Testing Data Quality at National Scale

States are required to transition to the newest version of the federal health data platform file layout for their regular monthly data submissions. This long-planned update addresses a wide range of stakeholder needs but also introduces risk. During the transition, data quality could regress if changes are not carefully validated.

To mitigate this risk, technical assistance teams agreed they needed a testing process capable of identifying regressions while meeting three critical requirements. The process needed to be scalable across 54 states and territories, consistent regardless of who conducted the review, and transparent enough to be clearly communicated to states through written guidance and webinars.

eSimplicity began by reviewing existing successful data quality review processes to identify components that could be adapted to the significant data model modernization effort. Building on those foundations, the team designed interactive dashboards well suited for scalable, standardized analysis.

The dashboards dramatically reduced the time required to access and review underlying data while eliminating common challenges associated with direct database querying, such as access limitations, inconsistent ad hoc queries, and data interpretation differences. By standardizing how data was reviewed and presented, the team could quickly identify regressions, provide consistent feedback, and deliver targeted coaching to states.

To support adoption and transparency, the dashboards were paired with comprehensive internal and external documentation, training materials, and procedures for sharing results across teams, with program stakeholders, and directly with states. This structure proved especially valuable for states that required multiple review cycles to achieve equivalent or improved data quality.

eSimplicity’s approach combined technical rigor with scalable enablement. Rather than simply validating data submissions, the team built a repeatable framework that paired standardized analytics with clear documentation, collaborative review process, and targeted support. This ensured consistency at scale while equipping partner teams to navigate a highly complex transition with confidence. This structure proved especially valuable for states that required multiple review cycles to achieve equivalent or improved data quality.

The equivalency testing method has now been applied to 80% of states and territories, demonstrating its ability to scale while maintaining consistency and quality.

Through multiple rounds of equivalency testing the process prevented over 50 critical and high priority data quality errors from moving into production. This is an immediate improvement to data users, prevents the need for costly historical resubmissions, and reduced the need for reactive state support.

As a result of this success, the team revisited the original processes that inspired the approach and redesigned them using lessons learned, such as training and collaboration on protocols with state support teams, and dashboard capabilities. The updated process is faster, more efficient, and repeatable, enabling a broader group of team members to conduct reviews rather than relying on a small number of specialists. This shift increases capacity and improves resilience while maintaining high-quality support.

eSimplicity’s focus is on building solutions that improve both immediate delivery outcomes and the operational practices required to sustain quality over time. By creating a scalable testing framework designed for consistency, transparency, and repeatability, the team established a stronger foundation for future data modernization efforts.