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Artificial intelligence (AI) and machine learning (ML) show promise in healthcare, but patient and provider concerns about safety and usability exist. This study explored patient needs and trust factors to ensure safe AI/ML use in diabetes management.

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Area of Science:

  • Healthcare Technology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Artificial intelligence (AI) and machine learning (ML) offer advanced computational methods for predicting health outcomes and addressing patient needs.
  • While promising for healthcare, particularly diabetes management, AI/ML technologies raise usability and safety concerns for patients and providers.

Purpose of the Study:

  • To understand patient information and training needs regarding AI/ML in healthcare.
  • To identify factors influencing patient trust and perceived value of AI/ML healthcare applications.
  • To determine how to best support the safe and appropriate use of AI/ML devices and applications for individuals with diabetes.

Main Methods:

  • Conducted focus groups (n=9) and interviews (n=3) with patients (n=40) and providers (n=6) across Alaska, Idaho, and Virginia.
  • Utilized Grounded Theory for data gathering, synthesis, and analysis, employing thematic content and constant comparison analysis.
  • Applied inductive approaches to identify key concepts such as patient-provider interactions, trust, accuracy, value, assurances, and information transparency.

Main Results:

  • Identified key themes and recommendations concerning patient preferences for AI/ML information, training needs for both patients and providers, and factors influencing trust.
  • Highlighted participant recommendations for improved device functionality, information labeling (e.g., online resources, 24/7 support), access to practice devices, community resource connections, and simplified display/alert systems.
  • Synthesized patient and provider perspectives on AI/ML enabled devices and applications in healthcare management.

Conclusions:

  • Patient and provider recommendations can inform Federal Oversight Agencies to enhance the utilization and safety of AI/ML technologies in diabetes monitoring.
  • Improving device safety and efficacy through informed oversight can lead to better patient outcomes in diabetes management.