• Clare Allison Parker, Nan Liu, Stella Xinzi Wu, Yuzeng Shen, Lam Sean Shao Wei SSW Duke-NUS Medical School, National University of Singapore, Singapore; Health Services Research Centre, Singapore Health Services, Singapore. Electro, and Ong Marcus Eng Hock MEH Duke-NUS Medical School, National University of Singapore, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore. Ele.
• Duke University School of Medicine, Durham, NC, United States of America. Electronic address: clare.parker@duke.edu.
• Am J Emerg Med. 2019 Aug 1; 37 (8): 1498-1504.

BackgroundEmergency 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.MethodsRetrospective 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.ResultsA 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.ConclusionsWe 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.Copyright © 2018 Elsevier Inc. All rights reserved.

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