IE Business School, IE University, Paseo de la Castellana 259E, Madrid, 28046, Madrid, Spain.
Health Care Manag Sci. 2024 Sep;27(3):313-327. doi: 10.1007/s10729-024-09681-8. Epub 2024 Jul 10.
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.
本研究提出了一种使用机器学习（ML）预测手术时间的方法。该方法包含了一组新的预测因子，强调了手术团队动态和组成的重要性，包括经验、熟悉度、社会行为和性别多样性。通过将 ML 技术应用于超过 77000 例手术的综合数据集，我们在平均绝对误差（MAE）方面取得了 24%的改进，优于模仿决策者当前方法的模型。我们的结果还强调了外科医生经验和团队组成动态在提高预测准确性方面的关键作用。这些进展可以导致医院更有效的运营规划和资源分配，有可能减少手术室的停机时间并改善医疗保健服务的提供。
