Comparison of electronic data capture (EDC) with the standard data capture method for clinical trial data

Journal article

BACKGROUND: Traditionally, clinical research studies rely on collecting data with case report forms, which are subsequently entered into a database to create electronic records. Although well established, this method is time-consuming and error-prone. This study compares four electronic data capture (EDC) methods with the conventional approach with respect to duration of data capture and accuracy. It was performed in a West African setting, where clinical trials involve data collection from urban, rural and often remote locations. METHODOLOGY/PRINCIPAL FINDINGS: Three types of commonly available EDC tools were assessed in face-to-face interviews; netbook, PDA, and tablet PC. EDC performance during telephone interviews via mobile phone was evaluated as a fourth method. The Graeco Latin square study design allowed comparison of all four methods to standard paper-based recording followed by data double entry while controlling simultaneously for possible confounding factors such as interview order, interviewer and interviewee. Over a study period of three weeks the error rates decreased considerably for all EDC methods. In the last week of the study the data accuracy for the netbook (5.1%, CI95%: 3.5-7.2%) and the tablet PC (5.2%, CI95%: 3.7-7.4%) was not significantly different from the accuracy of the conventional paper-based method (3.6%, CI95%: 2.2-5.5%), but error rates for the PDA (7.9%, CI95%: 6.0-10.5%) and telephone (6.3%, CI95% 4.6-8.6%) remained significantly higher. While EDC-interviews take slightly longer, data become readily available after download, making EDC more time effective. Free text and date fields were associated with higher error rates than numerical, single select and skip fields. CONCLUSIONS: EDC solutions have the potential to produce similar data accuracy compared to paper-based methods. Given the considerable reduction in the time from data collection to database lock, EDC holds the promise to reduce research-associated costs. However, the successful implementation of EDC requires adjustment of work processes and reallocation of resources.

Authors: Walther, B; Hossin, S; Townend, J; Abernethy, N; Parker, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS ONE
• Volume: 6
• Issue: 9
• Pages: e25348
• Publication date: 2011-09-23
• Acceptance date: 2011-09-01
• DOI: 10.1371/journal.pone.0025348
• EISSN: 1932-6203
• Language: English
• Keywords: Humans; Automatic Data Processing; SBTMR; Clinical Trials as Topic; Data Collection