{"title":"On-line strategy selection for reducing overcrowding in an Emergency Department","authors":"Cristiano Fabbri , Michele Lombardi , Enrico Malaguti , Michele Monaci","doi":"10.1016/j.omega.2024.103098","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0305048324000641/pdfft?md5=73a244287ddb46f885f084872bfe9105&pid=1-s2.0-S0305048324000641-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Omega-international Journal of Management Science","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305048324000641","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
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.
众所周知,过度拥挤是影响急诊室(ED)行为的一个主要问题,因为它会导致病人不满,并对工作人员的工作质量产生负面影响。由于急诊室过度拥挤缺乏准确的定义,而且服务请求具有外生性和随机性,因此处理急诊室过度拥挤问题非常复杂。在本文中,我们介绍了一种决策支持系统(DSS),该系统基于深度神经网络的集成,用于处理不确定性来源和模拟工具,以评估特定管理政策对急诊室行为的影响。该决策支持系统可在线运行,动态建议最适合在 ED 中实施的政策。我们在位于意大利北部的一个特定大型急诊室对 DSS 的性能进行了评估。数值结果表明,通过在有限的简单队列管理策略中进行动态选择,可以大大缓解拥挤状况。
期刊介绍:
Omega reports on developments in management, including the latest research results and applications. Original contributions and review articles describe the state of the art in specific fields or functions of management, while there are shorter critical assessments of particular management techniques. Other features of the journal are the "Memoranda" section for short communications and "Feedback", a correspondence column. Omega is both stimulating reading and an important source for practising managers, specialists in management services, operational research workers and management scientists, management consultants, academics, students and research personnel throughout the world. The material published is of high quality and relevance, written in a manner which makes it accessible to all of this wide-ranging readership. Preference will be given to papers with implications to the practice of management. Submissions of purely theoretical papers are discouraged. The review of material for publication in the journal reflects this aim.