The Future of VBC Is Context-Aware
Why AI Needs Longitudinal Context
AI models work best when they can see the full sequence of events. Episodic encounters capture only a moment in time. They leave out behavior, adherence patterns, and reactions to therapy.
Value-based care depends on prediction. Without continuity, prediction is weak.
How Longitudinal Data Strengthens AI and Decision Support
1. Richer Feature Sets
Time-based trends reveal real patterns.
2. Better Risk Stratification
Chronic conditions show distinct trajectories over months or years.
3. More Accurate Predictions
Models can detect deviations early.
4. Improved Recommendations
Decision tools can suggest next steps with more confidence.
Impact on Patient-Centered Care
1. Personalized Interventions
Continuous data enables tailored outreach.
2. Better Engagement
Patients receive relevant nudges based on real behavior.
3. Stronger VBC Scorecards
Complete data improves reporting accuracy.
4. Team Coordination
All teams rely on the same context-aware insights.
Read the full whitepaper to understand the architecture behind real-time harmonization and longitudinal records.
