John Snow Labs, the leader in AI for healthcare, has introduced a new end-to-end Hierarchical Condition Category (HCC) coding engine, aimed at enhancing risk adjustment accuracy and revenue integrity for healthcare providers and payers. Announced at the Healthcare NLP Summit, the solution reflects the company’s commitment to transforming value-based care through advanced, domain-specific AI.
The Importance of HCC Coding in Value-Based Care
Accurate HCC coding is critical for determining patient risk scores and ensuring appropriate reimbursement in Medicare Advantage and other value-based models. However, many conditions are often undercoded or missed due to their presence in unstructured clinical notes rather than structured EHR fields.
- Studies show up to 50% of prior conditions or complications are documented but not coded
- This results in underestimated risk scores and financial losses for providers and plans
- CMS’s recent 2026 Medicare Advantage rate increase (5.06%) underscores the urgency for accurate coding
“Our new HCC coding engine was developed to address the challenges of today’s healthcare industry,” said David Talby, CEO of John Snow Labs. “It enables a more accurate and consistent revenue cycle at a lower cost.”
What Makes John Snow Labs’ HCC Engine Unique?
The solution automates the detection of missed HCC codes within unstructured clinical data, including physician notes, discharge summaries, and encounter documentation. It delivers end-to-end functionality, from discovery to validation and audit.
Features:
1. Healthcare-Specific AI Models
- Powered by John Snow Labs’ state-of-the-art language models, purpose-built for clinical language
- Trained on HIPAA-compliant, real-world data for superior accuracy
2. Human-in-the-Loop Validation
- Ensures accuracy with expert coder oversight
- Combines AI speed with human judgment for trusted outcomes
3. Full Audit Trail
- Complete transparency into AI decision-making
- Facilitates compliance and retrospective reviews
4. Local Fine-Tuning for Precision
- Models can be adapted to reflect local patient populations
- Results in higher accuracy than generic or outsourced solutions
5. Seamless Integration with Existing Workflows
- Designed for in-house deployment
- Reduces dependency on external vendors, enhances data security, and cuts operational costs
Industry Impact and Future Outlook
This release comes at a pivotal time for the healthcare industry. As CMS increases expectations for accurate and transparent coding practices, healthcare organizations are under pressure to improve performance while managing costs.
John Snow Labs’ engine allows clinical teams to regain control, visibility, and cost-efficiency in their coding operations. By bringing this capability in-house, providers can better manage compliance, reduce errors, and protect revenue under evolving regulatory scrutiny.
John Snow Labs’ launch of its AI-powered HCC coding engine is a game-changing advancement in the healthcare revenue cycle. By combining clinical-grade language models with human validation and operational transparency, the company enables healthcare organizations to meet the rising demands of value-based care. With CMS calling for more precise risk assessments, this solution positions providers and payers to lead with integrity, innovation, and impact.
