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.
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:
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:
Most organizations implement governance at more than one level, both permanent governance bodies and those that are convened for launching new initiatives, for example:
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:
When governance for patient demographic data is implemented successfully and sustained over time, the organization can realize the following benefits:
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:
|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
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 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.
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
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?
2.1 Does data governance follow defined policies and processes?
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?