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Home Research Research Library Using Machine Learning to Predict Primary Care and Advance Workforce Research Using Machine Learning to Predict Primary Care and Advance Workforce Research 2020 Author(s) Wingrove, Peter M, Liaw, Winston R, Weiss, Jeremy, Petterson, Stephen M, Maier, John, and Bazemore, Andrew W Topic(s) Role of Primary Care Volume Annals of Family Medicine Source Annals of Family Medicine PURPOSE To develop and test a machine-learning–based model to predict primary care and other specialties using Medicare claims data. METHODS We used 2014-2016 prescription and procedure Medicare data to train 3 sets of random forest classifiers (prescription only, procedure only, and combined) to predict specialty. Self-reported specialties were condensed to 27 categories. Physicians were assigned to testing and training cohorts, and random forest models were trained and then applied to 2014-2016 data sets for the testing cohort to generate a series of specialty predictions. Comparing the predicted specialty to self-report, we assessed performance with F1 scores and area under the receiver operating characteristic curve (AUROC) values. RESULTS A total of 564,986 physicians were included. The combined model had a greater aggregate (macro) F1 score (0.876) than the prescription-only (0.745; P <.01) or procedure-only (0.821; P <.01) model. Mean F1 scores across specialties in the combined model ranged from 0.533 to 0.987. The mean F1 score was 0.920 for primary care. The mean AUROC value for the combined model was 0.992, with values ranging from 0.982 to 0.999. The AUROC value for primary care was 0.982. CONCLUSIONS This novel approach showed high performance and provides a near real-time assessment of current primary care practice. These findings have important implications for primary care workforce research in the absence of accurate data. ABFM Research Read all 2019 Primary Care Research Priorities in Low-and Middle-Income Countries Go to Primary Care Research Priorities in Low-and Middle-Income Countries 2017 Preserving Primary Care Robustness Despite Increasing Health System Integration Go to Preserving Primary Care Robustness Despite Increasing Health System Integration 2018 Rural Family Physicians in Patient Centered Medical Homes Have a Broader Scope of Practice Go to Rural Family Physicians in Patient Centered Medical Homes Have a Broader Scope of Practice 2016 Solo and Small Practices: A Vital, Diverse Part of Primary Care Go to Solo and Small Practices: A Vital, Diverse Part of Primary Care
Author(s) Wingrove, Peter M, Liaw, Winston R, Weiss, Jeremy, Petterson, Stephen M, Maier, John, and Bazemore, Andrew W Topic(s) Role of Primary Care Volume Annals of Family Medicine Source Annals of Family Medicine
ABFM Research Read all 2019 Primary Care Research Priorities in Low-and Middle-Income Countries Go to Primary Care Research Priorities in Low-and Middle-Income Countries 2017 Preserving Primary Care Robustness Despite Increasing Health System Integration Go to Preserving Primary Care Robustness Despite Increasing Health System Integration 2018 Rural Family Physicians in Patient Centered Medical Homes Have a Broader Scope of Practice Go to Rural Family Physicians in Patient Centered Medical Homes Have a Broader Scope of Practice 2016 Solo and Small Practices: A Vital, Diverse Part of Primary Care Go to Solo and Small Practices: A Vital, Diverse Part of Primary Care
2019 Primary Care Research Priorities in Low-and Middle-Income Countries Go to Primary Care Research Priorities in Low-and Middle-Income Countries
2017 Preserving Primary Care Robustness Despite Increasing Health System Integration Go to Preserving Primary Care Robustness Despite Increasing Health System Integration
2018 Rural Family Physicians in Patient Centered Medical Homes Have a Broader Scope of Practice Go to Rural Family Physicians in Patient Centered Medical Homes Have a Broader Scope of Practice
2016 Solo and Small Practices: A Vital, Diverse Part of Primary Care Go to Solo and Small Practices: A Vital, Diverse Part of Primary Care