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A novel approach to forecast surgery durations using machine learning techniques

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  • Marco Caserta

    (IE University)

  • Antonio García Romero

    (IE University)

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.

Suggested Citation

  • Marco Caserta & Antonio García Romero, 2024. "A novel approach to forecast surgery durations using machine learning techniques," Health Care Management Science, Springer, vol. 27(3), pages 313-327, September.
    • Handle: RePEc:kap:hcarem:v:27:y:2024:i:3:d:10.1007_s10729-024-09681-8
    DOI: 10.1007/s10729-024-09681-8
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    References listed on IDEAS

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    1. Robert S. Huckman & Bradley R. Staats, 2011. "Fluid Tasks and Fluid Teams: The Impact of Diversity in Experience and Team Familiarity on Team Performance," Manufacturing & Service Operations Management, INFORMS, vol. 13(3), pages 310-328, July.
    2. Bovim, Thomas Reiten & Christiansen, Marielle & Gullhav, Anders N. & Range, Troels Martin & Hellemo, Lars, 2020. "Stochastic master surgery scheduling," European Journal of Operational Research, Elsevier, vol. 285(2), pages 695-711.
    3. Enis Kayış & Taghi Khaniyev & Jaap Suermondt & Karl Sylvester, 2015. "A robust estimation model for surgery durations with temporal, operational, and surgery team effects," Health Care Management Science, Springer, vol. 18(3), pages 222-233, September.
    4. Shuwan Zhu & Wenjuan Fan & Shanlin Yang & Jun Pei & Panos M. Pardalos, 2019. "Operating room planning and surgical case scheduling: a review of literature," Journal of Combinatorial Optimization, Springer, vol. 37(3), pages 757-805, April.
    5. Robert S. Huckman & Bradley R. Staats & David M. Upton, 2009. "Team Familiarity, Role Experience, and Performance: Evidence from Indian Software Services," Management Science, INFORMS, vol. 55(1), pages 85-100, January.
    6. Zeynep Akşin & Sarang Deo & Jónas Oddur Jónasson & Kamalini Ramdas, 2021. "Learning from Many: Partner Exposure and Team Familiarity in Fluid Teams," Management Science, INFORMS, vol. 67(2), pages 854-874, February.
    7. Cardoen, Brecht & Demeulemeester, Erik & Beliën, Jeroen, 2010. "Operating room planning and scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 201(3), pages 921-932, March.
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