research Performance Evaluation of the Generative Pre-trained Transformer (GPT-4) on the Family Medicine In-Training Examination Read Performance Evaluation of the Generative Pre-trained Transformer (GPT-4) on the Family Medicine In-Training Examination
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
Diplomate Spotlight “Family Medicine Was All I Ever Wanted to Do” Dr. Phillip Wagner Read “Family Medicine Was All I Ever Wanted to Do”
Home Research Research Library The Gender Penalty: Reasons for Differences in Reported Weekly Work Hours Among Male and Female Family Physicians 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 2020 Well‐Being in the Nation: A Living Library of Measures to Drive Multi‐Sector Population Health Improvement and Address Social Determinants Go to Well‐Being in the Nation: A Living Library of Measures to Drive Multi‐Sector Population Health Improvement and Address Social Determinants 2024 Underlying reasons for primary care visits where chlamydia testing was performed in the United States, 2019-2022 Go to Underlying reasons for primary care visits where chlamydia testing was performed in the United States, 2019-2022 1990 Predictive validity of the American Board of Family Practice In-Training Examination Go to Predictive validity of the American Board of Family Practice In-Training Examination 2025 Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model Go to Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model
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 2020 Well‐Being in the Nation: A Living Library of Measures to Drive Multi‐Sector Population Health Improvement and Address Social Determinants Go to Well‐Being in the Nation: A Living Library of Measures to Drive Multi‐Sector Population Health Improvement and Address Social Determinants 2024 Underlying reasons for primary care visits where chlamydia testing was performed in the United States, 2019-2022 Go to Underlying reasons for primary care visits where chlamydia testing was performed in the United States, 2019-2022 1990 Predictive validity of the American Board of Family Practice In-Training Examination Go to Predictive validity of the American Board of Family Practice In-Training Examination 2025 Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model Go to Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model
2020 Well‐Being in the Nation: A Living Library of Measures to Drive Multi‐Sector Population Health Improvement and Address Social Determinants Go to Well‐Being in the Nation: A Living Library of Measures to Drive Multi‐Sector Population Health Improvement and Address Social Determinants
2024 Underlying reasons for primary care visits where chlamydia testing was performed in the United States, 2019-2022 Go to Underlying reasons for primary care visits where chlamydia testing was performed in the United States, 2019-2022
1990 Predictive validity of the American Board of Family Practice In-Training Examination Go to Predictive validity of the American Board of Family Practice In-Training Examination
2025 Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model Go to Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model
