卫生系统的实力以及最终人口的健康状况在很大程度上取决于卫生工作者的表现。然而,建设支持不足,管理和优化低收入和中等收入国家(LMICs)的卫生人力资源(HRH)导致卫生人力绩效不足,持续存在健康不平等和低质量的卫生服务。
美国国际开发署资助的2030年卫生人力资源计划(HRH2030)对记录监督增强和改善卫生工作者绩效的方法的研究进行了系统审查,以突出与这些干预措施相关的组成部分。由概念框架构建,对输入进行分类,进程,和结果,该审查评估了自2010年以来约29个低收入国家的57项监督研究.
在审查的57项研究中描述的成功监督方法中,44人是外部资助的飞行员,这是一个限制。30个侧重于社区卫生工作者(CHW)计划。卫生工作者的监督来自38种方法(67%)的卫生系统数据,22种方法使用了持续质量改进(QI)(39%)。许多成功的方法集成了数字监管技术(例如,智能手机,mHealth应用程序)以支持现有数据系统并补充其他卫生系统活动。很少有研究被改编,缩放,或持续,限制成本效益或影响的报告。
在审查结果的基础上,为了提高卫生工作者监督的有效性,我们建议:整合循证,QI工具和流程;将数字监督数据集成到监督流程中;在计划监督访问时增加对卫生系统信息和绩效数据的使用,以优先考虑表现最差的区域;跨服务提供区域和地理区域扩展和复制成功的模型;扩展和制度化监督以达到,准备,保护,并支持一线卫生工作者,特别是在卫生紧急情况下;利用国内人力和财力资源过渡和维持监督工作,包括社区,全面的劳动力支持。总之,有效的卫生工作者监督是由卫生系统数据,使用持续质量改进(QI),并将数字技术集成到其他卫生系统活动和现有数据系统中,以实现整个系统的方法。有效监管的加强和创新要更好地结合起来,缩放,并在现有系统内持续进行,以改善获得优质医疗保健的机会。
The strength of a health system-and ultimately the health of a population-depends to a large degree on health worker performance. However, insufficient support to build, manage and optimize human resources for health (HRH) in low- and middle-income countries (LMICs) results in inadequate health workforce performance, perpetuating health inequities and low-quality health services.
The USAID-funded Human Resources for Health in 2030 Program (HRH2030) conducted a systematic
review of studies documenting supervision enhancements and approaches that improved health worker performance to highlight components associated with these interventions\' effectiveness. Structured by a conceptual framework to classify the inputs, processes, and results, the
review assessed 57 supervision studies since 2010 in approximately 29 LMICs.
Of the successful supervision approaches described in the 57 studies reviewed, 44 were externally funded pilots, which is a limitation. Thirty focused on community health worker (CHW) programs. Health worker supervision was informed by health system data for 38 approaches (67%) and 22 approaches used continuous quality improvement (QI) (39%). Many successful approaches integrated digital supervision technologies (e.g., SmartPhones, mHealth applications) to support existing data systems and complement other health system activities. Few studies were adapted, scaled, or sustained, limiting reports of cost-effectiveness or impact.
Building on results from the
review, to increase health worker supervision effectiveness we recommend to: integrate evidence-based, QI tools and processes; integrate digital supervision data into supervision processes; increase use of health system information and performance data when planning supervision visits to prioritize lowest-performing areas; scale and replicate successful models across service delivery areas and geographies; expand and institutionalize supervision to reach, prepare, protect, and support frontline health workers, especially during health emergencies; transition and sustain supervision efforts with domestic human and financial resources, including communities, for holistic workforce support. In conclusion, effective health worker supervision is informed by health system data, uses continuous quality improvement (QI), and employs digital technologies integrated into other health system activities and existing data systems to enable a whole system approach. Effective supervision enhancements and innovations should be better integrated, scaled, and sustained within existing systems to improve access to quality health care.