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.

[1]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[2]  E. Steyerberg,et al.  Predicting the Unpredictable: A New Prediction Model for Operating Room Times Using Individual Characteristics and the Surgeon's Estimate , 2010, Anesthesiology.

[3]  A. Macario.  What does one minute of operating room time cost? , 2010, Journal of clinical anesthesia.

[4]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[5]  Joshua C. Vacanti,et al.  Contributors to Operating Room Underutilization and Implications for Hospital Administrators , 2018, The health care manager.

[6]  Peter L. Bartlett,et al.  Boosting Algorithms as Gradient Descent , 1999, NIPS.

[7]  Andreas Holzinger,et al.  Data Mining with Decision Trees: Theory and Applications , 2015, Online Inf. Rev..

[8]  C. Heij,et al.  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 , 2009, Anesthesia and analgesia.

[9]  F. Dexter,et al.  How to Schedule Elective Surgical Cases into Specific Operating Rooms to Maximize the Efficiency of Use of Operating Room Time , 2002, Anesthesia and analgesia.

[10]  F Dexter,et al.  Decrease in case duration required to complete an additional case during regularly scheduled hours in an operating room suite: a computer simulation study. , 1999, Anesthesia and analgesia.

[11]  Antti Peltokorpi,et al.  How do strategic decisions and operative practices affect operating room productivity? , 2011, Health care management science.

[12]  D. Laskin,et al.  Accuracy of predicting the duration of a surgical operation. , 2013, Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons.

[13]  Luis G. Vargas,et al.  The Surgical Scheduling Problem: Current Research and Future Opportunities , 2011 .

[14]  Joan Fountain,et al.  Surgical unit time utilization review: Resource utilization and management implications , 1988, Journal of Medical Systems.

[15]  R. Urman,et al.  Use of Historical Surgical Times to Predict Duration of Primary Total Knee Arthroplasty. , 2016, The Journal of arthroplasty.

[16]  Rodney A Gabriel,et al.  National incidences and predictors of inefficiencies in perioperative care. , 2016, Journal of clinical anesthesia.

[17]  G. Kazemier,et al.  Improving Operating Room Efficiency by Applying Bin-Packing and Portfolio Techniques to Surgical Case Scheduling , 2007, Anesthesia and analgesia.

[18]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[19]  J. Ledolter,et al.  Bayesian Prediction Bounds and Comparisons of Operating Room Times Even for Procedures with Few or No Historic Data , 2005, Anesthesiology.