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Can Claims Data Algorithms Identify the Physician of Record?

DuGoff, Eva, H., PhD, MPP*; Walden, Emily, MS; Ronk, Katie, BS; Palta, Mari, PhD*; Smith, Maureen, MD, PhD*,‡

doi: 10.1097/MLR.0000000000000709
Online Article: Applied Methods

Background: Claims-based algorithms based on administrative claims data are frequently used to identify an individual’s primary care physician (PCP). The validity of these algorithms in the US Medicare population has not been assessed.

Objective: To determine the agreement of the PCP identified by claims algorithms with the PCP of record in electronic health record data.

Data: Electronic health record and Medicare claims data from older adults with diabetes.

Subjects: Medicare fee-for-service beneficiaries with diabetes (N=3658) ages 65 years and older as of January 1, 2008, and medically housed at a large academic health system.

Measures: Assignment algorithms based on the plurality and majority of visits and tie breakers determined by either last visit, cost, or time from first to last visit.

Results: The study sample included 15,624 patient-years from 3658 older adults with diabetes. Agreement was higher for algorithms based on primary care visits (range, 78.0% for majority match without a tie breaker to 85.9% for majority match with the longest time from first to last visit) than for claims to all visits (range, 25.4% for majority match without a tie breaker to 63.3% for majority match with the amount billed tie breaker). Percent agreement was lower for nonwhite individuals, those enrolled in Medicaid, individuals experiencing a PCP change, and those with >10 physician visits.

Conclusions: Researchers may be more likely to identify a patient’s PCP when focusing on primary care visits only; however, these algorithms perform less well among vulnerable populations and those experiencing fragmented care.

Departments of *Population Health Sciences


Health Innovation Program, University of Wisconsin-Madison, Madison, WI

Supported by R21 HS021899 and R01 HS018368 under the Agency for Healthcare Research and Quality (M.S.) and the National Institute of Mental Health under Ruth L. Kirschstein National Research Service Award T32 MH18029 (E.W.). Additional support was provided by the Health Innovation Program, the UW School of Medicine and Public Health from The Wisconsin Partnership Program, and the Community-Academic Partnerships core of the University of Wisconsin Institute for Clinical and Translational Research (UW ICTR) through the National Center for Advancing Translational Sciences (NCATS), grant UL1TR000427.

The authors declare no conflict of interest.

Reprints: Eva H. DuGoff, PhD, MPP, Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 701 WARF, Madison, WI 53726. E-mail:

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