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Modeling and prediction of surgical procedure times

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  • Stepaniak, P.S.
  • Heij, C.
  • de Vries, G.

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 till 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 daytime. 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 1,250 surgical operations in 2009 is improved by up to more than 15 percent as compared to current planning procedures.

Suggested Citation

  • Stepaniak, P.S. & Heij, C. & de Vries, G., 2009. "Modeling and prediction of surgical procedure times," Econometric Institute Research Papers EI 2009-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:17017
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    Citations

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    Cited by:

    1. Cappanera, Paola & Visintin, Filippo & Banditori, Carlo, 2014. "Comparing resource balancing criteria in master surgical scheduling: A combined optimisation-simulation approach," International Journal of Production Economics, Elsevier, vol. 158(C), pages 179-196.
    2. Duma, Davide & Aringhieri, Roberto, 2019. "The management of non-elective patients: shared vs. dedicated policies," Omega, Elsevier, vol. 83(C), pages 199-212.
    3. van den Broek d’Obrenan, Anne & Ridder, Ad & Roubos, Dennis & Stougie, Leen, 2020. "Minimizing bed occupancy variance by scheduling patients under uncertainty," European Journal of Operational Research, Elsevier, vol. 286(1), pages 336-349.
    4. 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.
    5. 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.
    6. Gréanne Leeftink & Erwin W. Hans, 2018. "Case mix classification and a benchmark set for surgery scheduling," Journal of Scheduling, Springer, vol. 21(1), pages 17-33, February.
    7. 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.

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