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Objective <p>Artificial intelligence (AI) offers opportunities for managing the complexities of clinical care in the emergency department (ED), and Clinical Decision Support has been identified as a priority application. However, there is a lack of published guidance on how to rigorously develop and evaluate these tools. We sought to answer the question, “What methodological standards should be applied to the development of AI-based Clinical Decision Support tools in the ED?”.</p> Methods <p>We conducted an iterative consensus-establishing activity involving a subcommittee with AI expertise followed by surveys and a live facilitated discussion with participants of the 2024 Canadian Association of Emergency Physicians Research Symposium in Saskatoon. We augmented analysis of participant feedback with large language models.</p> Results <p>We established 11 recommendations AI-based Clinical Decision Support development including the selection of a relevant problem and team of experts, standards of data quality and quantity, novel AI-specific reporting guidelines, and adherence to principles of ethics and privacy. We removed the recommendation regarding model interpretability from the final list due to a lack of consensus.</p> Conclusion <p>These 11 recommendations provide guiding principles and methodological standards for emergency medicine researchers to rigorously develop AI-based Clinical Decision Support tools and for clinicians to gain knowledge and trust in using them.</p>

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Establishing methodological standards for the development of artificial intelligence-based Clinical Decision Support in emergency medicine

  • 期刊论文

  • 发表日期：

    2025年2月7日

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  • Hashim Kareemi,
  • Henry Li,
  • Akshay Rajaram,
  • Jessalyn K. Holodinsky,
  • Justin N. Hall,
  • Lars Grant,
  • Gautam Goel,
  • Jake Hayward,
  • Shaun Mehta,
  • Maxim Ben-Yakov,
  • Elyse Berger Pelletier,
  • Frank Scheuermeyer,
  • Kendall Ho

摘要

Objective

Artificial intelligence (AI) offers opportunities for managing the complexities of clinical care in the emergency department (ED), and Clinical Decision Support has been identified as a priority application. However, there is a lack of published guidance on how to rigorously develop and evaluate these tools. We sought to answer the question, “What methodological standards should be applied to the development of AI-based Clinical Decision Support tools in the ED?”.

Methods

We conducted an iterative consensus-establishing activity involving a subcommittee with AI expertise followed by surveys and a live facilitated discussion with participants of the 2024 Canadian Association of Emergency Physicians Research Symposium in Saskatoon. We augmented analysis of participant feedback with large language models.

Results

We established 11 recommendations AI-based Clinical Decision Support development including the selection of a relevant problem and team of experts, standards of data quality and quantity, novel AI-specific reporting guidelines, and adherence to principles of ethics and privacy. We removed the recommendation regarding model interpretability from the final list due to a lack of consensus.

Conclusion

These 11 recommendations provide guiding principles and methodological standards for emergency medicine researchers to rigorously develop AI-based Clinical Decision Support tools and for clinicians to gain knowledge and trust in using them.