Patient Demographic Data Quality Framework

Data Standards


Provides an approved set of expectations for governing architectural components of the data assets. Data standards address data representations, data access, and data distribution fundamental to establishing control over the data layer and the efficient use and exchange of information.

Introductory Notes

Data standards define the approach and practices for developing, approving, and instituting compliance for data representation, data access, and data distribution standards. These standards may include any of the following:

  • Data representation – business terms and definitions (See Business Glossary), allowed values (See Metadata Management), formats, logical and physical naming and abbreviation standards, model management standards, etc.
  • Data access – common data services, information exchange standards (e.g., XML), standard methods for bulk data movement and point-to-point interfaces, data integration standards (e.g., extract, transform, and load standards, etc.)
  • Data distribution – ownership and authority (See Governance Management), requesting and approving access, internal and external data provisioning (e.g., portals, web services, etc.), distribution controls and criteria-based access restrictions, distribution models (e.g., push, pull, publish, and subscribe, etc.), regulatory authority and audit, etc.

The organization’s standards influence platform choices, technology and tool selection, common integration rules, and the system development / data design process. Development of standards is essential. For example, variances in standards for patient demographic data across different vendor products and between organizations can cause increased incidence of duplicate records and sub-optimal results for matching algorithms. For organizations building their own systems, a standards-driven approach will prevent many potential issues in integrating data, migrating legacy systems, and redesigning existing data stores.

Consistently following standards for data formats at the point of capture / data entry is a critical activity in preventing incorrect or ambiguous patient identity. For example, entering both first and last names in a Patient First Name field or entering a birth date (where not restricted by screen edits) in MMDDYY versus MMDDYYY format, will create the potential for identification errors.

It is important for business representatives to take an active role through data governance in considering and approving data standards. If an organization can successfully employ a collaborative approach among stakeholders, it can define a systematic, governed process to develop, institutionalize, promote, and enforce standards that are aligned with organizational objectives and architectural decisions.

Once established and extended across the organization, supported by active governance, the collective set of data standards provides the following benefits:

  • Builds staff knowledge about and understanding of the data;
  • Improves the consistency of the patient data for all purposes;
  • Contributes to the population of the metadata repository;
  • Enables improvements to the design process for data stores;
  • Improves the accuracy and quality of data exchanged among organizations;
  • Improves the technology selection process; and
  • Reduces costs and effort by reusing established standards and preventing ad hoc creation of standards.

Additional Information

The motivation to adopt data standards can result from any number of internal and external events. For larger organizations, standards typically evolve from broader architectural initiatives: technology architecture transformation, data store integration, custom system migration to a vendor product, etc. They can also result from external drivers, such as new industry policies and mandates, regulatory reporting, data exchange standards, etc.

Patient identity integrity depends on matching records from disparate data stores back to a single person. The attributes needed to ensure a correct match tend to be similar along the continuum of care, however the naming conventions and formats often differ. As a result, patient records cannot be automatically reconciled without costly and inefficient manual intervention. Manual processes are prone to error, and incorrect patient record matching poses significant risks to patient safety. It is recommended to establish and follow consistent standards along the entire patient demographic data lifecycle to minimize risks.

Example Work Products

  • Data standards used by stakeholders creating and managing patient data
  • Validation of data stores against referenced standards

Additional Information

Successful adoption requires data standards to be relevant and meaningful to the people who interact directly with the data. For example, data representation standards should be developed based on approved business terms. If the organization develops its own standards instead of adopting those provided by a vendor or used by an industry association, governance representatives, who are responsible for creating, reading, updating, and deleting patient demographic data, should be involved in standards development.

The use of data standards ensures that consistent data descriptions and values persist across the patient data lifecycle, in new data stores, and over time, in redesign of legacy data stores in conformance with standards. The broader the adoption is within the organization, the greater the benefit. Accordingly, governance is needed to coordinate business stakeholders to maximize adoption and promote consistent use.

Example Work Products

  • Policy requiring adherence to standards
  • Standards specifications
  • Standards development, approval, and change request processes
  • Standards compliance process
  • Guidance for incorporating standards into design

Additional Information

Gaining broad adherence to selected standards can be challenging. It is important to take a consistent approach and to emphasize the criticality of effective communication. Specifically, standards and policy announcements should establish an effective date and require that any deviations must be posted after that date (See Communications).

Selected standards tend to be applied to new technology purchases and development. For example, the consideration for requiring certified Health IT Modules to capture the data attributes that would be required for standardized patient identifying attributes.

Applying standards retroactively can be prohibitively expensive, and in some cases the technologies in the legacy environment cannot be changed. When data standards cannot be physically implemented, they should be included in the documentation for the implemented environment (See Metadata Management) with cross-referencing between the standard and the existing system architecture to ensure traceability of critical attributes across the patient demographic data lifecycle (See Data Lifecycle Management).

Additional Information

Industry standards for data architecture are established to improve interoperability vertically along the supply chain, as well as horizontally among peer organizations. Many healthcare organizations share data but do not have a common standard. Consequently, they experience significant decreases in match rates when exchanging data. This has been evident even among organizations that have high internal patient demographic record matching rates.

The development of standard data attributes cannot occur accidentally. Accordingly, regulators and industry organizations typically play a critical role in motivating the creation and adoption of data architecture standards. To take an example from another industry, the Mortgage Industry Standards Maintenance Organization (MISMO) was created in 1999 to develop a common language for the exchange of information across the residential finance industry. While most regulators and housing agencies now require MISMO standards, achieving universal adoption of the standards has been a multi-year effort.

The implemented technology environment and workflow within large healthcare organizations is extensive. In some cases, it is not feasible to modify aged systems, especially those that will soon be retired. Organizations should examine the pros and cons of various standards for every system they build or buy, and document exceptions as they arise.

Example Work Products

  • Standards tailoring guidance (e.g., what criteria is applied to determine if some data stores are exempt from standards)
  • Standards exceptions documentation
  • Proposals for new or modified data standards based on regulatory requirements

Practice Evaluation Questions

Tier 1: Foundational

1.1 Are data standards, such as data representations, security, access, and provisioning, defined and followed?

Tier 2: Building

2.1 Are data standards reviewed with business stakeholders and approved by data governance?

Tier 3: Advanced

3.1 Does the organization align its data store designs, the exchange of data, and data access permissions with selected standards?

3.2 Is the organization aware of and selectively implementing external standards relevant to patient demographic data?