Building an AI-assisted medical coding platform
A clinical coding pipeline that reads documents, extracts concepts, and produces a principal ICD code grounded in the patient's medical history.
Healthcare technology provider · AI Engineering
Problem
Clinical coding requires interpreting large unstructured clinical documents and matching diagnoses and procedures against complex coding systems.
Challenges
Architecture
Clinical Document
Concept Extraction
ICD Candidates
Episode Reportability & Sequencing
Medical History
Discharge Notes
Principal ICD Code
Engineering decisions
Separating retrieval from generation
Retrieval runs as its own stage with its own evaluation, so it can be tuned and tested independently of the model.
Structured LLM output
Every model response is constrained to a typed schema — ranked codes with confidence — so downstream systems never parse free text.
Restricting model context
The prompt is built from retrieved snippets and coding guidance only, bounding what the model reasons over and shrinking the hallucination surface.
Validation layer
Suggestions pass a rules-based gate — code validity, formatting, and consistency — before anything is returned or stored.
Asynchronous processing
Document ingestion and analysis run as background jobs with retries and idempotency, so long-running work never blocks the API.
API architecture
A FastAPI service with typed request and response contracts, containerized and deployed behind Nginx.
Technology
Related reading
Demo
Try AutoMedicoder
Run a clinical document through the pipeline and inspect the ranked suggestions.
* This demo runs on a low tier cloud infrastructure and is backed by gpt-mini. High latency for some requests is expected