An Empirical Analysis Of The Basic Medical Insurance Risk-adjusted Payment Model

Purpose:The implementation of provincial-level co-ordination of medical insurance funds,the promotion of equitable distribution of medical resources among regions,and the realization of protection for all have become important measures in the reform of China's medical insurance system.The fair and effective distribution of insurance premiums at the provincial level is the prerequisite and basis for smoothly raising the overall level of medical insurance funds.Internationally,the risk adjustment mechanism has been used in the field of medical insurance premium distribution and has accumulated rich experience.This article summarizes the evolution and development rules of the risk-adjusted payment model in the world,comparatively studies the widely used DCG,ACG,and CRG models.Based on this,the 2016 CFPS survey data is used to try to build a risk-adjusted payment model,so as to introduce risk adjustment for China to summarize experience and provide useful reference for subsequent model construction.Method:First of all,the literature research method is used to understand the research status of China's risk adjustment mechanism,and the evolution and development rules of the risk adjustment payment model in the world are summarized.Second,the two-part model is constructed by the microeconometrics method,comparing the prediction effect of demographic model and survey factors model,and exploring the improvement effect of distinguishing urban and rural forecasts on model prediction accuracy.Result:The risk-adjusted payment model has generally experienced the development process from the initial simple demographic factor model to the diagnostic factor model.The diagnostic factor model continuously improves the model effect by improving the diagnostic grouping method,enriching the source of diagnostic information and adding drug information;The main differences in the models used such as DCG,ACG,and CRG include the risk adjustment factors and diagnostic grouping methods used;using our country s survey data modeling found that age,gender,whether to work,ability to live in daily life,the presence of chronic diseases,self-evaluation Health,hospitalization,and mental health are suitable as risk adjustment factors;the survey factor model is significantly better than the demographic factor model in terms of individual prediction accuracy and group prediction accuracy;distinguishing urban and rural predictions is conducive to improving the prediction effect of the model.Conclusion:The survey factor model that distinguishes between urban and rural forecasting has achieved significant improvements to the demographic factor model,but there are still insufficient and over-predicted forecasts for some groups.Before the development and utilization of the diagnostic factor model,the proportional method,reinsurance method,risk sharing method or Pre-event+post-event risk adjustment method to make up for the problem of insufficient prediction accuracy of the model.To solve the problem of insufficient prediction accuracy of the model,the model itself must be improved.During the use of the investigation factor model,it is necessary to prepare for the development of the diagnosis factor model later,such as improving the quality of diagnosis and drug use data,establishing a unified claims database,and developing a diagnostic data grouping system.