Data Capture, Data Management, and Quality Control; Single Versus Multicenter Trials

Kristin Knust (), Lauren Yesko (), Ashley Case () and Kate Bickett ()
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Kristin Knust: Emmes
Lauren Yesko: Emmes
Ashley Case: Emmes
Kate Bickett: Emmes

Chapter 17 in Principles and Practice of Clinical Trials, 2022, pp 303-320 from Springer

Abstract: Abstract Data capture, data management, and quality control processes are instrumental to the conduct of clinical trials. Obtaining quality data requires numerous considerations throughout the life cycle of the trial. Case report form design and data capture methodology are crucial components that ensure data are collected in a streamlined and accurate manner. Robust data quality and validation strategies must be employed early on in data collection to identify potential systemic errors. Data management guidance documents provide an opportunity to set clear expectations for stakeholders and establish communication pathways. These tools need to be supplemented with adequate training and ongoing support of trial staff. Trials may be conducted in a single or multicenter setting, which has implications for data management. Risk-based monitoring is one approach that can help data managers target quality issues in a multicenter setting. Evolving technologies such as electronic medical record and electronic data capture system integration, artificial intelligence, and big data analytics are changing the landscape of data capture and management.

Keywords: Data management; Data collection; Data quality; Multicenter trial; Case report form (CRF); Risk-based monitoring (RBM); Data review; Electronic medical record (EMR); Electronic data capture (EDC) (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-319-52636-2_40

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