22 Sep Health Plan Quality Is Improving. Trusted Data Helps Make It Measurable.

Top Takeaways

• More health plans achieved NCQA’s highest rating in 2026, with 18 plans earning 5 stars compared with 11 last year.
• Measuring quality improvement depends on having clinical data that is accurate, complete and usable across increasingly complex healthcare environments.
• As quality measurement becomes more digital, health plans need stronger data foundations that can support reporting, analytics and ongoing performance improvement.
• NCQA Data Aggregator Validation provides an important framework for establishing confidence in aggregated clinical data used for HEDIS reporting and other quality programs.

Health plan quality appears to be moving in the right direction. NCQA’s 2026 Health Plan Ratings show that 18 health plans earned the organization’s highest 5-star rating, up from 11 last year. Average ratings also increased slightly, while health plans improved performance across nearly four fifths of chronic disease management measures and made gains in behavioral health.

Those encouraging results also underscore the importance of reliable measurement in understanding and improving healthcare quality.

As health plans work to improve outcomes, close care gaps and demonstrate performance, the quality of the data supporting those efforts becomes increasingly important.

Better Quality Measurement Starts with Better Data

Health plans have access to more clinical information than ever before, flowing from providers, hospitals, HIEs and other sources. Turning all that information into reliable quality measurement is a different challenge.

Data can arrive in different formats, use inconsistent terminology, contain gaps or duplicates, and vary significantly in completeness and accuracy. Those issues can make it harder to establish an accurate picture of patient populations and measure performance consistently.

This becomes particularly important as healthcare moves toward more digital approaches to quality measurement.

NCQA says clinical data used for quality reporting needs to be accurate, complete and fit for use, with data quality assessed earlier in the lifecycle. Its evolving data quality framework is designed to support more automated and scalable digital measurement across interoperable healthcare environments.

For health plans, that makes data quality part of the quality improvement strategy itself.

Trusted Clinical Data Reduces Uncertainty

The challenge extends beyond collecting clinical data. Health plans need confidence that the information they receive can be trusted for quality measurement and reporting.

Validation is becoming increasingly important as health plans rely on aggregated clinical data from multiple sources. NCQA’s Data Aggregator Validation program evaluates the quality and integrity of clinical data and the processes used to manage it, from ingestion at primary sources through transmission to end users.

IMAT Solutions has earned NCQA’s Validated Data Stream designation, reflecting the rigorous standards applied to clinical data flowing through the validated stream. DAV validated data is also accepted as standard supplemental data in HEDIS audits, eliminating the need for primary source verification when health plans report data from validated sources.

For health plans, this can reduce the burden associated with quality measurement while providing greater confidence in the clinical data used to evaluate performance and identify opportunities for improvement.

Digital Quality Measurement Raises the Stakes

The transition toward digital quality measurement makes this foundation even more important.

Traditional quality reporting has often involved significant manual work to collect, reconcile and validate information. Digital measurement creates opportunities to make that process timelier and more scalable, but automation depends on the reliability of the underlying data.

NCQA is moving toward standardized, computable approaches that can integrate quality measurement more directly into healthcare workflows. Its digital strategy emphasizes interoperable data, automated quality checks and greater confidence in information earlier in the data lifecycle.

That means organizations need data infrastructure capable of doing more than moving information between systems. Clinical data must be normalized, validated and prepared so it can reliably support measurement and downstream use.

From Reporting Quality to Improving It

The ultimate value of quality measurement is not the rating itself. It is the ability to understand performance and identify where care can improve.

Reliable data gives health plans a stronger foundation for identifying gaps in care, tracking chronic disease management, evaluating population health initiatives and determining whether interventions are producing meaningful results.

It also creates a more consistent foundation for collaboration among health plans, providers and other organizations working from the same clinical information.

As this year’s NCQA ratings demonstrate, health plan performance can improve. The next challenge is ensuring organizations have the trusted data infrastructure needed to measure that progress accurately and turn those insights into continued improvement.

Building a Trusted Data Foundation for Quality

IMAT Intelligence helps healthcare organizations aggregate, normalize, validate and operationalize clinical data so it can support quality measurement, analytics and other critical healthcare initiatives.

For organizations that want to better understand the condition of their existing data, the IMAT Health Data Quality Assessment provides a measurable baseline across data integration, normalization, completeness, accuracy and AI readiness, helping identify gaps and prioritize opportunities for improvement.

As quality measurement becomes increasingly digital, establishing that trusted data foundation can help health plans move from simply reporting performance to using data more effectively to improve it.

Learn how the IMAT Health Data Quality Assessment can help your organization build a stronger foundation for quality measurement and digital transformation. Contact us to get started.

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Additional Insights

• Why Health Plans Are Shifting Focus from Data Exchange to Data Intelligence
• Why AI Ready Data Drives Sustainable Quality Performance for Health Plans
• What Senior Quality Leaders Must Do Now to Prepare for Digital HEDIS
• Why Health Plan Data Infrastructure Is Becoming a Competitive Advantage