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dc.contributor.authorKayış, Enis
dc.contributor.authorKhaniyev, T. T.
dc.contributor.authorSuermondt, J.
dc.contributor.authorSylvester, K.
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.en_US
dc.description.abstractFor 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.en_US
dc.description.sponsorshipHewlett-Packard Company
dc.publisherSpringer Science+Business Mediaen_US
dc.titleA robust estimation model for surgery durations with temporal, operational, and surgery team effectsen_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0001-8282-5572 & YÖK ID 29747) Kayış, Enis
dc.contributor.ozuauthorKayış, Enis
dc.subject.keywordsSurgery duration estimationen_US
dc.subject.keywordsOperating room planningen_US
dc.subject.keywordsEHR dataen_US
dc.subject.keywordsHealth care analyticsen_US
dc.subject.keywordsSurgical team composition and experienceen_US
dc.relation.publicationcategoryArticle - International Refereed Journal - Institutional Academic Staff

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