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Modeling and prediction of surgical procedure times

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  • Pieter S. Stepaniak
  • Christiaan Heij
  • Guus De Vries

Abstract

Accurate prediction of medical operation times is of crucial importance for cost‐efficient operation room planning in hospitals. This paper investigates the possible dependence of procedure times on surgeon factors like age, experience, gender and team composition. The effect of these factors is estimated for over 30 different types of medical operations in two hospitals, by means of ANOVA models for logarithmic case durations. The estimation data set contains about 30,000 observations from 2005 to 2008. The relevance of surgeon factors depends on the type of operation. The factors found most often to be significant are team composition, experience and time of the day. Contrary to widespread opinions among surgeons, gender has nearly never a significant effect. By incorporating surgeon factors, the accuracy of out‐of‐sample prediction of case durations of about 1250 surgical operations in 2009 is improved by up to more than 15% compared with current planning procedures.

Suggested Citation

  • Pieter S. Stepaniak & Christiaan Heij & Guus De Vries, 2010. "Modeling and prediction of surgical procedure times," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(1), pages 1-18, February.
    • Handle: RePEc:bla:stanee:v:64:y:2010:i:1:p:1-18
    DOI: 10.1111/j.1467-9574.2009.00440.x
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    Cited by:

    1. Oleg V. Shylo & Oleg A. Prokopyev & Andrew J. Schaefer, 2013. "Stochastic Operating Room Scheduling for High-Volume Specialties Under Block Booking," INFORMS Journal on Computing, INFORMS, vol. 25(4), pages 682-692, November.
    2. Javiera Barrera & Rodrigo A. Carrasco & Susana Mondschein & Gianpiero Canessa & David Rojas-Zalazar, 2020. "Operating room scheduling under waiting time constraints: the Chilean GES plan," Annals of Operations Research, Springer, vol. 286(1), pages 501-527, March.
    3. Vazquez, Alexei & Marasinou, Chrysostomos & Kalogridis, Georgios & Ellinas, Christos, 2024. "Activity delay patterns in project networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    4. Vandenberghe, Mathieu & De Vuyst, Stijn & Aghezzaf, El-Houssaine & Bruneel, Herwig, 2019. "Surgery sequencing to minimize the expected maximum waiting time of emergent patients," European Journal of Operational Research, Elsevier, vol. 275(3), pages 971-982.
    5. Pluyter, J.R., 2012. "Designing immersive surgical training against information technology-related overload in the operating room," Other publications TiSEM d48c5727-92fd-41b1-be5d-5, Tilburg University, School of Economics and Management.
    6. Sagnol, Guillaume & Barner, Christoph & Borndörfer, Ralf & Grima, Mickaël & Seeling, Matthes & Spies, Claudia & Wernecke, Klaus, 2018. "Robust allocation of operating rooms: A cutting plane approach to handle lognormal case durations," European Journal of Operational Research, Elsevier, vol. 271(2), pages 420-435.
    7. Duma, Davide & Aringhieri, Roberto, 2019. "The management of non-elective patients: shared vs. dedicated policies," Omega, Elsevier, vol. 83(C), pages 199-212.
    8. 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.
    9. Paul Joustra & Reinier Meester & Hans Ophem, 2013. "Can statisticians beat surgeons at the planning of operations?," Empirical Economics, Springer, vol. 44(3), pages 1697-1718, June.
    10. Azar, Macarena & Carrasco, Rodrigo A. & Mondschein, Susana, 2022. "Dealing with uncertain surgery times in operating room scheduling," European Journal of Operational Research, Elsevier, vol. 299(1), pages 377-394.
    11. Francesca Guerriero & Rosita Guido, 2011. "Operational research in the management of the operating theatre: a survey," Health Care Management Science, Springer, vol. 14(1), pages 89-114, March.
    12. Arlen Dean & Amirhossein Meisami & Henry Lam & Mark P. Van Oyen & Christopher Stromblad & Nick Kastango, 2022. "Quantile regression forests for individualized surgery scheduling," Health Care Management Science, Springer, vol. 25(4), pages 682-709, December.

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