在医疗保险信息化建设中应用大数据分析的研究

针对医疗保险信息化建设中大数据应用技术的不足,本研究以苏州工业园区医疗保险特病结算数据为分析对象,通过大数据算法对医疗保险信息化建设中的大数据进行梳理、分析、清洗、重构等,然后构建移动平均、指数平均模型实现对大数据的处理。本研究还通过随机矩阵理论算法实现医疗数据的能谱和本征态分析、统计,得出实际测量中的随机程度,揭示出医疗保险信息化建设大数据包含的整体关联事件特征,又利用数据挖掘算法再次对分析出的数据进行二次处理,使用户快速从海量的数据(比如尿毒症、白内障、再生障碍性贫血、血友病、恶性肿瘤康复期、冠心病合并心肌梗死、癫痫)中需求目标数据,实现对数据的分类、分析。实现数据表明,本研究方法具有明显的实用价值,为医保基金的可持续发展及医疗保险政策的制定、完善提供技术参考。

Research on Applying Big Data Analysis in Medical Insurance Information Construction

In view of the insufficiency of big data application technology in medical insurance information construction, this study takes the medical insurance special disease settlement data of Suzhou Industrial Park as the analysis object, and combs, analyzes and cleans the big data in medical insurance informationization construction through big data algorithm. , reconstruction, etc., and then build a moving average, exponential average model to achieve the processing of big data. This study also uses the random matrix theory algorithm to realize the energy spectrum and eigenstate analysis and statistics of medical data, and obtain the random degree in the actual measurement, revealing the characteristics of the overall associated event included in the medical insurance informationization construction big data, and using the data. The mining algorithm again reprocesses the analyzed data, so that users can quickly get from massive data (such as uremia, cataract, aplastic anemia, hemophilia, malignant tumor rehabilitation, coronary heart disease with myocardial infarction, epilepsy) Demand target data to achieve classification and analysis of data. The realization of the data shows that this research method has obvious practical value, and provides technical reference for the sustainable development of medical insurance fund and the formulation and improvement of medical insurance policy.