Closing gaps faster with a blend of traditional and next generation AI-enabled solutions

Artificial Intelligence (AI) continues to transform how health plans and providers approach risk adjustment and quality gap closure in value-based care. New technologies continue to emerge and increased data availability is enabling organizations to think bigger about how to accurately gain efficiencies to drive effective reimbursement while offering the highest standard of care to patients. The choices for methods and tools for each organization will continue to vary depending on where they are in their own risk adjustment and quality journey. And even as prospective risk adjustment gains traction, organizations are still going to need a blend of both prospective and retrospective strategies and solutions.

Balancing tried & true and next generation approaches

Providers are increasingly adopting proactive care strategies in value-based care. This involves leveraging predictive analytics and AI to identify high-risk patients earlier, close care gaps before claims are filed, improve documentation accuracy at the point of care, and reduce audit risk by ensuring conditions are captured correctly during patient visits.

Retrospective risk adjustment remains important for validating past documentation, capturing missed diagnoses, and ensuring accurate coding for reimbursement. However, this process is historically labor-intensive and often delayed by claims processing, leading to missed revenue and increased compliance risk. These challenges are driving providers toward prospective approaches that handle more of the workload upfront and deliver value without downstream issues.

Health plans are also exploring prospective risk adjustment for similar reasons. These models enable timely interventions at the point of care, improve cost forecasting across Medicare Advantage, Medicaid, and commercial lines, and reduce compliance risk through more specific documentation.

Despite growing interest in prospective models, retrospective strategies remain essential. They help recover missed revenue from under-coded claims, audit and validate submitted diagnoses, and serve as a safety net for capturing legitimate but unbilled conditions. When manual processes are replaced with automated platforms, retrospective reviews have consistently proven effective at recovering millions of dollars in missed revenue.

The blended approach: Best of both worlds

Most organizations now recognize that a blended model—combining both prospective and retrospective methods—is the most effective strategy. This approach allows payers and providers to leverage real-time insights for proactive care and use retrospective reviews to catch any gaps. With this blended approach and AI/technology, the need for retrospective workflows to catch any gaps should diminish over time.

Comparing prospective vs retrospective differences
‍

‍

Audit-ready confidence meets point-of-care action

AI-enabled retrospective solutions are designed primarily to enhance retrospective risk adjustment coding by improving the accuracy and speed of diagnosis and documentation identification. The core goal is to surface relevant diagnoses with high confidence scores, enabling coders to validate suggestions efficiently. These systems emphasize transparency and audit readiness, linking every diagnosis to its source evidence. The approach not only supports compliance, but also builds trust with users allowing teams to more quickly complete coding and validation. Technology like our EVE^TM Confidence Score (ECS) integrates intelligent routing and prioritization capabilities to automate workflows, reduce manual effort, and focus coder attention on high-value charts.

In contrast, prospective AI-enabled technology has a primary goal of extracting deeper clinical insights to inform physicians with the next best action to efficiently provide the right care to the patient, without introducing administrative burden to an already strained workflow. By combining agentic pipelines and deterministic clinical logic, especially in high-noise scenarios, our new Hybrid AI architecture allows payers and providers to find and filter the most likely suspects from a fragmented sea of structured and unstructured data.

The road ahead

As prospective AI technologies continue to mature, integration into clinical workflows will become increasingly seamless. As we continue to evolve towards that vision, predictive capabilities must drive to be as stabile and defensible as retrospective methods ensuring that they reliably deliver on the outcomes of both plans and providers at scale and across all relevant use cases. That’s why Reveleer is investing in both—building a platform that supports real-time suspecting and post-encounter validation, all within a unified, explainable framework.

‍

‍