Utilize the Database Architecture to Enhance the Performance and Efficiency of Large-Scale Medical Data Processing

Abstract

As the volume of medical data continues to grow, traditional database systems are increasingly challenged by demands for performance, scalability, and real-time responsiveness. Efficient database design is critical to meeting application needs in electronic medical record (EMR) systems, medical imaging storage, clinical decision support, and health data monitoring. This paper explores several architectural strategies to optimize database performance in large-scale medical environments. Techniques such as database sharding, table partitioning, index optimization, caching, and tiered storage of hot and cold data are shown to significantly improve system throughput, reduce latency, and enhance multi-threaded access efficiency. These methods collectively support the stable, secure, and scalable operation of modern healthcare information systems.