Scheduling a delay between different surgeons' cases in the same operating room on the same day using upper...

Area of Science:

• Surgical scheduling and operating room management
• Statistical modeling in healthcare operations
• Predictive analytics for surgical case durations

Background:

• Elective surgical cases are often scheduled within regular hours, requiring efficient operating room (OR) utilization.
• Sequential scheduling of surgeons in the same OR necessitates managing potential delays for subsequent cases.
• Accurate scheduling of delays is crucial for on-time case initiation and OR throughput.

Purpose of the Study:

• To develop and validate a statistical methodology for calculating scheduled delays between consecutive surgeons' cases in an OR.
• To enhance the reliability of operating room scheduling by minimizing delays for second surgeons.
• To provide a data-driven approach for optimizing OR turnover times.

Main Methods:

• Utilized statistical analysis of historical surgical case duration data.
• Applied the mathematics of upper prediction bounds to estimate case durations.
• Tested an analytical expression for single cases and a Monte Carlo simulation for pairs of cases.

Main Results:

• Developed 90% upper prediction bounds for surgical case durations.
• Validated bounds against actual durations, showing high accuracy for single (90% +/- 0.2%) and paired (92% +/- 0.6%) cases.
• Demonstrated that the calculated bounds are consistently longer than actual case durations.

Conclusions:

• The proposed statistical methodology accurately calculates appropriate scheduled delays between surgeons' cases.
• This approach can improve the predictability and efficiency of surgical scheduling in shared ORs.
• Implementing this method can lead to better resource allocation and reduced patient wait times.