How Public Health Agencies Are Harnessing AI And Advanced Analytics For Transformative Impact

Public health agencies have long been at the forefront of protecting and promoting the well-being of communities. In recent years, these agencies have started to embrace transformative technologies such as Artificial Intelligence (AI) and advanced analytics to enhance their capabilities and become more efficient. This integration of cutting-edge tools has opened up new avenues for data-driven decision-making, disease surveillance, and outbreak response, revolutionizing the field of public health.

AI and Advanced Analytics in Public Health

One of the key areas where AI and advanced analytics have made a significant impact at public health organizations is through the use of data to improve outcomes and better inform decision makers. It should come as no surprise that government agencies have vast amounts of data, and it’s near impossible to have humans analyze and make sense of this data. By using AI and advanced analytics approaches to analyze and classify these vast amounts of data, organizations are able to do things with greater accuracy and speed.

At the May 2023 GovFuture Forum event at George Mason University (GMU) in the Washington, DC region at George Mason University (GMU) in the Washington, DC region, experts from CDC’s National Institute for Occupational Safety and Health (NIOSH) shared their group’s perspectives on advanced analytics. Stacey Marovich, Lead Health Informatics Scientist at CDC NIOSH and Jennifer Cornell at CDC NIOSH provided their insights into how AI and advanced analytics are impacting their agency’s work. As a followup to the event, Stacey and Jennifer were interviewed on a GovFuture podcast where they dived deeper into how AI and analytics is being applied to NIOSH Industry and Occupation Computerized Coding System (NIOCCS), issues around data and transparency, and how technology is helping in some unexpected ways.

Technology making things easier

The NIOSH Industry and Occupation Computerized Coding System (NIOCCS) has been around for a little over 10 years, and currently in its fourth major version. At a high level, Stacy said the system “translates industry and occupation text data into standardized codes so that they can be used for research and analysis purposes. It’s free and it’s publicly available on the web so anyone can use it.”

With 10 years of use, this system has made coding more accurate and streamlined. Specifically, Jennifer shared that “Since the release of the latest version of NIOCCS, we’ve seen major advancements in the collection and coding of work data. And as Stacey mentioned, NIOCCS coding speed and capacity have increased significantly. I’ll share a couple of examples from our public health department partners. One jurisdiction reported they used the NIOCCS Web API extensively during their COVID-19 response.

The group codes health data for weekly fatalities collected from workplace outbreaks and survey data. The outcome provided a quick COVID-19 response. As Stacey shared, “NIOCCS reduced training time for new staff that were pulled in to help with the response and modernized high throughput, high quality data. Another jurisdiction reported they used NIOCCS together with real-time coded data on various communicable disease interview forms, and this made the rapid dissemination of essential workplace measures to local and tribal health departments possible. We anticipated jurisdictions would integrate the API as designed directly into their surveillance systems. The API branched out from COVID to begin collecting health data for other infectious diseases and use cases, so we were very pleased to see that. A few positive outcomes we didn’t predict included cross-checking of coding by our users. We have limitations on how much we can test because we only have access to our internal data, so it’s very, very valuable to have external validation.Further, private entities and nonprofits use the API to collect standardized codes for workers’ compensation data. We would love to see NIOCCS used more broadly in other scenarios beyond just public health, and we’re excited to see where it goes.”

Balancing Transparency and Privacy Protection

As public health departments harness the power of AI and advanced analytics, it is crucial to strike a balance between the need for transparency and protecting individuals’ private information. Transparency ensures that the public remains informed about health risks and mitigation strategies, fostering trust and cooperation. However, privacy concerns must also be addressed to safeguard sensitive data and preserve individual autonomy.

Around the idea of balancing transparency with the need to protect sensitive information when using advanced analytics and AI, Stacey said “ it’s definitely a challenge for us because much of the data that we deal with is health data. So there can be a lot of sensitivities around that. And with many of our projects, we also have fairly restrictive data use agreements that constrain what we can do with the data or how long we can keep the data, etc. So there’s challenges to our machine learning models and training data and to be able to persist that knowledge.

Although, with our program, we tend to get the industry and occupation data elements without any other needs for Personally Identifiable Information (PII). So we don’t tend to get other demographics like name, age, race, etc. So from that point, we’re not swimming in PII, but there’s still some sensitivities there because even industry and occupation by itself can sometimes be considered PII either on its own or when combined with other demographic data. Or, for instance, if you have an occupation of “mayor” and you have” city name” in the industry, that could be considered PII as well, just the industry and occupation on its own. So when we publish our data to the public, we ensure that there’s no small cell sized data that can be construed as PII. We tend to aggregate the data to higher levels rather than just posting the detailed data.

On the topic of the NIOCCS system, the two experts share that, “NIOCCS is a tool that’s used to code data. So it’s not like we’re not deciding if someone gets a bank loan or if they’re determining the type of medical care they get. It’s not to that level, but down the road, we could see that if NIOCCS was expanded into more and more areas, the stakes could become higher. So for example, NIOSH has been pushing for inclusion of work information in electronic health records. So work is a key social determinant of health that should be collected because I think everybody agrees that work has a major impact on health and vice versa. So it really should be prominent and available in EHRs, but currently work isn’t consistently collected in medical records in a standardized way. So this is something that NIOSH is pushing for and if that happens, coding of work data might possibly impact clinical decisions port or other areas of clinical care in the future.”

Advancing innovative approaches

The integration of AI and advanced analytics in public health departments represents a significant advancement not just for the so-called “back office” tasks but also in disease prevention, surveillance, and response. These transformative technologies enable faster and more accurate decision-making, increasing speed and efficiency as well. However, as advancements continue, it is imperative to maintain a delicate balance between transparency and privacy protection. By implementing robust privacy safeguards and engaging in open dialogue, public health departments can ensure that the benefits of technology, and the use of data, are harnessed responsibly fostering a healthier and more informed society.

Additional details and insights are provided in the GovFuture podcast on this topic.

Disclosure: Kathleen Walch is an Executive Director at GovFuture.