Data quality assessments stimulate improvements to health management information systems
Evidence from five African countries
Bibliographic Data
| ID | 19551291 |
|---|---|
| Authors | Jennifer Yourkavitch (0000-0002-7701-1918, ICF International (United States), corresponding author), Debra Prosnitz (ICF International (United States)), Samantha Herrera (0000-0001-7597-5249, Save the Children) |
| Year | 2019 |
| Volume | 9 |
| Issue | 1 |
| Pages | 010806-010806 |
| Publication date | 2019-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Global Health (JOURNAL) |
| Journal identifiers | ISSN: 2047-2978 • E-ISSN: 2047-2986 |
| Publisher | International Society of Global Health (PUBLISHER • GB) |
| DOI | 10.7189/jogh.09.010806 |
| PMID | 31263551 |
| OpenAlex | W2955021457 |
| Language | EN |
| Citations received | 5 |
| References cited | 3 |
BACKGROUND: Health service data are used to inform decisions about planning and implementation, as well as to evaluate performance and outcomes, and the quality of those data are important. Data quality assessments (DQA) afford the opportunity to collect information about health service data. Through its Rapid Access Expansion Programme (RAcE), the World Health Organization (WHO) funded non-governmental organizations (NGO) to support Ministries of Health (MOH) in implementing integrated community case management (iCCM) programs in the Democratic Republic of Congo, Malawi, Mozambique, Niger and Nigeria. WHO contracted ICF to support grantee monitoring and evaluation efforts, part of which was to conduct DQAs to enhance program monitoring and decision making. The contribution of DQAs to data-driven decision making has been documented and the purpose of this paper is to describe how DQAs contributed to health management information system (HMIS) strengthening and the findings of subsequent DQAs in those areas. METHODS: ICF created a mixed-methods DQA for iCCM data, comprising a review of the data collection and management system, a data tracing component and key informant interviews. The DQA was applied twice in each RAcE site, which enables a general comparison of system-level attributes before and after the first DQA application. For this qualitative assessment, we reviewed DQA reports to collate information about DQA recommendations and how they were addressed before a subsequent DQA, along with the findings of the second DQA. RESULTS: Findings from the first DQA in each RAcE site stimulated NGO and MOH efforts to strengthen different aspects of the HMIS in each country, including modifying data collection tools in the Democratic Republic of Congo; training community health workers (CHWs) and supervisors in Malawi; strengthening supervision in Mozambique; improving CHW registers and strengthening staff capacity at all levels to report data in Niger; establishing a data review system in Abia State, Nigeria; and, establishing processes to improve data use and quality in Niger State, Nigeria. CONCLUSION: Data quality assessments stimulated context-specific efforts by NGOs and MOHs to improve iCCM data quality. DQAs can serve as a collaborative and evidence-based activity to influence discussions of data quality and stimulate HMIS strengthening efforts
Business · Data collection · Data management · Data quality · Database · Information system · Knowledge management · Political science · Process management · Service delivery framework · Computer Science · Electronic Health Records Systems · Global Maternal and Child Health · Health Policy Implementation Science · Medicine
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| Citations per year | 0.71 |
|---|---|
| Citation span | 2019 - 2025 (7) |
| Highly cited | No |