RxHCC Suspecting in the Post-V28 Landscape

The transition to the V28 model in Medicare Advantage created new challenges for Risk Adjustment. V28 increased the number of Hierarchical Condition Categories (HCCs) from 86 to 115, but also removed more than 2,000 diagnosis codes. The result, value-based care leaders are concerned this transition will lower patient risk scores and reimbursement payments.

The transition to V28 has prompted value-based care organizations to incorporate prescription information alongside traditional diagnosis codes. Tapping into more clinical data sources allows payer and providers to create a comprehensive and accurate view of patient risk profiles, potentially uncovering previously unidentified health conditions. Leveraging additional data can enhance condition management strategies and ultimately improve patient care outcomes.  

Value-based care organizations have traditionally prioritized diagnosis codes due to their standardization, integration into clinical systems and their direct role in calculating reimbursements. Prescription data can be more challenging because of its variability, frequent changes, and integration challenges with existing healthcare systems.  

At the same time, prescription data provides concrete evidence of ongoing health conditions, even when patients don't have regular office visits, and analyzing this data can be more efficient than extensive chart reviews. As the Risk Adjustment landscape evolves, there's a growing recognition of the value of prescription data for suspecting diagnoses.

Advanced AI solutions can now analyze comprehensive medication databases to identify potential missed diagnoses. These systems examine all medications a patient is taking, not just those prescribed within a single organization, allowing for the detection of HCCs that might otherwise be missed.

Centers for Medicare & Medicaid Services (CMS) also recognized the value of this approach. In response to the V28 impact, CMS increased the weight for some medication-related HCCs and introduced a new RxHCC model for Part D Risk Adjustment. These changes encourage the use of comprehensive data, including prescriptions, to improve risk adjustment accuracy.

What next? Consider how AI-powered solutions could enhance Risk Adjustment strategies to incorporate broader data sets. Look for systems that can integrate with your existing workflows, analyze diverse data sources, and provide actionable insights in real-time. By leveraging advanced technologies, value-based care organizations can improve risk adjustment accuracy, enhance patient care, and maintain financial stability.

Moving forward, a balanced approach that combines traditional diagnosis-based HCC coding with robust RxHCC suspecting, supported by AI-driven software, will be crucial. This strategy can mitigate the financial impact of V28 changes, improve the accuracy of patient risk profiles, and streamline compliance.

Learn more about how Reveleer can reach across a fragmented health data landscape to create complete views of patient risk.