Gimbel Sarah, Mwanza Moses, Nisingizwe Marie Paul, Michel Cathy, Hirschhorn Lisa
School of Nursing, University of Washington, Magnuson Health Sciences Building, Box 357262, Seattle, WA, 98195-7262, USA.
Department of Global Health, University of Washington, Seattle, WA, USA.
BMC Health Serv Res. 2017 Dec 21;17(Suppl 3):828. doi: 10.1186/s12913-017-2660-y.
High-quality data are critical to inform, monitor and manage health programs. Over the seven-year African Health Initiative of the Doris Duke Charitable Foundation, three of the five Population Health Implementation and Training (PHIT) partnership projects in Mozambique, Rwanda, and Zambia introduced strategies to improve the quality and evaluation of routinely-collected data at the primary health care level, and stimulate its use in evidence-based decision-making. Using the Consolidated Framework for Implementation Research (CFIR) as a guide, this paper: 1) describes and categorizes data quality assessment and improvement activities of the projects, and 2) identifies core intervention components and implementation strategy adaptations introduced to improve data quality in each setting.
The CFIR was adapted through a qualitative theme reduction process involving discussions with key informants from each project, who identified two domains and ten constructs most relevant to the study aim of describing and comparing each country's data quality assessment approach and implementation process. Data were collected on each project's data quality improvement strategies, activities implemented, and results via a semi-structured questionnaire with closed and open-ended items administered to health management information systems leads in each country, with complementary data abstraction from project reports.
Across the three projects, intervention components that aligned with user priorities and government systems were perceived to be relatively advantageous, and more readily adapted and adopted. Activities that both assessed and improved data quality (including data quality assessments, mentorship and supportive supervision, establishment and/or strengthening of electronic medical record systems), received higher ranking scores from respondents.
Our findings suggest that, at a minimum, successful data quality improvement efforts should include routine audits linked to ongoing, on-the-job mentoring at the point of service. This pairing of interventions engages health workers in data collection, cleaning, and analysis of real-world data, and thus provides important skills building with on-site mentoring. The effect of these core components is strengthened by performance review meetings that unify multiple health system levels (provincial, district, facility, and community) to assess data quality, highlight areas of weakness, and plan improvements.
高质量数据对于为卫生项目提供信息、进行监测和管理至关重要。在多丽丝·杜克慈善基金会为期七年的非洲卫生倡议中，莫桑比克、卢旺达和赞比亚的五个“人口健康实施与培训”（PHIT）伙伴关系项目中有三个引入了相关策略，以提高初级卫生保健层面常规收集数据的质量和评估，并促进其在循证决策中的应用。本文以实施研究综合框架（CFIR）为指导：1）描述并分类项目的数据质量评估和改进活动；2）确定为在每个环境中提高数据质量而引入的核心干预组成部分和实施策略调整。
通过定性主题简化过程对CFIR进行了调整，该过程涉及与每个项目的关键信息提供者进行讨论，他们确定了与描述和比较每个国家数据质量评估方法及实施过程的研究目标最相关的两个领域和十个构建模块。通过向每个国家的卫生管理信息系统负责人发放包含封闭式和开放式问题的半结构化问卷，并从项目报告中进行补充数据提取，收集了每个项目的数据质量改进策略、实施的活动及结果。
在这三个项目中，与用户优先事项和政府系统相一致的干预组成部分被认为相对具有优势，并且更容易被调整和采用。同时评估和改进数据质量的活动（包括数据质量评估、指导和支持性监督、建立和/或加强电子病历系统）在受访者中获得了更高的排名分数。
我们的研究结果表明，至少成功的数据质量改进工作应包括与服务点持续的在职指导相关的常规审计。这种干预措施的结合让卫生工作者参与到实际数据的收集、清理和分析中，从而通过现场指导提供重要的技能培养。统一多个卫生系统层面（省级、地区级、机构级和社区级）以评估数据质量、突出薄弱环节并规划改进措施的绩效审查会议，强化了这些核心组成部分的效果。
