HIPAA-Compliant Patient Data Redaction in RAG Pipelines
When building clinical RAG engines (e.g., summarizing doctor-patient notes or querying electronic health records), systems must comply with HIPAA Privacy Rules. Sending raw Protected Health Information (PHI) to public, third-party LLM APIs (like OpenAI or Anthropic) violates federal regulations without a Business Associate Agreement (BAA), and even then, minimizing PHI leakage is a core requirement.
Describe a production-grade architecture that anonymizes clinical texts prior to sending them to an external LLM, and restores the original patient identifiers in the LLM's output response locally. What are the key trade-offs between pattern-matching redaction, Named Entity Recognition (NER) models, and LLM-based anonymizers?