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Optimizing management and financial performance of the teaching ambulatory care clinic
James E Stahl1, Mark S Roberts, Scott Gazelle
1Received from the Departments of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Mass. 02114, USA. James@thedatagroup.org
Objective:
To examine how to optimize teaching ambulatory care clinics performance with regard to access to care, access to teaching, and financial viability.
Design:
Optimization analysis using computer simulation.
Methods:
A discrete-event simulation model of the teaching ambulatory clinic setting was developed. This method captures flow time, waiting time, competition for resources, and the interdependency of events, providing insight into system dynamics. Sensitivity analyses were performed on staffing levels, room availability, patient characteristics such as "new" versus "established" status, and clinical complexity and pertinent probabilities.
Main Results:
In the base-case, 4 trainees:preceptor, patient flow time (registration to check out) was 148 minutes (SD 5), wait time was 20.6 minutes (SD 4.4), the wait for precepting was 6.2 minutes (SD 1.2), and average daily net clinic income was $1,413. Utilization rates were preceptors (59%), trainees (61%), medical assistants (64%), and room (68%). Flow time and the wait times remained relatively constant for strategies with trainee:preceptor ratios <4:1 but increased with number of trainees steadily thereafter. Maximum revenue occurred with 3 preceptors and 5 trainees per preceptor. The model was relatively insensitive to the proportion of patients presenting who were new, and relatively sensitive to average evaluation and management (E/M) level. Flow and wait times rose on average by 0.05 minutes and 0.01 minutes per percent new patient, respectively. For each increase in average E/M level, flow time increased 8.4 minutes, wait time 1.2 minutes, wait for precepting 0.8 minutes, and net income increased by $490.
Conclusion:
Teaching ambulatory care clinics appear to operate optimally, minimizing flow time and waiting time while maximizing revenue, with trainee-to-preceptor ratios between 3 and 7 to 1.
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