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?