The Emergency Severity Index (ESI) is the most commonly used system in over 70% of all U.S. emergency departments (ED) that uses predicted resource utilization as a means to triage [1Mccugh M., Tanabe P., Mcclelland M., Khare R.K. More patients are triaged using the emergency severity index than any other triage acuity system in the United States Acad. Emerg. Med. 2012 ;  19 (1) : 106-10910.1111/j.1553-2712.2011.01240.x

Cliquez ici pour aller à la section Références], Mistriage, which includes both undertriage and overtriage has been a persistent issue, affecting 32.2% of total ED visits [2Eitel D.R., Travers D.A., Rosenau A.M., Gilboy N., Wuerz R.C. The emergency severity index triage algorithm version 2 is reliable and valid Acad. Emerg. Med. 2003 ;  10 : 1070-1080 [cross-ref]

Cliquez ici pour aller à la section Références]. Our goal is to develop a machine learning framework that predicts patients' resource needs, thereby improving resource allocation during triage.

This retrospective study analyzed ED visits from the Medical Information Mart for Intensive Care IV, dividing the data into training (80%) and testing (20%) cohorts. We utilized data available during triage, including patient vital signs, age, gender, mode of arrival, medication history, and chief complaint. Azure AutoML was used to create different machine learning models trained to predict the 144 target columns including laboratory panels and imaging modalities as well as medications required during patients' ED visits. The 144 models' performance was evaluated using the area under the receiver operating characteristic curve (AUROC), F1 score, accuracy, precision and recall.

A total of 391,472 ED visits were analyzed. 144 Voting ensemble models were created for each target. All frameworks achieved on average an AUC score of 0.82 and accuracy of 0.76. We gathered the feature importance for each target and observed that ‘chief complaint’, among others, had a high aggregate feature importance across different targets.

This study shows the high accuracy in predicting resource needs for patients in the ED using a machine learning model. This can greatly improve patient flow and resource allocation in already resource limited emergency departments.