Mitigating ED Overcrowding Problem Using Machine Learning

June 23, 2022 | Chapter Event

Emergency department (ED) overcrowding is a major issue affecting hospitals globally. Overcrowding can result in many adverse effects, such as increased in-hospital mortality rates, long waiting and treatment times, ambulance diversions, etc. Early prediction of a patient admission status helps to manage the ED’s downstream recourses and mitigate overcrowding.

We propose a machine learning framework to predict the admission status of incoming patients at EDs. The initial triage information such as vital signs, demographic data and chief complaints are utilized to train and test the proposed models. A retrospective large dataset is obtained from a large hospital located in the Midwest for patient visit records between 2017 and 2019.

Hear more about the framework during this webinar brought to you by the HIMSS Alabama Chapter.


  • Abdulaziz Ahmed, PhD
    Assistant Professor, Health Informatics Graduate Programs, Department of Health Services Administration
    School of Health Professions, University of Alabama at Birmingham

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