A novel approach to forecast surgery durations using machine learning techniques

Marco Caserta () and Antonio Garcia Romero
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Marco Caserta: IE University

Health Care Management Science, 2024, vol. 27, issue 3, No 1, 313-327

Abstract: Abstract This study presents a methodology for predicting the duration of surgical procedures using Machine Learning (ML). The methodology incorporates a new set of predictors emphasizing the significance of surgical team dynamics and composition, including experience, familiarity, social behavior, and gender diversity. By applying ML techniques to a comprehensive dataset of over 77,000 surgeries, we achieved a 24% improvement in the mean absolute error (MAE) over a model that mimics the current approach of the decision maker. Our results also underscore the critical role of surgeon experience and team composition dynamics in enhancing prediction accuracy. These advancements can lead to more efficient operational planning and resource allocation in hospitals, potentially reducing downtime in operating rooms and improving healthcare delivery.

Keywords: Machine learning; Surgery duration; Feature importance; Team dynamics; Team composition (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10729-024-09681-8

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