Objective To provide a theoretical basis for improving the efficiency of public Chinese medicine hospitals in Hunan province by analyzing the efficiency and influencing factors of 112 public Chinese medicine hospitals in Hunan province in 2021,thereby promoting their high-quality development.Methods The DEA-BCC model was used to calculate the efficiency of the public Chinese medicine hospitals in Hunan province and the Tobit regression model was employed to explore the factors influencing their efficiency.Results The average comprehensive efficiency,pure technical efficiency,and scale efficiency of the 112 public Chinese medicine hospitals were 0.828 8,0.857 6,and 0.965 0,respectively.The proportion of hospitals with a comprehensive efficiency value of 1 was 26.79％.The average comprehensive efficiency of secondary hospitals in this province was higher than that of tertiary hospitals,but the average comprehensive efficiency of tertiary hospitals in four cities including Changsha was higher than that of secondary hospitals.More than 50％of the hospitals experienced decreasing returns to scale.The number of health professionals,personnel expenses,and fixed assets were statistically significant for the comprehensive efficiency of public Chinese medicine hospitals in Hunan province.Conclusion The efficiency of public Chinese medicine hospitals in Hunan province still has significant room for improvement.Technological level has emerged as the main factor restricting the improvement of their efficiency.There are significant differences in efficiency across public Chinese medicine hospitals in different regions,and the efficiency between secondary and tertiary hospitals is unbalanced.Further optimization of medical resource allocation is needed,focusing on connotative development and tailored measures to local conditions,to promote the efficiency improvement of public Chinese medicine hospitals in Hunan province.
关键词
公立中医医院/效率/DEA-BCC模型/Tobit回归模型/影响因素
Key words
public Chinese medicine hospital/efficiency/DEA-BCC model/Tobit regression model/influencing factor
维普
万方数据