Supporting ICU Admission Scheduling under Uncertainty

Abstract

Admission scheduling in an intensive care unit (ICU) poses a complex challenge that requires balancing the optimization of resources for the post-operative care of patients while managing the risk of capacity overload. This process is further complicated by uncertainties such as the unknown arrival of emergencies and the varying length-of-stay of each patient. We model this as a stochastic optimization problem with a planning horizon of one week, using Monte-Carlo simulation to approximate the uncertain and scenario-dependent evolution of ICU occupation. Specifically, the model aims at minimizing the risk of exceeding critical occupancy levels, represented as chance constraints, while efficiently utilizing available resources. The application of this model on real-world data highlights the potential for optimizing resource utilization without increasing the risk of surpassing critical occupancy thresholds by providing admission schedules that are robust to different outcomes of the uncertainties.