Real-time estimation of surgical procedure duration

Conference paper (2015)

Authors

A.C.P. Geudon Medical Instruments & Bio-Inspired Technology - Mechanical, Maritime and Materials Engineering
F.C. Meeuwsen Medical Instruments & Bio-Inspired Technology - Mechanical, Maritime and Materials Engineering
D.M.J. Tax Pattern Recognition and Bioinformatics - EEMCS
A.P. van Dijke Medical Instruments & Bio-Inspired Technology - Mechanical, Maritime and Materials Engineering
L.S.G.L. Wauben Medical Instruments & Bio-Inspired Technology - Mechanical, Maritime and Materials Engineering
M. Van Der Elst Reinier de Graaf Gasthuis
J. Dankelman Medical Instruments & Bio-Inspired Technology - Mechanical, Maritime and Materials Engineering
J.J. van den Dobbelsteen Medical Instruments & Bio-Inspired Technology - Mechanical, Maritime and Materials Engineering
Research Group
Medical Instruments & Bio-Inspired Technology (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2015 A.C.P. Guédon, M. Paalvast, F.C. Meeuwsen, D.M.J. Tax, A.P. van Dijke, L.S.G.L. Wauben, M. Van Der Elst, J. Dankelman, J.J. van den Dobbelsteen
DOI: https://doi.org/10.1109/HealthCom.2015.7454464
Pattern recognition Planning Workflow Support vector machines Operating room Procedure duration
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Published Date

2015

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Biomechanical Engineering

Research Group

Medical Instruments & Bio-Inspired Technology

Abstract

Efficiency in the Operating Room (OR) is a topic of growing interest. Planning of care is a crucial element to ensure optimal use of the ORs. Currently, OR scheduling is considered as a complex task based on predictions of surgery duration. The latter are often based on average times, but turn out to be inaccurate in practice because of various factors (such as complexity, patient's characteristics, unexpected events, etc). The aim of this study is to develop a prediction system that estimates in real-time the remaining duration of a surgical procedure. The prediction system was based on monitoring the progress of a procedure by recording the activation of a single piece of equipment in the OR, the electrosurgical device. Support Vector Machines was then used as a classifier to predict the remaining surgical procedure duration and thereby the optimal timing to start preparing the next patient for surgery. The classifier was trained with data on the activation of the electrosurgical device during 55 laparoscopic cholecystectomies. The performance tests showed a mean error rate about 0.2, which means that about 80% of the procedures were classified correctly. The real-time prediction system is a promising tool to improve OR planning and decrease unnecessary patients' waiting times.

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