Kari A Stephens1, Nicholas Anderson2, Ching-Ping Lin3, Hossein Estiri4. 1. 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. Electronic address: kstephen@uw.edu. 2. Department of Pathology and Laboratory Medicine, University of California Davis, Davis, CA 95616, United States. 3. Global REACH, Medical School, University of Michigan, 5113 Medical Science Building I, 1301 Catherine St, Ann Arbor, MI 48109-5611, United States. 4. Institute of Translational Health Sciences, University of Washington, Box 358051, Seattle, WA 98109, United States.
Abstract
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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