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Cross-institutional clinical federated querying in the CTSA consortium

Cross-institutional clinical federated querying in the CTSA consortium. October 17, 2008 Nick Anderson, Ph.D. IDR Working Group Division of Biomedical Health Informatics Institute of Translational Health Sciences University of Washington.

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Cross-institutional clinical federated querying in the CTSA consortium

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  1. Cross-institutional clinical federated querying in the CTSA consortium October 17, 2008 Nick Anderson, Ph.D. IDR Working Group Division of Biomedical Health Informatics Institute of Translational Health Sciences University of Washington

  2. Adapting the i2b2 Architecture to Support Cross-Institutional Clinical Translational Research • “Information exchange environment” pilot project • 2008-2010 • 4 partners • University of Washington ITHS • UC San Francisco CTSI • UC Davis CTSC • Recombinant Data Systems • www.i2b2.org • www.bioontology.org • www.i2b2cictr.org (later this month)

  3. I2B2CICTR aims/phases • Aim 1: (4 months) Establish a collaborative, information exchange and technical foundation by piloting a inter-institutional federated query system using test PHI data. • Aim 2: (9 months) Build on this technical foundation by piloting a cohort discovery service of limited, anonymized PHI data across the three sites. • Aim 3: (11 months) Extend the resolution of cohort data available to investigators by implementing an ontology-mapping tool at each institution to collectively pilot a inter-institutional “reference ontology”

  4. End-user needs and use-cases • Clinical translational investigators • Ability to determine if study subjects are available at local institution or within consortium • Create queries to explore retrospective clinical data characteristics • Ability to modify and reuse query criteria to reflect research process • CTSA informatics and clinical IT staff • Ability to lower resource of providing these services manually by developing high-level user-interface • Ability to easily generate data in formats suitable for analysis • Ability to capture re-occurring study queries and associated meta-data for modeling

  5. Driving use cases

  6. Phase 1: Technical interoperability • Goals • Common/secure development/test environment • Initiate DUA/MOA • Secure project management • Build harmonized test cases (software and process) for sharing test data (no PHI) • Challenges • Different DB’s • UW – SQL Server • UCSF – Sybase • UCD – Oracle • Different IT infrastructures • Different workflows

  7. Phase 2: Pilot Anonymous aggregate cohort discovery • Cautiously bridge institutional clinical IT environments • Share PHI data • Test DUA’s w/ end users • Data: • Demographics • ICD-9 Diagnosis • sss

  8. Phase 3: Pilot complex semantic data • Leverage “ontology mapper” • 2 disease model focus: • Cardiovascular disease • Diabetes • Semantic mapping • Pilot ability to search for “poorly characterized” disease criteria

  9. Source data systems

  10. Four parallel processes • Technical group - IT/development/testing • Ontology group - Terminologies/semantic alignment • Governance group - Data Use Agreements/institutional alignment • Evaluation group - Process, outcomes and usability evaluation

  11. Pandora’s box issues • The risk of being successful… • Business intelligence • Scope and control • Protecting the patients • Protecting the partners • Protecting the developers • “Surfing” the project on the wave of emergent approaches

  12. Questions? nicka@u.washington.edu

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