Real-Time, Risk-Based Clinical Trial Quality Management in China: Development of a Digital Monitoring Platform
Bibliographic record
Abstract
Background: With 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. Objective: The 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. Methods: Leveraging 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). Results: The 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. Conclusions: In 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".