摘要
目的:评价我国基层医疗卫生服务质量,为卫生行政部门制定及调整决策提供参考.方法:根据2022 年《中国卫生健康统计年鉴》《中国统计年鉴》数据,通过RSR法和PCA法对TOPSIS法进行改进,从人力资源、服务设施、服务效益 3 个维度 15 个指标对我国各省份基层医疗卫生机构服务质量进行综合评价.结果:15 个指标中提取出5 个主成分(Z1-Z5),特征值分别为λ1=4.89、λ2=3.82、λ3=1.91、λ4=1.47、λ5=0.96,累计贡献率达到86.96%;两种方法分档结果显示,山东、河南、江苏 3 个省份均被评定为"好",青海、宁夏、海南均被评定为"差";其中,山东省基层医疗卫生服务质量得分最高,为 0.64,海南省得分最低,为 0.21.结论:我国各地基层医疗卫生服务质量存在差异,分级诊疗制度仍需完善,村卫生室仍需进一步推进管理体制改革.应通过优秀案例推广、医联体建设、人才引进策略优化,综合提升我国基层医疗卫生服务质量.
Abstract
Objective:To evaluate the quality of grassroots medical and health services of China,so as to provide scientific ba-sis for health administration departments to make and adjust decisions.Methods:According to the data of China Health Statistical Yearbook and China Statistical Yearbook in 2022,TOPSIS method was improved by RSR method and PCA method,and the service quality of grassroots medical and health institutions in various provinces was comprehensively evaluated from 15 indicators including human resources,service facilities and service efficiency.Results:Five principal components(Z1-Z5)were extracted from 15 indi-cators,with characteristic values ofλ1=4.89,λ2=3.82,λ3=1.91,λ4=1.47,λ5=0.96,with a cumulative contribution rate of 86.96%.The results of the two methods showed that Shandong,Henan,and Jiangsu provinces were all rated as"good",while Qinghai,Ningxia,and Hainan were all rated as"poor".Among them,Shandong had the highest score of 0.64 for the quality of grassroots medical services,while Hainan had the lowest score of 0.21.Conclusion:There are differences in the quality of grassro-ots medical and health services in various parts of China,the hierarchical diagnosis and treatment system still needs to be strength-ened,and the village clinics still need to further promote the reform of the management system.It is necessary to comprehensively improve the quality of grassroots medical and health services in China through the promotion of excellent cases,the construction of medical alliances,and the optimization of talent introduction strategies.
基金项目
国家自然科学基金资助项目(32300941)
教育部产学合作协同育人项目(202102001001)
中国高校产学研创新基金资助课题(2021LDA12004)
NETL
NSTL
万方数据