Diaz-Garelli Jose-Franck, Bernstam Elmer V, Lee MinJae, Hwang Kevin O, Rahbar Mohammad H, Johnson Todd R
Clinical and Translational Science Institute, Wake Forest School of Medicine, US.
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, US.
EGEMS (Wash DC). 2019 Jul 25;7(1):32. doi: 10.5334/egems.286.
The well-known hazards of repurposing data make Data Quality (DQ) assessment a vital step towards ensuring valid results regardless of analytical methods. However, there is no systematic process to implement DQ assessments for secondary uses of clinical data. This paper presents DataGauge, a systematic process for designing and implementing DQ assessments to evaluate repurposed data for a specific secondary use. DataGauge is composed of five steps: (1) Define information needs, (2) Develop a formal Data Needs Model (DNM), (3) Use the DNM and DQ theory to develop goal-specific DQ assessment requirements, (4) Extract DNM-specified data, and (5) Evaluate according to DQ requirements. DataGauge's main contribution is integrating general DQ theory and DQ assessment methods into a systematic process. This process supports the integration and practical implementation of existing Electronic Health Record-specific DQ assessment guidelines. DataGauge also provides an initial theory-based guidance framework that ties the DNM to DQ testing methods for each DQ dimension to aid the design of DQ assessments. This framework can be augmented with existing DQ guidelines to enable systematic assessment. DataGauge sets the stage for future systematic DQ assessment research by defining an assessment process, capable of adapting to a broad range of clinical datasets and secondary uses. Defining DataGauge sets the stage for new research directions such as DQ theory integration, DQ requirements portability research, DQ assessment tool development and DQ assessment tool usability.
数据再利用的诸多已知风险使得数据质量（DQ）评估成为确保无论采用何种分析方法都能得出有效结果的关键步骤。然而，对于临床数据的二次利用，目前尚无实施DQ评估的系统流程。本文介绍了DataGauge，这是一种用于设计和实施DQ评估的系统流程，旨在评估用于特定二次利用的再利用数据。DataGauge由五个步骤组成：（1）定义信息需求，（2）开发正式的数据需求模型（DNM），（3）使用DNM和DQ理论制定针对特定目标的DQ评估要求，（4）提取DNM指定的数据，以及（5）根据DQ要求进行评估。DataGauge的主要贡献在于将通用的DQ理论和DQ评估方法整合到一个系统流程中。该流程支持现有针对电子健康记录的DQ评估指南的整合与实际应用。DataGauge还提供了一个基于理论的初始指导框架，将DNM与每个DQ维度的DQ测试方法联系起来，以辅助DQ评估的设计。通过结合现有的DQ指南，可以增强这个框架以实现系统评估。DataGauge通过定义一个能够适应广泛临床数据集和二次利用的评估流程，为未来系统的DQ评估研究奠定了基础。定义DataGauge为新的研究方向奠定了基础，如DQ理论整合、DQ要求可移植性研究、DQ评估工具开发和DQ评估工具可用性研究。
