基于数据包络分析的安徽省医疗卫生资源配置效率分析

Analysis of the Allocation Efficiency of Medical and Health Resources in Anhui Province Based on DEA

  • 摘要: 采用数据包络分析法(DEA),在规模报酬可变的假设条件下构建BCC模型,对2021年安徽省医疗卫生资源的部分投入和产出指标进行分析,为提高安徽省内医疗卫生资源配置的合理性提供参考。研究结果显示,安徽省整体纯技术效率取值为0.963,规模效率取值为0.950,综合效率取值为0.915。合肥、亳州、蚌埠、宣城四地呈现出DEA有效状态,淮北、铜陵和池州呈现出弱DEA有效状态,宿州、阜阳、淮南、滁州、六安、马鞍山、芜湖、安庆和黄山表现为DEA无效。安徽省DEA无效城市医疗卫生资源投入冗余相对集中于医疗卫生机构和医疗卫生机构床位上,产出不足则集中于诊疗人次上。可见安徽省医疗卫生资源配置效率总体水平相对较低,地区之间医疗卫生资源配置效率差异较大,医疗卫生资源投入和产出不均衡。主张因地制宜, 科学提高医疗卫生资源配置效率;注重人才培养和引进, 提高医技水平;利用辐射带动, 推动整体水平提高。

     

    Abstract: This paper adopted the data envelopment analysis(DEA) method, under the assumption of variable returns to scale, constructed the BCC model, and analyzed some of the input and output indicators of medical and healthcare resources in Anhui Province, to provide a reference for improving the reasonableness of the allocation of medical and health care resources within Anhui Province. The results of the study showed that the overall pure technical efficiency of Anhui province has a value of 0.963, the scale efficiency has a value of 0.95, and the comprehensive efficiency had a value of 0.915. Hefei, Bozhou, Bengbu, and Xuancheng show the DEA effective state, Huaibei, Tongling, and Chizhou showed a weak DEA effective state, and Suzhou, Fuyang, Huainan, Chuzhou, Lu'san, Ma'anshan, Wuhu, Anqing, and Huangshan showed a DEA ineffective state. The redundant investment in medical and health resources in DEA ineffective cities in Anhui Province was relatively concentrated in medical and health institutions and their beds, while the insufficient output was concentrated in the number of diagnosis and treatment patients. It can be concluded that the overall level of health resource allocation efficiency in Anhui Province is relatively low, and there are large differences in health resource allocation efficiency between regions, with significant imbalances in health resource input and output. The paper advocates adapting measures to local conditions and scientifically improving the efficiency of medical and health resource allocation; focusing on talent training and introduction to improve medical technology; using the radiation-driven effect to promote overall improvement.

     

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