1. Pharmaceutical Epistemology Jim Golden, Ph.D. Global Lead, Healthcare Data Analytics Accenture (james.golden@accenture.com)

2. The healthcare system is facing severe economic, effectiveness, and quality challenges. Transformation will come through data-driven decisions and improved insights. Economic Conditions Political Environment Health Care System Customer Needs Cost containment Shrinking budgets Profitability Fragmented Value Chain Consolidation Health Care Reform Funding Constraints Regulatory Pressures Meaningful Use Medical Advances Provider Shortages HIT Pay for Performance Evidence-based Medicine Comparative Effectiveness Quality Affordability Choice Safety Effectiveness Compliance & Adherence Demographics

3. “The Wharton School Study of the Health Care Value Chain”, Lawton, Burns, DeGraaff, Danzon, Kimberly, Kissick, Pauly Providers Fiscal Intermediaries Payers Purchasers Producers  Government  Federal  State & local  Employers  Individuals  Coalitions  Insurers  HMOs  Pharmacy  Benefit Managers  HIE’s  Hospitals  Physicians  Pharmacies  Fed Agencies  DoD, VA  Home Care  Staffing Providers  Wholesalers  Mail-Order  Distributors  Group Purchasing Organizations  Drug Mfgrs  CRO’s  Device Mfgrs  Medical- Surgical Mfgrs  SW Providers  IT Integrators Challenge: There Is No Real Healthcare Market “Value Chain”

4. The Data Needed to Empower Robust Health Analytics is Distributed throughout the Ecosystem Patients PMP Suppliers Public & Private Payers Providers Supply Chain Data Industry Intelligence Data Benchmarking Data Market Research Data Treatment & Rx Claims & Payment Data Clinical Outcomes Data Leading Practices Data Program Effectiveness Data Population/ Disease Data Drug Safety Data Drug Efficacy Data Medical Device Efficacy Clinical Trial Data Leading Practices Data Market Research Data Prescription Data Lab Data Radiological Data Product Utilization Data Treatment Protocol Data Admissions Data Physician Profile Data Benchmarking Data EBM Data Clinical Research Data Epidemiological Data Patient Profile Data Market Research Data Genomics Data Clinical Trial Data Other basic research Optimize Revenue Control Cost Quality Outcomes Clinical Evidence

5. Within the healthcare ecosystem there are very specific, near-term, high-value opportunities for data computability approaches: Inclusion / Exclusion Criteria Discovery Trial & Adaptive Design Simulation Pharmaco- economics Formulary Inclusion Strategy Physician Targeting / CLV Sales Force Optimization Evidence-Based Medicine Targeted Therapeutics Drug Safety & Signal Detection Institutional Safety Facility Utilization Commercial & RevenueOperationalClinical / Development Disease Management Drug Launch / Marketing Strategy Portfolio Optimization Fraud Detection Channel Optimization Patient Compliance Supply Chain Optimization Toxicity Genomics Investigator & Site Selection Drug Repurposing Meaningful Use CDHP Analysis & Forecasting Standards of Care Billing Quality Health Outcomes Price Optimization Rebate Optimization Comparative Effectiveness NowNextLater Animal Modeling

6. Inclusion / Exclusion Criteria Trial & Adaptive Design Evidence-Based Medicine Drug Safety & Signal Detection Clinical / Development  Critical areas of data aggregation across trials: –Demography –Adverse Events –Treatment Dose –Concomitant Medication Standardization (i.e. common variable names) Normalization (i.e. pounds to kilograms) Creation of derived/computed variables Aggregation (sum, mean, min, max) Current methodology for clinical data integration, warehousing and analytics:

7. Inclusion / Exclusion Criteria Trial & Adaptive Design Evidence-Based Medicine Drug Safety & Signal Detection Clinical / Development One possible desired future state:

8. http://www.wolframalpha.com/timeline.html Ramon Llull (1232 – 1315) Glymour, Ford and Hayes; Ramon Llull and the Infidels AI Magazine (1998) http://en.wikipedia.org/wiki/Ramon_Llull