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Appl Clin Inform. 2011 Aug 17;2(3):331-44. doi: 10.4338/ACI-2011-02-RA-0014. Print 2011.

TRIAD: The Translational Research Informatics and Data Management Grid.

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1
The Ohio State University, Department of Biomedical Informatics, Center for IT Innovations in Healthcare, and Center for Clinical and Translational Science , Columbus, OH.

Abstract

OBJECTIVE:

Multi-disciplinary and multi-site biomedical research programs frequently require infrastructures capable of enabling the collection, management, analysis, and dissemination of heterogeneous, multi-dimensional, and distributed data and knowledge collections spanning organizational boundaries. We report on the design and initial deployment of an extensible biomedical informatics platform that is intended to address such requirements.

METHODS:

A common approach to distributed data, information, and knowledge management needs in the healthcare and life science settings is the deployment and use of a service-oriented architecture (SOA). Such SOA technologies provide for strongly-typed, semantically annotated, and stateful data and analytical services that can be combined into data and knowledge integration and analysis "pipelines." Using this overall design pattern, we have implemented and evaluated an extensible SOA platform for clinical and translational science applications known as the Translational Research Informatics and Data-management grid (TRIAD). TRIAD is a derivative and extension of the caGrid middleware and has an emphasis on supporting agile "working interoperability" between data, information, and knowledge resources.

RESULTS:

Based upon initial verification and validation studies conducted in the context of a collection of driving clinical and translational research problems, we have been able to demonstrate that TRIAD achieves agile "working interoperability" between distributed data and knowledge sources.

CONCLUSION:

Informed by our initial verification and validation studies, we believe TRIAD provides an example instance of a lightweight and readily adoptable approach to the use of SOA technologies in the clinical and translational research setting. Furthermore, our initial use cases illustrate the importance and efficacy of enabling "working interoperability" in heterogeneous biomedical environments.

KEYWORDS:

Clinical research informatics; data access; data analysis; data integration; socio-organizational issues; standards; workflow

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