Home Research Research Library Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments 2020 Author(s) Irvin, Jeremy A, Kondrich, Andrew A, Ko, Michael, Rajpurkar, Pranav, Haghgoo, Behzad, Landon, Bruce E, Phillips, Robert L, Petterson, Stephen M, Ng, Andrew Y, and Basu, Sanjay Topic(s) Role of Primary Care Keyword(s) Payment Volume 20(1):608 Source BMC Public Health 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 (R2), 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 (R2 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 (R2 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. ABFM Research Read all 2015 More Comprehensive Care Among Family Physicians is Associated with Lower Costs and Fewer Hospitalizations Go to More Comprehensive Care Among Family Physicians is Associated with Lower Costs and Fewer Hospitalizations 2013 Toward Defining and Measuring Social Accountability in Graduate Medical Education: A Stakeholder Study Go to Toward Defining and Measuring Social Accountability in Graduate Medical Education: A Stakeholder Study 2016 Care Coordination and Population Management Services Are More Prevalent in Large Practices and Patient-centered Medical Homes Go to Care Coordination and Population Management Services Are More Prevalent in Large Practices and Patient-centered Medical Homes 2021 Uniting Public Health and Primary Care for Healthy Communities in the COVID-19 Era and Beyond Go to Uniting Public Health and Primary Care for Healthy Communities in the COVID-19 Era and Beyond
Author(s) Irvin, Jeremy A, Kondrich, Andrew A, Ko, Michael, Rajpurkar, Pranav, Haghgoo, Behzad, Landon, Bruce E, Phillips, Robert L, Petterson, Stephen M, Ng, Andrew Y, and Basu, Sanjay Topic(s) Role of Primary Care Keyword(s) Payment Volume 20(1):608 Source BMC Public Health
ABFM Research Read all 2015 More Comprehensive Care Among Family Physicians is Associated with Lower Costs and Fewer Hospitalizations Go to More Comprehensive Care Among Family Physicians is Associated with Lower Costs and Fewer Hospitalizations 2013 Toward Defining and Measuring Social Accountability in Graduate Medical Education: A Stakeholder Study Go to Toward Defining and Measuring Social Accountability in Graduate Medical Education: A Stakeholder Study 2016 Care Coordination and Population Management Services Are More Prevalent in Large Practices and Patient-centered Medical Homes Go to Care Coordination and Population Management Services Are More Prevalent in Large Practices and Patient-centered Medical Homes 2021 Uniting Public Health and Primary Care for Healthy Communities in the COVID-19 Era and Beyond Go to Uniting Public Health and Primary Care for Healthy Communities in the COVID-19 Era and Beyond
2015 More Comprehensive Care Among Family Physicians is Associated with Lower Costs and Fewer Hospitalizations Go to More Comprehensive Care Among Family Physicians is Associated with Lower Costs and Fewer Hospitalizations
2013 Toward Defining and Measuring Social Accountability in Graduate Medical Education: A Stakeholder Study Go to Toward Defining and Measuring Social Accountability in Graduate Medical Education: A Stakeholder Study
2016 Care Coordination and Population Management Services Are More Prevalent in Large Practices and Patient-centered Medical Homes Go to Care Coordination and Population Management Services Are More Prevalent in Large Practices and Patient-centered Medical Homes
2021 Uniting Public Health and Primary Care for Healthy Communities in the COVID-19 Era and Beyond Go to Uniting Public Health and Primary Care for Healthy Communities in the COVID-19 Era and Beyond
