Quality Is Only as Good as the Data Behind It

In Episode 2 of Data Quality Perspectives, host Burke Burnett sits down with NCQA Chief Product and Transformation Officer Vik Wadhwani to examine why modernizing quality measures is not enough on its own.

The conversation explores how standards such as FHIR make data easier to exchange, but do not automatically make it trustworthy. Even a technically sound measure can produce unreliable results when its underlying data is incomplete, inconsistent or lacks transparency. Wadhwani explains why organizations that digitize measurement without modernizing data practices risk automating old problems and moving uncertainty through the system faster.

Burnett and Wadhwani also discuss what a modern foundation for digital quality measurement requires: shared standards, clear data-quality expectations, traceability, automated and repeatable validation, continuous assurance and stronger alignment among data partners. These capabilities help organizations identify issues earlier, reduce duplicative work and distinguish true clinical performance from limitations in the available data.

Viewers will learn what health care leaders can prioritize now to get more value from digital quality measurement, including treating data quality as core infrastructure, building transparency and validation into workflows and aligning measurement modernization with data-assurance strategies.

Modern Measures Still Depend on Reliable Data
Digital quality measurement can improve efficiency and make insights easier to act on, but measure specifications and interoperable exchange cannot establish trust when the underlying data is incomplete, inconsistent or opaque.

• Modernization Can Automate Old Problems
  When measurement advances faster than data practices, organizations may move unusable data, missing context and uncertainty through the system more quickly instead of resolving the underlying problems.
• Verification Must Evolve for Continuous Data Flows
  Primary Source Verification and other established methods remain important, but point-in-time, document-based approaches are difficult to sustain as data moves continuously across systems and intermediaries.
• A Modern Foundation Combines Standards and Assurance
  Digital measures, interoperable data and execution infrastructure must work alongside data-quality specifications, traceability and automated, repeatable validation.
• Quality Signals Should Reflect Care, Not Data Limitations
  Modern measurement should help organizations evaluate the care delivered rather than produce rates that primarily reflect gaps or weaknesses in the supporting data.
• Data Quality Is Core Infrastructure
  Leaders should build data quality into investment roadmaps, organizational capabilities and workflows instead of treating it as a downstream audit or compliance activity.
• Trust Should Travel With the Data
  Health care organizations, vendors, infrastructure providers and data-source partners need common expectations for high-quality, high-fidelity data from the point of generation to the point of use.
• Reliable Information Is the North Star
  The goal of measurement modernization is dependable information that supports better decisions, better care, population health and patient outcomes.