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How can decision makers be supported in the improvement of an emergency department?: A simulation, optimization and data mining approach
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-4604-6429
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-4180-6003
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-5100-4077
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
2017 (English)In: Operations Research for Health Care, ISSN 2211-6923, E-ISSN 2211-6931, Vol. 15, p. 102-122Article in journal (Refereed) Published
Abstract [en]

The improvement of emergency department processes involves the need to take into considerationmultiple variables and objectives in a highly dynamic and unpredictable environment, which makes thedecision-making task extremely challenging. The use of different methodologies and tools to support thedecision-making process is therefore a key issue. This article presents a novel approach in healthcarein which Discrete Event Simulation, Simulation-Based Multi-Objective Optimization and Data Miningtechniques are used in combination. This methodology has been applied for a system improvementanalysis in a Swedish emergency department. As a result of the project, the decision makers were providedwith a range of nearly optimal solutions and design rules which reduce considerably the length of stayand waiting times for emergency department patients. These solutions include the optimal number ofresources and the required level of improvement in key processes. The article presents and discussesthe benefits achieved by applying this methodology, which has proven to be remarkably valuable fordecision-making support, with regard to complex healthcare system design and improvement.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 15, p. 102-122
Keywords [en]
Discrete Event Simulation, Simulation-Based Multi-Objective Optimization, Data mining, Decision support, Decision-making, Operational research in health care
National Category
Health Care Service and Management, Health Policy and Services and Health Economy Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-14404DOI: 10.1016/j.orhc.2017.10.003ISI: 000415311000010Scopus ID: 2-s2.0-85032745554OAI: oai:DiVA.org:his-14404DiVA, id: diva2:1157057
Available from: 2017-11-15 Created: 2017-11-15 Last updated: 2018-05-07Bibliographically approved

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Goienetxea et al. 2017(4495 kB)72 downloads
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Goienetxea Uriarte, AinhoaRuiz Zúñiga, EnriqueUrenda Moris, MatíasNg, Amos H. C.

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Goienetxea Uriarte, AinhoaRuiz Zúñiga, EnriqueUrenda Moris, MatíasNg, Amos H. C.
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Operations Research for Health Care
Health Care Service and Management, Health Policy and Services and Health EconomyProduction Engineering, Human Work Science and Ergonomics

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