Healthcare AI Evaluation Metrics — 7 Proprietary Clinical Metrics

Evaluate AI models for clinical use with our 7 proprietary metrics designed for patient safety, not just accuracy. Each metric addresses a specific dimension of healthcare AI readiness.

Healthcare AI Evaluation Framework

MetricFull NameWeightDescription
CDUIClinical Decision Utility Index25%Harm-weighted accuracy with CTCAE severity grading. Not just accuracy — clinical utility weighted by potential patient harm.
CSHIClinical Safety & Hallucination Index20%Detecting fabricated drugs, doses, and studies. The hallucinations that could kill.
RRSRegulatory Readiness Score15%HIPAA, SOC 2, and EU AI Act compliance evaluated at the model level, not just the vendor level.
CDCCClinical Decision Confidence Consistency15%Does the AI know what it doesn't know? Calibration, demographic consistency, and context robustness.
HEBTSHealth Equity & Bias Transparency Score10%AI that works equally well for all patients. Demographic parity, stereotype resistance, and bias detection.
PISIPharmaceutical Interaction Severity Index10%The only AI benchmark dedicated to the #1 cause of preventable patient harm — medication errors.
CCRIClinical Context Retention Index5%The first benchmark that tests AI the way clinicians actually use it — in multi-turn conversation.

Healthcare Readiness Score (HRS)

The Healthcare Readiness Score combines all 7 metrics into a single composite score using severity-weighted calculations:

HRS = (CDUI × 0.25) + (CSHI × 0.20) + (RRS × 0.15) + (CDCC × 0.15) + (HEBTS × 0.10) + (PISI × 0.10) + (CCRI × 0.05)

Scoring Tiers

  • Tier 1 (90-100): Clinical-grade — suitable for direct clinical decision support
  • Tier 2 (75-89): Clinical-ready — suitable with human oversight
  • Tier 3 (60-74): Clinical-adjacent — suitable for non-critical healthcare tasks
  • Tier 4 (below 60): Not recommended for healthcare applications

Read the full Methodology