Li Zhiguang, Zhang Wanying, Kong Aijie, Ding Zhiyuan, Wei Hua, Guo Yige
School of Economics and Management, Anhui University of Chinese Medicine, Hefei, Anhui, People's Republic of China.
King's Business School, King's College London, London, UK.
Risk Manag Healthc Policy. 2021 Jan 8;14:49-65. doi: 10.2147/RMHP.S282178. eCollection 2021.
This paper aims to measure the technical efficiency of China's medical and health institutions from 2012 to 2017 and outline the path to achieve high-quality development.
The DEA-Malmquist was used to evaluate the total factor productivity of medical and health institutions in 31 provinces. A fuzzy set Qualitative Comparative Analysis (fsQCA) was used for configuration analysis of determinants affecting technical efficiency.
The average total factor productivity (TFP) of those institutions was 0.965, namely TFP declined averagely by 3.5% annually. The efficiency change and the technical change were 0.998 and 0.967, respectively. The realization paths of high technical efficiency are composed of high fatality rate and high financial allocation-led, high population density and high GDP-led. Low dependency ratio and low financial allocation-led, low fatality rate and low financial allocation-led are the main reasons for low technical efficiency.
Due to advanced medical technology and economic development, major cities like Beijing, Shanghai, and Guangdong have attracted a large number of high-level health personnel, achieving long-term and stable health business growth. Hubei, Anhui, and Sichuan also have made rapid development of health care through appropriate financial subsidies and policy supports. The technical changes in Qinghai, Yunnan, and Inner Mongolia are higher than the national average, but the operation and management level of the medical and health institutions is relatively weak. Henan, Jiangxi, and Heilongjiang have a prominent performance in the efficiency change, but the technical change is weaker than the national average.
本文旨在测度2012—2017年中国医疗卫生机构的技术效率，并勾勒其实现高质量发展的路径。
运用DEA-Malmquist方法评估31个省份医疗卫生机构的全要素生产率。采用模糊集定性比较分析（fsQCA）对影响技术效率的决定因素进行组态分析。
这些机构的平均全要素生产率（TFP）为0.965，即TFP年均下降3.5%。效率变化和技术变化分别为0.998和0.967。高技术效率的实现路径由高死亡率和高财政投入主导型、高人口密度和高GDP主导型构成。低抚养比和低财政投入主导型、低死亡率和低财政投入主导型是技术效率低下的主要原因。
由于医疗技术先进和经济发展，北京、上海和广东等大城市吸引了大量高层次卫生人才，实现了医疗卫生事业的长期稳定增长。湖北、安徽和四川也通过适当的财政补贴和政策支持，实现了医疗卫生事业的快速发展。青海、云南和内蒙古的技术变化高于全国平均水平，但医疗卫生机构的运营管理水平相对较弱。河南、江西和黑龙江在效率变化方面表现突出，但技术变化弱于全国平均水平。
