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Consortium for Safe Artificial Intelligence in Healthcare

Consortium Overview

Artificial Intelligence (AI) offers new opportunities to improve patient outcomes, streamline workflows, and enhance clinical decision-making. However, the integration of AI technologies into healthcare settings also introduces unique patient safety challenges that require proactive, collaborative solutions.

Join us in developing a strategic approach to understanding, addressing, and advancing AI safety, with a focus on fostering collaboration, sharing best practices, and developing practical resources for organizations at every stage of AI adoption.

Through this consortium, we seek to broaden the conversation, engaging vendors and supporting organizations that may lack dedicated AI governance infrastructure. Through these efforts, we will help ensure that AI technologies are implemented safely, effectively, and equitably.

Consortium Working Groups

Based on participant feedback, we will be launching three working groups in late September 2026. 

Working Group 1: Best Practices for Safe AI Adoption and Use
  • General Focus: What we can learn from organizations and other experts on the best practices for safely adopting and using AI. This may include governance processes, technical and usability evaluations, implementation, and monitoring.
  • Deliverable: Report that describes current best practices, which can be used for AI-related process improvement.
Working Group 2: Reporting and Event Review Processes for AI-Related Safety Incidents
  • General Focus: Development of a reporting and event review process that enables safe AI use when hazards are present.
  • Deliverable: Guiding principles and processes for capturing AI-related safety issues and for conducting effective event review when AI is involved in a safety incident.
Working Group 3: Leveraging AI to Support Safety Report Analysis and Event Review
  • General Focus: Identify how AI can be used to improve safety event report analysis, identification of trends, event review, and mitigation strategies.
  • Deliverable: Use cases demonstrating how AI can be used to improve the safety report analysis and event review process.
Interested in learning more? Click Join the Consortium to receive working group updates.

Join the Consortium

Catch up on the discussion -recordings of the first two Consortium meetings are available on-demand!

  • Consortium for Safe AI in Healthcare Kickoff Session
    Contains 3 Component(s)

    The Mid Atlantic Patient Safety Center and the MedStar Health National Center for Human Factors in Healthcare invites you to join us in developing a strategic approach to understanding, addressing, and advancing AI safety, with a focus on fostering collaboration, sharing best practices, and developing practical resources for organizations at every stage of AI adoption. ​This kickoff session will feature a lecture that covers some of the basics of AI, including the difference between generative and rule-based AI, clinical applications of AI, and the possible risks associated with the use of AI in healthcare. Based on the needs identified by consortium members, future discussion topics may include: distinguishing between AI technology and medical devices when making procurement decisions, understanding existing AI governance processes, best practices and systems thinking for AI safety event analysis, and identifying organizational stakeholders involved in AI safety. Our goal is to create an open forum where consortium members can share and analyze real-world examples of AI-related safety issues, and then co-develop actionable recommendations, best practices, and toolkits that will be accessible and actionable for all.

  • Product Image: Consortium for Safe AI in Healthcare: Collaborative Planning Session

    Consortium for Safe AI in Healthcare: Collaborative Planning Session
    Contains 3 Component(s)

    The Mid Atlantic Patient Safety Center and the MedStar Health National Center for Human Factors in Healthcare invites you to join us in developing a strategic approach to understanding, addressing, and advancing AI safety, with a focus on fostering collaboration, sharing best practices, and developing practical resources for organizations at every stage of AI adoption.