Risk Adjustment
Cavo Articles
These articles examine how accurate, defensible diagnosis capture strengthens risk adjustment performance, reduces audit exposure, and protects revenue in an era of increasing CMS scrutiny.
Machine Learning AI Suggests. Precise Word Matching AI Delivers.
For health plans, risk adjustment success depends on one non-negotiable requirement. Every ICD-10 and HCC submitted must be accurate, specific, complete, and fully defensible under audit. As CMS oversight tightens and RADV exposure increases, payers can no longer evaluate risk adjustment technologies based solely on speed or automation claims. The real question is how completely a solution identifies diagnoses and [...]
What to Look for in Next-Gen HCC Coding Software: A Buyer’s Checklist
Risk adjustment and coding leaders face mounting pressure to deliver accuracy, reduce audit risk, and streamline workflows within their organizations. Next-generation HCC coding software must solve these fundamental problems, not perpetuate them. Machine learning-based suggestions with confidence intervals create more work, not less, even while they miss many HCCs or suggest codes with lower RAF scores than documentation warrants. Moreover, [...]
Missing Rare or Complex HCCs? Why Standard NLP Solutions Leave Money on the Table
Risk adjustment leaders face a harsh reality: standard NLP solutions systematically miss the rare and complex Hierarchical Condition Categories (HCCs) that drive the highest reimbursements. While health plans chase every documentation opportunity, legacy HCC coding software leaves substantial revenue uncaptured: and creates audit exposure that keeps executives awake at night. The financial stakes are unforgiving. Missing a single complex HCC [...]
Lost Revenue, Ballooning Budgets: When Risk Adjustment Gets Unsustainable (And How to Fix It)
Risk adjustment was supposed to level the playing field. Instead, it’s become a financial drain—amplified by machine learning/NLP coding tools that plateau in accuracy, add operational complexity, and fail to scale cleanly. What started as a system to ensure fair payments based on member health complexity has morphed into an expensive, error‑prone process. Many organizations leaned on statistical NLP to [...]
