Insights on AI governance, healthcare compliance, and responsible AI adoption.
A comprehensive guide to healthcare AI performance metrics including sensitivity, specificity, PPV, NPV, AUC, and F1 score. Learn which metrics matter most for AI scribes, clinical decision support, and medical imaging.
Read more →Building effective AI education programs for clinical staff, covering AI literacy foundations, tool-specific training, change management, and documentation requirements under RUAIH.
Read more →A practical approach to AI bias assessment for regional health systems, including local validation strategies, CHAI Model Cards, demographic stratification, and vendor engagement.
Read more →How to integrate AI safety event reporting into existing patient safety systems, leverage PSOs for protected reporting, and build psychological safety for AI issue disclosure.
Read more →Practical AI monitoring strategies for regional health systems, including risk stratification, vendor dashboard leverage, sampling-based review, and pre-deployment vendor questions.
Read more →Key contract clauses for AI vendor agreements beyond standard BAAs, including model training restrictions, audit rights, and data use protections aligned with RUAIH guidance.
Read more →A risk-based framework for patient notification about AI use in healthcare, including practical disclosure approaches, template language, and guidance on AI scribe transparency.
Read more →Practical AI governance models for resource-constrained health systems, including committee structures, board reporting approaches, and sample charter language aligned with RUAIH requirements.
Read more →An overview of the Joint Commission and CHAI's RUAIH framework for responsible AI use in healthcare, the 2026 certification timeline, and why regional health systems should start preparing now.
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