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A generic method for evaluating crowding in the emergency department

BACKGROUND: Crowding in the emergency department (ED) has been studied intensively using complicated non-generic methods that may prove difficult to implement in a clinical setting. This study sought to develop a generic method to describe and analyse crowding from measurements readily available in...

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Autores principales: Eiset, Andreas Halgreen, Erlandsen, Mogens, Møllekær, Anders Brøns, Mackenhauer, Julie, Kirkegaard, Hans
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4907010/
https://www.ncbi.nlm.nih.gov/pubmed/27301490
http://dx.doi.org/10.1186/s12873-016-0083-4
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author Eiset, Andreas Halgreen
Erlandsen, Mogens
Møllekær, Anders Brøns
Mackenhauer, Julie
Kirkegaard, Hans
author_facet Eiset, Andreas Halgreen
Erlandsen, Mogens
Møllekær, Anders Brøns
Mackenhauer, Julie
Kirkegaard, Hans
author_sort Eiset, Andreas Halgreen
collection PubMed
description BACKGROUND: Crowding in the emergency department (ED) has been studied intensively using complicated non-generic methods that may prove difficult to implement in a clinical setting. This study sought to develop a generic method to describe and analyse crowding from measurements readily available in the ED and to test the developed method empirically in a clinical setting. METHODS: We conceptualised a model with ED patient flow divided into separate queues identified by timestamps for predetermined events. With temporal resolution of 30 min, queue lengths were computed as Q(t + 1) = Q(t) + A(t) – D(t), with A(t) = number of arrivals, D(t) = number of departures and t = time interval. Maximum queue lengths for each shift of each day were found and risks of crowding computed. All tests were performed using non-parametric methods. The method was applied in the ED of Aarhus University Hospital, Denmark utilising an open cohort design with prospectively collected data from a one-year observation period. RESULTS: By employing the timestamps already assigned to the patients while in the ED, a generic queuing model can be computed from which crowding can be described and analysed in detail. Depending on availability of data, the model can be extended to include several queues increasing the level of information. When applying the method empirically, 41,693 patients were included. The studied ED had a high risk of bed occupancy rising above 100 % during day and evening shift, especially on weekdays. Further, a ‘carry over’ effect was shown between shifts and days. CONCLUSIONS: The presented method offers an easy and generic way to get detailed insight into the dynamics of crowding in an ED.
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spelling pubmed-49070102016-06-15 A generic method for evaluating crowding in the emergency department Eiset, Andreas Halgreen Erlandsen, Mogens Møllekær, Anders Brøns Mackenhauer, Julie Kirkegaard, Hans BMC Emerg Med Research Article BACKGROUND: Crowding in the emergency department (ED) has been studied intensively using complicated non-generic methods that may prove difficult to implement in a clinical setting. This study sought to develop a generic method to describe and analyse crowding from measurements readily available in the ED and to test the developed method empirically in a clinical setting. METHODS: We conceptualised a model with ED patient flow divided into separate queues identified by timestamps for predetermined events. With temporal resolution of 30 min, queue lengths were computed as Q(t + 1) = Q(t) + A(t) – D(t), with A(t) = number of arrivals, D(t) = number of departures and t = time interval. Maximum queue lengths for each shift of each day were found and risks of crowding computed. All tests were performed using non-parametric methods. The method was applied in the ED of Aarhus University Hospital, Denmark utilising an open cohort design with prospectively collected data from a one-year observation period. RESULTS: By employing the timestamps already assigned to the patients while in the ED, a generic queuing model can be computed from which crowding can be described and analysed in detail. Depending on availability of data, the model can be extended to include several queues increasing the level of information. When applying the method empirically, 41,693 patients were included. The studied ED had a high risk of bed occupancy rising above 100 % during day and evening shift, especially on weekdays. Further, a ‘carry over’ effect was shown between shifts and days. CONCLUSIONS: The presented method offers an easy and generic way to get detailed insight into the dynamics of crowding in an ED. BioMed Central 2016-06-14 /pmc/articles/PMC4907010/ /pubmed/27301490 http://dx.doi.org/10.1186/s12873-016-0083-4 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Eiset, Andreas Halgreen
Erlandsen, Mogens
Møllekær, Anders Brøns
Mackenhauer, Julie
Kirkegaard, Hans
A generic method for evaluating crowding in the emergency department
title A generic method for evaluating crowding in the emergency department
title_full A generic method for evaluating crowding in the emergency department
title_fullStr A generic method for evaluating crowding in the emergency department
title_full_unstemmed A generic method for evaluating crowding in the emergency department
title_short A generic method for evaluating crowding in the emergency department
title_sort generic method for evaluating crowding in the emergency department
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4907010/
https://www.ncbi.nlm.nih.gov/pubmed/27301490
http://dx.doi.org/10.1186/s12873-016-0083-4
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