Integrating AI Into Governmental Public Health Decision Making: Challenges, Considerations, and a Path Forward

Clinical Snapshot

Key Findings

• Effect Size: Not applicable - framework development study

• Primary Outcome: Development of AI Decision Support Lifecycle framework with six phases for governmental public health emergency response

• Nnt Or Sensitivity: Framework comprehensiveness across technical, ethical, and policy domains

Clinical Application

Moderate - requires significant infrastructure investment, workforce training, and governance framework development Highly relevant to Australian emergency management structures (AHPPC, CDNA, state health departments); aligns with TGA digital health initiatives and NHMRC ethical guidelines for research involving humans Public health agencies, emergency management officials, and elected officials involved in crisis response decision-making

Abstract

Public health emergencies such as pandemics, natural disasters, and epidemics may require rapid, high-stakes decisions often made by elected officials with limited public health training. Artificial intelligence (AI) holds significant promise to enhance the quality, transparency, and timeliness of governmental decision-making during such crises. This paper examines the potential of AI as a decision-support tool for elected officials while identifying key technical, logistical, ethical, and policy challenges. Technical considerations include model accuracy, data representativeness, and privacy protection, while ethical imperatives center on fairness, transparency, and accountability to prevent amplification of existing health disparities. The paper further explores workforce development needs, emphasizing AI literacy and cross-sector collaboration to enable informed use of AI insights. This viewpoint presents a novel AI Decision Support Lifecycle framework specifically designed for governmental public health emergency response, mapping six phases from problem definition through post-emergency evaluation. We provide stakeholder-specific recommendations for model developers, health agencies, and elected officials, and illustrate practical application through a detailed case example and use cases. Drawing on empirical evidence regarding digital health technologies and AI governance, we emphasize that technology deployment alone is insufficient. Successful implementation requires complementary investments in organizational capacity, data infrastructure, workforce training, community engagement, and continuous evaluation. AI integration also requires robust governance frameworks, continuous model evaluation, and alignment with existing crisis management structures. Policy recommendations highlight the importance of ethical AI frameworks, risk assessments, and public engagement to foster trust. Ultimately, AI can strengthen public health decision-making if developed and implemented responsibly within transparent and equitable systems.

References

1. 1.Campbell, E., Oyefolu, O., Gillani, S., Goodtree, H., Kelly, A., Rivers, C., & Watson, C. (2026). Integrating AI into governmental public health decision making: Challenges, considerations, and a path forward. JMIR Public Health and Surveillance, 12(1), e88470. https://doi.org/10.2196/88470