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Comparing traditional and AI-enhanced strategies for developing patient decision aids: a multiple case study.

Anik Giguere1,2, Delphine Auclair-Rochon2, Maéva Robin2

  • 1Department of Family Medicine and Emergency Medicine, Université Laval, Québec City, Québec, Canada anik.giguere@fmed.ulaval.ca.

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Summary

Artificial intelligence (AI) can enhance patient decision aid (DA) development by generating more evidence-based content. This AI-enhanced strategy produced richer information compared to traditional methods, improving efficiency and quality.

Keywords:
Clinical Decision-MakingEvidence-Based PracticeHealth Services ResearchInformation SciencePUBLIC HEALTH

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Area of Science:

  • Health Informatics
  • Medical Decision Making
  • Artificial Intelligence in Healthcare

Background:

  • Patient decision aids (DAs) are crucial for informed healthcare choices.
  • Traditional DA development is resource-intensive and can be time-consuming.
  • The integration of artificial intelligence (AI) offers potential for optimizing DA content creation.

Purpose of the Study:

  • To develop and evaluate AI-driven prompts for generating balanced, evidence-based patient DA content.
  • To compare the efficacy of an AI-enhanced DA development strategy against a traditional human-led approach.

Main Methods:

  • A comparative mixed-methods study analyzed eight DAs from the Ottawa Inventory.
  • Two researchers independently extracted content and developed AI-enhanced content using refined prompts.
  • Quantitative and qualitative analyses compared AI-enhanced and traditional DA development outputs.

Main Results:

  • AI-enhanced strategies generated a significantly higher percentage of unique benefits/harms for option-focused DAs (66%) and unique options for outcome-focused DAs (47%).
  • Evidence searches validated the AI-generated options, confirming their benefit and ruling out hallucinations.
  • Qualitative analysis indicated that AI-enhanced content was generally richer, though optimal combinations were not suggested.

Conclusions:

  • AI-powered prompt engineering can significantly improve the efficiency and quality of patient DA development.
  • This study offers practical insights for leveraging AI in creating evidence-based patient decision support tools.
  • Further research may explore AI's role in suggesting optimal combinations for DA content.