Eval directory
Evals for Bayesian Health
6 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Bayesian Health AI products.
About Bayesian Health
Bayesian Health provides a real-time clinical intelligence platform that continuously monitors patient data, surfaces patients needing attention, and gives clinicians guidance for proactive care.
Industry
Clinical AI / Predictive Analytics
Website
www.bayesianhealth.comHow complete this published benchmark library is across datasets, metrics, rubrics, use-case maps, and pack context. This is library coverage, not an agent performance score.
Test datasets
6/6 packs
Scoring metrics
6/6 packs
Judge rubrics
6/6 packs
Use-case maps
0/6 packs
Pack context
6/6 packs
Available eval packs for Bayesian Health
6 packs ready to run.
Alerting Routing Escalation
65 graded scenarios covering edge cases, failure modes, and quality checks.
Clinical Feature Engineering Data Quality
60 graded scenarios covering edge cases, failure modes, and quality checks.
Ehr Data Ingestion Interoperability
56 graded scenarios covering edge cases, failure modes, and quality checks.
Model Lifecycle Governance
78 graded scenarios covering edge cases, failure modes, and quality checks.
Model Performance Monitoring Drift Detection
46 graded scenarios covering edge cases, failure modes, and quality checks.
Real Time Prediction Risk Scoring Engine
54 graded scenarios covering edge cases, failure modes, and quality checks.
Why eval Bayesian Health AI
Bayesian Health's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.
The Corsac eval library for Bayesian Health measures four dimensions teams care about most when deploying medical & clinical ai agents:
- Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
- Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
- Safety gates — does it escalate or refuse when it should, and only then?
- Operator quality — does it preserve analyst trust by surfacing the right context at the right time?
Every eval pack above is hand-authored against Bayesian Health's public product surface and runnable in Corsac with your own data.