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

Timothy C. Y. Chan (), Simon Y. Huang () and Vahid Sarhangian ()
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Timothy C. Y. Chan: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada
Simon Y. Huang: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada
Vahid Sarhangian: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada

Operations Research, 2025, vol. 73, issue 4, 2242-2263

Abstract: 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 long-run 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.

Keywords: Stochastic; Models; Erlang-R queue; stochastic control; fluid control; bias optimality; hospital readmission (search for similar items in EconPapers)
Date: 2025
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