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

Title	A robust estimation model for surgery durations with temporal, operational, and surgery team effects
Author	Kayış, Enis, Khaniyev, T. T., Suermondt, J., Sylvester, K.
Publication Date:	2015-09
Publication Place	- Springer Science+Business Media
Subject	Surgery duration estimation, Operating room planning, EHR data, Health care analytics, Surgical team composition and experience
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1572-9389
Record ID	cd2acc48-0bfa-4c18-805c-b755764780ed
Library Location	Industrial Engineering
Date	2015-09
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	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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Özyeğin University

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

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

Publication Date 2015-09

Publication Place - Springer Science+Business Media

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

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1572-9389

Record ID cd2acc48-0bfa-4c18-805c-b755764780ed

Library Location Industrial Engineering

Date 2015-09

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

Sample Text 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