Research And Application On The Medical Insurance Reimbursement Fee’s Decision Model Based On Big Data

As the medical insurance coverage broaden, the medical insurance compensatory revenue will take up a larger proportion in the gross income of hospitals.Given this,the hospitals and medical insurance offices also need to adjust their management pattern and take measures to adapt themselves to this change.As the work on medical insurance gains greater importance in hospital’s management work,how to set up an reasonable and applicable medical insurance quota becomes the focus in the management work of hospitals,The traditional methods require large amount of labor to make the analysis report of medical insurance every month and analyze the medical insurance fee for the month.But with this manual method all layers of information can’t be comprehensively considered to make multi-dimensional business analyses,,resulting in the incapacity of analyzing the cause of those exceptional cases in time.In light of this,this paper uses the thought of data mining to have designed a model for medical reimbursement fee’s decision-making.And the main work is as follows:1.detecting data stream outliers: it can’t be avoided that there are some outliers existing in oceans of dynamic data,and if they are not found out and be added into calculation model,the errors of classifying result will become large.So we need to find a way to detect those outliers.2.researching the decision model:aiming at the situations of medical insurance reimbursement fee’s decision-making,analyze relevant problems,build up policy model and improve the inefficient manual evaluation mode for medical insurance decision-making.And also to design and realize the medical insurance reimbursement fee’s decision-making system and to finish the prediction of the medical insurance reimbursement fee by data mining.3.Designing and implementing a big data mining platform. The platform can complete the data mining and analysis work on the big data set. The functions this platform provided are as follows: data preprocessing, clustering, classification, recommendation, association rules and so on. The platform encapsulates the data mining algorithms based on hadoop and Spark. It also provides a flexible and configurable algorithm model and data model. According to the platform, users can complete the analysis work on the big data set easily and quickly.