A Method for Balancing Provider Schedules in Outpatient Specialty Clinics

Abstract

Background. Variability in outpatient specialty clinic schedules contributes to numerous adverse effects including chaotic clinic settings, provider burnout, increased patient waiting times, and inefficient use of resources. This research measures the benefit of balancing provider schedules in an outpatient specialty clinic. Design. We developed a constrained optimization model to minimize the variability in provider schedules in an outpatient specialty clinic. Schedule variability was defined as the variance in the number of providers scheduled for clinic during each hour the clinic is open. We compared the variance in the number of providers scheduled per hour resulting from the constrained optimization schedule with the actual schedule for three reference scenarios used in practice at M Health Fairview’s Clinics and Surgery Center as a case study. Results. Compared to the actual schedules, use of constrained optimization modeling reduced the variance in the number of providers scheduled per hour by 92% (1.70–0.14), 88% (1.98–0.24), and 94% (1.98–0.12). When compared with the reference scenarios, the total, and per provider, assigned clinic hours remained the same. Use of constrained optimization modeling also reduced the maximum number of providers scheduled during each of the actual schedules for each of the reference scenarios. The constrained optimization schedules utilized 100% of the available clinic time compared to the reference scenario schedules where providers were scheduled during 87%, 92%, and 82% of the open clinic time, respectively. Limitations. The scheduling model’s use requires a centralized provider scheduling process in the clinic. Conclusions. Constrained optimization can help balance provider schedules in outpatient specialty clinics, thereby reducing the risk of negative effects associated with highly variable clinic settings.

Research on outpatient capacity planning combining lean thinking and integer linear programming

“…Experimental results show that full patient scheduling significantly reduces waiting time, and real-time path optimization makes scheduling more efficient because it can immediately capture changes in the outpatient clinic. Berg et al studied the use of constraint optimization modeling to balance doctors’ work schedules in specialized outpatient clinics, minimizing the variability of timetables, improving work efficiency, and thus reducing the maximum number of doctors making visits [ 27 ]. Nguyen et al proposed precise and relaxing appointment scheduling rules to arrange the appointment of each new patient according to the uncertainty of future arrival [ 28 ].…”

Using Discrete-Event Simulation to Balance Staff Allocation and Patient Flow between Clinic and Surgery

“…A contributing factor to these delays is the requirement for physicians to perform multiple tasks within the process [6]. Physicians see patients in the clinic as well as perform required surgeries.…”