Real-Time, Risk-Based Clinical Trial Quality Management in China: Development of a Digital Monitoring Platform - Min Jiang; Shuhua Zhao; Yun Mei; Zhiying Fu; Yannan Yuan; Jie Ai; Yuan Sheng; Ying Gong; Jingjing Chen (JMIR Medical Informatics)
Abstract BackgroundWith the improvement of the drug evaluation system in China, an increasing number of clinical trials have been launched in Chinese hospitals. However, traditional clinical trial quality management models largely rely on human monitoring and counting, which can be time-consuming and are likely to generate errors and biases. There is an urgent need to upgrade and improve the efficiency and accuracy of clinical trial quality monitoring systems in hospital-based research institutions within China. ObjectiveThe objective of this study was to develop a digital monitoring platform that allows for the real-time monitoring and detection of risk points and provides warnings about risk points throughout the entire life cycle of clinical trials, on the basis of historical clinical trial quality control (QC) findings. MethodsLeveraging the risk-based quality management mindset, we built a digital dynamic monitoring platform by using big data analysis and automatic quantitative technology. Data from clinical trial QC reports generated during 2019 to 2023 in Beijing University Cancer Hospital, China, were used to train the automated classification tool, establish warning thresholds, and validate threshold values. Quality findings from the early-stage, interim-stage, and conclusion-stage QC rounds of clinical trials were rated by using 3 severity grades (minor, major, or critical) and classified into 5 categories (with 4 taxonomy levels under each category). QC report text was processed by using an automated natural language processing tool. All QC reports were grouped into 2 clusters via hierarchical clustering analysis. QC findings from the relatively high-risk cluster (reports that were more likely to have major and critical findings, as determined by experienced QC analysts) were used to determine warning threshold values for the monitoring platform (ie, the lowest number of findings was set as the threshold value for each specific study stage, Level-3 taxonomy, and severity grade combination). ResultsThe most frequently reported Level-3 taxonomies in QC reports from 2019 to 2022 were “Standard Procedure and Process,” “Safety Reporting,” and “Source Data Collection and/or Recording.” In total, 189 warning threshold values were established based on data from 1380 QC reports generated during 2019 to 2022, covering 3 severity grades, 21 Level-3 taxonomies, and 3 QC rounds. The warning thresholds were applied to 211 QC reports generated in 2023, of which 19.9% (n=42) triggered warnings. Similar patterns of QC findings, including the most frequently noted Level-3 QC findings, were observed between reports generated in 2023 and those from 2019 to 2022. ConclusionsIn clinical practice, our tool would enable the automated monitoring and detection of risk points throughout all clinical trial stages; accurately identify the most relevant trial procedure and function line; and notify quality management personnel, in real time, to take prompt actions and dynamically prevent the recurrence of quality issues.