Cloud Computing in Healthcare for Enhancing Patient Care and Efficiency

Abstract

Cloud computing has emerged as a transformative force in healthcare, offering scalable infrastructure, enhanced data accessibility, and advanced applications like AI diagnostics and telemedicine. However, its adoption faces critical challenges, including data privacy risks, regulatory complexities, and interoperability gaps between legacy systems and modern cloud architectures. This paper proposes a structured methodology to evaluate and optimize cloud platforms for healthcare, focusing on performance (throughput, latency), security (encryption, compliance), and cost efficiency. Through technical evaluation of AWS, Azure, and GCP—benchmarked using SPEC cloud, FHIR API testing, and a Total Cost of Ownership (TCO) model—we demonstrate AWS's superior throughput (1,200 req/sec at 10K users) and scalability compared to Azure (850 req/sec) and GCP (950 req/sec). Analysis of EHR audit logs reveals a 95% accuracy in detecting unauthorized access, while triangulation validation (????????=0.86Ts=0.86) ensures data reliability. Performance metrics for clinical AI tools show high precision (93%) and recall (90%), confirming robust diagnostic capabilities. These results provide actionable insights for healthcare providers to adopt cloud solutions that balance efficiency, security, and cost, ultimately enhancing patient care delivery.