Patient Demographic Data Quality Framework

Best practices for ensuring broad participation that results in informed, collaborative decision making for data management processes.

The Data Governance category addresses practices designed to help an organization achieve engaged participation representing the interests of the entire organization for making critical decisions affecting patient demographic data assets. It poses questions to help the organization determine how it is currently functioning in governing its data, such as how does the organization:

  • Stand up governance bodies that function consistently across a wide scope of shared responsibilities for patient demographic data activities?
  • Implement effective communication about patient demographic data?
  • Identify and resource a data management role to manage policies, processes and standards?
  • Build and manage a comprehensive set of approved business terms?
  • Capture and manage metadata to fully describe the organization’s data assets?

In addition to centralizing key decisions for the data assets, governance is needed to provide input into the organization’s implementation of external or regulatory requirements. Governance also includes monitoring data management results to ensure that the organization successfully realizes its desired outcomes and receives business value from data management activities.

Governance Management focuses on processes that foster collaborative decision making through governance bodies and effectively implement defining, sustaining, and compliance functions. Communications emphasizes the importance of bidirectional communication that facilitates stakeholder collaboration and determination of how data management information will be provided. Because the need for managing patient demographic data is permanent, communication should be carefully planned and sustained. Data Management Function examines scoping and resourcing data management activities as a sustained function within the organization. Business Glossary practices assist stakeholders in developing and maintaining a common understanding and shared meaning for a compendium of approved business terms. Metadata Management provides a top-down approach to designing, populating, and managing the metadata repository to describe the organization’s data assets, increasing the organization’s knowledge base about its critical data assets.

If implemented, practices within this category will greatly assist in creating a culture of shared responsibility for the data assets. Building capabilities in governance is a key factor in improving data quality, supporting sound design of data stores and interoperability, and creating a thorough and detailed knowledge base about the data assets for all relevant stakeholders.

Note that AHIMA’s Information Governance Adoption Model provides useful implementation guidance for data governance, in the areas of data modeling, data mapping, data audit, data quality controls, data quality management, data architecture, and data dictionaries.