ArticleAmerican journal of health economics2021
Improving the Performance of Risk Adjustment Systems: Constrained Regressions, Reinsurance, and Variable Selection.
Article in American journal of health economics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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.
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Who cites it
14 citing papers in PubMed, 28 citations in OpenAlex.
- Cross-sectional analysis of accuracy versus interpretability in Medicare Advantage risk adjustment.Health policy OPEN · 2026Article
- Does Health-Based Prospective Risk Adjustment Adequately Compensate for Individuals Diagnosed With a New Chronic Disease?Medical care research and review : MCRR · 2026Article
- A Machine Learning Model to Improve Risk Adjustment Accuracy in Medicare.Health services research · 2026Article
- Exploration of using constrained regression in Germany's morbidity-based risk adjustment.The European journal of health economics : HEPAC : health economics in prevention and care · 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
- Selecting risk adjusters with penalized regression and expert judgment: evidence from Colombia.Health economics review · 2025Article
- Algorithms to Improve Fairness in Medicare Risk Adjustment.JAMA health forum · 2025Article
- A critical review of the use of RThe European journal of health economics : HEPAC : health economics in prevention and care · 2025Review
- Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand.BMC medical informatics and decision making · 2024Article
- Comparing risk adjustment estimation methods under data availability constraints.Health economics · 2022Article
- How to deal with persistently low/high spenders in health plan payment systems?Health economics · 2022Article
- Development and Assessment of a New Framework for Disease Surveillance, Prediction, and Risk Adjustment: The Diagnostic Items Classification System.JAMA health forum · 2022Article
- Identifying undercompensated groups defined by multiple attributes in risk adjustment.BMJ health & care informatics · 2021Article
- Ethical Machine Learning in Healthcare.Annual review of biomedical data science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Modifications of risk-adjustment systems used to pay health plans in individual health insurance markets typically seek to reduce selection incentives at the individual and group levels by adding variables to the payment formula. Adding variables can be costly and lead to unintended incentives for upcoding or service utilization. While these drawbacks are recognized, they are hard to quantify and difficult to balance against the concrete, measurable improvements in fit that may be achieved by adding variables to the formula. This paper takes a different approach to improving the performance of health plan payment systems. Using the HHS-HHC V0519 model from the Marketplaces as a starting point, we constrain fit at the individual and group level to be as good or better than the current payment model while
Identifiers
What OpenQuestion holds
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.