care Act’s HHS-Operated Risk 

Adjustment Program’’ proposed rule. 

The proposed rule proposed several 

refinements to the HHS–RADV error 

rate calculation, and proposed to 

transition away from the current 

prospective application of HHS–RADV 

results.22 The proposals were designed 

to specifically address stakeholder 

feedback received after the first payment 

year of HHS–RADV. In addition to 

soliciting comments on the specific 

policy proposals in the proposed rule, 

we requested feedback on the potential 

impact of the COVID–19 public health 

emergency on the proposed effective 

dates for implementation of the 

proposals. We received 25 comments 

from health insurance issuers, industry 

trade associations, and other 

stakeholders. These comments ranged 

from general support of or opposition to 

the proposed changes to specific 

questions or comments regarding 

proposed changes. We also received a 

number of comments and suggestions 

that were outside the scope of the 

proposed rule that are not addressed in 

this final rule. In this final rule, we 

provide a summary of the proposed 

changes, a summary of the public 

comments received that directly relate 

to these proposals, our responses to 

these comments, and a description of 

the provisions we are finalizing. 

This rule finalizes the proposed 

changes to two aspects of HHS–RADV: 

(A) The error rate calculation, and (B) 

the application of HHS–RADV results, 

with the modifications described below. 

Beginning with the 2019 benefit year of 

HHS–RADV,23 we are finalizing as 

proposed the following refinements to 

the error rate calculation: (1) An 

adjustment to the HCC grouping 

methodology to address the influence of 

the HCC hierarchies and coefficient 

estimation groups; (2) a sliding scale 

adjustment for calculating an issuer’s 

adjustment factor that changes the 

confidence intervals for determining 

outliers and applies a sliding scale 

adjustment in cases where an outlier 

issuer is close to the edges of the 

confidence interval for one or more HCC 

failure rate groups; and (3) a 

modification to the error rate calculation 

in cases where a negative error rate 

outlier issuer also has a negative failure 

rate. We are also finalizing the transition 

from the current prospective application 

of HHS–RADV results 24 to an approach 

that would apply HHS–RADV results to 

the benefit year being audited. After 

consideration of comments, we will 

switch to the concurrent application of 

HHS–RADV results beginning with the 

2020 benefit year.25 We believe these 

policies address stakeholder feedback 

received and our experience with the 

first payment year of HHS–RADV on 

these issues. These finalized policies 

seek to further the integrity of HHS– 

RADV while maintaining stability, 

promoting fairness and improving the 

predictability of HHS–RADV. The 

following is a summary of the comments 

received on the proposed rule’s timeline 

for implementing these policies: 26

Comments: One commenter was 

concerned that the COVID–19 public 

health emergency would impact the 

completeness of 2019 (and possibly 

2020) data while another commenter 


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27https://www.cms.gov/files/document/2019- 

HHS-RADV-Postponement-Memo.pdf. 

28See the ‘‘2019 Benefit Year HHS–RADV 

Activities Timeline’’ https://www.regtap.info/ 

uploads/library/HRADV_Timeline_091020_5CR_

091020.pdf. 

29As noted above, the start of the 2019 benefit 

year HHS–RADV process was postponed until the 

2021 calendar year due to the COVID–19 public 

health emergency. 

30See the 2017 HHS–RADV timeline, available at: 

https://www.regtap.info/uploads/library/HRADV_

JobAid_timeline_5CR_032819.pdf; and https://

www.regtap.info/uploads/library/HRADV_

Timeline_073119_5CR_120219.pdf. Also see the 

2018 HHS–RADV timeline, available at: https://

www.regtap.info/uploads/library/HRADV_

Timeline_030420_V1_RETIRED_5CR_041320.pdf. 

31See the 2017 and 2018 HHS–RADV results 

memos, available at: https://www.cms.gov/CCIIO/ 

Programs-and-Initiatives/Premium-StabilizationPrograms/Downloads/2017-Benefit-Year-HHS-RiskAdjustment-Data-Validation-Results.pdf and 

https://www.cms.gov/CCIIO/Programs-andInitiatives/Premium-Stabilization-Programs/ 

Downloads/2018_BY_RADV_Results_Memo.pdf. 

32As detailed below, to effectuate the transition 

beginning with the 2020 benefit year, we will 

aggregate results from the 2019 and 2020 benefit 

years of HHS–RADV for non-exiting issuers using 

the average error rate approach and apply the 

aggregated results to 2020 benefit year risk scores 

and transfers. 

33See 83 FR 16930 at 16961 through 16965. 

34As detailed further below, these risk score 

changes are then used to adjust risk adjustment 

transfers for the applicable state market risk pool. 

35See 85 FR at 33599–33600. Also see, supra, 

note 26. 

36Exiting positive error rate 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. As 

detailed in Section II.B, we are finalizing the 

proposed transition from the prospective 

application of HHS–RADV results such that risk 

score error rates will also be applied to the benefit 

year being audited beginning with the 2020 benefit 

year of HHS–RADV for non-exiting issuers. 

expected COVID–19 to affect chart 

retrieval and provider documentation 

within the chart. One commenter did 

not see a need to further delay the 

stabilizing measures in the proposed 

rule due to COVID–19. 

Response: Recognizing the need for 

providers and provider organizations to 

focus exclusively on caring for patients 

during the COVID–19 public health 

emergency, we postponed the start of 

2019 benefit year HHS–RADV 

activities.27 As recently announced, IVA 

samples for 2019 benefit year HHS– 

RADV will be released in January 2021 

and we anticipate 2020 benefit year 

HHS–RADV will commence as usual 

with the release of IVA samples in May 

2021.28 We continue to monitor the 

COVID–19 pandemic, including 

potential medical record retrieval issues 

and will consider whether additional 

flexibilities for HHS–RADV are 

appropriate. However, we are not 

codifying or finalizing any specific 

COVID–19 policies in this rulemaking. 

Comments: Some commenters who 

supported the proposed error rate 

calculation changes asked HHS to also 

apply the changes to the 2017 and 2018 

benefit years of HHS–RADV. A different 

commenter opposed applying the 

proposed changes starting with the 2019 

benefit year HHS–RADV, expressing the 

belief it would be retroactive to do so, 

and instead supporting the adoption of 

these proposals for future benefit years. 

Other commenters supported policies in 

the rule applying beginning with the 

2019 benefit year. 

Response: The policies being finalized 

in this rule only impact the calculation 

of error rates and the application of the 

HHS–RADV results that occur at the end 

of the HHS–RADV process. Because the 

2019 benefit year of HHS–RADV has not 

begun 29 and, under the updated 

timeline, the calculation of the error 

rates for 2019 benefit year of HHS– 

RADV will not occur until February 

2022, we disagree that applying the 

error rate calculation refinements 

finalized in this rule to the 2019 benefit 

year would be retroactive. Further, for 

the reasons outlined in the proposed 

rule and this rule, we believe these 

refinements are important and should 

be applied as soon as practicable. 

However, we believe that application of 

this rule to 2017 and 2018 benefit years 

of HHS–RADV would not be 

appropriate because the applicable error 

rate calculations are complete.30 31 We 

are therefore applying the error rate 

calculation modifications finalized in 

this rule beginning with the 2019 

benefit year of HHS–RADV, as 

proposed. Similarly, for the application 

of HHS–RADV results, in light of the 

delay of 2019 benefit year HHS–RADV 

and for the reasons outlined below in 

Section II.B., we are finalizing the 

policy to begin applying HHS–RADV 

results to the benefit year audited 

beginning with the 2020 benefit year 

which is as soon as practicable.32

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,33 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 

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 metric. HHS defines the 

national statistically meaningful metric 

as the weighted mean and standard 

deviation of the failure rate calculated 

based on all issuers’ HHS–RADV 

results. Each issuer’s failure rates are 

compared to these national metrics to 

determine whether the issuer’s failure 

rate is an outlier. Based on outlier 

issuers’ failure rate results, their error 

rates are calculated and applied to their 

plan liability risk scores.34

In response to comments received on 

the 2019 RADV White Paper and to help 

put the proposed changes in context, the 

proposed rule outlined the current error 

rate calculation methodology.35 This 

included 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. 

Consistent with 45 CFR 153.350(c), 

HHS applies the outlier issuer’s error 

rate to adjust that issuer’s applicable 

benefit year plan liability risk score.36

This risk score change, which also 

impacts the state market average 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 risk 

adjustment 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. 

In response to stakeholder concerns, 

comments to the 2019 RADV White 

Paper, and our analyses of 2017 benefit 

year HHS–RADV results, HHS proposed 

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 subsequently adjusted 

transfers. We also proposed changes to 

mitigate the impact of HHS–RADV 


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37See 85 FR at 33601. 

38 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 at 29175. 

39The 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 June 17, 2019 

document ‘‘Potential Updates to HHS–HCCs for the 

HHS-operated Risk Adjustment Program’’ (2019 

HHS–HCC Potential Updates Paper), available at 

https://www.cms.gov/CCIIO/Resources/Regulationsand-Guidance/Downloads/Potential-Updates-toHHS-HCCs-HHS-operated-Risk-AdjustmentProgram.pdf#page=11. 

40As described in the ‘‘Potential Updates to 

HHS–HCCs for the HHS-operated Risk Adjustment 

Program’’ Paper, available at ‘‘https://www.cms.gov/ 

CCIIO/Resources/Regulations-and-Guidance/ 

Downloads/Potential-Updates-to-HHS-HCCs-HHSoperated-Risk-Adjustment-Program.pdf#page=11. 

41The shorthand ‘‘V05’’ refers to the current 

HHS–HCC classification for the HHS risk 

adjustment models, which applies through the 2020 

benefit year. V07 is the HHS–HCC classification for 

the HHS risk adjustment models, which applies 

beginning with the 2021 benefit year. 

adjustments that result from negative 

error rate outlier issuers with negative 

failure rates. After consideration of 

comments, we are finalizing the 

refinements to the error rate calculation, 

as proposed, beginning with the 2019 

benefit year of HHS–RADV. These 

targeted policies are intended as 

interim, incremental measures while we 

continue to analyze HHS–RADV results 

and consider potential further 

refinements and changes to the HHS– 

RADV methodology, including potential 

significant changes to the outlier 

determination process and the error rate 

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.37 For risk adjustment model 

development, this includes: (1) The 

hierarchies of HCCs, (2) HCC coefficient 

estimation groups, (3) a priori stability 

constraints, and (4) hierarchy violation 

constraints. For HHS–RADV, medical 

conditions are grouped for the HHS– 

RADV HCC failure rate groups. These 

grouping processes are not concurrent. 

More specifically, the grouping 

processes related to model development 

are implemented prior to the benefit 

year and the HHS–RADV HCC failure 

rate groups are implemented after the 

benefit year. Our experience in the 

initial years of HHS–RADV found that 

differences among these grouping 

processes interact in varying ways and 

may result in greater or lesser HHS– 

RADV adjustments than may be 

warranted in certain circumstances. 

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 38 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.39 Condition categories become 

HCCs once these hierarchies are 

imposed. 

As noted previously, 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 the risk adjustment model and plan 

liability risk score calculation. Although 

HCCs reflect hierarchies among related 

disease categories, multiple HCCs can 

accumulate for enrollees with unrelated 

diseases; 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. 

In the risk adjustment models, 

estimated coefficients of the various 

HCCs within a hierarchy 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 

such 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,40 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 

groups) in order to decrease the 

sensitivity of the models to coding 

proliferation. 

Although some of these HCC 

coefficient estimation groups occur 

within hierarchies, some HCC 

coefficient estimation groups include 

HCCs that do not share a hierarchy. 

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 41 are displayed in 

Table 1: 

TABLE 1—HCC COEFFICIENT ESTIMATION GROUPS FROM ADULT RISK ADJUSTMENT MODELS V05 

HHS HCC V05 HHS–HCC label 

Adult model HCC 

coefficient 

estimation group 

19 ...................... Diabetes with Acute Complications ................................................................................................................ G01 

20 ...................... Diabetes with Chronic Complications ............................................................................................................ G01 

21 ...................... Diabetes without Complication ....................................................................................................................... G01 

26 ...................... Mucopolysaccharidosis .................................................................................................................................. G02A 

27 ...................... Lipidoses and Glycogenosis .......................................................................................................................... G02A 

29 ...................... Amyloidosis, Porphyria, and Other Metabolic Disorders ............................................................................... G02A 

30 ...................... Adrenal, Pituitary, and Other Significant Endocrine Disorders ...................................................................... G02A 

54 ...................... Necrotizing Fasciitis ....................................................................................................................................... G03 

55 ...................... Bone/Joint/Muscle Infections/Necrosis ........................................................................................................... G03 

61 ...................... Osteogenesis Imperfecta and Other Osteodystrophies ................................................................................. G04 


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42 In the 2021 Payment Notice (85 FR at 29178), 

we finalized an additional a priori stability 

constraint to the child 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, 

and revised the single transplant stability constraint 

in the child models to be two stability constraints 

to better distinguish transplant cost differences. 

43For a table of the HCC failure rate groupings for 

2017 benefit year HHS–RADV, see the 2019 RADV 

White Paper, Appendix E. 

44See 85 FR at 33603–33604. Also see Section 3.3 

of the 2019 RADV White Paper. 

TABLE 1—HCC COEFFICIENT ESTIMATION GROUPS FROM ADULT RISK ADJUSTMENT MODELS V05—Continued 

HHS HCC V05 HHS–HCC label 

Adult model HCC 

coefficient 

estimation group 

62 ...................... Congenital/Developmental Skeletal and Connective Tissue Disorders ......................................................... G04 

67 ...................... Myelodysplastic Syndromes and Myelofibrosis ............................................................................................. G06 

68 ...................... Aplastic Anemia .............................................................................................................................................. G06 

69 ...................... Acquired Hemolytic Anemia, Including Hemolytic Disease of Newborn ....................................................... G07 

70 ...................... Sickle Cell Anemia (Hb-SS) ........................................................................................................................... G07 

71 ...................... Thalassemia Major ......................................................................................................................................... G07 

73 ...................... Combined and Other Severe Immunodeficiencies ........................................................................................ G08 

74 ...................... Disorders of the Immune Mechanism ............................................................................................................ G08 

81 ...................... Drug Psychosis .............................................................................................................................................. G09 

82 ...................... Drug Dependence .......................................................................................................................................... G09 

106 .................... Traumatic Complete Lesion Cervical Spinal Cord ......................................................................................... G10 

107 .................... Quadriplegia ................................................................................................................................................... G10 

108 .................... Traumatic Complete Lesion Dorsal Spinal Cord ........................................................................................... G11 

109 .................... Paraplegia ...................................................................................................................................................... G11 

117 .................... Muscular Dystrophy ........................................................................................................................................ G12 

119 .................... Parkinson’s, Huntington’s, and Spinocerebellar Disease, and Other Neurodegenerative Disorders ........... G12 

126 .................... Respiratory Arrest .......................................................................................................................................... G13 

127 .................... Cardio-Respiratory Failure and Shock, Including Respiratory Distress Syndromes ..................................... G13 

128 .................... Heart Assistive Device/Artificial Heart ............................................................................................................ G14 

129 .................... Heart Transplant ............................................................................................................................................. G14 

160 .................... Chronic Obstructive Pulmonary Disease, Including Bronchiectasis .............................................................. G15 

161 .................... Asthma ........................................................................................................................................................... G15 

187 .................... Chronic Kidney Disease, Stage 5 .................................................................................................................. G16 

188 .................... Chronic Kidney Disease, Severe (Stage 4) ................................................................................................... G16 

203 .................... Ectopic and Molar Pregnancy, Except with Renal Failure, Shock, or Embolism .......................................... G17 

204 .................... Miscarriage with Complications ...................................................................................................................... G17 

205 .................... Miscarriage with No or Minor Complications ................................................................................................. G17 

207 .................... Completed Pregnancy With Major Complications ......................................................................................... G18 

208 .................... Completed Pregnancy With Complications .................................................................................................... G18 

209 .................... Completed Pregnancy with No or Minor Complications ................................................................................ G18 

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. 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 

the same coefficient value) as long as 

the HCCs are not in the same hierarchy. 

Prior to the 2021 benefit year 

recalibration, only one a priori stability 

constraint was applied to the models, 

and this constraint was only applied to 

the child models.42

HCC coefficient estimation groups 

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). 

HCC coefficients may also be 

constrained to 0 if the estimates fitted 

by the regression model are negative. 

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. To 

create the HCC failure rate groupings for 

HHS–RADV, 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 

frequency 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.43

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 

ways that could result in 

misalignments.44

Based on HHS’s initial analysis of the 

2017 benefit year HHS–RADV results, 

and in response to comments to the 

2019 RADV White Paper, HHS 

considered an option in the proposed 

rule to address the influence of the HCC 

hierarchies and HCC coefficient 

estimation groups on the HCC failure 

rate groupings in HHS–RADV. We 

proposed to modify the creation of 

HHS–RADV HCC failure rate groupings 

to 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 proposed that, when 

HHS calculates EDGE and IVA 

frequencies for each individual HCC, we 

would aggregate HCCs that are in the 

same HCC coefficient estimation group 


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in the adult risk adjustment models 

(and, therefore, have coefficients 

constrained to be equal to one another) 

into one ‘‘Super’’ HCC, prior to 

calculating individual HCC failure rates 

and sorting the HCCs into low, medium, 

and high failure rate groups for HHS– 

RADV. These new frequencies, 

including the aggregated frequencies of 

HCC coefficient estimation groups and 

the individual frequencies of all other 

HCCs that are not aggregated with other 

HCCs because they are not in any 

coefficient estimation groups, would be 

considered frequencies of ‘‘Super 

HCCs.’’ 

Under the proposed methodology, we 

would modify the current HCC failure 

rate grouping methodology as follows: 

Where: 

c is the index of the cth Super HCC; 

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; 

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: 

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; 

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; 

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: 

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 would be grouped according to 

the current HHS–RADV failure rate 

grouping methodology. 

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 independently 

and additively to an issuer’s failure rate 

in a HCC failure rate grouping. This 

proposal would refine the current 

methodology to better identify and focus 

HCC failure rates used in outlier 

determination on actual differences in 

risk and costs. Our tests of this proposed 

policy on HHS–RADV results data 

revealed that between an estimated 85.2 

percent (2018 data) and 98.1 percent 

(2017 data) of the occurrences of HCCs 

on EDGE belong to HCCs that would be 

assigned to the same failure rate groups 

under the proposed ‘‘Super HCC’’ 

methodology as they have been under 

the current methodology as seen in 

Table 2. Although the impact on 

individual issuer results may vary 

depending upon the accuracy of their 

EDGE data submissions and the rate of 

occurrence of various HCCs in their 

enrollee population, the national 

metrics used for HHS–RADV, that is, the 

weighted means and weighted standard 

deviations, would only be slightly 

affected, as seen in Table 3. 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 would have 

similar failure rates under both 

methodologies. 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. 


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45For 2017, this was calculated after removing 

issuers in Massachusetts and incorporating cases 

where issuers failed pairwise and the SVA subsample was used. 

46Both a priori stability constraints and hierarchy 

violation constraints are described earlier in this 

section (Section II.A.1) of the rule. Also see 85 FR 

at 33602–33603. 

47See Section 11.3.1 of the 2018 HHS–RADV 

Protocols at https://www.regtap.info/uploads/ 

library/HRADV_2018Protocols_070319_RETIRED_

5CR_070519.pdf for a description of the process 

prior to the introduction of Super HCCs. Beginning 

with the 2019 benefit year of HHS–RADV, Super 

HCCs would take the place of HCCs in the process. 

The 2019 HHS–RADV Protocols have thus far only 

been published in part at https://www.regtap.info/ 

uploads/library/HRADV_2019_Protocols_111120_

5CR_111120.pdf. The section of the 2019 HHS– 

RADV Protocols pertaining to HCC grouping for 

failure rate calculations is not included in the 

current version. Once published, this section will 

be updated to include steps related to creation of 

Super HCCs. 

In testing this proposal to create 

Super HCCs in HHS–RADV, we grouped 

HCCs in the same HCC coefficient 

estimation group in the adult risk 

adjustment models. 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; 89.1 

percent for the 2018 benefit year).45 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 

accuracy of HCC failure rate estimates 

across the entire population in 

aggregate. Therefore, we proposed to use 

HCC coefficient estimation groups in the 

adult risk adjustment models to define 

Super HCCs for all HHS–RADV sample 

enrollees, regardless of the risk 

adjustment model to which they are 

subject. 

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 

did not consider including a priori 

stability constraints or hierarchy 

violation constraints in the aggregation 

of Super HCCs.46 We also did not 

consider hierarchy violation constraints 

as a part of the sorting algorithm in 

order to balance complexity and 

consistency. For example, if, in a given 

benefit year, the magnitudes of two 

coefficients that share a hierarchy 

happen to decrease in order of their 

conditions’ theoretical severity, the 

coefficients would violate the 

assumptions of the hierarchy structure 

and would be subject to a hierarchy 

violation constraint in that year’s risk 

adjustment models. However, if the 

magnitude of those two coefficients 

increase in the order of their conditions’ 

severity in the subsequent year, as 

would generally be expected, the 

coefficients would be consistent with 

the assumptions of the hierarchy 

structure and would not be constrained 

to be equal as a part of a hierarchy 

violation constraint. Because these yearto-year changes in hierarchy violation 

constraints are based solely on the 

magnitude of each year’s initial 

coefficient estimates, using them in the 

grouping of Super HCCs would make 

those groupings less stable and 

transparent, and would reduce 

predictability for issuers. 

Due to these considerations, we 

proposed to combine HCCs into Super 

HCCs defined only by HCC coefficient 

estimation groups in the adult risk 

adjustment models 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. As proposed, these Super HCC 

groupings would apply to all HHS– 

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 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 solicited comment on 

all aspects of this proposal. We also 

solicited comments on whether, in 

addition to the Super HCCs based on the 

adult risk adjustment models, HHS 

should create separate infant Super 

HCCs for each maturity and severity 

type in the infant risk adjustment 

models. Additionally, we solicited 

comments on whether we should 

consider incorporating a priori stability 

constraints from the child models or 

hierarchy violation constraints from the 

adult models when defining Super 

HCCs. 

After consideration of the comments 

received, we are finalizing this policy as 

proposed, and will combine HCCs in 

HCC coefficient estimation groups in the 

adult risk adjustment models, which 

effectively have equal coefficients, into 

Super HCCs prior to sorting the HCCs 

into low, medium and high failure rate 

groups for HHS–RADV. This refinement 

to the error rate calculation will apply 

starting with the 2019 benefit year of 

HHS–RADV. These Super HCC 

groupings will apply to all HHS–RADV 

sample enrollees, regardless of the risk 

adjustment models to which they are 

subject. Therefore, although the 

aggregation will be based upon the adult 

models, enrollees subject to the child 

and infant models will have their HCCs 

included in the aggregated counts when 

they have an HCC that is listed as 

sharing a coefficient estimation group 

with other HCCs in the adult models. 

The resulting Super HCCs will then be 

sorted into high, medium, and low 

failure rate groups using the sorting 

process described in the applicable 

benefit year’s HHS–RADV Protocols.47

Once sorted into failure rate groups, 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 

risk adjustment models, will be grouped 

according to the current HHS–RADV 

failure rate grouping methodology. 

Comments: All comments on this 

policy supported the proposal to adjust 

the HCC failure rate grouping 

methodology to define Super HCCs 

based upon the HCC coefficient 

estimation groups in the adult risk 

adjustment models. Several commenters 

requested we expand the proposed 

definition of Super HCCs to include the 

grouping of conditions used to create 

the variables for the infant models. 

Some of these commenters added that 

implementing this expansion for the 

infant models should be done in a way 

that avoids year-to-year stability 

concerns, if possible, while other 

comments requested that we publish an 

analysis on the impacts of such an 

expansion prior to implementing it. 

In addition, some commenters agreed 

that the inclusion of a priori stability 

constraints from the child models 

would be inappropriate due to their 

additive nature, with a few of these 

commenters also agreeing that hierarchy 

violation constraints should not factor 

into the definitions of Super HCCs. 

However, other commenters requested 

that HHS include HCCs involved in a 

hierarchy violation constraint in the 

same Super HCC. Some commenters 

requested we publish an analysis on 

including a priori stability constraints as 

part of the process to create Super 

HCCs. 

Response: We are finalizing the 

refinement to the HCC failure rate 

grouping methodology as proposed and 

will place all HCCs that share an HCC 

coefficient estimation group in the adult 

risk adjustment models into the same 

HCC failure rate grouping beginning 

with the 2019 benefit year of HHS– 

RADV. Although the aggregation will be 

based upon the adult models, the child 


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48For 2017, this was calculated after removing 

issuers in Massachusetts and incorporating cases 

where issuers failed pairwise agreement and the 

SVA sub-sample was used. 

49 Ibid. 

50The additive nature of HCCs subject to a priori 

stability constraints as opposed to other groupings 

and infant models will have their HCCs 

included in the aggregated counts when 

they have an HCC that is listed as 

sharing a coefficient estimation group 

with other HCCs in the adult models. As 

explained in the proposed rule and in 

this rule, we believe this change 

mitigates the misalignments that occur 

when HCCs with the same risk score 

coefficient are sorted into different HCC 

failure rate groupings while increasing 

the stability of year-to-year HCC failure 

rate grouping assignments. To promote 

fairness and ensure the integrity of the 

program, we do not believe that a RADV 

finding that reflects an EDGE data 

miscoding of one condition as another 

condition from the same coefficient 

estimation group should contribute to 

any of an issuer’s three failure rates. 

This refinement to the HHS–RADV 

failure rate grouping methodology 

ensures that these types of HCC 

miscodings with no risk score impact do 

not impact an issuer’s HHS–RADV error 

rate. 

We appreciate the comments about 

the creation of separate infant Super 

HCCs and investigated the potential 

adoption of separate infant model terms. 

Our analysis found that such an 

approach would likely result in more 

year-to-year uncertainty and instability 

due to the relatively small sample size 

for some infant model terms—notably, 

only around 5 percent of 2017 48 and 

2018 HHS–RADV sample enrollees in 

strata 1 through 9 with EDGE HCCs 

were infants. As a result, HCC counts 

and failure rates for potential infantonly Super HCCs would be more likely 

to vary due to random selection, 

yielding less year-to-year stability 

among HCC failure rate group 

assignments. Therefore, in the interest 

of stability, we believe that basing the 

definitions of Super HCCs on coefficient 

estimation groups from the adult risk 

adjustment models is more appropriate. 

As noted earlier, 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; 89.1 percent for the 2018 

benefit year).49

We also appreciate the comments 

regarding inclusion of hierarchy 

violation constraints when creating 

Super HCCs, such that HCCs involved 

in a hierarchy violation constraint 

would be included in the same Super 

HCC. As explained in the proposed rule, 

we did not consider hierarchy violation 

constraints when developing the Super 

HCC proposal in order to balance 

complexity and consistency, since these 

constraints can change from year-to-year 

as a natural result of the annual 

recalibration updates to the model 

coefficients. Similar to the concerns for 

the separate infant model Super HCCs, 

these year-to-year changes would make 

HCC groupings for these HCCs less 

stable and transparent, and would 

reduce predictability for issuers. 

Further, we note that hierarchy 

violation constraints may occur in a 

single metal-level and age group in just 

one of the three data years used to create 

the blended coefficients. For example, 

the 2021 benefit year coefficients reflect 

a weighted average of coefficients 

calculated separately from 2016, 2017, 

and 2018 benefit year EDGE data. If 

there is a hierarchy violation among 

three HCCs that share a hierarchy in the 

silver adult model fitted to 2018 EDGE 

data, a hierarchy violation constraint 

would be applied to the three 

coefficients calculated from that data set 

alone, excluding any coefficients from 

the 2016 and 2017 benefit years, and 

any other metal levels and age groups 

from the 2018 benefit year. As a result, 

when the coefficients from the separate 

data years are blended, the hierarchy 

violation constraint may not be apparent 

in the final coefficients and the final 

coefficients for the HCCs in the affected 

hierarchy may differ from one another. 

Additionally, even if a hierarchy 

violation constraint is necessary for the 

same hierarchy in all three data years, 

and is therefore apparent in the final 

risk adjustment coefficients, the 

hierarchy violation constraint could 

involve a very small number of 

enrollees specific to a particular metal 

level and age group model (for example, 

the gold metal level child model). 

Although the coefficients involved in 

such a hierarchy violation constraint 

would all be equal to one another, the 

coefficients from age group models 

unaffected by hierarchy violation 

constraints are likely to differ according 

to the severity of the HCCs in the 

hierarchy, and it would be appropriate 

to capture the resulting risk score 

differences in HHS–RADV. Therefore, a 

methodology that included hierarchy 

violation constraints in the definition of 

Super HCCs would have to keep the 

relevant HCCs in the applicable metal 

level and age group model affected by 

the hierarchy violation constraints 

separate from the same HCCs in metal 

levels and age group models that are 

unaffected. This would result in 

individual Super HCCs dedicated to 

only the HCCs affected by a given 

hierarchy violation constraint from 

HHS–RADV sample enrollees subject to 

the affected metal level and age group 

model. As such, the individual Super 

HCC failure rate calculation for that 

hierarchy violation constraint would be 

based on a very small sample, leading 

to instability for the HCC failure rate 

group assignment for that hierarchy 

violation constraint. It would also 

increase the complexity associated with 

adoption of this refinement to the HCC 

failure rate grouping methodology. In 

contrast, coefficient estimation groups 

are consistent across all five metal level 

adult models, and are almost identical 

to the coefficient estimation groups 

across all five metal level child models. 

As such, it is much more appropriate to 

define Super HCCs for all enrollees 

based on the adult coefficient estimation 

groups, because nearly all enrollees 

with an EDGE miscoding between two 

HCCs in a coefficient estimation group 

would be assigned the same risk score 

for either HCC. This consistency allows 

us to utilize a much larger sample size 

during the calculation of Super HCCspecific failure rates, namely, the entire 

HHS–RADV sample, resulting in more 

stable failure rate estimates and HCC 

failure rate group assignments. Defining 

Super HCCs based on the adult 

coefficient estimation groups is also 

easy to implement as an interim 

measure to address the identified 

misalignment that occurs in situations 

where HCCs in the same HCC 

coefficient estimation group are sorted 

into different HCC failure rate 

groupings. 

Finally, we appreciate the comments 

requesting more analysis on including a 

priori stability constraints from the 

child models in the definition of Super 

HCCs. For similar reasons to those noted 

in the discussion of the hierarchy 

violation constraints and variables from 

infant models, including a priori 

stability constraints from the child 

models in the definition of Super HCCs 

would result in very small sample sizes 

for the purposes of determining the 

Super HCC-level failure rate prior to 

sorting into HCC failure rate groups. As 

such, our analysis of the inclusion of a 

priori stability constraints for the child 

models found that it would likely result 

in less year-to-year uncertainty in that 

model than basing Super HCCs on 

coefficient estimation groups alone. 

Moreover, HCCs subject to a priori 

stability constraints are additive in the 

risk adjustment models, whereas HCCs 

within coefficient estimation groups are 

not.50 This difference is due to the fact 


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of HCCs in the risk adjustment models is discussed 

in greater detail in the proposed rule (85 FR 33605). 

We have also previously discussed this feature of 

a priori stability constraints in the 2019 HHS–HCC 

Potential Updates Paper, available at: https://

www.cms.gov/CCIIO/Resources/Regulations-andGuidance/Downloads/Potential-Updates-to-HHSHCCs-HHS-operated-Risk-Adjustment-Program.

pdf#page=11. 

51See Table 2 for a further comparison and 

analysis of the estimated changes reflecting 

implementation of the Super HCC refinement using 

2017 and 2018 HHS–RADV data. Also see Tables 

3 and 4 for a further analysis and comparison of the 

estimated changes reflecting implementation of the 

policies finalized in this rule using both 2017 and 

2018 benefit year HHS–RADV results. 

52Commenters should also refer to the illustrative 

example in the proposed rule. See 85 FR at 33605. 

53See Section 11.3.1 of the 2018 HHS–RADV 

Protocols at https://www.regtap.info/uploads/ 

library/HRADV_2018Protocols_070319_RETIRED_

5CR_070519.pdf for a description of the process 

prior to the introduction of Super HCCs. Beginning 

with the 2019 benefit year of HHS–RADV, Super 

HCCs would take the place of HCCs in the process. 

The 2019 HHS–RADV Protocols have thus far only 

been published in part at https://www.regtap.info/ 

uploads/library/HRADV_2019_Protocols_111120_

5CR_111120.pdf. The section of the 2019 HHS– 

RADV Protocols pertaining to HCC grouping for 

failure rate calculations is not included in the 

current version. Once published, this section will 

be updated to include steps related to creation of 

Super HCCs. 

that many of the a priori stability 

constraints reflect unrelated conditions, 

and therefore, a miscoding of one HCC 

within an a priori stability constraint 

would not be expected to impact the 

likelihood that another HCC in that a 

priori stability constraint would also be 

miscoded. In contrast, coefficient 

estimation groups reflect related 

conditions that could conceivably be 

miscoded as one another on EDGE. 

Therefore, we do not believe that it is 

appropriate to include a priori stability 

constraints from the child models in the 

definition of Super HCCs. 

Comments: A few commenters 

supported the proposed changes as 

valuable interim measures, but stated 

that the HCC failure rate grouping 

methodology may require additional 

improvements in the future and asked 

that HHS continue to analyze and 

propose refinements to the HCC 

grouping process for HHS–RADV. Some 

of these commenters emphasized that 

stability of HCC failure rate group 

assignment from year-to-year should be 

a priority when considering potential 

future changes. 

Response: We appreciate these 

comments. As noted in the proposed 

rule, the Super HCC refinement is 

intended to address the misalignment 

that occurs in situations where HCCs in 

the same HCC coefficient estimation 

group are sorted into different HCC 

failure rate groupings on an interim 

basis while we continue to assess 

different longer-term options. We 

remain committed to ensuring the 

integrity and reliability of HHS–RADV 

and agree that year-to-year stability is an 

important factor to consider when 

analyzing potential future changes. We 

continue to explore potential 

modifications to this program, including 

to the HCC grouping methodology, for 

future benefit years and will propose 

any such changes through notice-andcomment rulemaking. 

Comments: Several commenters 

requested that HHS release more 

information about the HCC failure rate 

grouping proposal to create Super HCCs. 

This included requests for more 

information about the degree to which 

validation failures relate to hierarchies 

for 2018 HHS–RADV, analysis on yearto-year stability, and a further 

explanation of the proposed refinement 

to the HCC failure rate grouping 

methodology. 

Response: Once the data became 

available, we conducted an additional 

analysis of the Super HCC proposal 

using 2018 benefit year HHS–RADV 

results. This further analysis provided 

roughly the same figure for the 

proportion of newly identified HCCs 

which could be attributed to a 

miscoding of an HCC in the same 

hierarchy, or in the same coefficient 

estimation group, as the analysis of 2017 

benefit year HHS–RADV results used to 

develop the Super HCC proposal, 

namely, about 1/3rd of newly identified 

HCCs. Among non-validated HCCs, the 

rate that could be attributed to 

miscoding of an HCC in the same 

hierarchy was slightly higher in our 

analysis of 2018 data (about 1/7th of 

non-validated HCCs) than it was for 

2017 data (about 1/8th of non-validated 

HCCs). Additionally, in response to 

comments, we note that in both 2017 

and 2018 HHS–RADV results, 

approximately 1/3rd of HCCs that could 

be attributed to miscoding of an HCC in 

the same hierarchy also shared a 

coefficient estimation group.51 The 

refinement to the HCC failure group rate 

methodology finalized in this rule will 

ensure that these HCCs will have no 

impact on failure rates. More 

specifically, adoption of this change for 

HCCs in the same coefficient group 

ensures they are not sorted into different 

HCC failure rate groupings and avoids 

making HHS–RADV adjustments to risk 

scores when they are not conceptually 

warranted. 

In response to the comments, we also 

provide the following additional 

example regarding the calculation of a 

Super’s HCC failure rate using 

freqEDGEc, freqIVAc, and FRc values for 

Super HCCs.52 HCC 54 Necrotizing 

Fasciitis and HCC 55 Bone/Joint/Muscle 

Infections/Necrosis share a HCC 

coefficient estimation group, and 

therefore those HCC failure rates would 

be grouped together to form a Super 

HCC. For example, if freqEDGEh54 is 30 

and freqEDGEh55 is 70, nationally, and if 

freqIVAh54 is 15 and freqIVAh55 is 65, 

nationally, then freqEDGEc54&55 is 100 

and freqIVAc54&55 is 80, yielding 

FRc54&55 = 1¥80/100 = 20%. This is in 

contrast to cases such as HCC 1 HIV/ 

AIDS, which does not share a coefficient 

estimation group with any other HCCs. 

In this second example, freqEDGEc will 

be equal to freqEDGEh, freqIVAc will be 

equal to freqIVAh, and FRc will be equal 

to FRh, the value of the national failure 

rate for HCC 1. 

As explained in the proposed rule, 

after the calculation of freqEDGEc, 

freqIVAc, and FRc, we will sort the 

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—using the sorting 

process under the current HHS–RADV 

failure rate grouping methodology. The 

sorting process and failure rate grouping 

methodology are described in the HHS– 

RADV Protocols.53 Specifically, HHS 

will calculate the HCC failure rate group 

for each Super HCC using the following 

method: 

• Create a list containing each Super 

HCC and its associated failure rate. 

• Sort Super HCCs from lowest to 

highest failure rate (FRc). 

• Put the Super HCC with the lowest 

failure rate in the low failure rate group, 

and update the size of this group 

(freqEDGElow) so that it is equal to 

freqEDGEc1, that is, the value of 

freqEDGEc for the first Super HCC from 

the sorted list. Put the next Super HCC 

from the sorted list in the low failure 

rate group, and update the group size to 

freqEDGElow + freqEDGEci, the value of 

freqEDGEc for the i-th Super HCC from 

the sorted list. Repeat this sorting 

process until the size of freqEDGElow

reaches or exceeds 1/3rd of the total 

frequency of HCCs recorded on EDGE 

(èfreqEDGEh across all HCCs, which is 

equal to èfreqEDGEc across all Super 

HCCs). 

• After the low failure rate group has 

reached the 1/3rd cut off, HHS will put 

the next Super HCC from the sorted list 

into the medium failure rate group, and 

will update the size of this group 

(freqEDGEmedium) so that it is equal to 

freqEDGEci. We will then put the next 

Super HCC from the sorted list into the 

medium failure rate group, and update 

the group size to freqEDGEmedium + 


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54An 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. 

55See, e.g., section 4.4.4 and 4.4.5 of the 2019 

RADV White Paper. 

56 In the 2020 Payment Notice, 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. 

57 In the 2019 RADV White Paper, we considered 

four different options for calculating and applying 

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. 

freqEDGEci. We will repeat this process 

until freqEDGElow + freqEDGEmedium

reaches or exceeds 2/3rds of the total 

number of HCCs recorded on EDGE 

(èfreqEDGEh across all HCCs, which is 

equal to èfreqEDGEc across all Super 

HCCs). 

• The remaining Super HCCs, those 

with the highest failure rates, will then 

be assigned to the high failure rate 

group. 

Because the inclusion of the final 

freqEDGEci in a given failure rate group 

may result in the total frequency for that 

group going beyond 1/3rd of the total 

èfreqEDGEc, consistent with the current 

sorting process and methodology, HHS 

will then reexamine the HCC allocations 

between failure rate groups to ensure an 

even distribution of HCCs between 

failure rate groups such that each HCC 

failure rate group contains as close as 

possible to 1/3rd of the HCCs reported 

in EDGE. To accomplish this, we will 

first identify the final Super HCCs in the 

low and medium failure rate groups that 

result in a total freqEDGElow or 

freqEDGEmedium that exceeds 1/3rd of the 

total èfreqEDGEc. Then we will generate 

multiple grouping scenarios such that 

the identified Super HCCs that cause 

freqEDGElow or freqEDGEmedium to 

exceed 1/3rd of the total èfreqEDGEc are 

instead included in the next higher 

failure rate group. These multiple 

grouping scenarios will contain all 

possible assignments of the two Super 

HCCs that cross the 1/3rd boundary for 

the low and medium failure rate 

groupings. For each grouping scenario, 

we will then calculate the potential 

values of freqEDGElow, freqEDGEmedium, 

and freqEDGEhigh and then calculate the 

absolute distance between in each HCC 

failure rate group and 1/3rd. HHS will 

then choose the scenario that is closest 

to an exact 1/3rd split of HCC 

frequencies across groups. This scenario 

will be used as the final HCC failure rate 

grouping assignment for that HHS– 

RADV benefit year. 

2. ‘‘Payment Cliff’’ Effect 

The HHS–RADV error rate calculation 

methodology is based on the 

identification of outliers, as determined 

using certain national thresholds. Those 

thresholds are used to determine 

whether an issuer is an outlier and the 

error rate that will be used to adjust 

outlier issuers’ risk scores. Under the 

current methodology, 1.96 standard 

deviations on both sides of the 

confidence interval around the weighted 

HCC group means are the thresholds 

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 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.54

Some stakeholders have expressed 

concern that issuers with failure rates 

that are just outside of the confidence 

intervals receive an adjustment to their 

risk scores, even though these issuers’ 

failure rates may not be significantly 

different from the failure rates of issuers 

just inside the confidence intervals who 

receive no risk score 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. 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 to identify outliers, 

some stakeholders suggested that HHS 

could mitigate 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. 

While HHS considered several 

possible methods to address the 

payment cliff,55 we proposed to address 

the payment cliff by adding a sliding 

scale adjustment to the current error rate 

calculation, such that the adjustments 

applied would vary based on the outlier 

issuer’s 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 help 

reduce the disparity of risk score 

adjustments by using a linear 

adjustment.56 We proposed to make this 

modification beginning with 2019 

benefit year HHS–RADV. 

To apply the sliding scale adjustment, 

we proposed to modify the calculation 

of the group adjustment factor (GAF) by 

providing a linear sliding scale 

adjustment for issuers whose failure 

rates are near the point at which the 

payment cliff occurs. To implement this 

policy, we needed to select the 

thresholds of the range (innerZr and 

outerZr) to calculate and apply the 

sliding scale adjustment.57 In the 

proposed rule, we proposed 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 be based on 

a 95 percent confidence interval or 1.96 

standard deviations from the mean, and 

would instead be based on a 90 percent 

confidence interval or 1.645 standard 

deviations from the mean. Specifically, 

this approach would adjust the upper 

and lower bounds of the confidence 

interval to be at 1.645 standard 

deviations from the mean, meaning that 

issuers with group failure rates outside 

of the 90 percent confidence interval in 

any HCC failure rate group will have 

their risk scores adjusted. This would 

result in more issuers being considered 

outliers under this methodology than 

under the current methodology, which 

uses a 95 percent confidence interval to 

detect outlier issuers, but these 

additional outlier issuers would face 

smaller GAFs due to the application of 

the sliding scale. 

To calculate the sliding scale 

adjustment, we proposed to add an 

additional step to the calculation of 

issuers’ GAFs that takes into 

consideration the distance of their group 

failure rates (GFRs) to the confidence 

interval. The present formula for an 

issuer’s GAF, GAFG,i = GFRG,i¥m{GFRG} 

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: 


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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 (from +/¥1.645 to 3 

standard deviations). This disZG,i,r value 

will 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: 

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 between +/¥1.645 to 3 

standard deviations. Specifically, 

coefficient a would be defined as: 

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 is 

calculated for a positive or negative 

outlier and would be defined as: 

In the absence of the constraints on 

negative failure rates that is being 

finalized later in this final rule, the final 

formula for the group adjustment when 

an outlier issuer is subject to the sliding 

scale (GAFG,i,r above) would be 

simplified to: 

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 HCC’s 

risk score adjustment factor for an issuer 

that had a failure rate with a z score 

within the range of values (from 

+/¥1.645 to 3 standard deviations) 

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,r. Under this approach, the above 

formulas would be implemented as 

follows: 


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76992 Federal Register / Vol. 85, No. 231 / Tuesday, December 1, 2020 / Rules and Regulations 

58See 85 FR at 33608. 

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. 

We sought comment on this proposal, 

including the proposed calculation of 

the sliding scale adjustment and the 

thresholds used to calculate and apply 

it. We also considered retaining the 95 

percent confidence interval (1.96 

standard deviations) as an alternative 

way to smooth the payment cliff. 

However, as noted in the proposed rule, 

while we recognize this option would 

also mitigate the payment cliff, we were 

concerned it would weaken the HHS– 

RADV program by reducing its overall 

impact and the magnitude of HHS– 

RADV adjustments to risk scores of 

outlier issuers.58

After consideration of comments 

received, we are finalizing the proposed 

sliding scale adjustment to smooth the 

payment cliff effect for those issuers 

whose failure rates are near the point at 

which the payment cliff occurs. We will 

calculate and apply a sliding scale 

adjustment between the 90 and 99.7 

percent confidence interval bounds 

(from +/¥1.645 to 3 standard 

deviations) beginning with 2019 benefit 

year HHS–RADV. For outlier issuers 

with failure rates more than 3 standard 

deviations from the mean, the GAF will 

not be impacted by the sliding scale 

adjustment, but will instead continue to 

be calculated as the difference between 

the weighted mean group failure rate 

and the issuers’ group failure rate. 

Comments: Some commenters 

supported the proposal to apply the 

sliding scale adjustment between the 

90–99.7 percent confidence interval. 

Several commenters supported the 

adoption of a sliding scale adjustment 

but wanted to retain the current 

confidence intervals and start the 

adjustment at the 95 percent confidence 

interval. These commenters were 

concerned with the increased number of 

outliers under the proposed sliding 

scale adjustment, which would result in 

more risk adjustment transfers being 

impacted by HHS–RADV results, 

arguing this would reduce predictability 

and stability of HHS–RADV. Other 

commenters expressed concern about 

the identification of more outliers under 

the proposed sliding scale adjustment, 

arguing it would be more disruptive 

especially during COVID–19. Some 

commenters stated that they did not 

believe that identifying outliers at the 

proposed 90 percent confidence interval 

would more accurately capture issuers’ 

actuarial risk and some thought the 

proposed 90 percent confidence interval 

could lead to an increase in ‘‘false 

positives’’ when identifying outliers. 

These commenters stated that the 95 

percent confidence interval imposes a 

more robust confidence interval for 

identifying ‘‘true outliers.’’ 

Some commenters wanted HHS to 

calculate error rates based on the 

difference between the edge of the 

confidence intervals and the outlier 

issuer’s failure rate (instead of the 

difference between the weighted group 

mean or a sliding scale adjustment and 

the outlier issuer’s failure rate). 

However, these commenters also 

supported the adoption of a sliding 

scale adjustment starting at the 95 

percent confidence intervals, if HHS 

were to finalize a sliding scale 

adjustment. One commenter wanted 

HHS to identify outliers and calculate 

their GAF based on state specific group 

means to address potential over and 

under adjustments of outlier issuers 

relative to their state-based competitors. 

One commenter supported the current 

methodology without a sliding scale 

adjustment, noting that the payment 

cliff effect resulted from the policy of 

only adjusting for outliers and that any 

measures to address the payment cliff 

would dampen the impact of HHS– 

RADV. Other commenters stated that it 

is appropriate for issuers who fall 

outside of the 99.7 percent confidence 

interval (beyond 3 standard deviations) 

to be assessed a full penalty. Another 

commenter, that supported the adoption 

of a sliding scale adjustment, expressed 

concerns that even with the proposed 

adjustment there would still be a 

payment cliff effect for issuers with very 

similar error rates. This commenter also 

asked HHS to address this effect for the 

current benefit year and beyond, as well 

as prior years, of HHS–RADV. 

Response: We are finalizing the 

sliding scale approach for calculating an 

outlier issuer’s error rate using modified 

group adjustment factors for issuers’ 

group failure rates between 1.645 to 3 

standard deviations from the mean on 

both sides of the confidence interval as 

proposed. We will apply this 

adjustment to the error rate calculation 

beginning with the 2019 benefit year of 

HHS–RADV. We believe that using a 

linear sliding scale adjustment will 

provide a smoothing effect in the 

current error rate calculation for issuers 

with failure rates just outside of the 

confidence interval of an HCC group 

and will retain the current significant 

adjustment to the HCC group weighted 

mean for issuers beyond three standard 

deviations. This approach ensures 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. 

We appreciate the comments 

supporting an alternative sliding scale 


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Federal Register / Vol. 85, No. 231 / Tuesday, December 1, 2020 / Rules and Regulations 76993 

59See section 4.4.2 of the 2019 RADV White 

Paper. 

60See section 4.4.5 and Appendix C of the 2019 

RADV White Paper. 

61 Ibid. 

62See, supra, notes 30 and 31. 63See FAQ ID 11290a (March 7, 2016) available 

at: https://www.regtap.info/faq_

viewu.php?id=11290 and HHS-Operated Risk 

Adjustment Data Validation (HHS–RADV)—2016 

Benefit Year Implementation and Enforcement (May 

3, 2017) available at: https://www.cms.gov/CCIIO/ 

Resources/Regulations-and-Guidance/Downloads/ 

HHS-Operated-Risk-Adjustment-Data-ValidationHHS-RADV-%E2%80%93-2016-Benefit-YearImplementation-and-Enforcement.pdf. 

adjustment that would begin at 1.96 

standard deviations. As detailed in the 

proposed rule, we recognize this 

alternative adjustment would also 

address the payment cliff and would 

provide stability by maintaining the 

current thresholds used in the error rate 

calculation. However, these benefits are 

outweighed by the concerns that such 

an adjustment would weaken HHS– 

RADV by reducing its overall impact 

and the magnitude of HHS–RADV 

adjustments to outlier issuer’s risk 

scores. As noted previously, the sliding 

scale adjustment that is finalized in this 

rule will mitigate the payment cliff 

effect while not impacting the error rate 

calculation for those outlier issuers who 

are not close the confidence intervals. 

While we did not propose adjusting 

issuers’ error rates to the state-specific 

means, we considered such an approach 

in response to comments. However, we 

do not believe that using state-specific 

means would address the payment cliff 

in the current error rate methodology. 

We also have concerns about using 

national metrics to determine outliers 

and then switching to state-specific 

means to calculate the GAFs. In 

addition, the adoption of a state-specific 

approach to calculate the GAF could 

create other issues, if states have small 

sample sizes (that is, a small number of 

issuers participated in HHS–RADV), 

this would create less confidence in the 

state mean metric being used to adjust 

issuers, and would introduce new 

complexities as each state would have a 

different calculation for the GAF. We 

therefore decline to adopt such an 

approach in this final rule. We also 

considered adjusting to the confidence 

intervals,59 but we have concerns that 

this option minimizes the impact of 

HHS–RADV adjustments on risk scores 

and risk adjustment transfers— 

including those outlier issuers with high 

error rates who are furthest away from 

the confidence intervals. 

While any outlier threshold by 

definition has the risk of flagging false 

positives, and that risk may be slightly 

greater at the 90 percent confidence 

interval, we believe that the 90 percent 

confidence interval will better 

encourage issuers to ensure accurate 

EDGE data reporting and the risk of 

flagging false positives is mitigated by 

the fact that the adjustments to these 

issuers will be small since they will be 

subject to the sliding scale adjustment. 

Furthermore, while we understand the 

concerns that use of the 90 percent 

confidence interval will increase the 

number of outliers, we have found that 

the overall impact of the proposed 

approach on risk adjustment transfers is 

less than the current methodology 

despite the increased number of 

outliers. As discussed in the 2019 RADV 

White Paper, we tested various potential 

sliding scale adjustments between the 

90 and 99.7 percent confidence interval 

bounds using 2017 HHS–RADV 

results.60 We found that even though 

including issuers whose failure rates fell 

between 1.645 and 1.96 standard 

deviations from the mean would 

increase the number of outliers, the 

sliding scale adjustment lowers the 

overall impact of HHS–RADV 

adjustments to transfers and results in 

the distribution of issuers’ error rates 

moving closer to zero compared to the 

current methodology.61 We also tested 

this policy on the 2018 benefit year 

HHS–RADV data once it became 

available and found similar results. We 

found that the sliding scale adjustment 

option between 1.645 and 1.96 standard 

deviations generally resulted in lower 

overall impact of HHS–RADV 

adjustment to risk adjustment transfers 

and the distribution of issuers’ error 

rates moving closer to zero compared to 

the current methodology. Furthermore, 

we believe that the 90 percent 

confidence interval will maintain the 

program integrity impact of HHS–RADV 

despite the estimated reduced impact of 

HHS–RADV on risk adjustment transfers 

using the 90 percent confidence 

interval, and we are not concerned that 

increasing the number of outliers will be 

more disruptive during the COVID–19 

public health emergency. More 

importantly, we believe that using the 

90 percent confidence interval will 

preserve 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 magnitude 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 methodology for 

calculating and applying the sliding 

scale adjustment provides a balanced 

approach to mitigating the payment cliff 

effect in the current methodology and 

disagree that adoption of the adjustment 

would reduce predictability and 

stability of HHS–RADV. 

We recognize the sliding scale 

adjustment finalized in this rule does 

not eliminate the payment cliff because 

the identification of outliers will still be 

based on the establishment and use of 

thresholds. As noted earlier, we are 

finalizing the targeted policies in this 

rule, such as the sliding scale 

adjustment, as incremental refinements 

to the current error rate methodology to 

address stakeholder feedback and our 

experience from the first payment year 

of HHS–RADV on these issues. We will 

continue to consider other potential 

changes to the error rate methodology 

for future benefit years, including 

potential significant changes to the 

outlier determination process, and as 

part of that process, we will also 

consider whether additional measures 

are necessary or appropriate to further 

mitigate the impact of the payment cliff 

after we have experience with the 

sliding scale adjustment finalized in this 

rule. 

We will apply the sliding scale 

adjustment beginning with the 2019 

benefit year of HHS–RADV, as 

proposed. We believe that application of 

this rule to the 2017 and 2018 HHS– 

RADV would not be appropriate 

because the error rate calculations for 

those benefit years are complete.62

Further, it would disrupt issuers’ wellsettled expectations with respect to the 

calculation of HHS–RADV error rates 

and adjustments if we were to extend 

this new policy to the 2017 and 2018 

benefit years. In addition, there is no 

need to apply the sliding scale 

adjustment to the earlier benefit years 

because HHS–RADV was not conducted 

for the 2014 benefit year and HHS– 

RADV was treated as a pilot for the 2015 

and 2016 benefit years.63

Comments: A few commenters noted 

that the increase in the number of 

issuers identified as outliers due to the 

introduction of the sliding scale 

adjustment could increase volatility by 

increasing the likelihood that an issuer 

would be an outlier in three HCC failure 

rate groups, leading to larger overall 

error rates despite the smaller GAF in 

each group, or by creating several 

negative outliers in one state market risk 

pool. One commenter, who was 

concerned about the increased number 

of outliers, noted that issuers can have 

a larger HHS–RADV adjustment under 

the proposed sliding scale adjustment 

than under the current methodology. 


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76994 Federal Register / Vol. 85, No. 231 / Tuesday, December 1, 2020 / Rules and Regulations 

64See, e.g., 85 FR at 33608. 

65See section 4.4.5 and Appendix C of the 2019 

RADV White Paper. 

66See 85 FR at 33613. 

67See section 4.4.5 and Appendix C of the 2019 

RADV White Paper. 

Some commenters were concerned that 

this volatility from the increased 

number and type of outliers could 

increase premiums or adversely affect 

issuers’ finanical planning. 

Response: We recognize that the 

sliding scale adjustment finalized in this 

rule will result in more issuers being 

identified as outliers than the current 

methodology.64 However, when testing 

various potential sliding scale 

adjustment options, we found that even 

though including issuers whose failure 

rates fell between 1.645 and 1.96 

standard deviations from the mean 

would increase the number of outliers, 

the sliding scale adjustment we are 

finalizing in this rule lowers the overall 

impact of HHS–RADV adjustments to 

risk adjustment transfers and results in 

the distribution of issuers’ error rates 

moving closer to zero compared to the 

current methodology.65 Therefore, we 

do not believe that using the sliding 

scale adjustment starting with the 1.645 

confidence interval will increase 

volatility or impact premiums more 

than the previous methodology. Instead, 

we believe that the sliding scale 

adjustment finalized in this rule will 

preserve 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 the incorporation of the 

sliding scale adjustment as proposed 

provides a balanced approach to 

mitigating the payment cliff effect. 

Under the new confidence intervals 

with the sliding scale adjustment 

beginning at 90 percent finalized in this 

rule, it is possible for an issuer to fail 

more HCC groups resulting in larger 

error rates than the previous 

methodology or for there to be more 

negative error rate outliers in a state 

market risk pool compared to the 

current methodology. In those cases, 

outlier issuers could have a higher error 

rate, or non-outlier issuers could be 

impacted by more outliers in their state 

market risk pool than under the current 

methodology that does not include a 

sliding scale adjustment. However, 

failure rates for the issuers newly 

identified as outliers due to the 

adoption of the sliding scale adjustment 

would be between 1.645 to 1.96 

standard deviations. Since these issuers’ 

failure rates are closer to the mean, the 

increase in error rates based on outlier 

status in several HCC failure rate groups 

would likely be small and could 

potentially be offset by reduced 

transfers from other issuers with failure 

rates between 1.96 and 3 standard 

deviations in the same state market risk 

pool. 

Comments: Some commenters 

expressed concern that issues other than 

actual HCC validation errors that impact 

the measurement of actuarial risk, such 

as medical record retrieval issues or 

incorrect provider coding, may 

contribute to the variance in failure 

rates, and that it is therefore not 

appropriate to adjust outlier issuers to 

the mean. Other commenters noted that 

changing the confidence intervals does 

not ensure that validation of HCCs that 

contribute to actuarial risk is accurately 

measured through HHS–RADV; these 

commenters supported maintaining the 

current confidence intervals. 

Response: HHS–RADV validates risk 

based upon the enrollee’s medical 

record which generally aligns with how 

the Medicare Advantage risk adjustment 

data validation (MA–RADV) program 

operates. Specifically, § 153.630(b)(7)(ii) 

requires that the validation of enrollee 

health status (that is, the medical 

diagnoses) occur through medical 

record review, that the validation of 

medical records include a check that the 

records originate from the provider of 

the medical services, that they align 

with the dates of service for the medical 

diagnosis, and that they reflect 

permitted providers and services. When 

an issuer fails to submit a medical 

record or has submitted an inaccurate 

medical record, the issuer has failed to 

validate the issuer’s risk under our 

regulations. We do not treat these 

medical record issues differently than 

other errors that can occur in HHS– 

RADV nor would we treat them 

differently for purposes of calculating 

GAF using the weighted group mean. 

While we are amending the 

calculation of the GAF, we did not 

propose and are not finalizing any 

changes to no longer use the mean in 

the calculation of the GAF. The purpose 

of the sliding scale adjustment is to 

mitigiate the payment cliff effect that 

was occuring by adjusting outlier 

issuers just outside the confidence 

interval to the weighted group mean. To 

ensure that the validation of HCCs that 

contribute to actuarial risk is accurately 

measured through HHS–RADV, we 

proposed the HCC failure rate grouping 

policy being finalized in this rule. That 

policy is another targeted refinement to 

the current methodology and it is 

focused on ensuring that miscoding of 

HCCs in the same coefficient estimation 

group with the same risk scores does not 

contribute to an issuer’s group failure 

rate. Additionally, in this rule, we are 

finalizing the application of HHS–RADV 

results to the benefit year being audited 

in response to stakeholder concerns 

about changes in population and risk 

score between benefit years. 

Comments: A commenter requested 

that HHS release prior HHS–RADV 

results and data if the sliding scale 

adjustment policy is finalized. 

Response: Summary information on 

issuers’ 2017 and 2018 benefit years 

HHS–RADV results are available on the 

Premium Stabilization Program page of 

the CCIIO website, which can be 

accessed at https://www.cms.gov/CCIIO/ 

Programs-and-Initiatives/PremiumStabilization-Programs. Issuers who 

participated in HHS–RADV for these 

benefit years also received issuerspecific and enrollee-specific results in 

the Audit Tool at the same time the 

summary information was released. 

Additionally, HHS conducted two pilot 

years of HHS–RADV for the 2015 and 

2016 benefit years to give HHS and 

issuers experience with how the audits 

would be conducted prior to applying 

HHS–RADV results to adjust issuers’ 

risk scores and risk adjustment transfers 

in the applicable state market risk pool 

and for the 2016 benefit year, 

participating issuers were provided 

illustrative 2016 benefit year HHS– 

RADV results based on the application 

of the current error rate methodology. 

As noted previously, HHS–RADV was 

not conducted for the 2014 benefit year 

so there were no results to release or 

otherwise share. We also point this 

commenter to the analysis in the 

proposed rule,66 as well as the results of 

the evaluation of the sliding scale 

adjustment options in the 2019 RADV 

White Paper, using 2017 benefit year 

HHS–RADV results.67 In addition, 

Tables 3 and 4 in this rule share an 

analysis and comparison of the 

estimated changes reflecting 

implementation of this policy using 

both 2017 and 2018 benefit year HHS– 

RADV results. 

3. Negative Error Rate Issuers With 

Negative Failure Rates 

HHS–RADV uses a two-sided outlier 

identification approach because the 

long-standing intent has been to account 

for identified material risk differences 

between what issuers submitted to their 

EDGE servers and what was validated in 


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Federal Register / Vol. 85, No. 231 / Tuesday, December 1, 2020 / Rules and Regulations 76995 

68An 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. 

69For 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. 

70See 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. 

71This calculation sequence is expressed here in 

a revised order compared to how the sequence is 

published in the 2021 Payment Notice (85 FR at 

29196–29198). This change was made to simplify 

the illustration of how this sequence will be 

combined with proposals finalized in this rule. The 

different display does not modify or otherwise 

change the amendments to the outlier identification 

process finalized in the 2021 Payment Notice. 

medical records through HHS–RADV, 

regardless of the direction of those 

differences.68 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. 

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 69 and the HCC 

failure rate approach to error estimation 

finalized in the 2019 Payment Notice.70

In response to stakeholder feedback 

about the impact of negative error rate 

issuer HHS–RADV adjustments on 

issuers who are not outliers, we 

proposed to adopt a constraint to the 

calculation of negative error rate outlier 

issuers’ error rates in cases when an 

outlier issuer’s failure rate is negative. 

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. For this reason, we 

proposed to refine the error rate 

calculation to mitigate the impact of 

adjustments that result from negative 

error rate outliers that are driven by 

newly found HCCs rather than by high 

validation rates. 

Beginning with 2019 benefit year 

HHS–RADV, we proposed to adopt an 

approach that constrains negative error 

rate outlier issuers’ error rate 

calculations in cases when an issuer’s 

failure rate is negative. For negative 

error rate outlier issuers with negative 

failure rates, the proposed constraint 

would be applied to the GAF such that 

this value 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 

||double barred|| terms and definitions 

into the error rate calculation 71 process: 


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72See, 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_RETIRED_5CR_070519.pdf. 

Where: GFRG,i is an issuer’s failure rate for the HCC 

failure rate grouping 

||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: 

UBG and LBG are the upper and lower bounds 

of the HCC failure rate grouping 

confidence interval, respectively. 

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, is the group adjustment factor for HCC 

failure rate group G for an issuer i. 

We would then compute total 

adjustments and error rates for each 

outlier issuer based on the weighted 

aggregates of the GAFG,i.72

We are finalizing this refinement to 

the error rate calculation as proposed. 

We will adjust the GAF calculation to be 

the difference between the weighted 

group mean and zero for negative error 

rate issuers with negative failure rates 

beginning with the 2019 benefit year of 

HHS–RADV. 

Comments: Most commenters 

supported the proposed negative failure 

rate constraint. These commenters 

tended to be concerned that the current 

methodology rewards issuers who fail to 

submit accurate data to the EDGE server, 

were concerned about predictability of 

HHS–RADV adjustments, or thought 

that the proposed constraint would 

result in more equitable HHS–RADV 

adjustments. A few commenters 

opposed the proposed negative failure 

rate constraint. These commenters, as 

well as another commenter that was not 

opposed to the negative failure rate 

constraint, expressed concerns that the 

proposed negative failure rate constraint 

would treat issuers with different 

validation rates and the same rate of 

found HCCs the same for calculating 

error rates, potentially penalizing 

issuers that submitted more verifiable 

HCCs. Some commenters argued that 

the potential for underreporting of risk 

in risk adjustment was minor, and one 

supported allowing issuers to get credit 

for the risk that they incurred including 

through newly found HCCs. 

Other commenters generally agreed 

that a change in methodology is needed 

to reduce the magnitude of HHS–RADV 

adjustments due to negative error rate 

issuers and the impact of these 

adjustments on non-outlier issuers in 

the same state market risk pool. Some 

commenters wanted HHS to abandon 

the two-sided nature of the outlier 

identification process and not adjust for 

any negative error rate outliers or urged 

HHS to look for ways to minimize 

adverse impact of negative error rate 

outliers on non-outliers. Other 

commenters recommended that HHS 

analyze the failure rates for negative 

error rate outliers without including 

found HCCs (meaning that only nonvalidated EDGE HCCs would be 

contributing to the issuer’s failure rate) 

and compare the results with the 

current methodology to assess if 

negative error rate outliers had better 

validation rates. Another commenter 

requested that HHS monitor data on the 

policy’s impact, if finalized. 

Response: We are finalizing the 

proposed approach to constrain negative 

error rate outlier issuers’ error rate 

calculations in cases when an outlier 

issuer’s failure rate is negative and will 

apply this constraint beginning with the 

2019 benefit year of HHS–RADV. We 

believe that the negative failure rate 

constraint to the GAF calculation in the 

error rate calculation will reduce 

potential incentives for issuers to use 

HHS–RADV to identify more HCCs than 

were reported to their EDGE servers and 

provide additional incentives for issuers 

to submit the most accurate data to the 

EDGE server. It also will mitigate the 

impact of HHS–RADV adjustments to 

transfers in the case of negative error 

rate issuers with negative failure rates 

and improve predictability. Specifically, 

this approach would limit the financial 

impact that negative error rate outliers 

with negative failure rates will have on 

other issuers in the same state market 

risk pool and can be easily implemented 

under the current error rate 

methodology. 

We understand that this constraint 

has limitations. We used 2017 and 2018 

benefit year HHS–RADV results to 

analyze the failure rates of negative 

error rate outliers and explore the 

impact of excluding found HCCs. We 

found that negative error rate outliers 

tended to have better than average 

validation rates, particularly when the 

HCC grouping methodology finalized in 

this rule is applied and those issues get 

credit for IVA findings that substitute 

for EDGE HCCs in the same HCC 

coefficient estimation group. However, 

at the same time, we recognize that 

there are limitations to the negative 

failure rate constraint policy as it does 

not distinguish between issuers with 

different validation rates and the same 

rate of found HCCs. Thus, as previously 

noted, this policy and the other changes 

to the error rate calculation in this rule 

are targeted refinements to the current 

methodology as we consider other 

potential long-term approaches. In 

proposing and finalizing these changes, 

we sought to balance the goals of 

promoting stability and predictability of 

HHS–RADV adjustments and adopting 

refinements as expeditiously as 

possible. The negative error rate 

constraint was designed with these 

goals in mind, as it builds on the current 

methodology, which issuers now have 

several years of experience with, and is 

easy to implement. It is an interim 

measure that will limit the financial 

impact that negative error rate outliers 

with negative failure rates have on other 

issuers in the same state market risk 


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Federal Register / Vol. 85, No. 231 / Tuesday, December 1, 2020 / Rules and Regulations 76997 

73See Section 3.3 on addressing the influence of 

HCC hierarchies on failure rate outlier 

determination (Pages 63–71). https://www.cms.gov/ 

files/document/2019-hhs-risk-adjustment-datavalidation-hhs-radv-white-paper.pdf. 74For 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. 75See 83 FR 16930 at 16962. 

76For the 2014 through 2016 benefit years, EDGE 

data was also used for the transitional reinsurance 

program established under section 1341 of the 

PPACA. The reinsurance program provided 

reimbursement based on the total amount of claims 

paid. Beginning with the 2018 benefit year, EDGE 

data is also used for calculating payments under the 

high-cost risk pool (HCRP) parameters added to the 

HHS risk adjustment methodology. Similar to the 

reinsurance program, HCRP payments are based on 

the amount of paid claims. Therefore, information 

on all claims paid—from all provider types—for a 

given benefit year should be submitted by issuers 

to their EDGE servers. 

pool. We remain committed to 

continuing to explore different longerterm options, including approaches that 

involve significant methodological 

changes, such as those described in the 

2019 RADV White Paper that would 

switch to identifying outliers based on 

risk score instead of number of HCCs.73

We also decline to abandon the twosided nature of the outlier identification 

process. 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. The increase to risk scores 

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 74 and the HCC failure 

rate approach to error estimation 

finalized in the 2019 Payment Notice.75

The two-sided approach also encourages 

issuers to ensure that their EDGEreported risk scores reflect the true 

actuarial risk of their enrollees. 

We agree with the commenter that 

supported allowing issuers to get credit 

for the risk that they incurred including 

through newly found HCCs. It ensures 

that risk adjustment transfers are made 

based on documented risk and that, 

consistent with the statute, the HHSoperated program assesses charges to 

plans with lower-than-average actuarial 

risk while making payments to plans 

with higher-than-average actuarial risk. 

As such, even with the adoption of this 

constraint, the calculation of error rates 

will still include found HCCs. The 

negative failure rate constrained value 

in the calculation of the GAF will only 

impact the negative failure rate portion 

of an issuer’s GAF. Therefore, this 

policy ensures that negative error rate 

outlier issuers with negative failure 

rates will only get credit in their error 

rate calculation for finding HCCs at a 

similar rate as they reported to EDGE 

and will not get credit for finding more 

HCCs in HHS–RADV than they reported 

on EDGE. We believe that any issuer 

with a negative failure rate is likely to 

review their internal processes to better 

capture missing HCCs in future EDGE 

data submissions. We intend to monitor 

the impact of this policy on future 

benefit years of HHS–RADV data. 

Comments: One commenter noted 

that it is not evident that issuers with 

negative failure rates in one HCC group 

are adding more diagnoses given that 

the three HCC grouping structure allows 

for HCCs to be found in one grouping 

and missing in another grouping. One 

commenter noted that the proposal to 

calculate the GAF between zero and the 

weighted mean for negative failure rate 

issuers does not reflect the outlier 

portion of the negative error rate outlier 

(because the adjustment is within the 

confidence intervals for two of three 

HCC groupings). Another commenter 

expressed concerns that the national 

mean is not adjusted for found HCCs 

under the proposal leading to concerns 

that the national mean is being inflated 

and proposed adjusting negative error 

rate outliers to the edge of the 

confidence intervals as an alternative to 

the proposed negative failure rate 

constraint. 

Response: The purpose of this 

negative failure rate constraint policy is 

to mitigate the impact of HHS–RADV 

adjustments due to negative error rate 

issuers with negative failure rates. We 

understand that the HCC failure rate 

grouping methodology can result in an 

issuer finding HCCs in one HCC failure 

rate group when the HCC may be 

missing in another HCC failure rate 

grouping. We are finalizing the HCC 

grouping refinement discussed earlier in 

this rule to help prevent those cases 

from occurring when the HCCs are in 

the same HCC coefficient estimation 

group in the adult risk adjustment 

models. We also acknowledge that this 

constraint would not affect the 

calculation of the national mean, which 

would continue to consider all found 

HCCs and that the calculation of the 

GAF under this constraint policy may 

not fully reflect the outlier portion. We 

considered these limitations and 

weighted them against the benefits of 

this policy. While we do have concerns 

about the impact of adjustments 

resulting from negative error rate issuers 

with negative failure rates, we believe 

that issuers should retain the ability to 

find HCCs in HHS–RADV. Having the 

ability to find HCCs in HHS–RADV is 

important to ensure that issuers’ actual 

actuarial risk is reflected in HHS– 

RADV, especially when those HCCs 

replace related HCCs that were reported 

to EDGE. As such, we believe that found 

HCCs should continue to contribute to 

the national mean. At the same time, 

given the number of negative error rate 

issuers with negative failure rates, we 

believe that it is important to refine the 

current methodology to reduce the 

incentives for issuers to find HCCs in 

HHS–RADV that are not reported in 

EDGE. We intend to monitor the impact 

of this policy on HHS–RADV 

adjustments and will continue to 

explore potential further refinements 

and changes to the HHS–RADV 

methodology and program requirements 

for future benefit years. 

Comment: Some commenters stated 

that the HHS–RADV Protocols and the 

applicable EDGE data submission 

requirements did not align and 

recommended that HHS align these 

documents. One of these commenters 

recommended aligning these rules as an 

alternative to constraining negative error 

rate outliers with negative failure rates. 

Response: We did not propose and are 

not finalizing any changes to the EDGE 

data submission requirements. As noted 

earlier, 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. 

This includes allowing issuers to get 

credit for the risk that they incurred 

including through newly found HCCs. 

However, in response to stakeholder 

feedback, we are adopting the negative 

failure rate constraint to limit the 

impact of HHS–RADV adjustments due 

to negative error rate issuers with 

negative failure rates beginning with the 

2019 benefit year of HHS–RADV. We 

disagree that the HHS–RADV Protocols 

and the EDGE data submission are not 

appropriately aligned as the EDGE data 

submissions and HHS–RADV Protocols 

are different processes. Specifically, the 

EDGE data submission process for risk 

adjustment requires issuers to submit all 

paid claims to their respective EDGE 

servers, regardless of provider type, for 

the applicable benefit year. These paid 

claims provide the diagnoses that are 

used to calculate risk adjustment 

transfers at the state market risk pool 

level under the state payment transfer 

formula.76 HHS–RADV is a review of an 

enrollee’s medical records to confirm 

the diagnoses used to perform the 


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77See, for example, Appendix E of the 2018 

Benefit Year HHS–RADV Protocols, which 

describes the guidelines for abstracting Lifelong 

Permanent Conditions from medical records for 

purposes of the 2018 benefit year of HHS–RADV. 

78The illustration of the error rate calculation 

methodology formulas that will apply beginning 

with the 2019 benefit year of HHS–RADV also 

includes the policy finalized in the 2021 Payment 

Notice to not consider issuers with fewer than 30 

HCCs in an HCC failure rate group to be outliers in 

that HCC failure rate group but continue to include 

such issuers in the calculation of national metrics. 

See 85 FR at 29196–29198. 

79See Section 11.3.1 of the 2018 HHS–RADV 

Protocols at https://www.regtap.info/uploads/ 

library/HRADV_2018Protocols_070319_RETIRED_

5CR_070519.pdf for a description of the process 

prior to the introduction of Super HCCs. Beginning 

with the 2019 benefit year of HHS–RADV, Super 

HCCs would take the place of HCCs in the process. 

The 2019 HHS–RADV Protocols have thus far only 

been published in part at https://www.regtap.info/ 

uploads/library/HRADV_2019_Protocols_111120_

5CR_111120.pdf. The section of the 2019 HHS– 

RADV Protocols pertaining to HCC grouping for 

failure rate calculations is not included in the 

current version. Once published, this section will 

be updated to include steps related to creation of 

Super HCCs. 

calculations under the state payment 

transfer formula. HHS- RADV allows 

issuers to take into account an issuer’s 

paid claims for the applicable benefit 

year for medical record review and this 

process also allows issuers to take into 

account certain diagnoses found during 

the review of the medical records of the 

enrollee to provide a more complete and 

accurate picture of an enrollee’s risk to 

the issuer. Further, while HHS–RADV 

Protocols allow IVA and SVA auditors 

to abstract documented ‘‘Lifelong 

Permanent Conditions’’ 77 that may not 

be captured in EDGE data submissions, 

we disagree that such an approach is 

inappropriate. The list of Lifelong 

Permanent Conditions is a set of health 

conditions that require ongoing medical 

attention and where all associated 

diagnoses are typically unresolved once 

diagnosed. Allowing abstraction of 

diagnosis codes for those conditions 

from medical records submitted during 

HHS–RADV if the Lifelong Permanent 

Condition is identified in the enrollee’s 

medical history included in a medical 

record for the applicable benefit year 

ensures that an enrollee’s full health 

risk is captured and reflected in risk 

adjustment transfers for that state 

market risk pool. 

a. Combining the HCC Grouping 

Constraint, Negative Failure Rate 

Constraint and the Sliding Scale 

Proposals 

As discussed elsewhere in this final 

rule, we are finalizing as proposed each 

of the three constituent proposals to 

refine the current error rate calculation. 

To illustrate the interaction of the 

finalized policies to create Super HCCs 

for HHS–RADV grouping purposes, 

apply the sliding scale adjustment, and 

constrain negative failure rates for 

negative error rate outliers, this section 

outlines the complete finalized revised 

error rate calculation methodology 

formulas that will apply beginning with 

the 2019 benefit year of HHS–RADV, 

integrating all the changes finalized in 

this rule.78

First, HHS will 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 finalized approach, Super 

HCCs will be defined as HCCs that have 

been aggregated such that HCCs that are 

in the same HCC coefficient estimation 

group in the adult models are aggregated 

together and all other HCCs each 

compose a Super HCC individually. 

Using the Super HCCs, we will calculate 

the HCC failure rate as follows: 

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: 

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: 

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, will be grouped 

according to the current sorting 

algorithm in the current HHS–RADV 

failure rate grouping methodology.79

These HCC groupings will 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, thereby assigning each Super 

HCC into a high, medium, or low HCC 

failure rate grouping. This process 

ensures that all HCCs in a Super HCC 

are grouped into the same HCC failure 

rate grouping in HHS–RADV. 

Next, an issuer’s HCC group failure 

rate would be calculated as follows: 

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 (or SVA, 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: 


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Federal Register / Vol. 85, No. 231 / Tuesday, December 1, 2020 / Rules and Regulations 76999 

80This calculation sequence is expressed here in 

a revised order compared to how the sequence is 

published in the 2021 Payment Notice (85 FR at 

29196–29198). This change was made to simplify 

the illustration of how this sequence would be 

combined with proposals finalized in this rule. The 

different display does not modify or otherwise 

change the amendments to the outlier identification 

process finalized in the 2021 Payment Notice. 

Where: 

m{GFRG} 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} is the weighted standard deviation 

of GFRG,i of all issuers for the HCC group 

G. 

Each issuer’s HCC group failure rates 

will then be compared to the national 

metrics for each HCC failure rate 

grouping. If an issuer’s failure rate for an 

HCC failure rate 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 failure rate 

group, the failure rate for the issuer’s 

HCCs in that group will be considered 

an outlier (if the issuer meets the 

minimum number of HCCs for the HCC 

failure rate group). Based on issuers’ 

failure rates for each HCC failure rate 

group, outlier status will 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 EDGE HCCs. If 

no issuers’ HCC group failure rates in a 

state market risk pool materially deviate 

from the national mean of failure rates 

or if those issuers whose failure rates do 

materially deviate from the national 

mean do not also meet the minimum 

HCC frequency requirement (that is, if 

no issuers in the state market risk pool 

are outliers), HHS will not apply any 

HHS–RADV adjustments to issuers’ risk 

scores or to transfers in that state market 

risk pool. 

Then, once the outlier issuers are 

determined, we will calculate the GAF 

taking into consideration the outlier 

issuer’s distance from the confidence 

interval and limiting calculation of the 

GAF when if the issuer is a negative 

error rate outlier with a negative failure 

rate. The formula 80 will apply as 

follows: 

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: 

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: 


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77000 Federal Register / Vol. 85, No. 231 / Tuesday, December 1, 2020 / Rules and Regulations 

81Some enrollees sampled in Strata 1–3 will only 

have RXCs, which are not considered as part of the 

determination of an enrollee adjustment factor. 

• 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 adjustment for range r, calculated 

as: 

With zG,i defined as the z-score of i 

issuers’ GFRG,i: 

• GAFG,i is the group adjustment 

factor for HCC failure rate group G for 

an issuer i; 

• Sd{GFRG} is the weighted national 

standard deviation of all issuers’ GFRs 

for HCC failure rate group G; 

• m{GFRG} is the weighted national 

mean of all issuers’ GFRs for HCC 

failure rate group G. 

Once an outlier issuer’s GAF is 

calculated, the enrollee adjustment will 

be calculated by applying the GAF to an 

enrollee’s individual EDGE HCCs. For 

example, if an issuer has an 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 

EDGE HCC risk score coefficients 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 strata 1 through 9 with EDGE 

HCCs: 81

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 

adjustments for that enrollee and 

adjusted enrollee risk scores are only 

computed for sampled enrollees with 

EDGE HCCs in strata 1 through 9. 

Next, for each sampled enrollee with 

EDGE HCCs, HHS will calculate the 

total adjusted enrollee risk score as: 

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 

IVA or SVA 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 with 

EDGE HCCs. 

After calculating the outlier issuers’ 

sample enrollees with HCCs’ adjusted 

EDGE risk scores, HHS will calculate an 

outlier issuer’s error rate by 

extrapolating the difference between the 

amended risk score and EDGE risk score 

for all enrollees (strata 1 through 10) in 

the sample. The extrapolation formula 

will 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 

EDGE HCCs will 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 will be: 


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Federal Register / Vol. 85, No. 231 / Tuesday, December 1, 2020 / Rules and Regulations 77001 

82Exiting 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. As detailed in Section II.B, we 

are finalizing the transition to the concurrent 

application of HHS–RADV results such that issuer 

risk score error rates for non-exiting issuers will 

also be applied to the risk scores and transfer 

amounts for the benefit year being audited 

beginning with the 2020 benefit year of HHS– 

RADV. 

83See 45 CFR 153.350(c). 

84These estimates reflect the exclusion from 

outlier status of those 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 or 2018 benefit 

year HHS–RADV. We excluded issuers with fewer 

than 30 HCCs 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. 

85This analysis reflects the sliding scale policy 

finalized in Section II.A.2. of this rule which creates 

a sliding scale adjustment from +/¥1.645 to 3 

standard deviations. 

Consistent with 45 CFR 153.350(b), 

HHS then will apply the outlier issuer’s 

error rate to adjust that issuer’s 

applicable benefit year’s plan liability 

risk score.82 This risk score change, 

which also will impact the state market 

average risk score, will then be used to 

adjust the applicable benefit year’s risk 

adjustment transfers for the applicable 

state market risk pool.83 Due to the 

budget-neutral nature of the HHSoperated risk adjustment program, 

adjustments to one issuer’s risk scores 

and risk adjustment transfers based on 

HHS–RADV findings affect 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. 

In the proposed rule, 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. We performed a 

similar analysis using 2018 benefit year 

HHS–RADV results, once the data 

became available. Table 3 provides a 

comparison of the national failure rate 

metrics under the current and new, 

finalized methodologies using 2017 and 

2018 benefit year HHS–RADV results. 

Additionally, using the 2017 and 2018 

HHS–RADV data, Table 4 provides a 

comparison between the estimated 

mean error rates using the current 

methodology for sorting HCCs for HHS– 

RADV grouping or the finalized Super 

HCC aggregation for sorting of HCCs for 

HHS–RADV groupings, with the 

finalized negative failure rate constraint 

and the finalized sliding scale 

adjustment also being applied. As 

shown in Tables 3 and 4, the analysis 

of 2018 HHS–RADV results provided 

roughly the same figures as the 2017 

HHS–RADV results, and offers further 

support for finalizing these refinements 

to the error rate calculation. 

TABLE 3—A COMPARISON OF HHS–RADV NATIONAL FAILURE RATE METRICS BASED ON PRIOR BENEFIT YEAR HHS– 

RADV DATA

HHS–RADV data benefit year Group 

Weighted mean failure rate Weighted std. dev. Lower threshold Upper threshold 

Current 

grouping 

New 

grouping 

Current 

grouping 

New 

grouping 

Current 

grouping 

and 95% CI 

New 

grouping 

and 90% CI 

Current 

grouping 

and 95% CI 

New 

grouping 

and 90% CI 

2017 Data .............................. Low ............... 0.0476 0.0496 0.0973 0.0959 ¥0.1431 ¥0.1082 0.2382 0.2074 

Med ............... 0.1549 0.1557 0.0992 0.0994 ¥0.0395 ¥0.0078 0.3493 0.3192 

High .............. 0.2621 0.2595 0.1064 0.1065 0.0536 0.0843 0.4706 0.4347 

2018 Data .............................. Low ............... 0.0337 0.0369 0.0884 0.0856 ¥0.1396 ¥0.1038 0.2070 0.1777 

Med ............... 0.1198 0.1225 0.0862 0.0856 ¥0.0490 ¥0.0184 0.2887 0.2633 

High .............. 0.2262 0.2283 0.0919 0.0914 0.0461 0.0779 0.4062 0.3787 

TABLE 4—A COMPARISON OF HHS–RADV ERROR RATE (ER) ESTIMATED CHANGES BASED ON PRIOR BENEFIT YEAR 84

HHS–RADV DATA

Scenario 

2017 Data 2018 Data 

Current sorting method New sorting method Current sorting method New sorting method 

Mean neg. 

ER 

(%) 

Mean pos. 

ER 

(%) 

Mean neg. 

ER 

(%) 

Mean pos. 

ER 

(%) 

Mean neg. 

ER 

(%) 

Mean pos. 

ER 

(%) 

Mean neg. 

ER 

(%) 

Mean pos. 

ER 

(%) 

Sorting Method Only ......................................... ¥5.68 9.96 ¥5.98 9.91 ¥6.92 5.43 ¥7.06 5.71 

Sorting Method with Negative Constraint ......... ¥3.11 9.96 ¥3.38 9.91 ¥3.35 5.43 ¥3.16 5.89 

Sorting Method with Sliding Scale 85 ................ ¥2.27 5.28 ¥2.49 5.32 ¥3.07 2.21 ¥3.21 2.45 

Sorting Method, Sliding Scale & Negative Constraint (Finalized) ........................................... ¥1.50 5.28 ¥1.66 5.32 ¥1.71 2.21 ¥1.86 2.47 


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86See 78 FR 15410 at 15438. 

87See 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-andInitiatives/Premium-Stabilization-Programs/ 

Downloads/BY2017-HHSRADV-Adjustments-to-RATransfers-Summary-Report.pdf. 

88 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 plan 

liability risk scores, resulting in adjustments to 

2017 benefit year risk adjustment transfer amounts. 

See 83 FR at 16965–16966. We updated this policy 

to only apply HHS–RADV results for exiting issuers 

that are positive error rate outliers beginning with 

the 2018 benefit year. See the 2020 Payment Notice, 

84 FR at 17503–17504. 

89See 84 FR at 17504 through 17508. 

90See 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-HoldbackPolicy-for-the-2018-Benefit-Year-and-Beyond.pdf. 

91As 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. 

92See Section 5.2 of the 2019 RADV White Paper. 

93For 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. 

94 84 FR at 17504. 

95 Ibid. 

B. Application of HHS–RADV Results 

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.86 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.87 88

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 timeline for the reporting, 

collection, and disbursement of HHS– 

RADV adjustments to transfers 89 and 

the changes to the risk adjustment 

holdback policy,90 both of which lead to 

reopening of prior year risk adjustment 

transfers, we proposed to switch away 

from the prospective approach for nonexiting issuers. We proposed to make 

the transition and apply HHS–RADV 

results to the benefit year being audited 

for all issuers starting with the 2021 

benefit year of HHS–RADV. We 

proposed applying HHS–RADV results 

to the benefit year being audited for all 

issuers in an effort 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. 

In the proposed rule, we explained 

that if we finalized and implemented 

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. Then, 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).91

In order to effectuate this transition, 

we proposed 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 plan liability risk 

scores and risk adjustment transfers.92

This approach would result in one final 

HHS–RADV adjustment to 2021 benefit 

year plan liability risk scores and risk 

adjustment 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.93

However, in an effort to be consistent 

with our current risk score error rate 

application and calculation and ensure 

that both years of HHS–RADV results 

were taken into consideration in 

calculating risk adjustment plan liability 

risk scores, we also proposed an 

alternative approach: the ‘‘combined 

plan liability risk score option.’’ 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. We sought 

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. 

We also explained in the proposed 

rule that the transition to a policy to 

apply HHS–RADV results to the benefit 

year being audited for all issuers would 

remove the need to continue the current 

policy on issuers entering sole issuer 

markets finalized in the 2020 Payment 

Notice.94 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.95

We further explained that 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 

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 if there was an outlier issuer 

in the same state market risk pool. 

We also sought comment on an 

alternative timeline, in which HHS 

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. We explained that under 

the alternative timeframe, 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. Lastly, we sought 


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96 In addition, positive error rate outlier issuers’ 

2019 and 2020 HHS–RADV results will be applied 

to the risk scores and transfers for the benefit year 

being audited. The average error rate approach is 

not applicable because exiting issuers who 

participated in 2019 HHS–RADV would not have 

2020 benefit year risk scores or transfers to adjust. 

97As 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. 

98 Ibid. 

99For example, in the 2021 Payment Notice, we 

finalized several updates to the HHS–HCC clinical 

classification to develop updated risk factors that 

apply beginning with the 2021 benefit year risk 

adjustment models. See 85 FR at 29175. 

100As noted above, a new entrant to a state 

market risk pool in 2020 would see its risk score(s) 

and transfer impacted by the new issuer’s own 2020 

HHS–RADV results, the combined 2019 and 2020 

HHS–RADV results of other non-exiting issuers in 

the same state market risk pool, and the 2020 HHS– 

RADV results for positive error rate outlier exiting 

Continued 

comment on whether, if we finalized 

and implemented either of the transition 

options using the alternative timeline, 

we should also pilot RXCs for the 2020 

benefit year HHS–RADV. 

We are finalizing the proposed 

transition from the current prospective 

application of HHS–RADV results for 

non-exiting issuers and will apply 

HHS–RADV audit findings to the benefit 

year being audited for all issuers, 

starting with the 2020 benefit year 

HHS–RADV, by combining 2019 and 

2020 benefit years HHS–RADV results 

for non-exiting issuers following the 

average error rate approach. We also 

reaffirm that, as a result of finalizing 

these changes, we will not need to 

continue the current policy on issuers 

entering sole issuer markets after the 

transition is effectuated. Therefore, if a 

new issuer entered a state market risk 

pool in 2020, its risk adjustment plan 

liability risk score(s) and transfer for 

2020 benefit year risk adjustment could 

be impacted by the new issuer’s own 

2020 HHS–RADV results and the 

combined 2019 and 2020 HHS–RADV 

results of other issuers in the same state 

market risk pool. For exiting issuers, 

HHS will continue to adjust only for 

positive error rate outliers, as opposed 

to both positive and negative error rate 

outliers.96 Beginning with the 2021 

benefit year of HHS–RADV, plan 

liability risk scores and risk adjustment 

transfers will only be adjusted once 

based on the same benefit year’s HHS– 

RADV results (that is, 2021 benefit year 

HHS–RADV results would adjust 2021 

benefit year plan liability risk scores 

and transfers for all issuers).97

Additionally, HHS will continue to pilot 

RXCs for the 2020 benefit year. 

We are finalizing this change to apply 

HHS–RADV results to the benefit year 

being audited for all issuers 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. In addition, this 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. Once the 

transition is effectuated, it will also 

prevent situations in which an issuer 

who newly enters a state market risk 

pool, including new market entrants to 

a sole issuer market, is subject to HHS– 

RADV adjustments from the prior 

benefit year for which they did not 

participate. 

Comments: The majority of 

commenters supported switching from 

the prospective application of the HHS– 

RADV results to the benefit year being 

audited. These commenters generally 

agreed that having a concurrent 

application would maintain actuarial 

soundness in the application of an 

issuer’s HHS–RADV error rate, provide 

stability to HHS–RADV results, and 

promote fairness in the HHS–RADV 

process. One commenter suggested that 

HHS should consider maintaining the 

current prospective application of HHS– 

RADV findings; another commenter 

suggested HHS exempt new issuers from 

having their transfers adjusted due to 

HHS–RADV. 

Regarding the transition year, some 

commenters supported switching to the 

concurrent application in the 2021 

benefit year as proposed due to 

concerns that changing the transition 

year to the 2020 benefit year of HHS– 

RADV would heighten the already 

significant uncertainty surrounding 

2020 as a result of COVID–19, with one 

commenter noting that issuers did not 

account for this change in their 2020 

pricing. However, most commenters 

supported switching to the concurrent 

application with the 2020 benefit year, 

suggesting that it would be most 

appropriate to transition to a concurrent 

application as early as possible and one 

cited to the various changes to the HHSoperated risk adjustment program 

beginning with the 2021 benefit year as 

further support for the alternative 

timeline for the transition. One 

commenter requested additional 

information on the 2020 benefit year 

HHS–RADV timeline. 

Response: We are finalizing the 

proposal to switch from the current 

prospective application of the HHS– 

RADV results to the benefit year being 

audited, starting with the 2020 benefit 

year. As previously noted, when we 

finalized the prospective HHS–RADV 

results application policy, we did not 

anticipate the extent of changes that 

could occur in the risk profile of 

enrollees or market participation by 

issuers from benefit year to benefit year. 

As a result of experience over the early 

years of the program, we believe that 

transitioning to apply HHS–RADV 

results on a concurrent basis for all 

issuers will provide greater stability, 

promote fairness, and enhance actuarial 

soundness, specifically in the event that 

an issuer’s risk profile, enrollment, or 

market participation changes 

significantly from benefit year to benefit 

year. In light of the other changes to 

HHS–RADV program operations 

described in this rule which will lead to 

reopening of prior benefit year risk 

adjustment transfers,98 it is also no 

longer necessary to apply HHS–RADV 

results on a prospective basis to allow 

time to complete the discrepancy and 

appeals processes to avoid having to 

reopen prior year transfers. We also 

agree that we should begin the 

application of the results on a 

concurrent basis as soon as possible and 

will implement the policy starting with 

the 2020 benefit year. We believe that 

starting with the 2020 benefit year will 

add stability in the midst of the COVID– 

19 pandemic, as the results from the 

2019 and 2020 benefit years of HHS– 

RADV will be averaged together to 

calculate the adjustment to 2020 benefit 

year risk adjustment risk scores. We 

believe this added stability will account 

for concerns that issuers did not take 

this proposed change into consideration 

when setting rates for the 2020 benefit 

year. We also agree with the commenter 

who cited the risk adjustment program 

updates that apply beginning with the 

2021 benefit year as further support for 

effectuating the transition beginning 

with the 2020 benefit year.99

We did not propose and are not 

finalizing a new exemption from HHS– 

RADV for new market entrants. The 

inclusion of new market entrants in 

HHS–RADV ensures that those issuers’ 

actuarial risk for the applicable benefit 

year is accurately reflected in risk 

adjustment transfers, and that the HHSoperated risk adjustment program 

assesses charges to plans with lowerthan-average actuarial risk while making 

payments to plans with higher-thanaverage actuarial risk. However, new 

market entrants will no longer be 

impacted by a prior year’s HHS–RADV 

results and will only be impacted by the 

results from the benefit year under 

which they participated in the state 

market risk pool after the transition is 

effectuated.100


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issuers in the same state market risk pool. However, 

a new entrant to a state market risk pool in 2021 

would see its risk score(s) and transfer impacted by 

2021 HHS–RADV results only. 

101See the ‘‘2019 Benefit Year HHS–RADV 

Activities Timeline’’ https://www.regtap.info/ 

uploads/library/HRADV_Timeline_091020_5CR_

091020.pdf. 

102https://www.cms.gov/files/document/2019- 

HHS-RADV-Postponement-Memo.pdf. 103See the ‘‘2019 Benefit Year HHS–RADV 

Activities Timeline’’ https://www.regtap.info/ 

uploads/library/HRADV_Timeline_091020_5CR_

091020.pdf. 

HHS intends to provide more 

information on the 2020 benefit year 

HHS–RADV timeline in the future, but 

generally anticipates it will commence 

as usual with the release of samples in 

May 2021. As previously noted in this 

rule, HHS has provided details on the 

updated timeline on the activities for 

2019 benefit year HHS–RADV.101

Comments: Most commenters who 

submitted comments on the options for 

combining HHS–RADV results during 

the transition period supported using 

the average error rate approach, noting 

that it would provide more stability and 

transparency than the combined plan 

liability risk score option. One 

commenter who expressed a preference 

for the average error rate approach cited 

concerns with the amplifying effect of 

adjusting risk scores twice under the 

plan liability risk score option. Most 

commenters who supported the average 

error rate approach supported 

effectuating the transition using 2019 

and 2020 benefit years’ error rate 

results. These commenters noted that 

aggregating the results of these 2 years 

could reduce volatility and smooth over 

potential challenges issuers may face 

when conducting HHS–RADV audits for 

these benefit years due to the COVID– 

19 public health emergency. A few 

commenters who supported use of the 

average error rate approach urged HHS 

to implement the transition and use 

2020 and 2021 benefit years’ results, 

suggesting it would be the most 

straightforward approach. One 

commenter requested clarification as to 

whether the average error rate approach 

would use a weighted average error rate. 

A few commenters supported the 

combined plan liability risk score 

option for the transition years of HHS– 

RADV. One of these commenters 

believed that the combined plan 

liability risk score option would be a 

fairer way to provide consistency, while 

a different commenter that supported 

the combined plan liability risk score 

option was concerned that the average 

error rate approach would reduce the 

otherwise applicable HHS–RADV 

adjustment. Another commenter 

compared the two alternative 

approaches, noting that the average 

error rate would align well with some 

issuers’ practices, while the combined 

liability risk score option would align 

better with other issuers’ financial 

reporting. 

Response: We are finalizing the use of 

the average error rate approach to 

transition to the concurrent application 

of HHS–RADV results for non-exiting 

issuers by combining their 2019 and 

2020 benefit years’ HHS–RADV results. 

In response to comments we clarify that 

for simplification purposes, HHS will 

apply an unweighted average value of 

the 2019 and 2020 benefit years’ HHS– 

RADV results to adjust 2020 benefit year 

risk scores and transfers. We proposed 

using a combined plan liability risk 

score as an alternative option, believing 

that it could provide a more consistent 

transition to a concurrent application of 

HHS–RADV results. However, the 

majority of comments on these 

transition options emphasized the 

extent to which they believed an 

average error rate approach will actually 

provide greater stability and 

transparency for the HHS–RADV 

adjustments applied during the 

transition period. After consideration of 

comments, we agree that the average 

error rate approach will be the optimal 

transitional approach. More specifically, 

aggregating the 2019 and 2020 benefit 

years’ results for non-exiting issuers and 

using the unweighted average value of 

those benefit years’ HHS–RADV results 

to adjust transfers will allow for more 

consistency, reduce potential volatility, 

and better accommodate any potential 

disparities or challenges due to COVID– 

19. As noted previously, we also believe 

the transition to the application of the 

results on a concurrent basis should be 

implemented as soon as possible and 

therefore will start the concurrent 

application of HHS–RADV results for all 

issuers starting with the 2020 benefit 

year. We recognize that there are 

advantages to the combined plan 

liability risk score option, which is why 

we proposed it for combining HHS– 

RADV results for the transition years. 

However, for the reasons outlined 

above, we believe the average error rate 

method is the more balanced approach 

to effectuate the transition and combine 

2019 and 2020 HHS–RADV results for 

non-exiting issuers. 

Comments: Some commenters 

suggested HHS cancel either the 2019 or 

2020 benefit years of HHS–RADV. One 

of these commenters expressed concern 

that the COVID–19 pandemic could 

potentially skew the 2020 benefit year 

HHS–RADV results. Other commenters 

stated that COVID–19 would make it 

difficult for providers to respond to 

issuer requests for the medical 

documentation needed to complete 

audits, which they noted could skew 

HHS–RADV results. 

Response: We appreciate the concerns 

related to the potential impact of 

COVID–19, but are not cancelling HHS– 

RADV for either the 2019 or 2020 

benefit year. We believe that cancelling 

either year of this program would be 

detrimental to program integrity and 

would result in future difficulties 

monitoring HHS–RADV trends. We 

acknowledge that the COVID–19 

pandemic puts a number of stressors on 

providers and issuers. Recognizing the 

impact of the public health emergency 

on HHS–RADV activities, we postponed 

the start of 2019 benefit year HHS– 

RADV activities.102 As recently 

announced, IVA samples for 2019 

benefit year HHS–RADV will be 

released in January 2021 and we 

anticipate 2020 benefit year HHS–RADV 

will commence as usual.103 We will 

continue to monitor the COVID–19 

public health emergency and will 

consider whether additional flexibilities 

for HHS–RADV are appropriate. 

Further, as noted above, the adoption of 

the average error rate approach for the 

transition to the concurrent application 

of HHS–RADV is intended to help 

reduce volatility related to potential 

challenges issuers may face when 

conducting HHS–RADV audits for these 

benefit years due to the COVID–19 

public health emergency. 

Comments: Most commenters 

supported continuing the pilot of RXCs 

for the 2020 benefit year. Some of these 

commenters suggested that continuing 

to pilot RXCs would allow for more 

consistency between 2019 and 2020 and 

support transitioning to the concurrent 

application of HHS–RADV results 

starting with the 2020 benefit year, 

while another commenter believed that 

it would minimize the amount of 

changes occurring at once. One 

commenter noted that extending the 

RXC pilot would benefit the issuers who 

are still learning how to conduct HHS– 

RADV for RXCs. Another commenter 

did not believe it would be necessary to 

continue piloting RXCs in 2020, but 

acknowledged that an additional pilot 

period would allow issuers to focus on 

HHS–RADV during the COVID–19 

pandemic, rather than adjusting to new 

aspects of HHS–RADV reporting. 

Response: After consideration of 

comments, we are finalizing the 

continuation of the pilot for RXCs for 

the 2020 benefit year. Extending the 

RXC pilot an additional benefit year will 

increase consistency between the 


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104The exception to the current prospective 

application of HHS–RADV results is for exiting 

issuers identified as positive error rate outliers, 

whose HHS–RADV results are applied to the risk 

scores and transfer amounts for the benefit year 

being audited. 

105Since the 2017 benefit year, HHS has been 

responsible for operating risk adjustment in all 50 

states and the District of Columbia. 

106See 83 FR 16961 and 16965. 

107See 79 FR 13755–13770. 

108See the 2019 RADV White Paper at pages 78– 

79 and Appendix B. 

109See 84 FR 17507–17508. See also the 2019 

RADV White Paper at page 80. 

operations of the 2019 and 2020 benefit 

years’ HHS–RADV and facilitate the 

combination of the HHS–RADV 

adjustments for these benefit years as 

we transition to a concurrent 

application of HHS–RADV results 

starting with the 2020 benefit year. We 

agree with commenters who suggested 

that an additional pilot year for RXCs 

would benefit issuers and provide an 

opportunity to continue to improve 

their internal process for conducting 

HHS–RADV for RXCs. 

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 final rule, we are finalizing 

the modifications to the calculation of 

error rates to modify the HCC failure 

rate grouping methodology for HCCs 

that share an HCC coefficient estimation 

group in the adult risk adjustment 

models; to calculate and apply a sliding 

scale adjustment for cases where outlier 

issuers are near the confidence 

intervals; and to constrain the error rate 

calculation for issuers with negative 

failure rates. We are also finalizing the 

transition from the current prospective 

application of HHS–RADV results 104 to 

apply the results to the benefit year 

being audited. These are methodological 

changes to the error estimation 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 did 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.105

IV. Regulatory Impact Statement 

A. Statement of Need 

This rule finalizes standards related to 

HHS–RADV, 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 earlier 

provided detail on the implementation 

of HHS–RADV. 

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 final rule, we considered 

numerous alternatives to the presented 

policies. Below we discuss the key 

regulatory alternatives considered. 

We considered an alternative 

approach to the 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 finalized approach that 

focuses on HCCs that share an HCC 

coefficient estimation group and have 

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.106

We considered several other options 

for addressing the payment cliff effect 

besides the specific sliding scale 

adjustment that we are finalizing. 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.107 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 less 

stability, since the vast majority of 

outlier issuers had their original failure 

rates applied without the benefit of 

subtracting the weighted mean 

difference.108 In addition, while the 

original methodology did not 

specifically result in a payment cliff 

effect, it would have resulted in more 

and larger adjustments to transfers. 

The second option we considered to 

mitigate the impact of the payment cliff 

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,109 we had 

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 


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77006 Federal Register / Vol. 85, No. 231 / Tuesday, December 1, 2020 / Rules and Regulations 

110 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. 

111See section 5.2 of the 2019 RADV White 

Paper. 

112For a general description of the current 

timeline for publication, collection, and 

distribution of HHS–RADV adjustments to transfers, 

see 84 FR at 17506 –17507. 

113https://www.sba.gov/document/support-- 

table-size-standards. 

114Available at https://www.cms.gov/CCIIO/ 

Resources/Data-Resources/mlr.html. 

accurate risk adjustment data to their 

EDGE servers. 

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 

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.110 In addition, the 

two-sided outlier identification, and the 

resulting adjustments to outlier issuer 

risk scores that have significantly betterthan-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 during HHS–RADV. 

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. 

We also considered various other 

options for the thresholds under the 

sliding scale option to mitigate 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 

99.7 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 99.7 percent 

confidence interval (beyond three 

standard deviations), and the current 

significant adjustment to the HCC group 

weighted mean after three standard 

deviations. Commenters supported this 

sliding scale option because it 

addressed the payment cliff issue 

without increasing the number of 

issuers identified as outliers. However, 

while we recognized that this 

alternative also would mitigate the 

payment cliff effect, it would weaken 

HHS–RADV 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 this rule, we described two 

of those options. The third option is the 

‘‘RA transfer option.’’ The RA transfer 

option would separately calculate 2019 

benefit year HHS–RADV adjustments to 

2020 benefit year transfers and 2020 

benefit year HHS–RADV adjustments to 

2020 benefit year transfers.111 Under 

this option, we would then calculate the 

difference between each of these values 

and the unadjusted 2020 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 2020 benefit year 

transfers. That is, HHS would separately 

calculate adjustments for the 2019 and 

2020 benefit year HHS–RADV results 

and incorporate 2019 and 2020 benefit 

year HHS–RADV results in one final 

adjustment to 2020 benefit year transfers 

that would be collected and paid in 

accordance with the 2020 benefit year 

HHS–RADV timeline.112 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 being finalized. 

V. Regulatory Flexibility Act 

The RFA (5 U.S.C. 601 et seq.) 

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-forprofit 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 final rule, we establish 

standards for HHS–RADV. 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.113 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 114 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. 

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 604 

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 final rule would not affect 

small rural hospitals. Therefore, the 

Secretary has determined that this final 


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Federal Register / Vol. 85, No. 231 / Tuesday, December 1, 2020 / Rules and Regulations 77007 

rule will not have a significant impact 

on the operations of a substantial 

number of small rural hospitals. 

VI. 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 2020, that 

threshold is approximately $156 

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. 

VII. 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 final rule, we 

attempted to balance the states’ interests 

in regulating health insurance issuers 

with the need to ensure market stability 

and adopt refinements to HHS–RADV 

standards. 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 Exchangerelated 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, 

HHS has operated risk adjustment for all 

50 states and the District of Columbia. 

In our view, while this final 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. 

VIII. 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 final rule is 

not subject to the requirements of 

Executive Order 13771 because it is 

expected to result in no more than de 

minimis costs. 

IX. Conclusion 

In accordance with the provisions of 

Executive Order 12866, this regulation 

was reviewed by the Office of 

Management and Budget. 

Dated: November 18, 2020. 

Seema Verma, 

Administrator, Centers for Medicare & 

Medicaid Services. 

Dated: November 23, 2020. 

Alex M. Azar II, 

Secretary, Department of Health and Human 

Services. 

[FR Doc. 2020–26338 Filed 11–25–20; 4:15 pm] 

BILLING CODE 4120–01–P 

DEPARTMENT OF COMMERCE 

National Oceanic and Atmospheric 

Administration 

50 CFR Part 635 

[Docket No.: 201124–0317] 

RTID 0648–XT038 

Atlantic Highly Migratory Species; 

2021 Atlantic Shark Commercial 

Fishing Year 

AGENCY: National Marine Fisheries 

Service (NMFS), National Oceanic and 

Atmospheric Administration (NOAA), 

Commerce. 

ACTION: Final rule; fishing season 

notification. 

SUMMARY: This final rule establishes the 

2021 opening date for all Atlantic shark 

fisheries, including the fisheries in the 

Gulf of Mexico and Caribbean. This 

final rule also establishes the shark 

fisheries quotas for the 2021 fishing 

year, with adjustments based on harvest 

levels during 2020, and establishes the 

large coastal shark (LCS) retention limits 

for directed shark limited access permit 

holders. NMFS may increase or decrease 

these retention limits for directed shark 

limited access permit holders during the 

year, in accordance with existing 

regulations, to provide equitable fishing 

opportunities for commercial shark 

fishermen in all regions and areas, to the 

extent practicable. These actions could 

affect fishing opportunities for 

commercial shark fishermen in the 

northwestern Atlantic Ocean, including 

the Gulf of Mexico and Caribbean Sea. 

DATES: This rule is effective on January 

1, 2021. The 2021 Atlantic commercial 

shark fishing year opening dates and 

quotas are provided in Table 1 under 

SUPPLEMENTARY INFORMATION. 

ADDRESSES: Atlantic Highly Migratory 

Species (HMS) Management Division, 

1315 East-West Highway, Silver Spring, 

MD 20910; https://

www.fisheries.noaa.gov/topic/atlantichighly-migratory-species. 

FOR FURTHER INFORMATION CONTACT: 

Lauren Latchford (lauren.latchford@

noaa.gov), Guy Eroh (guy.eroh@

noaa.gov), or Karyl Brewster-Geisz 

(karyl.brewster-geisz@noaa.gov) at 301– 

427–8503. 

SUPPLEMENTARY INFORMATION: 

Background 

The Atlantic commercial shark 

fisheries are managed primarily under 

the authority of the Magnuson-Stevens 

Fishery Conservation and Management 

Act (Magnuson-Stevens Act). The 2006 

Consolidated Atlantic HMS Fishery 

Management Plan (FMP) and its 

amendments are implemented by 

regulations at 50 CFR part 635. For the 

Atlantic commercial shark fisheries, the 

2006 Consolidated HMS FMP and its 

amendments established, among other 

things, measures related to commercial 

shark retention limits, commercial 

quotas for species and management 

groups, and accounting for under- and 

overharvests in the shark fisheries. 

Regulations include adaptive 

management measures, such as 

flexibility in establishing opening dates 

for the fishing season and the ability to 

make inseason trip limit adjustments, 

which provide management flexibility 

in furtherance of equitable fishing 

opportunities, to the extent practicable, 

for commercial shark fishermen in all 

regions and areas. 

On September 29, 2020, NMFS 

published a proposed rule (85 FR 

60947) regarding management measures 

for the commercial shark fisheries for 

the 2021 fishing year. The rule proposed 

opening all Atlantic commercial shark 

management groups on January 1, 2021, 

setting initial retention limits for large 

coastal sharks (LCS) by directed shark 


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