Artificial intelligence is rapidly becoming embedded across risk adjustment, coding, and quality workflows. We see this every day in conversations with health plans that are under increasing pressure to improve accuracy while navigating a far more aggressive audit environment.
But as AI adoption accelerates, one reality has become clear: not all AI reduces risk. In fact, when implemented without the right guardrails, AI can introduce new exposure instead of eliminating it.
When built correctly, AI becomes a defensive asset. It helps health plans reduce audit risk, strengthen documentation integrity, and protect revenue. When built poorly, it becomes a liability.
RADV Pressure Has Changed the Risk Equation
RADV audits are no longer a theoretical concern. CMS has made it clear that every diagnosis submitted must be backed by airtight documentation. Unsupported or poorly evidenced diagnoses now carry real financial and reputational consequences.
Historically, many organizations relied on retrospective reviews to clean up issues after the fact. That approach is no longer sufficient. Missing documentation can’t always be reconstructed months later, and late discovery limits corrective action.
The risk equation has changed. Health plans need earlier visibility into documentation issues, not downstream surprises.
What Defensive AI Actually Means in Risk Adjustment
Defensive AI is not about maximizing code capture. It is about accuracy, validation, and compliance.
A defensive AI strategy focuses on identifying risk before it becomes exposure. It prioritizes documentation integrity over volume and supports consistent decision-making across teams. The goal is simple: ensure that every diagnosis submitted is defensible.
This approach recognizes that both overcoding and undercoding are costly. Overcoding increases audit risk. Undercoding leaves legitimate revenue on the table. Defensive AI helps organizations avoid both by focusing on what is clinically supported and properly documented.
Flagging Risky Diagnoses Before They Reach Claims
One of the most powerful advantages of defensive AI is its ability to flag unsupported, unvalidated, or high-risk diagnoses before they ever reach a claim.
Rather than discovering issues months later during an audit, AI can surface documentation gaps in near real time. These insights allow compliance teams and clinicians to intervene while corrections are still possible.
This early visibility reduces rework, minimizes provider disruption, and gives health plans a proactive line of defense instead of a reactive cleanup effort.
Building Reproducible Evidence Trails for Every HCC
RADV audits don’t just ask what was coded. They ask why it was coded.
Defensive AI supports audit readiness by linking diagnoses directly to supporting clinical evidence. It helps create consistent, reproducible evidence trails that can be reviewed, validated, and defended.
This traceability matters. When every HCC is tied to clear documentation and reasoning, organizations are better prepared to withstand audit scrutiny. Audit readiness becomes a continuous state rather than a scramble triggered by an audit notice.
Avoiding Both Overcoding and Undercoding
One misconception we often encounter is that defensive strategies limit performance. In reality, accuracy protects both compliance and revenue.
Defensive AI helps organizations strike the right balance. By validating diagnoses against evidence and clinical criteria, plans avoid inflated risk scores while ensuring legitimate conditions are not missed. Accuracy, not aggression, is what sustains long-term performance in a heightened audit environment.
Governance and Ongoing Review Are Non-Negotiable
AI does not operate in a vacuum. Regulations evolve. CMS guidance changes. Clinical standards shift. Without oversight, even well-designed AI systems can drift out of alignment.
Compliance leaders must regularly review AI outputs, validate logic, and ensure continued alignment with CMS expectations. Governance frameworks are what keep AI defensible over time.
When oversight is embedded into the strategy, AI remains an asset instead of becoming an unchecked risk.
AI as a Defensive Strategy
The future of risk adjustment will not be defined by how aggressively organizations deploy AI, but by how responsibly they govern it.
Defensive AI allows health plans to move forward with confidence. It strengthens documentation integrity, reduces audit exposure, and supports sustainable performance in an increasingly scrutinized environment.
This is a topic we’ll explore further at the Risk Adjustment Innovations Forum, where we’ll discuss how AI can be operationalized safely across coding, quality, and compliance.
AI does not replace judgment. When designed correctly, it protects it.
