The scheduling of surgeries in a hospital operating room is a process fraught with uncertainty. The most carefully planned day can be upended by the unexpected arrival of a patient requiring an emergency operation. If a surgeon needs more time than expected to complete a procedure, if a patient shows up late or if a nurse has to call out sick, an entire day’s procedures can be delayed.
Operating rooms (ORs) consume about 40 percent of a hospital’s budget, says Miao (Mark) Bai, who earned his Ph.D. in industrial and systems engineering (ISE) at Lehigh earlier this year. Not surprisingly, OR scheduling has been the object of much scrutiny from researchers.
Bai and his colleagues in Lehigh’s ISE department have developed a scheduling model that they believe outperforms other scheduling methods by dealing both proactively and reactively with disruptions and by accounting for bottlenecks in post-surgery recovery rooms.
The model is designed for a surgical suite with multiple ORs that share a post-anesthesia care unit (PACU) where patients recover from anesthesia. The model schedules surgeries one day in advance and is then adjusted dynamically on the day of surgery to respond to disruptions.
The model’s proactive algorithm develops a robust OR schedule that can accommodate a certain level of disruptions, says Bai, who is now a research associate at the Mayo Clinic in Minnesota.
Read the full story at the Lehigh University News Center.
February 6, 2018
- Department of Industrial and Systems Engineering, Lehigh University
- Healthcare Systems Engineering Program, Lehigh University
- Profile: Miao Bai
- Faculty profile: Robert H. Storer
- Faculty profile: Greg L. Tonkay
- Paper: A sample gradient-based algorithm for a multiple-OR and PACU surgery scheduling problem