01 Apr The Shift to Digital Quality Measures: What CMS’s Next Move Means for Healthcare Data

By Mark Coetzer, VP of Business Development at IMAT Solutions

CMS and NCQA are accelerating the transition to digital quality measures, moving healthcare away from manual reporting and toward automated, data-driven performance measurement. While this shift promises better alignment, reduced administrative burden, and more timely insights, it also exposes persistent challenges around data quality, interoperability, and standardization. Organizations that invest in strong, unified data foundations will be best positioned to succeed in this next phase of quality reporting.

A Turning Point for Quality Measurement

At RISE National 2026 and across recent industry discussions, one theme stood out clearly, which is that healthcare quality measurement is entering a new era.

CMS and NCQA are aligning around a shared vision of digital quality measures, often referred to as dQMs. These measures are designed to move beyond manual abstraction and retrospective reporting toward automated, real-time evaluation of care using standardized clinical data. This is not a small evolution. It represents a fundamental shift in how quality is measured, reported, and ultimately improved.

For years, healthcare organizations have relied on a mix of claims data, chart reviews, and manual workflows to calculate performance. That model is now being replaced by computable measures that can be executed directly within digital systems, reducing interpretation and human error while improving consistency.

From Manual Reporting to Digital Measurement

The move to digital quality measures is part of a broader transformation across CMS programs. Electronic clinical quality measures and standardized reporting formats like QRDA are already required across multiple programs for the 2026 reporting period, reinforcing the expectation that quality data must be captured and submitted electronically.

At the same time, NCQA is advancing its transition to fully digital HEDIS measures, with increasing emphasis on Electronic Clinical Data Systems and FHIR-aligned specifications.

The long-term direction is clear. By the end of the decade, quality measurement will be largely digital, automated, and integrated into the normal flow of clinical care rather than treated as a separate reporting exercise.

Why This Shift Matters Now

On paper, the benefits of digital quality measures are compelling. They promise reduced administrative burden, improved alignment across programs, and more timely insights into patient outcomes. They also create the potential for continuous, year-round measurement rather than episodic reporting tied to specific deadlines.

More importantly, they allow quality measurement to reflect the full patient journey. Instead of relying primarily on claims or limited samples, digital measures can draw from EHRs, registries, and other real-time data sources to provide a more complete picture of care.

However, as many organizations are discovering, the shift to digital does not eliminate complexity, and in many cases, it reveals it.

The Data Challenge Beneath Digital Quality

The biggest barrier to digital quality measurement is not the measures themselves. It is the data. Healthcare data remains fragmented across EHR systems, payer platforms, laboratories, and other sources. Even when data is available, it is often inconsistent, incomplete, or stored in formats that do not align with reporting requirements.

Digital measures depend on structured, standardized data. Without it, organizations struggle to accurately calculate performance, regardless of how advanced the measurement framework becomes.

This is why many industry conversations, including those at RISE National 2026, are shifting away from simply adopting new standards and toward a deeper focus on data readiness.
Because in a digital quality environment, there is no fallback to manual chart abstraction. The data must be right the first time.

Interoperability Is Evolving, But Not Enough

There is no question that interoperability has improved. FHIR-based APIs, TEFCA, and broader data exchange initiatives are helping organizations connect systems and move data more efficiently. However, interoperability alone does not solve the quality problem.

Moving data between systems does not guarantee that it is usable, complete, or aligned with measure logic. Variability in how data is captured, coded, and exchanged continues to create gaps that directly impact reporting accuracy. This is where the next phase of interoperability is emerging. It is no longer just about access, but about usability.

A Secondary but Important Shift: Leaving Manual Processes Behind

Another signal of this transformation is CMS’s push toward fully electronic transactions. Recent regulatory updates emphasize the transition away from fax and paper-based workflows in areas like claims and documentation exchange, reinforcing the broader move toward standardized digital processes.

While this may seem like an operational detail, it reflects a larger reality. Healthcare is being forced to modernize not just how data is measured, but how it is captured, exchanged, and validated across the ecosystem.

Building the Foundation for What Comes Next

As CMS and NCQA continue aligning quality programs and advancing digital measurement, the industry is moving toward a future where quality, interoperability, and analytics are tightly integrated. This is where platforms like IMAT Intelligence play a critical role.

By aggregating, normalizing, and validating clinical data across disparate systems, organizations can create a trusted data foundation that supports not only digital quality reporting, but also care gap closure, risk adjustment, and broader population health initiatives.

Because ultimately, digital quality measures are not just about reporting performance. They are about improving it. Healthcare has spent years building the ability to exchange data. Now, the focus is shifting to something more important. The ability to trust and use that data will define success in the era of digital quality measurement.

Contact IMAT Solutions to learn how IMAT Intelligence can help you build a trusted data foundation for digital quality measurement.

About the Author
Mark Coetzer is VP of Business Development at IMAT Solutions, with more than 30 years of technology experience and a decade dedicated to healthcare. He brings deep expertise in clinical data integration, interoperability, and population health, and is passionate about helping organizations build trusted data foundations for better care and smarter outcomes.

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