By Nathan Elliott, Director of Professional Services, J2 Interactive
LinkedIn: C. Nathan Elliott
LinkedIn: J2 Interactive
When NCQA validated its first ten clinical data streams in late 2021, Data Aggregator Validation looked like an experiment. Four years on, it looks like infrastructure. The program’s eleventh cohort opened in January 2026, and NCQA reports that fifty organizations across twenty-one states have now completed more than 130 validations. For health information exchanges (HIEs), a validated data stream has become something close to a credential of legitimacy. This is proof to health plans that the clinical data flowing out the door can be trusted for HEDIS reporting without redundant and costly work to prove data integrity all the way back to the source electronic health records at hospitals and doctors’ offices.
Jason Buckner, CIO of Manifest MedEx in California, recently spoke about data quality during a webinar hosted by CIVITAS Networks for Health called “NCQA DAV for HIEs: The Journey and What to Expect.” In a follow-up conversation, Buckner reiterated one of his key points during the presentation: “HIEs must be willing to take responsibility for data quality, extending back to where the data was first entered.”
Manifest MedEx has a track record of success with the NCQA DAV program and the data quality outcomes it supports. That very success is why HIE leaders should resist treating validation as a finish line. The most consequential development in this space is not that more organizations are passing data quality checks; it is that NCQA is quietly redefining what passing will mean. NCQA’s Data Quality Solutions portfolio, built on a set of HEDIS Data Quality Specifications, points toward a future in which data quality is assessed continuously and automatically rather than verified once per cohort. The specifications are explicitly designed to be embedded in vendors’ tools and integrated into existing pipelines, so issues are caught upstream—near ingestion—rather than discovered in an annual spot-checking audit, with a dedicated stream-validation offering slated for 2027.
This should change how leaders think about the work itself. Primary source verification, a labor-intensive process to confirm that the final CCD or FHIR file matches what the clinician actually documented, exists to solve a “many-to-many” problem: dozens of health plans would otherwise each verify the same data from the same sources. The DAV program lets an aggregator do that once on everyone’s behalf. But PSV is a heavy lift, and as sources multiply and data volumes climb, manual verification does not scale. Neither do the deeper questions the new specifications assume. NCQA’s framework asks whether data is complete, accurate, plausible, and conformant. These are four properties that are easy to confuse and quite different in practice. A document can be perfectly conformant to the C-CDA implementation guide and still be clinically implausible; a field can be populated and still be wrong. Leaders who equate “structurally valid” with “high quality” will keep getting surprised.
They will also keep relearning that an HIE can only be as good as its feeds. Flawless internal transformation logic cannot rescue a stream built on inconsistent source coding or incomplete submissions from a participating practice. Some of the highest-leverage quality work is therefore not technical at all; it is the governance, source remediation, and code-mapping integrity that DAV’s process standards have always emphasized, and that a continuous model makes far harder to ignore.
All of this brings the software question into focus. The market for clinical data quality and FHIR tooling is crowded and growing, and the temptation is to buy whatever gets you through this cohort’s audit. That might be the wrong frame. The better question is whether a tool operationalizes where NCQA is heading, which is where data quality is part of standard year-round operations.
The most valuable software investments now involve capabilities that specifically surface data quality problems, both upon ingestion and as data leaves the HIE. Emerging technologies are moving past simple structural validation, offering advanced capabilities to assess completeness, accuracy, and clinical plausibility across the entire data lifecycle. This technological shift is essential for operationalizing the new standard: transforming data quality from episodic tasks—performed largely to pass an annual audit—into a continuous, integrated part of an organization’s culture. This is the spirit of the change NCQA DAV encourages. By proactively identifying issues near the source and maintaining fidelity checks throughout the data aggregator pipeline, HIEs can build the standing discipline required for continuous validation.
The exchanges that thrive over the next few years will be those that shift from engaging with data quality as a badge to be earned to treating it as the visibility layer of an always-on data quality function. The badge will still matter. The durable work, and the real competitive advantage, is building the steady operational discipline and selecting tools that help an exchange stand behind its data every day for all uses, not just for the next NCQA DAV cohort.