How CDC’s data office is applying AI to public health

As federal agencies push forward on their IT modernization goals, many agencies are exploring the potential use of artificial intelligence tools that can supplement human employees. Federal agencies are currently applying AI to a variety of missions, and public health is no different. The Centers for Disease Control and Prevention’s new Office of Public Health Data Surveillance and Technology (DST) is looking into ways to apply AI to public health data, as well as ways to leverage generative AI to bolster their efforts.

“There was actually a series of 15 pilots that were run across different centers and in offices across the agency,” Jennifer Layden, director of DST, said on Federal Monthly Insights – Operationalizing AI. “These were used to help evaluate the type of infrastructure we would need, what type of capabilities we would use, what would be the security factors that we’d have to consider? And the variety of these projects or pilots ranged from more like programmatic work to more operational work, such as website redesign, evaluating, comments back on a protocol or whatnot.”

CDC stood up DST last year to coordinate its data strategy. That includes improving data exchange with other federal, state and local agencies and non-governmental partners; improving the ways data informs public health initiatives; and ways to better visualize and distribute data for public consumption. AI is quickly becoming a part of those efforts.

AI use cases

For example, automated processes can flag potential health threats quicker, facilitating more rapid notifications and communication. But it can also improve internal workflows, making CDC employees more efficient at their jobs. And generative AI can quickly produce a fact sheet about a new public health threat to educate both those at risk and the medical professionals who may need to treat them.

For example, Layden discussed one test use case where AI is examining public cooling sites to identify what areas could be at risk for spreading Legionella, a disease spread through contaminated water.

Ensuring data privacy and reducing bias

Amidst a variety of potential use cases, Layden said DST is focusing on putting guardrails around the use of these tools.

“What we’re trying to do in the process is ensure that one, we establish guidance by which programs and scientists can have some basic playbook by which to use such tools to ensuring that people do it safely and securely,” she told The Federal Drive with Tom Temin. “[Two:] recognizing that we don’t want to create any risks to de-identification or information sharing that that should not be shared. And then three, how to also factor in ethical and bias considerations.”

Data privacy and ethical and bias considerations are especially important when working with public health data. One major concern around AI tools is that bad actors can leverage them to violate the data privacy of patients and citizens by manipulating the tools to reveal personally identifiable information. That’s where de-identification and determinations about what data is appropriate to share come into play. But that data also has to be as equitable and diverse as possible so as not to introduce any biases and potentially create new underserved populations, or exacerbate the conditions of existing ones.

Picking the right teams and AI tools

That’s why Layden said DST encourages the use of multidisciplinary teams when working with public health data. She advocated for teams that include experts in the disease or other public health threat, people who understand the populations affected or at risk, and people who grasp the data tools and methodologies to perform advanced analytics.

“It is really a multidisciplinary team that needs to come together to understand what the question is that we’re trying to answer,” Layden said. “What are the considerations we need to factor in, as we understand the data that we’re using? And then what are the best tools to help answer that question? So not just using a new tool because it’s a new tool, but is it the best tool to answer the question at hand?”

Another consideration revolving around these tools is the fact that they evolve; after all, AI tools have been around for some time now, but generative AI only hit the spotlight about a year ago. As that evolution occurs, experts need to continuously reevaluate them for a number of reasons: Are they still the best tool for the job? Has the nature of identifiable information changed in any way?

Sharing information and tools

The appropriate community also needs access to that information. Best practices and lessons learned can prevent other teams from making similar mistakes, or save them time in evaluating their own tools. Layden said that stakeholders need to continuously build out, test and validate that framework for it to keep doing its job.

That’s because the capabilities have to continue to evolve, because so will the threats. So public health professionals have to keep pace.

“One of the challenges in public health broadly — and not unique or not new — is bringing in the more advanced analytic capabilities, the workforce expertise,” Layden said. “We’ve also looked at ways to partner with academic and private partners, recognizing that our bandwidth, our capabilities to understand the full spectrum of tools and how they could be used … Will be slower to build up those capabilities in-house. So how we can partner with experts either in academic or private is another way for us to build up the capabilities, our understanding, as well as expertise.”

One way CDC accomplishes that is through the use of shared tools. For example, Layden said more than half of state jurisdictions use a tool for case investigation that the CDC operates and maintains. Similarly, there’s a shared surveillance system for tracking emergency room data. And there’s a shared governance model to help support the development and sharing of even more tools.

One of the benefits of sharing tools like this is it encourages sharing data more broadly, and in the same formats, reducing the amount of work data scientists have to do to reconcile the data before they can begin analyzing it.

“So in my mind, public health, the more we can share, build up enterprisewide tools that can be used and leveraged appropriately is one step that we need to continue to take and to grow, but then also sharing the best practices.

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