中国全科医学 ›› 2021, Vol. 24 ›› Issue (1): 46-51.DOI: 10.12114/j.issn.1007-9572.2021.00.037

• 专题研究 • 上一篇    下一篇

我国基层医疗卫生机构医护人员的医防整合行为及影响因素研究

于梦根,赵璇,李惠文,于亚航,袁蓓蓓*,孟庆跃   

  1. 100191北京市,北京大学中国卫生发展研究中心
    *通信作者:袁蓓蓓,副研究员,硕士生导师;E-mail:beibeiyuan@bjmu.edu.cn
  • 出版日期:2021-01-05 发布日期:2021-01-05
  • 基金资助:
    基金项目:中国工程院重大咨询研究项目(2018-ZD-09)——整合医学战略研究(2035)

Primary Care Doctors and Nurses' Behaviors in the Delivery of Integrated Medical and Preventive Services and Its Influencing Factors 

YU Menggen,ZHAO Xuan,LI Huiwen,YU Yahang,YUAN Beibei*,MENG Qingyue   

  1. PKU China Center for Health Development Studies,Beijing 100191,China
    *Corresponding author:YUAN Beibei,Associate professor,Master supervisor;E-mail:beibeiyuan@bjmu.edu.cn
  • Published:2021-01-05 Online:2021-01-05

摘要: 背景 构建医防整合型卫生服务体系,基层卫生服务不可或缺,医护人员在服务过程中的行为直接影响患者接受服务的质量和健康状况,揭示整合服务行为的影响因素对准确、科学制定医防整合服务政策有重要意义。目的 分析基层医护人员的医防整合行为及其影响因素,为加强基层医防整合服务提供政策建议。方法 于2019年4—10月,采用全国性多阶段抽样,使用自设问卷对基层医护人员进行调查,主要包括基本信息、医防整合认识和服务行为指标等,利用两水平线性回归和多元线性回归模型分析服务行为的影响因素。结果 共计发放问卷810份,基层医护人员自报预防服务时间占比有效问卷数量是624份,有效率为77.4%;固定患者服务比例有效问卷数量是609份,有效率为75.2%。基层医护人员自报预防服务时间占比和固定患者服务比例的均值分别为(37.7±23.033)%和(27.3±24.312)%。预防服务时间占比资料层级结构明显,两水平线性回归模型结果显示日常服务过程中的预防服务时间占比影响因素包括是否参与家庭医生团队、合作互动和专业界限认识(P<0.05);多元线性回归模型获得的固定患者服务比例影响因素包括人员类型、是否参与家庭医生团队、合作互动和本机构工作年限(P<0.05)。结论 推进家庭医生团队建设和签约服务;明确服务分工,加强科室和多学科合作;转变医防人为分割的固化思维等有利于医务人员为患者提供连续协调的医防整合服务。

关键词: 基层医疗卫生机构, 医防整合, 医务人员, 服务行为, 影响因素分析, 两水平线性回归模型

Abstract: Background Primary care is an indispensable part of integrated medical and preventive care system that is constructed currently. Primary care doctors and nurses' behaviors in service delivery may directly affect care quality and health status of patients,so identifying the associated factors of these medical workers' behaviors is necessary for the development of appropriate and scientific policies related to such services. Objective To analyze primary care doctors and nurses' behaviors and associated factors in the delivery of integrated medical and preventive services,to provide policy suggestions for the development of such services. Methods From April to October 2019,we carried out a survey among a nationwide multi-stage sample of primary care doctors and nurses using a self-designed questionnaire for investigating their demographics and perception of integrated medical and preventive services,as well as evaluation indicators for behaviors in service delivery.We used two-level linear regression and multiple linear regression models to analyze the influencing factors of their behaviors in service delivery. Results Of the 810 cases attending the survey,624(77.4%) gave responsive answers to questions about self-reported percentage of preventive service delivery time with notable hierarchical structure of answer responses,and 609(75.2%) gave responsive answers to questions about the percentage of regular patients encountered. The mean self-reported percentage of preventive service delivery time was(37.7±23.033)%. And the mean percentage of regular patients encountered was(27.3±24.312)%. Two-level linear regression analysis showed that the influencing factors for self-reported percentage of preventive service delivery time included whether being a family physician team member,cooperation and interaction,and understanding of professional boundary(P<0.05). Factors influencing self-reported percentage of regular patients encountered obtained by multiple linear regression analysis included demographic information,whether being a family physician team member,cooperation and interaction,and years of working in the institution(P<0.05). Conclusion To improve the delivery of sustainable and coordinated integrated medical and preventive services,efforts should be made to promote the development of family physician teams and contracted services,determine the roles of each member,strengthen inter-departmental and multidisciplinary cooperation,and change the rigid thinking of medical and preventive services being unconnected.

Key words: Primary health care institutions, Integrated medical and preventive services, Medical staff, Services behavior, Root cause analysis, Two-level linear regression models