RESOURCES
Complete transparency, explainability, and governance for every AI decision. From input to reasoning to output—every step is documented, auditable, and explainable.
Last reviewed: 2026-06-01
Every data point used in a decision is tracked to its source: EHR system, policy document, user input, external API. Data lineage ensures traceability.
Example: 'Patient BMI (32.4) sourced from EHR record dated 2026-01-03, recorded by Dr. Smith (NPI: 1234567890).'
The AI reasoning process is captured step-by-step: rules evaluated, criteria checked, evidence considered, alternatives explored, confidence calculated.
Example: 'Evaluated medical necessity criteria: 1) Failed conservative treatment ✓, 2) BMI >30 ✓, 3) Comorbidity present ✓ → Medical necessity MET (confidence: 92%)'
Human-readable explanations generated for every decision: why this decision was made, what evidence supported it, what factors were most influential.
Example: 'Authorization APPROVED because patient meets all 3 medical necessity criteria per Aetna Policy #MED-2024-001.'
Every decision, input, and reasoning step logged to tamper-proof audit trail with timestamps, user context, system version, and environmental factors.
Example: '[2026-01-06 14:23:45 UTC] User: jane.doe@health.org | Decision ID: PA-2026-00123 | Outcome: APPROVED'
Role-based access controls, approval workflows, compliance checks, and policy enforcement ensure AI operates within organizational and regulatory boundaries.
Example: 'High-value authorization ($50K) triggered automatic medical director review. Approved by Dr. Johnson on 2026-01-06.'
Subject matter experts review AI decisions, provide feedback, override when necessary, and contribute to continuous model improvement—all actions logged.
Example: 'Adjuster override: AI recommended DENY but adjuster approved based on special circumstances. Override logged.'
Meet HIPAA audit requirements, SOC 2 change controls, insurance regulatory compliance, FDA 21 CFR Part 11, and EU AI Act transparency mandates.
Defend against claims or audits with complete documentation: here's exactly what data was used, how the decision was made, and who approved it.
Internal QA teams can review AI decisions, identify improvement opportunities, and ensure AI operates consistently with organizational policies.
Human feedback and overrides create a learning loop: AI learns from expert corrections, improving accuracy and reducing future overrides.
Stakeholders (patients, customers, regulators) can understand how decisions are made, building trust in AI-assisted operations.
Experience AI that explains every decision. See the Proof Stack in action.