Transforming Health Research Through Use of a Secure Health Data Cloud

1. Transforming Health ResearchThrough Use of a Secure Health Data Cloud Thomas P. Caruso, PhD, MBA, PMP CEO, Quantal Semantics, Inc. 2nd Annual King Abdullah International Medical Research Center Scientific Meeting

2. Overview The U.S. Health Information Exchange Vision Problems with Standards Approach to Interoperability Solutions to interoperability problems with Metadata and a Secure Health Data Cloud Opportunities resulting from use of Metadata Future of Health Information Exchange

3. U.S. Health Info Exchange Vision A Standards Approach to Interoperability Imagine more than 100,000!

4. All proprietary EHRs must use the same standards and a complex infrastructure to protect highly sensitive data

5. Problems with Standards Approach Failed • Problem: Data Relationships • FOR STANDARDS TO WORK: Each proprietary EHR must use an identical Standard Object Model (HL7 RIM V3) • Conversion to HL7 V3 is large investment • Regulation to motivate use of HL7 CDA (Common Document Architecture) • Increased government costs when governments are cutting costs

6. Problems with Standards Approach SNOMED LOINC HCPCS NDC ICD-9 UMLS MeSH GALEN ICD-10 HL7 CVX NextBio RxNorm CPT Problem: Semantics (Vocabulary Complexity)

7. We’re Building a Tower of Bable for Health Information!

8. Problems with Standards Approach Data Sharing Agreement Health Info Provider 1 Health Info Provider 2 Health Info Provider N Data Sharing Agreement Data Sharing Agreement Data Sharing Agreement Data Sharing Agreement Data Sharing Agreement Nationwide Health Info Exchange Problem: Complex Infrastructure to Protect Data

9. U.S. Federal Query Model Problems with Standards Approach http://wiki.siframework.org/Query+Health+Abstract+Model+and+Terminology

10. Alternative Vision for Health Info Exchange Health Info Producer Secure Health Data Cloud A Metadata Approach to Interoperability Health Info Consumer

11. Solutions with Metadata Approach • Relationships Solution: Triplet “Nets” Patient Asia Area X Visited = 12 mo Is Mother has has Associated with Patient Exudative Effusions in X-rays has has Indicates Is TB Dyspnea Age = 9 mo Is Caused By Is Caused By Is Caused By Is Caused By Emphysema Lung Cancer Bronchitis Has behaviour Causes Causes Causes Smoking

12. Solutions with Metadata Approach Secure Health Data Cloud Conversion Utility De-Identified Health Info Health Info Producer Converts to Adopted Standards, Clarifies Relationships & Reduces or Eliminates Inconsistences Semantics Solution: Inconsistency Management

13. Solutions with Metadata Approach Secure Health Data Cloud Personal Identity Info “BANK” Health Info Producer/ Consumer De-Identified Health Info Firewall Firewall Controls ID-Based Access Infrastructure Solution: Personal Identity Information “Banks”

14. Solutions with Metadata Approach Fine-Grain Consented & De-identified Health Info Secure Health Data Cloud Personal Identity Info “BANK” Individual Firewall Firewall Individual Control, NOT Provider Control

15. Risk Shifted to Personal Identity Information Banks • Access ONLY through Personal Identity Info “Banks” • Thus ID info controlled by Personal Id Info “Banks” • And Personal Identity Info “Banks” assume risk • Personal Identity Info “Banks” compensated for risk • Healthcare transactions at no cost • Other transactions provide revenue • Research uses • Public health (surveillance) uses • Clinical decision support system uses

16. Opportunities with Metadata Approach < Exudative Effusions in Xrays| indicates | TB>=95, 10 <dyspnea | is caused by | TB OR lung cancer OR bronchitisOR emphysema>=90,10 < Smoking | causes | Bronchitis >=1, 70 < Smoking | causes | lung cancer >=2, 5 < Smoking| causes | emphysema >=0, 4 <TB | is associated with | area X in Asia>=90,90 <Patient | visited | area X in Asia>=95 Probabilities Generated from Data Mining Probability Information in Tags

17. Opportunities with Metadata Approach <BRCA del 185AG Gene | associated with | breast cancer ANDage .LT. 50>=2, 50 <rofecoxibAND(AORB)| causes| heart attack >=0.00001, 0.01 Probabilities all based on inference. Summary Statistic Tags

18. Opportunities with Metadata Approach • Use of mathematics: number, graph, information and linguistic theories, as well as quantum mechanics Analytics • High Dimensionality  Vast Amounts of Summary Rules • Number of combinations of factors that “describe” patients  Astronomical Amounts of Information Enhances Data Mining, Security & CDSS

19. Future of Health Information Secure Health Data Cloud with Universal Exchange Language Technology

20. Report about Future of Health Information Technology U.S. President’s Council of Advisors on Science & Technology (PCAST) produced report entitled: “Realizing the Full Potential of Health Information Technology to Improve Healthcare for Americans: The Path Forward” Released in December 2010

21. Build a Universal Exchange Language Create a next generation infrastructure for health information that optimizes use of health information for healthcare and research purposes while strengthening the privacy and security of that information for the individuals who provide it.

22. A Universal Exchange Language (UEL) Patient/PHR EHR UEL CDC Improved Public Health Surveillance Patient-UEL Improved Quality of Care at Lower Cost Secure Health Data Cloud UEL-Rules Provider Lower Costs Payer Development of Safer & More Effective Drugs Health Researcher Use of Evidence-Based Medicine Pharma

23. A Secure Health Data Cloud More at: http://QuantalSemantics.com Accurate association Secure dis-aggregation/re-aggregation Metadata UEL compatible with Semantic Web UEL uses probabilistic semantics UEL with summary rules based on best practice UEL provides fine-grained access control Mathematical basis for health analytics