Hybrid-Code: Reliable Local Clinical Coding with Privacy
Published:Dec 26, 2025 02:27
•1 min read
•ArXiv
Analysis
This paper addresses the critical need for privacy and reliability in AI-driven clinical coding. It proposes a novel hybrid architecture (Hybrid-Code) that combines the strengths of language models with deterministic methods and symbolic verification to overcome the limitations of cloud-based LLMs in healthcare settings. The focus on redundancy and verification is particularly important for ensuring system reliability in a domain where errors can have serious consequences.
Key Takeaways
- •Proposes Hybrid-Code, a hybrid neuro-symbolic multi-agent framework for local clinical coding.
- •Emphasizes privacy preservation by operating within the hospital firewall.
- •Prioritizes reliability through redundancy and verification, crucial for healthcare applications.
- •Demonstrates high language model utilization while maintaining a low hallucination rate.
- •Highlights the importance of reliability over raw model performance in production environments.
Reference
“Our key finding is that reliability through redundancy is more valuable than pure model performance in production healthcare systems, where system failures are unacceptable.”