Enhancing Data Quality and Remote Accessibility in Clinical Trials: A SeroSelectTB Case Study in Ethiopia, Tanzania, and South Africa

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. 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. 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. 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. 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.