Reducing the Validation Burden with Safe and Trustworthy Automation
As early detection programs expand, manual validation of clinical findings has become a primary operational constraint. This white paper explores how embedding validation directly into AI infrastructure enables health systems to safely automate downstream care workflows and significantly reduce manual review.
- 500k Annual radiology reports processed by a sample large health system
- 23,000 Incidental findings exams requiring validation each year in a large health system
- 15 mins Approx. time required to review each finding and activate a care plan in typical EMR-based workflows
Independent analysis is the foundation of trust
Eon Ensemble AI pairs two fundamentally different models to independently interpret clinical documentation without relying on a single authority.
Core components:
- Independent analysis of radiology reports
- Deterministic Computational Linguistics engine
- Probabilistic Large Language Model
- Safe automation through comparison
Consensus drives safe workflow automation
The system compares independent conclusions and advances downstream workflows only when the AI engines reach consensus and exceed safety thresholds.
Operational enablers:
- Autonomous clinical data validation
- Predefined safety and accuracy thresholds
- Automated guideline-based next steps
- Intelligent Care Plan™ activation
Streamlined workflows process exceptions quickly
For cases requiring human judgment, Smart Validation dramatically reduces the manual effort needed to review findings and move patient care forward.
Workflow Outcomes:
- Review time cut to < 1 min per case
- Single annotated report view
- Auto-generated ACR Lung-RADS recommendations
- One-click activation of patient care plans