Danielle Larese Michigan State University Advisor: Raymond Brock ATLAS 08 Aug 2007
Biorepository Software Selection University of Michigan 31-Aug-2012
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Transcript of Biorepository Software Selection University of Michigan 31-Aug-2012
Biorepository Software SelectionUniversity of Michigan
31-Aug-2012
Frank Manion, Chief Information OfficerPaul McGhee, Lead Business Analyst
Cancer Center Informatics
Agenda
• Project Objectives • Overview of Software Selection Process• Biorepository – Business Processes• Biorepository Application – Context Within the University• Scripted Vendor Demos• Establishing a U-M Biorepository Capability• Critical Success Factors• Governance and (half-baked!) informatics plan
Project Objectives
• Support the University’s personalized medicine strategy– Enable linking biosamples with highly annotated clinical and laboratory data
• Provide compliant environment for biosample management– Collection– Storage in a centralized repository– Receipt of samples and processing by individual research labs– Recording of assay results (including links to large datasets such as DNA sequencing)– Ability to readily analyze and share information
• Support specific research studies (clinical, population-based, laboratory)– Demographic data– Clinical data– Epidemiologic survey data– Biosamples– Lab assay results
• Provide capability to query across all University biorepositories to identify patients or samples for specific, protocol-driven research.
• Operationalize robust biorepository capability identified as one of the “strategic enablers” for UMHS
Overview of Software Selection Process
Preliminary Screening Formal RFP Process Final Recommendation
Interviewed contacts with cancer centers across U.S.
Broadened project scope to include entire medical school
Rigorous analysis of RFP responses
Interviewed 2 large Cancer Center research teams over
3-month period
Interviewed numerous additional key stakeholders
Summarized weighted scores for each step of the scripted
demos
Created 177 requirements based on 38 use cases
Added additional requirements for final total of 189
Conducted 1-hour interviews with at least 2 vendor-provided
customer references
7 vendors scored applications against our requirements Issued RFP to 3 top vendors Prepared final recommendation
Internally scored 5 other applications in use at U-M
Engaged stakeholders to finalize 34 scripted demos
based on U-M requirements
Result: 3 vendors met 90% of requirements (other vendors
significantly lower)
51 stakeholders scored each step at full-day demos (was requirement met & usability)
Biorepository – Business Processes
Scripted Vendor Demos• Scripted vendor demos organized around U-M requirements • Allowed attendees to evaluate whether software would really help them
in their daily research processes• Simple, unambiguous rating categories• Attendees indicated they really liked this scripted approach
Establishing a U-M Biorepository Capability
• Biorepository leadership team formed to create business case and gain funding approval– Included key leaders from Office of Research– Included key biorepository stakeholders from across U-M
• Selection of diverse pilot programs based on scientific value and opportunity for learning– Head & Neck SPORE– Breast Cancer– Chronic Kidney Disease– Michigan Genomics Initiative
Critical Success Factors
• Stakeholder engagement– Spending time with Business Analyst to create use cases– Reviewing requirements necessary to perform each use case– Reviewing step-by-step scripted user demos to facilitate evaluating how
well vendor solution will meet U-M needs– Scoring vendor demos based on U-M scripts (each step scored both on
how well requirement met and usability)
• Using use cases to document user interviews– Allowed documenting requirements in context meaningful to user– Facilitated quick creation of scripted demo scenarios organized around user
business processes
• Initial screening process included scoring current U-M applications that were not serious contenders– During key stakeholder reviews results from prior formal scoring quickly
answered the question “Why don’t we use XXX?”
SPECIMEN BANKS(Assoc Research Manager)
Collection/Processing/Storage/Inventory•Tissue
Fresh/FrozenFFPE
•Serum•Germ Line DNA
WBCBuccal Swabs
•SpecialtyUrineStoolBreast fluidother?
CLINICAL DATABASE(Assoc Research Manager)
Collection/Entry/Retrieval•Demographics•Special data elements (appropriate for each disease)•Treatment•Outcomes (response, recurrence/progression, mortality)
ADMINISTRATOR REGULATORY(Assoc Research
Manager)•IRB•OHPR•NCI•OTHER
SCIENTIFIC DIRECTOR (MD OR PHD)
STANDARDIZED ELEMENTS for all:Specimen Collection/processingSpecimen StorageSpecimen distributionInformation ModelsData CollectionData storage systemsQC/QA data entryData retrievalEtc.
BIOINFORMATICS/BIOSTATISTICS• Generation and Analysis of “omics” data from specimens• Association with clinical outcomes• Compliant with Regulatory Standards
Investigational Data Generated by
Investigational Labs
Informatics Framework
Biospecimen System
Common Lab Identifier System
Reporting System
Sequencing Facility
Research Data
WarehouseSparql Query Framework
OBI Framework CDE to OBI
Mapping
CBM?
Note: Not fully baked yet…Questions: What are pro’s/con’s to CBM? What other issues can this group suggest?
Various Labs…
Questions?
Comments?