Spending Patterns in Region of Residency Training and Subsequent Expenditures for Care Provided by Practicing Physicians for Medicare Beneficiaries | Medical Education and Training | JAMA | JAMA Network
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1.
Fisher  ES, Wennberg  DE, Stukel  TA, Gottlieb  DJ, Lucas  FL, Pinder  EL.  The implications of regional variations in Medicare spending, part 1: the content, quality, and accessibility of care.  Ann Intern Med. 2003;138(4):273-287.PubMedGoogle ScholarCrossref
2.
Geographic Variations in Healthcare Spending. Washington, DC: Congressional Budget Office; 2008. http://www.cbo.gov/sites/default/files/cbofiles/ftpdocs/89xx/doc8972/02-15-geoghealth.pdf. Accessed March 14, 2014.
3.
Fisher  ES, Wennberg  DE, Stukel  TA, Gottlieb  DJ.  Variations in the longitudinal efficiency of academic medical centers.  Health Aff (Millwood). 2004;(suppl var):var19-var32. doi:10.1377/hlthaff.var.19.PubMedGoogle Scholar
4.
Asch  DA, Nicholson  S, Srinivas  S, Herrin  J, Epstein  AJ.  Evaluating obstetrical residency programs using patient outcomes.  JAMA. 2009;302(12):1277-1283.PubMedGoogle ScholarCrossref
5.
Centers for Medicare & Medicaid Services.  Hospital cost reports. http://www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/CostReports/index.html?redirect=/costreports/ . Accessed January 17, 2014 .
6.
Medicaid Graduate Medical Education Payments: A 50-State Survey. Washington, DC: Association of American Medical Colleges; 2013. https://members.aamc.org/eweb/upload/Medicaid%20Graduate%20Medical%20Education%20Payments%20A%2050-State%20Survey.pdf. Accessed March 14, 2014.
7.
Kuo  YF, Sharma  G, Freeman  JL, Goodwin  JS.  Growth in the care of older patients by hospitalists in the United States.  N Engl J Med. 2009;360(11):1102-1112.PubMedGoogle ScholarCrossref
8.
Pham  HH, O’Malley  AS, Bach  PB, Saiontz-Martinez  C, Schrag  D.  Primary care physicians’ links to other physicians through Medicare patients: the scope of care coordination.  Ann Intern Med. 2009;150(4):236-242.PubMedGoogle ScholarCrossref
9.
Charlson  ME, Pompei  P, Ales  KL, MacKenzie  CR.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.  J Chronic Dis. 1987;40(5):373-383.PubMedGoogle ScholarCrossref
10.
Wennberg  DE, Sharp  SM, Bevan  G, Skinner  JS, Gottlieb  DJ, Wennberg  JE.  A population health approach to reducing observational intensity bias in health risk adjustment: cross sectional analysis of insurance claims.  BMJ. 2014;348:g2392.PubMedGoogle ScholarCrossref
11.
Eicheldinger  C, Bonito  A.  More accurate racial and ethnic codes for Medicare administrative data.  Health Care Financ Rev. 2013;29:27-42.Google Scholar
12.
WWAMI Rural Health Research Center. RUCA data travel distance and time, remote, isolated, and frontier. http://depts.washington.edu/uwruca/ruca-travel-dist.php. Accessed December 8, 2013.
13.
Improving Value in Graduate Medical Education. Washington, DC: Council on Graduate Medical Education; 2013. http://www.hrsa.gov/advisorycommittees/bhpradvisory/cogme/Reports/twentyfirstreport.pdf. Accessed May 21, 2014.
14.
Graduate Medical Education That Meets the Nation’s Health Needs. Washington, DC: Institute of Medicine; 2014. http://www.iom.edu/Reports/2014/Graduate-Medical-Education-That-Meets-the-Nations-Health-Needs.aspx. Accessed September 2, 2014.
15.
Mullan  F, Chen  C, Steinmetz  E.  The geography of graduate medical education: imbalances signal need for new distribution policies.  Health Aff (Millwood). 2013;32(11):1914-1921.PubMedGoogle ScholarCrossref
16.
Geographic Variation in Health Care Spending and Promotion of High-Value Care—Interim Report. Washington, DC: Institute of Medicine; 2013. http://www.iom.edu/Reports/2013/Geographic-Variation-in-Health-Care-Spending-and-Promotion-of-High-Care-Value-Interim-Report.aspx. Accessed March 21, 2013.
Original Investigation
December 10, 2014

Spending Patterns in Region of Residency Training and Subsequent Expenditures for Care Provided by Practicing Physicians for Medicare Beneficiaries

Author Affiliations
  • 1Department of Health Policy, Milken Institute School of Public Health, George Washington University, Washington, DC
  • 2Dr Chen is now with the Health Resources and Services Administration, Rockville, Maryland
  • 3Robert Graham Center, Washington, DC
  • 4American Board of Family Medicine, Washington, DC
JAMA. 2014;312(22):2385-2393. doi:10.1001/jama.2014.15973
Abstract

Importance  Graduate medical education training may imprint young physicians with skills and experiences, but few studies have evaluated imprinting on physician spending patterns.

Objective  To examine the relationship between spending patterns in the region of a physician’s graduate medical education training and subsequent mean Medicare spending per beneficiary.

Design, Setting, and Participants  Secondary multilevel multivariable analysis of 2011 Medicare claims data (Part A hospital and Part B physician) for a random, nationally representative sample of family medicine and internal medicine physicians completing residency between 1992 and 2010 with Medicare patient panels of 40 or more patients (2851 physicians providing care to 491 948 Medicare beneficiaries).

Exposures  Locations of practice and residency training were matched with Dartmouth Atlas Hospital Referral Region (HRR) files. Training and practice HRRs were categorized into low-, average-, and high-spending groups, with approximately equal distribution of beneficiary numbers. There were 674 physicians in low-spending training and low-spending practice HRRs, 180 in average-spending training/low-spending practice, 178 in high-spending training/low-spending practice, 253 in low-spending training/average-spending practice, 417 in average-spending training/average-spending practice, 210 in high-spending training/average-spending practice, 97 in low-spending training/high-spending practice, 275 in average-spending training/high-spending practice, and 567 in high-spending training/high-spending practice.

Main Outcomes and Measures  Mean physician spending per Medicare beneficiary.

Results  For physicians practicing in high-spending regions, those trained in high-spending regions had a mean spending per beneficiary per year $1926 higher (95% CI, $889-$2963) than those trained in low-spending regions. For practice in average-spending HRRs, mean spending was $897 higher (95% CI, $71-$1723) for physicians trained in high- vs low-spending regions. For practice in low-spending HRRs, the difference across training HRR levels was not significant ($533; 95% CI, –$46 to $1112). After controlling for patient, community, and physician characteristics, there was a 7% difference (95% CI, 2%-12%) in patient expenditures between low- and high-spending training HRRs. Across all practice HRRs, this corresponded to an estimated $522 difference (95% CI, $146-$919) between low- and high-spending training regions. For physicians 1 to 7 years in practice, there was a 29% difference ($2434; 95% CI, $1004-$4111) in spending between those trained in low- and high-spending regions; however, after 16 to 19 years, there was no significant difference.

Conclusions and Relevance  Among general internists and family physicians who completed residency training between 1992 and 2010, the spending patterns in the HRR in which their residency program was located were associated with expenditures for subsequent care they provided as practicing physicians for Medicare beneficiaries. Interventions during residency training may have the potential to help control future health care spending.

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