Mitigating Emergency Department Overcrowding Problem Using Machine Learning
Overview:
Overcrowding of Emergency Department (ED) is a major issue affecting hospitals globally and result in increased in-hospital mortality rates, long waiting and treatment times, ambulance diversions, etc. We propose an artificial intelligence-based framework to help manage the ED’s downstream recourses and mitigate overcrowding for a large hospital located in the Midwest. Particularly, this talk will discuss the application of text mining and machine learning methodologies to predict the admission status of incoming ED patients.
Speaker:
Abdulaziz Ahmed, PhD, is an assistant professor in the Health Informatics Graduate Programs in the Department of Health Services Administration, School of Health Professions at the University of Alabama at Birmingham. Before joining UAB, Ahmed worked as Assistant Professor at the Business Department at the University of Minnesota Crookston. Ahmed received his PhD in Industrial and Systems Engineering from the State University of New York at Binghamton. He is also a Lean Six Sigma Green and Black Belt certified. His research focuses on the applications of novel optimization and machine learning techniques in improving complex systems in healthcare. He has published in prestigious journals such as Expert Systems with Applications, Operations Research for Health Care, and Healthcare Management Science.
Mitigating Emergency Department Overcrowding Problem Using Machine Learning
Dr. Ahmad proposes an artificial intelligence based framework to help manage the ED’s downstream recourses and mitigate overcrowding for a large hospital located in the Midwest. Particularly, this talk will discuss the application of text mining and machine learning methodologies to predict the admission status of incoming ED patients.
Overview:
Overcrowding of Emergency Department (ED) is a major issue affecting hospitals globally and result in increased in-hospital mortality rates, long waiting and treatment times, ambulance diversions, etc. We propose an artificial intelligence-based framework to help manage the ED’s downstream recourses and mitigate overcrowding for a large hospital located in the Midwest. Particularly, this talk will discuss the application of text mining and machine learning methodologies to predict the admission status of incoming ED patients.
Speaker:
Abdulaziz Ahmed, PhD, is an assistant professor in the Health Informatics Graduate Programs in the Department of Health Services Administration, School of Health Professions at the University of Alabama at Birmingham. Before joining UAB, Ahmed worked as Assistant Professor at the Business Department at the University of Minnesota Crookston. Ahmed received his PhD in Industrial and Systems Engineering from the State University of New York at Binghamton. He is also a Lean Six Sigma Green and Black Belt certified. His research focuses on the applications of novel optimization and machine learning techniques in improving complex systems in healthcare. He has published in prestigious journals such as Expert Systems with Applications, Operations Research for Health Care, and Healthcare Management Science.