ArticleBiostatistics (Oxford, England)2017
Computational health economics for identification of unprofitable health care enrollees.
Article in Biostatistics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 24 citations in OpenAlex.
- Cross-sectional analysis of accuracy versus interpretability in Medicare Advantage risk adjustment.Health policy OPEN · 2026Article
- Algorithms to Improve Fairness in Medicare Risk Adjustment.JAMA health forum · 2025Article
- Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets.Health economics review · 2023Article
- Identifying undercompensated groups defined by multiple attributes in risk adjustment.BMJ health & care informatics · 2021Article
- Intersections of machine learning and epidemiological methods for health services research.International journal of epidemiology · 2021Article
- Improving the Performance of Risk Adjustment Systems: Constrained Regressions, Reinsurance, and Variable Selection.American journal of health economics · 2021Article
- Fair regression for health care spending.Biometrics · 2020Article
- Estimating treatment effects with machine learning.Health services research · 2019Observational
- Data transformations to improve the performance of health plan payment methods.Journal of health economics · 2019Article
- Classifying Stage IV Lung Cancer From Health Care Claims: A Comparison of Multiple Analytic Approaches.JCO clinical cancer informatics · 2019Article
- Exploiting machine learning for end-to-end drug discovery and development.Nature materials · 2019Article
- Sample Selection for Medicare Risk Adjustment Due to Systematically Missing Data.Health services research · 2018Article
- Mental Health Risk Adjustment with Clinical Categories and Machine Learning.Health services research · 2018Article
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
Health insurers may attempt to design their health plans to attract profitable enrollees while deterring unprofitable ones. Such insurers would not be delivering socially efficient levels of care by providing health plans that maximize societal benefit, but rather intentionally distorting plan benefits to avoid high-cost enrollees, potentially to the detriment of health and efficiency. In this work, we focus on a specific component of health plan design at risk for health insurer distortion in the Health Insurance Marketplaces: the prescription drug formulary. We introduce an ensembled machine learning function to determine whether drug utilization variables are predictive of a new measure of enrollee unprofitability we derive, and thus vulnerable to distortions by insurers. Our implementation also contains a unique application-specific variable selection tool. This study demonstrates that super learning is effective in extracting the relevant signal for this prediction problem, and that a small number of drug variables can be used to identify unprofitable enrollees. The results are both encouraging and concerning. While risk adjustment appears to have been reasonably successful at weakening the relationship between therapeutic-class-specific drug utilization and unprofitability, some classes remain predictive of insurer losses. The vulnerable enrollees whose prescription drug regimens include drugs in these classes may need special protection from regulators in health insurance market design.
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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.