NCQA’s data quality beta puts digital quality measurement under scrutiny
Availity has joined the National Committee for Quality Assurance’s Data Quality Solutions Implementation Beta, an industry effort built around a familiar but stubborn problem in modern healthcare: quality measurement collapses when the underlying data cannot be trusted, interpreted, or moved into the right workflow. The program is testing how proposed HEDIS® Data Quality Specifications can be turned into machine-readable logic and applied programmatically at scale.
The practical stakes are high. Health plans and providers increasingly depend on structured clinical data to power quality reporting, care gap closure, and performance analytics, yet even small failures in completeness, accuracy, or usability can distort the picture of what care actually occurred. A gap on paper may reflect missing care, but it can also reflect missing evidence, delayed documentation, or data that never arrives in a form the measurement system can use.
That distinction sits at the center of this beta. It is also the reason the project matters beyond a narrow technical exercise.
Why unusable evidence can look like missed care
The release frames quality measurement as an evidence problem as much as a care-delivery problem. When clinical information is incomplete or trapped in incompatible formats, downstream systems can misclassify a patient’s status, leave a care gap open, or weaken confidence in the resulting measurement. In a value-based environment, those errors affect more than dashboards; they influence workflow, prioritization, and the way organizations decide where to intervene.
Russ Thomas, Availity’s chief executive officer, described the industry’s current burden as spending too much time deciding whether information can be trusted and too little time acting on it. That is an accurate description of what often happens when digital quality measurement is built on fragmented data pipelines rather than validated, standards-based exchange. The promise of the beta is not simply cleaner reporting. It is a more reliable chain of evidence from the point of care to the measurement engine.
HEDIS® has long been the dominant performance measurement framework in healthcare, but the move toward digital quality measurement changes the technical burden. Manual, point-in-time verification is slow and labor intensive. Programmatic assessment requires data that can be validated consistently, interpreted automatically, and exchanged without losing context. If those pieces fail, the measurement system becomes an obstacle rather than a quality tool.
Machine-readable logic could change how quality checks happen
Availity’s role in the beta is to help evaluate whether NCQA’s proposed HEDIS® Data Quality Specifications can be translated into logic that software can process at scale. That is a significant shift in how quality checks are conceived. Instead of treating data validation as an occasional review step, the industry would move toward embedded quality assessment that can happen as data flows through existing systems.
From a health information exchange perspective, the appeal is obvious. Data quality checks performed earlier in the workflow can identify missing fields, inconsistent coding, or unusable documentation before those defects reach reporting systems. In theory, that could reduce downstream rework and make it easier for plans and providers to understand whether an open gap reflects a true service deficit or a documentation failure.
This is where the beta becomes more than an interoperability project. It asks whether standards-based assessment can be woven into everyday operational systems without forcing organizations to rebuild the entire quality infrastructure around a new measurement model. If the answer is yes, then the industry gains a way to scale validation rather than chase discrepancies manually after the fact.
Care gap closure depends on evidence that survives the workflow
Availity’s broader message is that many open care gaps persist not because care never happened, but because the evidence did not make it to the quality measurement system in a usable form. That framing is clinically important. A patient can receive recommended care and still appear noncompliant if lab results, encounter data, or other documentation are delayed, incomplete, or formatted in a way the receiving system cannot interpret.
Madeline Hasegawa, senior product manager at Availity, linked the beta to the industry’s move toward digital quality measurement and said earlier detection of data quality issues may help organizations distinguish unprovided care from missing or unusable evidence. In operational terms, that means less ambiguity for care managers and a clearer target for outreach. In measurement terms, it means fewer false positives in gap reports and a better chance of aligning quality metrics with actual clinical activity.
There is also a systems-level consequence here. When evidence exchange is unreliable, teams may spend valuable time chasing documentation instead of closing real gaps in screening, follow-up, or chronic disease management. A more standardized data quality framework could reduce that waste by sorting real care deficits from technical failures of transmission or classification.
The beta’s value will depend on how well it fits real workflows
The central question is not whether data quality matters. It does. The harder test is whether standards-based assessment can operate inside the complexity of payer and provider workflows without creating new burdens of its own. Availity’s network spans payer, provider, administrative, and clinical-data environments, which gives it a direct view of where evidence exchange breaks down and where automation might help.
The initiative also reflects a broader industry shift: quality measurement is moving away from retrospective cleanup and toward more continuous digital verification. That shift only works if the underlying specifications are not merely technically elegant, but usable across heterogeneous systems and real-world documentation patterns. Machine-readable logic is only as useful as the data sources it can reliably evaluate.
For now, the most concrete clinical implication is narrow but important. Better data quality assessment could make it easier to tell whether a care gap represents absent care, absent evidence, or evidence that never reached the system responsible for measuring performance. In a healthcare environment increasingly shaped by digital reporting, that is not a semantic difference. It is the difference between accurate measurement and a misleading signal.