Houston Miss Lauren, Yu A/Prof Ping, Martin Dr Allison, Probst Dr Yasmine
School of Medicine, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong NSW 2522, Australia.
Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong NSW 2522, Australia.
AMIA Annu Symp Proc. 2018 Dec 5;2018:1300-1309. eCollection 2018.
Evidence for the need for high data quality in clinical research is well established. The rigor of clinical research conclusions rely heavily on good quality data, which relies on good documentation practices. Little attention has been given to clear guidelines and definitions to monitor data quality. To address this, a "fit-for-use" data quality monitoring framework (DQMF) for clinical research was developed based on a holistic design-oriented approach. An integrated literature review and feasibility study underpinned the framework development. Ontology of key terms, concepts, methods, and standards were recorded using a consensus approach and mind mapping technique. The DQMF is presented as a nested concentric network illustrating concept relationships and hierarchy. Face validation was conducted, and common terminology and definitions are listed. The consolidated DQMF can be adapted according to study context and data availability aiding in the development of a long-term strategy with increased efficacy for clinical data quality monitoring.
临床研究中对高数据质量的需求已有充分证据。临床研究结论的严谨性在很大程度上依赖于高质量的数据，而高质量的数据又依赖于良好的文档记录实践。对于监测数据质量的明确指南和定义关注甚少。为解决这一问题，基于整体设计导向方法开发了一个用于临床研究的“适用”数据质量监测框架（DQMF）。综合文献综述和可行性研究为框架开发提供了支撑。使用共识方法和思维导图技术记录关键术语、概念、方法和标准的本体。DQMF以嵌套同心圆网络的形式呈现，展示概念关系和层次结构。进行了表面效度验证，并列出了通用术语和定义。整合后的DQMF可根据研究背景和数据可用性进行调整，有助于制定长期策略，提高临床数据质量监测的效率。
