Shared Health Research Information Network

1. Shared Health Research Information Network Andrew McMurrySr. Research Software DeveloperHarvard Medical School Center for BioMedical InformaticsChildren's Hospital Informatics Program at Harvard-MIT HSTAndrew_McMurry(@) hms.harvard.edu https://catalyst.harvard.edu/shrine Three axis for rapid production grade deployment: 1. POLICY 2. TECHNOLOGY3. RESEARCH SCENARIOS

2. 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

3. 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)

4. History of cross-institutional IRB agreementshttp://spin.chip.org/irb.html 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 • Across independent HIPAA covered entities • SPIN: Federated query over locally controlled de-identified databases • Distributed pathology database shared by

5. 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

6. 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

7. SHRINE Technical Architecture

8. Technical Architecture, “cell” view2009 deliverable Architecture

9. Architecture, sequence diagram view

10. SHRINE Technical Architecture • Current Status • Harvard EffortPrototype system running liveat 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

11. 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)

12. 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

13. SHRINE Technical Architecture • I2b2 single site query demo • http://I2b2.org/software • SHRINE multi-site demo • http://cbmi-lab.med.harvard.edu:8443/i2b2

14. 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

15. 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

16. 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

17. 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/content/abstract/14/4/527

18. 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

19. 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

20. 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

21. for Translational Research Requiring Human Specimens

22. for Translational Research Requiring Human Specimens

23. 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

24. For Population Health Surveillance: disease outbreaks

25. For Population Health Surveillance: seasonal influenza http://aegis.chip.org/flu

26. For Population Health Surveillance: pharmacovigilance http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0000840

27. 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

28. 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

29. 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

30. 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

31. 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

32. Acknowledgements: Core I2b2 team • https://www.i2b2.org/about/structure.html

33. Thank You • http://catalyst.harvard.edu/shrine • Andrew_McMurry (@) hms.harvard.edu