Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

Abstract

Of 137 million annual emergency department (ED) visits in the United States, 30 million visits are made by children.^1-3 With the steady increase in the volume and acuity of patient visits to EDs,⁴ accurate differentiation and prioritization of patients at the ED triage is important. However, current triage systems have suboptimal ability to differentiate critically ill children,^5-7 and the proportion of children seen by a physician within the time recommended by triage has been declining because of pervasive ED crowding.⁸ Therefore, it is essential to optimize triage systems to not only avoid undertriaging critically ill children but also reduce overtriaging in order to provide high-quality and timely care and to achieve efficient resource allocation in the ED.