Research Electronic Data Capture Using REDCap and External Data Quality Pipeline Development

Objective: Research Electronic Data CAPture (REDCap) is a powerful web-based data management tool commonly used in academic research centers. Proper use, development, and execution are crucial in facilitating high-quality data capture, ensuring robust future analysis. While REDCap provides integrated tools for assessing data quality and status, these often fail to address the intricate demands of multisite longitudinal studies. Methods: We present a framework to optimize REDCap project development and introduce a Python-based data quality pipeline. Results: By focusing on strategic pre-production project design and implementing rapid-response quality assurance during post-production, we substantially improved the quality and accessibility of data for analysis. The Blackbox was first released in November 2024 for a double-blind clinical trial. A review of the three output files, Query Library Flags, Missingness Flags, and Missingness Summary revealed that there were 1949 queries, with the violations occurring between 85 and 500 days. Most of these were due to changes to the protocol or to missing branching logic (resulting in unnecessary queries). The study team addressed and resolved queries where able, and fields with missing branching logic were fixed. Black Box execution was applied against the corrected data set. A comparison between the two runs shows all queries were resolved, and there were no violations. Conclusion: This approach can empower other researchers to enhance both the accuracy and usability of data in complex research projects by leveraging REDCap's capabilities.