Scalable AI Enabled Cloud Native Framework for Secure Healthcare Governance and Intelligent Digital Health Systems
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706029Keywords:
Cloud-native architecture, healthcare data governance, artificial intelligence, data security, interoperability, machine learning, blockchain, privacy preservation, predictive analyticsAbstract
The rapid digitization of healthcare systems has resulted in unprecedented volumes of sensitive patient data, necessitating robust, scalable, and secure frameworks for data governance. This research proposes a cloud-native, AI-enabled architecture designed to ensure secure healthcare data management while enabling intelligent digital health transformation. The framework integrates microservices-based cloud infrastructure with artificial intelligence techniques such as machine learning, natural language processing, and predictive analytics to optimize data governance, interoperability, and clinical decision-making. Security is reinforced through zero-trust architecture, encryption protocols, blockchain-based audit trails, and privacy-preserving AI mechanisms. The proposed system supports real-time data processing, ensures compliance with global healthcare regulations, and enhances data accessibility across distributed healthcare ecosystems. Furthermore, it enables advanced analytics for disease prediction, patient monitoring, and personalized treatment planning. The scalability of cloud-native technologies allows seamless handling of growing healthcare data volumes, while AI-driven automation reduces operational inefficiencies. This framework not only addresses current challenges in healthcare data governance but also lays the foundation for future innovations such as smart hospitals, telemedicine expansion, and population health management. The study demonstrates how integrating AI with cloud-native systems can transform healthcare delivery into a secure, efficient, and intelligent ecosystem.
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