Stephens Kari A, Anderson Nicholas, Lin Ching-Ping, Estiri Hossein
Department of Psychiatry & Behavioral Sciences, University of Washington, Box 356560, Seattle, WA 98195, United States; Department of Biomedical Informatics & Medical Education, University of Washington, Box 358051, Seattle, WA 98109, United States; Institute of Translational Health Sciences, University of Washington, Box 358051, Seattle, WA 98109, United States.
Department of Pathology and Laboratory Medicine, University of California Davis, Davis, CA 95616, United States.
Int J Med Inform. 2016 Sep;93:26-33. doi: 10.1016/j.ijmedinf.2016.05.008. Epub 2016 Jun 1.
Building federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users. Establishing these resources requires development methodologies that support internal validation of data extraction and translation processes, sustaining meaningful partnerships, and delivering clear and measurable system utility. We describe findings from two federated data sharing case examples that detail critical factors, shared outcomes, and production environment results.
Two federated data sharing pilot architectures developed to support network-based research associated with the University of Washington's Institute of Translational Health Sciences provided the basis for the findings. A spiral model for implementation and evaluation was used to structure iterations of development and support knowledge share between the two network development teams, which cross collaborated to support and manage common stages.
We found that using a spiral model of software development and multiple cycles of iteration was effective in achieving early network design goals. Both networks required time and resource intensive efforts to establish a trusted environment to create the data sharing architectures. Both networks were challenged by the need for adaptive use cases to define and test utility.
An iterative cyclical model of development provided a process for developing trust with data partners and refining the design, and supported measureable success in the development of new federated data sharing architectures.
构建联邦数据共享架构需要支持一系列数据所有者，实现数据资源之间有效且经过验证的语义对齐，并始终关注最终用户。建立这些资源需要开发方法来支持数据提取和转换过程的内部验证，维持有意义的伙伴关系，并提供清晰且可衡量的系统效用。我们描述了两个联邦数据共享案例的结果，这些案例详细说明了关键因素、共同成果和生产环境结果。
为支持与华盛顿大学转化健康科学研究所相关的基于网络的研究而开发的两个联邦数据共享试点架构为这些结果提供了基础。采用了一种实施和评估的螺旋模型来构建开发迭代，并支持两个网络开发团队之间的知识共享，这两个团队交叉协作以支持和管理共同阶段。
我们发现，使用软件开发的螺旋模型和多个迭代周期有效地实现了早期网络设计目标。两个网络都需要投入大量时间和资源来建立一个可信的环境以创建数据共享架构。两个网络都面临着需要适应性用例来定义和测试效用的挑战。
迭代循环的开发模型为与数据伙伴建立信任和完善设计提供了一个过程，并支持新的联邦数据共享架构开发取得可衡量的成功。
