Medical RAG System for Automated Billing Code Generation
NLP
GenAI
Healthcare
Built a Retrieval-Augmented Generation (RAG) system at NeuroReef Labs to automate medical billing code generation from clinical notes and patient visit records.
Problem: Medical coders manually assign ICD-10, CPT, SNOMED, and HCC codes to every patient visit — a slow, error-prone process that delays billing and reimbursement.
Solution:
- RAG pipeline over clinical documentation to retrieve relevant coding guidelines and past examples
- LLM generates billing codes grounded in retrieved context, reducing hallucinations
- HyDE (Hypothetical Document Embeddings) used in the medical chatbot over Athena EHR data for improved retrieval quality
- Applied few-shot prompt tuning for medical guideline-consistent outputs
Codes Automated:
| Code Type | Purpose |
|---|---|
| ICD-10 | Diagnosis classification |
| CPT | Procedure billing |
| SNOMED | Clinical terminology |
| HCC | Risk adjustment for Medicare |
Tech Stack: Python OpenAI API RAG HyDE HuggingFace Athena EHR Prompt Engineering
Impact: Reduced manual coding effort for medical billing, improving accuracy and turnaround time for reimbursement workflows.