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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. [ABSTRACT FROM AUTHOR]

OBJECTIVE — Population-level strategies may improve primary care for diabetes. We designed a controlled study to assess the impact of population management versus usual care on metabolic risk factor testing and management in patients with type 2 diabetes. We also identified potential patient-related barriers to effective diabetes management. RESEARCH DESIGN AND METHODS — We used novel clinical software to rank 910 patients in a diabetes registry at a single primary care clinic and thereby identify the 149 patients with the highest HbA[sub 1c] and cholesterol levels. After review of the medical records of these 149 patients, evidence-based guideline recommendations regarding metabolic testing and management were sent via e-mail to each intervention patient's primary care provider (PCP). Over a 3-month follow-up period, we assessed changes in the evidence-based management of intervention patients compared with a matched cohort of control patients receiving usual care at a second primary care clinic affiliated with the same academic medical center. RESULTS — In the intervention cohort, PCPs followed testing recommendations more often (78%) than therapeutic change recommendations (36%, P = 0.001). Compared with the usual care control cohort, population management resulted in a greater overall proportion of evidencebased guideline practices being followed (59 vs. 45%, P = 0.02). Most intervention patients (62%) had potential barriers to effective care, including depression (35%), substance abuse (26%), and prior nonadherence to care plans (18%). CONCLUSIONS — Population management with clinical recommendations sent to PCPs had a modest but statistically significant impact on the evidence-based management of diabetes compared with usual care. Depression and substance abuse are prevalent patient-level adherence barriers in patients with poor metabolic control. [ABSTRACT FROM AUTHOR]

Abstract: Diabetes management continues to fall short of evidence-based goals of care. Population management represents a new approach to diabetes care for large numbers of patients with diabetes cared for within a single clinical system. This method is information intensive and generally requires an advanced informatics infrastructure. While Information Processing is a critical first step in population management, to have a significant impact on disease control population-based intervention must also employ potent Clinical Action tools that lower barriers to effective care. In this review we present two recent population management interventions within our health system that illustrate the principles of Information Processing and Clinical Action in diabetes care. [Copyright &y& Elsevier]