Optimal ICU Admission Control With Premature Discharge

Abstract

The intensive care unit (ICU) delivers care to critically ill patients with high resource intensity. Stochastic patient arrival and uncertain length of stay in ICUs present tremendous challenge to establish the admission and discharge policy to maximize the number of surviving patients and improve operational efficiency. In practice, ICU schedulers reserve some beds for potential patients with most critical conditions. Moreover, they prematurely discharge current ICU patients who are in stable status to accommodate for new and more urgent arrivals. We develop an analytical framework to quantify the impact of the number of reserved beds and suggest when to prematurely discharge current patients. A Markov decision process model is established to strike a balance between the rejection of incoming patient and the premature discharge in the near future. Monotonicity, concavity, and structural properties of the optimal control policy are presented. Using the inpatient record at a tertiary-level hospital in China, we conduct a case study and propose an effective threshold policy. Extensive numerical experiments are performed to analyze the effect of each parameter on the total survival benefits and compare different policies.