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Key Information Influencing Patient Decision-Making About AI in Health Care: Survey Experiment Study.

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Journal of Medical Internet Research
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Summary

Patients value information on AI device performance, oversight, and regulatory approval for trust and acceptance in healthcare. Tailoring information to patient characteristics is key for informed decision-making regarding AI-enabled care.

Keywords:
AI labelingartificial intelligencehealth communicationhealth decision-makingpatient preferencepatient-centered care

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Patient Engagement

Background:

  • Artificial intelligence (AI)-enabled devices are increasingly integrated into healthcare settings.
  • Limited research exists on patient informational preferences for AI devices, impacting trust and acceptance.
  • Effective patient-facing communication is crucial for addressing concerns and supporting informed decisions about AI in care.

Purpose of the Study:

  • To identify key information elements influencing patient trust and acceptance of AI devices.
  • To examine how patient characteristics affect responses to AI device labeling.
  • To explore patient evaluation of AI label content and its effectiveness in decision-making, trust-building, and usage intention.

Main Methods:

  • A web-based survey with 340 US patients using two experiments: a discrete choice experiment with simulated AI device labels and a single profile factorial experiment.
  • Participants evaluated simulated AI device labels based on information needs, legibility, comprehensibility, credibility, and perceived effectiveness.
  • Data analyzed using mixed-effects logistic regression to assess the impact of information elements and patient characteristics.

Main Results:

  • Information on regulatory approval, device performance, provider oversight, and AI's added value significantly increased patient trust and acceptance.
  • Patient characteristics like AI familiarity and health literacy influenced the impact of information elements.
  • While label comprehension was generally good, information on data privacy and safety protocols had less influence compared to performance and regulatory details.

Conclusions:

  • Patients prioritize information on AI device performance, oversight, and regulatory status for decision-making.
  • Transparent and understandable information is vital for fostering patient trust and acceptance of AI in healthcare.
  • A tailored approach to communicating AI information is necessary due to varying patient characteristics and concerns.