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. 2006 Sep;21(9):973-8.
doi: 10.1111/j.1525-1497.2006.00509.x.

Is this "my" patient? Development and validation of a predictive model to link patients to primary care providers

Steven J Atlas  1 , Yuchiao Chang, Thomas A Lasko, Henry C Chueh, Richard W Grant, Michael J Barry
Affiliations

Is this "my" patient? Development and validation of a predictive model to link patients to primary care providers

Steven J Atlas et al. J Gen Intern Med. 2006 Sep.

Abstract

Background: Evaluating the quality of care provided by individual primary care physicians (PCPs) may be limited by failing to know which patients the PCP feels personally responsible for.

Objective: To develop and validate a model for linking patients to specific PCPs.

Design: Retrospective convenience sample.

Participants: Eighteen PCPs from 10 practice sites within an academic adult primary care network.

Measurements: Each PCP reviewed the records for all outpatients seen over the preceding 3 years (16,435 patients reviewed) and designated each patient as "My Patient" or "Not My Patient." Using this reference standard, we developed an algorithm with logistic regression modeling to predict "My Patient" using development and validation subsets drawn from the same patient set. Quality of care was then assessed by "My Patient" or "Not My Patient" designation by analyzing cancer screening test rates.

Results: Overall, PCPs designated 11,226 patients (68.3%, range per provider 15% to 93%) to be "My Patient." The model accurately categorized patients in development and validation subsets (combined sensitivity 80.4%, specificity 93.7%, and positive predictive value 96.5%). To achieve positive predictive values of > 90% for individual PCPs, the model excluded 19.6% of PCP "My Patients" (range 5.5% to 75.3%). Cancer screening rates were higher among model-predicted "My Patients."

Conclusions: Nearly one-third of patients seen were considered "Not My Patient" by the PCP, although this proportion varied widely. We developed and validated a simple model to link specific patients and PCPs. Such efforts may help effectively target interventions to improve primary care quality.

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Figures

FIGURE 1
FIGURE 1
Schematic of algorithm with Logistic Regression Models.
FIGURE 2
FIGURE 2
Percentage of all patients seen by each primary care provider rated as “My Patient.” In general, physicians rating a low percentage as “My Patient” primarily worked as urgent care providers within their practice.
See this image and copyright information in PMC

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