Clinical Data-Backed LLM Chat

Problem

• Prescribers and clinical staff needed fast answers from trusted clinical reference material while they were actively working.
• The relevant information was spread across too many clinical sources for manual lookup to be practical.
• A generic chatbot was not enough: answers needed to be grounded, source-aware, and reliable at production scale.

Solution

Built a clinical RAG chat product around source ingestion, embeddings, vector search, and retrieval pipelines spanning 1M+ clinical sources so answers could be generated from the right reference content instead of model memory alone. The system paired an LLM chat interface with clinical-data-backed retrieval, making it easier for prescribers to ask natural-language questions while keeping responses tied to authoritative source material.

Conversation Actions

The chat flow also supported workflow-specific actions around the clinical answer: prior authorization templating, insurance checks, and pharma contact forms for specific drugs.