Objective To estimate the service efficiency level of traditional Chinese medicine(TCM)hospitals in China,analyze regional differences and influencing factors,and provide a reference for optimizing the allocation of TCM resources.Methods The super-efficiency SBM model was used to estimate the service efficiency of TCM hospitals from 2012 to 2021,the Dagum Gini coefficient was used to analyze the regional differences in service efficiency of TCM hospitals,and the influencing factors and spillover effects were explored with the help of the spatial Durbin model.Re-sults The average annual service efficiency of TCM hospitals from 2012 to 2021 was 0.907,with the eastern,central,westward,and northeast having respective averages of 0.955,0.886,0.947,and 0.669.The service efficiency of TCM hospitals in the central region was the biggest of 0.159,while the average annual Gini coefficient of service efficiency of TCM hospitals in the western region was the smallest of 0.107.The difference of service efficiency of TCM hospitals was the biggest between central region and northeast region.Supervariable density was the main source of the overall differ-ence of service efficiency in TCM hospitals.The spatial autocorrelation coefficient was significantly negative,the direct effect coefficient of per capita education level and per capita GDP was significantly positive,the spillover effect coefficient of urban ratio and population density was significantly negative,and the direct effect coefficient and spillover effect coeffi-cient of bed-to-cover ratio were significant.Conclusion The development of TCM hospitals in China has room for optimization,and TCM hospitals in the northeast region should be the focus of attention and support in the next stage.The overlapping problem between different regions was the key factor that leads to the gap of service efficiency in TCM hospitals.The service efficiency of TCM hospitals in China is affected by many factors,such as per capita education lev-el,per capita GDP,urban rate,population density and bed-to-care ratio.
Chinese Medicine HospitalsService EfficiencyRegional DifferenceInfluencing Factor
NETL
NSTL
万方数据
