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A robust estimation model for surgery durations with temporal, operational, and surgery team effects

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  • Enis Kayış
  • Taghi Khaniyev
  • Jaap Suermondt
  • Karl Sylvester

Abstract

For effective operating room (OR) planning, surgery duration estimation is critical. Overestimation leads to underutilization of expensive hospital resources (e.g., OR time) whereas underestimation leads to overtime and high waiting times for the patients. In this paper, we consider a particular estimation method currently in use and using additional temporal, operational, and staff-related factors provide a statistical model to adjust these estimates for higher accuracy. The results show that our method increases the accuracy of the estimates, in particular by reducing large errors. For the 8093 cases we have in our data, our model decreases the mean absolute deviation of the currently used scheduled duration (42.65 ± 0.59 minutes) by 1.98 ± 0.28 minutes. For the cases with large negative errors, however, the decrease in the mean absolute deviation is 20.35 ± 0.74 minutes (with a respective increase of 0.89 ± 0.66 minutes in large positive errors). We find that not only operational and temporal factors, but also medical staff and team experience related factors (such as number of nurses and the frequency of the medical team working together) could be used to improve the currently used estimates. Finally, we conclude that one could further improve these predictions by combining our model with other good prediction models proposed in the literature. Specifically, one could decrease the mean absolute deviation of 39.98 ± 0.58 minutes obtained via the method of Dexter et al (Anesth Analg 117(1):204–209, 2013 ) by 1.02 ± 0.21 minutes by combining our method with theirs. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • 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.
    • Handle: RePEc:kap:hcarem:v:18:y:2015:i:3:p:222-233
    DOI: 10.1007/s10729-014-9309-8
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Gary P. Pisano & Richard M.J. Bohmer & Amy C. Edmondson, 2001. "Organizational Differences in Rates of Learning: Evidence from the Adoption of Minimally Invasive Cardiac Surgery," Management Science, INFORMS, vol. 47(6), pages 752-768, June.
    4. 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.
    5. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    6. Ying Li & Saijuan Zhang & Reginald Baugh & Jianhua Huang, 2010. "Predicting surgical case durations using ill-conditioned CPT code matrix," IISE Transactions, Taylor & Francis Journals, vol. 42(2), pages 121-135.
    7. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    Cited by:

    1. Khaniyev, Taghi & Kayış, Enis & Güllü, Refik, 2020. "Next-day operating room scheduling with uncertain surgery durations: Exact analysis and heuristics," European Journal of Operational Research, Elsevier, vol. 286(1), pages 49-62.
    2. 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.

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