Medicare Advantage

Start with the gap the plan actually has. With over 33 million Medicare Advantage enrollees in 2024, the industry-wide pattern is well documented: Annual Wellness Visit completion sits below 35 percent and in-home assessment engagement is often under 25 percent. That combination produces coding gaps, care gaps, and revenue leakage at the same time.

Against that, four capabilities are worth more than a feature list. AI-based suspecting that surfaces undocumented conditions using adaptive modeling and longitudinal analysis. NLP-powered chart review that finds high-risk documentation gaps and prioritizes coders against them. Real-time RAF analytics rather than a quarterly report. And prioritized member targeting, so AWV and IHA outreach goes to the members where a visit changes both the care and the score.

Then ask two harder questions: does it handle duals in one view, and can it prove its own output in a RADV audit.

By closing the loop between finding a gap and acting on it. The MAO solution runs AI-driven HCC suspecting and recapture tracking to surface undocumented risk conditions using adaptive modeling and longitudinal analysis, then NLP-powered chart review to identify high-risk documentation gaps and streamline coder prioritization.

Smart member chase lists prioritize AWV and IHA targeting to drive visit completion and documentation rates, so prospective and retrospective work point at the same members rather than competing for them.

Provider coding gap insights close it out, reporting on documentation accuracy, missed recapture opportunities, and improvement trends by provider, which is what turns a one-time chart chase into a durable documentation improvement.

Models: CMS-HCC, ESRD, and RxHCC. Plan types: MA, MAPD, and D-SNP.

Dual-eligible populations are handled through cross-line-of-business data integration, which merges Medicare, Medicaid, and historical data into a unified view of the member. For a D-SNP population that is the difference between suspecting on a partial history and suspecting on the whole one.

By scoring the evidence, not just the code. Submitted HCCs are scored by the strength of their supporting documentation, conditions with weak MEAT support are flagged, and CMS sampling exposure is simulated so a plan can find unsupported diagnoses before an audit rather than after a recoupment.

This matters more under extrapolation. CMS now extrapolates RADV findings across the contract, so the financial exposure of an unsupported diagnosis is no longer bounded by the sampled member.

Yes. In-year financial impact modeling tracks RAF trends, projects score impact, and simulates interventions in real time.

The practical use is budget allocation. Chart chase, in-home assessments, and provider engagement all compete for the same dollars, and modeling the projected RAF impact of each before committing is how that decision stops being a matter of precedent.

The published example on this page is a Use Case Snapshot rather than a named customer result: a plan with 250,000 Medicare Advantage lives and 60,000 D-SNP lives, running CMS-HCC, ESRD, and RxHCC models across 50,000 chart reviews per year at 62 percent aggregate IHA and AWV participation.

Invent Health treats those as capability illustrations. For a figure specific to your population, the accuracy benchmark from a chart sample is the honest starting point.