摘要
目的探索大数据背景下甘肃省分级诊疗政策在贫困人口中的实践情况,为贫困人口的分级诊疗工作进行探索性研究,为甘肃省健康扶贫和分级诊疗政策的完善提供参考依据。方法通过定性研究和定量研究相结合,进行统计分析。结果甘肃省分级诊疗政策在贫困人口中年龄组间分布差异无统计学意义(X^2=6.398,P=0.603>0.05);分级诊疗政策在民族间的分布差异有统计学意义(X^2=24.396,P=0.000<0.05);分级诊疗政策在精准扶贫人员中的分布存在明显差异,差异有统计学意义(X^2=6.528,P=0.038<0.05);贫困人口就诊机构间分布差异有统计学意义(X^2=1219.758,P=0.000<0.05)。结论甘肃省贫困人口的分级诊疗工作取得了较为显著的成效,形成了"分级诊疗+健康扶贫"的新工作模式,探索出了符合地域特色的工作方法。
Objective To explore the practice of hierarchical diagnosis and treatment policy in Gansu Province in the context of big data,and provide reference for the improvement of health poverty alleviation and hierarchical diagnosis and treatment policies in Gansu Province.Methods Statistical analysis was performed by combining qualitative research with quantitative research.Results There was no significant difference in the distribution of hierarchical diagnosis and treatment policies among the poor groups in the population of Gansu Province(X^2=6.398,P=0.603 >0.05).The distribution of hierarchical diagnosis and treatment policies among ethnic groups was statistically significant(X^2=24.396,P<0.05);There was a significant difference in the distribution of grading diagnosis and treatment policies among the precise poverty alleviation personnel,the difference was statistically significant(X^2=6.528,P=0.038<0.05);the distribution difference among the poor people's treatment institutions was statistically significant(X^2=1219.758),P<0.05).Conclusion The hierarchical diagnosis and treatment of poor people in Gansu Province had made remarkable achievements,forming a new working mode of"grading diagnosis and treatment"plus"healthy poverty alleviation",and exploring working methods that conform to regional characteristics.
作者
姚进文
路杰
闫宣辰
陶生鑫
白焕莉
王伟
李建苗
高歆
蒲旭虹
殷利霞
胡晓斌
YAO Jinwen;LU Jie;YAN Xuanchen(Information Center,Gansu Provincial Health and Family Planning Commission,Lanzhou,Gansu,730030,China)
出处
《中国卫生质量管理》
2019年第1期109-112,共4页
Chinese Health Quality Management
基金
甘肃省科技厅软科学项目
项目编号:17CX1ZA008
关键词
分级诊疗
健康扶贫
大数据
Hierarchical Diagnosis and Treatment
Health Poverty Alleviation
Big Data
