Use Cases / Healthcare

PHI redaction and audit trail for clinical LLM deployments

Clinical documentation LLMs process PHI by design. Meibel's HIPAA-aware entity detection intercepts patient identifiers before they reach any upstream model. Meibel extends your existing HIPAA compliance posture to AI workloads — it is not a turnkey HIPAA compliance program.

Request Access PHI in RAG pipelines
HIPAA Safe Harbor identifiers
Patient name
Date of birth
MRN
NPI (provider)
Phone / address
Diagnosis codes
16 of 18 HIPAA Safe Harbor identifiers detectable. Custom patterns for quasi-identifiers.
What Meibel handles
Direct and quasi-identifier detection
HIPAA Safe Harbor direct identifiers (name, DOB, MRN, NPI) plus quasi-identifiers: rare diagnosis codes, age+condition combinations that create re-identification risk.
De-identification modes
Safe Harbor mask mode (replace with [PHI_X] tokens) or Expert Determination mode (suppress entity category). Clinical context is preserved for model reasoning.
Per-encounter audit trail
Every clinical LLM call logged with encounter-scoped tenant ID. Your privacy officer can query all calls that involved PHI detection for any patient-related system.
RAG pipeline coverage
PHI in retrieved context chunks — not just user prompts. Meibel scans the full assembled prompt including retrieved EHR snippets before sending to the model.
In production at health systems

The PHI redaction layer gave our HIPAA officer the confidence to proceed with the clinical documentation deployment. The entity detection accuracy on medical identifiers is noticeably better than what we tested from other tools.

VP of Digital Health — Multi-Hospital Health System (12 hospitals)

Give your HIPAA officer the evidence they need.

PHI detection active on the first call. Request access and configure your clinical AI governance in a single session.

Request Access Read: PHI in RAG pipelines