Purpose: Teaching clinics must satisfy often conflicting demands. They need to give to provide timely access to patients while giving trainees a good consistent teaching experience, a broad range of clinical experiences to learn from, timely access to preceptors, maximize continuity and remain financially solvent.
Method: We collected encounter data from primary care residency clinics at Mass General Hospitals from 7/2008 - 5/2010. (258 residents and ~17,000 patients). Patients were characterized by gender, age (subdivided into ten-year increments), major disease category, both acute and chronic, such as, Neuro Acute, Neuro Chronic; Cardio Acute, Cardio Chronic, Psych Acute, Psych Chronic etc. (total 44 disease categories), number of disease categories. Measure of imbalance: standard deviation for each disease category, patient panel size, annual visit frequency. Patient reassignment algorithms were executed in 2 parts: first, patients are sorted in decreasing order of complexity, second, patients are allocated sequentially to the clinician panel with the least overall complexity. These algorithms were applied in 3 different reassignment scenarios: 1) within preceptor, 2) within a group of preceptors, 3) across entire practice. Preceptor group size was varied from 1 to 12 reflecting a wide range of potential organizational structures.
Result: Reassignment within preceptor (RwP) did not produce significant reductions in the standard deviation of either number of disease categories or diagnoses across residents. Reassignment in preceptor groupings of 4 preceptors or larger resulted in a significant reduction in category and diagnostic variance relative to basecase. Reassignment across all preceptors reduced variance the most but comes at the cost of reduced patient-preceptor continuity.
Conclusion: Systematically reallocating patient panels annually in teaching clinics potentially can improve the consistency and breadth of the educational experience while at the same time improving patient access at little cost. This method in principle can be extended to any scenario where there is either patient or clinician turnover.
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