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Summary
This summary is machine-generated.

This study developed a Clinical AI Sociotechnical Framework checklist to guide the successful integration of artificial intelligence (AI) in healthcare. The 35-item checklist ensures AI systems align with clinical needs and sociotechnical factors for improved patient outcomes.

Keywords:
AI deploymentAI integrationalgorithmanalyticsartificial intelligencechecklistclinical settingclinical workflowhuman-AI interactionliterature reviewmachine learningmodel

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Area of Science:

  • Healthcare technology
  • Clinical informatics
  • Artificial intelligence in medicine

Background:

  • Integrating artificial intelligence (AI) in healthcare requires careful consideration of both technical performance and sociotechnical factors.
  • Existing approaches often overlook the complex social and organizational dynamics influencing AI adoption in clinical settings.

Purpose of the Study:

  • To develop a comprehensive checklist addressing the sociotechnical aspects of AI deployment in healthcare.
  • To provide a structured, holistic guide for healthcare teams throughout the AI system lifecycle.

Main Methods:

  • A literature synthesis of 20 studies informed the initial checklist.
  • A modified Delphi study involving 35 global healthcare professionals refined the checklist items.
  • Consensus was established using an 80% threshold, with reliability assessed via IQRs and Cronbach's alpha.

Main Results:

  • The initial 45-item checklist was refined to 34 items based on expert feedback.
  • A final 35-item checklist achieved 100% consensus among participants.
  • The checklist effectively addresses planning, design, development, and implementation stages of AI in healthcare.

Conclusions:

  • The Clinical AI Sociotechnical Framework checklist offers a practical tool for developing and implementing AI in clinical settings.
  • It addresses critical technical and social factors essential for successful AI adoption and integration.
  • The checklist aims to enhance patient outcomes and streamline AI integration into healthcare workflows.