Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments

Jeremy Irvin · Andrew Kondrich · Michael Kwan-Lung Ko · Pranav Rajpurkar · Behzad Haghgoo · Bruce E. Landon +4 more

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Healthcare Policy and Management Medical Coding and Health Information Machine Learning in Healthcare

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Abstract Background Risk 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. Methods We 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 2 ), discrimination (C-statistic), and mean absolute error (MAE) for the overall population, and predictive ratio and net compensation for vulnerable subgroups. We provide 95% confidence intervals (CI) around each performance measure. Results Linear regression without SDH indicators achieved moderate determination (R 2 0.327, 95% CI: 0.300, 0.353), error ($6992; 95% CI: $6889, $7094), and discrimination (C-statistic 0.703; 95% CI: 0.701, 0.705). ML without SDH indicators improved all metrics (R 2 0.388; 95% CI: 0.357, 0.420; error $6637; 95% CI: $6539, $6735; C-statistic 0.717; 95% CI: 0.715, 0.718), reducing misestimation of cost by $3.5 M per 10,000 members. Among people living in areas with high poverty, high wealth inequality, or high prevalence of uninsured, SDH indicators reduced underestimation of cost, improving the predictive ratio by 3% (~$200/person/year). Conclusions ML improved risk adjustment models and the incorporation of SDH indicators reduced underpayment in several vulnerable populations.

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2019Dissecting racial bias in an algorithm used to manage the health of populations 2004Risk adjustment of Medicare capitation payments using the CMS-HCC model.2016Accountable Health Communities — Addressing Social Needs through Medicare and Medicaid 2014Social Epidemiology 2000Using diagnoses to describe populations and predict costs.2006Confidence Intervals for Rank Statistics: Somers’ D and Extensions 2001Socio-economic and locational determinants of accessibility and utilization of primary health-care 2019Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program

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