An entropy-based approach to improve clinic performance and patient satisfaction

The patient scheduling problem in outpatient clinics has been studied extensively in literature with several mathematical, simulation and heuristic based solutions. Factors that influence a clinic's decision to follow a specific scheduling method depend on the patient arrival factors and the expected encounter time. A significant number of small clinics use Bailey's rule or an adaptation of the Bailey's rule for patient scheduling due to its simplicity and lack of resources to invest in a complex scheduling software system. Often there are competing factors that a scheduler or decision maker has to evaluate. These include maximizing clinical resource utilization levels from an economic standpoint versus attempting to minimize waiting time for patients from a patient satisfaction/quality of care standpoint. Additional parameters that make the scheduling problem challenging are the variability in patient arrival time, no-shows, variability in patient-physician encounter times, emergency patients, and several related factors. This research studies the patient scheduling problem in an outpatient clinic using entropy as a common measure to classify the dominating factors that contribute towards intended clinic performance criteria and patient satisfaction criteria. The goal is to provide an effective and insightful method to study the clinic outpatient scheduling problem which can benefit the clinic and the patients.

An entropy-based approach to improve clinic performance and patient satisfaction Conference Paper