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Estimating the waiting time of multi-priority emergency patients with downstream blocking

To characterize the coupling effect between patient flow to access the emergency department (ED) and that to access the inpatient unit (IU), we develop a model with two connected queues: one upstream queue for the patient flow to access the ED and one downstream queue for the patient flow to access...

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Detalles Bibliográficos
Autores principales: Lin, Di, Patrick, Jonathan, Labeau, Fabrice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3950617/
https://www.ncbi.nlm.nih.gov/pubmed/23690253
http://dx.doi.org/10.1007/s10729-013-9241-3
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author Lin, Di
Patrick, Jonathan
Labeau, Fabrice
author_facet Lin, Di
Patrick, Jonathan
Labeau, Fabrice
author_sort Lin, Di
collection PubMed
description To characterize the coupling effect between patient flow to access the emergency department (ED) and that to access the inpatient unit (IU), we develop a model with two connected queues: one upstream queue for the patient flow to access the ED and one downstream queue for the patient flow to access the IU. Building on this patient flow model, we employ queueing theory to estimate the average waiting time across patients. Using priority specific wait time targets, we further estimate the necessary number of ED and IU resources. Finally, we investigate how an alternative way of accessing ED (Fast Track) impacts the average waiting time of patients as well as the necessary number of ED/IU resources. This model as well as the analysis on patient flow can help the designer or manager of a hospital make decisions on the allocation of ED/IU resources in a hospital.
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spelling pubmed-39506172014-03-20 Estimating the waiting time of multi-priority emergency patients with downstream blocking Lin, Di Patrick, Jonathan Labeau, Fabrice Health Care Manag Sci Article To characterize the coupling effect between patient flow to access the emergency department (ED) and that to access the inpatient unit (IU), we develop a model with two connected queues: one upstream queue for the patient flow to access the ED and one downstream queue for the patient flow to access the IU. Building on this patient flow model, we employ queueing theory to estimate the average waiting time across patients. Using priority specific wait time targets, we further estimate the necessary number of ED and IU resources. Finally, we investigate how an alternative way of accessing ED (Fast Track) impacts the average waiting time of patients as well as the necessary number of ED/IU resources. This model as well as the analysis on patient flow can help the designer or manager of a hospital make decisions on the allocation of ED/IU resources in a hospital. Springer US 2013-05-21 2014 /pmc/articles/PMC3950617/ /pubmed/23690253 http://dx.doi.org/10.1007/s10729-013-9241-3 Text en © The Author(s) 2013 https://creativecommons.org/licenses/by/2.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
spellingShingle Article
Lin, Di
Patrick, Jonathan
Labeau, Fabrice
Estimating the waiting time of multi-priority emergency patients with downstream blocking
title Estimating the waiting time of multi-priority emergency patients with downstream blocking
title_full Estimating the waiting time of multi-priority emergency patients with downstream blocking
title_fullStr Estimating the waiting time of multi-priority emergency patients with downstream blocking
title_full_unstemmed Estimating the waiting time of multi-priority emergency patients with downstream blocking
title_short Estimating the waiting time of multi-priority emergency patients with downstream blocking
title_sort estimating the waiting time of multi-priority emergency patients with downstream blocking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3950617/
https://www.ncbi.nlm.nih.gov/pubmed/23690253
http://dx.doi.org/10.1007/s10729-013-9241-3
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