(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2025)
The rapid digitization of healthcare systems has enabled unprecedented data generation from electronic health records (EHRs), wearable devices, medical imaging systems, and IoT-enabled monitoring tools. While this data offers immense potential for predictive analytics and intelligent decision-making, it raises critical concerns regarding patient privacy, data security, and regulatory compliance. This paper proposes a privacy-preserving healthcare intelligence system built on cloud-native architecture integrated with secure automation and predictive analytics capabilities. The system leverages advanced cryptographic techniques such as homomorphic encryption, federated learning, and differential privacy to ensure sensitive medical data remains protected throughout its lifecycle. Cloud-native technologies, including microservices, containerization, and Kubernetes orchestration, enable scalability, resilience, and real-time processing of healthcare workloads. Predictive analytics models powered by machine learning are embedded to support early disease detection, patient risk stratification, and operational optimization in healthcare delivery. Additionally, secure automation workflows using policy-driven access control and AI-assisted decision systems enhance efficiency while maintaining compliance with healthcare regulations such as HIPAA and GDPR. The proposed framework demonstrates how privacy-preserving computation and cloud-native intelligence can coexist to transform healthcare systems into secure, adaptive, and predictive ecosystems. The study highlights architectural design, implementation strategies, and performance considerations for deploying such systems in real-world healthcare environments.