We strip patient identifiers before any model call. Prompts log only a structured payload and an input hash.
Useful AI. Auditable AI. Production-ready AI.
MedIQ is AI-native — but we never bet patient outcomes on a single black box. Deterministic rules do the heavy lifting; governed AI adds judgment on top, fully audited and fully reversible.
Every AI call writes a row: input hash, decision, rationale, confidence — exportable for audit.
Rules run before AI. If a rule blocks, we never ship the prompt — and AI can never override a rule.
MedIQ centrally vets and assigns models per task. No tenant keys, no model selection, no metered surprise.
Tenant configuration is encrypted at rest with AES-256-GCM and per-record IVs, gated to tenant admins.
Scrubber AI pass typically completes in under 1.5s p95 — fast enough for inline review during charge capture.
Deterministic 60+ rule engine; AI risk pass adds context only humans usually catch.
15-stage pipeline over the national catalogs (ICD-10, CPT, HCPCS, NCCI, MUE) with governed AI refinement.
Auto-drafted appeal letters with payer-shaped tone, citations, and tracked revisions.
A claim's trip through the AI layer.
Plain-English description of the path every claim takes — deterministic checks first, then AI, then a reversible patch your biller can accept or reject.
- 1IngestClaim arrives from encounter or batch upload. Tenant + role checked.
- 2Rule engine60+ deterministic rules: crosswalks, modifiers, duplicates, payer rules.
- 3De-identifyPHI stripped; structured payload + input hash prepared.
- 4Model callGoverned AI returns a risk score and rationale — logged with input hash.
- 5PatchSuggested reversible change synthesized from rule + AI output.
- 6AuditDecision + confidence + input hash written to the audit log.
Want the governance brief?
We'll send the architecture overview, model review checklist, and audit-log schema.