Clinical RAG Platform
Production retrieval-augmented generation for healthcare decision support
The Challenge
Healthcare organizations have vast knowledge bases—clinical protocols, research papers, regulatory guidelines—but clinicians can't access the right information at the point of care. Traditional search fails because medical queries are nuanced and context-dependent.
A query like "anticoagulation protocol for post-surgical AFib patient with renal impairment" requires understanding medical terminology, patient context, and institutional guidelines simultaneously.
The Solution
Multi-tenant RAG platform on GCP serving 180+ hospitals with safety-critical design:
Hybrid Retrieval
Combines Vertex AI Vector Search for semantic understanding with BM25 for exact medical terminology matching. Reciprocal rank fusion ensures both approaches contribute to results.
Clinical Embeddings
Fine-tuned embeddings on clinical text that understand medical abbreviations, drug names, and procedure codes that general models miss.
Safety-Critical Design
Explicit confidence thresholds, mandatory source attribution, and hallucination detection. The system refuses to answer rather than risk incorrect medical information.
Tenant Isolation
Different medical specialties have different knowledge bases and protocols. Full isolation ensures cardiology queries don't surface oncology protocols.
Mathematical Formulation
Reciprocal Rank Fusion (RRF)
Combines dense (semantic) and sparse (BM25) retrieval results using rank-based fusion:
where (smoothing constant), = set of rankings (dense + sparse), and = position of document in ranking .
Hybrid Retrieval Score
Final relevance score combines semantic similarity with lexical matching:
where are clinical embeddings, weights semantic similarity, and BM25 captures exact medical terminology matches.
Confidence-Gated Response
Safety-critical threshold determines whether to respond or defer:
where for clinical applications (conservative threshold), = retrieved context, and confidence is estimated via calibrated model outputs.
Architecture
1class ClinicalRAGPipeline:
2 """Safety-critical RAG for healthcare decision support."""
3
4 def __init__(self, config: RAGConfig):
5 self.embedder = ClinicalEmbedder(config.embedding_model)
6 self.retriever = HybridRetriever(
7 vector_store=VertexVectorSearch(config.index_id),
8 bm25_index=ClinicalBM25Index(config.corpus_path)
9 )
10 self.generator = SafeGenerator(
11 model=config.llm_model,
12 safety_threshold=config.confidence_threshold
13 )
14
15 async def query(self, question: str, context: ClinicalContext) -> RAGResponse:
16 # Hybrid retrieval: dense + sparse
17 dense_results = await self.retriever.vector_search(
18 self.embedder.encode(question), k=10
19 )
20 sparse_results = await self.retriever.bm25_search(question, k=10)
21
22 # Reciprocal rank fusion
23 merged = self.retriever.rrf_merge(dense_results, sparse_results)
24
25 # Generate with safety checks
26 response = await self.generator.generate(
27 question=question,
28 context=merged,
29 clinical_context=context
30 )
31
32 # Enforce citation and confidence requirements
33 if response.confidence < self.config.confidence_threshold:
34 return RAGResponse.low_confidence(response)
35
36 return response.with_citations(merged)Production Impact
- HIPAA-compliant infrastructure with full audit logging
- Safety mechanisms prevent potentially harmful hallucinations
- Real-time monitoring with BigQuery analytics and alerting
Technical Stack
Infrastructure
- • GCP Cloud Run (serverless compute)
- • Vertex AI Vector Search
- • Cloud Storage (document store)
- • BigQuery (analytics)
Application
- • Python FastAPI backend
- • Custom chunking strategies
- • Async processing pipeline
- • Comprehensive monitoring
Why It Matters
In healthcare, incorrect AI responses aren't just annoying—they're dangerous. This platform prioritizes safety over helpfulness, with explicit confidence thresholds and source attribution on every response.
The system is designed to say "I don't know" rather than risk providing incorrect medical information. This conservative approach is essential for clinical deployment where patient safety is paramount.