Evaluating the informatics for integrating biology and the bedside system for clinical research
Evaluating the informatics for integrating biology and the bedside system for clinical research
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DOI：
10.1186/1471-2288-9-70
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发表时间：
2009-10-28
影响因子：
4
通讯作者：
Mitchell, Joyce A.
中科院分区：
文献类型：
--
作者：
Deshmukh, Vikrant G.;Meystre, Stephane M.;Mitchell, Joyce A.
背景资料：选择患者队列是涉及人类受试者的研究的一个关键、迭代且通常耗时的方面;用于帮助简化过程的信息学工具已被确定为实现临床和转化研究的重要基础设施组件。我们描述了一个免费的和开源的队列选择工具，从信息学整合生物学和床边（i2 b2）组的评估：i2 b2 hive.Methods：我们的评估包括使用几个真实的世界的例子，在犹他州健康科学中心之间的电子接收的研究数据请求的可用性和功能的i2 b2蜂巢2006-2008。根据所请求的数据元素类型，以及单独使用i2 b2 hive满足每个研究数据请求所需的工作量，评估了hive服务器组件和可视化查询工具应用程序作为队列选择工具的适用性。结果：我们发现i2 b2 hive适用于获得队列规模的估计值，并基于简单的纳入/排除标准生成研究队列，其中包括我们机构抽样的约44%的临床研究数据请求。仅使用i2 b2 hive无法满足依赖于协调后的临床概念、临床结果的汇总值或其入选/排除标准中的时间条件的数据请求，并且需要一个或多个中间数据步骤，其形式为预处理或后处理、修改hive元数据等。结论：i2 b2 hive被发现是一种有用的队列选择工具，用于满足常见类型的研究数据请求，特别是在估计初始队列规模时。对于另一个可能希望使用i2 b2 hive进行临床研究的机构，我们建议该机构需要有结构化的、编码的临床数据和元数据，这些数据和元数据可以进行转换以适应i2 b2 hive的逻辑数据模型，从源系统中提取相关临床数据的策略，以及对这些数据进行大量预处理和后处理的能力。
Background: Selecting patient cohorts is a critical, iterative, and often time-consuming aspect of studies involving human subjects; informatics tools for helping streamline the process have been identified as important infrastructure components for enabling clinical and translational research. We describe the evaluation of a free and open source cohort selection tool from the Informatics for Integrating Biology and the Bedside (i2b2) group: the i2b2 hive.Methods: Our evaluation included the usability and functionality of the i2b2 hive using several real world examples of research data requests received electronically at the University of Utah Health Sciences Center between 2006-2008. The hive server component and the visual query tool application were evaluated for their suitability as a cohort selection tool on the basis of the types of data elements requested, as well as the effort required to fulfill each research data request using the i2b2 hive alone.Results: We found the i2b2 hive to be suitable for obtaining estimates of cohort sizes and generating research cohorts based on simple inclusion/exclusion criteria, which consisted of about 44% of the clinical research data requests sampled at our institution. Data requests that relied on post-coordinated clinical concepts, aggregate values of clinical findings, or temporal conditions in their inclusion/exclusion criteria could not be fulfilled using the i2b2 hive alone, and required one or more intermediate data steps in the form of pre- or post-processing, modifications to the hive metadata, etc.Conclusion: The i2b2 hive was found to be a useful cohort-selection tool for fulfilling common types of requests for research data, and especially in the estimation of initial cohort sizes. For another institution that might want to use the i2b2 hive for clinical research, we recommend that the institution would need to have structured, coded clinical data and metadata available that can be transformed to fit the logical data models of the i2b2 hive, strategies for extracting relevant clinical data from source systems, and the ability to perform substantial pre- and post-processing of these data.