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Can OR Schedule Optimizing Software Improve OR Efficiency?
Mark Meyer, David Craft, Suzanne Sokal, Keri McCrensky, Wilton Levine, Warren Sandberg, David Berger
Massachusetts General Hospital, Boston, MA
Objective: Develop software for automating schedule optimization in a resource-constrained operating environment.
Design: Descriptive study.
Setting: High velocity, parallel processing OR environment (three ORs and a PACU) in an urban, academic medical center.
Patients: Surgical cases via historical timestamp database.
Interventions: The schedule optimizer software modifies a surgical case list to limit both frequency and duration of disruptive events. The major disruptive event is defined as all three operating rooms having simultaneous anesthesia induction or emergence, measured as three rooms having concurrent turnover. Once disruptive events are limited, the optimizer then selects the best schedule to maximize desirable outcomes, including fewer projected overtime case hours and preferentially scheduling ambulatory cases in the morning to help alleviate hospital bed congestion. The optimizer utilizes historical average case and turnover durations for similar cases (case type/surgeon) to simulate a day’s progression based on the booked case list. Once an optimal schedule is obtained, staff and scheduling offices are sent the optimized schedule via email and patients are informed of their OR time.
Main Outcome Measures: Number of disruptive events before/after optimization.
Results: Using real case lists and historical time data, the optimizer successfully re-ordered cases in a mock three OR parallel processing environment. Disruptive events present in the unoptimized schedule were eliminated while minimizing overtime and maximizing morning ambulatory case load.
Conclusions: Case scheduling in a high velocity environment with limited resources requires knowledge and experience to prevent disruptive events. The extensive aggregated historical data used in simulations by the optimizer, combined with logic rules dictating desirable characteristics, allow for automatic development of rational case schedules.
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