Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel.
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
/
Aglomeração
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Redes Neurais de Computação
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Serviço Hospitalar de Emergência
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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