Making All Your Health Data LLM-Ready

The LLM problem you don’t realise you have (until it bites)

Even before the launch of ChatGPT Health, it was clear that consumer LLMs are becoming a mainstream way people make sense of health questions, prepare for appointments, and interpret information. 

That’s genuinely useful. But there’s a structural limitation most users (and many developers) misunderstand: LLMs don’t reliably “read everything” you give them, especially when the input is long, messy, or spread across multiple files.

Even where a tool accepts uploads and “supports health records”, the model is still operating under constraints: limited attention over long context, imperfect parsing of real-world documents, and retrieval that can silently miss relevant parts (you wouldn't even know which or whether it happened at all). One well-documented example is the “lost-in-the-middle” effect: performance is often strongest at the beginning and end of long inputs, while information in the middle can be effectively underused. 

In health, that failure mode is uniquely risky.

What HDA unlocks 

HDA is not “a smarter health chatbot”.

HDA is a secure health data layer that turns fragmented records combined with health logs in natural language into a structured, source-linked dataset first—and only then lets you query, share and connect it to LLMs, including ChatGPT Health.

That changes what’s technically possible:

Coverage you can trust: HDA ingests your corpus into a canonical store (including messy PDFs/scans), so your answers don’t depend on what happened to be parsed or randomly taken into account. 

Grounded outputs, not vibes: HDA retrieves the most relevant evidence from your own records and returns answers with traceable sources (file + page/section), so results are genuinely data-based rather than plausible-sounding.

Stable personal context over time: Health is longitudinal. HDA maintains structured history (diagnoses, letters, medications (including self-medication), symptoms, trends, imaging, timeline, labs, diaries, lifestyle data and - soon - wearables and apps data) so updates don’t become “context soup” across conversations.

This is the difference between “a conversation about health” and “a system that can reliably work with your health record at scale” and confidently unlock medical advice.

We're even launching this document pipeline as its own tool, Canonizr, so everyone can benefit from LLM-ready data.