A Flexible Framework of General Clinical Data Quality Control System

Standardized and comprehensive data is essential for conducting statistical analyses in clinical research and ensuring the credibility of clinical trial results. Traditional methods of data quality control primarily rely on monitoring database table fields. However, the complexity and diversity of clinical data structures pose challenges in maintaining all constructed database tables and limit flexibility. This research addresses these issues and proposes a clinical data quality control system that fulfills the requirements of clinical data management. The system utilizes flexible software methods to abstractly partition clinical data, enabling support for multiple standards of clinical data. It employs a generalized structured query language to express rules, facilitating data integrity, consistency, accuracy, timeliness, relevance, and user-defined rule models. Additionally, the system incorporates statistical algorithms to accomplish quality control tasks such as identifying data outliers, conducting correlation and comparative analyses, as well as supporting data similarity control using Transformers text vectorization to calculate cosine similarity and numerical data fusion. Furthermore, the designed system leverages Spark distributed computing by partitioning and allocating data to different nodes for computation. This approach significantly enhances system stability and scalability. Overall, the proposed clinical data quality control system offers a comprehensive solution to address the complexities of clinical data, ensuring standardized and reliable data for statistical analysis in clinical research.