摘要
目的:构建患者感知的家庭医生团队服务融合度调查工具,分析医防融合服务现状.方法:通过文献检索和半结构化访谈初步构建调查工具;通过专家咨询修改和完善调查工具;实证研究验证调查工具的适用性并分析医防融合度现状.结果:构建了包括服务综合性、服务闭环、服务深度、关系连续性、以患者为中心和服务协调性共6 个维度、18 个条目的调查工具;患者感知家庭医生团队医防融合度总得分为(54.30±12.45)分.结论:本研究构建的调查工具为基层家庭医生团队医防融合度评估提供了患者视角.患者感知医防融合度总体水平较高,但服务协调性和深度欠佳.建议优化服务流程、明晰服务路径、细化服务管理、构建医防融合激励机制,迈向医防深度融合.
Abstract
Objective:This study aims to develop a validated instrument for assessing the degree of integration perceived by patients in the services provided by general practitioner(GP)teams,and to evaluate the current state of integrated medical-prevention services.Methods:The survey tool was initially created through a review of existing literature and semi-structured interviews.It was subsequently refined and enhanced following consultations with experts.The utility of the instrument was confirmed through empirical research,which also facilitated an analysis of the status quo regarding the integration of medical and prevention services.Results:The devised instrument encompasses 18 items distributed across six dimensions:service comprehensiveness,service continuity,depth of service,continuity of relationship,patient-centricity,and service coordination.The cumulative score reflecting patients'perception of the integration level in GP teams'medical-prevention services was 54.30±12.45.Conclusion:The survey instrument developed in this research is characterized by its rationality,novelty,and practical relevance.It offers a patient-centered perspective for assessing the quality of integrated medical and prevention services for primary chronic diseases.While the overall perception of integration among patients is positive,there is a notable deficiency in the depth of services provided.There is a need for optimization of the service process,clarification of the service path,refinement of service management,and development of an integrated incentive mechanism for medical and preventive care,aiming for a deeper integration between medical services and preventive care.
基金项目
国家自然科学基金面上项目(72074087)
教育部人文社会科学研究青年项目(20YJC630193)
NETL
NSTL
万方数据