A Case Study of the International Niemann-Pick Disease Registry (INPDR)

Clinical research registries need to be driven by data quality to improve the outcome of clinical trials and to provide the possibility to facilitate new research initiatives. The International Niemann-Pick Disease Registry (INPDR) is one such example of a clinical research registry. Unlike other registries where data quality is largely based around best effort manual data entry, the INPDR registry supports ongoing data completeness evaluation and feedback to those entering data. To assess the efficiency of this feedback, a research framework evaluating data completeness with different types of user feedback was applied. In this paper we consider the impact on data quality and user activity over time. Data accuracy and data completeness was measured by participating centers and combined into an overall data quality score. The results show that clinical research registries can benefit from an ongoing data quality assurance framework with targeted data quality feedback. This has implications for future clinical trials and clinical databases more generally.