Abstract
Background
Emergency department (ED) overcrowding is a growing international patient safety issue.
A major contributor to overcrowding is long wait times for inpatient hospital admission.
The objective of this study is to create a model that can predict a patient's need
for hospital admission at the time of triage.
Methods
Retrospective observational study of electronic clinical records of all ED visits
over ten years to a large urban hospital in Singapore. The data was randomly divided
into a derivation set and a validation set. We used the derivation set to develop
a logistic regression model that predicts probability of hospital admission for patients
presenting to the ED. We tested the model on the validation set and evaluated the
performance with receiver operating characteristic (ROC) curve analysis.
Results
A total of 1,232,016 visits were included for final analysis, of which 38.7% were
admitted. Eight variables were included in the final model: age group, race, postal
code, day of week, time of day, triage category, mode of arrival, and fever status.
The model performed well on the validation set with an area under the curve of 0.825
(95% CI 0.824–0.827). Increasing age, increasing triage acuity, and mode of arrival
via private patient transport were most predictive of the need for admission.
Conclusions
We developed a model that accurately predicts admission for patients presenting to
the ED using demographic, administrative, and clinical data routinely collected at
triage. Implementation of the model into the electronic health record could help reduce
the burden of overcrowding.
Keywords
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Article Info
Publication History
Published online: October 29, 2018
Accepted:
October 28,
2018
Received in revised form:
October 27,
2018
Received:
May 6,
2018
Identification
Copyright
© 2018 Elsevier Inc. All rights reserved.
