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Transcript of Shared Health Research Information Network Andrew McMurry Sr. Research Software Developer Harvard...
Shared Health Research Information Network
Andrew McMurrySr. Research Software Developer
Harvard Medical School Center for BioMedical InformaticsChildren's Hospital Informatics Program at Harvard-MIT HST
Andrew_McMurry(@) hms.harvard.edu
https://catalyst.harvard.edu/shrine
Three axis for rapid production grade deployment:
1. POLICY 2. TECHNOLOGY
3. RESEARCH SCENARIOS
Outline of topics coveredPolicy 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
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)
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 byBrigham & 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
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
SHRINE Technical Architecture
Bird’s 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
SHRINE Technical Architecture
ArchitectureTechnical Architecture, “cell” view2009 deliverable
Architecture, sequence diagram view
SHRINE Technical Architecture
Current Status Harvard Effort
Prototype system running live at Harvard across BIDMC, Children’s, and Partners representing both BWH and MGH.
Uses 1 year of real patient data Demographics and diagnosis Under tight IRB control
SHRINE Technical Architecture
Current Status
National Effort: west coast partners
University of WashingtonUCSF UC DavisRecombinant
End-to-End Demo March 18th (3 week turn around time)
SHRINE Technical Architecture
Current Status
National Effort: sleep study partners
Case Western Reserve InstituteUniversity of Washington-Madison Marshfield Clinic(potentially others as well)
I2B2 users interested in using SHRINE for sleep studies
SHRINE Technical Architecture
I2b2 single site query demo http://I2b2.org/software
SHRINE multi-site demo http://cbmi-lab.med.harvard.edu:8443/i2b2
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
Development Challenges and Opportunities
1. 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
2. Open Source strategy
1. Writing plug-ins for the SHRINE network
Development Challenges and Opportunities
1. Grid computing makes multi-threading look simple by comparison
2. 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
3. Writing analysis plug-ins for the SHRINE network
Development Challenges and Opportunities
1. Grid computing makes multi-threading look simple by comparison
2. Open Source strategy
1. 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/content/abstract/14/4/527
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
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?
AvailabilityMillions of Paraffin Embedded TissuesSmaller Collections of Fresh / Frozen Tissues
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 Markle’s Common Framework federated
query TMA construction using specimens from four sites
http://spin.chip.org
for Translational Research Requiring Human Specimens
for Translational Research Requiring Human Specimens
For Population Health Surveillance
For translational research requiring human specimensFor 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
For Population Health Surveillance: disease outbreaks
For Population Health Surveillance: seasonal influenza
http://aegis.chip.org/flu
For Population Health Surveillance: pharmacovigilance
http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0000840
SHRINE: Intended Investigation Use Cases
For translational research requiring human specimensFor population health surveillanceFor 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
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
Summary of topics coveredOvercome 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
Acknowledgements: Core SHRINE team
Zak Kohane (SHRINE Lead / HMS)Griffin Weber (HMS CTO / bidmc)Shawn Murphy (I2B2 CRC / partners) Dan Nigrin (Children’s 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
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 / Children’s)Mike Banos (PSL Developer / BWH )Ken Mandl (Biosurviellance PI/ Children’s) Clint Gilbert (Biosurviellance Dev Lead / Children’s) Greg Polumbo (SPIN Developer/ HMS) Ricardo Delima (SPIN Developer / NCI at HMS) Britt Fitch (SPIN Developer / HMS
http://spin.chip.org/community.html
Acknowledgements: Core I2b2 team
https://www.i2b2.org/about/structure.html
Thank You
http://catalyst.harvard.edu/shrine
Andrew_McMurry (@) hms.harvard.edu