Improving data quality across 3 sub-Saharan African countries using the Consolidated Framework for Implementation Research (CFIR): results from the African Health Initiative.
Improving data quality across 3 sub-Saharan African countries using the Consolidated Framework for Implementation Research (CFIR): results from the African Health Initiative.
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DOI：
10.1186/s12913-017-2660-y
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发表时间：
2017-12-21
影响因子：
2.8
通讯作者：
AHI PHIT Partnership Collaborative
中科院分区：
文献类型：
--
作者：
Gimbel S;Mwanza M;Nisingizwe MP;Michel C;Hirschhorn L;AHI PHIT Partnership Collaborative
高质量的数据对于告知、监测和管理卫生项目至关重要。在多丽丝·杜克慈善基金会为期七年的非洲健康倡议期间，莫桑比克、卢旺达和赞比亚的五个人口健康实施和培训伙伴关系项目中的三个引入了战略，以改进初级保健一级例行收集的数据的质量和评估，并鼓励将其用于循证决策。以实施研究综合框架(CFIR)为指导，本文：1)对项目的数据质量评估和改进活动进行描述和分类，2)确定在每种情况下为提高数据质量而引入的核心干预组成部分和实施战略调整。通过与每个项目的主要信息提供人进行讨论的定性主题缩减过程对CFIR进行了调整，这些信息提供人确定了与研究目的最相关的两个领域和十个结构，目的是描述和比较每个国家的数据质量评估方法和实施过程。通过半结构问卷收集关于每个项目的数据质量改进战略、实施的活动和结果的数据，其中包括向每个国家的健康管理信息系统线索管理封闭式和开放式项目，并从项目报告中提取补充数据。在这三个项目中，与用户优先事项和政府系统保持一致的干预部分被认为是相对有利的，更容易调整和采用。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)从受访者那里获得了较高的排名分数。我们的发现表明，至少，成功的数据质量改进努力应该包括与正在进行的、在服务点进行的在职指导相关的例行审计。这对干预措施使卫生工作者参与数据收集、清理和分析真实世界的数据，从而通过现场指导提供重要的技能培养。这些核心组成部分的效果通过绩效审查会议得到加强，这些会议统一了多个卫生系统级别(省、区、设施和社区)，以评估数据质量、突出薄弱领域并计划改进。
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.
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影响因子：
2.8
作者：
Sherr K;Cuembelo F;Michel C;Gimbel S;Micek M;Kariaganis M;Pio A;Manuel JL;Pfeiffer J;Gloyd S
通讯作者：
Gloyd S
DOI：
10.2471/blt.11.99580
发表时间：
2012-05-01
期刊：
Bulletin of the World Health Organization: International Journal of Public Health
影响因子：
--
作者：
Braa, Jørn;Heywood, Arthur;Sahay, Sundeep
通讯作者：
Sahay, Sundeep
DOI：
10.1186/1748-5908-4-50
发表时间：
2009-08-07
期刊：
Implementation science : IS
影响因子：
--
作者：
Damschroder LJ;Aron DC;Keith RE;Kirsh SR;Alexander JA;Lowery JC
通讯作者：
Lowery JC
影响因子：
2.6
作者：
Nahimana E;McBain R;Manzi A;Iyer H;Uwingabiye A;Gupta N;Muzungu G;Drobac P;Hirschhorn LR
通讯作者：
Hirschhorn LR
影响因子：
2.8
作者：
Stringer, Jeffrey S. A.;Chisembele-Taylor, Angela;Chilengi, Roma
通讯作者：
Chilengi, Roma