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A generic method for evaluating crowding in the emergency department

Overview of attention for article published in BMC Emergency Medicine, June 2016
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Title
A generic method for evaluating crowding in the emergency department
Published in
BMC Emergency Medicine, June 2016
DOI 10.1186/s12873-016-0083-4
Pubmed ID
Authors

Andreas Halgreen Eiset, Mogens Erlandsen, Anders Brøns Møllekær, Julie Mackenhauer, Hans Kirkegaard

Abstract

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. 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. 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. 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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Mendeley readers

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The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 16%
Student > Master 5 13%
Researcher 4 11%
Student > Doctoral Student 4 11%
Librarian 3 8%
Other 7 18%
Unknown 9 24%
Readers by discipline Count As %
Medicine and Dentistry 10 26%
Nursing and Health Professions 6 16%
Engineering 3 8%
Psychology 2 5%
Mathematics 1 3%
Other 1 3%
Unknown 15 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 December 2017.
All research outputs
#19,292,491
of 23,881,329 outputs
Outputs from BMC Emergency Medicine
#611
of 781 outputs
Outputs of similar age
#273,054
of 356,634 outputs
Outputs of similar age from BMC Emergency Medicine
#10
of 11 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
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