Predicting Patient Admission From the Emergency Department Using Administrative and Diagnostic Data

Predicting Patient Admission From the Emergency Department Using Administrative and Diagnostic Data

David W. Savage, Douglas G. Woolford, Mackenzie Simpson, David Wood, Robert Ohle
Copyright: © 2020 |Pages: 11
DOI: 10.4018/IJEACH.2020070101
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Abstract

Emergency department (ED) overcrowding is a growing problem in Canada. Many interventions have been proposed to increase patient flow. The objective of this study was to predict patient admission early in the visit with the goal of reducing waiting time in ED for admitted patients. ED data for a one-year period from Thunder Bay, Canada was obtained. Initial logistic regression models were developed using age, sex, mode of arrival, and patient acuity as explanatory variables and admission yes or no as the outcome. A second stage prediction was made using the diagnostic tests ordered to further refine the predictive models. Predictive accuracy of the logistic regression model was adequate. The AUC was approximately 81%. By summing the probabilities of patients in the ED, the hourly prediction improved. This study has shown that the number of hospital beds required on an hourly basis can be predicted using triage administrative data.
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Methods

Study Setting

The Thunder Bay Regional Health Sciences Centre (TBRHSC) is a regional referral centre in Northwestern Ontario, Canada for both pediatric and adult patients. The ED experiences annual patient volumes of approximately 108,000 visits per year. Arriving patients are placed in either an acute care or fast-track queue depending on acuity. Higher acuity patients classified under the Canadian Triage and Acuity Scale (CTAS) as either a level I, II, of III are treated in the acute care area while CTAS IV and V are typically but not always treated in the fast-track area.

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