A HYBRID EDGE-CLOUD MODEL FOR ACCELERATING HEALTHCARE TRANSACTION PROCESSING WITH EXPLAINABLE AI

Abstract

Healthcare transaction processing plays a crucial role in the secure and efficient exchange of sensitive data, such as patient reports, payment information, and insurance claims. Traditional centralized systems struggle with latency, scalability, data security, and resource constraints. This paper introduces a Hybrid Edge-Cloud Model to address these challenges by reducing latency, improving data security, and enhancing scalability. By combining edge computing for real-time data processing with cloud computing for sophisticated analytics, the model optimizes transaction processing in healthcare systems. Additionally, Explainable AI (XAI) fosters trust and transparency in AI-driven decisions, ensuring better decision-making in patient care. The proposed solution offers significant benefits, including lower operational costs, enhanced data security, and more reliable healthcare outcomes. Practical implementations in billing, patient data management, and telemedicine are discussed, alongside challenges such as system integration and data privacy concerns. The paper concludes with recommendations for future research and development, highlighting the collaboration needed among healthcare providers, technology vendors, and researchers to fully realize this model.