Many outpatient clinics are experimenting with open access scheduling. Under open access, patients see their physicians within a day or two of making their appointment request, and long-term patient booking is very limited. The hope is that these short appointment lead times will improve patient access and reduce uncertainty in clinic operations by reducing patient no-shows. Practice shows that successful implementation can be strongly influenced by clinic characteristics, indicating that open access policies must be designed to account for local clinical conditions. The effects of four variables on clinic performance are examined: (1) the fraction of patients being served on open access, (2) the scheduling horizon for patients on longer-term appointment scheduling, (3) provider care groups, and (4) overbooking. Discrete event simulation, designed experimentation, and data drawn from an intercity clinic in central Indiana are used to study the effects of these variables on clinic throughput and patient continuity of care. Results show that, if correctly configured, open access can lead to significant improvements in clinic throughput with little sacrifice in continuity of care.
In this paper, we propose a two-phase approach for designing a weekly scheduling template for outpatient clinics providing multiple types of services. In many outpatient clinics, various service types are categorized to address the operational challenge of substantial changeover time between certain pairs of services. In the first phase of our approach, a mixed-integer program is formulated to assign service categories to clinic sessions during a week and determine the optimal number of appointments reserved for each service type in each clinic session. The objective in the first phase is to balance the workload of the providers among clinic sessions. In the second phase, a stochastic mixed-integer program is formulated for each clinic session to assign each contained appointment with a starting time based on several time-based performance measures. To solve the formulated stochastic program, we develop a Monte Carlo sampling based genetic algorithm. The two-phase approach is tested numerically with cases derived from a real women's clinic. Our results demonstrate that the two-phase approach can efficiently find promising weekly appointment scheduling templates for outpatient clinics. In addition, our results suggest that the best suboptimal scheduling templates found become more sensitive to the weighting coefficients of the time-based measures as the provider workload increases.
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