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