Rapid Deployment and Adoption of
Health Information Technology for
Real Time Biosurveillance
Primary support: NCI, NLM, CDC, and the DF/HCC
Outline
 Overview of the SPIN Architecture
 SPIN National Demonstration for Cancer Research
 Lessons Learned for Rapid National Deployment
 SPIN enabled Biosurveillance
 Summary
SPIN addresses (3) pervasive issues
1. Linking existing patient care systems
2. Protecting patient privacy
3. Ensuring that hospitals remain in control
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… and has been deployed for:
Cancer Research Requiring Human Specimens
Public Health Biosurveillance
With the potential for other clinical applications
How SPIN works
1.
Link existing databases
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2.
3.
Extract from existing hospital systems
Transform patient encounters into HIPAA-safe vocabulary
Load into hospital controlled “SPIN peer”
Protect Patient Privacy per HIPAA
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De-Identified:
Statistical Level query
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Limited:
When authorized for individual cases
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PHI is rarely used, and only with permission from each IRB
Hospital Control
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No central governing body
Remain in control over disclosures at all times
(1) Linking routine care systems
Extract from routine care delivery systems
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Databases or XML
Transform free text reports
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“Scrub” patient identifiers (per HIPAA)
Autocode into controlled vocabularies such as UMLS
Load into the hospital controlled PEER database
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Assign a randomly generated ID to each case
(2) Protecting Patient Privacy
Increasing levels of investigator access commensurate
with investigator need and hospital policy.
SPIN has enabled
Statistical Queries
Limited Dataset
PHI
Cancer Research
Feasibility Studies
Case Selection
Specimens
Biosurveillance
Automated Analysis
Investigation
Emergencies
Potential Applications
Clinical Aggregates
QA/QC
Informed Care
(3) Hospitals remain in control
Each hospital (Peer) chooses who to share with
And what to share (Clinical Reports, ED feeds, .. )
SPIN Applications & Timeline
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(2001 – Present) Translational Research (cancer)
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(2006 – Present) Biosurveillance
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(2007 - Present) Harvard CTSA
SPIN enabled Translational Research
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Motivation:
Vast collections of human specimens and clinical
data exist all over the country, yet are infrequently
shared for cancer research.
SPIN enabled Translational Research
(1) Link Existing Pathology Databases
 Extract Pathology Reports (coded XML) from each site
 Scrub HIPAA identifiers & Autocode diagnosis for UMLS
 Generate random ID and load into local SPIN peer
(2) Patient Privacy: Increasing levels of investigator access
 Statistical query
(With UMLS and keywords)
 Individual case query
(De-Identified Path Reports)
 Specimen request
(Remap case UUID to accession #)
(3) Hospital Control
 De-Identified reports for Harvard researchers (ecommons)
 PHI has never been released
SPIN enabled Translational Research
SPIN enabled Translational Research
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Motivation:
Vast collections of human specimens and clinical data exist
all over the country, yet are infrequently shared for cancer
research.
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Results:
National prototype including 14 sites
Virtual Specimen Locator (all HMS hospitals)
UMLS/scrubber adopted by caBIG (caTIES)
Direct influence on Markle’s CFH Common Framework
Sites Participating in the National Demonstration
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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 ongoing “Virtual Specimen Locator” collaboration
Lessons Learned for Rapid National Deployment
We have the principles, now here are the challenges:
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For each participant and for each type of data exchange, we
need to map heterogeneous databases
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Building agreement to share: IRBs and the political will
Lessons Learned for Rapid National Deployment
mapping heterogeneous DBs
VS
Start SMALL : Grow the number of common terms!
Lessons Learned for Rapid National Deployment
Applying lessons learned: mapping heterogeneous DBs
1.
2.
3.
Request for Capabilities (What is available?)
Availability limits scope of the vocabulary
What big questions can be asked with only a few data elements?
Pathology:
age, gender, collection, free text “diagnosis”
Public Health: age, gender, location, free text “complaint”
CTSA:
age, gender, …………,
free text mining
4.
Parallel tracks: autocoding and standard vocabulary approach
 Different low hanging fruit: diagnosis vs MRN
5.
Quick End-To-End lifecyles
 Question, development, research, new question
Lessons Learned for Rapid National Deployment
Agreement to share: IRBs and political will
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SPIN has addressed the “Distributed IRB” issue
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Statistical level queries easy are OK by IRBs
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Difficulty arises going to the next step
HIPAA limited data set
PHI
2006 to present:
SPIN enabled Biosurveillance
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Motivation:
Detect infectious disease outbreaks
Track the spread of influenza
Provide early warning signs of bioterrorism
Re-identify patients as fast as possible during
public health emergency
AEGIS:
Automated Epidemiologic Geotemporal Integrated Surveillance
2006 to present:
SPIN enabled Biosurveillance
(1) Link Routine Care Delivery Systems
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Extract Emergency Department visits from each site
Write a simple SQL statement & set routine extraction time
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Transform
Autocode free text of the “Chief Complaint”
Anonymize (blur) home addresses & preserve spatial clusters
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Load into local SPIN peer
Generate the random linked identifier
Peer database is encrypted ( key does NOT live on disk! )
2006 to present:
SPIN enabled Biosurveillance
Preserving both spatial clusters & patient privacy
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Calculate latitude/longitude for each patient address.
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Skew the latitude/longitude with respect to the underlying
population density.
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Return only the anonymized coordinates during routine analysis
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Return real address during emergencies
2006 to present:
SPIN enabled Biosurveillance
(1) Link Routine Care Delivery Systems
 Extract Emergency Department visits from each site
 Anonymize patient addresses & Autocode “Chief complaint”
 Generate random identifier and load into local SPIN peer
(2) Patient Privacy: Increasing levels of investigator access
 Statistical query
(Automated routine analysis)
 Limited Disclosure
(Alarm Investigation )
 Patient Re-Identification (Emergency Investigation)
(3) Hospital Control
 Which public health agencies do I trust? (CDC, DPH)
 What do I want to allow in each investigation scenario?
2006 to present:
SPIN enabled Biosurveillance
DPH/CDC is responsible for assigning Identity  Role
Each Hospital is responsible for assigning Role  Policy
2006 to present:
SPIN enabled Biosurveillance
One Example of Hospital Authorization Policies
Visit ID
Gender
Chief Complaint
Location
Disposition
Temperature
Checkin Time
Discharge Time
MRN
Routine Analysis Alarm Investigation Emergency Investigation
Anonymize
Permit
Permit
Permit
Permit
Permit
Permit
Permit
Permit
Anonymize
Anonymize
Permit
Permit
Permit
Permit
Permit
Permit
Permit
Permit
Permit
Permit
DPH/CDC is responsible for assigning Identity  Role
Each Hospital is responsible for assigning Role  Policy
2006 to present:
SPIN enabled Biosurveillance
2006 to present:
Biosurveillance using SPIN & AEGIS
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Motivation:
Detect infectious disease outbreaks
Track the spread of influenza
Provide early warning signs of bioterrorism
Re-identify patients as fast as possible during public
health emergency
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Results:
Fulfills AHIC Biosurveillance Use Case
One of four federally funded NHIN architectures
Enables our existing biosurveillance application
(aegis.chip.org)
Summary
 SPIN addresses 3 pervasive issues
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

Linking routine care systems
Protecting patient privacy
Ensuring that hospitals remain in control
 SPIN enables increasing levels of access:
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
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Automated real-time biosurveillance
Alarm Investigation
Emergency Investigation
 SPIN is decentralized and builds agreement for
timely national adoption