Using REDCap to enhance longitudinal data management: a case study from Nepal

REDCap is a widely used platform that supports the entire research data lifecycle, from collection to deidentification, linkage, and analysis, within an integrated platform. In Nepal, road traffic injuries (RTIs) pose major public health and economic challenges, hindered by limited data availability. Within the SafeTrip Nepal road safety research programme (2022-2026), REDCap and Data Governance (DG) practices are being utilized to address these gaps to inform policy decisions. This poster demonstrates constructing longitudinal datasets for analysis from repeated collection points, managing data sensitivity levels, and aligning processing stages with DG plans to improve auditing, risk management, and research outcomes.

A four-stage DG plan was implemented: Planning (REDCap installation, training, and role assignments); Data Collection (real-time data entry, monthly follow-ups, and quality control at two sites with four hospitals, two data collectors per hospital, and one supervisor per site); Analysis (using SPSS and Power BI for data analysis and visualisation); and Output (secure storage, deidentification, role-based access, and ethical compliance). Post-project data will be archived for five years and securely destroyed.

REDCap enabled efficient follow-up data management. Key strengths included robust quality control, confidentiality, secure data handling, and efficient role-based access. Implementing a DG framework further enhanced data practices by promoting standardisation, accountability, compliance, and risk management. Challenges such as maintaining consistent training and addressing technical issues were mitigated through regular technical support, iterative training sessions, and assistance from the REDCap community.

This study demonstrates the feasibility and efficiency of managing RTI research data using REDCap and DG. The method can be expanded to address more social/public health issues and enhance data security, quality, and ethical compliance. It provides a framework for improving data management procedures, assisting with well-informed decisions, and achieving high-quality and ethical research outcomes.