Quantifying data quality for clinical trials using electronic data capture

• Published in: PloS one 2008-08, Vol.3 (8), p.e3049-e3049
• Main Authors: Nahm, Meredith L, Pieper, Carl F, Cunningham, Maureen M
• Format: Article
• Language: English
• Subjects: Accuracy; Audits; Automatic Data Processing - methods; Benchmarks; Clinical Audit - standards; Clinical decision making; Clinical trials; Clinical Trials as Topic - standards; Commission on Professional and Hospital Activities - standards; Data bases; Data capture; Data collection; Decision making; Drug abuse; Error analysis; Errors; Evidence-Based Healthcare/Statistical Methodologies and Health Informatics; Health sciences; Heart surgery; Historical account; Humans; Information management; Medical equipment; Medical errors; Medical records; Medical research; National Institute on Drug Abuse (U.S.); National Institutes of Health (U.S.); Non-Clinical Medicine/Academic Medicine; Non-Clinical Medicine/Research Methods; Organizational Case Studies - standards; Ovarian cancer; Product development; Quality; Quality assessment; Quality control; R&D; Research & development; Research Design; Studies; Transcription; United States; Validity
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0003049

Historically, only partial assessments of data quality have been performed in clinical trials, for which the most common method of measuring database error rates has been to compare the case report form (CRF) to database entries and count discrepancies. Importantly, errors arising from medical record abstraction and transcription are rarely evaluated as part of such quality assessments. Electronic Data Capture (EDC) technology has had a further impact, as paper CRFs typically leveraged for quality measurement are not used in EDC processes. The National Institute on Drug Abuse Treatment Clinical Trials Network has developed, implemented, and evaluated methodology for holistically assessing data quality on EDC trials. We characterize the average source-to-database error rate (14.3 errors per 10,000 fields) for the first year of use of the new evaluation method. This error rate was significantly lower than the average of published error rates for source-to-database audits, and was similar to CRF-to-database error rates reported in the published literature. We attribute this largely to an absence of medical record abstraction on the trials we examined, and to an outpatient setting characterized by less acute patient conditions. Historically, medical record abstraction is the most significant source of error by an order of magnitude, and should be measured and managed during the course of clinical trials. Source-to-database error rates are highly dependent on the amount of structured data collection in the clinical setting and on the complexity of the medical record, dependencies that should be considered when developing data quality benchmarks.