Implementation of a deidentified federated data network for population-based cohort discovery
The Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers. The project adapted software f...
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Published in | Journal of the American Medical Informatics Association : JAMIA Vol. 19; no. e1; pp. e60 - e67 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
BMJ Group
01.06.2012
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Series | FOCUS on clinical research informatics |
Subjects | |
Online Access | Get full text |
ISSN | 1067-5027 1527-974X 1527-974X |
DOI | 10.1136/amiajnl-2011-000133 |
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Abstract | The Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers.
The project adapted software from the Informatics for Integrating Biology and the Bedside (i2b2) program to connect three Clinical Translational Research Award sites: University of Washington, Seattle, University of California, Davis, and University of California, San Francisco. The project developed an iterative spiral software development model to support the implementation and coordination of this multisite data resource.
By standardizing technical infrastructures, policies, and semantics, the project enabled federated querying of deidentified clinical datasets stored in separate institutional environments and identified barriers to engaging users for measuring utility.
The authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned.
The common system architecture and translational processes provide high-level (aggregate) deidentified access to a large patient population (>5 million patients), and represent a novel and extensible resource. Enhancing the network for more focused disease areas will require research-driven partnerships represented across all partner sites. |
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AbstractList | The Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers.
The project adapted software from the Informatics for Integrating Biology and the Bedside (i2b2) program to connect three Clinical Translational Research Award sites: University of Washington, Seattle, University of California, Davis, and University of California, San Francisco. The project developed an iterative spiral software development model to support the implementation and coordination of this multisite data resource.
By standardizing technical infrastructures, policies, and semantics, the project enabled federated querying of deidentified clinical datasets stored in separate institutional environments and identified barriers to engaging users for measuring utility.
The authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned.
The common system architecture and translational processes provide high-level (aggregate) deidentified access to a large patient population (>5 million patients), and represent a novel and extensible resource. Enhancing the network for more focused disease areas will require research-driven partnerships represented across all partner sites. The Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers.OBJECTIVEThe Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers.The project adapted software from the Informatics for Integrating Biology and the Bedside (i2b2) program to connect three Clinical Translational Research Award sites: University of Washington, Seattle, University of California, Davis, and University of California, San Francisco. The project developed an iterative spiral software development model to support the implementation and coordination of this multisite data resource.METHODSThe project adapted software from the Informatics for Integrating Biology and the Bedside (i2b2) program to connect three Clinical Translational Research Award sites: University of Washington, Seattle, University of California, Davis, and University of California, San Francisco. The project developed an iterative spiral software development model to support the implementation and coordination of this multisite data resource.By standardizing technical infrastructures, policies, and semantics, the project enabled federated querying of deidentified clinical datasets stored in separate institutional environments and identified barriers to engaging users for measuring utility.RESULTSBy standardizing technical infrastructures, policies, and semantics, the project enabled federated querying of deidentified clinical datasets stored in separate institutional environments and identified barriers to engaging users for measuring utility.The authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned.DISCUSSIONThe authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned.The common system architecture and translational processes provide high-level (aggregate) deidentified access to a large patient population (>5 million patients), and represent a novel and extensible resource. Enhancing the network for more focused disease areas will require research-driven partnerships represented across all partner sites.CONCLUSIONThe common system architecture and translational processes provide high-level (aggregate) deidentified access to a large patient population (>5 million patients), and represent a novel and extensible resource. Enhancing the network for more focused disease areas will require research-driven partnerships represented across all partner sites. ObjectiveThe Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers.MethodsThe project adapted software from the Informatics for Integrating Biology and the Bedside (i2b2) program to connect three Clinical Translational Research Award sites: University of Washington, Seattle, University of California, Davis, and University of California, San Francisco. The project developed an iterative spiral software development model to support the implementation and coordination of this multisite data resource.ResultsBy standardizing technical infrastructures, policies, and semantics, the project enabled federated querying of deidentified clinical datasets stored in separate institutional environments and identified barriers to engaging users for measuring utility.DiscussionThe authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned.ConclusionThe common system architecture and translational processes provide high-level (aggregate) deidentified access to a large patient population (>5 million patients), and represent a novel and extensible resource. Enhancing the network for more focused disease areas will require research-driven partnerships represented across all partner sites. |
Author | Anderson, Nicholas Wynden, Rob Geraghty, Estella Gabriel, Davera Tarczy-Hornoch, Peter Rainwater, Julie Mandel, Aaron Anderson, Kent Abend, Aaron Kamerick, Michael |
AuthorAffiliation | 4 University of California San Francisco, San Francisco, California, USA 1 Department of Biomedical Health Informatics, University of Washington, Seattle, Washington, USA 2 Recombinant Data Corporation, Newton, Massachusetts, USA 3 University of California Davis, Davis, Sacramento, California, USA |
AuthorAffiliation_xml | – name: 3 University of California Davis, Davis, Sacramento, California, USA – name: 1 Department of Biomedical Health Informatics, University of Washington, Seattle, Washington, USA – name: 2 Recombinant Data Corporation, Newton, Massachusetts, USA – name: 4 University of California San Francisco, San Francisco, California, USA |
Author_xml | – sequence: 1 givenname: Nicholas surname: Anderson fullname: Anderson, Nicholas – sequence: 2 givenname: Aaron surname: Abend fullname: Abend, Aaron – sequence: 3 givenname: Aaron surname: Mandel fullname: Mandel, Aaron – sequence: 4 givenname: Estella surname: Geraghty fullname: Geraghty, Estella – sequence: 5 givenname: Davera surname: Gabriel fullname: Gabriel, Davera – sequence: 6 givenname: Rob surname: Wynden fullname: Wynden, Rob – sequence: 7 givenname: Michael surname: Kamerick fullname: Kamerick, Michael – sequence: 8 givenname: Kent surname: Anderson fullname: Anderson, Kent – sequence: 9 givenname: Julie surname: Rainwater fullname: Rainwater, Julie – sequence: 10 givenname: Peter surname: Tarczy-Hornoch fullname: Tarczy-Hornoch, Peter |
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Cites_doi | 10.1038/461145a 10.1186/1471-2288-9-70 10.1016/j.artmed.2007.04.002 10.1145/12944.12948 |
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Snippet | The Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial... ObjectiveThe Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical... |
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Title | Implementation of a deidentified federated data network for population-based cohort discovery |
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