uc-rex: a model for cross-campus collaboration and data sharing
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UC-Rex: A model for cross-campus collaboration and data sharing. UC-CSC Meeting San Francisco August 4, 2014 Doug Berman UCSF and Ayan Patel UCLA. Today’s discussion:. What is UC- ReX ? Who is involved and what did we do? What technology was involved? How we worked together: - PowerPoint PPT PresentationTRANSCRIPT
ucrex.org
UC-Rex:A model for cross-campus collaboration
and data sharing
UC-CSC Meeting
San Francisco
August 4, 2014
Doug Berman UCSF and Ayan Patel UCLA
ucrex.org2
Today’s discussion:
• What is UC-ReX?• Who is involved and what did we do?• What technology was involved?• How we worked together:
• Project structure• Sponsorship, leadership, governance, workgroups and
coordination
• Results and outcomes• Results• What we learned about working together
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UC- Research Exchange (UC-ReX)Background
• During the past few years UC Medical Campuses have made significant investment in Electronic Health Records
• Research is a key mission at each campus• We recognize the power in working
together in research• Medical campuses may share data in order
to achieve a large population for our work
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UC-ReX Goals• Five-Year Goal:
Enable researchers and quality improvement specialists to query and analyze clinical data collected at the point of care at all UC medical campuses for research or quality improvement purposes under a common cross-institutional IRB approval process (Trust/Rely) and in a manner that preserves privacy.
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UC-ReX Sponsors
• UC Office of the President Funding for Cross-UC Data Sharing $5 million/5 years (July 2011- 2016)
• UC - BRAID Biomedical Research Acceleration, Integration & Development
• Local CTSA’s (Clinical Translational Science Awards)• Campus CIO’s
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Demonstration :UCReX Data Explorer (SHRINE)
https://ucrexi2b2.ucsf.edu/
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Governance Structure
UC BRAID UC ReX Steering
Technology Strategy
(Lisa Dahm, UCI)
Technical Implementation
(Lisa Dahm, UCI)
Data Harmonization
(Davera Gabriel, UCD)
User Support
(Mini Kahlon, UCSF)
Data Quality (Doug Bell, UCLA)
Working Groups
1 voting + 1 non-voting member from each UCSimple majorityRotating Chair
PI from each UC CTSABudgetary oversightReview quarterly status reports
http://www.ucbraid.org/informaticsmdashuc-rex.html
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Working Groups• Specify use cases• Provide functional gap analysis• Propose technology roadmap
Technology Strategy
• Ensure that infrastructure & critical software are deployed & maintained
Technical Implementation
• Ensure semantic interoperability• Oversee data quality
Data Harmonization
• Define processes, create SOPs• Coordinate user training & support• Design UCReX Website, roll-out pilot
User Support
• Review completeness and consistency of data across campuses
• Identify opportunities to improve data qualityData Quality
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Technology Strategy
• Identify use cases• Review and select technologies and
partners• Sets technical direction
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UC-ReX Use Cases
Support Clinical Trials and Recruitment• Clinical studies are challenging• It is difficult to identify and recruit appropriate research subjects.• Clinical studies may take years to recruit sufficient populations
to support conclusions; many fail for lack of patients
Performing studies in larger populations may make many studies possible.
Quality comparisons among sites to identify best practices
Research Questions on Retrospective data
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Technical Implementation• Open source software developed at Harvard
• i2b2 – Informatics for Integrating Biology and the Bedside• Scalable informatics framework that enables clinical researchers to use existing clinical data for
discovery research• https://www.i2b2.org/
• SHRINE - Shared Health Research Information Network• System for enabling clinical researchers to query across distributed hospital electronic medical
record systems• https://open.med.harvard.edu/wiki/display/SHRINE/SHRINE++Basics
• 1 Proxy Server, 2 Application Servers, 1 Database Server• Proxy Server – Apache• i2b2 Application Server – jBoss• SHRINE Application Server – Tomcat• Database Server – Oracle/SQL Server
• UCSF, UCLA, UCD – Oracle• UCI, UCSD – SQL Server
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What’s at each site
• Unique ETL - Moves data to harmonized dataset
• i2b2 database• Web-site for queries• Management agent• Local provisioning for access• Local support for users
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Central Node (located at UC Davis-MC)
Data remain at each UC
UCReX Network Topology
• The Central Management Node (CMN) is a webapp deployed to complement SHRINE – as agent or manager
• Provides a central point for monitoring nodes and gathering information
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Data Harmonization
• Develop/determine ontology to reference source data from each site• In order to query all sites, we all must speak the same language• 5 different EMR implementations (4 sites with Epic EMR – Not much help)
• Clinical workflows implemented differently• Different modules at different stages of deployment• What about legacy data?
• Types of Medical Data• Demographics – Local source data mapped to various standards from CDC,
WHO, ISO, HL7• Diagnoses and Procedures – ICD9 Standard Terminology used consistently
across all sites for billing/finance• Lab Results
• Different laboratories with different equipment• Different reference ranges and units for the same lab
• Medications• Maintaining Consistent Ontology
• GitHub repository set up to ensure each site has the same ontology
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User Support
• Communicate with local users• Develop websites, documentation• Provide training and support• Develop support protocols and communication
among site-based support groups• Implement processes for sharing identified data
(IRB approvals, data sharing agreements, request process and secure delivery of results)
• Receive feedback from use community
16
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Data Quality• Discovery data anomalies by querying the i2b2 databases
• Look at trends of counts of data types by year from each site • View distribution of demographics
• One site had a unreasonably high percentage of a certain race, upon investigation it was discovered that that race was used as a default value
• Lab Results• Look at medians and means for each lab type• Discovered some lab units were not converted to the
appropriate unit agreed upon in Data Harmonization• Continue to slice and dice data and look at it from different
perspectives• Investigate potential issues - feedback discoveries to Technical
Implementation and Data Harmonization
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Lessons learned
• Balance Project Goals• Research project versus IT project to support
research• Focus on delivery
• Expect institutional differences• Infrastructure, organization, approval and
change processes will differ at each institution• Time lines needed to adapt to diversity among
institutions
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Lessons learned (continued)
• Project management was key• Program management - central coordination
and decision making• Site level – planning resources and deliverables
• Virtual work – Conference calls and screen shares are effective• Team members from each site work together
directly
• ‘Perfect is the enemy of good’
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UCReX –Team
UCD Kent Anderson
Nicholas Anderson UCSD Elizabeth Bell
Davera Gabriel Hyeon-eui Kim Samuel Morley Lucila Ohno-Machado
Travis Nagler Paulina Paul
UCI Yi-Cheng (Andrea )Hwang UCSF Douglas Berman
Lisa Dahm Robert Hink
Ray Pablo Bhuwan Karki
Dana Ludwig
UCLA Douglas Bell Vijay Rayanaker
Robert Follett Kimberly Romero
Ayan Patel Leslie Yuan
Marianne Zachariah
Program Manager Lattice Armstead