Abstract
OBJECTIVE: To implement a dynamic data management and control framework that meets the multiple demands of high data quality, rigorous information technology security, and flexibility to continuously incorporate new methodology for a large disease registry. MATERIALS AND METHODS: Guided by relevant sections of the COBIT framework and ISO 27001 standard, we created a data control framework supporting high-quality real-world data (RWD) studies in multiple disease areas. We first mapped and described the entire data journey and identified potential risks for data loss or inconsistencies. Based on this map, we implemented a control framework adhering to best practices and tested its effectiveness through an analysis of random data samples. An internal strategy board was set up to regularly identify and implement potential improvements. RESULTS: We herein describe the implementation of a data management and control framework for multiple sclerosis, one disease area in the NeuroTransData (NTD) registry that exemplifies the dynamic needs for high-quality RWD analysis. Regular manual and automated analysis of random data samples at multiple checkpoints guided the development and implementation of the framework and continue to ensure timely identification of potential threats to data accuracy. DISCUSSION AND CONCLUSIONS: High-quality RWD, especially those derived from long-term disease registries, are of increasing importance from regulatory and reimbursement perspectives, requiring owners to provide data of comparable quality to clinical trials. The framework presented herein responds to the call for transparency in real-world analyses and allows doctors and patients to experience an immediate benefit of the collected data for individualized optimal care.