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Predicting patient disposition in paediatric emergency department

Overview of attention for article published in Journal of Paediatrics & Child Health, December 2012
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Title
Predicting patient disposition in paediatric emergency department
Published in
Journal of Paediatrics & Child Health, December 2012
DOI 10.1111/jpc.12011
Pubmed ID
Authors

Kate Bradman, Meredith Borland, Elaine Pascoe

Abstract

AIM: The aim of this study is to directly compare published prediction tools with triage nurse (TN) predictions within a defined paediatric population. METHOD: A prospective observational study carried out over a week in May 2010 in the Emergency Department (ED) at Princess Margaret Hospital for Children in Perth, Western Australia. TN predicted which patients would be admitted to hospital at the time of ED presentation. Data required for the other prediction tools (paediatric early warning score (PEWS); triage category and the Pediatric Risk of Admission Score (PRISA) and PRISA II were obtained from the notes following the patient's ED attendance. RESULTS: A total of 1223 patients presented during the study week, 91 patients were excluded and a total of 946 patients (83.6%) had TN predictions and were included in the analysis. TN predictions were compared against a PEWS ≥ 4, triage category 1, 2 and 3, PRISA ≥ 9 and PRISA II ≥ 2. TNs had the highest prediction accuracy (87.7%), followed by an elevated PEWS (82.9%), triage category of 1, 2, or 3 (82.9%). The PRISA and PRISA II score had an accuracy of 80.1% and 79.7%, respectively. CONCLUSION: When compared with validated prediction tools, the TN is the most accurate predictor of need to admit. This study provides valuable information in planning efficient flow of patients through the ED.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Canada 1 2%
Unknown 50 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 27%
Student > Postgraduate 6 12%
Student > Bachelor 6 12%
Researcher 4 8%
Student > Doctoral Student 2 4%
Other 8 15%
Unknown 12 23%
Readers by discipline Count As %
Medicine and Dentistry 20 38%
Nursing and Health Professions 9 17%
Social Sciences 2 4%
Computer Science 1 2%
Psychology 1 2%
Other 6 12%
Unknown 13 25%
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 06 December 2012.
All research outputs
#22,830,981
of 25,457,858 outputs
Outputs from Journal of Paediatrics & Child Health
#3,147
of 3,371 outputs
Outputs of similar age
#256,307
of 286,309 outputs
Outputs of similar age from Journal of Paediatrics & Child Health
#23
of 26 outputs
Altmetric has tracked 25,457,858 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,371 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.