Cloud-based telemedicine service platforms have emerged as a critical component of modern healthcare systems, particularly during global pandemics when physical access to healthcare facilities becomes restricted. These platforms leverage cloud computing technologies to enable remote patient monitoring (RPM), virtual consultations, real-time data sharing, and continuous health assessment without requiring in-person visits. During pandemics such as COVID-19, healthcare systems worldwide experienced unprecedented strain, highlighting the need for scalable, accessible, and resilient digital healthcare infrastructures. Cloud-based telemedicine systems integrate wearable IoT devices, mobile health applications, electronic health records (EHRs), and AI-driven analytics to monitor patients’ vital signs including temperature, oxygen saturation, heart rate, and respiratory activity. The centralized cloud infrastructure ensures efficient storage, processing, and analysis of large-scale medical data while enabling seamless communication between patients and healthcare providers. These systems enhance early diagnosis, reduce hospital overcrowding, and minimize infection risks by limiting physical contact. However, challenges such as data privacy concerns, network dependency, cybersecurity risks, and unequal digital access remain significant barriers. This study explores the architecture, benefits, limitations, and methodologies of cloud-based telemedicine platforms for remote patient monitoring during global pandemics, emphasizing their role in ensuring continuous, scalable, and accessible healthcare delivery under crisis conditions.
