Clinical Data Analysis
Preventing Clinical Data Errors with Quality Built into Design
Reduce errors before analysis using data dictionaries, range checks, verification, audit trails and risk-based quality management.
Admin
June 20, 2026
8 min read
Data quality is designed, not cleaned in later
Detection matters, but prevention through electronic controls, standard definitions and ownership is more effective.
Practical framework
Create a data dictionary.
Build range and logic checks.
Use proportionate source verification.
Maintain audit trails.
Reporting
Track queries, missingness, deviations and corrections by site.
Common mistakes
Using colour as data.
Overwriting without trace.
Treating every field as equally critical.
References
Accessed 20 June 2026. General methodological information.