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Implementing Triage-Bot: Supporting the Current Practice for Triage Nurses.

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This study introduces Triage-Bot, an AI system designed to assist emergency department (ED) nurses with patient triage. The AI tool uses voice, video, and vital sign analysis to streamline care and improve efficiency in Canadian emergency departments.

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Nursing Informatics
  • Emergency Medicine Technology

Background:

  • Canadian emergency departments face high patient volumes, with 15.1 million annual unscheduled visits.
  • A projected shortage of 100,000 nurses by 2030 will strain emergency department resources.
  • Triage systems are critical for immediate patient support and resource allocation in emergency settings.

Purpose of the Study:

  • To propose Triage-Bot, an AI system to aid emergency department nurses in patient triage.
  • To enhance patient outcomes and streamline clinical processes through technological innovation.
  • To improve the overall quality and efficiency of healthcare delivery in emergency departments.

Main Methods:

  • Triage-Bot utilizes the Canadian Triage and Acuity Scale (CTAS) for patient prioritization.
  • The system employs voice and video interactions for patients to describe health concerns.
  • AI models, including deep learning, analyze facial expressions and voice tone, alongside automated vital sign measurements (HR, HRV, SpO2, RR, BP, BG, stress).

Main Results:

  • A systematic review indicated AI's potential in nursing for personalized patient instructions and remote monitoring.
  • Triage-Bot can be implemented in ED waiting rooms to collect patient data.
  • Collected data supports nurses in assessing patient condition severity and prioritizing care.

Conclusions:

  • AI-powered tools like Triage-Bot can significantly assist ED nurses in managing patient flow and care.
  • The system's integration into healthcare facilities can improve triage efficiency and patient management.
  • Further development is needed to ensure accessibility and address potential limitations related to user understanding and varying abilities.