Good Clinical Practice, often abbreviated as GCP, is an international ethical and scientific quality standard for designing, conducting, recording, and reporting clinical research involving human participants. Although learners may study GCP in dedicated training, data managers need to understand its relevance to everyday data work. GCP is not only about consent forms and monitoring visits. It is deeply connected to data credibility and participant protection.
GCP emphasizes that research should be scientifically sound and ethically conducted. It requires that participant rights, safety, and well-being are protected, and that clinical trial data are credible. For data managers, this means that data systems must support accurate documentation, confidentiality, traceability, and quality assurance. A database that allows uncontrolled changes without audit trails would weaken GCP compliance. A data export that includes unnecessary identifiers may threaten confidentiality. A poorly documented cleaning process may make it difficult to reproduce results.
One helpful way to connect GCP to data management is through the principle that data should be attributable, legible, contemporaneous, original or source-verifiable, accurate, complete, consistent, enduring, and available. These terms are often discussed in relation to data integrity. They remind research teams that a final dataset is not enough; the study must also preserve evidence of how the data were generated, reviewed, corrected, and used.
In a REDCap project, GCP-aligned practices may include assigning individual user accounts, defining role-based permissions, maintaining audit logs, avoiding shared passwords, documenting database changes, validating fields, training users, preserving export records, and ensuring that data corrections are traceable. In R-based cleaning workflows, GCP-aligned practices may include using scripted transformations, preserving raw data, documenting exclusions, versioning scripts, and generating reproducible reports.