The document outlines the transformation towards value-based, personalized healthcare through a synergy of healthcare IT and scientific advancements. It emphasizes the need for integrated data systems to support personalized medicine and improve healthcare delivery while highlighting collaborative efforts across the healthcare ecosystem. Additionally, it discusses the evolution of drug development and the necessity for innovative partnerships and analytics to enhance patient care and outcomes.

1. The Data Driven Healthcare Enterprise The Transformation to Value Based, Personalized Healthcare Brett J. Davis Senior Director, Personalized Healthcare Oracle Health Sciences Global Business Unit

2. The following is intended to outline our general product direction. It is intended for information purposes only, and may be incorporated into any contract. It is not a commitment to deliver any material code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle's products remains at the sole discretion of Oracle. Roadmaps previously communicated by Phase Forward are subject to Oracle review and amendment.

3. “ To wrest from nature the secrets which have perplexed philosophers in all ages, to track to their sources the causes of disease, to correlate the vast stores of knowledge , that they are quickly available for the prevention and cure of disease – these are our ambitions.” - Sir William Osler, 1906 Personalized Healthcare: Bringing a century old vision to reality…

4. “ Supposing is good, but finding out is better.” - Mark Twain … Put More Simply

5. A Changing Healthcare Landscape © 2010 Oracle and/or its affiliates. All rights reserved. Oracle confidential

6. Life Sciences and Healthcare are converging Predictive, Preventive, Personalized and Participatory Healthcare HEALTHCARE LIFE SCIENCES “ Trial and Error” Healthcare “ Evidence Based” Healthcare “ Precision” Healthcare Blockbusters and mass-production of novel drugs Targeted Therapies Increased regulation and efficacy standards Analytics LIFE SCIENCES HEALTHCARE DNA chemistry and advanced technology “ Managed” Healthcare Paper based Records Electronic Data Capture Pharmacovigilance and Risk Mgmt Safety at Point of Care Electronic Medical Records Paper based Systems Patient Care and Disease Mgmt Translational Med Personalized Healthcare

7. The Future of the Healthcare Industry Lean. Global. Networked. A transformation is only possible through the synergy of healthcare information technology (HIT) with scientific breakthroughs in the molecular understanding of disease, novel therapeutics and diagnostics, as well as a redesign of our healthcare delivery models. Leveraging data from multiple sources and diverse populations from across the healthcare system will be essential. Not only is this approach necessary for providing the “best care for the right patient,” but it has major business model implications for many constituents in the healthcare ecosystem including pharma/biotech, health providers, PBMs and payers, and as a result, their IT strategies. — Bill Dalton, PhD, MD, President/CEO, Moffitt Cancer Center

8. The Future of the Life Sciences Industry Lean. Global. Networked. Our scientists are working in new ways. By using highly-specialized techniques, we're aiming to design treatments personalized for the unique needs of individual people. Science is making this type of personalized medicine possible for the first time in history… This personalized medicine approach means new opportunities for people who are sick, for physicians and for our shareholders. … finding new treatments depends on collaboration . No company, hospital, lab, or scientist can do it alone. That's why we've formed partnerships with governments, independent medical and scientific groups, companies and advocates…. — Jeff Kindler, CEO Pfizer Corporation

9. Systemic Collaboration to Improve Health These systems supported individual silos across the health science ecosystem, but must now provide data for an integrated view Investment is shifting towards R&D and clinical collaboration, personalized / translational medicine and care management Increasing demand and capabilities for personalized medicine will drive new business models, including more value-based healthcare Initial customer investment focused on transactional capabilities (EMR, Claims, EDC, Trial Management, etc.) The new healthcare delivery paradigm requires collaboration © 2010 Oracle and/or its affiliates. All rights reserved. Patient Researcher Citizen / Member Individual / Family Care Management Personalized / Translational Medicine Value-Based Healthcare

10. The new drug development paradigm A networked healthcare and life science model Basic Research Discovery & Development Point of Care Source: adapted from DataMonitor CRO Academia, CRO & Sponsor Sponsor Healthcare

11. The new drug development paradigm Aerospace transformation… Boeing has rapidly shifted the company to embrace a “network” of partners Partners Across the Globe Are Bringing the Boeing 787 Together Clinical development transformation… Merck is shifting drug development toward embracing a “network” of partners Merck External Basic Research (EBR) team expects to deliver 25% of early pipeline from external partners by 2013 (Source: Pharma Focus Asia) Piramal PoC Oncology Drug Discovery Patheon Commercial Manufacturing Pharmaceutical Dev. Services PPD Vaccine Testing Central Lab and Sample Storage Advinus Candidate Drugs for Metabolic Disorders Orchid Chemicals Bacterial and Fungal Infection Dev Ranbaxy Antifungal and Antibiotic Target Programs Moffitt Cancer Center Total Cancer Care WuXi AppTec Discovery Chemistry

12. Patient-centered, collaborative care “ Care that is safe, effective, patient-centered, timely, efficient, and equitable.” -- Institute of Medicine, 2001 Reid, Compton, Grossman, and Fanjiang, Editors, Committee on Engineering and the Health Care System, National Academy of Engineering and the Institute of Medicine, National Academies Press, 2005, pg. 20. Adapted from Ferlie and Shortell, 2001, Improving the quality of health care in the United Kingdom and the United States: A framework for change, Milbank Quarterly 79(2): 281-315

13. What is the data driven healthcare enterprise? © 2010 Oracle and/or its affiliates. All rights reserved. Oracle confidential

14. Page The Learning Healthcare Organization Accountable Care Organizations Enterprise Quality Standards Clinical Effectiveness Comparative Effectiveness Automating “Today’s” Healthcare Enterprise Need for Secure, Interoperable Healthcare Data and Analytics Impact on HC Transformation / Value to Healthcare System Today Performance Management Implications for healthcare providers This evolution has both clinical and operational implications Today’s “Transactional” Systems Were Not Designed to Enable this Transformation Evidence Based Medicine Value-based, Personalized Healthcare Trial and Error Medicine Core Transactional Systems Clinical & Enterprise Integration Enterprise Data Warehouse Context Specific Analytics and Applications Core Systems Requirements:

15. Framework for PHC Enabled by HIT Achieving this will create a “learning healthcare” paradigm -Basic / Translational Research -Clinical Research/Trials -CER -Deep Analytics / Informatics “ Learning Healthcare” Paradigm Supported by Robust, Interoperable Informatics -Trustworthy data from EHRs -Longitudinal Biobank data -Imaging Translate guidelines and empirical results into specific process steps Leverage workflow driven informatics processes to drive to point of care with decision support analytics Trustworthy data to measure protocol with analytics to track outcomes or deviations Employ analytics to measure results and teach people, activate patients and transform care

16. Framework for PHC Enabled by HIT Achieving this will create a “learning healthcare” paradigm -Basic / Translational Research -Adaptive Clinical Research/Trials -CER -Deep Analytics / Informatics “ Learning Healthcare” Paradigm Supported by Robust, Interoperable Informatics -Trustworthy data from EHRs -Longitudinal Biobank data -Imaging Translate guidelines and empirical results into specific process steps Leverage workflow driven informatics processes to drive to point of care with decision support analytics Trustworthy data to measure protocol with analytics to track outcomes or deviations Employ analytics to measure results and teach people, activate patients and transform care Health Management Platform Enterprise Health Analytics Context Specific Analytics Existing Clinical Systems Novel Research Methods for Enabling Rapid Learning Networks (e.g. adaptive trial design, signal detection) Real-time Feedback

17. Personalized Healthcare Requires Deep Analytics Departmental “Dashboards”are Insufficient DASHBOARDS “ Visible” PLUMBING Mappings to applications and data transformations take expertise & time The technology infrastructure has its own complexities

18. Personalized Healthcare Requires An Integrated View Insights From Combining Clinical, Biomedical, Operational and Financial Data “ How is our nurse overtime policy affecting our ICU quality measures? Patient satisfaction?” FINANCIAL HR SUPPLY CHAIN CLINICAL “ Do our cardiac care reimbursements reflect our improved quality measures?” “ How many patients with a particular molecular profile and clinical attributes are in our system?” “ INTEGRATED VIEW ANALYTICS” “ GENOTYPE” “ SILO ANALYTICS” “ What’s our overall performance? Our quality performance and cost to deliver that quality?”

19. What does this ultimately mean for a health system? Data and applications must be decoupled for robust use of the data and for applications to draw upon multiple data sources Fulfill Health Information Request Health Information Services Agreement For Secondary Use Health Information Request Authenticate Requester Transform and Normalize Health Informatio n Patient Health Information Aggregated for Normalization Transformed into Information Service Format Patient Health Information Gathered Clinical Innovations Clinical & Operational Benefit

20. Analytics coupled with the right health management platform can support longitudinal care Focus for Today: Citizen/Patient Care Cycle Identifying, stratifying and targeting patients Attracting patients Improving administrative processes to Improve utilisation of resources Help Patients get treatment Improve patient experience Support after discharge Adherence Patient monitoring Supporting patient behavior modification Unaware Patient Aware Patient Patient Visits HC Professional Get Treatment Patient Discharged Patient is Compliant Patient is Stable Health Consumer Gateway Pre-Acute Acute Intervention Post-Acute/ Disease Management

21. Effective Care Cycle Management Has a Positive Impact on Both Cost and Quality Source: Intel Corp 2006 Cost of Care/Day 0% 100% $1 $10 $100 $1,000 $10,000 Acute Care Residential Care Home Care ICU Community Hospital Specialty Clinic Assisted Living Skilled Nursing Facility Doctor’s Office Community Clinic Chronic Disease Management Healthy Independent Living Quality of Life

22. These platforms also create opportunities for biopharma industry innovation and novel collaboration Real-time Clinical Information Exchange Across the System Patient Recruitment Clinical Data PROVIDERS Hospital Clinics Bio-banks CRO Pharma/ Biotech Life Sciences Clinical Trials Safety & Pharmacovigilance HEALTHCARE LIFE SCIENCES

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25. Oracle Health Sciences Institute In Partnership With Sun Labs Focused on research that will accelerate IT innovation to advance personalized medicine and the delivery of safe and effective treatments and health care services to patients around the globe Works in collaboration with academic research centers. Current collaborations include: Dana Farber Cancer Center (translational research) The Cleveland Clinic (cardiac decision support) St. Francis Xavier University (parallelizing ontological reasoning) Focuses on areas fundamental to the challenges facing health sciences organizations. Research priorities include: Artificial intelligence and semantic technology Genomic, genetic and phenotypic data analysis Data mining to support optimization of clinical trials Predictive algorithms to enable earlier detection of adverse events Advanced decision support at the point of care