Study on Service Efficiency Evaluation of Traditional Chinese Medicine Hospitals Based on DEA-Malmquist Index Model
Objective To evaluate the service efficiency of Traditional Chinese Medicine hospitals in 30 provinces(autonomous regions and municipalities)in China from 2017 to 2023,and to provide reference for promoting the high-quality and efficient development of Traditional Chinese Medicine hospitals.Methods The BCC model and Malmquist index model in data envelopment analysis method are used to measure the static and dynamic changes of service efficiency in Traditional Chinese Medicine hospitals,and the time series prediction method is used to predict the index changes from 2022 to 2023 and make dynamic evaluation.Results In 2021,the average comprehensive efficiency of Traditional Chinese Medicine hospitals in China was 0.852,and 50％of the provinces(autonomous regions and municipalities)were in the state of invalid DEA.From 2017 to 2021,the overall service efficiency of Traditional Chinese Medicine hospitals in China showed a downward trend.The average total factor productivity index was 0.958,with an average decrease of 4.2％.The average index values of the eastern,central and western regions were 0.978,0.946 and 0.951,respectively.The three regions all experienced different degrees of decline,and the overall efficiency showed little change trend from 2017 to 2023 after prediction and analysis.Conclusion The overall service efficiency of Traditional Chinese Medicine hospitals in China is at a low level,and the regional development differences are obvious,among which the technical progress index is an important influencing factor.All provinces(autonomous regions and municipalities)should reasonably control the development scale of Traditional Chinese Medicine hospitals,constantly promote technological progress,improve the utilization rate of resources,gradually narrow regional differences,and then improve the service efficiency of Traditional Chinese Medicine hospitals.
data envelopment analysisTraditional Chinese Medicine hospitalservice efficiencyevaluation
万方数据
维普
