Undergraduate Program
Client Project Challenge
Optimizing Scheduling of the Epilepsy Monitoring Unit at Lurie Children’s Hospital

The Epilepsy Monitoring Unit (EMU) at Lurie Children’s Hospital provides monitoring via video electroencephalography (VEEG), which allows technologists and physicians to track patients around the clock and record seizure events as they happen. The Lurie EMU currently hosts patients in 8 in-patient beds for a variety of stays: 2-, 3-, and 5-day appointments. However, these appointment stays are subject to variability as physicians may be able to capture sufficient information and release patients early, or may need a longer stay to capture more data. This is particularly true for the patients booked for 2-day stays; about 80% of these patients leave within the first day of monitoring. A patient leaving early cannot be easily replaced by another patient, so beds can go unused for significant lengths of time. This has led to bed utilization being significantly reduced to around 62% and the patient waitlist increasing to over a 16-week wait. We used queuing theory and discrete event simulation to model the appointment waitlist and patient processing at the EMU and to track these metrics while accounting for simulated randomness. We have also written a program that will generate the best weekly schedules of 2-, 3-, and 5-day appointments to respect the desired threshold of probability of no weekly overlaps. Each of these schedules were tested with multiple replications of one year of simulated time and compared on metrics of utilization, average time in the waitlist, percentage of appointments that overlap, and weekly throughput.  Our recommended booking schedule improves on the current EMU schedule in three of these four metrics while maintaining at least one 3-day appointment and at least one 5-day appointment during the week.

A full report on this project is available by request to

Team members: Maximiliaan Van Mieghem Kevin O’Brien Brunner Patrick Delos Reyes

Advisor:  Prof. Julia Gaudio