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Data Governance in Healthcare: Where the Industry Needs to Improve

by iSphere on August 23, 2022 in Healthcare IT Solutions

In the time since organizations first began thinking about managing the standards and policies governing their data, the quantity and variety of that data has exploded. That’s especially true in the healthcare sector.

The emphasis on value-based care has put a greater administrative and bureaucratic emphasis on usable, high-integrity data to justify care-model accountability and defend reimbursement. Contact tracing and quarantine enforcement during the pandemic has required policy changes to offer secure, reliable, up-to-the-minute data. And data breaches in 2021, up 7.5% from the year before, continued to show the need for air-tight data standards to prevent misuse and protect PHI.

Providers and payers recognize the importance of data governance, but implementation has lagged. Responses in the HIMSS 2022 State of Healthcare Report showed only 21% of American healthcare systems have reached the point of digital implementation. For many healthcare IT leaders, part of the challenge is fighting against the limitation of IT budgets, even as general organization budgets and investments rise.

Can autonomous data governance help barebones IT staff maximize their budgets? Here’s why we think it’s a possibility.

What to Know about Autonomous Data Governance

Organizations will continue to need data governance analysts, managers, or executives to set the standards, policies, and best practices as well as communicate those outcomes and opportunities to non-technical stakeholders. Having an actual person as a data owner and steward prevents the governance principles from getting sidelined and positions someone to handle big-picture analytics and strategy. However, the routine or repetitive elements of data governance can be given to algorithms.

Here’s an example. With the ever-changing regulatory landscape and cybersecurity threats out there, your data governance team would traditionally have to spend a fair amount of time immersed in oversight, monitoring, and reporting. Yet much of that activity can be automated, using algorithms to continuously audit EHR and other critical data as well as streamlining workflows across health information networks. This can better drive defense against hackers and prevent HIPAA violations.

That’s only one of the advantages of autonomous data governance. From a holistic perspective, automation can improve your total data scalability, allowing for more vigorous analysis and greater care outcomes. Who doesn’t want a self-optimizing and self-healing system that opens up copious amounts of bandwidth? Increasing data quality and lowering your organization’s material risks will make IT indispensable to executives, augmenting large-scale decisions that influence quality care or member satisfaction.

How to Implement Autonomous Data Governance

The actual process of automating data governance is elaborate during planning and implementation but requires a fraction of the direct effort related to manual processes. Here are some of the critical steps that your team or an IT consulting solutions partner should take to deliver autonomous and secure governance.

Involving the Right Stakeholders

As with any IT project, decision making by committee can delay the transition or even risk your project deliverables. On the other hand, you don’t want any glaring omissions in the requirements or current state assessments. The trick is to bring together a select group of data, compliance, and automation specialists in addition to any necessary executive stakeholders.

Conducting Existing State Assessments

The complexity of data governance across multiple cloud or hybrid cloud environments cannot be overstated. As a result, your efforts might be determined by how much work your organization has already done to break down data silos. If you have not yet established interoperability across your data ecosystem or built a data lake to act as a centralized repository for all your data, there’s a key first step to take.

Determining Automation Potential

Which aspects of data governance can and should be automated? What budget is available? Where are in-house data governance stewards spending the majority of their time? Start by identifying high-value processes (robust cybersecurity, compliance monitoring, proactive deduplication, etc.) that can justify the initial investment. If you reach out to the right partner, they can also provide you with a list of success stories to help open possibilities that your organization might not recognize.

Training the Algorithms

This is the most difficult part of the process. You need an in-house specialist or an outsourced partner who understands the fundamentals of training algorithms on confirmed high-quality data sets and building a reliable test infrastructure. Coming back to the earlier example, you would need to train algorithms to identify specific schema and tags that apply data governance rules to sensitive PHI. Once trained, it also helps to have the ability to verify that your work is operating seamlessly outside of the testing environment.

Prepare to Always Adapt

Once your proof of concept is in place, that’s not the end of the automation process. Not only will your organization change, requiring some modifications to the algorithms and meta triggers, but so will industry standards, regulations, and cyberthreats. You’ll need people who can make adjustments without altering the autonomous functions that will remain the same, whether that expertise is owned by an internal person or an external partner.

The Importance of an Autonomous Future

Many of the greatest advancements in healthcare have stemmed from breakthrough usage of data. For example, the tracking of location data about cases of cholera outbreaks in London to prevent the spread of disease or Florence Nightingale’s pioneering data visualization of sanitation conditions during the Crimean War. Now, autonomous data governance has a chance to handle the mundane-but-essential, so your team can focus their time and attention on equally essential breakthroughs. You just need the right people to put self-optimizing and self-healing systems in place so you can better heal others.

Are you looking to learn about data governance in healthcare? Or are you ready to hire experts to help your own transition? Our healthcare IT staffing team can help your business elevate your systems to enhance your operations and overall care outcomes.

 

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