Why Healthcare Needs Data Governance

If You Think Governance Is Simply About Compliance, Think Again

Julie SkeenJune 19, 2019

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What do you think of when you hear “Data Governance?”  If you’re like a lot of people, the phrase brings to mind red tape and bureaucracy, a lot of time and effort, and an urge to run in the opposite direction. But data governance has evolved far beyond these old stereotypes. In reality, modern or ambient data governance safeguards an organization’s most critical data assets, ensuring not only that improper use is avoided, but that the data is used for its highest and best purpose. Of any industry, this concept is perhaps most critical to healthcare. After all, when could it be more important to get the most value out of data than when our health and very lives are at stake?

There are numerous examples of how data could be better leveraged across an entire organization, if only other departments or areas were aware of it.  Too often, data is siloed within a single system or department. For example, pharmacy data and test results might be received from external sources, but if they remain in just one system or department (such as billing), they have limited value. As the use of AI becomes more prominent as a diagnostic tool, shouldn’t all relevant data be available for use in the algorithm?  As data proliferates and the potential of data to positively impact patient health continues to increase, it also raises questions of patient rights, privacy and maintaining compliance with all applicable laws and regulations.

Of course we’re all in favor of protecting patients’ rights and handling data in a manner that’s legally and ethically responsible, but organizations need a formal structure for doing so. Data governance can ensure that the most critical data is identified and available to produce real value for both patients and the organization, while also safeguarding patients’ rights in accordance with policies and regulations.

The Impact of Ambient Data Governance in Healthcare

Not all data is created equal in healthcare, and by prioritizing those data assets that are most important to an organization, they can maximize returns, through analytics and operational improvements, and minimize risks, by ensuring compliance. By implementing ambient data governance, organizations ensure the following:

  1. Data is identified as critical. An important step in ambient data governance is determining what data assets are most valuable, and there are several approaches organizations can take in this assessment. This can happen from the “top down,” with critical reporting requirements such as HEDIS measures being linked to the data elements that impact those scores.  It can also happen from the bottom up, by surfacing frequently used data elements.
  2. Accountability is established. Once the critical data is identified, then someone needs to assume ownership and responsibility for it. Who is responsible for how this data is obtained and used?  Once this is determined and documented, those individuals will be accountable for ensuring that the data is used appropriately.
  3. Data quality is improved. Data quality has a number of definitions.  The IT team may say that it means that fields are complete and conform to a certain expected standards such as a format for a patient or member ID.   While this is important, it is not the whole story.  High quality data must not only consistently have the right data in the right field, but it must also remain unaffected as it moves between systems and processes.  This means that everyone can trust that the expected values in that field are really there.
  4. Policies are monitored. Data is associated with relevant policies so that the owner of those policies can ensure that they are being adhered to consistently. These policies may be externally enforced regulations such as the EU’s General Data Protection Regulation (GDPR) and California’s statute, CCPA (among others under consideration in a variety of states), not to mention existing laws such as HIPAA and more.  Policies could also include internal policies for operations, security, etc.
  5. New data is automated. Data governance can also assure that as data is created or ingested into the organization, it will be effectively managed from the very beginning. What happens when new data comes in?  Who makes sure that the data is received on time and in the proper format?  What happens if the format changes? How is this data integrated?  How do you ensure the critical data is accurately updated to downstream systems and processes?  How is the data integrated with other data?  How are exceptions found and worked by those responsible for data accuracy?  All of these steps can and should be automated.  Without automation, a backlog will occur and issues will be missed until they resurface further downstream.  When this happens, users will stop trusting and using data.

When the above steps have been followed to implement ambient data governance, critical data will be used in a manner that is not only legal and ethical, but a way that provides the highest possible value to the patient. If I were the patient, I know I would not only hope, but expect this to happen.

If you’d like to learn more about how data governance helps get the most out of data, check out the ebook below.

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