Healthcare RAG / Clinical Knowledge Retrieval Development

Reference architecture

1. Clinical Sources (guidelines, formulary, literature)
2. Ingestion + Chunking
3. Embedding + Vector Index
4. Hybrid Retrieval + Re-rank
5. Grounded LLM Answer
6. Citations + Clinician Review

A typical clinical RAG system: source documents are ingested and chunked, embedded into a vector index, and retrieved by a hybrid search + re-ranker at query time. The LLM answers strictly from the retrieved passages, returns citations to the exact source, and routes anything outside the corpus to a clinician rather than guessing.

Frequently asked questions

• How do you stop the assistant from hallucinating clinical advice?+

  Grounding is an architectural decision. The model answers strictly from retrieved passages, every claim carries a citation to its source section, and when the corpus has no relevant passage the system says so instead of inventing an answer. We measure that with faithfulness and retrieval-quality evals, so you can see exactly how grounded the answers are.

• What happens when clinical guidelines change?+

  We track version and effective-date metadata on every source, so retrieval serves the current protocol and superseded guidance is filtered out rather than quietly returned. When a guideline is updated, re-ingesting the new version updates the index without rebuilding the system. You keep an audit trail of what the assistant knew at any point in time.

• Can it retrieve from PubMed and our internal documents together?+

  Yes. We ingest public sources like PubMed / MEDLINE, RxNorm, and UMLS alongside your own guidelines, formulary, and protocols into a single retrieval layer, with source attribution on every passage. You control which sources are authoritative and which are supplementary, so internal policy can outrank general literature where it should.

• Does PHI ever leave a HIPAA-eligible boundary?+

  No. When patient context enters a query, it only touches embedding and LLM providers that are HIPAA-eligible and under a Business Associate Agreement: Azure OpenAI and AWS Bedrock, or self-hosted models where no BAA option exists. That boundary is built into the retrieval pipeline, and the guideline corpus itself is de-identified, so most retrieval never involves PHI at all.

• How do you evaluate retrieval quality before it goes live?+

  We build an evaluation harness with a clinician-reviewed question set, then measure retrieval hit-rate, answer faithfulness, and citation correctness against it, using tooling like Ragas and LangSmith plus custom evals. New corpus versions and model changes are scored against that baseline before promotion, so quality is a gate rather than a hope.