Most cost containment efforts in public health systems have focused on regulating the use of hospital resources, especially operative time. As such, attempting to maximize the efficiency of limited operative time is important. Typically, hospital operating room (OR) scheduling of time is performed in two tiers: 1) master surgical scheduling (annual allocation of time between surgical services and surgeons) and 2) daily scheduling (a surgeon's selection of cases per operative day). Master surgical scheduling is based on a hospital's annual case mix and depends on the annual throughput rate per case type. This throughput rate depends on the efficiency of surgeons’ daily scheduling. However, daily scheduling is predominantly performed manually, which requires that the human planner simultaneously reasons about unknowns such as case-specific length-of-surgery and variability while attempting to maximize throughput. This often leads to OR overtime and likely sub-optimal throughput rate. In contrast, scheduling using mathematical and optimization methods can produce maximum systems efficiency, and is extensively used in the business world. As such, the purpose of our study was to compare the efficiency of 1) manual and 2) optimized OR scheduling at an academic-affiliated community hospital representative of most North American centres.

Historic OR data was collected over a four year period for seven surgeons. The actual scheduling, surgical duration, overtime and number of OR days were extracted. This data was first configured to represent the historic manual scheduling process. Following this, the data was then used as the input to an integer linear programming model with the goal of determining the minimum number of OR days to complete the same number of cases while not exceeding the historic overtime values. Parameters included the use of a different quantile for each case type's surgical duration in order to ensure a schedule within five percent of the historic overtime value per OR day.

All surgeons saw a median 10% (range: 9.2% to 18.3%) reduction in the number of OR days needed to complete their annual case-load compared to their historical scheduling practices. Meanwhile, the OR overtime varied by a maximum of 5%. The daily OR configurations differed from historic configurations in 87% of cases. In addition, the number of configurations per surgeon was reduced from an average of six to four.

Our study demonstrates a significant increase in OR throughput rate (10%) with no change in operative time required. This has considerable implications in terms of cost reduction, surgical wait lists and surgeon satisfaction. A limitation of this study was that the potential gains are based on the efficiency of the pre-existing manual scheduling at our hospital. However, given the range of scenarios tested, number of surgeons included and the similarity of our hospital size and configuration to the majority of North American hospitals with an orthopedic service, these results are generalizable. Further optimization may be achieved by taking into account factors that could predict case duration such as surgeon experience, patients characteristics, and institutional attributes via machine learning.