2019
DOI: 10.1016/j.jamcollsurg.2019.05.029
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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 t… Show more

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Cited by 86 publications
(104 citation statements)
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“…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.…”
Section: Resultsmentioning
confidence: 63%
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“…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.…”
Section: Resultsmentioning
confidence: 63%
“…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.…”
Section: Discussionmentioning
confidence: 61%
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