Patient Demographic Data Quality Framework

Governance Management


Establish the ownership, stewardship, and operational structures needed to ensure that data is managed as a critical asset and implemented in an effective and sustainable manner.

Introductory Notes

Governance focuses on implementing processes that facilitate collaborative agreements, decision making, and approvals. This leads to effectively implementing building, sustaining, and compliance functions for the data assets, as described below:

  • Building - governance bodies with stakeholder representation need to be actively engaged in making consensus decisions about important aspects of patient demographic data and involved in efforts to improve it, such as contributing terms or definitions to the business glossary (e.g., clarifying use of a term for their business activities), providing metadata (e.g., identifying a data owner for Patient Street Address), approving standards (e.g., Patient Last Name must not exceed 60 characters), and defining quality rules (e.g., Patient Middle Name must not contain an initial).
  • Sustaining - governance participants need to remain engaged over time, as new processes are added (e.g., a data quality assessment process), as new data requirements are considered, as business terms are added, and as metadata and standards are modified. In addition, governance needs to exercise control over data management functions, such as reviewing and approving a process for requesting data access, etc.
  • Compliance - governance should have the authority to determine effective compliance processes, such as thorough reviews, audits, quality assurance, etc., to ensure that: approved standards are being followed; approved processes are implemented; approved metadata is being captured; and that the right stakeholders are involved in decisions that affect them.

Strengthening capabilities with these practices creates a corporate culture of shared engagement and responsibility for patient demographic data that is created and managed. The business owns the data it creates and manages; therefore, lasting improvements cannot be realized without significant engagement with the business.

The data governance structure that is selected should correlate with the organizational hierarchy and its primary business processes. Key functions for data governance usually include the following actions:

  • Approve data management plans, policies, processes, and standards;
  • Assign accountability and responsibility for owners, stewards, and custodians (physical data stores);
  • Develop decision rights and change processes;
  • Address regulatory and other external requirements; and
  • Enforce compliance.

Most organizations implement governance at more than one level, both permanent governance bodies and those that are convened for launching new initiatives, for example:

  • An executive governance body may be charged with oversight for ensuring that business, technology, and operations are communicating and following defined processes to manage and improve the patient demographic data, determining priorities, approving technology selection decisions, etc.
  • A committee of data stewards, representatives with delegated responsibility for making decisions about shared data as it impacts their business processes, may be created to participate in collective decisions, reviews, and approvals for processes, standards, and compliance mechanisms, etc.
  • Temporary data working groups may be formed from the data steward group and other delegated staff for specific purposes, for example to develop metrics and milestones for data quality improvements.
  • Tactical work groups, permanent or temporary may be created at the behest of permanent governance bodies to achieve specific objectives, such as: to assure the quality of critical data attributes; to analyze business process for modifications that will improve data quality; and to resolve specific business challenges with patient data.

The data governance function may begin by focusing on a small set of activities and processes, but it typically expands over time to include additional tasks and responsibilities. For example, implementing a Master Patient Index is an effort that would engage numerous business representatives across the patient demographic data lifecycle.

Key factors for successful implementation of data governance include the following:

  • An approved governance charter(s);
  • Executive oversight, governance policies, goals, and objectives;
  • Clearly defined roles and responsibilities; and
  • Well-designed supporting processes, such as issues escalation.

When governance for patient demographic data is implemented successfully and sustained over time, the organization can realize the following benefits:

  • Increased awareness and shared responsibility for data standards and quality on the part of all stakeholders;
  • Increased efficiency of arriving at informed decisions about patient data;
  • Increased assurance that all business needs have been factored into decisions;
  • Strengthened compliance for policies, processes, and standards; and
  • Increased teamwork and likelihood of success for group efforts (e.g., business glossary, data standards, etc.).

Additional Information

From scheduling a doctor visit to fillling a prescription, patients encounter people performing different processes at various stages of receiving health care services. In some cases, applications may be used that share data with other applications; in other cases, the applications may be linked, but have inadequate controls for ensuring the integrity of records in the data store. Either way, duplicate patient records are a common result of inadequate collaboration across the lifecycle and can present significant risks to patient safety.

Patient identity integrity is often compromised as a result of differences in the terminology, design, and processing of common data used along the patient care continuum. Therefore, it is important to improve communications to increase collaboration between the people and processes that collectively impact the quality of patient care.

Data governance is the mechanism for facilitating common agreements about shared data. It should have a structure that includes roles, responsibilities, and accountability for key stakeholders. The goal is to improve the flow of data across the lifecycle with minimal confusion or need for rework (See Data Lifecycle Management).

Data governance roles typically include:

Role3 Responsibility
Data Steward The responsibility for management of data entrusted to someone whose knowledge, skills, and responsibilities highly impact that data in the normal course of performing his/her job.
Data Owner The responsibility assigned to a party for a business data asset, typically the individual who is in charge of a business process or application data store.
Data Custodian The repsonsibility assigned to a party for ensuring that approved business requirements for data and functionality are supported in the technology stack of the organization.

Example Work Products

  • Governance process documentation
  • Evidence of implemented governance processes
  • Description of data governance roles and responsibilities

3 See Glossary of Terms, for additional information.

Additional Information

Data governance policies and processes should lay the foundation for establishing and monitoring data governance functions (See Introductory Notes above). Each organization will need to consider its current organizational chart and relevant lines of business to determine the most useful governance structure. In order to support consistency and collaboration across the patient demographic data lifecycle, governance processes should be clearly defined and kept up to date.

Example Work Products

  • Data governance charter
  • Data governance policy
  • Defined data governance processes, including operations, the decision process, and issue resolution

Additional Information

Data governance is the central hub responsible for data management oversight. Stakeholders should collectively review and guide all critical data management activities that are being performed on patient demographic data across the lifecycle. Accordingly, metrics are essential to monitoring progress that is being made, as well as to informing collective decisions that impact the quality objectives established for patient demographic data.

Data governance metrics should be consistent with the organization’s business objectives and should be updated to maintain alignment with business goals (e.g., patient safety). The progress of data governance best reflects the organization’s increasing capabilities and maturity.

Additional Information

Data governance processes should be standardized to improve collaboration among all stakeholders impacting patient demographic data. Typically, data governance roles and responsibilities are only part of a stakeholder’s overall responsibilities. Training on data governance processes is necessary to ensure successful alignment of activities significantly impacting patient demographic data quality objectives.

Data governance processes should extend to data management activities not owned by the data management function, but are needed to support improved patient demographic data quality across the lifecycle. For example, data governance processes should exist to guide development and delivery of scalable training to support the consistent capture and verification of critical patient demographic data attributes.4 Training should also address best practices for defining business terms and metadata, which can be rolled out to business stakeholders beyond governance delegates.

Example Work Products

  • Metrics to evaluate data governance effectiveness
  • Data governance training materials

4 Patient Identification and Matching Final Report, see pg. 4

Practice Evaluation Questions

Tier 1: Foundational

1.1 Is a data governance structure defined and established with roles, responsibilities, and accountability assigned to all supplying and consuming business units involved in activities and decisions about patient demographic data?

Tier 2: Building

2.1 Does data governance follow defined policies and processes?

Tier 3: Advanced

3.1 Does data governance define and approve appropriate metrics to evaluate the effectiveness of quality improvements to patient demographic data?

3.2 Is training in data governance processes available and required for participating stakeholders?