ArticleApplied clinical informatics2026
Enhancing Data Quality and Remote Accessibility in Clinical Trials: A SeroSelectTB Case Study in Ethiopia, Tanzania, and South Africa.
Article in Applied clinical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Ensuring data quality and maintaining remote accessibility remain key challenges in clinical trials conducted in low-resource settings. Although several software platforms and digital tools attempt to address these challenges, no single, comprehensive solution adequately meets the complex demands of decentralized and intermittently connected trial environments. Consequently, context-specific tools must be developed and adapted to ensure reliable performance under such constraints. Objective: The aim of this study was to enhance data quality and enable uninterrupted participant randomization in settings with limited internet and electricity through the implementation of an automated Quality Control (QC) reporting system and an online and offline participant randomization application. Methods: A centralized automated QC reporting system was developed using a Python-based script, which was integrated with the Research Electronic Data Capture system to extract and process data, and generate a web-based monitoring dashboard, which displayed data summaries, recruitment charts, and flags on missing or inconsistent records. Thereafter, a mobile randomization application built with React Native was developed for online and offline participant randomization, employing block randomization to ensure balanced allocation between study arms and enrollment continuity. Results: The automated QC system reduced data verification time from months to 1 hour, enabling real-time oversight and early error correction. The randomization application maintained balanced study arms and allowed uninterrupted participant enrollment during internet outages. These digital tools improved data completeness, reduced bias and human error, and increased trial efficiency across all study sites. Conclusion: Automation and offline functionality can significantly enhance data quality, efficiency, and integrity in clinical trials conducted in resource-constrained environments. The experience from this study demonstrates the value of combining automated QC and offline randomization tools to ensure robust and continuous study operations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.