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Economic and Financial Impacts of Cancer · Journal article
Health Services Research · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a methodological replication study that re-estimates the CMS HCC version 28 risk adjustment model using 2016–2022 data and compares Traditional Medicare claims–based coefficients to Medicare Advantage encounter data–based coefficients. The analysis finds that MA-based HCC scores are 8.9% lower than TM-based scores and that coefficient estimates have shifted modestly over time, with implications for policy recalibration but no definitive evidence yet supporting a specific payment adjustment.
Ordinary least squares regression replication study with sensitivity and robustness testing. Community-dwelling, non-dual aged and disabled individuals with Traditional Medicare claims or Medicare Advantage encounter records, 2016–2022.. Intervention: Re-estimation of HCC v28 risk model coefficients using 2016–2022 Traditional Medicare and Medicare Advantage data. Compared with: CMS HCC version 28 model coefficients estimated from 2018–2019 Traditional Medicare data. United States (20% national sample of TM and MA data).
Using TM data from 2016 to 2022 results in 1.7% lower average HCC scores relative to 2018–2019 TM data used for CMS HCC model v28 Using MA encounter data results in 8.9% lower average HCC scores than TM-based scores Differences between MA- and TM-based HCC scores vary across the distribution of scores
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Clinicians and payers should recognize that risk adjustment coefficients derived from MA encounter data differ systematically from those based on Traditional Medicare claims, with implications for MA payment policy recalibration. However, this analysis does not provide definitive guidance on whether or how payment rates should change; further analysis integrating enrollment incentives and broader MA payment policy is needed.
A methodological study replicating and stress-testing an existing risk model using observational data; findings suggest potential recalibration needs but lack a prospective design, randomization, or definitive clinical outcome to warrant stronger classification.
As stated by the source record.
Quoted from the source exactly as published.
Clinicians and payers should recognize that risk adjustment coefficients derived from MA encounter data differ systematically from those based on Traditional Medicare claims, with implications for MA payment policy recalibration. However, this analysis does not provide definitive guidance on whether or how payment rates should change; further analysis integrating enrollment incentives and broader MA payment policy is needed.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
OBJECTIVE: To estimate the Centers for Medicare and Medicaid Services (CMS) Hierarchical Condition Category (HCC) risk model using Medicare Advantage (MA) encounter data, as an initial step toward recalibrating risk-adjusted MA payments. DATA SOURCES AND STUDY SETTING: A 20% sample of Traditional Medicare (TM) claims and MA encounter data for 2016-2022. Standardized fee schedules provide a measure of resource use in TM claims and MA encounters. STUDY DESIGN: Ordinary least squares regression replication of the CMS HCC version 28 model for relative resource use among community-dwelling, non-dual aged and disabled individuals. Robustness testing includes replication with version 22 model structure, sensitivity to MA chart review records, MA contracts with complete encounter data, and patterns of care during the COVID pandemic. PRINCIPAL FINDINGS: Using TM data from 2016 to 2022 results in modest changes in estimated coefficients and 1.7% lower average HCC scores, relative to 2018-2019 TM data used for CMS's HCC model v28. Using MA data result in 8.9% lower average scores than TM-based scores. The differences between MA- and TM-based HCC scores vary across the distribution of scores. When re-estimating HCC v28 coefficients, increasing trends in TM and MA diagnosis prevalence are associated with smaller (diluted) coefficients in TM and MA-based HCC risk models. Trends in medical technology can increase (e.g., high-cost targeted cancer therapies) or decrease (e.g., lower-cost biosimilars) HCC model coefficients, with evidence of larger technology-related decreases in an MA-based model. The decrease in MA-based scores does not create new disincentives to enroll beneficiaries who are racial/ethnic minorities or rural residents. CONCLUSIONS: We make an important contribution to the policy debate about MA risk adjustment. Any changes in risk-adjusted MA payment need to be reviewed in the full context of MA payment policy and MA plan enrollment incentives.
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