Business School, Guilin University of Electronic Technology, Guilin, China.
School of Economics and Management, Wuhan University, Wuhan, China.
Front Public Health. 2021 Aug 23;9:724736. doi: 10.3389/fpubh.2021.724736. eCollection 2021.
With the rapid development of the economy of China, the interactivity between provinces and the mobility of the population is increasing. Some patients who could have received the same treatment in their residential areas still choose to receive services in areas with higher economic development and concentrated high-quality medical resources, resulting in a huge waste of medical resources. Blindly increasing medical resources everywhere does not necessarily increase the output effectively. In this study, the data envelopment analysis (DEA) model, social network analysis (SNA), cluster analysis, and regression analysis are used to analyze the structural characteristics of the economic network structure and efficiency of health care in China. The results show that indegree and eigenvector centrality have a significant positive correlation with the efficiency of health care, and the clustering coefficient has a significant negative correlation with the efficiency of health care in China. This study uses a k-means algorithm to classify 31 provinces into three groups and extract their characteristics. As for the supply of health care resources, the government should command and dispatch the resources in the whole country through a top-down design based on the characteristics of each province.
随着中国经济的快速发展，各省之间的互动和人口流动日益增加。一些原本可以在居住地接受相同治疗的患者，仍然选择在经济发展水平较高、集中了高质量医疗资源的地区接受服务，这导致了医疗资源的巨大浪费。盲目地在各地增加医疗资源，不一定能有效地提高产出。本研究运用数据包络分析（DEA）模型、社会网络分析（SNA）、聚类分析和回归分析，对中国经济网络结构和医疗保健效率的结构特征进行分析。结果表明，入度和特征向量中心度与医疗保健效率呈显著正相关，聚类系数与中国医疗保健效率呈显著负相关。本研究运用 k-均值算法将 31 个省份分为三组，并提取其特征。至于医疗保健资源的供应，政府应根据各省的特点，通过自上而下的设计来指挥和调度全国的资源。
