Predictive AI in Chronic Disease: Catching Deterioration Before the ER Visit

Chronic disease management has long relied on reactive care, treating patients after exacerbations occur rather than preventing crises before they escalate. The promise of predictive AI is fundamentally different: identifying patients at risk of heart failure, COPD flare-ups, or diabetic emergencies sometimes days before acute events, creating intervention windows that can prevent hospitalizations entirely. The challenge lies in moving beyond predictions to actionable interventions, determining what care teams should do when AI flags high-risk patients, how to avoid alert fatigue when models generate hundreds of warnings, and whether prevented admissions translate to measurable financial and clinical returns.

This roundtable examines real-world implementations of predictive chronic disease AI that are demonstrating outcome improvements beyond pilot metrics. The discussion will address both the technical performance of predictive models and the care delivery redesign necessary to capture value from early warning systems.

Join us to discuss:

• What predictive AI models are reliably identifying chronic disease exacerbations early enough to enable meaningful clinical intervention?

• How are care teams operationalizing AI predictions - what specific interventions work when patients are flagged as high-risk but not yet symptomatic?

• What ROI frameworks and outcome metrics demonstrate the value of prevented hospitalizations versus the costs of predictive AI infrastructure and intervention programs?