Measuring Data Quality Through a Source Data Verification Audit in a Clinical Research Setting.

Health data has long been scrutinised in relation to data quality and integrity problems. Currently, no internationally accepted or "gold standard" method exists measuring data quality and error rates within datasets. We conducted a source data verification (SDV) audit on a prospective clinical trial dataset. An audit plan was applied to conduct 100% manual verification checks on a 10% random sample of participant files. A quality assurance rule was developed, whereby if >5% of data variables were incorrect a second 10% random sample would be extracted from the trial data set. Error was coded: correct, incorrect (valid or invalid), not recorded or not entered. Audit-1 had a total error of 33% and audit-2 36%. The physiological section was the only audit section to have <5% error. Data not recorded to case report forms had the greatest impact on error calculations. A significant association (p=0.00) was found between audit-1 and audit-2 and whether or not data was deemed correct or incorrect. Our study developed a straightforward method to perform a SDV audit. An audit rule was identified and error coding was implemented. Findings demonstrate that monitoring data quality by a SDV audit can identify data quality and integrity issues within clinical research settings allowing quality improvement to be made. The authors suggest this approach be implemented for future research.