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Improving Operating Room Efficiency: Machine Learning Approach to Predict Case-Time Duration
| Content Provider | Scilit |
|---|---|
| Author | Bartek, Matthew A. Saxena, Rajeev C. Solomon, Stuart Fong, Christine T. Behara, Lakshmana D. Venigandla, Ravitheja Velagapudi, Kalyani Lang, John D. Nair, Bala G. |
| Copyright Year | 2019 |
| Description | Journal: Journal of the American College of Surgeons 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. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7077507/pdf http://www.journalacs.org/article/S1072751519304053/pdf |
| ISSN | 10727515 |
| e-ISSN | 18791190 |
| DOI | 10.1016/j.jamcollsurg.2019.05.029 |
| Journal | Journal of the American College of Surgeons |
| Issue Number | 4 |
| Volume Number | 229 |
| Language | English |
| Publisher | Ovid Technologies (Wolters Kluwer Health) |
| Publisher Date | 2019-07-13 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of the American College of Surgeons Otorhinolaryngology Electronic Medical Record Extreme Gradient Boosting |
| Content Type | Text |
| Resource Type | Article |
| Subject | Surgery |
