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Care Variation in Treatment of Sepsis
Scanning Lab Variation Through a Cost and Quality Lens
When Imaging Becomes Unwarranted for Non Specific Low Back Pain
Care Variation in Treatment of Sepsis
Scanning Lab Variation Through a Cost and Quality Lens
When Imaging Becomes Unwarranted for Non Specific Low Back Pain
Shining a light on care variation in clinical practice
Tenets of Risk Adjustment for Commercial market
Aligning Value-Based Healthcare and Cost Efficiency Through Engagement
Data Governance
The Fate of Obamacare - Speculation Abounds
PHM Strategies Based on Patient-Generated Health Data
Delving Into the ROI Equation for PHM
Six Great Data-Driven PHM Strategies
External Provider Relationships and PHM Ecosystem/Partner Selection
Clinical Best Practices and Treatment Gap Analysis
Corporate Culture Transformation and Alignment
Care Coordination and Population Stratification
Provider And Patient Engagement
Reasons for Embarking on a Population Health Management Initiative
Understanding CMS approach to attain Triple Aim goals for Medicare
Dec 14th, 2016

Past issues of this newsletter have explored the many ways data can be leveraged to help healthcare achieve the Triple Aim. The successes of all of the data-driven strategies for PHM that have been presented depend upon data integrity. In addition to being a vital asset, the effective use of data is now mission-critical. For payers and providers, success requires a very high standard regarding data integrity, distribution, usability, and security. On the downside, poor standards can lead to non-payment, poor treatment outcomes, and demoralization. Data governance is the field that addresses these issues.

A report published by Chilmark Research stated that "most organizations have fairly rudimentary governance structures and that only 15 percent to 20 percent have full-fledged data governance frameworks in place". (cited in Dataversity, February 2016)


Data governance is a framework whose purpose is to assure data integrity, usability, appropriate distribution, and security. The data governance framework establishes executive-level stewardship, controls on data quality, accountability, and the inculcation of responsible information management so that it becomes a cultural value.


There are several drivers for data governance. All are widely acknowledged. The drivers include: (1) The need to improve data quality. (2) Risk management to prevent financial misstatements, inadvertent release of sensitive data, and risk that results from key decisions based on poor quality data. (3) External regulations such as HIPAA.


Here is a list of important data governance goals:

  • Data must be valid, complete, and timely

  • Data must represent "a single source of truth"

  • Accountability for data quality must be assigned to data stewards and others

  • Constantly evolving how data are leveraged throughout the enterprise

  • Increase data literacy

  • Increase data security

  • Assure appropriate distribution

  • Increase confidence in the quality of data that supports decision making

  • Decreasing the risk of regulatory fines

  • Strike correct balance between over and under managing data


Data Governance Committee. Successful implementation requires organizations to launch a data governance initiative. The first step is to create a Data Governance Committee. The committee must have Executive and Board support and be adequately empowered. This is essential because the committee will very likely want to make some changes that are controversial. Examples include changes to work flows, data acquisition strategies, and accountability.

Stakeholders. Stakeholders should be identified and represented in the Data Governance Committee. Data and analytics are an organizational resource and there are many stakeholders. They will include patients, members of the community, clinical departments, business operations, and Information Systems. Individuals with delegated responsibilities must have adequate bandwidth to be successful. These individuals include data stewards, data trustees, data custodians, data users, executives, data architects, and analysts.

Roles of Those Responsible for Data Governance

Data stewards are business representatives (stewards) who have direct operational level responsibility for the management of one or more types of organizational data and who are empowered to make decisions regarding that data.

According to an article posted on Teradata Magazine Online, "a data steward needs to be a seasoned analyst who understands the business and data management concepts and is able to recommend and gain reasonable compromises that enhance the value of an organization's data assets. Specific competencies include business knowledge, business-area respect, data management knowledge, analysis, facilitation and negotiation, and communication."

Data trustees. The business definition of data, access, and use of that data is described in corporate policies and procedures created by corporate officers known as data trustees. These individuals appoint data stewards.

Data custodians are information system administrators. The operation and management of the health information systems (HIS) that collect, transport, provide safe custody, manage, and provide access to organizational data is their responsibility.

Data users are all individuals that enter and use data as part of their work. Data users must be aware of established standard of data use. They should participate in training on data standards and data entry.

Cultural philosophy. The Data Governance Committee should practice and promote a corporate cultural philosophy that includes a high level of analytics maturity. It should also strive for a careful balance between too much and too little governance. Promoting the value of high standards for data is essential.

Training. Physicians, nurses, managers, and staff, all need training if the data governance initiative is to succeed. Today's payer and provider organizations install a wide variety of very sophisticated software applications and without proper training in their use and how data are to be handled, the potential of these applications will be wasted.

Time frame. The initial time to achieve the goals set forth here may be one or more years. Beyond that, it will be the ongoing effort to maintain what has been achieved and to build upon the foundation.


Most Healthcare organizations have very significant investment in their information infrastructure. Additionally, management has a growing understanding of the opportunities and risks inherent in the way their data are handled. Thus, an appreciation of the importance of data governance should be apparent. However, if the Chilmark research results cited at the beginning of this newsletter are accurate, most organizations have their work cut out in regard to data governance.

The ACA includes provisions for rewards and penalties for the delivery of value-based care. This carrot and stick approach has served as a motivation for some to strive for a higher level of analytics maturity and to assure high quality of data. My view is that when the opportunities and risks regarding data and analytics are fully appreciated, other motivations kick in that better serve us all.

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About the author:
Bob Eige - PHM Lead Consultant