Due time driven surgery scheduling

Due time driven surgery scheduling In many hospitals there are patients who receive surgery later than what is medically indicated. In one of Europe’s largest hospitals, the University Hospital Leuven, this is the case for approximately every third patient. Serving patients late cannot always be avoided as a highly utilized OR department will sometimes suffer capacity shortage, occasionally leading to unavoidable delays in patient care. Nevertheless, serving patients late is a problem as it exposes them to an increased health risk and hence should be avoided whenever possible. In order to improve the current situation, the delay in patient scheduling had to be quantified and the responsible mechanism, the scheduling process, had to be better understood. Drawing from this understanding, we implemented and tested realistic patient scheduling methods in a discrete event simulation model. We found that it is important to model non-elective arrivals and to include elective rescheduling decisions made on surgery day itself. Rescheduling ensures that OR related performance measures, such as overtime, will only loosely depend on the chosen patient scheduling method. We also found that capacity considerations should guide actions performed before the surgery day such as patient scheduling and patient replanning. This is the case as those scheduling strategies that ensure that OR capacity is efficiently used will also result in a high number of patients served within their medically indicated time limit. An efficient use of OR capacity can be achieved, for instance, by serving patients first come, first served. As applying first come, first served might not always be possible in a real setting, we found it is important to allow for patient replanning. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Health Care Management Science Springer Journals

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Publisher
Springer US
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Business and Management; Operation Research/Decision Theory; Health Administration; Health Informatics; Management; Econometrics; Business and Management, general
ISSN
1386-9620
eISSN
1572-9389
D.O.I.
10.1007/s10729-016-9356-4
Publisher site
See Article on Publisher Site

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