Improving Operating Room Efficiency: Machine Learning Approach to Predict Case-Time Duration

Abstract

Background-Accurately estimating operative case-time duration is critical for optimizing operating room utilization. Current estimates are inaccurate and prior models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective dataset to improve estimation of case-time duration relative to current standards. Study Design-We developed models to predict case-time duration using linear regression and supervised machine learning (ML). For each of these models, we generated: 1) service-specific models and 2) surgeon-specific models in which surgeons were modeled individually. Our dataset included 46,986 scheduled surgeries performed at our center from January 2014 to December 2017, with 80% used for training and 20% for model testing/validation. Predictions derived from each model were compared to our institutional standard. Models were evaluated based on accuracy, overage (case duration > predicted + 10%), underage (case duration < predicted-10%), and the predictive capability of being within a 10% tolerance threshold. Results-The ML algorithm resulted in the highest predictive capability. The surgeon-specific model was superior to the service-specific model, with higher accuracies, lower percentage of overage and underage, and higher percentage of cases within the 10% threshold. The ability to predict cases within 10% improved from 32% using our institutional standard to 39% with the ML surgeon-specific model. The majority of the information utilized in the models was based on procedure and personnel data rather than patient health status.

A Machine Learning Study to Improve Surgical Case Duration Prediction

“…Although the performance of the RF model was close to that of the XGB model, the XGB model was more computationally e cient in that it took a shorter time to complete the training process. For the XGB model built in this study, the coe cient of determination (R 2 ) was higher than that in other ML studies, while the percentages of under-and overprediction were lower [15,19,7]. Moreover, this model improves the current OR scheduling method at CMUH, which is based on estimates made by surgeons.…”

“…It has been reported in the past studies that primary surgeons contributed the largest variability in surgical case duration prediction compared to other factors attributed to patients [15,16,14]. These studies provide evidence and rationale that more factors relating to primary surgeons should be added as input variables in the training of ML models.…”

“…First, the data set used in this study contained more than 140,000 cases (after exclusion) and more than 400 different types of surgical procedures, establishing a new benchmark for a massive quantity of data with high diversity. The maximal number of cases that had been used in other studies was in the range of 40,000 to 60,000 [15,7]. Second, OR events were modeled as dependent events instead of independent.…”

“…We applied 15 minutes as a tolerance threshold for the percentage of underage, overage and within because ± 15 minutes is an acceptable periodic range at CMUH to be considered as accurate booking. To avoid having an excessively stringent standard and to better compare our outcomes with those of other studies [15,19], a tolerance threshold of 10 % was also applied.…”

“…(2019) increased the percentage of accurately booked cases for robot-assisted surgery from 35 % to 52 %. Bartek et al (2019) reported that they were able to improve predicted cases within 10 % of the threshold tolerance from 32 % to 39 % using an extreme gradient boosting model [15]. Nonetheless, these ML studies included only 5-12 different types of procedures and specialties to train their ML models, which may limit the generalization of these models.…”