AI is no longer a future concept—it’s an essential strategy for improving care quality, reducing adverse events and empowering clinicians. Houston Methodist Hospital combined real-time AI risk models, AI-generated chart summaries and care navigation to redesign post-discharge care coordination. 

Data showed that the highest-risk patients accounted for 70% of 30-day post-discharge deaths. Using AI-powered risk stratification, Houston Methodist prioritized timely follow-up for these patients, ensuring they were seen within 14 days of discharge. The result? Higher follow-up rates, reduced mortality and readmissions and a scalable model for precision care delivery. 

Join this session to discover a practical framework for implementing AI-driven quality improvement initiatives that deliver measurable results and strengthen care teams. 

Learn How To:

• Use AI risk models, chart summaries, and care navigation to support high-risk patient follow-up.
• Launch and scale AI-powered quality improvement projects within clinical workflows.
• Implement strategies to reduce readmissions and post-discharge mortality through AI-driven care coordination.
• Leverage insights from Houston Methodist’s approach to building a sustainable model for precision care.
• Align AI tools with quality improvement goals and clinical team needs.