Stochastic Analysis Of Add-on Cases On An Operating Room Schedule With Dedicated Block Time
Edgar Soto1, Carter J. Boyd, BS1, Shivani Ananthasekar, BS1, Timothy W. King, MD, PhD1, Samir S. Award, MD, MPH2, Jorge I. De La Torre, MD, MHSA1.
1University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA, 2Baylor College of Medicine, Houston, TX, USA.
Purpose: We present an accessible and applicable method to apply basic stochastic analysis and operations management principles to increase operating room scheduling efficiency. The objective of this study was to determine if application of basic stochastic analysis can tame the chaotic nature of add-on cases on an operating room schedule with dedicated block time. Methods: This was a retrospective review and statistical analysis of all add-on surgeries, as defined as cases added within 24hours of set schedule, from January 2015 to December 2016. This was conducted at a single institution, university-based, metropolitan Veterans Affairs Administration Medical Center. Outcomes were measured to test if stochastic analysis is a viable method to study the random nature of surgical add-on cases and determine if the resulting insights can empower practical OR scheduling optimization. Results: The service with the highest percentage of days with at least one add-on was General surgery (47.2%), followed by Vascular Surgery (36.6%), then Orthopedic Surgery (23.1%) (see figure 1 and table 1). The average duration of add-ons per day for these services were: General 226±144mins, Vascular 186±144mins, and Orthopedics 163±103mins (see figure 2 and table 2). Cardiothoracic Surgery add-ons had the largest mean duration (288±159mins), but they were relatively infrequent (8.1% of days). General Surgery also had the highest percentage of add-ons for every day—except for Monday, which Vascular Surgery had the highest share (63.5%). Mondays and Thursdays had the highest combined add-on share by the three busiest services. The presented analysis can be used by other institutions to generate analogous insights and increase OR utilization and resource efficiency. Conclusion: Today's clinician-manager must be agile in applying a basic understanding of data analysis and operations management to drive quality improvement initiatives. We demonstrate a straightforward method to apply stochastic analysis to optimize operating room scheduling efficiency.
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