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Using Machine Learning Technique in Managing Emergency Triage Flow.

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Acta Informatica Medica : AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku Bih
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Summary

A new machine learning model for emergency department triage significantly reduces mis-triage rates compared to traditional nursing assessments. This AI tool enhances patient classification accuracy and efficiency in emergency care.

Keywords:
Canadian Triage and Acuity Scale Machine LearningEmergency Department Mis-triageRandom Forest

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Area of Science:

  • Emergency Medicine
  • Artificial Intelligence
  • Machine Learning

Background:

  • Triage is essential in emergency departments, but current systems often lead to erroneous patient classification.
  • Artificial intelligence (AI) and machine learning (ML) offer potential solutions for improving patient sorting and triage accuracy.

Purpose of the Study:

  • To develop and evaluate a machine learning model for predicting emergency department patient triage levels.
  • To compare the performance of the ML model against the standard nursing triage system.

Main Methods:

  • A retrospective pilot study utilized emergency department records from King Fahad Hospital of the University (January 2020 - December 2022).
  • A dataset of 998 randomly selected patients was used, with the ML model trained via 10-fold cross-validation.
  • Two experimental setups were employed: one with five triage levels and another combining levels 2-5.

Main Results:

  • The ML model achieved 84% accuracy in the first experiment and 64% in the second.
  • Crucially, the machine learning model demonstrated significantly lower mis-triage rates than the standard nursing triage system.

Conclusions:

  • The proposed machine learning model offers superior accuracy and reduced mis-triage rates compared to conventional nursing triage.
  • This AI-driven approach can serve as a valuable tool for optimizing patient management in emergency departments through more efficient and accurate triage.