Posted Dec. 2, 2009

The National Quality Forum (NQF) has released its Quality Data Set (QDS), a common technological framework for defining clinical data necessary to measure performance and accelerate improvement in patients' quality of care. The QDS framework provides a standardized set of data that should be captured in patients' electronic health records (EHRs) and is applicable to all care settings a patient is likely to use in his or her lifetime.

"Providing a common data resource for all stakeholders in the quality-measures supply chain will allow us to align our efforts and improve the comparability of quality reports while dramatically reducing the burden of quality measurement," said Paul Tang, MD, MS, chair of the expert panel that drafted the QDS. "This is a dynamic structure that will continue to grow and expand to meet future needs of quality measurement." Tang is vice president and chief medical information officer at the Palo Alto Medical Foundation and consulting associate professor of medicine (Biomedical Informatics) at Stanford University.

To date, collecting and reporting meaningful healthcare performance data has been a largely manual process, which not only creates burden, but can be inefficient and lead to inconsistent results. The QDS acts as a dictionary for quality measurement, providing a standardized core set of data. Common definitions are the foundation of strong benchmarking and performance comparison. NQF soon will begin requiring measures submitted for endorsement to include "e-specifications" that align with the QDS framework.

The QDS framework ensures the latest health information technology requirements are seamlessly woven into quality measures. By providing a common language to describe the information within quality measures, the QDS enables quality measurement from a variety of electronic sources, including EHRs, personal health records (PHRs), registries and health information exchanges (HIEs).

The framework consists of standard elements (a code list for a specific condition such as diabetes, or a medication such as aspirin); quality data elements (information describing the context of use in the clinical care process such as a past history of diabetes or the administration of aspirin); and data flow attributes (the sources of the information, who is providing the standard and quality data elements, what the care setting is, etc.)