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- What is Data cleaning?
- Where is it used in clinical trials?
- How does Clinion implement it?
- What does it look like in practice?
- Related Terms
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What is Data cleaning?
Data cleaning is the process of carefully examining clinical trial data to identify and correct errors, inconsistencies, or gaps. Through query resolution, standardization, and verification against study protocols, clinical trial data cleaning maintains the integrity and reliability of the dataset throughout the study.
Where is it used in clinical trials?
In clinical trials, data cleaning is used throughout the data collection and management phase to ensure that trial data is accurate, complete, and consistent.
It is applied after data is entered into systems such as the Electronic Data Capture (EDC) platform and continues on an ongoing basis during the study. Data cleaning activities include checking for missing values, inconsistencies, outliers, and protocol deviations, and resolving queries with study sites. This process helps ensure that the data is reliable and ready for final database lock and study completion.
How does Clinion implement it?
Clinion supports data cleaning through its integrated EDC, CTMS, and RTSM platforms, using configurable workflows to identify data issues early and support efficient review by data management teams. Built-in query management, audit trails, and role-based access ensure that corrections are tracked clearly and data quality is maintained consistently across the study.
What does it look like in practice?
In practice, data cleaning in Clinion works as a continuous process running alongside the trial. As staff enter data into eCRFs, the system automatically checks each entry for errors like missing values, unusual numbers, or conflicting information. When problems are found, the system creates queries and sends them to site staff to fix. Data managers use real-time dashboards to monitor everything: how many queries are open, which sites need help, and what issues need immediate attention. Every change is recorded in audit trails, showing exactly what happened and when. This combination of automatic checks, dashboard monitoring, and detailed tracking ensures data quality is maintained throughout the study without slowing down the trial.
Category
EDC & Data Management