The Mayo Clinic Department of Orthopedic Surgery was facing low utilization of their operating rooms (ORs) for spine surgical procedures, combined with fluctuating empty days and days with overtime to complete scheduled surgeries. Extremely long days ended up being unsafe days with increased provider fatigue and higher likelihood of errors.
Investigation revealed the cause to be inaccurate estimation of surgical and non-surgical duration and scheduling of surgeries rather than limited surgery demand. Existing scheduling optimization research in the literature was inadequate, as they provided a single “optimal” solution. Often, the single optimal solution was not satisfactory to the patients and the providers.
The team 1) conducted descriptive research using historical data to identify clinical and operational factors; 2) developed and implemented predictive models for the duration of surgical and nonsurgical times in the OR based on these factors; and 3) developed and implemented a prescriptive scheduling search algorithm that suggests multiple slots for a given surgery thereby providing flexibility while ensuring high probability of completion of surgical day without much overtime (completion closer to 5 pm).