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


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.

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The authors thank a superb team of anonymous reviewers and editors for helpful suggestions that improved the content and structure of the paper. This research was funded in part by the Hewlett-Packard Company.

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Correspondence to Enis Kayış.

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Kayış, E., Khaniyev, T.T., Suermondt, J. et al. A robust estimation model for surgery durations with temporal, operational, and surgery team effects. Health Care Manag Sci 18, 222–233 (2015).

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  • Surgery duration estimation
  • Operating room planning
  • EHR data
  • Health care analytics
  • Surgical team composition and experience