Measuring Data Quality in Clinical Trials Using Electronic Data Capture
Author Information
Author(s): Meredith L. Nahm, Carl F. Pieper, Maureen M. Cunningham
Primary Institution: Duke University
Hypothesis
For EDC to substantially improve quality, it would have to facilitate improvements to the process of medical record abstraction.
Conclusion
Medical record abstraction is the most significant source of error in clinical trials, and its management is crucial for data quality.
Supporting Evidence
- The average error rate was significantly lower than published error rates for source-to-database audits.
- 14% of errors were in fields critical to the analysis.
- Data quality assessments showed that structured data collection reduces error rates.
Takeaway
This study looked at how well data is collected in clinical trials using electronic systems, finding that errors are mostly caused by how medical records are handled.
Methodology
The study involved source-to-database audits comparing data from clinical trials to ensure accuracy.
Potential Biases
The study's findings may not apply to trials that require significant medical record abstraction.
Limitations
Results may not be generalizable to other therapeutic areas or inexperienced research sites.
Participant Demographics
Participants were from outpatient settings with chronic conditions.
Statistical Information
P-Value
14.3 errors per 10,000 fields
Confidence Interval
12–39 per 10,000 fields
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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