Can artificial intelligence help ED nurses more accurately triage patients?

Area of Science:

• Emergency Medicine
• Health Informatics
• Artificial Intelligence in Healthcare

Background:

• The Emergency Severity Index (ESI) is a widely adopted patient triage tool in the United States and internationally.
• Existing evidence highlights limitations of the ESI in accurately determining patient acuity.
• Inaccurate triage can lead to suboptimal patient care and resource allocation.

Purpose of the Study:

• To explore the potential of artificial intelligence (AI) in enhancing the accuracy of the Emergency Severity Index (ESI) triage process.
• To investigate methods for integrating AI into emergency department workflows for improved patient acuity assignment.

Main Methods:

• Review of current literature on ESI triage and AI applications in healthcare.
• Analysis of potential AI algorithms for acuity assessment.
• Conceptual framework for AI integration into the ESI process.

Main Results:

• AI demonstrates potential to analyze complex patient data for more precise acuity scoring.
• Integration of AI could mitigate human bias and variability in ESI assignments.
• AI-powered tools may offer real-time decision support for triage nurses.

Conclusions:

• Artificial intelligence offers a promising avenue to address the limitations of the Emergency Severity Index.
• Implementing AI in triage can potentially decrease the incidence of incorrect ESI level assignments.
• Further research and validation are needed to develop and deploy AI-driven triage systems effectively.