1. A novel node in the Learning Health Care System December 2014 Paul.Wallace@optum.com

2. Confidential property of Optum. Do not distribute or reproduce without express permission from Optum. 2 A Learning Health Care System… Publication Improved Outcomes New Technology System Improvements Support research, incent innovation and guide organizational change through real-world translation across diverse clinical settings

3. Confidential property of Optum. Do not distribute or reproduce without express permission from Optum. 3 Optum Labs: Five key assets to help solve many problems Linked medical claims/EHR data Forums to convene collaboration Translation partners Experts on staff, within partners and alongside Optum Data visualization “power tools”1 2 3 4 5

4. Confidential property of Optum. Do not distribute or reproduce without express permission from Optum. 4 Optum Labs Partners

5. Confidential property of Optum. Do not distribute or reproduce without express permission from Optum. 5 Research project pipeline is ramping up

6. Confidential property of Optum. Do not distribute or reproduce without express permission from Optum. 6 Translation: Systematically Moving Knowledge into Action Optum Labs as a Facilitator of Translation among partners via “The Optum Labs Translation Network” 1. Translation Analysis as Part of Research Review 2. Research Summaries (lay language ) 3. Interpretation of Findings for Impact & Translation Priority 4. Formalization of Translation Plan 5. Dissemination: Multiple Audiences, Channels 6. Implementation through Guidelines, Performance Measures, etc. Action Steps

7. Confidential property of Optum. Do not distribute or reproduce without express permission from Optum. 7 Research Clinical Translation Innovation Policy Change “Constellations”: National initiatives addressing big problems In development: Heart failure Performance measurement incubator Alzheimer’s disease Complex co-morbidity Cancer prevention Diabetes

8. Confidential property of Optum. Do not distribute or reproduce without express permission from Optum. 8 Policy Implications for the Learning Health Care System Methods Development- Substantial potential for the safe and innovative use of de-identified data –Extend observational approaches (RCT replication, efficacy to effectiveness extension) –Coordination of de-identification approaches with use of HPI (Registry specifications, care personalization) –Machine learning Governance opportunities –Informing and engaging IRBs, especially as methods and data sources evolve –Refining the relationship between QI and research –Guidance in use of observational methods and de-identification Sustainability Balance of research, translation and commercialization –Institutional needs for rapidly implementable knowledge and –Time-related aspects of competitive advantage and market positioning.

9. Thanks…

10. Confidential property of Optum. Do not distribute or reproduce without express permission from Optum. 10 Our Data Today: Claims, EHRs & Consumer 2,000+ data fields: Medical claims Pharmacy claims Lab claims and results Health risk assessments Standardized costs of care Race Income Education level Language preference Household Geography Mortality Tests, Treatments 315 million U.S. population >149 million Administrative 37 million Clinical Mayo Expanded insights with deeper clinical context 500+ additional data fields: Encounters Vitals Labs Medication orders Procedures Admissions, discharges and transfers Patient appointments PHQ-9 Patient-provided information >38 million Consumer Expanded insights with consumer data 300+ additional data fields: Purchase Behavior: general trends Demographic view including Income, Assets, Home Value, Education Level, Marital Status, Occupation, Home Ownership, Household Make-Up (multi- generational, presence of: children, grandchildren, grandparents), Ethnicity Data Psychographic Data including interest and participation in : travel, various leisure activities, charitable giving, advocacy, volunteering, community involvement

11. Confidential property of Optum. Do not distribute or reproduce without express permission from Optum. 11 Optum Labs employs certified de-identified data sets, together with a hashing methodology to enable matching individuals from multiple sources, yet preserving statistical de-identification. Shared Salt Code (same for all contributors) Data is then hashed by contributors at their site. Uses Optum Labs’ Confidential Salt Statistically de-identified views Name | Address | Birthdate SSN | Phone, etc. Direct identifiers (EMR / Clinical) Name | Address | Birthdate Member ID | Phone, etc. Direct identifiers (Insurer example) Primary hash Secondary hash De-identification Data from disparate sources can be linked and de-identified

12. Confidential property of Optum. Do not distribute or reproduce without express permission from Optum. 12 OLDW Linked logical database Linking by secure double-hash algorithm Research views (certified as de-identified) Controlled granularity e.g., State, Zip3, Date of Death Research sandboxes: Specific choice of data view and statistical/visualization tools E.g., NHD, SAS, R, Spotfire Virtual computing environment Policy-based security Housed in Optum Labs Data Center Optum Labs Data Warehouse Ingestion of de-identified data De-ID with first-stage hash/salt at source Etc. Humedica Mayo dNHI Source databases Virtual Sandboxes: How to Access The Data