Machine Learning Can Improve Estimation of Surgical Case Duration: A Pilot Study
Operating room (OR) utilization is a significant determinant of hospital profitability. One aspect of this is surgical scheduling, which depends on accurate predictions of case duration. This has been done historically by either the surgeon based on personal experience, or by an electronic health record (EHR) based on averaged historical means for case duration. Here, we compare the predicted case duration (pCD) accuracy of a novel machine-learning algorithm over a 3-month period. A proprietary machine learning algorithm was applied utilizing operating room factors such as patient demographic data, pre-surgical milestones, and hospital logistics and compared to that of a conventional EHR. Actual case duration and pCD (Leap Rail vs EHR) was obtained at one institution over the span of 3 months. Actual case duration was defined as time between patient entry into an OR and time of exit. pCD was defined as case time allotted by either Leap Rail or EHR. Cases where Leap Rail was unable to generate a pCD were excluded. A total of 1059 surgical cases were performed during the study period, with 990 cases being eligible for the study. Over all sub-specialties, Leap Rail showed a 7 min improvement in absolute difference between pCD and actual case duration when compared to conventional EHR (p < 0.0001). In aggregate, the Leap Rail method resulted in a 70% reduction in overall scheduling inaccuracy. Machine-learning algorithms are a promising method of increasing pCD accuracy and represent one means of improving OR planning and efficiency.
KeywordsOperating room Case duration Prediction Model Natural language processing Efficiency
None. Data were provided by Leap Rail, Inc.
Compliance with Ethical Standards
Conflict of Interest
Justin P. Tuwatananurak declares that he has no conflict of interest. Shayan Zadeh serves as the CEO of Leap Rail, Inc. Xinling Xu PhD declares that she has no conflict of interest. Joshua A. Vacanti declares that he has no conflict of interest. William R. Fulton declares that he has no conflict of interest. Jesse M. Ehrenfeld declares that he has no conflict of interest. Richard D. Urman has received funding from Medtronic, Merck and Mallinckrodt for unrelated research and honorarium from 3 M and Posimir.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent requirement was waived given the deidentified nature of the data and observational nature of the study.
- 8.Stepaniak, P. S., Heij, C., Mannaerts, G. H., De quelerij, M., and De vries, G., Modeling procedure and surgical times for current procedural terminology-anesthesia-surgeon combinations and evaluation in terms of case-duration prediction and operating room efficiency: A multicenter study. Anesth. Analg. 109(4):1232–1245, 2009.CrossRefGoogle Scholar
- 10.Bishop, C., Pattern recognition and machine learning. Berlin: Springer, 2006, ISBN 0-387-31073-8.Google Scholar
- 11.Mason, L., Baxter, J. Bartlett, P. L., and Frean, M., Boosting algorithms as gradient descent. In S.A. Solla and T.K. Leen and K. Müller. Advances in neural information processing systems 12. MIT Press. 512–518, 1999.Google Scholar
- 12.Rokach, Lior, and Maimon, O., Data mining with decision trees: Theory and applications. World Scientific Pub Co Inc. ISBN 978-9812771711, 2008.Google Scholar
- 13.Breiman, L. Machine Learning 45: 5, 2001. https://doi.org/10.1023/A:1010933404324.
- 14.Russell, S. J., and Norvig, P., Artificial intelligence: A modern approach. Pearson, 2016.Google Scholar