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

Dynamic Control of Service Systems with Returns Postdischarge interventions (e.g., follow-up phone calls, outpatient appointments) are commonly used to reduce unplanned hospital readmissions. Such interventions have been shown to be effective (to varying degrees) in reducing the probability of readmission (or return). The interventions are, however, costly, and hence, their benefits need to be carefully balanced with the costs. In “Dynamic Control of Service Systems with Returns: Application to Design of Postdischarge Hospital Readmission Prevention Programs,” Chan, Huang, and Sarhangian investigate how postservice interventions should be dynamically allocated, accounting for their costs as well as their benefits in terms of reducing returns and congestion costs. To this end, they study a transient queueing control problem and examine the structure of the optimal policy by analyzing associated fluid-control problems. The structural results motivate the design of intuitive surge protocols whereby different intensities of interventions are provided based on the congestion level of the system. 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