The Office of the National Coordinator for Health IT (ONC) developed a framework to help health systems, large practices, health information exchanges and payers to improve their patient demographic data quality.
As ONC notes in an introduction to the framework, which can accessed here, accurately and consistently matching patient data both within and across organizations is pivotal to ensuring safe and effective care to patients.
Patient demographic data is the primary resource used for matching patient records. Unfortunately, patient demographic data is often of poor data quality, resulting in both inaccurate matching of patient records and low match rates, particularly when data is exchanged across organizations, ONC stated.
The PDDQ Framework is comprised of five primary categories—data governance, data quality, data operations, platforms and standards and supporting processes.
ONC worked with the CMMI Institute to develop the Patient Demographic Data Quality (PDDQ) Framework, with a goal of helping organizations ensure that formulation, agreements, approvals and implementation of adopted standards and processes will be effective and sustainable and support the minimization of the number of duplicate records across the industry, ultimately improving patient safety.
ONC says that the PDDQ Framework allows organizations to evaluate themselves against key questions designed to foster collaborative discussion and consensus among all involved stakeholders. “Its content reflects a path that organizations can follow when building proactive, defined data quality processes to positively influence behavioral changes in the management of patient demographic data,” ONC stated.
The PDDQ Framework enables organizations to quickly assess the current state of data management practices, discover gaps, and formulate actionable plans and initiatives to improve management of the organization’s data assets across functional, departmental, and geographic boundaries. The PDDQ Framework is designed to serve as both a proven yardstick against which progress can be measured as well as an accelerator for an organization-wide approach to improving data quality, ONC stated.
The PDDQ Framework advocates organization-wide alignment on the following key factors:
- Implementing governance functions;
- Planning data quality;
- Implementing quality improvements and assurance;
- Managing operational components;
- Defining and mapping data dependencies;
- Supporting access to shared data interoperability; and
- Ensuring that data is understood and trusted across the organization.
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