AI Data Governance in Healthcare: What’s New and What’s Changing?

What Are the Benefits of AI and Data Governance?

A robust data governance framework ensures the AI model is receiving high-quality information, which reduces risk. “Data governance is like having a glass box around the AI,” says Laine. “It provides transparency into what’s feeding the AI model and who has touched that data.”

At the same time, AI itself can improve data management. It can be used for policy enforcement and security pattern analysis. For example, AI can monitor and verify that sensitive patient data is being accessed and handled properly. 

Chatbots can enhance the end-user experience by helping analysts more efficiently sort and interpret information from large data sets. 

In addition, machine learning tools can help healthcare organizations leverage larger influxes of data. The AI automatically processes and learns from the data it collects, which enables the system to continuously improve. 

How Can Organizations Set Realistic Expectations for AI Data Governance?

A common challenge, Godden says, is when leaders think they need to revitalize all of the organization’s data sets before they can generate value from an AI tool. Instead, he encourages them to adjust expectations and start with smaller goals: “Identify a business opportunity and focus on governing and cleaning only the data you need to solve that specific problem.” 

It’s important to clearly define the organization’s values and ensure that employees understand them. This provides necessary guidelines so that when a data anomaly occurs, employees can properly identify and fix it in accordance with the company’s expectations. “AI models are going to have biases, and corrections will come down to individuals making value calls,” Laine says. 

She adds that healthcare systems need to remember AI isn’t perfect. Human intervention is critical, especially when determining why an anomaly occurred in the data. “If I were a doctor, I would feel more reassured knowing a data governance team is behind the scenes verifying that the data makes sense,” Laine says.

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Who Should Lead AI Data Governance Efforts?

The chief data officer typically leads governance efforts, with support from data quality analysts and architects. Prompt specialists are also now being used to better train AI training models. 

When getting started with AI and data governance, Laine emphasizes, data management professionals should help lead the way: “These are the people who understand how the data moves and changes. I think relying on their expertise is key to an organization getting it right.” 

Godden adds that when establishing a healthcare AI program, there should be a diverse team involved in crafting the policies and procedures that will govern the technology. That includes the IT and data teams; medical professionals; and people from the legal, marketing and HR departments.

“You need everyone involved in building and using the AI to understand it and have their antennas up,” Godden says, noting that all team members have a role in monitoring AI for inconsistencies. “This is not an IT problem. This is an everyone problem.”