ArticleHealth affairs (Project Hope)2025
Use Of Patient Health Survey Data For Risk Adjustment To Limit Distortionary Coding Incentives In Medicare.
Article in Health affairs (Project Hope), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Streamlining oversight in Medicare advantage: measuring coding intensity via contract rankings.Health affairs scholar · 2026Article
- Access to care affects electronic health record reliability and AI-driven disease prediction.Nature health · 2026Article
- Differences in the Measurement of Comorbidities Based on ICD-10-CM Coding Definitions Using Medicare Advantage Encounter Data and Fee-For-Service Claims.Medical care · 2026Article
- Accountable Care Organization Efficiency on Entry and Shared Savings Bonuses.JAMA network open · 2026Article
- Risk Governance of Payment Exceptions Under China's DRG/DIP Reform: A Structured Policy-Text Analysis of Special Case Negotiation Rules.Risk management and healthcare policy · 2026Article
- Incidence, Persistence, and Steady-State Prevalence in Coding Intensity for Health Plan Payment.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
A core problem with the current risk-adjustment system in Medicare Advantage and accountable care organization (ACO) programs-the Hierarchical Condition Categories (HCC) model-is that the inputs (coded diagnoses) can be influenced for gain by risk-bearing plans or providers. Using existing survey data on health status (which provide less manipulable inputs), we found that the use of a hybrid risk score drawing from survey data and a scaled-back set of HCCs would, in addition to mitigating coding incentives, modestly lessen risk-selection incentives, strengthen payment incentives to deliver efficient care, allocate payment across ACOs more efficiently according to markers of population health that are not as affected by practice patterns or coding efforts, and redistribute payment in a manner that supports equity goals. Although sampling error and survey nonresponse present challenges, analyses suggest that these should not be prohibitive. Overall, our proof-of-concept analysis suggests that using survey data to improve risk-adjustment performance is a promising strategy that merits further development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.