We’ve all been there: staring at a stack of printed lab results or a folder full of cryptic report_final_v2_NEW.pdf files, trying to remember if our cholesterol was higher or lower two years ago. For developers, this isn't just a filing problem—it's a data engineering challenge.
In the world of healthcare, data is messy, siloed, and often locked in "unstructured" formats. To build a truly personal Electronic Health Record (EHR) system, we need more than just a folder; we need a RAG (Retrieval-Augmented Generation) pipeline that can parse PDFs, map them to the FHIR (Fast Healthcare Interoperability Resources) standard, and provide natural language insights.
In this guide, we’ll leverage Unstructured.io, Milvus, and DuckDB to turn chaotic medical PDFs into a queryable, structured knowledge base.
The Architecture: From Raw Pixels to Structured Insights
Before we dive into the code, let’s look at how the data flows from a messy lab report to a structured answer.
