Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany.
Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
BMC Med Inform Decis Mak. 2022 Aug 11;22(1):213. doi: 10.1186/s12911-022-01961-z.
With the growing impact of observational research studies, there is also a growing focus on data quality (DQ). As opposed to experimental study designs, observational research studies are performed using data mostly collected in a non-research context (secondary use). Depending on the number of data elements to be analyzed, DQ reports of data stored within research networks can grow very large. They might be cumbersome to read and important information could be overseen quickly. To address this issue, a DQ assessment (DQA) tool with a graphical user interface (GUI) was developed and provided as a web application.
The aim was to provide an easy-to-use interface for users without prior programming knowledge to carry out DQ checks and to present the results in a clearly structured way. This interface serves as a starting point for a more detailed investigation of possible DQ irregularities. A user-centered development process ensured the practical feasibility of the interactive GUI. The interface was implemented in the R programming language and aligned to Kahn et al.'s DQ categories conformance, completeness and plausibility.
With DQAgui, an R package with a web-app frontend for DQ assessment was developed. The GUI allows users to perform DQ analyses of tabular data sets and to systematically evaluate the results. During the development of the GUI, additional features were implemented, such as analyzing a subset of the data by defining time periods and restricting the analyses to certain data elements.
As part of the MIRACUM project, DQAgui is now being used at ten German university hospitals for DQ assessment and to provide a central overview of the availability of important data elements in a datamap over 2 years. Future development efforts should focus on design optimization and include a usability evaluation.
随着观察性研究的影响不断增加，人们也越来越关注数据质量（DQ）。与实验研究设计不同，观察性研究使用的是主要在非研究环境中收集的数据（二次使用）。根据要分析的数据元素的数量，存储在研究网络中的数据的 DQ 报告可能会变得非常大。它们可能难以阅读，重要信息可能会被迅速忽略。为了解决这个问题，开发了一个带有图形用户界面（GUI）的 DQ 评估（DQA）工具，并将其作为网络应用程序提供。
目的是为没有编程知识的用户提供一个易于使用的界面，以便进行 DQ 检查，并以清晰结构化的方式呈现结果。该界面是对可能存在的 DQ 异常情况进行更详细调查的起点。用户为中心的开发过程确保了交互 GUI 的实际可行性。该界面是用 R 编程语言实现的，并与 Kahn 等人的 DQ 类别一致性、完整性和合理性保持一致。
通过 DQAgui，开发了一个带有 R 程序包和前端网络应用程序的 DQ 评估工具。该 GUI 允许用户对表格数据集进行 DQ 分析，并对结果进行系统评估。在开发 GUI 期间，实现了其他功能，例如通过定义时间段分析数据的子集，并将分析限制在某些数据元素。
作为 MIRACUM 项目的一部分，DQAgui 目前正在德国的十所大学医院用于 DQ 评估，并提供了在两年多时间内数据图中重要数据元素可用性的集中概览。未来的开发工作应侧重于设计优化，并包括可用性评估。
