How Precina Health Uses Memgraph and GraphRAG to Revolutionize Type 2 Diabetes Care with Real-Time Insights
In a recent community call, Josiah Bryan, CTO of Precina Health, walked us through how they use GraphRAG to help patients with Type 2 diabetes. Spoiler alert: they’re crushing it with an approach that scales behavioral care and AI-powered insights for rural and low-income patients.
Large Language Models (LLMs) like GPT are powerful, but have limits—particularly around context size. Retrieving vast amounts of specific data, especially in something as critical as healthcare, requires better tools. That’s where Retrieval-Augmented Generation (RAG) comes in. Combine it with graph databases, and you get GraphRAG, which allows you to efficiently retrieve and reason through complex, evolving data.
Precina Health isn't just throwing AI at the wall to see what sticks. They’ve systematized Type 2 diabetes management, getting patients to reduce their hemoglobin A1C (HbA1C) by 1% per month (compared to typical annual reductions). Their approach factors in everything—clinical care, social determinants, and behavioral insights.
Precina’s approach highlights something missed in tech-driven healthcare: technology is a tool, not the solution. Their system makes small, patient-specific adjustments using AI, but the provider-patient interaction drives real change.
