Medical Document Intelligence
Project Summary
Client: Healthcare / Clinical Operations Industry: Medical / Health Tech Status: Working demonstrator
Focus Areas:
- Citation-first retrieval: physicians see cited hit lists, never generated answers
- Clinical entity extraction and normalization across heterogeneous document formats
- A self-growing medical thesaurus for clinical variants, acronyms, and OCR errors
- Evaluation framework for medical-grade accuracy requirements
Designed and built an OpenSearch-based search solution that turns unsorted patient documents (PDFs, scans, DOCX) into a structured, searchable per-patient index. Built on one firm constraint: physicians see cited hit lists, never generated answers: a retrieval machine with provenance, not an answer generator.
Challenge
Medical documents are among the hardest to process automatically. Clinical lab results and doctor reports come in wildly inconsistent formats: different labs, different templates, multi-page reports with scattered data points. The accuracy requirements are exceptionally high because downstream decisions affect patient care, and an accountable clinician needs to verify every result against its source.
Approach
A search solution built for the full complexity of real medical documents:
- Bounded-context pipeline: ingestion, extraction, normalization, indexing, and query rewriting as separate, testable stages, with a provider-agnostic LLM layer
- Traceable index: an OpenSearch index with a per-mention entity graph and source deep links, so every hit traces back to the exact document and passage
- Self-growing medical thesaurus: matches a clinician's query against clinical variants, abbreviations, and OCR errors across the corpus
- Evaluation-first design: golden-standard benchmarks and recall experiments that show where structured-only retrieval misses clinically relevant documents
Current Status
A working demonstrator: unsorted patient documents in, a structured, searchable per-patient index out, with cited hit lists and full source traceability. Ongoing work expands document coverage and improves accuracy across edge cases.
Tech Stack
- Python
- OpenSearch (per-mention entity graph, source deep links)
- Document AI / OCR pipeline
- Medical entity recognition, normalization, and self-growing thesaurus
- Custom evaluation framework
- On-premise LLM inference
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Working with complex medical documents?
Medical document processing requires specialized expertise. Let's discuss your document challenges.