While AI is everywhere in healthcare strategy discussions, most initiatives never make it past pilot. Nearly 80 percent of healthcare AI efforts stall because the underlying data is fragmented, unreliable or difficult to govern at scale.
The discussion focuses on how health systems are supporting use cases like predictive readmissions, operational optimization and precision medicine by strengthening data accuracy, integration, security, explainability and access across the enterprise.
Key takeaways include:
- The five data barriers preventing 80 percent of healthcare AI initiatives from scaling
- A step-by-step data readiness framework that reduces manual preparation by 60–80 percent
- How trusted data foundations drive 3–5x faster deployment and 30–40 percent accuracy gains
