Dataset Information

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

ABSTRACT: BACKGROUND:Accurate estimation of operative case-time duration is critical for optimizing operating room use. Current estimates are inaccurate and earlier models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective data set 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. For each of these models, we generated an all-inclusive model, service-specific models, and surgeon-specific models. In the latter 2 approaches, individual models were created for each surgical service and surgeon, respectively. Our data set included 46,986 scheduled operations performed at a large academic medical 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 with our institutional standard of using average historic procedure times and surgeon estimates. 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 machine learning algorithm resulted in the highest predictive capability. The surgeon-specific model was superior to the service-specific model, with higher accuracy, 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 machine learning surgeon-specific model. CONCLUSIONS:Our study is a notable advancement toward statistical modeling of case-time duration across all surgical departments in a large tertiary medical center. Machine learning approaches can improve case duration estimations, enabling improved operating room scheduling, efficiency, and reduced costs.

SUBMITTER: Bartek MA

PROVIDER: S-EPMC7077507 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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

Bartek Matthew A MA Saxena Rajeev C RC Solomon Stuart S Fong Christine T CT Behara Lakshmana D LD Venigandla Ravitheja R Velagapudi Kalyani K Lang John D JD Nair Bala G BG

Journal of the American College of Surgeons 20190713 4

<h4>Background</h4>Accurate estimation of operative case-time duration is critical for optimizing operating room use. Current estimates are inaccurate and earlier models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective data set to improve estimation of case-time duration relative to current standards.<h4>Study design</h4>We developed models to predict case-time duration using linear regression and supervised machine le ...[more]

PMID: 31310851

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Project description:BACKGROUND:Although the surgical pause or time-out is a required part of most hospitals' standard operating procedures, little is known about the quality of execution of the time-out in routine clinical practice. An interactive electronic time-out was implemented to increase surgical team compliance with the time-out procedure and to improve communication among team members in the operating room. We sought to identify nonroutine events that occur during the time-out procedure in the operating room, including distractions and interruptions, deviations from protocol, and the problem-solving strategies used by operating room team members to mitigate them. METHODS:Direct observations of surgical time-outs were performed on 166 nonemergent surgeries in 2016. For each time-out, the observers recorded compliance with each step, any nonroutine events that may have occurred, and whether any operating room team members were distracted. RESULTS:The time-out procedure was performed before the first incision in 100% of cases. An announcement was made to indicate the start of the time-out procedure in 163 of 166 observed surgeries. Most observed time-outs were completed in <1 minute. Most time-outs were completed without interruption (92.8%). The most common reason for an interruption was to verify patient information. Ten time-out procedures were stopped due to a safety concern. At least 1 member of the operating room team was actively distracted in 10.2% of the time-out procedures observed. CONCLUSIONS:Compliance with preincision time-outs is high at our institution, and nonroutine events are a rare occurrence. It is common for ?1 member of the operating room team to be actively distracted during time-out procedures, even though most time-outs are completed in under 1 minute. Despite distractions, there were no wrong-site or wrong-person surgeries reported at our hospital during the study period. We conclude that the simple act of performing a preprocedure checklist may be completed quickly, but that distractions are common.

Dataset Information

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

Publications

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

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