Intensive Care Units (ICUs) play a vital role in hospitals and they often have capacity strain. Using data from a major teaching hospital, we empirically examine the effect of ICU capacity strain on patient discharge. Our data has a dynamic measure of patient health status, allowing us to study...

We explore the challenges of re-using machine learning models originally developed for patient-level predictions to forecast system-wide quantities in hospitals. As a running example, we examine the use of patient-level predictions of length of stay to predict short-term occupancy in an...

This work examines the admission process to hospitals' Intensive Care Units (ICUs), which currently lacks well-defined admission criteria. A major challenge that has impeded the progress of developing ICU admission standards is that the impact of ICU admission on patient outcomes has not been...

Advancements in algorithms hold promise to enhance operations by improving users' decision-making. However, people sometimes fail to use algorithms, which could be a barrier to achieving such improvements. Using the bolus calculator (algorithm) use behavior in over 221,000 bolus insulin...

Problem definition: Do physicians exhibit predictable behavioral bias when making admission control decisions under patient diagnosis uncertainty with stochastic arrivals and lengths of stay? How can we structure feedback to help improve their decision-making?Methodology/results: We use a...

Because a hospital is an interconnected, interdependent network of care units, allocating resources — beds, nurses, and improvement initiatives — to one unit to reduce its congestion may have spillover effects on other units. As such, to identify the bottleneck unit and improve access to...