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Connecting with caBIG ® : Lessons Learned from caBIG ® Adoption Robert A. Dennis UCLA School of Medicine January 2009
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Overview Background and Context ICRi next steps Introduction to CTSI group Goals and Objectives Scope of Adoption Activity Lessons Learned: Key Findings Energy activation barrier Heterogeneous data systems Unfamiliarity of tools Mapping/migration of legacy data Conclusion & Next Steps
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Introduction to CTSI Group UCLA School of Medicine Cedars Sinai Medical Center Charles Drew Medical School LaBio Med/Harbor UCLA County Hospital Authors: Denise Aberle, M.D UCLA Liz Chen, LA BioMed/Harbor UCLA Robert Dennis, PhD, UCLA Paul Fu, MD, MPH, LA BioMed/Harbor UCLA Andrew Helsley, UCLA Richard Linstorm, Charles Drew University Omolola Ogunyemi, PhD., Charles Drew University Sukrit Mukherjee, Charles Drew University
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Institutional Context and Setting Priority score in low 150s- not funded (2009). Informatics program rated excellent, but… “… no evidence that [they] have considered the institutional and organizational barriers likely to appear especially with the federated data warehouse.” “Very high level description of the complexity of the federated research data warehouse and what can be leveraged (caBIG, SPIN, LBNH …” “No evidence that any data owners have agreed to participate in the data federation. No data governance, oversight or quality organizational models…”
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Goal and Objective Facilitate the implementation of specific caBIG technologies in the context of the UCLA Clinical Translational Science Institute, and Document the difficulties, barriers, workarounds, and solutions encountered and devised by our group.
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Scope of Adoption Activity Interoperable Informatics infrastructure: caTissue Suite at the corners of 4 institutions 2 sites implementing as internal biobank systems. 2 systems developing conduits from internal commercial biobanks UCLA Pathology partnership to utilize caTissue Suite as external data store. caBIG technology mentors
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Lessons Learned Overview 1.Energy Activation Barrier 2.Heterogeneous data systems 3.Unfamiliarity with technologies 4.Data mapping of legacy data.
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Key Finding #1:Energy Activation Barrier -- politics Description: Formation of group across consortium was delayed thus loss of valuable time within period of performance lost. Situation: Politics are high when stakes are low. Recommended Actions: Plan for delays in assembling needed representatives. Keep pressure (persistent communication) with leadership
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Key Finding #2 - Heterogeneous data systems Description: A morass of heterogonous data systems makes a single solution and set of strategies impossible. Situation: Four institutions each having different existing systems and practices and to some extend different priorities. Recommended Actions: Establish a common base- in our case bio samples. View appropriate tool as common standard-- caTissue.
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Key Finding #3- Unfamiliarity with technologies Description: Across consortium there was uneven familiarity with available technologies. Situation: Our group has informatics people, researchers, physicians, and bio bankers. There was little familiarity of the caBIG program and the available technologies. Recommended Actions: Utilize caBIG WS expertise. Reaching out to request presentations/ Q&A sessions proved extremely effective in our groups’ case.
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Key Finding #4 - Data mapping of legacy data Description:Data mapping of legacy data is singles greatest source of time and effort -- takes n X10 times the effort required to setup and install one of the tools. Situation: Heterogeneous systems and practices has resulted in non standardized data. Mapping legacy data into caBIG requires a metric butt load of time and effort. Recommended Actions:No magic bullet. Need to plan for this aspect appropriately. Recommend to leadership that support is needed.
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Conclusion and Next Steps Groups’ work not done. Continue work to populate with data. caB2B as a query portal - need to define canned queries. Challenge - Linking image data to patients Access permissions and policies need to be established
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