Year: 2024
Type: conference-abstract
Abstract: Background Ensuring data quality and integrity is fundamental to the Enroll-HD study. Quality control (QC) and assurance measures are implemented and monitored at a participant, site, and study level, alongside a suite of other key performance indicators (KPIs). Aims QC measures are designed to maximize data consistency, completeness, accuracy, and timeliness; KPI monitoring (including data quality indicators) facilitates efficient site management and study oversight. Methods/Techniques Data quality and performance monitoring measures are centrally defined and implemented. Systems and procedures utilized include: 1) electronic data capture system; 2) remote centralized statistical monitoring (CSM) of participant data (cross-sectional and longitudinal) ; 3) remote CSM of site data, comprising KPI outlier analyses; 4) review of study level data, including tracking of recruitment, dropout, data quality, and completeness; 5) onsite monitoring to ensure compliance with protocol and applicable regulations; 6) site management underpinned by data quality measurement tools e.g., site metrics cards; 7) training, SOPs, and manuals to support data collection and monitoring. Results/Outcome Data quality processes allow us to achieve and maintain a high standard of data quality, while KPI monitoring allows study staff to support sites, providing tailored support and feedback where appropriate. Conclusion Data quality and performance monitoring are critical operational processes in ensuring high quality data releases, supporting study sites, and ultimately facilitating high quality clinical research. We are currently working to unify all remote data QC procedures into a singular framework, and developing a web-based application enabling real-time centralized KPI tracking.
Authors: Jenny Callaghan, Jennifer Ware, on behalf of the Enroll-HD Platform Team
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4402373824