7 Essential Practices for Data Governance in Healthcare


hand drawing best practiceData is now one of the most valuable assets in any organization, especially healthcare as we transition into a more analytically driven industry. Data is now the longest lasting asset in any organization, outliving facilities, devices and people.

According to the Oxford Dictionary, governance, in general, suggests the act of controlling, influencing, or regulating a person, action, or course of events. The Latin origins are found in gubernare, which means to steer or rule.

In the past few years, as the value and longevity of data were better realized, the term data governance emerged to describe the concept of managing and influencing the collection and utilization of data in an organization. The adoption and creation of accountable care organizations is motivated as much by the acquisition of more data to manage risk and understand outcomes, as it is motivated to acquire clinicians, patients, and facilities. If we accept the assertion that healthcare is a knowledge delivery industry—that is, the application of specialized skills and knowledge, along with specialized tools—it is our obligation to exploit the data assets in our environment to augment and optimize that knowledge and those skills.

Healthcare Data Governance: Sorting through the details

While information and data security is a long-standing body of practice and knowledge in corporations, data governance is less mature, especially in healthcare.  As a result of this lower maturity, there is a tendency to operate in extremes, either too much governance or too little.  Over time, as data and analytic maturity increases, the healthcare industry will find a natural equilibrium.  For example, in the Healthcare Analytic Adoption Model, a robust data governance function is required in order to achieve the conditions of Level 5 maturity.

A new body of knowledge can be a ripe ground for confusion and over-complication, and there are many vendors and consultants that have an inclination to benefit unfairly from this confusion and complexity in these formative stages.  Below are the seven simple practices of data governance that can be used as a self-guided tour through the maze of puzzling advice.

1. Balanced, Lean Governance

The Data Governance Committee should practice a cultural philosophy that believes in governing data to the least extent necessary to achieve the greatest common good.  Quite often, organizations will either over-apply data governance in their enthusiasm for the new function; or under apply data governance due to their lack of experience.  The best approach is to start off with a broad vision and framework, but limited application, and expand the governance function incrementally, only as needed, and no more.

The Data Governance Committee should be a subcommittee to an existing governance structure, with the influence necessary to institute inevitably controversial changes to workflows, resolve data quality conflicts, and develop complex data acquisition strategies to support the strategic clinical and financial optimization of the organization.  The Data Governance Committee should also enlist front-line employees as Data Stewards who are knowledgeable about the collection of data in the source transaction systems such as the EMR, cost accounting, scheduling, registration, and materials management systems. Data Stewards are invaluable to the mission of the Data Governance Committee. CIOs who function horizontally, across business lines, at the application and data content layers of the information technology stack (as opposed to those who operate primarily at the infrastructure layers) are a natural fit for facilitating and leading the Data Governance Committee.

When in doubt, govern less, not more. Keep it lean. Grow slowly and carefully into the need for more.

2. Data Quality

Overseeing and ensuring data quality is probably the single most important function of data governance.  When low quality data has a negative impact on the accuracy or timeliness of the organization’s decision making, the Data Governance Committee must be capable of quickly reacting to these issues and enforcing the changes required in source data systems (not the analytic systems) and workflows that are necessary for raising data quality.  Simply defined, Data Quality is equal to the Completeness of Data x Validity of Data x Timeliness of Data. The Data Governance Committee must make each of these variables in the data quality equation a leadership priority.

3. Data Access

Increasing access to data, across all members of the enterprise, including external

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