2020
DOI: 10.1055/s-0040-1714393
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Enhancing Traceability in Clinical Research Data through a Metadata Framework

Abstract: Background The clinical research data lifecycle, from data collection to analysis results, functions in silos that restrict traceability. Traceability is a requirement for regulated clinical research studies and an important attribute of nonregulated studies. Current clinical research software tools provide limited metadata traceability capabilities and are unable to query variables across all phases of the data lifecycle. Objectives To develop a metadata traceability framework that can help query an… Show more

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Cited by 14 publications
(9 citation statements)
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“…Systematic software platforms to organize large metadata and clinical data [also called “clinotype” ( Nguyen et al, 2021 )] is essential in biomedical research ( Burgun and Bodenreider, 2008 ; Ohmann and Kuchinke, 2009 ). These software platforms, such as ( Ta et al, 2018 ; Kim et al, 2019 ; Hume et al, 2020 ), have two key objectives. First, it allows the biomedical researcher to perform manual cohort selection quickly.…”
Section: Introductionmentioning
confidence: 99%
“…Systematic software platforms to organize large metadata and clinical data [also called “clinotype” ( Nguyen et al, 2021 )] is essential in biomedical research ( Burgun and Bodenreider, 2008 ; Ohmann and Kuchinke, 2009 ). These software platforms, such as ( Ta et al, 2018 ; Kim et al, 2019 ; Hume et al, 2020 ), have two key objectives. First, it allows the biomedical researcher to perform manual cohort selection quickly.…”
Section: Introductionmentioning
confidence: 99%
“…We were interested to learn how organizational and document resources might help to generate metadata and to embed metadata within the digital object itself. Hume et al raised the importance of metadata development for traceability reasons [21]. Detailed results are available in an additional le [see additional le 4, Fig.…”
Section: Metadata Exploitation During Data Managementmentioning
confidence: 99%
“…In addition to clinically relevant contextual information, use of PGD in health care or research also requires metadata [ 19 , 20 ]—data about the data. Examples include the name, model, and unique ID of the source device, and the unique ID of the app [ 21 ] installed on the patient’s smartphone that acquired the data.…”
Section: Standardized Pgd Integration Pipelinementioning
confidence: 99%