Best Practices for Data Governance in Healthcare

health care data governance

Multiple investigations are currently in progress to analyze the data breach at Anthem insurance, in which over 80 million records were stolen including SSN, credit card and medical history.  The incident shows how hackers are increasingly targeting health care industry, mainly for its rich data. Today, data is one of the most important resources for any organization, especially in healthcare as we approach an industry driven by analytic. Data is one of the longest-lasting assets within an organization, outliving people, devices and facilities alike.

Throughout the recent years, as the longevity and value of data continue to be recognize, the concept of “data governance” began to describe the idea of influencing and managing the use and collection of data through an organization. The creation and adoption of accountable organizations for care is prompted as much by the need to acquire more data for managing and understanding risk outcomes, as it is motivated to get more facilities, patients and clinicians. If we agree that healthcare is an industry critically dependent on knowledge creation and delivery, it is important to use data in our environment to optimize for skills and knowledge. Here are some best practices for data governance in healthcare.

  1. Use Lean, Balanced Governance

A committee for data governance should exist alongside the existing governance structure, with the influence needed to institute changes to workflow, develop complex data strategies for acquisition and resolve quality conflicts. The committee will also need to use front-line employees who understand data collection in transaction systems such as cost accounting, registration, materials and scheduling.

The committee for data governance should utilize a cultural philosophy that understands that governing data is necessary to some extent for achieving the common good. Generally, some organizations have been known to either over-apply data governance as a result of their enthusiasm for the function, or under-apply it because they don’t fully understand it. Often, the best approach is to begin with a broad framework and vision, but limited application, which allows them to expand their function incrementally, as it is needed. Keep data governance lean and grow cautiously and carefully into requirements for more.

  1. Data Quality

Managing and ensuring the quality of data is one of the single most important aspects of data governance. When data that is low in quality has an impact on the timeliness or accuracy of an organization’s ability to make decisions, the committee needs to be able to react quickly to the issues and enforce any required changes. In simple terms, data quality should be measured in regards to the result of evaluating data, validity and timeliness, and the committee must make each of these variable a priority.

  1. Data Access

Improving the ability to access data across various members of a single enterprise, including community members, external shareholders, and patients is an important part of the data governance committee’s job. While information security tends to protect and restrict access to certain data, the committee for data governance needs to create a useful tension in the opposite motion. In highly effective organizations, the information security and data governance committees are often combined, forcing members to balance their tension and streamline what may otherwise a time-consuming decision making process.

  1. Data Literacy

It’s useless for any industry, including healthcare, to increase the access to or quality of data, if the beneficiaries of that data aren’t literate about its interpretation and use in regards to their organization. Literacy can be increased by teaching your users how to determine which data is good and bad, in the context of their environment, and using data analysis tools. What’s more, statistical techniques can be applied to improve decision making, and metadata can be carefully collected and disseminated.  Typically, the committee for data governance should be commending the cause when it comes to decisions driven by data.

  1. Data Content

A multi-year strategy should be planned when it comes to data acquisition and data provisioning within the healthcare industry, so that the data ecosystem may be constantly expanded and improved. The more this ecosystem thrives, the more it will be available for healthcare delivery and management. For example, activity-based data such as patient observations, bedside data, familial and genetic data are all important to the utilization of analytics in the industry. Acquiring the systems that are needed to collect this data is the first step in your analytic journey, and it can take a long time to complete.

  1. Prioritization of Analytics

Committees for data governance need to be diligent in playing a role for the development of strategic plans for C-level suites. They also need to play an active role in ensuring that the necessary requirements of that plan can be implemented. Generally, there is often more of a demand for services than there is resources for that demand to be met. It’s important to recognize that the committee for data governance cannot always resolve every issues. However, top-down corporate priorities can be balanced alongside bottom-up requests. As a rule of thumb, often 80% of the analytic resources of an organization are directed to top-down needs, and centrally managed issues, whereas 40% can be distributed towards the support of departments, clinical service lines, business units and research.

  1. Master Data Management

As a healthcare organization continues to progress in utilization and analytic maturity, the committee for data governance will need to encourage, design and resolve conflicts in data management on a master level. Alongside coded standards of data, the committee will also need to use algorithms as standard in order to bind certain parts of data into algorithms that can be used on a consistent basis throughout an organization. For example, this could include defining the criteria for re-admission, calculating a length for stay, attributing patients to providers that are necessary for certain care arrangements, and defining patient cohorts.


These practices could help you to avoid the common pitfalls associated with governing. Remember, a balanced and lean governance of data should help your organization to make the most out of your data, therefore allowing you to provide the best possible care.

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 Author: Rahul Sharma

 images courtesy: Stuart Miles