There was a time when choosing a risk adjustment solution came down to two questions: How fast can it review charts? How many HCCs can it find? Those questions still matter. But in 2026, they are no longer be enough.
CMS is auditing all eligible Medicare Advantage contracts annually. The agency plans to use AI to support its own coding reviews. DOJ enforcement actions have shown that the design intent behind coding systems, not just their accuracy rates, is now subject to investigation. For health plans evaluating risk adjustment vendors today, the stakes have fundamentally changed.
The New Evaluation Framework
Health plan leaders, from VPs of Risk Adjustment to CFOs and Chief Compliance Officers, are asking different questions now. The shift reflects a market that has moved from “capture” to “care,” from revenue optimization to defensible accuracy.
Does the platform add and remove codes?
This is the baseline question. Any risk adjustment software that only adds diagnoses operates as a one-way system. That pattern is now directly associated with RADV audit risk and DOJ scrutiny. The right risk adjustment solution validates supported diagnoses while systematically flagging and removing codes that lack adequate clinical evidence.
Can the AI explain every recommendation?
Explainability is no longer a nice-to-have feature. It is a compliance requirement in practice. When a RADV auditor reviews a submitted diagnosis, the health plan must be able to show why that code was captured, what clinical evidence supports it, and how the decision was made. Risk adjustment software powered by unexplainable automation cannot meet this standard.
Does it prepare you for audits automatically?
With CMS initiating new audit cycles roughly every three months and limiting record submissions to two per audited HCC, health plans need risk adjustment vendors that produce audit-ready documentation as part of the normal workflow. Scrambling to build an evidence trail after receiving an audit notice is no longer viable.
Is the AI governable at the enterprise level?
Shadow AI and ungoverned automation tools are a growing concern for CIOs and compliance leaders. The right risk adjustment solution is transparent, auditable, and fully controllable by your IT and compliance teams.
Why RAAPID Is the Standard for Defensible Coding
RAAPID’s Novel Clinical AI Platform answers every one of these questions.
Its Neuro-Symbolic AI does not operate as ungovernable automation. It combines deep learning with clinical reasoning logic, producing a transparent evidence trail for every HCC suggestion grounded in MEAT-based documentation. Every recommendation is explainable, traceable, and defensible.
RAAPID’s two-way retrospective model adds supported codes and removes unsupported ones, the exact compliance posture CMS expects. The platform delivers over 98% coding accuracy and cuts chart review time from 40+ minutes to under 8 minutes. Coding teams see 60 to 80% productivity gains without sacrificing clinical rigour.
The platform is HITRUST certified, SOC 2 Type II compliant, and deploys natively on Microsoft Azure. Plans can run it within their own Azure tenant for full data sovereignty, or use RAAPID’s hosted environment for reduced IT overhead. Either way, PHI stays protected, and governance stays tight.
The Real Test for Risk Adjustment Vendors
The market is separating into two camps: risk adjustment vendors still selling speed and volume, and those selling defensible accuracy and audit readiness. CMS has made clear which direction it expects the industry to go.
Before your next vendor evaluation, ask yourself: if CMS reviewed every coding decision your risk adjustment software helped produce, would the evidence trail show clinical truth or revenue intent?
RAAPID was built so the answer is always clinical truth. That is why health plans making the shift from capture to care are choosing RAAPID as their risk adjustment solution.
