用于分析 i2b2 临床数据仓库中用户交互模式的交互式仪表板。

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS BMC Medical Informatics and Decision Making Pub Date : 2024-11-11 DOI:10.1186/s12911-024-02748-0
Lena Baum, Armin Müller, Marco Johns, Hammam Abu Attieh, Mehmed Halilovic, Vladimir Milicevic, Diogo Telmo Neves, Karen Otte, Anna Pasquier, Felix Nikolaus Wirth, Patrick Segelitz, Katharina Schönrath, Joachim E Weber, Fabian Prasser
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引用次数: 0

摘要

背景:临床数据仓库为医学研究人员提供了统一的医疗数据访问途径。生物与床旁整合信息学(i2b2)是一个成熟的开源解决方案,其主要优点是可以定制数据表示以支持特定的使用案例。可以与领域专家和使用该平台的医学研究人员一起,通过迭代方法定义和改进这些数据表示。为了促进这些讨论,了解用户与系统的交互方式非常重要:这项工作的目的是开发描述用户与临床数据仓库(尤其是 i2b2)交互情况的指标。此外,我们还旨在开发一个以交互式可视化为特色的仪表盘,让数据工程师和数据管理员了解潜在的改进措施:我们首先确定了不同数据使用维度的度量指标,并从 i2b2 数据库模式中提取了用户以前查询的相关元数据,以便进一步分析。然后,我们用 Python 实现了相关的可视化,并使用 Dash 将结果集成到交互式仪表板中:确定的指标类别包括使用频率、会话持续时间以及功能和特性的使用。我们创建了一个仪表盘,扩展了本地 i2b2 数据仓库平台,重点关注后一类,并进一步细分为查询次数、经常查询的概念和查询复杂性。该实施方案以开源软件的形式提供:可以从 i2b2 数据库模式中记录的元数据中得出一系列指标,让数据工程师和数据管理员全面了解用户如何与平台交互。这有助于确定特定用例的特定平台实例的优势和局限性,并帮助它们不断改进。
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An interactive dashboard for analyzing user interaction patterns in the i2b2 clinical data warehouse.

Background: Clinical data warehouses provide harmonized access to healthcare data for medical researchers. Informatics for Integrating Biology and the Bedside (i2b2) is a well-established open-source solution with the major benefit that data representations can be tailored to support specific use cases. These data representations can be defined and improved via an iterative approach together with domain experts and the medical researchers using the platform. To facilitate these discussions, it is important to understand how users interact with the system.

Objective: The objective of this work was to develop metrics for describing user interactions with clinical data warehouses in general and i2b2 in particular. Moreover, we aimed to develop a dashboard featuring interactive visualizations that inform data engineers and data stewards about potential improvements.

Methods: We first identified metrics for different data usage dimensions and extracted the relevant metadata about previous user queries from the i2b2 database schema for further analysis. We then implemented associated visualizations in Python and integrated the results into an interactive dashboard using Dash.

Results: The identified categories of metrics include frequency of use, session duration, and use of functionality and features. We created a dashboard that extends our local i2b2 data warehouse platform, focusing on the latter category, further broken down into the number of queries, frequently queried concepts, and query complexity. The implementation is available as open-source software.

Conclusion: A range of metrics can be derived from metadata logged in the i2b2 database schema to provide data engineers and data stewards with a comprehensive understanding of how users interact with the platform. This can help to identify the strengths and limitations of specific instances of the platform for specific use cases and aid their iterative improvement.

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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
期刊最新文献
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