ArticleGlobal health, science and practice2022
Drivers and Barriers to Improved Data Quality and Data-Use Practices: An Interpretative Qualitative Study in Addis Ababa, Ethiopia.
Article in Global health, science and practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 13 citations in OpenAlex.
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- Behavioral factors affecting data quality and information utilization in health facilities in ethiopia, 2023: an exploratory qualitative study.Archives of public health = Archives belges de sante publique · 2025Article
- Practice, enablers and barriers of health information system accountability framework in Northwest Ethiopia 2023.BMC medical informatics and decision making · 2025Article
- Early Effects of Information Revolution Interventions on Health Information System Performance in Ethiopia.Global health, science and practice · 2024Article
- What could improve surgical data system at health facilities with high surgical volume in Ethiopia?BMC health services research · 2024Article
- Implementation status of maternal death surveillance and response system in Ethiopia: Evidence from a national-level system evaluation.PloS one · 2024Article
- Implementation status of maternal death surveillance and response system in Ethiopia: Evidence from a national-level system evaluation.PloS one · 2024Article
- Implementation status of maternal death surveillance and response system in Ethiopia: Evidence from a national-level system evaluation.PloS one · 2024Article
- Learning Health Systems to Bridge the Evidence-Policy-Practice Gap in Primary Health Care: Lessons From the African Health Initiative.Global health, science and practice · 2022Article
- Implementation context, determinants, and outcomes of eCHIS and DHIS2 for maternal health reporting in Southwest Ethiopia.Digital healthArticle
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAn objective of the Information Revolution Roadmap of Ethiopia's Health Sector Transformation Plan was to improve health management information system (HMIS) data quality and data use at the point of health service delivery. We aimed to assess drivers of and barriers to improving HMIS data quality and use, focusing on key Information Revolution strategies including Connected Woreda, capacity building, performance monitoring teams, and motivational incentives.
methodsWe conducted an interpretative qualitative study across all 11 health centers in 3 subcities of Addis Ababa, Ethiopia: Yeka, Akaki-Kaliti, and Ledeta. A total of 40 key informant interviews and 6 focus group discussions with a total of 43 discussants were conducted. We coded information gathered line-by-line and grouped responses under thematic codes as they emerged. Findings were triangulated and validated.
resultsOur findings indicate that the main drivers of data quality and use at the point of service delivery were the use of the Connected Woreda strategy and its tools, capacity-building activities including mentorship, performance monitoring-team activities that led to active leadership engagement, and motivational incentives for data producers and users. Barriers to optimal data-use practices were the use of duplicative data collection tools at health facilities, under-developed health information system infrastructure, inadequate health information technician staffing and capacity limitations at the health facility level, insufficient leadership commitment, and unfavorable health worker attitudes toward data. DISCUSSION: Improvements in quality and use of HMIS data at health facilities are expected to result in delivering better-quality health services to the community as data enable health workers to identify gaps in health care, fix them, and monitor improvements. Future investments should focus on strengthening the promising data-use practices, resolving bottlenecks caused by duplicative data collection tools, enhancing individual and institutional capacity, addressing suboptimal health worker attitudes toward data, and overcoming infrastructure and connectivity challenges.
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