A Study On Fund Risk Early Warning Model Of New Rural Cooperative Medical System Of A County In Xinjiang

Objective:To design a fund risk management index system,then build a fund risk early-warning model and a fund risk early-warning system on the basis of risk identification through the analysis of NCMS operationg datas of a county.Methods:Colected the NCMS annual accounts and Statistical Yearbook data of a county in Xinjiang.Key information interview method was applied to know the operation status and existing problems on NCMS.Dephin method,variation coefficient method and fuzzy corprehensive evaluation method were used to establish a fund risk management index system.Selected variables with the MIV altorithrm based on BP neural network and framed the early-warning model with Grey GM?1,n?model.Results:?1?The coverage on NCMS continued to expand,the participation rate approached to 100%in 2015.The fund-raising standard increased from 120 to 520 yuan/person·year during 2009-2015.There were 104563 outpatients and 7523 inpatients benefited from the NCMS fund,the effective compensation rate were 75.12%on outpatient and 51.78%on hospatiliation in 2015.The payment capacity of fund balance decreased from 0.53 to-0.04 year during 2009-2015.?2?The authority of Dephin professors is 0.8,the professors' accommodation coefficient of three domensions are all 0.3,the consistency test(?_{index importan}² =27.65;?_{risk possibility}²=26.33;?_{azard degree}² =26·54,P<0.05)makes sense.The final index system include five level indexes:environmental risk,policy risk,fund raising risk,fund payment risk,manahgement risk,and 30 two level indexes.The first three indexes of the ordering on the risk degree mean values were payment risk,policy risk and management risk in first class.The hospital ratio of outside the county,hospital compensation amount outside the county account for a proportion of the total hospitalization compensation,and the average hospitalization costs in second class.?3?Four variables were chosen by the MiV altorithrm based on BP neural network,including the prevalence rate of chronic diease,reimbursement proportion of basic drug list,hospitalization rate of peasents,average hospitalization costs,then,the grey GM?l,n?model was built with the four variables and its precision is 92.44%.Conclusion:The fund risk resouses of the county mainly come from the sectors of fund finacing and payment.The results of Dephin is realible,the designed index system is complete and truthful.Th e established model after selecting four variables has higher precision,can be forcasted.