Case Study: Digital Phenotyping — Harnessing AI & Unstructured Data to Revolutionize Life-Saving Diagnostics
How AI-driven EMR analysis helped identify high-risk cardiovascular patients and support earlier intervention.
What You’ll Learn:
- AI-Enabled Risk Prediction – Using structured + unstructured EMR data to identify mortality predictors.
- Digital Phenotyping Methods – Curated datasets powering predictive modeling.
- Model Deployment – Operationalizing algorithms across EMR networks.
- Clinical Impact Analysis – Evaluating algorithm-driven diagnostics and outcomes.
Key Outcomes:
- Development of a diagnostic risk fingerprint.
- Predictive model developed, validated, and deployed across EMR networks.
- Framework established to assess whether AI alerts drive improved patient outcomes.
See how Magnolia Market Access applies AI and unstructured EMR data to advance diagnostic evaluation.
