Shared Health Research Information Network

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Shared Health Research Information Network
Three axis for rapid production grade deployment
1. POLICY 2. TECHNOLOGY3. RESEARCH SCENARIOS

• Andrew McMurrySr. Research Software
  DeveloperHarvard Medical School Center for
  BioMedical InformaticsChildren's Hospital
  Informatics Program at Harvard-MIT
  HSTAndrew_McMurry(_at_) hms.harvard.edu
• https//catalyst.harvard.edu/shrine

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Outline of topics covered

• Policy
• History of success cross-institutional IRB
  agreements
• ? Integrated health care entities
• ? Across independent HIPAA covered entities
• Technology
• SHRINE Architecture
• Current status and roadmap
• Development Challenges and Opportunities
• Intended future translational research scenarios
• for Translational Research Requiring Human
  Specimens
• for Population Health Surveillance
• for Observational Studies of Genetic Variants

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History of cross-institutional IRB agreements

• Integrated health care entities
• Partners RPDR ? i2b2 Clinical Research Chart
• Everyday patient encounters ? huge research
  cohorts
• Shawn Murphy et all (wont steal their thunder
  here)
• Centralized Research Patient Data Repository
  shared among Massachusetts General Hospital
  (MGH), Brigham and Women's Hospital (BWH),
  Faulkner Hospital (FH), Spaulding
  Rehabilitation Hospital (SRH), and Newton
  Wellesley Hospital (NWH)

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History of cross-institutional IRB
agreementshttp//spin.chip.org/irb.html

• Across independent HIPAA covered entities
• SPIN Federated query over locally controlled
  de-identified databases
• Distributed pathology database shared by

Brigham Women's Hospital Beth Israel Deaconess
Medical Center Cedars-Sinai Medical Center
Dana-Farber Cancer Institute Children's
Hospital Boston Harvard Medical School
Massachusetts General Hospital National
Institutes of Health National Cancer Institute
Olive View Medical Center Regenstrief Institute
University of California at Los Angeles Medical
Center University of Pittsburgh Medical Center
VA Greater LA Healthcare System Participate
in live Pathology Specimen Locator collaboration
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History of cross-institutional IRB agreements

• SHRINE approach leverage has worked in the past
• Secure IRB approvals for I2b2 local database at
  each site
• Separate set of approvals for federated queries
  across sites
• SHRINE governance principles
• Hospital Autonomy each site remains in control
  over all disclosures
• Patient privacy no attempts to re-identify
  patients
• Non compete no attempts to compare quality of
  care across sites

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SHRINE Technical Architecture

• Birds Eye View
• Leverage local i2b2 deployments
• Broadcast queries and aggregate responses across
  autonomous sites as if they were one clinical
  data warehouse
• There is no central database
• Connect sites in a peer-to-peer or hub-spoke
  fashion

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SHRINE Technical Architecture
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Architecture
Technical Architecture, cell view2009
deliverable
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Architecture, sequence diagram view
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SHRINE Technical Architecture

• Current Status
• Harvard EffortPrototype system running live at
  Harvard across BIDMC, Childrens, and Partners
  representing both BWH and MGH.
• Uses 1 year of real patient data
• Demographics and diagnosis
• Under tight IRB control

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SHRINE Technical Architecture

• Current Status
• National Effort west coast partners
• University of Washington
• UCSF
• UC Davis
• Recombinant
• End-to-End Demo March 18th (3 week turn around
  time)

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SHRINE Technical Architecture

• Current Status
• National Effort sleep study partners
• Case Western Reserve Institute
• University of Washington-Madison
• Marshfield Clinic
• (potentially others as well)
• I2B2 users interested in using SHRINE for sleep
  studies

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SHRINE Technical Architecture

• I2b2 single site query demo
• http//I2b2.org/software
• SHRINE multi-site demo
• http//cbmi-lab.med.harvard.edu8443/i2b2

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SHRINE Technical Architecture

• Timeline and Roadmap
• By end of 2009, Harvard SHRINE queries for
  aggregate counts
• Demographics ICD9 Diagnosis
• Current work
• Polishing demostration software for relase
• Medications and Labs
• Next Steps
• Browseable random LDS datasets
• Downloadable LDS
• No plans for PHI

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Development Challenges and Opportunities

• Grid computing makes multi-threading look simple
  by comparison
• Politically impossible to send patient data to
  each grid node
• Grid computing and federated queries are VERY
  different
• Pre-processing can be used effectively as shown
  in our use cases
• Open Source strategy
• Writing plug-ins for the SHRINE network

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Development Challenges and Opportunities

• Grid computing makes multi-threading look simple
  by comparison
• Hosted retreat to address Open Source strategy
• Harvard CTSA, CHIP, I2B2, Partners, DFCI, private
  companies
• Science Commons, jQuery
• Actively launching an open source portal
• Test driven development with continuous
  integration
• Release early release often
• All milestones measured by what we can get IRB
  approved and deployed with real clinical data
• Writing analysis plug-ins for the SHRINE network

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Development Challenges and Opportunities

• Grid computing makes multi-threading look simple
  by comparison
• Open Source strategy
• Writing analysis plug-ins for the SHRINE network
• Using I2b2 Java Workbench (Shawn Murphy et all)
• Using I2b2 Web Querytool (Griffin Weber et all)
• By pre-processing results when required for
  patient privacy http//www.jamia.org/cgi/cont
  ent/abstract/14/4/527

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SHRINE Intended Investigation Use Cases

• For translational studies requiring human
  specimens
• For Population Health Surveillance
• For Observational Studies of Genetic Variants
• Examples shown here reflect current projects
  which will use the SHRINE infrastructure

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for Translational Research Requiring Human
Specimens

• NCI vision 2001 Vast collections of human
  specimens and relevant clinical data exist all
  over the country, yet are infrequently shared for
  cancer research.
• Challenges
• How to link existing pathology systems for cancer
  research?
• How to ensure patient privacy in accordance with
  HIPAA?
• How to encourage hospital participation?
• Availability
• Millions of Paraffin Embedded Tissues
• Smaller Collections of Fresh / Frozen Tissues

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for Translational Research Requiring Human
Specimens

• Shared Pathology Informatics Network
• National prototype including HMS, UCLA, Indiana,
  UPMC,
• Live Production instance at HMS including 4
  hospitals
• Created Open Source Tools
• caBIG adopted caTIES from SPIN
• Influenced Markles Common Framework federated
  query
• TMA construction using specimens from four sites

http//spin.chip.org
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for Translational Research Requiring Human
Specimens
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for Translational Research Requiring Human
Specimens
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For Population Health Surveillance

• For translational research requiring human
  specimens
• For Population Health Surveillance
• Geotemporal cancer disease incidence rates
• Seasonal infectious diseases such as influenza
• Disease flares such as Irritable Bowel Disease
  (IBD)
• Other use cases exist, these are the ones under
  concentrated study

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For Population Health Surveillance disease
outbreaks
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For Population Health Surveillance seasonal
influenza
http//aegis.chip.org/flu
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For Population Health Surveillance
pharmacovigilance
http//www.plosone.org/article/infodoi2F10.1371
2Fjournal.pone.0000840
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SHRINE Intended Investigation Use Cases

• For translational research requiring human
  specimens
• For population health surveillance
• For Observational Studies of Genetic Variants
• High throughput genotyping
• High throughput phenotyping
• High throughput sample acquisition
• Orders of magnitude
• Faster to obtain huge populations for genomic
  studies
• Cheaper
• Courtesy of Zak Kohane

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For observational studies of genetic variants

• High throughput sample acquisition
• CRIMSON
• High throughput genotyping
• CRIMSON samples ? SNP arrays
• High throughput phenotyping
• Natural language processing smoking status
• Orders of magnitude
• Faster to obtain huge populations for genomic
  studies
• Cheaper
• disruptive technology
• Courtesy of Zak Kohane

Lynn Bry, MD, PHD et all
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Summary of topics covered

• Overcome statistical noise and reproducibility
  with large patient populations
• Policy
• History of cross-institutional IRB agreements
• Technology
• Architecture
• Current status and roadmap
• Development Challenges and Opportunities
• Intended future translational research scenarios
• for Translational Research Requiring Human
  Specimens
• for Population Health Surveillance
• for Observational Studies of Genetic Variants

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Acknowledgements Core SHRINE team

• Zak Kohane (SHRINE Lead / HMS)
• Griffin Weber (HMS CTO / bidmc)
• Shawn Murphy (I2B2 CRC / partners)
• Dan Nigrin (Childrens CIO)
• Ken Mandl (Public Health Use Cases/ CHIP IHL)
• Sussane Churchill (I2B2 Executive director)
• Doug Macfadden (HMS CBMI IT Director)
• Matvey Palchuck (Ontology Lead / HMS)
• Andrew McMurry (Architect / HMS)
• Could give an entire talk on all the
  collaborators, multi-institutional effort. Asking
  forgiveness from those not listed

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Acknowledgements Core SPIN team

• Zak Kohane (SPIN PI / HMS)
• Frank Kuo (PSL Program Director / BWH)
• John Gilbertson (PSL Pathologist / MGH)
• Mark Boguski (PSL Pathologist / BIDMC)
• Antonio Perez (PSL Pathologist / Childrens)
• Mike Banos (PSL Developer / BWH )
• Ken Mandl (Biosurviellance PI/ Childrens)
• Clint Gilbert (Biosurviellance Dev Lead /
  Childrens)
• Greg Polumbo (SPIN Developer/ HMS)
• Ricardo Delima (SPIN Developer / NCI at HMS)
• Britt Fitch (SPIN Developer / HMS
• http//spin.chip.org/community.html

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Acknowledgements Core I2b2 team

• https//www.i2b2.org/about/structure.html

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Thank You

• http//catalyst.harvard.edu/shrine
• Andrew_McMurry (_at_) hms.harvard.edu