Accelerating gap closure with data precision and abstraction

With growing demands to improve Star Ratings, close care gaps, and meet NCQA and CMS standards, Artificial Intelligence (AI) has become essential for health plans in quality measurement. At Reveleer, we’ve embraced this shift with a clear, outcome-driven AI strategy that combines intelligent workflows and data precision across both seasonal and year round quality improvement programs for both payers and providers.

From seasonal blitzes to year-round precision

Traditional HEDIS abstraction has long relied on seasonal staffing surges, manual chart reviews, and fragmented systems. Reveleer’s solution and technology breaks this mold by supporting both project-based abstraction (e.g., HEDIS audits) and year-round quality programs. Our approach enables health plans to maintain compliance, maximize Star Ratings, and engage providers continuously during and beyond HEDIS season, providing a platform for payers and providers to close gaps more efficiently together.

AI-enabled evidence validation and matching for quality teams

Next gen quality is Reveleer’s latest clinical quality abstraction solution, designed to modernize how health plans manage HEDIS and year-round quality initiatives to impact Star Ratings. It combines  AI, an intuitive UX, and intelligent workflows to deliver a faster, more accurate, and more user-friendly abstraction experience. Built on Reveleer’s full-featured platform; our next gen quality improvement technology also evolves with changing NCQA and CMS standards all year long. Initial benchmark testing shows this new solution accelerating abstraction by up to 30% for the average coder for a blended set of measures.  

At the heart of our Clinical Quality Solution is EVE^TM (Evidence Validation Engine), a proprietary AI system that combines natural language processing (NLP), optical character recognition (OCR), and quality measure logic to find and surface results in our best-in-class workflow experience. EVE enables abstractors by surfacing evidence from structured and unstructured data sources, linking abstracted results directly to clinical documentation, automating routing of documents for efficient review, overread, and audit trail creation with support for hybrid, admin, and digital measures. This results in up to 75% efficiency gains in abstraction workflows and 88%+ prediction accuracy on the most common measures.

As a complement to that solution, Care Gap Manager (CGM) is Reveleer’s year-round, provider-first application designed to help health plans and their provider groups proactively close care gaps, improve Star Ratings, and streamline quality operations. It’s built to support continuous engagement, real-time performance tracking, and incentive-driven provider collaboration across all lines of business.

Reveleer’s Care Gap Manager uses AI to scan unstructured clinical documentation and match relevant evidence to care gap closure criteria. This dramatically reduces time spent on manual chart review and improves abstraction accuracy. Some plans achieve up to 95% average measure accuracy across HEDIS measures in evidence matching, eliminating inefficient chart reviews by setting aside “zero-value” charts with no gap closure evidence.
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A more efficient abstraction experience supported by AI

Efficiency in abstraction isn’t just about speed, it’s about reducing cognitive load, minimizing errors, and enabling abstractors to focus on high-value tasks. Here’s how Reveleer’s AI transforms the abstraction experience:

• Smart evidence surfacing: AI automatically identifies relevant clinical evidence from disparate data formats. Abstractors are presented with pre-validated evidence snippets, reducing time spent per chart.

• Contextual understanding: AI clinically reasons by analyzing text to grasp meaning beyond words from their context. It uses techniques like  part-of-speech tagging, word sense disambiguation, and  theme extraction  to identify key entities, sentiments, and relationships in data, enabling automation of reporting, intelligent search, and trend spotting in project documentation for better quality control.

• Real-time feedback and audit trails: Every AI-suggested abstraction is linked to its source, with built-in audit trails and overread capabilities. Abstractors can accept or reject as valid or invalid creating a feedback loop.

• Integrated workflows: AI-driven abstraction is embedded within Reveleer’s workflows, enabling seamless transitions between retrieval, abstraction, and gap closure. This reduces handoffs and improves operational throughput.

• Zero value chart avoidance – When AI determines there is no evidence present in a chart, it is set aside, saving valuable Abstractor time that would have been wasted in a fruitless review of a zero-value chart.
  

A strategic choice for care gap closure

Reveleer’s Quality Improvement AI technology delivers measurable value across key dimensions including proactive care gap closure and retrospective reviews:

• Operational efficiency: Automate repetitive tasks and reduce manual abstraction burden

• Regulatory readiness: Meet NCQA, CMS, and state program requirements with confidence

• Star optimization: Accelerate gap closure and maximize bonus capture

• Scalability: Support national and regional plans with flexible architecture

• Continuous improvement: Enable year-round quality performance and provider engagement

In a crowded landscape of value-based care quality vendors, Reveleer leads the way by delivering unified, AI-enabled solutions that go beyond abstraction to drive real outcomes. While competitors may offer strong stand-alone solutions, only Reveleer combines advanced evidence validation, real-time provider engagement, and scalable year-round quality workflows backed by a proven track record across 72 million covered lives. For health plans seeking operational efficiency, Star optimization, and continuous improvement, Reveleer offers a strategic partnership built for the future of value-based care technology rooted in critical fundamentals.