ArticleHealth services research2016
A Machine Learning Framework for Plan Payment Risk Adjustment.
Article in Health services research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- Nursing Process Data for Health Care Cost Prediction Using Machine Learning: Longitudinal Study.JMIR nursing · 2026Observational
- A Multi-Schedule Machine Learning Pipeline for Medicare Reimbursement Change Prediction and Operational Risk Stratification.medRxiv : the preprint server for health sciences · 2026Article
- Cross-sectional analysis of accuracy versus interpretability in Medicare Advantage risk adjustment.Health policy OPEN · 2026Article
- Development of Machine Learning Models to Predict Health Insurance Claim Costs Among Older Indonesians: A Retrospective Predictive Modeling Study.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 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
- Setting incentives right with long-term risk adjustment.The European journal of health economics : HEPAC : health economics in prevention and care · 2025Article
- A Framework for the Design of Risk-Adjustment Models in Health care Provider Payment Systems.Medical care research and review : MCRR · 2025Review
- Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation.The European journal of health economics : HEPAC : health economics in prevention and care · 2024Article
- Identifying low acuity Emergency Department visits with a machine learning approach: The low acuity visit algorithms (LAVA).Health services research · 2024Article
- A Novel Machine Learning Algorithm for Creating Risk-Adjusted Payment Formulas.JAMA health forum · 2024Article
- High-risk pooling for mitigating risk selection incentives in health insurance markets with sophisticated risk equalization: an application based on health survey information.BMC health services research · 2024Article
- Using machine learning to predict acute myocardial infarction and ischemic heart disease in primary care cardiovascular patients.PloS one · 2024Article
- Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks.Sensors (Basel, Switzerland) · 2023Article
- Use of race in clinical algorithms.Science advances · 2023Article
- Using machine-learning algorithms to improve imputation in the medical expenditure panel survey.Health services research · 2023Article
- Drivers of Individual and Regional Variation in CMS Hierarchical Condition Categories Among Florida Beneficiaries.Risk management and healthcare policy · 2023Article
- A two-stage super learner for healthcare expenditures.Health services & outcomes research methodology · 2022Article
- The Lumbar Stenosis Prognostic Subgroups for Personalizing Care and Treatment (PROSPECTS) study: protocol for an inception cohort study.BMC musculoskeletal disorders · 2022Article
- Comparing risk adjustment estimation methods under data availability constraints.Health economics · 2022Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
objectiveTo introduce cross-validation and a nonparametric machine learning framework for plan payment risk adjustment and then assess whether they have the potential to improve risk adjustment. DATA SOURCES: 2011-2012 Truven MarketScan database. STUDY
designWe compare the performance of multiple statistical approaches within a broad machine learning framework for estimation of risk adjustment formulas. Total annual expenditure was predicted using age, sex, geography, inpatient diagnoses, and hierarchical condition category variables. The methods included regression, penalized regression, decision trees, neural networks, and an ensemble super learner, all in concert with screening algorithms that reduce the set of variables considered. The performance of these methods was compared based on cross-validated R PRINCIPAL
findingsOur results indicate that a simplified risk adjustment formula selected via this nonparametric framework maintains much of the efficiency of a traditional larger formula. The ensemble approach also outperformed classical regression and all other algorithms studied.
conclusionsThe implementation of cross-validated machine learning techniques provides novel insight into risk adjustment estimation, possibly allowing for a simplified formula, thereby reducing incentives for increased coding intensity as well as the ability of insurers to "game" the system with aggressive diagnostic upcoding.
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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.