Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation.

Autor:	Zaribafzadeh H; Department of Biostatistics and Bioinformatics, and Department of Surgery, Duke University, Durham, NC., Webster WL; Department of Surgery, Duke University, Durham, NC., Vail CJ; Department of Surgery, Duke University, Durham, NC., Daigle T; Duke Health Technology Solutions, Duke University Health System, Durham, NC., Kirk AD; Department of Surgery, Duke University, Durham, NC., Allen PJ; Department of Surgery, Duke University, Durham, NC., Henao R; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC., Buckland DM; Department of Surgery, Duke University, Durham, NC.; Department of Emergency Medicine and Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC.
Jazyk:	angličtina
Zdroj:	Annals of surgery [Ann Surg] 2023 Dec 01; Vol. 278 (6), pp. 890-895. Date of Electronic Publication: 2023 Jun 02.
DOI:	10.1097/SLA.0000000000005936
Abstrakt:	Objective: To implement a machine learning model using only the restricted data available at case creation time to predict surgical case length for multiple services at different locations. Background: The operating room is one of the most expensive resources in a health system, estimated to cost $22 to $133 per minute and generate about 40% of hospital revenue. Accurate prediction of surgical case length is necessary for efficient scheduling and cost-effective utilization of the operating room and other resources. Methods: We introduced a similarity cascade to capture the complexity of cases and surgeon influence on the case length and incorporated that into a gradient-boosting machine learning model. The model loss function was customized to improve the balance between over- and under-prediction of the case length. A production pipeline was created to seamlessly deploy and implement the model across our institution. Results: The prospective analysis showed that the model output was gradually adopted by the schedulers and outperformed the scheduler-predicted case length from August to December 2022. In 33,815 surgical cases across outpatient and inpatient platforms, the operational implementation predicted 11.2% fewer underpredicted cases and 5.9% more cases within 20% of the actual case length compared with the schedulers and only overpredicted 5.3% more. The model assisted schedulers to predict 3.4% more cases within 20% of the actual case length and 4.3% fewer underpredicted cases. Conclusions: We created a unique framework that is being leveraged every day to predict surgical case length more accurately at case posting time and could be potentially utilized to deploy future machine learning models. Competing Interests: The authors report no conflicts of interest. (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu