Medical information management system based on multi-source heterogeneous big data

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ABSTRACT This study aims to provide a medical information management system using multi-source heterogeneous big data to improve medical service quality and efficiency, with a motivation on its potential in medical insurance DRG payment. The system framework uses Back Propagation Neural Network (BPNN) technology to efficiently process and analyze multi-source medical data. Comparative experiments and parameter adjustments evaluated the system’s performance. Results show that the BPNN model achieved excellent accuracy (92.5%), recall (93%), and F1 value (92.8%) on the test data set, outperforming other models such as PSO(88%), CNN(89%), and RNN(90%). The system’s response speed was also significantly improved, with an average response time of 0.38 seconds, compared to 0.89 seconds for traditional systems. A 72-hour stability test confirmed the system’s reliability and ability to meet user needs. The proposed system demonstrates improved performance and user experience, making it a promising solution for medical information management and DRG payment applications.