Exploring the Clinical Informatics Landscape in Europe, Asia, and Beyond

October 19, 2010

Presentation Document

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Real world data emerging as a basis for decision making

▪ Payors are increasingly asking for evidence demonstrating cost effectiveness and real-world value of drugs

▪ Head-to-head comparators by sub-populations on the rise for drugs that have proven cost-effective

Early but definitive signs that payors are demanding more real-world proofs of net value of treatments

▪ U.S.: EHR/eRx providing access to clinical data sets that are much larger than clinical trial data

▪ UK: GPRD provides real-world data on >10mn patients

▪ India: Collecting medical data on 10s of millions of patients

In addition to claims data, increasing availability of real world clinical data

▪ Researchers at academic medical centers analyzing real-world data for safety signals and comparative efficacy

▪ FDA launched the Sentinel Initiative in 2008 to query EMR and medical claims systems for safety signals

Providers and regulators are increasingly analyzing real-world impact of pharma products

▪ New products face coverage challenges without sufficient cost-benefit case presentation

▪ Preferential formulary status is increasingly granted to pharmacos based on real-world clinical data

▪ Safety concerns could escalate into high-profile cases

▪ Phamacos can expand market access by targeting sub-population for whom treatments are most efficacious

▪ Real-world outcomes provide fact base for pharmacos to confidently take on risk-based pricing

▪ Pharmacos’ ability to stay ahead of the curve using real-world clinical data will be critical to winning in the future

Threats and opportunities as rules of the game change

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▪ Since 2004, 11 drug assessment reports completed, often considering only head-to-head comparison trials,disregarding indirect comparisons

▪ On June 26, 2009, recommendation made against Lantus (glargine) use based on analysis of real-world clinical data

Private payors and gatekeepers for public funding are requiring real-world proofs of cost-effectiveness for drug treatments

SOURCE: Press articles, team analysis

▪ Drug submission guidelines to require cost-effectiveness data for drug submissions in 2010

▪ Cost-effectiveness based on real-world clinical data will also be required forcoverage renewal submissions

▪ All cost-effectiveness claims to be expressed in terms that allow for monitoring and verification

▪ Collaboration with Wisconsin HIE to encourage ER doctors to use EMRs to reduce redundant treatments

▪ Partnership expected to be expanded nationwide, possibly in conjunction with other payors

▪ Numerous payors lobbied US Congress to establish an entity to analyze real-world data and understand treatment cost-effectiveness

▪ Efforts resulted in introduction of Comparative Effectiveness Research Act of 2009 to create an institute funded by both public and private payors to identify most cost-effective treatments

▪ NICE already evaluates value of product compared to price

▪ Moving towards value-based pricing with lower prices at launch and potentially increased prices after cost-effectiveness is proven

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Academic medical centers, health systems and regulators increasingly mining real world data to conduct their own safety surveillance and comparative efficacy reviews

SOURCE: Press articles, team analysis

▪ FDA increasing use of real-world data analysis for pharmacovigilance through launch of Sentinel Initiative on May 22, 2008 to query EMR and medical claims systems for safety signals

▪ ARRA investing $1.1 Bn into comparative efficacy research through AHRQ, HHS, and NIH

▪ UK’s General Practitioner Research Database (GPRD) provides real-world data on >10mn patients

▪ 500+ publications on treatment outcomes of various interventions have been published by various academic and commercial researchers over the past 10 years

▪ Several industry players have purchased full access to database

▪ Providers working to leverage their own EMR systems to provide new services and improve existing operations

▪ Kaiser already secured $600k grant from AHRQ evaluating heart disease management and prevention

▪ Regenstrief has created nation's only state-wide EMR system

▪ Allows ED physicians to view all previous care as a single virtual record in 6MM patient database, with 900MM online coded results and 20 MM full reports

▪ Also created center to provide access to its EMR data to other institutions

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Real-world data comes from diverse sources

Types of data

Claims data

ePrescription / pharmacy fulfillment data

Laboratory data

EMR data

Potential vendors/partnersTypes of organization

▪ Academic Medical Centers

▪ Health Information Exchanges

▪ EMR vendors

▪ Data aggregators

▪ Public payors

▪ Claims data vendors

▪ PBMs

▪ Retail pharmacies

▪ Electronic prescription companies

▪ Laboratory and diagnostic services provider

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Representative quotesKey challenges

Significant challenges in capturing value – creating an advantage for those that crack the code

“Currently, it takes 6 months

and a few 100K to answer any

question”

“We have to be in it for the long

run and have to find the right

strategic partners… and maybe

even explore strategic funding

to make this work.”

“Effectiveness research itself is

a bit of a white space… R&D is

watching safety signals,

commercial analysis is watching

sales patterns, but

effectiveness of in-market

products is not anyone's priority

yet.”

Lack of organizational readiness to use

▪ Pharmacos are swamped by today’s priorities

▪ Fragmentation of responsibilities within pharma organizations limits their ability to launch cohesive effort to capture the opportunity

Gathering fragmented real-world clinical data

▪ Sources of data highly fragmented across providers and data vendors

▪ Inconsistent data quality may limit usefulness

Difficulty in aggregating multiple data sources

▪ Regulations regarding patient data privacy limit data exchange and linking

▪ Advanced technical expertise required to integrate non-standardized data formats

Lack of expertise to extract business insights

▪ Analytic expertise typically approached more as scientific exercise rather than business analysis

▪ Clinical researchers face difficulties in translating clinical findings into business strategies

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Focus of today’s discussion

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While most of the UK is fragmented with multiple stakeholders and little consolidation…

Source: UK Monitor website; NHS website; McKinsey analysis

Providers public

▪ ~250 Hospital Trusts (HT) managing hospitals and specialist care (operating ~400 NHS hospital sites)

Providers private

▪ ~34K primary care GPs, who are mostly self-employed▪ Private hospitals (concentrated into 5 large chains)

Payers public ▪ Fragmented but important source of claims data – Previously organized into ~150 Primary Care Trusts (PCTs)

with decision-making authority over ~75% of NHS budget– Currently undergoing reform

Payers private ▪ Low-priority source due to fragmentation, e.g., provide only supplementary insurance and serve ~10% of the population

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▪ Government Multi-disciplinary team based at the Medicines & Healthcare products Regulatory Agency

▪ Longitudinal primary-care EMR data on 13 million UK lives

▪ Data is aggregated, normalized, and linked with other healthcare data

▪ Online access to data

▪ Wide array of analytics including

– Clinical epidemiology, treatment patterns, and drug utilization

– Drug safety / pharmacovigilance

– Health outcomes, economics, drug effectiveness

– Health service planning and disease management

▪ Consulting services, primarily for research

▪ Private company with longitudinal primary-care EMR data on 12 million UK lives

▪ 602 general practices using EMIS clinical system

▪ Sample sizes limited to 100,000 patients

▪ Analytic services available

▪ Strictly for academic research purposes by universities or pharmacovigilance activities by pharma through a 3rd party

▪ 49 publications to date since 2004

Two entities stand out as leaders in the clinical informatics space

Source: UK Monitor website; NHS website; McKinsey analysis

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In Germany, while fragmentation exists, a number of coalitions are forming creating pockets of meaningful data

Source: UK Monitor website; NHS website; McKinsey analysis

Providers public ▪ Fragmented network of ~700 small public hospitals

Providers private

▪ ~1200 hospitals and ~140K GPs / specialists– HELIOS group is a leading network with 42 hospitals, 19 rehabilitation

centers, 24 clinics and 4 senior care facilities

Payers public ▪ ~280 “sickness funds” (non-profit, quasi-public, self-governed organizations) covering 80-90% of population– Highly concentrated and consolidating rapidly– AOK and vdek are the dominant sickness funds

Payers private ▪ ~50 private payers covering only ~10% of population▪ Typically branches of larger insurance companies

Others ▪ Bremen Institute for Prevention Research and Social Medicine (BIPS)▪ Pharmacy Data Center, a centralized provider of claims data (diagnosis

and prescription) sells anonymized data that AZ could access

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▪ Funded by government and University of Bremen

▪ Full data sets from four different sources (including an AOK) covering ~14 million lives

▪ ~70 faculty and staff including epidemiologists, statisticians, and analysts

▪ Over 50 peer-reviewed articles per year

▪ Collaborates with outside stakeholders, requires approval by Ministry of Health

▪ Group of 14 “sickness funds” that provide health insurance to ~1/3 of Germans

▪ Each AOK is independent but non-competitive and coordinated through the “AOK-Bundesverband”

▪ Claims, demographic, procedure code and medication data on ~24m Germans, but siloed amongst the 14 AOKs

▪ Demonstrated capability for analyzing claims data, e.g., quality management programs with HELIOS group

Data and analytic capabilities are being developed both through academia and payers

Source: BIPS website; AOK website; press releases; McKinsey analysis

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Additional sources of data across Europe

IMS ▪ Traditional provider of prescription sales data▪ Developing data assets across Europe with established product lines,

e.g., IMS “XX” Analyzer

France ▪ Single payer system with a National Insurance Database▪ Échantillon Généraliste de Bénéficiaires (EGB) – 3% extract of insurance

database made available for academic researchers

Nordics1 ▪ In Sweden, 70 National Quality Registries cover 80% of the population▪ Quality registries cover 68% of the population across the Nordics

Italy ▪ Health Search Database (HSD), a research unit of the Italian College of General Practitioners, aggregating clinical information contributed by Italian GPs with records from ~2M patients

▪ Datasets include patient EMRs, drug prescriptions and prices, lab and diagnostic tests, and hospital DRG tariffs

Cegedim ▪ 3rd party commercial provider of sales force performance data

▪ Has been developing longitudinal EMR data in Europe, particularly in France with ~1.6M primary care lives and specialty care lives ~100K

1 Denmark, Sweden, Finland, Norway, IcelandSource: Company websites and documents; ISPOR; interviews; team analysis

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In Asia, some markets are developing data although still in early stages

Source: Websites and documents; ISPOR; interviews; team analysis

South Korea

▪ National Health Insurance Corporation covers 98% of 48M population captured in database since 2005 but currently not linked to other sources

▪ Hospitals investing in EMR systems and beginning to mine data, e.g., Catholic Medical Centers

Taiwan ▪ National Health Insurance Data containing administrative claims data

▪ Multiple registries, including cancer, birth, death, rare disease, and dialysis

Thailand ▪ Multiple registries including cardiovascular, diabetes, cancer

▪ Developing databases for prescription sales, inpatient, and outpatient care

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Large investments into infrastructure offer opportunities to leapfrog ahead of traditional health system evolution

China

▪ Existing infrastructure through Ministry of Human Resources and Social Security (MOHRSS) largely paper-based

▪ Undergoing healthcare reform with objective to provide increased coverage to population

▪ Past transformative ventures have created massive infrastructure builds through turnkey solutions

Canada

▪ Alberta Health and Wellness (AHW) serves as the primary payer and primary inpatient provider for the province of Alberta

▪ Actively promoting EMR adoption to cover Alberta’s ~4M people

▪ 46% of community physicians use EMR and ~34,000 providers share (some) clinical data through Alberta Netcare (2009)

United Arab Emirates (Abu Dhabi)

▪ Designed healthcare system bottom-up, including integrated EMR system

▪ Beginning to collect data on ~0.9M population

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Key takeaways

▪ The data landscape outside of the US is complex with differing data owners with variable interests

▪ Regulatory and reimbursement agencies are requesting data perceived as relevant to their markets, often data from their own markets

▪ Countries with infrastructure investments present unique opportunities to develop intelligently designed data assets for secondary use