📊IMSPARK: Public Health Cannot Protect What It Cannot See📊

📊Imagine… Health Data Access Is Public Health Capacity📊

💡 Imagined Endstate:

Imagine state and territorial health agencies able to detect emerging problems earlier, understand who is being missed, and evaluate whether interventions are actually working . Public health decisions would be informed by a more complete picture of how people move through the health system.

 📚 Source:

Budurlean, L., Joseph, R., & Offutt-Powell, T. (2026, August 25). Closing the real-world data gap for public health. Association of State and Territorial Health Officials. Link.

💥 What’s the Big Deal:  

Imagine public health agencies moving from partial visibility to a shared operating picture🔎. Instead of waiting for fragmented reports to reveal what has already happened, health leaders could identify patterns earlier and act with greater confidence. 

Budurelean et al. (2026) argue that state and territorial public health agencies have historically lacked access to the breadth of real-world data routinely used by health systems, insurers, researchers, and life-sciences organizations🧭. That gap limits surveillance, analysis, and the ability to respond quickly to changing community health needs.

Public health departments already hold some of the most important datasets in government📂. Vital records, disease surveillance systems, immunization registries, cancer registries, environmental data, and Medicaid claims all provide valuable information. But those systems were never designed to provide a complete view of how the entire population experiences health care.

That matters because large portions of the population can remain partially invisible🌱. People covered through commercial insurance, employer-sponsored plans, or Medicare Advantage may not appear consistently in traditional state surveillance systems. When those populations are missing, agencies can struggle to understand chronic disease, preventive-care gaps, behavioral health trends, or emerging patterns across whole communities.

The problem is not simply that the data exist somewhere else💻. Private health systems, insurers, pharmacies, laboratories, and technology companies already operate inside a much broader real-world data ecosystem. The asymmetry is that public health often carries the responsibility for population-level decision-making without equivalent access to those information resources.

ASTHO’s Public Health Data Consortium is designed to change that balance🪢. Launched in 2026 with Veritas Data Research and HealthVerity, the model gives participating jurisdictions access to linked real-world data, technical support, and dashboards designed around state-defined priorities.

The governance structure may be the most important part⚖️. Instead of states becoming passive consumers of commercially assembled data, the consortium places public health agencies inside the governance process. States help determine how data are used, how privacy is protected, and what kinds of questions the system should answer.

That turns data modernization into something larger than a technology project. It becomes an issue of data sovereignty and institutional capacity🚉. Public health agencies need the ability not only to receive information but to shape the rules governing how that information supports public purposes.

The initial priorities show why that matters 🧪. Maternal health, infant and child health, substance use, rural health, and chronic disease all require timely information across multiple systems. Fragmented data can hide patterns that become obvious only when datasets are linked.

There is also a speed problem🧬. When agencies have to build a new data pipeline every time a new question arises, public health becomes reactive. A shared infrastructure allows analysts to ask new questions without starting from zero each time.

For territorial and island health systems, that challenge can be even more pronounced 🌊. Smaller jurisdictions frequently operate with limited informatics staff and fragmented systems. Data modernization therefore cannot mean simply buying more software. It has to include workforce capacity, interoperability, governance, and sustained access to usable information.

The broader lesson is that data themselves do not create public-health value. Value comes from whether institutions can connect, interpret, and act on them🧬. A sophisticated dataset sitting outside the reach of the agency responsible for population health does little to strengthen resilience.

Public health cannot protect populations it cannot fully see🔌. Closing the real-world data gap is not simply about better analytics, it is about giving public institutions the information capacity required to govern health effectively.

#PublicHealthData, #DataModernization, #RealWorldData, #HealthEquity, #PublicHealthInfrastructure, #DataGovernance, #TerritorialHealth, #PublicHealthInnovation, #CommunityResilience, #IMSPARK

🤖IMSPARK: AI Becoming a Capacity Divide🤖

🤖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