Pharmaceutical EpistemologyJim Golden, Ph.D.

Global Lead, Healthcare Data Analytics

Accenture

(james.golden@accenture.com)

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

“The Wharton School Study of the Health Care Value Chain”, Lawton, Burns, DeGraaff, Danzon, Kimberly, Kissick, Pauly

ProvidersFiscal

IntermediariesPayers Purchasers Producers

Government Federal

State & local

Employers IndividualsCoalitions

InsurersHMOsPharmacyBenefit Managers

HIE’s

HospitalsPhysiciansPharmaciesFed Agencies

DoD, VA

Home CareStaffing Providers

WholesalersMail-OrderDistributorsGroup Purchasing Organizations

Drug MfgrsCRO’sDevice Mfgrs

Medical-Surgical Mfgrs

SW Providers

IT Integrators

Challenge: There Is No Real Healthcare Market “Value Chain”

The Data Needed to Empower Robust Health Analytics is Distributed throughout the Ecosystem

Patients

PMPSuppliers

Public &Private Payers

Providers

Supply Chain DataIndustry Intelligence DataBenchmarking DataMarket Research Data

Treatment & Rx Claims & Payment DataClinical Outcomes DataLeading Practices DataProgram Effectiveness DataPopulation/ Disease Data

Drug Safety DataDrug Efficacy DataMedical Device EfficacyClinical Trial DataLeading Practices DataMarket Research Data

Prescription DataLab DataRadiological DataProduct Utilization Data Treatment Protocol Data

Admissions DataPhysician Profile DataBenchmarking DataEBM DataClinical Research Data

Epidemiological DataPatient Profile DataMarket Research DataGenomics DataClinical Trial DataOther basic research

Optimize Revenue

Control Cost

Quality Outcomes

Clinical Evidence

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 & Revenue OperationalClinical / 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 CareBilling Quality

Health Outcomes

Price Optimization

Rebate Optimization

Comparative Effectiveness

Now Next Later

Animal Modeling

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:

Inclusion / Exclusion

Criteria

Trial & Adaptive Design

Evidence-Based Medicine

Drug Safety & Signal Detection

Clinical / Development

One possible desired future state:

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