Development and validation of a machine learning framework for improved resource allocation in the emergency department.

Journal: The American journal of emergency medicine
PMID: 39127019

Abstract

OBJECTIVE: 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 [1], Mistriage, which includes both undertriage and overtriage has been a persistent issue, affecting 32.2% of total ED visits [2]. Our goal is to develop a machine learning framework that predicts patients' resource needs, thereby improving resource allocation during triage.

Authors

  • Abdel Badih El Ariss
    Emergency Department, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.
  • Norawit Kijpaisalratana
    Department of Emergency Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand.
  • Saadh Ahmed
    Georgia State University, Department of computer science, Atlanta, Georgia.
  • Jeffrey Yuan
    Northwestern University, Department of Data science, Evanston, IL, United States of America.
  • Adriana Coleska
    Emergency Department, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.
  • Andrew Marshall
    Emergency Department, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America.
  • Andrew D Luo
    Emergency Department, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America; Emergency Department, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America.
  • Shuhan He
    Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA. Electronic address: She8@partners.org.

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