Systematic OR Block Allocation at a Large Academic Medical Center Comprehensive Review on a Data-driven Surgical Scheduling Strategy

Objective: To alleviate the surgical patient flow congestion in the perioperative environment without additional resources. Background: Massachusetts General Hospital experienced increasing overcrowding of the perioperative environment in 2008. The Post-Anesthesia Care Unit would often be at capacity, forcing patients to wait in the operating room. The cause of congestion was traced back to significant variability in the surgical inpatient-bed occupancy across the days of the week due to elective surgery scheduling practices. Methods: We constructed an optimization model to find a rearrangement of the elective block schedule to smooth the average inpatient census by reducing the maximum average occupancy throughout the week. The model was revised iteratively as it was used in the organizational change process that led to an implementable schedule. Results: Approximately 21% of the blocks were rearranged. The setting of study is very dynamic. We constructed a hypothetical scenario to analyze the patient population most representative of the circumstances under which the model was built. For this group, the patient volume remained constant, the average census peak decreased by 3.2% (P < 0.05), and the average weekday census decreased by 2.8% (P < 0.001). When considering all patients, the volume increased by 9%, the census peak increased 1.6% (P < 0.05), and the average weekday census increased by 2% (P < 0.001). Conclusions: This work describes the successful implementation of a data-driven scheduling strategy that increased the effective capacity of the surgical units. The use of the model as an instrument for change and strong managerial leadership was paramount to implement and sustain the new scheduling practices.