Admission and Discharge Control in Intensive Care Units

Intensive Care Units (ICUs) are scarce resources that usually operate under high occupancy rates. When faced with limited bed availability, arriving patients are sometimes rejected or admitted by discharging an existing patient early which may result in increased readmission rates or patient/hospital related costs. Yet, there is not a well-defined admission and discharge policy to decrease such adverse consequences. We formulate a comprehensive ICU model with multiple patient types, multiple health stages, and a so-called readmission orbit whose population consists of patients who will seek readmission after an ICU discharge. The ICU model also differentiates first-time and recurring patients. We propose two easy-to-implement heuristic control policies, one based on a Markov Decision Processes model and the other based on a deterministic fluid model. We compare the performances of these policies with the ones of two state-of-the-art policies via discrete-event simulation. According to the numerical results, our proposed policies significantly outperform the state-of-the-art policies by carefully balancing rejection and early-discharge decisions. Moreover, they complement each other since the fluid-based policy performs well in large ICUs that operate in an overloaded regime, while the one-bed policy performs well in small ICUs or ICUs operating in critically or under-loaded regimes.