Application of automated feedback for the improvement of data quality in web-based clinical collaborations

Background: Clinical research registries are rarely driven by data quality assurance. However, quality of data can have a huge impact on the performance and outcome of any given trial using registry data. Therefore, data quality assurance procedures for cost reduction and data process improvements have to be implemented in research registries. Hypothesis: This research proposes that web-based data quality feedback can motivate registry users, increase their contributions and ultimately improve the quality of registry data and its (re-)use to support clinical trials; thereby reducing the costs and need for study monitors. Method: To explore causes of low data quality and user motivation, a survey and an assessment of quality indicators in a multicentre clinical setting was performed. Subsequently, a development and evaluation of a web-based feedback framework was conducted. This was explored in the international Niemann-Pick disease registry (INPDR) and two clinical trials associated with the European Network for the Study of Adrenal Tumours (ENSAT). Results: The survey and framework evaluation highlight effectiveness of web-based automated data quality feedback. Case studies showed an increase of data quality within observation time. Conclusion: Centralised data monitoring requires a general framework that can be adjusted for a variety of trials and studies. This research highlights how biomedical research registries have to be designed with focus on data quality and feedback mechanisms