Predicting patient disposition in a paediatric emergency department

Bradman, Kate, Borland, Meredith and Pascoe, Elaine (2012) Predicting patient disposition in a paediatric emergency department. Journal of Paediatrics and Child Health, 50 10: E39-E44. doi:10.1111/jpc.12011

Author Bradman, Kate
Borland, Meredith
Pascoe, Elaine
Title Predicting patient disposition in a paediatric emergency department
Journal name Journal of Paediatrics and Child Health   Check publisher's open access policy
ISSN 1034-4810
Publication date 2012-12-02
Year available 2012
Sub-type Article (original research)
DOI 10.1111/jpc.12011
Open Access Status Not yet assessed
Volume 50
Issue 10
Start page E39
End page E44
Total pages 6
Place of publication Chichester, West Sussex, United Kingdom
Publisher Wiley-Blackwell Publishing
Language eng
Formatted 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.

What is already known on this topic?
1. There have been many studies looking into predicting admission of patients from the emergency department using a variety of different tools with varying success.
2. Studies in the adult population appear to show that the best predictor of disposition from the emergency department is the triage nurse.
3. Introduction of the four hour rule has prioritised the importance of patient flow through the emergency department.

What this paper adds?
1. This is the first study to use all the different published prediction tools, within the same population, to predict patient disposition and therefore directly compare them.
2. It confirms that the trained triage nurse is the best predictor of patient disposition.
3. Early identification of patients requiring admission could improve flow through the emergency department.
Keyword Emergency department management
Prediction tool
Triage nurse
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Article first published online: 2 DEC 2012

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Medicine Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 10 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Mon, 09 Jun 2014, 21:38:04 EST by Elaine Pascoe on behalf of Medicine - Princess Alexandra Hospital