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Users’ experiences of an emergency department patient admission predictive tool: A qualitative evaluation

Overview of attention for article published in Health Informatics Journal, July 2016
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Article details
Title
Users’ experiences of an emergency department patient admission predictive tool: A qualitative evaluation
Published in
Health Informatics Journal, July 2016
DOI 10.1177/1460458215577993
Pubmed ID
Authors
Abstract

Emergency department overcrowding is an increasing issue impacting patients, staff and quality of care, resulting in poor patient and system outcomes. In order to facilitate better management of emergency department resources, a patient admission predictive tool was developed and implemented. Evaluation of the tool's accuracy and efficacy was complemented with a qualitative component that explicated the experiences of users and its impact upon their management strategies, and is the focus of this article. Semi-structured interviews were conducted with 15 pertinent users, including bed managers, after-hours managers, specialty department heads, nurse unit managers and hospital executives. Analysis realised dynamics of accuracy, facilitating communication and enabling group decision-making. Users generally welcomed the enhanced potential to predict and plan following the incorporation of the patient admission predictive tool into their daily and weekly decision-making processes. They offered astute feedback with regard to their responses when faced with issues of capacity and communication. Participants reported an growing confidence in making informed decisions in a cultural context that is continually moving from reactive to proactive. This information will inform further patient admission predictive tool development specifically and implementation processes generally.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 72 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 10 14%
Student > Master 9 13%
Student > Bachelor 6 8%
Student > Ph. D. Student 4 6%
Student > Postgraduate 4 6%
Other 10 14%
Unknown 29 40%
Readers by discipline
Readers by discipline Count As %
Nursing and Health Professions 16 22%
Medicine and Dentistry 9 13%
Engineering 6 8%
Agricultural and Biological Sciences 2 3%
Economics, Econometrics and Finance 2 3%
Other 4 6%
Unknown 33 46%
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 07 September 2016.
All research outputs
#18,469,995
of 22,886,568 outputs
Outputs from Health Informatics Journal
#423
of 537 outputs
Outputs of similar age
#281,980
of 365,454 outputs
Outputs of similar age from Health Informatics Journal
#62
of 77 outputs
Altmetric has tracked 22,886,568 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 537 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
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We're also able to compare this research output to 77 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.