Evaluating the Reliability of a Remote Acuity Prediction Tool in a Canadian Academic Emergency Department.

Journal: Annals of emergency medicine
Published Date: Jan 3, 2024

Abstract

STUDY OBJECTIVE: There is increasing interest in harnessing artificial intelligence to virtually triage patients seeking care. The objective was to examine the reliability of a virtual machine learning algorithm to remotely predict acuity scores for patients seeking emergency department (ED) care by applying the algorithm to retrospective ED data.

Authors

  • Laila Nasser
    Department of Emergency Medicine, Sunnybrook Health Sciences Centre, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department for Continuing Education, Oxford University, Oxford, England. Electronic address: laila.nasser@medportal.ca.
  • Shelley L McLeod
    Schwartz/Reisman Emergency Medicine Institute, Sinai Health, Toronto, Ontario, Canada.
  • Justin N Hall
    Department of Emergency Medicine, Sunnybrook Health Sciences Centre, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.

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