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Understanding Clinicians' Informational Needs for AI-Driven Clinical Decision Support Systems: Qualitative Interview

Simone Mingels1, Hannah Piehl1, Madeline Therrien1

  • 1Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht University, Paul Henri Spaaklaan 1, Maastricht, 6229 EN, The Netherlands, +31 (0)43 38 81863.

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Summary
This summary is machine-generated.

Clinicians need clear, layered information on AI-CDSS training data and performance metrics for safe use. Reporting standards must align with clinical workflows to improve AI adoption in healthcare.

Keywords:
AI implementationartificial intelligenceco-creationdelivery of health careinformational needsreporting standardtransparency

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems

Background:

  • Artificial intelligence (AI) is transforming healthcare with AI-driven clinical decision support systems (AI-CDSS).
  • Clinician adoption of AI-CDSS is hindered by concerns regarding algorithm misuse, misinterpretation, and transparency.
  • Understanding clinician informational needs is crucial for effective AI-CDSS integration.

Purpose of the Study:

  • To explore clinicians' informational needs and preferences for using AI-CDSS.
  • To gather AI experts' perspectives on essential information for safe AI-CDSS use.
  • To identify optimal reporting standards for AI-CDSS in clinical practice.

Main Methods:

  • Qualitative descriptive study using semistructured interviews with 16 participants (8 clinicians, 8 AI experts).
  • Exploration of experiences with AI, informational needs, and feedback on reporting standards (Model Cards, Model Facts, TRIPOD-AI).
  • Analysis of interview transcripts using codebook thematic analysis.

Main Results:

  • Clinicians require clear data on AI training sets (origin, size, criteria) for applicability assessment.
  • Performance metrics beyond AUC are needed, focusing on clinical relevance.
  • Specific warnings and limitations are essential to prevent AI-CDSS misuse.
  • Information delivery should be layered, customizable, jargon-free, and integrated into clinical workflows.

Conclusions:

  • Reporting standards for AI-CDSS must prioritize clinician comprehension and usability.
  • Enhanced transparency in training data and performance metrics can improve AI-CDSS assessment.
  • A clinician-centered, layered information approach integrated into workflows is vital.
  • Co-creation with clinicians during AI-CDSS development is key for practical usability.