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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Implementation of a deidentified federated data network for population-based cohort discovery
Nicholas Anderson1, Aaron Abend, Aaron Mandel
1Department of Biomedical Health Informatics, University of Washington, Seattle, Washington 98109, USA. nicka@uw.edu
Journal of the American Medical Informatics Association : JAMIA
|August 30, 2011
Summary
A federated query tool enabled clinical trial cohort discovery across multiple academic medical centers by accessing aggregate patient data. This system provides deidentified access to over 5 million patients, creating a novel research resource.
Area of Science:
- Translational Science
- Health Informatics
- Clinical Research Informatics
Background:
- Clinical trial cohort discovery is often hindered by fragmented patient data across institutions.
- Accessing aggregate patient data from multiple academic medical centers presents significant technical and policy challenges.
Purpose of the Study:
- To explore a federated query tool for facilitating clinical trial cohort discovery.
- To manage access to aggregate, deidentified patient data across unaffiliated academic medical centers.
Main Methods:
- Adapted Informatics for Integrating Biology and the Bedside (i2b2) software to link three Clinical Translational Research Award sites.
- Developed an iterative spiral software development model for multisite data resource coordination.
- Standardized technical infrastructures, policies, and semantics for data integration.
Main Results:
- Enabled federated querying of deidentified clinical datasets across separate institutional environments.
- Provided high-level, deidentified access to a large patient population (>5 million patients).
- Identified barriers to user engagement for utility measurement.
Conclusions:
- The common system architecture and translational processes offer a novel and extensible resource for clinical research.
- Iterative development and evaluation highlighted key challenges and lessons learned.
- Future enhancements require research-driven partnerships across all participating sites for focused disease areas.
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