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Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel.
Boresta, Marco; Giovannelli, Tommaso; Roma, Massimo.
Afiliação
  • Boresta M; Institute for System Analysis and Computer Science "A. Ruberti", National Research Council of Italy, via dei Taurini, 19, Rome, 00185, Italy.
  • Giovannelli T; Department of Industrial and Systems Engineering, Lehigh University, 200 W Packer Ave, Bethlehem, PA, 18015, USA.
  • Roma M; Department of Computer, Control and Management Engineering "A. Ruberti", SAPIENZA - University of Rome, via Ariosto 25, Rome, 00185, Italy. roma@diag.uniroma1.it.
Health Care Manag Sci ; 27(3): 415-435, 2024 Sep.
Article em En | MEDLINE | ID: mdl-38856785
ABSTRACT
This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acuity patients, with the aim of minimizing patient waiting times and ED operating costs. We formulate this problem as a general multiobjective simulation-based optimization problem where some of the objectives are expensive black-box functions that can only be evaluated through a time-consuming simulation. To efficiently solve this problem, we propose a metamodeling approach that uses an artificial neural network to replace a black-box objective function with a suitable model. This approach allows us to obtain a set of Pareto optimal points for the multiobjective problem we consider, from which decision-makers can select the most appropriate solutions for different situations. We present the results of computational experiments conducted on a real case study involving the ED of a large hospital in Italy. The results show the reliability and effectiveness of our proposed approach, compared to the standard approach based on derivative-free optimization.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Simulação por Computador / Aglomeração / Redes Neurais de Computação / Serviço Hospitalar de Emergência / Gravidade do Paciente Limite: Humans País/Região como assunto: Europa Idioma: En Revista: Health Care Manag Sci Assunto da revista: SERVICOS DE SAUDE Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Itália

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Simulação por Computador / Aglomeração / Redes Neurais de Computação / Serviço Hospitalar de Emergência / Gravidade do Paciente Limite: Humans País/Região como assunto: Europa Idioma: En Revista: Health Care Manag Sci Assunto da revista: SERVICOS DE SAUDE Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Itália