• Alexander Fenn, Connor Davis, Daniel M Buckland, Neel Kapadia, Marshall Nichols, Michael Gao, William Knechtle, Suresh Balu, Mark Sendak, and B Jason Theiling.
• Duke University School of Medicine, Durham, NC; Duke Institute of Health Innovation, Durham, NC. Electronic address: alexanderfenn@gmail.com.
• Ann Emerg Med. 2021 Aug 1; 78 (2): 290-302.

Study ObjectiveThis study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU at each hour (up to 24 hours) of an emergency department (ED) encounter. The secondary goal was to provide a framework for the operational implementation of these machine learning models.MethodsData were curated from 468,167 adult patient encounters in 3 EDs (1 academic and 2 community-based EDs) of a large academic health system from August 1, 2015, to October 31, 2018. The models were validated using encounter data from January 1, 2019, to December 31, 2019. An operational user dashboard was developed, and the models were run on real-time encounter data.ResultsFor the intermediate admission model, the area under the receiver operating characteristic curve was 0.873 and the area under the precision-recall curve was 0.636. For the ICU admission model, the area under the receiver operating characteristic curve was 0.951 and the area under the precision-recall curve was 0.461. The models had similar performance in both the academic- and community-based settings as well as across the 2019 and real-time encounter data.ConclusionMachine learning models were developed to accurately make predictions regarding the probability of inpatient or ICU admission throughout the entire duration of a patient's encounter in ED and not just at the time of triage. These models remained accurate for a patient cohort beyond the time period of the initial training data and were integrated to run on live electronic health record data, with similar performance.Copyright © 2021 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

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