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An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department

This study presents an agent-based simulation modeling in an emergency department. In a traditional approach, a supervisor (or a manager) allocates the resources (receptionist, nurses, doctors, etc.) to different sections based on personal experience or by using decision-support tools. In this study...

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Detalles Bibliográficos
Autores principales: Yousefi, M., Ferreira, R.P.M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Associação Brasileira de Divulgação Científica 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423739/
https://www.ncbi.nlm.nih.gov/pubmed/28380196
http://dx.doi.org/10.1590/1414-431X20175955
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author Yousefi, M.
Ferreira, R.P.M.
author_facet Yousefi, M.
Ferreira, R.P.M.
author_sort Yousefi, M.
collection PubMed
description This study presents an agent-based simulation modeling in an emergency department. In a traditional approach, a supervisor (or a manager) allocates the resources (receptionist, nurses, doctors, etc.) to different sections based on personal experience or by using decision-support tools. In this study, each staff agent took part in the process of allocating resources based on their observation in their respective sections, which gave the system the advantage of utilizing all the available human resources during the workday by being allocated to a different section. In this simulation, unlike previous studies, all staff agents took part in the decision-making process to re-allocate the resources in the emergency department. The simulation modeled the behavior of patients, receptionists, triage nurses, emergency room nurses and doctors. Patients were able to decide whether to stay in the system or leave the department at any stage of treatment. In order to evaluate the performance of this approach, 6 different scenarios were introduced. In each scenario, various key performance indicators were investigated before and after applying the group decision-making. The outputs of each simulation were number of deaths, number of patients who leave the emergency department without being attended, length of stay, waiting time and total number of discharged patients from the emergency department. Applying the self-organizing approach in the simulation showed an average of 12.7 and 14.4% decrease in total waiting time and number of patients who left without being seen, respectively. The results showed an average increase of 11.5% in total number of discharged patients from emergency department.
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spelling pubmed-54237392017-05-24 An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department Yousefi, M. Ferreira, R.P.M. Braz J Med Biol Res Clinical Investigation This study presents an agent-based simulation modeling in an emergency department. In a traditional approach, a supervisor (or a manager) allocates the resources (receptionist, nurses, doctors, etc.) to different sections based on personal experience or by using decision-support tools. In this study, each staff agent took part in the process of allocating resources based on their observation in their respective sections, which gave the system the advantage of utilizing all the available human resources during the workday by being allocated to a different section. In this simulation, unlike previous studies, all staff agents took part in the decision-making process to re-allocate the resources in the emergency department. The simulation modeled the behavior of patients, receptionists, triage nurses, emergency room nurses and doctors. Patients were able to decide whether to stay in the system or leave the department at any stage of treatment. In order to evaluate the performance of this approach, 6 different scenarios were introduced. In each scenario, various key performance indicators were investigated before and after applying the group decision-making. The outputs of each simulation were number of deaths, number of patients who leave the emergency department without being attended, length of stay, waiting time and total number of discharged patients from the emergency department. Applying the self-organizing approach in the simulation showed an average of 12.7 and 14.4% decrease in total waiting time and number of patients who left without being seen, respectively. The results showed an average increase of 11.5% in total number of discharged patients from emergency department. Associação Brasileira de Divulgação Científica 2017-03-30 /pmc/articles/PMC5423739/ /pubmed/28380196 http://dx.doi.org/10.1590/1414-431X20175955 Text en http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Investigation
Yousefi, M.
Ferreira, R.P.M.
An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department
title An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department
title_full An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department
title_fullStr An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department
title_full_unstemmed An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department
title_short An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department
title_sort agent-based simulation combined with group decision-making technique for improving the performance of an emergency department
topic Clinical Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423739/
https://www.ncbi.nlm.nih.gov/pubmed/28380196
http://dx.doi.org/10.1590/1414-431X20175955
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