Dynamic Control of Service Systems with Returns: Application to Design of Postdischarge Hospital Readmission Prevention Programs

• 成果类型: Article
• 署名作者: Chan, Timothy C. Y.; Huang, Simon Y.; Sarhangian, Vahid
• 署名单位: University of Toronto
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.0066
• 发表日期: 2025
• 页码: 2242-2263
• 关键词: model; QUEUE

摘要

We study a control problem for queueing systems in which customers may return for additional episodes of service after their initial service completion. At each service completion epoch, the decision maker can choose to reduce the probability of return for the departing customer but at a cost that is convex increasing in the amount of reduction in the return probability. Other costs are incurred as customers wait in the queue and every time they return for service. Our primary motivation comes from postdischarge quality improvement interventions (e.g., follow-up phone calls, outpatient appointments) frequently used in a variety of healthcare settings to reduce unplanned hospital readmissions. Our objective is to understand how the cost of interventions should be balanced with the reductions in congestion and service costs. To this end, we consider a fluid approximation of the queueing system and characterize the structure of optimal long-run average and bias-optimal transient control policies for the fluid model. Our structural results motivate the design of intuitive surge protocols whereby different intensities of interventions (corresponding to different levels of reduction in the return probability) are provided based on the congestion in the system. Through extensive simulation experiments, we study the performance of the fluid policy for the stochastic system and identify parameter regimes in which it leads to significant cost savings compared with a fixed longrun average optimal policy that ignores holding costs and a simple policy that uses the highest level of intervention whenever the queue is nonempty. In particular, we find that, in a parameter regime relevant to our motivating application, dynamically adjusting the intensity of interventions could result in up to 25.4% reduction in long-run average cost and 33.7% in finite-horizon costs compared with the simple aggressive policy.