Home Research Research Library How the Gender Wage Gap for Primary Care Physicians Differs by Compensation Approach : A Microsimulation Study How the Gender Wage Gap for Primary Care Physicians Differs by Compensation Approach : A Microsimulation Study 2022 Author(s) Ganguli, Ishani, Mulligan, Kathleen L, Phillips, Robert L, and Basu, Sanjay Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Measurement, and Quality Of Care Volume Annals of Internal Medicine Source Annals of Internal Medicine BACKGROUND: The physician gender wage gap may be due, in part, to productivity-based compensation models that undervalue female practice patterns. OBJECTIVE: To determine how primary care physician (PCP) compensation by gender differs when applying existing productivity-based and alternative compensation models. DESIGN: Microsimulation. SETTING: 2016 to 2019 national clinical registry of 1222 primary care practices. PARTICIPANTS: Male and female PCPs matched on specialty, years since medical school graduation, practice site, and sessions worked. MEASUREMENTS: Net annual, full-time-equivalent compensation for male versus female PCPs, under productivity-based fee-for-service, panel size-based capitation without or with risk adjustment, and hybrid payment models. Microsimulation inputs included patient and visit characteristics and overhead expenses. RESULTS: Among 1435 matched male (n = 881) and female (n = 554) PCPs, female PCP panels included patients who were, on average, younger, had lower diagnosis-based risk scores, were more often female, and were more often uninsured or insured by Medicaid rather than by Medicare. Under productivity-based payment, female PCPs earned a median of $58 829 (interquartile range [IQR], $39 553 to $120 353; 21%) less than male PCPs. This gap was similar under capitation ($58 723 [IQR, $42 141 to $140 192]). It was larger under capitation risk-adjusted for age alone ($74 695 [IQR, $42 884 to $152 423]), for diagnosis-based scores alone ($114 792 [IQR, $49 080 to $215 326] and $89 974 [IQR, $26 175 to $173 760]), and for age-, sex-, and diagnosis-based scores ($83 438 [IQR, $28 927 to $129 414] and $66 195 [IQR, $11 899 to $96 566]). The gap was smaller and nonsignificant under capitation risk-adjusted for age and sex ($36 631 [IQR, $12 743 to $73 898]). LIMITATION: Panel attribution based on office visits. CONCLUSION: The gender wage gap varied by compensation model, with capitation risk-adjusted for patient age and sex resulting in a smaller gap. Future models might better align with primary care effort and outcomes. PRIMARY FUNDING SOURCE: None. ABFM Research Read all 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments 2021 FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS Go to FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS 2021 Implementing High-Quality Primary Care: A Report From the National Academies of Sciences, Engineering, and Medicine Go to Implementing High-Quality Primary Care: A Report From the National Academies of Sciences, Engineering, and Medicine 2013 Do residents who train in safety net settings return for practice? Go to Do residents who train in safety net settings return for practice?
Author(s) Ganguli, Ishani, Mulligan, Kathleen L, Phillips, Robert L, and Basu, Sanjay Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Measurement, and Quality Of Care Volume Annals of Internal Medicine Source Annals of Internal Medicine
ABFM Research Read all 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments 2021 FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS Go to FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS 2021 Implementing High-Quality Primary Care: A Report From the National Academies of Sciences, Engineering, and Medicine Go to Implementing High-Quality Primary Care: A Report From the National Academies of Sciences, Engineering, and Medicine 2013 Do residents who train in safety net settings return for practice? Go to Do residents who train in safety net settings return for practice?
2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments
2021 FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS Go to FROM ABFM: IMPLEMENTING A NATIONAL VISION FOR HIGH QUALITY PRIMARY CARE: NEXT STEPS
2021 Implementing High-Quality Primary Care: A Report From the National Academies of Sciences, Engineering, and Medicine Go to Implementing High-Quality Primary Care: A Report From the National Academies of Sciences, Engineering, and Medicine
2013 Do residents who train in safety net settings return for practice? Go to Do residents who train in safety net settings return for practice?
