Analysis of data errors in clinical research databases.

TLDR

Errors in clinical research databases are common, yet little is known about their characteristics and optimal detection and prevention strategies. The study analyzes data from several clinical research databases at a single academic medical center to assess the frequency, distribution, and features of data entry errors and highlights the need for further investigation into detection and prevention methods. The authors examined data from multiple databases, employing double‑entry and constraint‑failure methods to identify and characterize data entry errors. Error rates ranged from 2.3 % to 26.9 %, with errors arising from both entry mistakes and misinterpretation; constraint‑based detection underestimated total errors, prevented only a small fraction, and many errors were non‑random, clustered, and potentially impactful on study results.

Abstract

Errors in clinical research databases are common but relatively little is known about their characteristics and optimal detection and prevention strategies. We have analyzed data from several clinical research databases at a single academic medical center to assess frequency, distribution and features of data entry errors. Error rates detected by the double-entry method ranged from 2.3 to 26.9%. Errors were due to both mistakes in data entry and to misinterpretation of the information in the original documents. Error detection based on data constraint failure significantly underestimated total error rates and constraint-based alarms integrated into the database appear to prevent only a small fraction of errors. Many errors were non-random, organized in special and cognitive clusters, and some could potentially affect the interpretation of the study results. Further investigation is needed into the methods for detection and prevention of data errors in research.