Data Quality Review

lut 28, 2025

What Does 'Data Quality Review’ Mean?

Data Quality Review refers to the systematic process of examining and evaluating the accuracy, completeness, and reliability of data collected during clinical trials. This critical step ensures that the data used for analysis and decision-making meets predetermined quality standards and regulatory requirements.

The review typically involves checking for inconsistencies, missing information, and potential errors in the collected data. It may include comparing source documents with entered data, verifying adherence to the study protocol, and assessing the overall integrity of the dataset to support the validity of the clinical trial results.

Why Is the 'Data Quality Review’ Important in Clinical Research?

Data Quality Review is crucial in clinical research as it ensures the integrity and reliability of study results. By identifying and correcting errors or inconsistencies in the data, it helps maintain the scientific validity of clinical trials and supports informed decision-making in drug development and patient care.

Furthermore, Data Quality Review is essential for regulatory compliance and the acceptance of clinical trial data by health authorities. It enhances the credibility of research findings, reduces the risk of drawing incorrect conclusions, and ultimately contributes to the safety and efficacy of new medical treatments.

Good Practices and Procedures

Implement a risk-based approach, focusing more resources on critical data points and high-risk areas of the study
Utilize automated data validation tools to flag potential inconsistencies and outliers in real-time
Conduct regular cross-functional team reviews involving data managers, statisticians, and clinical operations personnel
Perform targeted source data verification (SDV) on a subset of data, based on predefined triggers or random sampling
Implement a continuous feedback loop to refine data collection processes and improve data quality throughout the study lifecycle