Health plans frequently encounter challenges in managing separate workflows for quality improvement and risk adjustment programs.

This leads to duplicated efforts, inefficiencies, and provider frustration from repeated data requests. AI-powered chart review offers a practical way to unify these distinct processes into one streamlined workflow.

Creating Collaboration and Efficiency with a Unified Workflow

Traditionally, quality and risk teams within health plans have operated independently, each handling their own chart reviews, data collection, and analysis.

With advanced AI technology, health plans now have an opportunity to merge these previously siloed workflows into one cohesive workflow. By encouraging collaboration between quality and risk teams with AI as an enabling technology, organizations eliminate duplicated efforts and improve operational efficiency.

AI-driven workflows also allow for effective recycling of previously collected charts, meaning that data already captured for one purpose can seamlessly serve another, further accelerating gap closure efforts. For example, a chart initially reviewed for a quality measure may also contain clinical evidence valuable to risk adjustment outcomes. The reverse is often true: a chart obtained for risk adjustment outcomes may also contain evidence valuable to quality improvement outcomes.

Imagine if a chart could be processed once and its results used by both quality and risk teams rather than re-requesting the chart or processing it twice. Both teams would benefit from a single-pass processing approach.

Key benefits include:

• Reduced manual chart reviews
• More accurate and faster gap closure
• Improved provider-payer relationships
• Better utilization of existing data sources

Enhancing Clinical Insight Through AI-driven Reasoning

Next-generation AI technology is advanced enough to simultaneously analyze patient charts for relevant information across both quality improvement and risk adjustment initiatives. For example, AI can effectively detect Social Determinants of Health (SDoH) indicators important for quality programs, alongside Hierarchical Condition Category (HCC) data needed for accurate risk adjustment. This capability significantly reduces manual workloads, speeds gap closure processes, and increases compliance and performance for both risk and quality teams.

Reducing Provider Frustration through Better Data Practices

Provider groups often struggle under significant administrative burdens due to multiple and repetitive data requests from payers. These redundant requests can strain relationships between payers and providers. By utilizing AI-driven solutions, payers can better leverage the supplemental data they already have and reduce unnecessary data collection. This not only decreases provider abrasion but also fosters healthier, more cooperative relationships, allowing providers to dedicate more resources and attention to direct patient care.

Unlocking Value from Unstructured Clinical Data

One of the greatest strengths of next-generation AI is its ability to analyze and interpret unstructured clinical notes and standardized clinical documents like Consolidated Clinical Document Architecture (C-CDAs) from Health Information Exchanges (HIEs). Previously, extracting actionable insights from this type of data posed a significant challenge.

Advanced AI now enables payers to fully utilize these valuable yet underused resources. By deriving critical insights from previously overlooked information, health plans gain a richer and more comprehensive view of patient health, ultimately leading to better-informed decisions and improved patient outcomes and greater revenue capture.

Conclusion

With the emergence of advanced AI, now health plans can:

• Reduce manual chart reviews
• Achieve more accurate and faster gap closure
• Improve provider-payer relationships
• Better utilize existing data sources

Learn More

Novillus’ Curate is leading this transformation to unify quality and risk adjustment workflows.  Click here to learn more about Curate.