Beyond the Clinic Family Medicine on a Mission Part 1: How Air Force Physicians Achieve Humanitarian Goals Read Family Medicine on a Mission Part 1: How Air Force Physicians Achieve Humanitarian Goals
Phoenix Newsletter - March 2025 President’s Message: ABFM’s Unwavering Commitment to Diplomates and the Specialty Read President’s Message: ABFM’s Unwavering Commitment to Diplomates and the Specialty
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 BMC Public Health 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. Read More ABFM Research Read all 2022 Measuring the Value Functions of Primary Care: Physician-Level Continuity of Care Quality Measure Go to Measuring the Value Functions of Primary Care: Physician-Level Continuity of Care Quality Measure 2014 Electronic health record functionality needed to better support primary care Go to Electronic health record functionality needed to better support primary care 2011 Establishing a baseline: health information technology adoption among family medicine diplomates Go to Establishing a baseline: health information technology adoption among family medicine diplomates 2013 The primary care extension program: a catalyst for change Go to The primary care extension program: a catalyst for change
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 BMC Public Health Source BMC Public Health
ABFM Research Read all 2022 Measuring the Value Functions of Primary Care: Physician-Level Continuity of Care Quality Measure Go to Measuring the Value Functions of Primary Care: Physician-Level Continuity of Care Quality Measure 2014 Electronic health record functionality needed to better support primary care Go to Electronic health record functionality needed to better support primary care 2011 Establishing a baseline: health information technology adoption among family medicine diplomates Go to Establishing a baseline: health information technology adoption among family medicine diplomates 2013 The primary care extension program: a catalyst for change Go to The primary care extension program: a catalyst for change
2022 Measuring the Value Functions of Primary Care: Physician-Level Continuity of Care Quality Measure Go to Measuring the Value Functions of Primary Care: Physician-Level Continuity of Care Quality Measure
2014 Electronic health record functionality needed to better support primary care Go to Electronic health record functionality needed to better support primary care
2011 Establishing a baseline: health information technology adoption among family medicine diplomates Go to Establishing a baseline: health information technology adoption among family medicine diplomates
2013 The primary care extension program: a catalyst for change Go to The primary care extension program: a catalyst for change