Accessible Requires Authentication Published by De Gruyter December 14, 2019

Billing Codes Determine Lower Physician Income for Primary Care and Non-Procedural Specialties

Arielle L. Langer ORCID logo and Miriam Laugesen

Abstract

The income gap between specialists and primary care physicians and among specialists is well established, but the drivers of this difference are not well delineated. Using the Community Tracking Study (CTS) Physician Survey, we sought to isolate and compare premiums paid to physicians for specialization and the proportion of time spent on offices visit rather than procedures. We divided medical subspecialties according the proportion of Medicare billing for Evaluation and Management (E&M) codes for the specialty as a whole. We report substantial differences in income across physician specialty, and over 70 percent of the difference in income remained controlling for factors that may confound the relationship between income and specialty including gender, location and type of practice, and hours. We note a large variation in premiums for specialization: 11.3–46.8 percent above family medicine after controlling for confounders. Classifying medical subspecialties by E&M billing as procedural versus non-procedural specialties revealed clear income differences. Controlling for confounders, procedural medical specialties earned 37.5 percent more than family medicine, as compared with 15.3 percent for non-procedural medical specialties. This analysis suggests that differences in physician income and resulting incentives are a direct consequence of the payment structure itself, rather than compensation for additional years of training or a reflection of different underlying demographics.

Acknowledgements

Tow Foundation. National Institute of Health Care Management Foundation.

References

ABMS. 2011. “Specialties & Subspecialties.” American Board of Medical Specialties, accessed Mar 31. http://www.abms.org/who_we_help/physicians/specialties.aspx. Search in Google Scholar

Abowd, John M., Francis Kramarz, and David N. Margolis. 1999. “High Wage Workers and High Wage Firms.” Econometrica 67 (2): 251–333.10.1111/1468-0262.00020 Search in Google Scholar

AMA. 2007. “American Medical Association’s Relative Value Scale Update Committee. Medicare Charges by Specialty: Comparison by Type of Service.” RUC Analysis. Search in Google Scholar

Baker, L. C. 1996. “Differences in Earnings between Male and Female Physicians.” The New England Journal of Medicine 334 (15): 960–964.10.1056/NEJM1996041133415068596598 Search in Google Scholar

Biddle, Jeff E., and Daniel S. Hamermesh. 1998. “Beauty, Productivity, and Discrimination: Lawyers’ Looks and Lucre.” Journal of Labor Economics 16 (1): 172–201.10.1086/209886 Search in Google Scholar

BLS. 2011. “CPI Inflation Calculator.” Bureau of Labor Statistics, accessed Apr. 1. http://www.bls.gov/data/inflation_calculator.htm. Search in Google Scholar

Bodenheimer, T., R. A. Berenson, and P. Rudolf. 2007. “The Primary Care-Specialty Income Gap: Why it Matters.” Annals of Internal Medicine 146 (4): 301–306.10.7326/0003-4819-146-4-200702200-0001117310054 Search in Google Scholar

Chernozhukov, V., and H. Hong. 2002. “Three-Step Censored Quantile Regression and Extramarital Affairs.” Journal of the American Statistical Association 97 (459): 872–882.10.1198/016214502388618663 Search in Google Scholar

CMS. 2011. “Berenson-Eggers Type of Service (BETOS).” Centers for Medicare & Medicaid Services, accessed Oct. 20. https://www.cms.gov/hcpcsreleasecodesets/20_betos.asp. Search in Google Scholar

CMS. 2018. “Final Policy, Payment, and Quality Provisions Changes to the Medicare Physician Fee Schedule for Calendar Year 2019.” cms.gov. Search in Google Scholar

Effland, A., J. Stout, J. Cooper, R. Dismukes, E. O’Donoghue, and P. Westcott. 2009. “FIPS Codes and County Names, by State.” USDA, accessed Mar 31. http://www.ers.usda.gov/data/baseacres/Data/Counties.xls. Search in Google Scholar

Gillis, K. D., R. J. Willke, and R. A. Reynolds. 1993. “Assessing the Validity of the Geographic Practice Cost Indexes.” Inquiry 30 (3): 265–280.8406784 Search in Google Scholar

HSC. 2011. “CTS Physician Surveys and the HSC 2008 Health Tracking Physician Survey.” Center for Studying Health System Change, accessed 3/31. http://www.hschange.org/index.cgi?data=04. Search in Google Scholar

Kowalski, Amanda E. 2011. “Research: Stata Code.” Amanda E. Kowalski, Yale Department of Economics, accessed Oct 21. http://www.econ.yale.edu/∼ak669/research.html. Search in Google Scholar

Leigh, J. P., D. Tancredi, A. Jerant, and R. L. Kravitz. 2010. “Physician Wages Across Specialties: Informing the Physician Reimbursement Debate.” Archives of Internal Medicine 170 (19): 1728–1734.20975019 Search in Google Scholar

Lo Sasso, A. T., M. R. Richards, C. F. Chou, and S. E. Gerber. 2011. “The $16,819 Pay Gap for Newly Trained Physicians: The Unexplained Trend of Men Earning more than Women.” Health Affairs 30 (2): 193–201.10.1377/hlthaff.2010.0597 Search in Google Scholar

LOC. 2011. “Regions of the United States: Regions Defined.” Library of Congress, accessed Mar. 31. http://memory.loc.gov/ammem/gmdhtml/rrhtml/regdef.html. Search in Google Scholar

MacKinney, A. C. 2010. “Increases in Primary Care Physician Income Due to the Patient Protection and Affordable Care Act of 2010 – Continued Tweaking of Physician Payment.” Rural Policy Brief (2010 2): 1–8.20737732 Search in Google Scholar

NRMP. 2011. Charting Outcomes in the Match. Washington, DC: National Resident Matching Program. Search in Google Scholar

Phillips, J. P., D. M. Wilbanks, D. F. Rodriguez-Salinas, and D. M. Doberneck. 2019. “Specialty Income and Career Decision Making: A Qualitative Study of Medical Student Perceptions.” Medical Education 53: 593–604.10.1111/medu.1382030821014 Search in Google Scholar

Schaefer, Elizabeth, Frank Potter, Stephen Williams, Nuria Diaz-Tena, James D. Reschovsky, and Garry Moore. 2003. “Comparison of Selected Statistical Software Packages for Variance Estimation in the CTS Surveys.” Center for Studying Health System Change, Technical Publication No. 40. Search in Google Scholar

Sigsbee, B. 2011. “The Income Gap: Specialties vs Primary Care or Procedural vs Nonprocedural Specialties?” Neurology 76 (10): 923–926.10.1212/WNL.0b013e31820f2dfd21383329 Search in Google Scholar

Simon, C. J., D. Dranove, and W. D. White. 1997. “The Impact of Managed Care on the Physician Marketplace.” Public Health Reports 112 (3): 222–230. Search in Google Scholar

Simon, C. J., D. Dranove, and W. D. White. 1998. “The Effect of Managed Care on the Incomes of Primary Care and Specialty Physicians.” Health Services Research 33 (3 Pt 1): 549–569.9685122 Search in Google Scholar

Staiger, D. O., D. I. Auerbach, and P. I. Buerhaus. 2010. “Trends in the Work Hours of Physicians in the United States.” The Journal of the American Medical Association 303 (8): 747–753.10.1001/jama.2010.168 Search in Google Scholar

Tu, H. T., and P. B. Ginsburg. 2006. “Losing Ground: Physician Income, 1995–2003.” Tracking Report/Center for Studying Health System Change (15): 1–8. Search in Google Scholar

Vaughn, B. T., S. R. DeVrieze, S. D. Reed, and K. A. Schulman. 2010. “Can we Close the Income and Wealth Gap between Specialists and Primary Care Physicians?” Health Affairs 29 (5): 933–940.10.1377/hlthaff.2009.0675 Search in Google Scholar

Weeks, W. B., T. A. Wallace, and A. E. Wallace. 2009. “How do Race and Sex Affect the Earnings of Primary Care Physicians?” Health Affairs 28 (2): 557–566.10.1377/hlthaff.28.2.557 Search in Google Scholar

Woo, B. 2006. “Primary Care–the Best Job in Medicine?” The New England Journal of Medicine 355 (9): 864–866.10.1056/NEJMp06815416943397 Search in Google Scholar

Supplementary Material

The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/fhep-2019-0009).

Published Online: 2019-12-14

© 2019 Walter de Gruyter GmbH, Berlin/Boston