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A new classification system helps categorize patient safety risks from generative artificial intelligence (AI) in healthcare. This system aids in monitoring errors from AI tools like large language models (LLMs) and ambient digital scribes.

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Area of Science:

  • Healthcare Technology
  • Artificial Intelligence
  • Patient Safety

Background:

  • Generative artificial intelligence (AI) offers transformative potential in healthcare.
  • Effective classification and monitoring of AI-related patient safety risks are crucial.
  • Existing frameworks for AI safety in healthcare are limited.

Purpose of the Study:

  • To develop and evaluate a preliminary classification system for generative AI patient safety errors.
  • To assess the system's utility in categorizing errors from diverse AI healthcare applications.
  • To establish a foundation for robust AI error monitoring in clinical settings.

Main Methods:

  • A novel classification system was designed, structured around AI input and output stages with specific error typologies.
  • The system was applied to two distinct generative AI applications: patient-facing large language models (LLMs) and an ambient digital scribe (ADS) system.
  • Error categorization focused on type, frequency, and clinical significance.

Main Results:

  • In LLM analysis (27 queries), 45 errors were identified, with omissions being most frequent (42%); 25% of errors were high clinical significance.
  • In ADS simulation (11 visits), 66 errors were identified, with omissions predominating (83%); no high clinical significance errors were noted.
  • The classification system effectively categorized output errors across both LLM and ADS applications.

Conclusions:

  • The developed classification system demonstrates utility in identifying and categorizing generative AI patient safety errors in healthcare.
  • Omission errors were prevalent in both analyzed AI applications, highlighting a key area for improvement.
  • This system provides a foundational framework for enhancing the safety and reliability of AI in clinical practice.