Comparative Effectiveness Research, Personalized Medicine

1

Department of Health ServicesSchool of Public Health

U. Washington

Comparative Effectiveness Research, Personalized Medicine, and Health Reform:

A View from the Other Washington

April 6, 2011

Clifford Goodman, PhDSr. Vice President

[email protected]

2

Why CER?

• Evidence of inappropriate use of health care technologies, including over-use, under-use, and improper use

• Evidence of large variations in practice

• Evidence for FDA market approval/clearance often not sufficient to support clinical and policy decisions

• Inconsistent, insufficiently rigorous evidence for many technologies not regulated by FDA (i.e., many medical and surgical procedures)

• Lack of evidence on “head-to-head” comparisons of alternative interventions for particular health problems

• Lack of evidence in “real-world” practice (efficacy vs. effectiveness)

• Continued rapid increases in health care costs

3

Timeline: Getting to CER

1 RCT of streptomycin for pulmonary tuberculosis, sponsored by Medical Research Council (UK): 19482 Origin of TA (not focused on health) in 1965: US Congressman Daddario; first “experimental” HTA by National Academy of Engineering in 1969 (multiphasic screening); Office of Technology Assessment published first HTA in 1974

3 Patient Outcomes Assessment Research Program (later, PORTs) initiated by NCHSR (later renamed AHCPR; now AHRQ) in 1986 (“promote research with respect to patient outcomes of selected medical treatments and surgical procedures for the purpose of assessing their appropriateness, necessity and effectiveness “)

4 HCFA (later renamed CMS) Effectiveness Initiative: 19885 Early published appearance of “pharmacoeconomics”: Bootman et al. 19896 “Evidence-based”: Eddy 1990; “Evidence-based medicine”: Guyatt et al. 19927 Medicare Prescription Drug, Improvement, and Modernization Act of 2003 (MMA) specifies AHRQ role in “comparative clinical effectiveness”; American Recovery and Reinvestment Act of 2009 (ARRA) authorizes major national investment in CER

8 CMS draft guidance in 2005; formalized in 2006. Medicare and other payers began linking coverage to clinical research in 1990s

Source: C. Goodman © 2009 The Lewin Group

1970 1980 2000 2010

Health TechnologyAssessment2

(1974) Outcomes Research3

(1986)

Effectiveness Research4

(1988)

Pharmacoeconomics5

(1989)

Evidence-basedMedicine6

(1990)

Coverage withEvidence Development8

(2006)

Comparative EffectivenessResearch7 (2003, 2009)

19901940

1st RandomizedControl Trial1

(1948)

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CER Attributes

Generally common attributes:

• Direct comparisons of alternative interventions (as opposed to comparison with placebo or indirect comparisons)

• Applies to all types of interventions

� pharma, biotech, devices/equip’t, medical and surgical procedures; organization, delivery, management, financing

• Effectiveness (in realistic health care settings) rather than efficacy (in ideal circumstances)

• Health care outcomes (e.g., morbidity, mortality, QoL, adverse events, and symptoms) rather than surrogates or other intermediate endpoints

• Evolving portfolio of primary, secondary research methods

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CER Methods Portfolio (Evolving)Clinical Trials

• Randomized clinical trials

• Practical (pragmatic) clinical trials

• Other non-randomized controlled trials

• Adaptive clinical trials and other trial designs

• Other, e.g., randomized consent, regression discontinuity, combined single-subject (“n of 1”) trials

Observational Studies (prospective or retrospective)

• Population-based longitudinal cohort studies

• Patient registries

• Claims databases

• Clinical data networks

• Electronic health record data analyses

• Post-marketing surveillance (passive and active)

Syntheses of Existing Evidence

• Systematic reviews (comparative effectiveness reviews)

• Meta-analyses

• Modeling

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Priority Conditions

• Arthritis and nontraumatic joint disorders

• Cancer

• Cardiovascular disease, including stroke and hypertension

• Dementia, including Alzheimer's disease

• Depression and other mental health disorders

• Developmental delays, attention-deficit hyperactivity disorder, and autism

• Diabetes mellitus

• Functional limitations and disability

• Infectious diseases, including HIV/AIDS

• Obesity

• Peptic ulcer disease and dyspepsia

• Pregnancy, including preterm birth

• Pulmonary disease/asthma

• Substance abuse

Source: Agency for Healthcare Research and Quality, 2010

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Priority Populations, Health Disparities

Priority populations:

• Racial and ethnic minorities

• People with disabilities

• Children

• People with multiple chronic conditions

• Elderly

Health disparities:

• Significant gaps or differences in the overall rate of disease incidence, prevalence, morbidity, mortality, or survival rates in the priority population as compared to the health status of the general population

Source: Federal Coordinating Council for Comparative Effectiveness Research. Report to The President and The Congress. June 2009.

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CER in the American Recovery and Reinvestment Act of 2009 (ARRA)

• Provides $1.1 billion, to be obligated by Sept. 30, 2010

$300 M - Agency for Healthcare Research and Quality

$400 M - National Institutes of Health

$400 M - Secretary of Health and Human Services

• Designates two groups to provide recommendations on national CER priorities and other advice by June 30, 2009:

• Federal Coordinating Council for CER

• Institute of Medicine

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ARRA Funding for CER: Real $$ ?

Funding in 2009 $ Billions

AHRQ budget (original) 0.326

CER in ARRA 1.1

- AHRQ 0.300

- NIH 0.400

- HHS Sec’y 0.400

NIH budget 30.395

Pharma/bio R&D 65

Total U.S. health care 2,510

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ARRA Mandated Two Reports on CER Priorities

Both reports were released June 30, 2009

• Institute of Medicine

� 100 priorities (4 tiers X 25) clinical and other health care problems

• Federal Coordinating Council on CER

� Coordination across federal CER assets

� Research (in comparative effectiveness)

� Human and scientific capital (training, methods, etc.)

� CER data infrastructure

� Dissemination and translation of CER

� Priority populations and other subgroups

� In addition to pharma, behavioral, procedures, prevention, and delivery system interventions

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12

Using the CER Strategic Framework for Inventory and Investment Decisions

Source: FCCCER. Report to The President and The Congress. June 2009.

CER InvestmentOpportunities

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June 2009

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IOM Recommended CER Priorities

In top tier:

• Compare the effectiveness of genetic and biomarker testing and usual care in preventing and treating breast, colorectal, prostate, lung, and ovarian cancer, and possibly other clinical conditions for which promising biomarkers exist.

Source: Institute of Medicine. Initial National Priorities for Comparative Effectiveness Research, 2009.

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CER in Health Reform 2010: Patient-Centered Outcomes Research Institute

• Established by Patient Protection and Affordable Care Act, Sec. 6301

• Private, non-profit organization

• Identify research priorities; establish & implement research agenda

• Overseen by 21-member Board of Governors, including the Directors of AHRQ and NIH; 19 members appointed by Comptroller General

� Assisted by expert advisory panels and methodology committee

• Funding: appropriations; transfers from Medicare Federal Hospital Insurance and Federal Supplementary Medical Insurance Trust Funds; transfers from health insurance and self-insured health plans

• Limitations on PCORI’s and the Secretary’s ability to use PCORI research findings for coverage and reimbursement

� Cannot “mandate coverage, reimbursement, or other policies for any public or private payer”

� Gov’t may use findings in coverage “if such use is through an iterative and transparent process which includes public comment and considers the effect on subpopulations,” subject to other constraints

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Patient-Centered Outcomes Research Institute

Act establishes Patient-Centered Outcomes Research Trust Fund (PCORTF) in U.S. Treasury. Appropriations:

• FY 2010: $10 million

• FY 2011: $50 million

• FY 2012: $150 million

FYs 2013-19: $150 million in appropriations plus transfers from:

• Medicare Federal Hospital Insurance and Federal Supplementary Medical Insurance Trust Funds

• Health insurance and self-insured health plans

• Formula: avg. number of enrollees in the plans (Medicare, healthinsurance policies, and self-insured plans) multiplied by:

� $1 for FY 2013

� $2 for FY 2014

� $2 increased by annual medical inflation for FYs 2015-19

• No amounts available for expenditure after September 30, 2019

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Defining PM

“Personalized medicine” refers to the tailoring of

medical treatment to the individual characteristics

of each patient. It does not literally mean the

creation of drugs or medical devices that are unique

to a patient but rather the ability to classify

individuals into subpopulations that differ in their

susceptibility to a particular disease or their

response to a specific treatment. Preventive or

therapeutic interventions can then be concentrated

on those who will benefit, sparing expense and side

effects for those who will not.” ― President’s

Council of Advisors on Science and Technology

2008

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Timeline: Getting to PM, Too

Source: C. Goodman © 2009 The Lewin Group

1970 1980 2000 2010

HTA(1974)

Outcomes Research

(1986)

Effectiveness Research

(1988)

Pharmacoeconomics(1989)

EBM (1990)

CED(2006)

CER(2003, 2009)

19901950

1st RCT(1948)

1960

DNA Structure Described

(1953)

“Pharmacogenetics”(1959)

Genetic Code

Cracked(1967)

CYP450 Metabolic Enzymes Identified

(1977)

Human Genome

Sequenced(2003)

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CER and PM: Contradiction?

1. CER has been largely oriented toward population-based evaluations and applications. In contrast, PM focuses on using individuals’ genomic information and other personal traits to inform their health care decisions.

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The Trouble With Averages

2. Like other forms of evaluation of health care interventions, CER generally has focused on identifying interventions that are effective, on average, across a broad patient population.

• Interventions that yield a statistically significant treatment effect across a study population may not necessarily work for all treated patients; they may be ineffective for some patients and harmful for others.

• Interventions that do not yield a statistically significant treatment effect across a study population―and that may be dismissed as ineffective―may work for certain subsets of the population.

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Could Mislead Gatekeeping Function

3. The absence of PM considerations in CER could be suboptimal for patient interests, particularly to the extent that CER findings are used to support gatekeeping or other authoritative functions, such as product labeling, clinical practice guidelines, coverage policies, and quality measures and criteria.

• To the extent that PM is incorporated into CER, the resulting evidence will be more relevant and useful for these same functions.

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Incorporating PM into CER

4. For CER to contribute to PM, it will have to emphasize priorities and study designs that account for individuals’ genetic, behavioral, environmental, and other personal traits that mediate the impact of screening, diagnostic, therapeutic, and other interventions on patient outcomes.

• To date, only a small percentage of published comparative effectiveness studies have focused on treatment effectiveness in patient subgroups.

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Study Designs to Detect HTEs*

5. The extent to which population-based evidence can be used to inform health care decisions for specific individuals depends not only on how well the study population represents those individuals …

• It also depends on whether the study designs and analytical methods used are capable of detecting important treatment effects and adverse outcomes for the patient subgroups representing those individuals.

* Heterogeneity of treatment effects

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Subgroup Analyses – Are Apparent Differences in Treatment Effect Real?

• Is the magnitude of difference clinically important?

• Was the difference clinically significant?

• Did the hypothesis precede rather than follow the analysis?

• Was the subgroup analysis one of a small number of hypotheses tested?

• Was the difference suggested by the comparisons within rather than between studies?

• Was the difference consistent across studies?

• Is there indirect evidence that supports the hypothesized difference?

Source: Oxman AD, Guyatt G. A consumers guide to subgroup analyses. Ann Intern Med 1992;116:76-84.

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PM Interventions Subject to Evidence Req’ts

6. PM interventions are subject to prevailing requirements for rigorous evidence demonstrating how well they work compared to standard care.

• Increasingly, this means showing that an intervention has some direct, or least demonstrably indirect, favorable impact on health outcomes in real-world practice settings.

• For genetic/genomic testing and other aspects of molecular-based PM, this means demonstrating not only technical accuracy of a test, but further downstream impact on health care decisions and outcomes.

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EGAPP Hierarchy of Data Sources and Study Designs

Source: Teutsch SM, Bradley LA, Palomaki GE, et al. The Evaluation of Genomic Applications in Practice and

Prevention (EGAPP) initiative: methods of the EGAPP Working Group. Genet Med 2009;11(1):3-14.

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Analytical Framework: CYP450 for SSRIs

Source: Teutsch SM et al. EGAPP Working Group. Genet Med 2009;11(1):3-14.

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Genomics to Effectiveness: Unpredictable Path

Genomics ≠ Physiology ≠ Efficacy ≠ Effectiveness

“[M]uch more research will be required before we

can be confident that our genomic and

pathophysiological understanding of disease

processes can provide a valid basis for estimating

intervention effects in health outcomes for

genetically defined subpopulations.”

Source: Khoury MJ, et al. Comparative effectiveness and genomic medicine: an evolving partnership for 21st century medicine. Genet Med 2009;11(10):707-11.

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Essential Role of HIT for CER-PM

7. HIT can help align CER and PM in two main ways:

• Through EHR capture of genetic and other personal health information in clinical trials and clinical practice, HIT can support CER to augment the evidence base for PM.

• Clinical decision support systems and other forms of HIT can ensure that evidence pertaining to PM is present and actionable at the point of decision-making by patients and clinicians.

• However, slow adoption of HIT thus far

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Advanced Methods Portfolio Needed

8. CER offers an evolving portfolio of methods with great potential for meeting the needs of PM.

• CER methods development supported by AHRQ

• Ongoing work in the public and private sectors on data mining and analysis of claims and other administrative and observational data.

• Adaptive clinical trial designs, other variations on clinical trials that focus on deriving evidence efficiently for responsive vs. nonresponsive patient subgroups.

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PM Puts Pressure on Traditional Trials

Expanding findings about gene-based risk factors increase difficulty of conducting traditional clinical trials to incorporate these and other person-level traits. CER methods must address the:

• Sheer volume of gene-based applications and other diagnostic tests

• Timeliness for collecting evidence

• Costs of doing large-scale research in this field

• Standards of evidence for these new applications


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“Fusion” of Data Sources Using Advanced HIT?

Potential in large networks linking clinical, other data

• Interoperable EHRs

• Biobanks (e.g., tissue/tumor repositories)

• Clinical registries

• Claims data

• Potential to conduct “virtual” clinical trials, observational (e.g,. cross-sectional, longitudinal cohort) studies, advanced modeling

However:

• Methodological, statistical challenges remain in selection bias, information bias, other confounders

• Current data infrastructure for linking genetic testing data to other sources is far from adequate …

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Infrastructure to Monitor Utilization and Outcomes of Gene-Based Tests (2)

Objectives:

• Conduct an assessment of existing databases in the US health care system for monitoring the utilization and outcomes of gene-based applications (including tests and related interventions) in the health care system

• Provide recommendations to establish appropriate and practical systems to assess use and outcomes of gene-based clinical applications.

Source: AHRQ. Infrastructure to Monitor Utilization and Outcomes of Gene-Based Applications: An Assessment. AHRQ Pub. No. 08-EHC012. May 2008.

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Findings:

• Only limited, sporadic information is available on the utilization of gene-based tests over time.

• Some research and surveys suggest that knowledge on the part of some providers about the availability and utility of tests may be reasonably widespread and accurate.

• Little or nothing is known about the extent to which patients and their families are aware of tests and knowledgeable about their benefits and harms.

• There are few longitudinal data to indicate the benefits and risks of using genetic tests to guide interventions and medical decisions, such as in the selection of therapies, and their short- or long-term outcomes.


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Recommendations:

• Improve coding of gene-based tests in many relevant databases so that test types, reason for test, and test results can be readily determined

• Develop or adopt standards for the proper collection and storageof data from genetic testing laboratories for archiving the tests performed and facilitating interoperability between databases

• Explore possibility of adding questions to ongoing surveys or developing new surveys to monitor availability of genetic testing centers, adequate counseling, barriers to accessing counseling

• Consider establishing survey of genetic testing laboratories similar to National Ambulatory Medical Care Survey for medical clinics and the National Hospital Discharge Survey for hospitals

• Develop pilot studies for small set of diseases and tests


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CER Affecting Innovation, Including in PM

9. CER is likely to alter value propositions for innovation in PM. It will provide new opportunities and hasten some shakeouts.

• The need to generate comparative evidence at more discrete levels raises the risk of innovation and forces choices about its direction and sequence. Targeted therapies that can demonstrate comparative effectiveness may gain market advantages.

• Federal support of CER trials, other studies could reduce development costs of some new interventions. Federal and private sector support of linked databases may help to identify new genetic determinants of drug response, related biomarkers.

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Target Group Translation

10. As CER further reflects patient risk factors, comorbidities, HTEs, and other individual factors that can affect the use and outcomes of health care interventions, communications and applications of these findings must be more adaptive and targeted to clinicians, patients, payers, public.

• These messages should address limitations of this evidence for decision-making and evidence gaps that are priorities for further CER.

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Spectrum of CER-PM Alignment

Aligning CER and PM depends on several key factors, including:

• Research questions being addressed

• Type of interventions being studied

• Data infrastructure

• Study design and implementation

• How findings are communicated to and applied by patients, clinicians, payers, and others

• Ability of health care organization, delivery, management, and payment to support and enable evidence-based PM

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Most CER Has Not Focused on Subgroups, Even Large, Aggregated Ones

• CRS analysis: only 13% of comparative clinical effectiveness studies published in the peer-reviewed literature Jan. ‘04-Aug. ‘07 focused on effectiveness of treatments in subpopulations other than white middle-age adults (or females for diseases that only occur in females), e.g., children, the elderly, and non-white populations.

• Only about 5% of these CER studies included patients with comorbidities, even though nearly 60% of hospitalized patients have one comorbidity and more than a third have at least two comorbidities.*

*Comparative clinical effectiveness and cost-effectiveness research: background, history, and overview. Washington, DC: Congressional Research Service. October 15, 2007.

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MEDCAC: Pharmacogenomic Testing for Anticancer Therapies, Jan. 27, 2010

1. How confident are you that there is sufficient evidence to determine whether pharmacogenomic testing affects health outcomes (including benefits and harms) for patients with cancer whose anticancer treatment strategy is guided by the results of testing as described below?

a) CYP2D6 for breast cancer patients who are candidates for tamoxifen

b) UGT1A1 for colon cancer patients who are candidates for irinotecan

c) HER2/neu for breast cancer patients who are candidates for trastuzumab

d) BCR-ABL for chronic myelogenous leukemia patients who are candidates for imatinib

e) e) K-RAS for metastatic colorectal cancer patients who are candidates for cetuximab and/or panitumumab

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2. For those items where the answer to Question 1 is at least in the intermediate range (mean score > 2.5), how confident are you that pharmacogenomic testing improves health outcomesfor patients with cancer whose anticancer treatment strategy is guided by the results of testing as described below?

a) CYP2D6 for breast cancer patients who are candidates for tamoxifen

b) UGT1A1 for colon cancer patients who are candidates for irinotecan

c) HER2/neu for breast cancer patients who are candidates for trastuzumab

d) BCR-ABL for chronic myelogenous leukemia patients who are candidates for imatinib

e) e) K-RAS for metastatic colorectal cancer patients who are candidates for cetuximab and/or panitumumab

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3. How confident are you that these conclusions are generalizable to

a. community based settings;b. the Medicare beneficiary population?

4. Please discuss any important evidence gaps and recommend how they should be addressed.

46

When Is One Intervention Truly Better?

PM is influencing how CER methods demonstrate that an intervention is superior to usual care in various scenarios. Thresholds for “better” include:

• Anticipated effect sizes

• Nature of risks and benefits

� e.g., high standards of evidence for high-stakes decisions

• Patient-specific factors

• Individual preferences for various outcomes, including patient-reported outcomes (PROs)

These considerations influence the CER methods required to answer research/evidence questions


47

How Do These Issues Arise in Policies by Major Payers? An Example …

• Genetic testing for warfarin anticoagulation response

48

CMS Findings on Genetic Testing for Warfarin

Anticoagulation Response

• In 2007, the FDA determined that available PGx information warranted a change in the labeling of warfarin to call attention to the potential relevance of genetic information to prescribing of warfarin.1

• Regarding Medicare coverage, CMS stated in 2009 that …

1FDA approves updated warfarin (Coumadin) prescribing information. Press release of the Food and Drug Administration, Rockville, MD, August 16, 2007.

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CMS Findings on Genetic Testing for Warfarin Anticoagulation Response (2)

Based on the evidence reviewed, we believe that the

evidence is insufficient to determine that

pharmacogenomic testing of CYP2C9 or VKORC1

alleles to predict warfarin responsiveness improves

patient oriented health outcomes related to the

underlying indication for warfarin anticoagulation or

adverse events related to warfarin therapy itself. In

addition, we believe that the evidence is insufficient to

determine that pharmacogenomic testing to predict

warfarin responsiveness leads to changes in

physician management of beneficiaries’

anticoagulation therapy that would result in positive

outcomes.

Source: CMS Decision Memorandum for Pharmacogenomic Testing to Predict Warfarin Responsiveness, August 3, 2009.

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Therefore, we have determined that pharmacogenomic

testing of CYP2C9 or VKORC1 alleles to predict

warfarin responsiveness is not reasonable and

necessary under §1862(a)(1)(A) of the Social Security

Act. However, we do believe the available evidence

supports that Coverage with Evidence Development

(CED) under §1862(a)(1)(E) of the Social Security Act is

appropriate. Thus, we are making the following

decision …


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Pharmacogenomic testing of CYP2C9 or VKORC1 alleles to predict warfarin responsiveness is covered only when provided to Medicare beneficiaries who are candidates for anticoagulation therapy with warfarin who

1. have not been previously tested for CYP2C9 or VKORC1 alleles; and

2. have received fewer than five days of warfarin in the anticoagulation regimen for which the testing is ordered; and

3. are enrolled in a prospective, randomized, controlled clinical study when that study meets the following standards …


52


[W]hat is the frequency and severity of the following

outcomes, compared to subjects whose warfarin

therapy management does not include

pharmacogenomic testing?

• Major hemorrhage

• Minor hemorrhage

• Thromboembolism related to the primary indication for

anticoagulation

• Other thromboembolic event

• Mortality


53

CMS Findings on Genetic Testing for Warfarin

Anticoagulation Response (6)

• Other research continues on how genotypes affect sensitivity to warfarin and how well genetic tests predict safer and more effective doses of warfarin, including a large, multicenter RCT designed to determine whether genetic information provides additional benefit to what can be accomplished with traditional clinically-based warfarin information alone.1

1Shurin SB, Nabel EG. Pharmacogenomics--ready for prime time? N Engl J Med 2008;358(10):1061-3.

54

Department of Health ServicesSchool of Public Health

U. Washington

Comparative Effectiveness Research, Personalized Medicine, and Health Reform:

A View from the Other Washington

April 6, 2011

Clifford Goodman, PhDSr. Vice President

[email protected]

HEALTH CARE AND HUMAN SERVICES POLICY, RESEARCH, AND CONSULTING - WITH REAL-WORLD PERSPECTIVE

Ingenix Data Assets and ResourcesTaylor Dennen, PhD, Clifford Goodman, PhD

April 6, 2011

1www.lewin.com

� Introductions

� Foundation of our data assets

� Data tools

� Analytical services and technical support

� Representative current projects

Introduction to Ingenix Data Assets and Resources

2www.lewin.com

UnitedHealth Group – Our Parent Company

� Thought leadership in every discipline of health care

� Leader in technology-driven efficiencies

� Broad coverage: Interacts with 560,000 physicians and other health care providers and 4,800 hospitals across the U.S.

Individual & Employer Enterprise ServicesPublic & Social Sector

Fully-Insured • ASO

3www.lewin.com

Electronic Data Assets Bridge Gaps, Facilitate/Extend Research Capacity

CompleteAll personal health record data during all periods of eligibility, regardless of intermediary providing transmission

AccurateMember ID linked and validated – no matching algorithms to link medical and pharmacy data

Full DetailPhysician, retail & specialty pharmacy, mail order, hospital codes and diagnoses detailed by individual member ID

RepresentativeDemographic and economic composition of patients reflects U.S. commercially insured population

4www.lewin.com

Core Data Originates as Electronic Information Describing Health Care Encounters

Patient receives

Rx and goes to

pharmacy to fill.

Hospital claims

Pharmacy

claims

Physician

claims

Patient goes

to see their

physician.

Lab test results

and lab claims

Patient goes

for lab tests.

Data linking & enrichment

Unique Identifiers, Episode groupers,

Cohort Groupers, etc.

IVDM Data Warehouse

Validated data elements, standardized formats

InVision Data Mart (IVDM)

5www.lewin.com

Value of the Data

� Nationally projectable; supports horizon scanning

� Measure disease burden: prevalence, incidence, cost

� Investigate current treatment interventions: medical, surgical, devices, pharmaceuticals

� Drug use by indication, off-label use

� Utilization by patient sociodemographics: race/ethnicity, income, etc.

� Physician characteristics, including education, ethnicity, compliance with guidelines/standards

� Track physician prescribing (based on fills), patient compliance

� Outcomes of care

� Ability (with IRB approval) to link for:

� Mortality, physician surveys, patient surveys, registries, trials recruitment, charts and EMRs

� Track changes in treatments over time: impact of CER findings dissemination

� Currency of data: pharmacy with 1-month lag, medical/surgical with 3-month lag

6www.lewin.com

Unique Features that Distinguish Our Data from Other Public Use Datasets …

� Detailed current information for key population segments

� Current work force, spouses, and dependents (family view)

� All health care categories represented

� Provider, facility (including dialysis), facility- and self-administered drugs, DME, ancillary (PT, OT), home care, lab & diagnostics, imaging, etc.

� Diverse care settings

� Hospital inpatient stays and home care

� Hospital clinics and ambulatory surgical centers, emergency rooms, free-standing labs and imaging centers, dialysis centers, outpatient visits to physician offices, walk-in retail clinics and Urgent Care, etc.

� No limitations on provider models

� Staff, group, network models

� Solo, group and clinic-based physician practices, retail clinics

� Profit and non-profit providers

7www.lewin.com

…and Address Shortcomings of Other Datasets

� Patient and encounter-level detail retained

� Actual dates of service, geographic detail, ICD-9 codes, HCPCS & CPT codes, etc.

� Unique approach to patient-level data aggregation

� Supports longitudinal analysis but remains de-identified and HIPAA compliant

� Available within 90-180 days of encounter

� Accelerated detection of the impact of environmental change

8www.lewin.com

Data Assets, Linkages for CER, HEOR, etc.

Per-Project Data

Data Warehouse

Administrative

Data

� Member Identifier

� Plan� Gender� Age� Dates of Eligibility

Pharmacy

Claims Data


� De-identified Prescribing Physician

� Drug Dispensed (NDC)

� Quantity and Date Dispensed

� Drug Strength� Days Supply� Dollar Amounts

Physician

and Facility

Claims Data

� Member Identifier� Physician or Facility Identifier

� Procedures (CPT-4, revenue codes, ICD-9)

� Diagnosis (ICD-9-CM, DRG)

� Admission and Discharge Dates

� Date and Place of Service

� Dollar Amounts

Lab Test

Results Data


� Lab Test Name� Result

Socioeconomic

Elements

� Member identifier

� Income� Net Worth � Education� Race & Ethnicity

� Life Stage� Life Style Indicators

Surveys Ingenix

Oncology Survey

Chart Reviews

EMR DataIn-Patient

Detail Mortality Data

9www.lewin.com

External Linkages Further Enrich the Data

� Family and household aggregation

� Person/household/community-level socioeconomic data

� Laboratory results

� Disease specific registry data (where applicable)

� Mortality data

10www.lewin.com

Data Quality - Subject to Rigorous Validation

� Originate from electronic data that is used to determine benefit eligibility and pay health claims

� Data elements must undergo rigorous checks of data integrity.

� Subjected to numerous quality checks and audits, both during the ETL process and afterwards

� A Formal Data Quality Program (DQP) Since 2003

� To monitor, measure and report on the quality of these data assets to senior executives at Ingenix

The Ingenix Data Quality Program was named recipient of the 2007

Best Practices Award byThe Data Warehousing Institute

11www.lewin.com

Large and Representative Membership

� Individual records spanning more than 7 years

� Included members required to have >3 months of continuous enrollment

� 20-23 million unique lives per year drawn from U.S. commercially insured and employer group health plans

� Pooling across 4 years, currently 38 million lives available for analysis

� These lives are nationally projected to represent about 61% of the U.S. population. For example, in 2007:

6%

13%

9%

3%

14%

55% Commercial, Employer-based

Commercial, Direct

Medicare

Medicaid

Military

Uninsured

2007 U.S. Population Insurance Coverage

Source: US Census Current Population Survey(46.1)

(11.1)

(31.0)

(41.6)

(19.3)

(178.6)

12www.lewin.com

Geographic Diversity and Depth

2009 coverage as a percentage of each state’s projected total population

13www.lewin.com

NA$45,000$62,500

Median Household Income

2%1%9%Other/Unk

5%5%4%Asian

9%14%9%Hispanic

9%13%4%African American

75%68%73%White

Total Lives (%)Total Lives (%)Total Lives (%)Ethnicity

US Privately Insured(2006)2

US Population(2005)1Lives

1. Statistical Abstract, US Census

2. Medical Expenditure Population Survey (MEPS)

Data Representation vs. U.S. Benchmarks

14www.lewin.com

11%13%2%65+

17%15%13%45-54

13%11%3%55-64

15%14%17%35-44

13%13%17%25-34

32%34%37%00-24

Total Lives (%)Total Lives (%)Total Lives (%)Age Band

23%23%16%West

35%37%48%South

24%22%26%Midwest

19%18%10%Northeast

Total Lives (%)Total Lives (%)Total Lives (%)US Region

51%51%50%Female

49%49%50%Male

US Privately Insured(2008)1

US Population(2008)1

Lives for IVDM2008Attributes

1. Statistical Abstract, US Census

Data Representation vs. U.S. Benchmarks

15www.lewin.com

Continuous Enrollment Extends Timeframe Beyond Single Event

Data Catalog – More Data, More Possibilities

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� Additional 9M annual lives with medical and pharmacy benefit data to broaden our data breadth beyond the affiliated plans set of patients

� Individual linked (name/address/DOB/phone) data on over 20 consumer characteristics including race/ethnicity, income, home ownership and marital status

� Match rate is > 90%, fill rates from 5% to over 90% based on attribute

� Access to other affiliate plan lives not currently available in our licensed HEOR data product

� Typically newer acquisitions

� Largest single source of privately insured Medicare patients; 5.5M lives annually

� To be included in our IVMM tool in 2Q 2009

Non-affiliate Plan Lives

Socio-economicData

Additional Affiliate Lives

Medicare Data

InVision DataMart

� Our standard licensed HEOR offering, rich claims detail, totally de-identified

� Repository of about 2M lives collected through an EMR process

� Survey-based data collected on actual cancer patients which can be linked to our APLD claims data

� Breast, lung and CRC are currently being collected

EMR (2011)

Oncology Stage & Histology

Provider360

� A comprehensive and accurate database of provider demographic information

� Used for address scrubbing and target list development

� Access to patient-linked inpatient medical, lab tests performed and pharmacy data for over 500 hospitals

PremierData

� With the appropriate HIPAA and affiliates approvals, we can survey physicians and patients for further insights

� With the appropriate HIPAA and affiliates approvals, we can conduct chart reviews for further detail on medical conditions and other factors

ChartReviews

Surveys

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What Types of Questions Can Data Address?

� Healthcare Access and Utilization – Evaluate usage in actual practice

� Treatment Patterns – Opportunities to understand how diseases progress and are managed as well as costs over time

� Adherence – To treatments and medication

� Subgroup Analyses – Identify and understand patterns of care among smaller physician and patient types

� Predictive Modeling - Precursor conditions detected

� Integration with other secondary and primary data sources

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Data Assets

� Data Mart

� InVision Data Mart

� Impact Data Mart

� InVision Multiplan

� InVision Explorer / Database Builder

� InVision for Market Intelligence

� Natural History of Disease

� Analytic Services and Technical Support

www.lewin.com

24 M

46 regional plus Affiliates

All Therapeutic Markets

Clinically rich claims data to conduct custom research projects representing multiple plans and formularies. Delivered in the IMPACT DB format.

Epidemiology

HEOR Researchers

InVision Data Mart Multiplan (IHCIS &

Affiliate)

15 M

Affiliate Commercial ASO + Fully Insured


Clinically rich claims data to conduct custom research projects from one of the largest national plans

Epidemiology

HEOR Researchers

InVision Data Mart

Annual Lives

Data platform

Coverage

Customer Benefit

Secondary Audience

Primary Audience

Data Mart Suite

9 M

46 regional


Clinically rich claims data to conduct custom research projects representing multiple plans and formularies. Delivered in the IMPACT DB format.

Epidemiology

HEOR Researchers

IMPACT Data Mart (IHCIS)

Choosing the Right Dataset for the Job

Standardized fees, patient copay tiers (RBRBS)

Encrypted by plan

Inpatient

Mostly positive enrollment, some inferred

Transactions – de-identified Physician and Patient level data

Lab Results

Un-projected

180 Day

Semi-annually/Quarterly

36+ Months

InVision Data Mart Multiplan (IHCIS &

Affiliate)

Actual provider charges, actual patient copays

Common Encrypted

None

Full positive enrollment


Lab Results

Un-projected

90 Day

Semi-annually/ Quarterly

36+ Months

InVision Data Mart

InpatientEpisodes of care

Some inferredEnrollment

Financial Information

MD Reporting

Data Level

Unique Data Elements

Projection

Data Lag

Update Frequency

Duration

Standardized fees, patient copay tiers (RBRBS)

Encrypted by plan


Lab Results

Un-projected

180 Day

Semi-annually/Quarterly

36+ Months

IMPACT Data Mart (IHCIS)

Choosing the Right Dataset for the Job (cont’d)

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A web-based tool that provides easy access to the InVision Data Mart database for preliminary analysis

� User-friendly interface allows interactive interrogation of the data to determine the size of the sample available for specific studies

� Covers all markets / therapeutic areas in the U.S. commercial and Medicare Part D populations

� Allows user to qualify patients on the basis of drugs, diagnosis, or procedures

� Provides baseline information on the patients who qualify

� Medication-based

� Diagnosis-based

� Procedure-based

InVision Explorer / Database Builder

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An online web-based tool that enables access to descriptive patient level data not found in off-the-shelf reports or custom analyses, enabling better market assessments

� User-friendly interface allows interactive interrogation of the data and enables more extensive extraction

� Covers all markets / therapeutic areas in the U.S. commercial and Medicare populations

� Multi-plan patient-level longitudinal data sources, projected nationally

� Contains patient-level diagnosis, medical service utilization, drug utilization, demographics, physician specialty and demographics

InVision Market Intelligence (IVMI)

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Natural History of Disease

� New online application that allows interactive probing of costs and progress of disease

� A population is defined on the basis of diagnosis and/or treatment

� The population is time-aligned around the “event date”

� A matched control population is created

� Health care utilization patterns of the two populations are compared using a sophisticated, intuitive, user interface

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Analytical Services and Technical Support

� Experienced staff from across Ingenix can provide highly customized analytic services and technical support.

� User guides & technical training for Data Marts

� Consulting on project design and execution

� Full service offering for conducting analyses to address key questions

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Representative Current Projects

� NIMH - Autism Study

� NICHD – BPCA

� ASPE/CMS - MPCD

� NCI-CDC – Mammogram Compliance

� CDC – H1N1

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� NIMH - Autism Study

� Study of health outcomes and health care use in children with ASD and their families

� Sample research questions

� What are vaccination rates in siblings of children with autism relative to controls?

� How does health care use change over time relative to ASD diagnosis?

� Can administrative data be used to reliably identify risk factors for ASD such as antibiotic use, illness during pregnancy, or vaccination receipt?

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� NICHD – BPCA

� Support of the Best Pharmaceuticals for Children Act (BPCA) Program

� Sample research questions

� What is the actual use (prevalence and days supply) of off-label drugs for common childhood conditions including headaches, hypertension, respiratory tract infections and asthma?

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� ASPE/CMS – MPCD

� Comparative Effectiveness Research and Multi-Payer Claims Database (MPCD) Implementation

� Potential research questions related to Institute of Medicine (IOM) priorities

� Which of the research questions identified as IOM priorities can be supported or answered by the MPCD?

� Data may be useful for comparing therapies, preventive measures, and delivery system strategies and their impact on health outcomes (diagnoses) and use of health care services (such as inpatient readmission or emergency care use)

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� NCI-NCHS – Mammogram Compliance

� “Are Women Getting Mammograms Anyway? Utilization Based on a National Commercial Health Insurer Database”

� Sample research questions:

� What is the relationship between screening recommendations/guidelines and mammography screenings in the US?

� What is the difference in adherence to guidelines in US (annual screening recommended) and Canada (screening every two years recommended)?

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� CDC – H1N1

� “Safety analysis of H1N1 vaccine”

� Sample research questions:

� What are the associations between H1N1 vaccine receipt and adverse events including allergic reactions, stroke, other?

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Contacts

� Taylor Dennen, PhD, Managing Director, The Lewin Group

� Phone: 215-699-1935

� Email: [email protected]

� Nancy Walczak, FSA, PhD – Managing Director, The Lewin Group

� Phone: 952-833-7556


� Clifford Goodman, PhD, Sr. Vice President, The Lewin Group

� Phone: 703-269-5626


Documents

Comparative Effectiveness Research, Personalized Medicine