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Healthcare AI Governance: Lifecycle Management for Clinical Systems

7 chapters · 36 lessons

1. 1. Defining Intended Use and Scope Boundaries

   5 lessons

   Establish clear task definitions, user roles, patient populations, and decision impact statements that anchor governance throughout the AI lifecycle.

2. 2. Evidence Review and Multi-Stakeholder Risk Assessment

   6 lessons

   Coordinate clinical safety, data protection, information security, and procurement teams to evaluate AI tools before deployment.

3. 3. Deployment Controls and Workflow Integration

   5 lessons

   Implement technical and procedural safeguards that enforce governance rules in production clinical environments.

4. 4. Post-Launch Monitoring and Performance Tracking

   5 lessons

   Build systems to detect drift, degradation, and safety signals after AI tools enter clinical use.

5. 5. Incident Response and Root Cause Investigation

   5 lessons

   Establish protocols to identify, contain, investigate, and learn from AI safety incidents and performance failures.

6. 6. Change Management and Model Update Governance

   5 lessons

   Control how AI systems evolve in production through versioning, testing, and approval workflows for updates.

7. 7. Building Governance Programs That Scale

   5 lessons

   Design organizational structures, documentation systems, and continuous improvement processes for multi-tool AI portfolios.