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Implementation of a deidentified federated data network for population-based cohort discovery
OBJECTIVE: 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. METHODS: The projec...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Group
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392860/ https://www.ncbi.nlm.nih.gov/pubmed/21873473 http://dx.doi.org/10.1136/amiajnl-2011-000133 |
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author | Anderson, Nicholas Abend, Aaron Mandel, Aaron Geraghty, Estella Gabriel, Davera Wynden, Rob Kamerick, Michael Anderson, Kent Rainwater, Julie Tarczy-Hornoch, Peter |
author_facet | Anderson, Nicholas Abend, Aaron Mandel, Aaron Geraghty, Estella Gabriel, Davera Wynden, Rob Kamerick, Michael Anderson, Kent Rainwater, Julie Tarczy-Hornoch, Peter |
author_sort | Anderson, Nicholas |
collection | PubMed |
description | OBJECTIVE: 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. METHODS: 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. RESULTS: 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. DISCUSSION: The authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned. CONCLUSION: 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. |
format | Online Article Text |
id | pubmed-3392860 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BMJ Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-33928602012-07-10 Implementation of a deidentified federated data network for population-based cohort discovery Anderson, Nicholas Abend, Aaron Mandel, Aaron Geraghty, Estella Gabriel, Davera Wynden, Rob Kamerick, Michael Anderson, Kent Rainwater, Julie Tarczy-Hornoch, Peter J Am Med Inform Assoc Research and Applications OBJECTIVE: 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. METHODS: 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. RESULTS: 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. DISCUSSION: The authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned. CONCLUSION: 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. BMJ Group 2011-08-26 2012-06 /pmc/articles/PMC3392860/ /pubmed/21873473 http://dx.doi.org/10.1136/amiajnl-2011-000133 Text en © 2012, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode. |
spellingShingle | Research and Applications Anderson, Nicholas Abend, Aaron Mandel, Aaron Geraghty, Estella Gabriel, Davera Wynden, Rob Kamerick, Michael Anderson, Kent Rainwater, Julie Tarczy-Hornoch, Peter Implementation of a deidentified federated data network for population-based cohort discovery |
title | Implementation of a deidentified federated data network for population-based cohort discovery |
title_full | Implementation of a deidentified federated data network for population-based cohort discovery |
title_fullStr | Implementation of a deidentified federated data network for population-based cohort discovery |
title_full_unstemmed | Implementation of a deidentified federated data network for population-based cohort discovery |
title_short | Implementation of a deidentified federated data network for population-based cohort discovery |
title_sort | implementation of a deidentified federated data network for population-based cohort discovery |
topic | Research and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392860/ https://www.ncbi.nlm.nih.gov/pubmed/21873473 http://dx.doi.org/10.1136/amiajnl-2011-000133 |
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