Analysis of data errors in clinical research databases.
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.
Goldberg
Saveli I
SI
Massachusetts General Hospital, Boston, MA, USA.
Niemierko
Andrzej
A
Turchin
Alexander
A
D001185:Artificial Intelligence; D035843:Biomedical Research / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D019300:Medical Errors / Q000517:prevention & control* / Q000706:statistics & numerical data*; D009323:Natural Language Processing; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D014481:United States