Assessing data quality and the variability of source data verification auditing methods in clinical research settings

Journal of Biomedical Informatics
Lauren HoustonAllison Martin

Abstract

Data audits within clinical settings are extensively used as a major strategy to identify errors, monitor study operations and ensure high-quality data. However, clinical trial guidelines are non-specific in regards to recommended frequency, timing and nature of data audits. The absence of a well-defined data quality definition and method to measure error undermines the reliability of data quality assessment. This review aimed to assess the variability of source data verification (SDV) auditing methods to monitor data quality in a clinical research setting. The scientific databases MEDLINE, Scopus and Science Direct were searched for English language publications, with no date limits applied. Studies were considered if they included data from a clinical trial or clinical research setting and measured and/or reported data quality using a SDV auditing method. In total 15 publications were included. The nature and extent of SDV audit methods in the articles varied widely, depending upon the complexity of the source document, type of study, variables measured (primary or secondary), data audit proportion (3-100%) and collection frequency (6-24 months). Methods for coding, classifying and calculating error were also inconsistent. Tr...Continue Reading

Citations

Mar 2, 2021·Journal of Surgical Oncology·Yong-Bo AnZhong-Tao Zhang
Jul 9, 2021·Arthritis Research & Therapy·Lisa LindnerAnne C Regierer
Jun 24, 2020·Journal of Biomedical Informatics·Lauren HoustonYasmine Probst
Aug 1, 2021·Neonatal Network : NN·Sharon LiskaStacy Brunquist

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