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Abstract
Historically, the United States (U.S.) public health system consists of siloed data systems that limit interoperability. Health Information Organizations (HIOs) are positioned to facilitate data sharing between healthcare and public health, yet their utility in supporting public health reporting remains underexamined. The COVID-19 pandemic exposed weaknesses of the U.S. public health system, underscoring the urgency of modernization. Recognizing the need for action, the Centers for Disease Control and Prevention (CDC) launched the Data Modernization Initiative (DMI) and the Public Health Data Strategy (PHDS) in 2023 to create a more efficient and responsive system. Although these strategies articulate ambitious goals, questions persist about how HIOs can contribute to public health. This dissertation addresses the public health interoperability gap through two aims. The first aim uses a 2023 survey of HIOs, explicitly focusing on organizations with a state public health agency (PHA) as their primary PHA to determine HIO challenges and capabilities. Findings show that most HIOs operate as nonprofit entities with hybrid governance models and support some PHDS reporting services, including syndromic surveillance, immunization registries, electronic case reporting (eCR), and electronic laboratory reporting (ELR). Persistent barriers include insufficient funding, limited PHA technical capacity, and workforce shortages. The second aim seeks to understand how key components of CDC’s PHDS are reflected in the literature, employing a scoping review of peer-reviewed and grey literature documents. The review demonstrates the adoption of modernization activities across PHDS components, such as syndromic surveillance, eCR, ELR, and wastewater monitoring. Still, it highlights persistent challenges in workforce capacity, policy alignment, interoperability, and scalability across jurisdictions. These findings suggest that modernization cannot be achieved through federal mandates or technology alone. Progress requires aligning policy with operational realities and sustained investment in people and systems. Future research could evaluate whether HIOs are fit to support public health surveillance activities and the continued integration of emerging surveillance domains and technology, such as Artificial Intelligence (AI) and machine learning (ML), for a responsive public health data ecosystem.