INTRODUCTION: Physicians are vital to health-care delivery, but assessing their impact on care can be challenging given limited data. Historically, health services researchers have obtained physician characteristics data from the American Medical Association (AMA) Physician Masterfile. The Center for Medicare and Medicaid Services' Medicare Data on Provider Practice and Specialty (MD-PPAS) file was assessed, as an alternative source of physician data, particularly in the context of cancer health services research. METHODS: We used physician National Provider Identifiers in the MD-PPAS data (2008-2014) to identify physicians in the AMA data current as of July 18, 2016. Within each source, we grouped physicians into six broad specialty groups. Percent agreement and Cohen's kappa coefficient (k) were calculated for age, sex, specialty, and practice state. RESULTS: Among the 698 202 included physicians, there was excellent agreement for age (percent agreement = 97.7%, k = 0.97) and sex (99.4%, k = 0.99) and good agreement for specialty (86.1%, k = 0.80). Within specialty, using AMA as the reference, agreement was lowest for oncologists (77%). Approximately 85.9% of physicians reported the same practice state in both data sets. CONCLUSION: Although AMA data have been commonly used to account for physician-level factors in health services research, MD-PPAS data provide researchers with an alternative option depending on study needs. MD-PPAS data may be optimal if nonphysicians, provider utilization, practice characteristics, and/or temporal changes are of interest. In contrast, the AMA data may be optimal if more granular specialty, physician training, and/or a broader inclusion of physicians is of interest. Published by Oxford University Press 2020. This work is written by US Government employees and is in the public domain in the US.
INTRODUCTION: Physicians are vital to health-care delivery, but assessing their impact on care can be challenging given limited data. Historically, health services researchers have obtained physician characteristics data from the American Medical Association (AMA) Physician Masterfile. The Center for Medicare and Medicaid Services' Medicare Data on Provider Practice and Specialty (MD-PPAS) file was assessed, as an alternative source of physician data, particularly in the context of cancer health services research. METHODS: We used physician National Provider Identifiers in the MD-PPAS data (2008-2014) to identify physicians in the AMA data current as of July 18, 2016. Within each source, we grouped physicians into six broad specialty groups. Percent agreement and Cohen's kappa coefficient (k) were calculated for age, sex, specialty, and practice state. RESULTS: Among the 698 202 included physicians, there was excellent agreement for age (percent agreement = 97.7%, k = 0.97) and sex (99.4%, k = 0.99) and good agreement for specialty (86.1%, k = 0.80). Within specialty, using AMA as the reference, agreement was lowest for oncologists (77%). Approximately 85.9% of physicians reported the same practice state in both data sets. CONCLUSION: Although AMA data have been commonly used to account for physician-level factors in health services research, MD-PPAS data provide researchers with an alternative option depending on study needs. MD-PPAS data may be optimal if nonphysicians, provider utilization, practice characteristics, and/or temporal changes are of interest. In contrast, the AMA data may be optimal if more granular specialty, physician training, and/or a broader inclusion of physicians is of interest. Published by Oxford University Press 2020. This work is written by US Government employees and is in the public domain in the US.
Authors: Laura-Mae Baldwin; Walter Adamache; Carrie N Klabunde; Kevin Kenward; Celia Dahlman; Joan L Warren Journal: Med Care Date: 2002-08 Impact factor: 2.983
Authors: Lori A Pollack; Walter Adamache; Christie R Eheman; A Blythe Ryerson; Lisa C Richardson Journal: Health Serv Res Date: 2008-12-15 Impact factor: 3.402
Authors: Carrie N Klabunde; Anita Ambs; Nancy L Keating; Yulei He; William R Doucette; Diana Tisnado; Steven Clauser; Katherine L Kahn Journal: J Gen Intern Med Date: 2009-07-14 Impact factor: 5.128
Authors: Elizabeth A Gage-Bouchard; Elisa M Rodriguez; Frances G Saad-Harfouche; Austin Miller; Deborah O Erwin Journal: PLoS One Date: 2014-10-20 Impact factor: 3.240