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

Name: A robust estimation model for surgery durations with temporal, operational, and surgery team effects
Author: Kayış, Enis, Khaniyev, T. T., Suermondt, J., Sylvester, K.

İsim	A robust estimation model for surgery durations with temporal, operational, and surgery team effects
Yazar	Kayış, Enis, Khaniyev, T. T., Suermondt, J., Sylvester, K.
Basım Tarihi:	2015-09
Basım Yeri	- Springer Science+Business Media
Konu	Surgery duration estimation, Operating room planning, EHR data, Health care analytics, Surgical team composition and experience
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1572-9389
Kayıt Numarası	cd2acc48-0bfa-4c18-805c-b755764780ed
Lokasyon	Industrial Engineering
Tarih	2015-09
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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.
DOI	10.1007/s10729-014-9309-8
Cilt	18

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

Yazar Kayış, Enis, Khaniyev, T. T., Suermondt, J., Sylvester, K.

Basım Tarihi 2015-09

Basım Yeri - Springer Science+Business Media

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

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1572-9389

Kayıt Numarası cd2acc48-0bfa-4c18-805c-b755764780ed

Lokasyon Industrial Engineering

Tarih 2015-09

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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.

DOI 10.1007/s10729-014-9309-8

Cilt 18