Published Paper Details:

Cloud computing combined with big data analytics offers transformative opportunities for healthcare--enabling scalable storage, multimodal data fusion, and advanced machine learning for clinical and operational decision support. This paper presents a comprehensive framework and experimental plan for deploying cloud-based analytics to improve patient care. We describe architectures (centralized Lakehouse, hybrid edge-cloud, and federated setups), data engineering pipelines for electronic health records (EHR), imaging, and device telemetry, and privacy-preserving machine learning strategies (federated learning, differential privacy). We propose a set of experiments using public and partner datasets to evaluate predictive performance, latency, cost, and privacy-utility tradeoffs. We also outline deployment considerations, regulatory compliance, and ethical safeguards for real-world adoption. Our work aims to provide a reproducible blueprint for researchers and healthcare IT teams to build cloud-centered analytics that improve clinical outcomes while minimizing privacy risks.