University of Texas Health Science Center at Houston, Houston, Texas, USA.
Vanderbilt University, Nashville, Tennessee, USA.
Stud Health Technol Inform. 2024 Aug 22;316:1214-1218. doi: 10.3233/SHTI240629.
The increasing use of MAUDE reports in patient safety research highlights the importance of understanding the processing and dissemination of open-access MAUDE data. However, the absence of a structured data pipeline undermines the reproducibility and transparency of studies relying on MAUDE data. In response, we conducted a comprehensive analysis of a recent study on endoscopic clips, assessing methodologies and results. We advocate for implementing an extract, transform, and load (ETL) pipeline, utilizing openFDA and integrating keyword search strategies and data visualization techniques. This approach aims to enhance the quality of MAUDE-based studies, ensuring their reproducibility and transparency. Moreover, ETL serves as a cornerstone in data engineering, enabling real-time data management and quality assurance, thus promoting the sustainability and collaboration of MAUDE-based patient safety research.
MAUDE 报告在患者安全研究中的应用日益增多，这凸显了理解开放获取 MAUDE 数据的处理和传播的重要性。然而，缺乏结构化的数据管道破坏了依赖 MAUDE 数据的研究的可重复性和透明度。有鉴于此，我们对最近一项关于内镜夹的研究进行了全面分析，评估了其方法和结果。我们提倡实施提取、转换和加载（ETL）管道，利用 openFDA 并整合关键字搜索策略和数据可视化技术。这种方法旨在提高基于 MAUDE 的研究的质量，确保其可重复性和透明度。此外，ETL 是数据工程的基石，能够实现实时数据管理和质量保证，从而促进基于 MAUDE 的患者安全研究的可持续性和协作。
