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Automated Identification of a Physician's Primary Patients

Affiliations of the authors: Decision Systems Group, Brigham and Women's Hospital (TAL); Laboratory of Computer Science, Massachusetts General Hospital, Harvard Medical School (TAL, HCC); Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (TAL); General Medicine Division and the Clinical Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School (SJA, MJB), Boston, MA.

Correspondence and reprints: Thomas A. Lasko, MD, MS, MIT CSAIL, 32 Vassar Street, 32-252, Cambridge, MA 02139; e-mail: <tlasko{at}mit.edu>.

Received for publication: 05/30/05; accepted for publication: 09/21/05.

Objective: To develop and validate an automated method for determining the set of patients for whom a given primary care physician holds overall clinical responsibility.

Design: The study included all adult patients (16,185) seen at least once in an ambulatory setting during a three-year period by 18 primary care physicians in ten practices. The physicians indicated whether they considered themselves to be the physician primarily responsible for the overall clinical care of each visiting patient. Statistical models were constructed to predict the physicians' designations using predictor variables derived from electronically available appointment schedules and demographic information.

Measurements: Predictive accuracy was assessed primarily using the area under the receiver-operating characteristic curve (AUC), and secondarily using positive predictive value (PPV) and sensitivity.

Results: A minimal set of six variables was identified as predictive of the physicians' designations. The constructed model had a median AUC for individual physicians of 0.92 (interquartile interval: 0.90–0.96), a PPV of 0.94 (interquartile interval: 0.87–0.95), and a sensitivity of 0.95 (interquartile interval: 0.87–0.97).

Conclusion: A statistical model using a minimal set of commonly available electronic data can accurately predict the set of patients for whom a physician holds primary clinical responsibility. Further research examining the generalization of the model to other settings would be valuable.

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