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
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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 2024 Policy Briefs With Personality: How to Innovatively Disseminate Evidence for Advocacy Go to Policy Briefs With Personality: How to Innovatively Disseminate Evidence for Advocacy 2016 Intended vs Reported Scope of Practice–Reply Go to Intended vs Reported Scope of Practice–Reply 2022 Physician versus Practice-Level Primary Care Continuity and Association with Outcomes in Medicare Beneficiaries Go to Physician versus Practice-Level Primary Care Continuity and Association with Outcomes in Medicare Beneficiaries 2003 Computer-based testing in family practice certification and recertification Go to Computer-based testing in family practice certification and recertification
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 2024 Policy Briefs With Personality: How to Innovatively Disseminate Evidence for Advocacy Go to Policy Briefs With Personality: How to Innovatively Disseminate Evidence for Advocacy 2016 Intended vs Reported Scope of Practice–Reply Go to Intended vs Reported Scope of Practice–Reply 2022 Physician versus Practice-Level Primary Care Continuity and Association with Outcomes in Medicare Beneficiaries Go to Physician versus Practice-Level Primary Care Continuity and Association with Outcomes in Medicare Beneficiaries 2003 Computer-based testing in family practice certification and recertification Go to Computer-based testing in family practice certification and recertification
2024 Policy Briefs With Personality: How to Innovatively Disseminate Evidence for Advocacy Go to Policy Briefs With Personality: How to Innovatively Disseminate Evidence for Advocacy
2022 Physician versus Practice-Level Primary Care Continuity and Association with Outcomes in Medicare Beneficiaries Go to Physician versus Practice-Level Primary Care Continuity and Association with Outcomes in Medicare Beneficiaries
2003 Computer-based testing in family practice certification and recertification Go to Computer-based testing in family practice certification and recertification
