1. Biorepository Software Selection University of Michigan 31-Aug-2012 Frank Manion, Chief Information Officer Paul McGhee, Lead Business Analyst Cancer Center Informatics

2. 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

3. 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

4. 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 vendorsPrepared 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)

5. Biorepository – Business Processes

6. Biorepository Application - Context Within the University Focus on “Scope of Proposed Application” while being aware of bigger picture

7. 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

8. 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

9. 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?”

10. SPECIMEN BANKS (Assoc Research Manager) Collection/Processing/Storage/ Inventory Tissue Fresh/Frozen FFPE Serum Germ Line DNA WBC Buccal Swabs Specialty Urine Stool Breast fluid other? 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/processing Specimen Storage Specimen distribution Information Models Data Collection Data storage systems QC/QA data entry Data retrieval Etc. BIOINFORMATICS/BIOSTATISTICS Generation and Analysis of “omics” data from specimens Association with clinical outcomes Compliant with Regulatory Standards Investigational Data Generated by Investigational Labs

11. Informatics Framework Biospecimen System Common Lab Identifier System Reporting System Sequencing Facility Research Data Warehouse Sparql 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…

12. Questions? Comments?