Making Medical Guidelines Smarter with RAG

LLMs Healthcare RAG

Medical guidelines are essential — but juggling dozens of complex, sometimes conflicting documents in real time? Not easy.

We're building a RAG (Retrieval-Augmented Generation) system to fix that. Here's what we're doing:

The Problem:

  • Tons of guidelines, constant updates.
  • Conflicting recommendations across sources.
  • Hard to access in the rush of clinical work.

Our Solution:

  • Document Processing: Smart parsing, semantic chunking, metadata tagging.
  • Knowledge Base: Embedded guideline sections, version tracking, mapping relationships.
  • Retrieval Engine: Context-aware queries, relevance scoring, evidence-level awareness.

What It Can Do:

  • Compare guidelines side-by-side (e.g., ADA vs EASD on Type 2 Diabetes).
  • Synthesize evidence across sources.
  • Support real-time decisions at the bedside.

What We Learned:

  • Chunk size matters for keeping medical meaning intact.
  • Fine-tuning embeddings on medical data massively improves retrieval.
  • Query reformulation boosts relevance (especially for complex cases).

The Impact:

  • Faster access to the right info.
  • More consistent, evidence-based decisions.
  • A practical tool for clinicians and a great training aid for new doctors.

What's Next:

Real-time updates, EHR integration, specialty expansion — and making it even more patient-specific.

In short: We're making it way easier for doctors to actually use the best evidence at the right moment — without replacing the human touch.