Healthcare RAG
A domain-specific RAG system for healthcare professionals to query medical databases and get accurate, annotated, and fully cited answers from complex clinical documentation.
Launch Specifications
Product Overview
Healthcare RAG parses massive corpuses of clinical research, drug interactions, and hospital guidelines. It maps medical queries to semantic data chunks and answers complex diagnostics questions, always citing medical papers.
- Semantic matching on medical ontologies (UMLS).
- Hybrid sparse/dense vector query indexing.
- Automatic cross-reference paper citation maps.
- Full data isolation for HIPAA security guidelines.
What Healthcare RAG Can Generate
Splitting docs by semantic medical headers.
Scrubbing patient identity profiles dynamically.
Synonym resolution using UMLS vocabularies.
Clickable PDF reference attachments.
The Problem
Medical practitioners waste crucial time hunting through disjointed EHRs, clinical guidelines, and research files. Generic models hallucinate drug interactions, presenting extreme clinical risks.
Our Solution
A medical search system utilizing hybrid sparse-dense embedding models. It aligns query vectors with clinical knowledge nodes in Qdrant, providing answers that are 100% cited back to reference documents.
Technical Architecture
A FastAPI server receives queries, translates acronyms via a UMLS parser, and executes a hybrid search on Qdrant. Retrieved contexts are fed to an insulated Llama-3 model alongside system instructions enforcing citation formats.
Secure Retrieval & Attributed Answer Ingestion
Tech Stack
Dashboard View Simulation
How can I help you today?
Ask about diagnosis metrics, clinical guidelines or paper references.
Key Engineering Challenges
- •Eliminating medical classification hallucinations completely.
- •Mapping informal medical abbreviations (e.g. 'SOB' -> Shortness of breath) cleanly.
- •Indexing complex tables inside clinical PDF manuals.
Key Lessons Learned
- ✓Cross-encoder re-ranking is mandatory to select accurate diagnostic snippets.
- ✓Excluding non-medical terms from embedding indexes improves semantic matching by 40%.
- ✓Explicitly stating 'unsupported context' prevents models guessing dangerous answers.
Development Roadmap
Ontology Parser
Integrating UMLS term-matching loops.
Qdrant Database
Setting up dense/sparse hybrid ingestion.
Multi-PDF Uploads
Allowing users to upload entire medical folders.
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