AI-driven Knowledge Management in Medical Insurance Department: Towards Efficient Supervision and Payment Processing Using Scalable Computing

Abstract

Knowledge management systems leveraging artificial intelligence (AI) capabilities in the medical insurance sector are essential to improve efficiency, accuracy, and efficiency in care and payment management. Comprehending the ever-increasing complexity and sophistication of medical information and data requires modern technology that can handle massive amounts of data and provide ongoing insights. Data confidentiality, resilient and scalable computing infrastructure, connectivity to legacy systems, and AI algorithm correctness and reliability are all obstacles when implementing AI-driven systems in this field. Varying medical data and changing healthcare regulations complicate program implementation. Using customizable computing resources, the research presents an all-inclusive AI-driven knowledge management framework (AI-DKMF). Integrating machine learning techniques, big data analytics, and natural language processing allows the system to process services and payments. Distributed computing systems, robust storage methods, and adaptive algorithms are needed to manage big data, keep sensitive information secure, and comply with healthcare regulations as it is constantly changing. The medical insurance sector can greatly benefit from the proposed AI-driven system in many ways, such as claims authentication, fraud detection, risk assessment the use of predictive analytics, and support for individual customers. Medical insurance departments can reduce operating costs, improve service quality, and increase patient satisfaction by streamlining these processes. The performance and flexibility of the proposed system are evaluated using simulation experiments. The results prove the ability of the system to handle multiple feedbacks efficiently and accurately. The evaluation additionally demonstrates how well the system works with data types and how it can adapt to different codes. The proposed AI-DKMF model increases the Algorithmic Efficiency Analysis by 98.4%, Data Volume Scalability Analysis by 96.8%, Privacy Protection Analysis by 96.9%, Operational Cost Reduction Analysis by 97.5%, Fraud Detection Accuracy Analysis by 8.9% compared to other existing models.