John Snow Labs Unveils New AI-Powered HCC Coding Engine To Enhance Risk Adjustment Accuracy And Revenue Integrity

The end-to-end solution enables payers and providers to run patient risk adjustment programs in-house, lowering costs and improving accuracy

LEWES, Del., April 22, 2025 (GLOBE NEWSWIRE) -- John Snow Labs, the AI for healthcare company, today announced its new end-to-end Hierarchical Condition Category (HCC) coding engine designed to help healthcare providers and payers improve risk adjustment accuracy and revenue integrity. This was introduced at the company’s annual Healthcare NLP Summit in a session titled, “Transforming HCC Coding with Healthcare-Specific Language Models.”

Accurate HCC coding is critical for patient risk adjustment, as it directly impacts reimbursement models and financial sustainability in value-based care. However, studies indicate that as many as half of all patients may have prior conditions, complications, or severity indicators documented in clinical notes but not reflected in claims or electronic health records (EHRs).

The new end-to-end solution automates the discovery of missed clinical codes that are evidenced in unstructured clinical notes, but not properly coded. The solution includes a human-in-the-loop validation as well as a full audit trail. The ability to fine-tune the model to a local patient population results in higher accuracy compared to off-the-shelf models and services.

Powered by state‑of‑the‑art, healthcare‑specific language models from John Snow Labs, healthcare organizations can bring AI‑powered HCC coding in‑house, empowering clinical teams with greater control, scalability, and cost efficiency. Additionally, integrating the engine into existing coding workflows can reduce dependency on outsourced services, which can significantly reduce costs and maintain better quality control.

These enhancements come at a critical time. Earlier this month, the Centers for Medicare & Medicaid Services (CMS) released its 2026 Medicare Advantage (MA) Rate Announcement, projecting a 5.06% average increase in payments to MA plans, signifying the largest rate hike in a decade. With the additional funding comes an expectation for plans to deliver more accurate risk scores, improve coding integrity, and prove that the MA model can deliver better value. John Snow Labs can help organizations do this in a way that meets the specific demands of the healthcare industry.

“Our new HCC coding engine was developed to address the challenges of today’s healthcare industry—creating a more accurate and consistent revenue cycle at a lower cost,” said David Talby, CEO, John Snow Labs. “By leveraging the latest healthcare-specific AI models and human-in-the-loop workflows to improve them, both payers and providers can run HCC coding in-house at lower cost, with higher accuracy, and tighter control compared to outsourced or black-box services.”

To learn more about John Snow Labs’ AI-powered HCC coding solution, visit https://www.johnsnowlabs.com/.

About John Snow Labs
John Snow Labs, the AI for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations put AI to good use. Developer of Medical LLMs, Healthcare NLP, Spark NLP, the Generative AI Lab No-Code Platform, and the Medical Chatbot, John Snow Labs’ award-winning medical AI software powers the world’s leading pharmaceuticals, academic medical centers, and health technology companies. Creator and host of The NLP Summit, the company is committed to further educating and advancing the global AI community.

Contact
Gina Devine
Head of Communications
John Snow Labs
gina@johnsnowlabs.com

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/490e7235-dc9a-4b8d-8daf-a73854c095e4

Frequently Asked Questions

What is John Snow Labs' new HCC coding engine?

It's an end-to-end solution that automates the discovery of missed clinical codes in unstructured notes, improving risk adjustment accuracy and revenue integrity for healthcare payers and providers.

How does it improve HCC coding?

By leveraging AI-powered language models, it identifies conditions not reflected in claims or EHRs, improving accuracy and reducing reliance on outsourced services. It also includes human-in-the-loop validation and a full audit trail.

What are the benefits of using this solution?

Lower costs, higher accuracy, greater control over the process, improved efficiency, and better compliance with CMS requirements.

First published on Tue, Apr 22, 2025

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HCC coding risk adjustment healthcare AI revenue integrity John Snow Labs AI-powered HCC coding Medicare Advantage value-based care

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