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Error rates in a clinical data repository: lessons from the transition to electronic data transfer--a descriptive
Matthew K H Hong1, Henry H I Yao, John S Pedersen
1Division of Urology, Department of Surgery, University of Melbourne, Royal Melbourne Hospital and the Australian Prostate Cancer Research Centre Epworth, Melbourne, Victoria, Australia.
BMJ Open
|June 25, 2013
Summary
Manually entered prostate cancer pathology data showed a 2.8% error rate, with text fields being more error-prone. Electronic checking of source data is feasible for improving data quality.
Area of Science:
- Urology
- Pathology informatics
- Data quality management
Background:
- Clinical datasets are prone to data errors, potentially confounding analyses.
- Manual data transcription is common in pathology, raising concerns about accuracy.
Purpose of the Study:
- To assess the reliability of manually transcribed pathology data in a prostate cancer database.
- To quantify error rates in manually entered data compared to electronically imported data.
- To evaluate the feasibility of electronic checking for source data errors.
Main Methods:
- A descriptive study comparing manually entered clinicopathological variables with electronically imported pathology reports.
- Data from 421 radical prostatectomy patients (2004-2011) were analyzed for concordance.
- Error rates were calculated for individual fields, distinguishing between text and numerical data.
Main Results:
- 76% of patients had concordant data across a median of 12 pathology fields.
- The overall error rate for manually entered data was 2.8%, with individual fields ranging from 0.5% to 6.4%.
- Text-based fields demonstrated significantly higher error rates (p<0.001) compared to numerical fields.
Conclusions:
- Manually entered pathology data has a low overall error rate, but specific fields are variably prone to errors.
- High-quality pathology data is achievable for both prospective and retrospective datasets.
- Electronic verification of source pathology data is a practical method for error detection.
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