7 Essential Practices for Data Governance in Healthcare

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stakeholders, members of the community, and especially patients is a critical function of the Committee. While the information security committee tends to protect data and restrict access to data, the Data Governance Committee should create a productive tension in the opposite direction. In the most effective organizations, the data governance and information security committees are combined, thus forcing the members to balance the tension internally and streamlining what can otherwise be lengthy decision making and reconciliation between the two committees.

4. Data Literacy

It serves no purpose to increase the quality of or access to data if the intended beneficiaries of the data are not literate about the interpretation and meaningful use of data as it applies to their role in the organization. Data literacy can be increased by:

  1. teaching the users how to distinguish good data from bad data in the context of their decision making environment and role in the organization;
  2. data analysis tools;
  3. process improvement techniques that are driven by data;
  4. statistical techniques that can be applied to improve decision making when data is incomplete or scarce; and
  5. the very deliberate collection and dissemination of metadata, especially that which is associated with enterprise data warehouse (EDW) content.

The Data Governance Committee should champion the cause of data-driven decision-making and data transparency around quality and cost. These campaigns should include the use of slogans, spokespeople, role models and other attributes of successful causes.

5. Data Content

The Data Governance Committee should plot a multi-year strategy for data acquisition and data provisioning, seeking to constantly expand the data ecosystem that is available for analysis in the business of healthcare delivery and health management. For example, activity-based-costing data, genetic and familial data, bedside devices data, and patient reported observations and outcomes data are all critically important to the evolution of analytics in the industry. Building and acquiring the systems to collect this data is the first step in the analytic journey and can take as long as five years to complete. All of the aforementioned data sources are required to progress through the Healthcare Analytic Adoption Model.

6. Analytic Prioritization

The Data Governance Committee should play a major role in developing the strategic analytic plan for the C-level suite, and then play an active role in ensuring the requirements of that plan are implemented. Inevitably, there will be more demand for analytic services than there are resources available to meet that demand. The Data Governance Committee cannot resolve every priority, but it can balance top-down corporate priorities with bottom-up requests from the clinical and business units by advocating a resource allocation of 60/40 between centralized and decentralized analytic resources—that is, 60% of the organization’s analytic resources should be dedicated to top-down, centrally managed priorities, while 40% of the resources should be distributed to support the tactical requirements of departments, business units, clinical service lines, and research.

7. Master Data Management

As the organization progresses in analytic maturity and utilization, the Data Governance Committee will become the steward for defining, encouraging the utilization of, and resolving conflicts in master data management. This role will cover local data standards (facility codes, department codes, etc.); as well as regional and industry standards (CPT, ICD, SNOMED, LOINC, etc.). In addition to coded data standards, the Committee will also become involved in the standard use of algorithms to bind data into analytic algorithms that should be consistently used throughout the organization, such as calculating length of stay, defining readmission criteria, defining patient cohorts, and attributing patients to providers in accountable care arrangements.

In Closing

If you are struggling to understand and implement a healthcare data governance function in your organization, following these seven simple practices will help you avoid all of the major pitfalls of either under-governing or over-governing. Of utmost importance, a lean and balanced data governance function will help your healthcare organization maximize the value of your data to deliver the best possible care and provide for the highest possible health, at the lowest price.

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