Download From Retrospective to Prospective Designs

From Retrospective to Prospective Designs:
Leveraging Clinical, Administrative, and Patient Reported Data for
Clinically Meaningful Research
Jennifer Christian, PharmD, MPH, PhD
Vice President
Real World & Late-Phase Research
David Thompson, PhD
Senior Vice President,
Advisory Services
© Copyright 2015 Quintiles
Your Presenters
David Thompson, PhD
Presenter
Photo
Senior Vice President, Advisory Services, Quintiles
David Thompson, PhD is Senior Vice President of the Advisory Services division of
Quintiles Transnational Corporation, with responsibilities for Real-World Data &
Analytics and Health Economics & Outcomes Research (HEOR).
Dr. Thompson is a health economist with 25+ years of experience in the health
economics arena, including work in economic modeling, retrospective database
analysis, trial-based economic evaluations, and patient-reported outcomes. His work
has been global in nature, with extensive experience in North American & European
markets as well as the emerging markets of the Asia-Pacific and Latin American
regions. Prior to joining Quintiles, he held leadership positions at i3
Innovus/OptumInsight (2000-2012) and PAI (1988-2000).
Quintiles Confidential
2
Your Presenters
Jennifer Christian, PharmD, MPH, PhD
Presenter
Photo
Vice President, Real World & Late-Phase Research, Quintiles
Jennifer Christian is Vice President of Clinical Evidence within the Real World & Late
Phase Research Scientific Affairs group at Quintiles where she provides scientific
oversight to the design and conduct of real world studies, including clinical effectiveness
and safety evaluations. Dr. Christian has conducted numerous studies using large
claims databases, electronic medical records, prospective clinical studies, and, more
recently, she has focused on pragmatic and hybrid study design approaches for
enhancing safety and effectiveness evaluations. She is a graduate of the UNC- Chapel
Hill School of Pharmacy and School of Public Health and from the Doctoral
Epidemiology program at Brown University. Prior to joining Quintiles, Dr. Christian was a
Senior Director of Clinical Effectiveness and Safety within the Chief Medical Office of
GlaxoSmithKline, where she provided strategic and methodological expertise to clinical
development teams to strengthen their proposed study questions and methodological
approaches.
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Agenda
Introduction
Retrospective hybrid designs
Enriched prospective study designs that leverage EMR data including
randomized, pragmatic studies
Q&A
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Today’s Webinar Audience
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Academia
Biostatistician
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1.66%
Epidemiology
25.58%
Health Economics/Health Outcomes
9.30%
Market Access
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5.98%
Risk Management
Other
10.96%
15.28%
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Polling Questions
A small number of
polling questions have
been added to today’s
webinar to make the
session more
interactive
?
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From Retrospective to Prospective Designs
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Sources of Real-World Data
Six Identified by ISPOR Real-World Data Task Force
• Supplements to traditional RCTs:
> Commonly known as trial-based or ‘piggyback’
evaluations
> Inherent tension between internal & external
validity
• Large simple trials:
> Commonly known as pragmatic or naturalistic
trials
> To date not commonly performed due to high risk,
high cost
• Registries:
> Include prospective cohort studies
• Administrative data:
> Also known as claims data
• Health surveys:
> Useful for basic epidemiologic data or macrolevel views on utilization
• Electronic health records & medical chart
review:
> Electronic health records also called electronic
medical records (EMRs)
> Better to separate EHR/EMR data from medical
chart review
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Typology of Real-World Data
Retrospective Designs
Prospective Designs
Primary Data
Collection
Medical Chart Review
RCT Piggybacks
Pragmatic Trials
Registries
Health Surveys
Secondary Data
Collection
A Two-by-Two Approach
Administrative Claims
EMR
Automated EMR Data Feeds
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From Retrospective to Prospective
Two Key Questions
Primary Data
Collection
Medical Chart Review
RCT Piggybacks
Pragmatic Trials
Registries
Health Surveys
Secondary
Data Collection
Prospective Designs
Administrative Claims
EMR
Automated EMR Data Feeds
Retrospective
Designs
Prospective Designs
Primary Data
Collection
2. How can we utilize secondary
sources of retrospective data for
prospective research?
Retrospective
Designs
Medical Chart Review
RCT Piggybacks
Pragmatic Trials
Registries
Health Surveys
Secondary
Data Collection
1. For secondary sources of
retrospective data, should we use
EMRs? Claims? Both?
Administrative Claims
EMR
Automated EMR Data Feeds
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Claims vs EMRs Scorecard
Content Comparisons
Characteristic
•Verdict
Administrative Claims
Electronic Medical Records
Patient Detail
Basic demographics (age, sex) plus
enrollment
Demographics plus vital signs, BMI,
allergies, smoking status
Clear win for EMRs
Medications Detail
Drug code (name, form, strength),
Rx fill date, amt supplied, dose &
freq for pharmacy-dispensed drugs;
no OTC
Mostly same detail for Rx’s written
(but no Rx fill date); current meds,
including OTC products, available
EMRs have medication
history & OTC use, but lose
to claims on lack of fill data
Diagnostic Detail
ICD-9 codes
ICD-9 codes, problem lists,
severity, symptoms
Clear win for EMRs
Procedures Detail
CPT codes
CPT codes
Tie
Laboratory Detail
CPT codes, date; limited availability
of lab results
CPT codes, date, & e-feed of lab
results sometimes including
pathology & radiology
Clear win for EMRs
Hospital Detail
Dates of admission & discharge,
diagnoses, major procedures;
usually nothing on inpatient drugs
Hospital EMR: detail on all aspects
of inpatient care, including day:time
info; ambulatory EMR: not much
Ambulatory EMRs lose vs
claims, hospital EMRs win
Financial Detail
Charges, amounts reimbursed, copays
Usually not available
Clear win for claims
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Claims vs EMRs Scorecard
Practical Comparisons
Characteristic
Administrative Claims
•Verdict
Electronic Medical Records
Insurance Coverage
Insured only (usually one type)
Treatment independent of
insurance, includes uninsured
EMRs win on variety,
claims on specificity
Geographic Settings
Mostly US only
EMRs proliferating in US & ex-US
settings
Narrow (but widening) lead
for EMRs
Ease of Analysis
Relatively easy
Harder, particularly for
unstructured data
Claims win on ease, but
EMRs reward extra effort
Ease of Linkage
Do-able but not easy without
compromising PHI
Do-able but not easy without
compromising PHI
Tie
Data Completeness
High for elements essential to
reimbursement
High for elements essential to
patient management
Depends on what’s most
important
Timeliness
Time lag usually measured in
months or quarters
Time lag usually measured in days
or weeks
Clear win for EMRs
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Claims & EMRs
Substitutes or Complements?
• Choice between claims versus EMR data requires consideration of their
relative strengths & limitations:
> Claims offer detail on health-care utilization & cost, but considerably less clinical
detail
> EMR has relative abundance of clinical detail but no cost and sometimes incomplete
picture of care
> Note also that, for drug studies, pharmacy dispensing a potential blind spot for EMR
• Sometimes analyses require level of detail offered by both claims & EMR
> EMR-claims data vendor linkages exist but overlap is typically small, limiting
usefulness to less common disease areas
> Some integrated delivery networks have access to claims, allowing do-it-yourself
data aggregation customized to study needs
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Claims & EMRs
Linkage to Take Advantages of Complementariness
Electronic Medical
Records
Administrative Claims.
Patient Detail
Patient Detail
• Demographics
• BMI, smoking status
• Vital signs
• Demographics
• Health plan enrollment
Clinical Detail
• ICD-9-CM diagnoses &
procedures
• Prescriptions filled
• Hospitalizations
• Lab tests (but no results)
Clinical Detail
• ICD-9-CM diagnoses &
procedures
• Drugs prescribed
• OTC medications taken
• Free-text physician notes
• Lab tests & results
Other
Other
One Patient, Two Data Sources
• Reflects all care settings
• Includes financial detail
• Limited to ambulatory setting
• No detail on OTC meds
• No detail on prescriptions
filled
• No physician notes
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EMRs as a Point of Entrée into Provider Networks
EMR Systems Are Implicit Provider Networks for Outreach to Patients
Patients
Patients
Patients
Providers
Providers
Patients
Providers
Providers
EMR
Data
Providers
Patients
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‘Retro Plus’ or ‘Hybrid Lite’
Collecting Additional Data from the Patients & Physicians in a Database Analysis
Combine insights from
retrospective analyses of
EMR or EMR-claims data
With direct surveys of the
patients included in these
analyses
As well as the physicians
who treated them
To better understand the reasons for the observed results
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Case Study: Type 2 Diabetes Screening Behavior
EMR Data Extraction + Surveys of Physicians
Study Overview
Objective: To understand the factors that
influence physician screening practices for
type 2 diabetes mellitus (T2DM)
Approach: Physician Survey +
Retrospective Cohort Study
Physician Survey
•
•
•
Pre-diabetes & T2DM screening
practices
Knowledge of screening guidelines
Characteristics of medical practice
Value added
• Evaluation of concordance between physicians’
description of their own behaviour with empirical
evidence from EMR data
• Improved understanding of how to focus support
resources for the medical profession in
identifying pre-diabetes and diabetes
• Reduce subsequent morbidity & mortality by
providing patients with appropriate and timely
care
Retrospective Data Analysis
• Q-EMR used to create cohort eligible
patients whose physicians returned a
survey
• Evaluate screening activity, clinical
characteristics, behavioral/lifestyle
changes, prescribed medications
Study Cohort: 300 physicians
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Study Design: Hybrid Survey & Retrospective
EMR Data
Patients of PCPs who
respond to online survey
Primary Care Physicians
(PCPs) from EMR Network
Online Survey
Patients ≥ 18 years eligible for
receiving diabetes screening
test (based on screening
guidelines criteria)
Study Sample
Survey Responses
Link
Summarize patient
records for each
physician to create
concordance measures
Physician Level
Analysis
Evaluate treatments
and outcomes in
patients diagnosed with
diabetes or prediabetes
Patient Level Analysis
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Planned Analyses
Concordance between Self-reported Attitude/Behavior towards Diabetes Screening Guidelines
and Evidence from EMR
Physician Level Survey
Measures
Physician Level Composite
Measures
• Attitudes and Behaviors towards
Screening Activity
• Proportion of each Physician’s
• Likelihood to Screen Patients
Eligible* Patients:
possessing guidelines criteria
• Receiving Screening Test
• Familiarity with recent guidelines
• Receiving Different Treatments
and preferences regarding its
• Receiving Follow-up Testing
use
• Summarized Patient Characteristics at
• Preferred Treatment Approaches
Physician level
• By Diabetes Severity
• By Age
*Eligible patients are defined based on
• Frequency of Follow-up Testing
screening guidelines criteria
• Pre-diabetes
• Diabetes
Physician
• Normal Results
• Physician & Practice Characteristics Level
Composite
Measure
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Example: Linking Survey-EMR Data
Patient Level EMR Data
US Preventative Services
Task Force Guidelines
 Adults aged >18 years whose physicians
participated in the online survey
 3 or more years of consecutive activity with the
surveyed physician between 2009 and 2014
 Eligible for diabetes screening based USPSTF
guidelines:
 Age ≥ 18 years and blood pressure
level ≥ 135/80 mm Hg
The USPSTF recommends screening for
type 2 diabetes in asymptomatic adults
with sustained blood pressure (either
treated or untreated) greater than 135/80
mmHg
Physician Survey Data
Physician Level EMR Data
Q. Please indicate how likely you would be to
conduct type 2 diabetes screening in patients
with blood pressure level ≥ 135/80 mm Hg?
Extremely
likely
Not at all
likely
𝑝𝑖,𝑗 =
𝑁𝑜. 𝑜𝑓 𝑝𝑎𝑡𝑖𝑒𝑛𝑡𝑠 𝑟𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝑠𝑐𝑟𝑒𝑒𝑛𝑖𝑛𝑔 𝑢𝑛𝑑𝑒𝑟 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑎 𝑗
𝑁𝑜. 𝑜𝑓 𝑝𝑎𝑡𝑖𝑒𝑛𝑡𝑠 𝑒𝑙𝑖𝑔𝑖𝑏𝑙𝑒 𝑓𝑜𝑟 𝑠𝑐𝑟𝑒𝑒𝑛𝑖𝑛𝑔 𝑢𝑛𝑑𝑒𝑟 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑎 𝑗
Concordance Measures Analysis
Survey
Response
Concordant
or
Non-Concordant?
Physician
Level EMR
Data
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Enhanced prospective study designs that leverage
secondary data sources
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Patient Case
“A clinician performs an experiment every time he treats a patient” –
Alvan Feinstein
a. Chief complaint (why patient came to the hospital) EMR, Patient, Caretaker,
EMS or First Responder
b. History of present illness Patient, Caretaker, Recorded in medical note
c. Past medical history Variable depending on system
d. Medications taking prior to admission Patient, Caretaker, Call outpatient
pharmacy
e. Drug allergies Patient, pharmacy database, EMR
f. Family/social history Patient, Caretakers
g. Physical exam and review of systems Clinicians, EMR
h. Problem list (assessment and plan) Clinicians, EMR
i. Hospital Course EMR, Clinicians
j. Baseline labs and pertinent labs throughout hospital course Lab database
k. Drug therapy throughout their hospital course Pharmacy database
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Greater insights using enriched real-world evidence
Pharmacy
Hospital
In-Patient
EMR
Ambulatory
EMR
Existing Data Sources
Claims
+
Physician
Reported Data
Patient Reported
Data
Primary Data
Integrated multi-source data can be used provide a
comprehensive view of the patient’s condition, medical care and
outcomes
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How can we utilize secondary sources of health data for
prospective research?
Mosaic
•
Utilizing primary &
secondary data
collection under one
common protocol
without integration
Enhanced
Prospective
Studies
Recruitment
•
Efficiencies in
targeting site
identification and
recruitment with
EMR
•
•
•
•
•
•
Full integration
Address additional
questions such as
healthcare utilization
and costs
Enhanced analysis
with adjustments for
additional confounders
Reduce burden of
data collection on sites
Baseline and historic
data
Reduce source data
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verification
Mosaic
What is it? Why use this approach?
• What? Utilizing primary and secondary data under one common protocol
without necessarily integrating
• Why? Secondary data can provide a more efficient solution to evaluating how
patients are treated in the real world, while primary data collection can be used
to supplement the gaps and in countries where secondary data is not available
• How? Common protocol with different solutions for data collection
› Matched or parallel cohort studies with primary or secondary data approaches
› Country level approaches with primary data in some and secondary data in others
• Challenges? Disease vary by how they are recorded and treated within and
across countries and data sources; Common, standardized definitions are
challenging; Regulatory requirements vary and may alter protocol in some
countries
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Example: Real-World Comparators / Matched or Parallel
Cohort Studies
Matched or Parallel Cohort Studies
Phase 3b or 4 – Patients treated in clinical trial
Parallel cohort to examine representativeness & effectiveness in pops of interest
Trial eligible pts
Broader pt pool
Subgroup specific analyses
block1
Pharmacy
Hospital
In-Patient
EMR
Claims
Ambulatory EMR
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Example: Common Protocol with Primary & Secondary Data
Global Type 2 Diabetes Drug Registry
Study Overview
Value added & Lessons learned
Objective: To describe the disease
management patterns in type 2
diabetics initiating a second line antidiabetic treatment
Value Added
• Efficiency in leveraging secondary data
sources, where available
• Expect earlier findings in countries with
secondary sources while primary data
collection can be more detailed, fit for
purpose but take longer
• Global approach allows for comparison in
disease management by country
Approach: Mosaic design. Multicountry, multicenter, observational,
longitudinal, prospective cohort study
Patients: Type 2 diabetics initiating
their second line anti-diabetic therapy
Target sample size: >10,000 subjects
with more than 20 countries
Challenges:
• Feasibility is crucial
• Regulatory differences by country
• Recruitment estimates
• Terminology aligned
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Example: Common study design with different data
collection methods by country
Initiation of
second-line
therapy
Diabetes
diagnosis
and initial
treatment
Enrolment, Day 0, On 6 month
site-visit, collection
data
of baseline variables collection
point
12 month
data
collection
point
24 month
data
collection
point
36 month data
collection
point,
End of study
Follow-up,
collection of
follow-up
variables
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Available Options and Recommended Approach
Full prospective
France
United Kingdom
Germany
Italy
Spain
Denmark
Norway
Sweden
Australia
United States
Canada
Japan
China (Mainland)













Secondary Data
Collection











Integrated approach









 = available option
= recommended approach
29
How can we utilize secondary sources of health data for
prospective research?
Mosaic
•
Utilizing primary &
secondary data
collection under one
common protocol
without integration
Site &
Patient
Recruitment
•
•
Efficiencies in
targeting site
identification and
patient recruitment
with EMR
Establish baseline
and historic data
Enhanced
Prospective
Studies
•
•
•
•
•
Full integration
Address additional
questions such as
healthcare utilization
and costs
Enhanced analysis
with adjustments for
additional confounders
Reduce burden of
data collection on sites
Reduce source data
verification
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How can secondary sources of data assist with
recruitment for prospective studies?
• Many studies can be challenging to recruit
> [Examples: rare diseases, strict inclusion/exclusion criteria, large
number of patients needed]
• EMR and/or healthcare claims (receipt) data can provide additional
ways to recruit patients and identify patients who may be eligible for
the study
• Studies have reported a high yield of patients at a lower cost by using
an EMR database to develop a weekly list of potential patients for
recruitment that meet inclusion/exclusion criteria and contacting directly
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Case study: Determinants of Receiving Shingles Vaccine
Targeting site recruitment with EMR data for improved representativeness & efficiencies
Situation
• Understand determinants of receiving the
shingles vaccine in adults eligible for the
NHS shingles vaccination program
• Proposal: To conduct a non-interventional,
multicenter, primary healthcare-based,
case-control study of patients who were
eligible to receive the Zoster vaccine
through the NHS program
• Need to identify 500 patients who were
around 80 years of age
Solution
• NHS data was identified as an
efficient solution to identify and recruit
GP sites’ and patients’ recruitment
• Data partner provided counts for GPs
across the UK and patients counts
aged 80-81 years old per practice
Result
Customer Goal
• Targeted recruitment strategy plan
• Sites are being identified & contracted
• Recruit 60 sites (GP practices) and
500 patients aged 80-81 years old
across the UK
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How can we utilize secondary sources of health data for
prospective research?
Mosaic
•
Utilizing primary &
secondary data
collection under one
common protocol
without integration
Enhanced
Prospective
Studies
Recruitment
•
•
Efficiencies in
targeting site
identification and
recruitment with
EMR
Establish baseline
and historic data
•
•
•
•
•
Full integration
Address additional
questions such as
healthcare utilization
and costs
Enhanced analysis
with adjustments for
additional confounders
Reduce burden of
data collection on sites
Reduce source data
verification
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Enhanced Prospective Study Designs
Product of Interest
Study
Start-up
Identify
sites and
patients
Feasibility
of data
elements
Visit 1
Consent
Visit 2
n months of normal care
Randomize?
Clinical
Assessments
Baseline
Visit
Switching
permitted
Comparator(s)
Collect health care interventions & monitor safety through
existing records
Pharmacy
Hospital
In-Patient
EMR
Ambulatory
EMR
Claims
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COMPASS: COMparative effectiveness and PAtient Safety & Surveillance
~8 million active patients with linked out-patient and in-patient data
Current Pts: 681K
Hospitals: 4
Current Pts: 425K
Hospitals: 1
Medical & Pharmacy
Claims: 0
Medical Claims: 425K
Pharmacy Claims: 0
Total Pts: 1.5M
Hospitals: 1
Current Pts: 200K
Hospitals: 0
Medical & Pharmacy
Claims: 0
Medical & Pharmacy
Claims: 100K
Total Pts: 360K
Hospitals: 9
Medical & Pharmacy
Claims: 0
Total Pts: 1M
Hospitals: 3
Medical & Pharmacy
Claims: 100K
Total Pts: 3.5 M
Hospitals: yes
Current Pts: 282K
Hospitals: 1
Medical & Pharmacy
Claims: 282K
Medical & Pharmacy
Claims: 0
Current Pts: 2M
Hospitals: 32
Medical & Pharmacy
Claims: 50K
Pts: 166K
Hospitals: 0
Medical & Pharmacy
Claims: No
Partnership with nationwide network of specialty asthma and
allergy clinics with robust EMR data
35
COMPASS: COMparative effectiveness and PAtient Safety & Surveillance
Integrated Healthcare Delivery Networks (IDNs)
• An integrated delivery network (IDN) is a network of health care providers and
organizations which provides a coordinated continuum of services to a defined
population. An IDN may own or could be closely aligned with an insurance product.
• IDNs can be leveraged to:
› Access patient data across the continuum of care (ambulatory and in-hospital)
› Link lab, pharmacy and, in some cases, imaging data
EMR
› Follow patients longitudinally for specific outcomes
› Collect Quality of Life and other PRO data
› Assess healthcare resource utilization
› Evaluate treatment patterns
Patient
Survey
Labs
Hospital
Procedures
Physician
Survey
Data
Warehouse
Claims
Pharmacy
Pathology
Imaging
36
Main Messages
• There are numerous innovative designs that combine
primary and secondary data collection, each with strengths
and limitations
• Through experience, we have found that each study
requires a robust feasibility to assess the specific study
criteria prior to initiating the protocol
• For enriched studies, early engagement with local expertise
is critical for feasibility and successful implementation
37
Quintiles-IMS Health
Real-World Evidence
Collaboration
Quintiles and IMS Health Announce Global
Collaboration to Advance the Use of Next-Generation
Real-World Evidence in Late-Stage Clinical Research
Network of complementary data sources, innovative
technologies, scientific expertise to generate insights
faster and demonstrate value to healthcare stakeholders
38
IMS Health & Quintiles Announce Real World
Evidence Collaboration
 Largest global data and technology
company exclusively committed to
healthcare industry
 Vast data networks and information
assets
 Leading retrospective analytics
across clinical and commercial
applications and services
 Largest global Clinical Research
Organization
 Leading scientific partner
supporting clinical trial and
prospective observational study
execution
 Standard-setting voice in
observational study design and
prospective study execution
• Best in class solutions for comprehensive real world and late phase evidence
development focused on increasing strength of evidence, providing a faster
path to insight, and enhanced brand value
• Complementary strength of leading scientific research and enabling
information and analytics
• Global scale with market relevant capabilities
39
Thank you
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Previous & Upcoming Events
Quintiles experts run regular webinars on
Real-World & Late Phase services.
Visit Quintiles to learn more at one of the
following upcoming meetings:
Topics include:
OBSERVATIONAL RESEARCH &
•
ISPOR – EUROPEAN CONGRESS
REGISTRIES
•
WORLD VACCINES CONGRESS
•
SAFETY & RISK MANAGEMENT
•
WORLD ORPHAN DRUG CONGRESS
•
HTA & MARKET ACCESS
•
MAXIMIZING VALUE AND QUALITY IN
•
DIA JAPAN
PHASE IV
•
PARTNERSHIPS WITH CLINICAL TRIALS
•
•
RARE DISEASE REGISTRIES
•
COMPARATIVE EFFECTIVENESS
EUROPE
EUROPE
•
CMSS 2015 ANNUAL MEETING
RESEARCH
•
CLINICAL OUTCOME ASSESSMENTS
To register or view previous webinars please go to
http://www.quintiles.com/landing-pages/real-world-and-latephase-research-webinars
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