 On-line strategy selection for reducing overcrowding in an Emergency DepartmentCristiano Fabbri, Michele Lombardi, Enrico Malaguti and Michele Monaci Omega, 2024, vol. 127, issue C Abstract: Overcrowding is a well-known major issue affecting the behavior of an Emergency Department (ED), as it is responsible for patients’ dissatisfaction and has a negative impact on the quality of workers’ performance. Dealing with overcrowding in an ED is complicated by lack of its precise definition and by exogenous and stochastic nature of requests to be served. In this paper, we present a Decision Support System (DSS) based on the integration of a Deep Neural Network for dealing with the sources of uncertainty and a simulation tool to evaluate how specific management policies affect the ED behavior. The DSS is designed to be run on-line, dynamically suggesting the most suitable policy to be implemented in the ED. We evaluate the performance of the DSS on a specific major ED located in northern Italy. Numerical results show that overcrowding can be considerably reduced by allowing a dynamic selection among a limited set of simple policies for queue management. Keywords: Decision Support System; Deep Neural Network; Natural Language Processing; Simulation; Emergency Department (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:127:y:2024:i:c:s0305048324000641 Ordering information: This journal article can be ordered from DOI: 10.1016/j.omega.2024.103098 Access Statistics for this article Omega is currently edited by B. Lev More articles in Omega from Elsevier |
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