ArticleBMC public health2020
Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments.
Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Nursing Process Data for Health Care Cost Prediction Using Machine Learning: Longitudinal Study.JMIR nursing · 2026Observational
- Artificial Intelligence in Social Health: A Narrative Review of Uses, Advantages, Challenges, and Future Directions.Healthcare (Basel, Switzerland) · 2026Review
- Cross-sectional analysis of accuracy versus interpretability in Medicare Advantage risk adjustment.Health policy OPEN · 2026Article
- Identification of Patients for a Community Health Worker Program Using an Artificial Intelligence Algorithm.Learning health systems · 2026Article
- Social determinants of health diagnosis codes in in-person and telehealth visits during pregnancy in the United States, 2020-2025: a repeated cross-sectional and case-control study.BMC pregnancy and childbirth · 2026Article
- Validation of an Electronic Health Record Algorithm for Identifying Housing-Related Needs in a Safety-Net Health System.Health services research · 2026Article
- Article
- Social and environmental determinants of Neglected infectious diseases in quilombola communities of the Brazilian Amazon: An epidemiological and machine learning analysis.PLoS neglected tropical diseases · 2026Article
- Healthcare utilization and cost analysis by social determinants of health: a prospective cohort study in Catalonia, Spain.BMC health services research · 2026Article
- Enhancing Medicaid Social Risk Identification: Comparing Linear and Non-Linear Machine Learning Models with Expert Heuristics.IEEE International Conference on Digital Health. IEEE International Conference on Digital Health · 2026Article
- Algorithms to Improve Fairness in Medicare Risk Adjustment.JAMA health forum · 2025Article
- Predicting quality measure completion among 14 million low-income patients enrolled in medicaid.NPJ digital medicine · 2025Article
- Help us document what we already do: pilot study of clinical decision support tools targeting social risk-informed care.JAMIA open · 2025Article
- Risk Adjustment in Capitation Payments to Primary Care Providers: Does It Matter How We Account for Patients' Socioeconomic Status?Medical care · 2025Article
- A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data.Frontiers in psychiatry · 2025Article
- Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities.Journal of pathology informatics · 2024Review
- Variational Bayes machine learning for risk adjustment of general outcome indicators with examples in urology.NPJ digital medicine · 2024Article
- 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
- Examining Diabetes Status by the Social Determinants of Health Among Adults in Hawai'i.Hawai'i journal of health & social welfare · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRisk adjustment models are employed to prevent adverse selection, anticipate budgetary reserve needs, and offer care management services to high-risk individuals. We aimed to address two unknowns about risk adjustment: whether machine learning (ML) and inclusion of social determinants of health (SDH) indicators improve prospective risk adjustment for health plan payments.
methodsWe employed a 2-by-2 factorial design comparing: (i) linear regression versus ML (gradient boosting) and (ii) demographics and diagnostic codes alone, versus additional ZIP code-level SDH indicators. Healthcare claims from privately-insured US adults (2016-2017), and Census data were used for analysis. Data from 1.02 million adults were used for derivation, and data from 0.26 million to assess performance. Model performance was measured using coefficient of determination (R
resultsLinear regression without SDH indicators achieved moderate determination (R
conclusionsML improved risk adjustment models and the incorporation of SDH indicators reduced underpayment in several vulnerable populations.
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