🤖Imagine… Public Health Agencies With the Skills🤖
💡 Imagined Endstate:
Imagine state and territorial public health agencies able to use artificial intelligence for real public-health value without sacrificing privacy, accuracy, or trust. Agencies would have both the guardrails to manage risk and the workforce capability to move beyond basic administrative uses into surveillance, emergency response, and other higher-value functions.
📚 Source:
Whiteman, A., Subramaniam, H., Papillon, G., Marshall, M., Strickland, C., Offutt-Powell, T., Layden, J., & Bharel, M. (2026). The artificial intelligence divide: A call to action for state and territorial public health. American Journal of Public Health. Published online September 9, 2026. Link.
💥 What’s the Big Deal:
Whiteman et al. (2026) show that public health agencies are moving toward AI, but unevenly⚖️. Among 44 state and territorial health agencies, 73 percent reported having an AI policy in place or in development. Yet those policies focused much more heavily on privacy, governance, and security than on preparing people to use the technology effectively.
That imbalance matters🪢. 81 percent of reported policies addressed data governance, privacy, and security, while only about 34 percent addressed leadership and workforce readiness. Guardrails are essential, but a system built mostly around restriction can still leave staff without the skills needed to use AI responsibly.
The implementation gap is even clearer🔎. AI use is currently concentrated in administrative efficiency and content creation, while only 14 percent of jurisdictions reported using AI for more advanced functions such as disease surveillance, anomaly detection, or predictive modeling.
That suggests the divide is not primarily about interest🌱. The article points to infrastructure and capacity. Agencies reported lack of guidance, workforce knowledge, funding, and concerns about data accuracy as major barriers to adoption.
The workforce issue may be the most important👩🏽💻. More than half of responding agencies identified limited AI skills or knowledge as a challenge. Public health cannot move into sophisticated AI applications if the people expected to oversee those systems are not given the time and training to understand them.
Accuracy creates another concern🧪. Nearly half of agencies reported worries about AI-generated data accuracy. In public health, that is not a minor technical issue. An unreliable output can affect how risk is understood and how resources are directed.
For territorial and Pacific health systems, the divide may be especially consequential🌊. Smaller jurisdictions often operate with fewer staff and more limited technical resources. If advanced AI capability becomes concentrated only in well-funded systems, existing public-health disparities could widen rather than shrink.
That is why AI literacy should be treated as part of public-health infrastructure🧩. The goal should not be for every employee to become a programmer. It should be for the workforce to understand where AI adds value, where it introduces risk, and when human judgment must remain decisive.
Imagine a future where public health agencies are not divided into those that can safely operationalize AI and those that can only watch from the sidelines🌺. The big deal is this: the next AI divide may not be about who has access to the technology. It may be about who has the workforce, infrastructure, and confidence to use it well.
#ArtificialIntelligence, #PublicHealth, #AILiteracy, #HealthEquity, #WorkforceDevelopment, #TerritorialHealth, #PacificHealth, #IMSPARK
