ArticlePublic health action2019
Overcoming limitations of tuberculosis information systems: researcher and clinician perspectives.
Article in Public health action, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Quantifying emergent drug-resistant TB using laboratory data and record-linkage methods.IJTLD open · 2026Article
- Designing a digital tuberculosis treatment support and monitoring system for primary care: a user-centered qualitative study of patients, families, and healthcare workers.BMC health services research · 2026Article
- Optimizing use of data and evidence-related tools to complement routine surveillance systems for tuberculosis program planning.Frontiers in tuberculosis · 2025Article
- Article
- Data quality assessment of a South African electronic registry for drug-resistant TB, 2015-2016.Public health action · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
settingTuberculosis (TB) diagnosis and treatment requires patients to have multiple encounters with health care systems and the different stakeholders who play a role in curing them to coordinate their efforts. To optimize this process, high-quality, readily available data are required. Data systems to facilitate these linkages are a neglected priority which, if weak, fundamentally undermine TB control interventions.
objectiveTo describe lessons learnt from the use of programmatic data for TB patient care and research.
designWe did a survey of researcher and clinical provider experiences with information systems and developed a tiered approach to addressing frequently reported barriers to high-quality care.
resultsUnreliable linkages, incomplete data, lack of a reliable unique patient identifier, and lack of data management expertise were the most important data-related barriers to high-quality patient care and research. We propose the creation of health service delivery environments that facilitate, prioritize, and evaluate high-quality data entry during patient or specimen registration.
conclusionAn integrated approach, focused on high-quality data, and centered on unique patient identification will form the foundation for linkages across health systems that reduce patient management errors, bolster surveillance, and enhance the quality of research based on programmatic data.
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Registered trials
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