rapid deployment and adoption of health information technology for real time biosurveillance primary...
TRANSCRIPT
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 privacy3. Ensuring that hospitals remain in control
… 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• Extract from existing hospital systems • Transform patient encounters into HIPAA-safe vocabulary • Load into hospital controlled “SPIN peer”
2. Protect Patient Privacy per HIPAA• De-Identified: Statistical Level query
• Limited: When authorized for individual cases
• PHI is rarely used, and only with permission from each IRB
3. Hospital Control • No central governing body• Remain in control over disclosures at all times
(1) Linking routine care systems
Extract from routine care delivery systems Databases or XML
Transform free text reports “Scrub” patient identifiers (per HIPAA) Autocode into controlled vocabularies such as
UMLS
Load into the hospital controlled PEER database 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
(2001 – Present) Translational Research (cancer)
(2006 – Present) Biosurveillance
(2007 - Present) Harvard CTSA
SPIN enabled Translational Research
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
Motivation:
Vast collections of human specimens and clinical data exist all over the country, yet are infrequently shared for cancer research.
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
1. Brigham & Women's Hospital*2. Beth Israel Deaconess Medical Center*3. Cedars-Sinai Medical Center 4. Dana-Farber Cancer Institute*5. Children's Hospital Boston* 6. Harvard Medical School* 7. Massachusetts General Hospital*8. National Institutes of Health 9. National Cancer Institute 10.Olive View Medical Center 11.Regenstrief Institute 12.University of California at Los Angeles Medical Center 13.University of Pittsburgh Medical Center 14.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:
For each participant and for each type of data exchange, we need to map heterogeneous databases
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. Request for Capabilities (What is available?) 2. Availability limits scope of the vocabulary 3. 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
SPIN has addressed the “Distributed IRB” issue
Statistical level queries easy are OK by IRBs
Difficulty arises going to the next stepHIPAA limited data setPHI
2006 to present: SPIN enabled Biosurveillance
Motivation: Detect infectious disease outbreaksTrack the spread of influenzaProvide early warning signs of bioterrorismRe-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
Extract Emergency Department visits from each siteWrite a simple SQL statement & set routine extraction time
TransformAutocode free text of the “Chief Complaint”Anonymize (blur) home addresses & preserve spatial clusters
Load into local SPIN peerGenerate 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
Calculate latitude/longitude for each patient address.
Skew the latitude/longitude with respect to the underlying population density.
Return only the anonymized coordinates during routine analysis
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
Routine Analysis Alarm Investigation Emergency InvestigationVisit ID Anonymize Permit PermitGender Permit Permit PermitChief Complaint Permit Permit PermitLocation Anonymize Anonymize PermitDisposition Permit PermitTemperature Permit PermitCheckin Time Permit PermitDischarge Time Permit PermitMRN Permit
One Example of Hospital Authorization Policies
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
Motivation: Detect infectious disease outbreaksTrack the spread of influenzaProvide early warning signs of bioterrorismRe-identify patients as fast as possible during public health emergency
Results:Fulfills AHIC Biosurveillance Use CaseOne of four federally funded NHIN architecturesEnables our existing biosurveillance application (aegis.chip.org)
Summary
SPIN addresses 3 pervasive issues Linking routine care systems Protecting patient privacy Ensuring that hospitals remain in control
SPIN enables increasing levels of access: Automated real-time biosurveillance Alarm Investigation Emergency Investigation
SPIN is decentralized and builds agreement for timely national adoption