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Assessing Risk in Implementing New Artificial Intelligence Triage Tools-How Much Risk is Reasonable in an Already

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Summary
This summary is machine-generated.

Artificial intelligence (AI) risk prediction tools in emergency medicine face implementation challenges. A risk-benefit analysis supports an ongoing evaluation strategy for the Score for Emergency Risk Prediction (SERP) tool, aligning with learning health systems.

Keywords:
Artificial intelligenceImplementationRiskTriage

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Area of Science:

  • Emergency Medicine
  • Health Informatics
  • Artificial Intelligence

Background:

  • Emergency medicine (EM) faces unique challenges in risk prediction due to urgency and high-pressure environments.
  • Artificial intelligence (AI) tools aim to streamline triage and mitigate issues like overcrowding in emergency departments (EDs).
  • Implementation risks include over/under-triage, false positives, and potential healthcare professional bias, complicating AI tool adoption.

Purpose of the Study:

  • To explore risks associated with AI risk prediction tools in emergency medicine.
  • To analyze the case study of the Score for Emergency Risk Prediction (SERP) machine learning triage tool in Singapore.
  • To evaluate the ethical appropriateness of an implementation strategy using a risk-benefit analysis and the learning health systems framework.

Main Methods:

  • Analysis of a case study involving the Score for Emergency Risk Prediction (SERP) tool.
  • Risk-benefit analysis of the proposed implementation strategy.
  • Consideration of ongoing-evaluation studies as an alternative to pre-implementation randomized controlled trials (RCTs).

Main Results:

  • Pre-implementation RCTs were deemed infeasible for SERP due to its interaction with clinical judgment.
  • Ongoing-evaluation studies are proposed as a method to assess SERP's real-world capabilities.
  • A risk-benefit analysis suggests the proposed implementation strategy is ethically appropriate.

Conclusions:

  • The implementation strategy for SERP aligns with improvement-focused and systemic approaches.
  • The learning health systems (LHS) framework is crucial for ensuring safety, efficacy, and ongoing learning.
  • Ethical considerations and risk mitigation are paramount for successful AI tool integration in emergency medicine.