Browsing School, Graduate by Subject "remote data capture"
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A comparison of paper-based data submission to remote data capture for minimizing data entry errors in cancer clinical researchBackground. Patient data are essential for judging the safety and efficacy of cancer clinical trials. The current process of paper-based data entry provides opportunities for incurring data discrepancies. Automated systems have shown potential to reduce the number of data entry errors and preserve the quality of clinical trial data. To test this potential, this study examined case report forms (CRFs) to test for differences in the proportion of discrepancies and the time to resolve these discrepancies between a paper-based data entry and OracleRTM Clinical (OC) Remote Data Capture (RDC). Objective. The purpose of this study was to examine differences in the proportion of errors and the time to resolve specific errors between a paper-based CRF and an electronic RDC format. Reason's conceptual framework for error detection and recovery feedback loop was used to guide this research where the warning environmental cueing function provided feedback to the end-user. Results. The sample consisted of 445 RDC and 445 paper-based CRFs submitted to the Cancer Trial Support Unit (CTSU) from March 12, 2004 through March 28, 2005. There was a significant reduction in the proportion of overall data discrepancies for RDC as compared to paper-based CRFs (46.5% vs. 31.7%, p<.001). Similar results were found for univariate (58.6% vs. 41.2%, p<.001) and multivariate (64% vs. 36%, p<.001) discrepancies. Of the total sample of 890 CRFs analyzed for this study, 509 (57.2%) had no discrepancies. For the 381 (42.5%) forms with discrepancies there was no difference in the mean number of days to resolve discrepancies between RDC and paper-based (43 vs. 35). However, RDC had a greater proportion of resolved discrepancies (52% vs. 48%, p<.001).;Conclusion. The results from this study supported Reason's concept of error detection and recovery. RDC data entry format decreased overall, univariate and multivariate data discrepancies for patient information collected on a colon cancer study; however, there was no difference in the timeline for discrepancy resolution between the two formats. Further studies are recommended to test alternate definitions of discrepancy resolution time points. Results from this study can only be generalized to automated systems that use Oracle RTM Clinical and the instance configuration specific for the programmed edit checks used for the colon cancer study.