Coordinated and Priority-based Surgical Care: An Integrated Distributionally Robust Stochastic Optimization Approach
Posted: 18 Nov 2020
Date Written: October 1, 2020
We study a Coordinated clinic and surgery Appointment Scheduling (CAS) problem in a surgical suite. Our models seek to provide timely access to care by coordinating clinic and surgery appointments to ensure that patients can see a surgeon in the clinic and (if needed) schedule their surgery within a maximum wait time target based on patient classes. There are different types of uncertainty including the number of appointment requests, whether a patient requires surgery, and surgery duration. We develop an Integrated Multi-stage Stochastic and Distribution-ally Robust Optimization (IMSDRO) approach to determine the optimal clinic and surgery dates for patients such that the access constraints are satisfied, and the clinical and surgical overtimes are minimized. The IMSDRO approach integrates a multi-stage stochastic program with a Distribution-ally robust optimization approach to simultaneously incorporate multiple types of uncertainties by including stochastic scenarios for appointment request arrivals and ambiguity sets for surgery duration. Several new transformations are introduced to turn the nonlinear program derived from the IMSDRO approach to a tractable one, and a constraint generation algorithm is developed to solve it efficiently. We propose a data-driven Rolling Horizon Procedure (RHP) to facilitate implementation. We use case data to assess the performance of our policies. The results suggest our policy can significantly improve surgical access delay times compared with the current practice. Our methodology is not limited to a particular setting and can be applied to other service industries where access matters.
Keywords: Healthcare Coordination, Access Delay to Care, Multi-Stage Stochastic Programming, Distribution-Ally Robust Optimization, Data-Driven Rolling Horizon
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