Conceptual Use Case for Learning Public Health Systems as a Service

Abstract

Public health systems face mounting challenges from pandemics, environmental disruptions, and persistent health inequities, exposing the limitations of static, siloed infrastructures. This paper introduces a conceptual model for Learning Public Health Systems as a Service (LPHSaaS), integrating principles from service science, resilience theory, and decision systems architecture. The novelty lies in reimagining public health as a continuously adaptive, stakeholder-informed service ecosystem, enabled by emerging technologies such as AI, IoT, cloud computing, and distributed ledger technologies. Methodologically, the paper presents a cyclical workflowevent monitoring, validation, decision-making, feedback, and knowledge translation, illustrated through a use case that demonstrates how real-time analytics and participatory design enhance responsiveness and trust. Findings suggest that LPHSaaS can improve situational awareness, resource allocation, and intervention precision while fostering resilience and continuity during crises. The proposed model offers a blueprint for transforming public health infrastructures into dynamic, learning ecosystems, with implications for both theoretical advancement and practical implementation across diverse health contexts.