Traditional public health surveillance systems remain largely reactive and fragmented, limiting their ability to support real-time outbreak detection, chronic disease monitoring, and equitable healthcare delivery—particularly for vulnerable and underserved populations. Recent advances in the Internet of Things (IoT) and Artificial Intelligence (AI) offer a path- way toward proactive, predictive, and data-driven public health systems; however, their practical deployment raises critical architectural challenges related to data security, regula- tory compliance, interoperability, and patient-centric data governance. This paper examines the fundamental trade-offs between centralized cloud-based plat- forms and decentralized ledger technologies when applied to AI-powered health surveillance. While centralized Platform-as-a-Service solutions enable rapid development, scalability, and real-time analytics, they introduce concerns regarding privacy, compliance, and institutional data silos. Conversely, decentralized architectures provide immutability, auditability, and patient sovereignty but are poorly suited for high-frequency biomedical data streams gener- ated by IoT devices. To address these limitations, we propose a hybrid architectural framework based on polyglot persistence that strategically integrates low-cost IoT biosensors, centralized cloud services for real-time operational data, and decentralized ledgers for secure access control and immutable health record management. The proposed system supports advanced capabili- ties such as edge-based data validation, AI-driven geospatial disease risk prediction, portable digital health identities, and offline-first operation for resource-constrained settings. This framework offers a scalable, ethical, and interoperable blueprint for next-generation public health surveillance and contributes toward advancing United Nations Sustainable Develop- ment Goal 3: Good Health and Well-being.
Rawat et al. (Mon,) studied this question.
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