A Precise Health Follow-up Management Information System for Community Chronic Diseases based on Big Data Analysis

Abstract

To enhance the efficiency of medical big data utilization, the author introduces a study on a precise health follow-up management information system tailored for community chronic diseases, leveraging advanced big data analytics. The system includes a meticulously designed chronic disease health record management framework, optimized for efficient ETL (Extract, Transform, Load) processes of medical data. This approach aims to minimize indexing overhead, enhance query execution speed and search capabilities, and maximize the use of aggregated computing resources. The system can be decomposed into four modules: ELT, data block creation, index creation, and querying. As an intermediate layer between users and distributed data management systems, this system can provide data upload, query execution mechanisms, and provide indexes to facilitate data search operations. The experimental results show that when the simulation step size is 50t, the maximum amount of data can reach 10 × 104. This method has a much higher retrieval efficiency than heuristic algorithms when processing massive data, further verifying the effectiveness and practicality of the chronic disease health record management system.