By David Talby, CEO, John Snow Labs
LinkedIn: David Talby
LinkedIn: John Snow Labs
Hierarchical Condition Category (HCC) coding supports risk scores that drive Medicare Advantage and other value-based payments. With more than half of Medicare beneficiaries now enrolled in Medicare Advantage for 2025 (that equates to roughly 35.7 million people), precision in coding directly affects financial performance and compliance.
To manage this, many organizations outsource HCC coding to third parties that promise scale and turnkey accuracy. However, this has proven to be a costly, difficult, and risky undertaking. It’s not all bad news, though. Advances in generative AI have made it far easier and safer to bring HCC coding in-house, cutting costs, and improving audit readiness in the process.
The downside of outsourcing
The business model behind outsourced HCC coding creates misaligned incentives. You’re effectively trading off higher spend for softer accuracy guarantees. Health plans can spend millions under per-chart pricing models, but vendors rarely provide the transparent, auditable evidence needed to show that coding accuracy is actually better.
To boot, audit exposure is the responsibility of your organization, not the vendor. While CMS has mechanisms in place to claw back overpayments, it’s not a perfect system. If an outsourced partner “pushes” codes, you keep the liability when auditors review the records and they keep their fees.
Additionally, with most outsourced models, you ship protected health information (PHI) to a third party and accept someone else’s thresholds, edit logic, and risk tolerance. That lack of control and transparency is a problem if CMS asks “why was this HCC assigned?” and you can’t produce an explainable, defensible trail.
Regulatory change is a moving target
Imagine hiring a tax firm that charges 20% of your deductions instead of an hourly rate. They have every incentive to find more deductions and to push the envelope. If you get audited, you’re liable and they keep their cut. That’s the risk dynamic of many outsourced HCC models. Vendors maximize near-term revenue, while you pay the price and face the long-tail audit exposure.
In Medicare Advantage, the stakes are extremely high. Payments in 2025 continue to rise as enrollment grows, intensifying scrutiny on the accuracy of risk scores and coding practices. Policy updates project ongoing payment increases tied partly to risk score changes, fueling further attention from CMS and watchdogs (Reuters).
Regulators are taking a hard stance, making the risks even more clear. The Office of Inspector General (OIG) has repeatedly warned about diagnoses that come only from health risk assessments (HRAs) or chart reviews, but aren’t backed up anywhere else in the medical record. These types of codes raise payments but often don’t hold up under an audit. By default, you’re taking calculated regulatory risks if coding isn’t buttoned up.
A better alternative
With advances in generative AI, bringing HCC coding in-house can eliminate many of these issues at a fraction of the cost and risk profile. Your organization is in the driver’s seat when it comes to edit logic, thresholds, evidence requirements, and escalation paths. That means audit readiness is built into the design, with full provenance for every suggested and accepted code. Not dependent on a third-party.
It may sound daunting, but it’s actually quite practical. You already employ clinical coders, and when equipped with the right tools, they can pre-review charts, surface high-yield evidence, and accelerate second-level review without adding headcount. Perhaps most importantly, solutions that run inside your environment avoid sharing PHI while giving your team full observability.
Not too long ago, doing it yourself meant building a natural language processing (NLP) platform from the ground up. Now, generative AI-powered HCC coding tools can be integrated into existing workflows to read messy, siloed, multimodal data, and keep pace with evolving models, operate on-prem or in a private cloud environment, and let you customize to meet the needs of your own organization.
The safer, smarter path forward
The writing is on the wall: Unsupported diagnoses will be found and funds will be recovered. The OIG continues to spotlight vulnerable coding channels like HRAs and chart reviews when they’re not supported elsewhere in the medical record. And CMS’s Part C error-rate work shows billions at stake each year.
Outsourcing made sense when the technology gap was wide, but that gap is closing fast. Today, organizations can deploy AI-native HCC platforms behind their own firewall, tailor them to their own compliance standards, and operate at a predictable per-patient cost, while remaining audit-ready.
Risk adjustment is simply too important to leave it up to someone else. The future of HCC coding is in-house, and with a combination of generative AI and clinical coders, organizations can directly address HCC coding with control, transparency, and cost savings.