IMS Health Real World Evidence Access Point

News, views and insights from leading international experts in RWE and HEOR
IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR
VOLUME 5, ISSUE 9 • NOVEMBER 2014
Putting RWE at
the heart of
decision making
Diabetes special focus
Propelling stakeholder
engagement and collaboration
Optimizing resource allocation
in primary care
Harnessing transformational
methodologies
RWE x 6
1 2
3
6 = $1bn
4
5
Six ways to release
untapped RWE potential

“WeIn hsaIvgeh otbsser?v?e?d?? s?e??v?e?r?al important trends that could shape the
way companies create or use RWE, which will be of importance to
our industry moving forward.”
Headline
Headline
IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR
"RWE is transforming a broad
understanding in diabetes with
real insights into differential
patient cohort responses, based
on powerful clinical and even
genomic data."
Welcome
Welcome to our latest AccessPoint as we continue to explore the
dynamics shaping the HEOR and real-world evidence (RWE) landscape.
In our last edition, we highlighted our evolving understanding of
oncology innovations and outcomes and the role of RWE in these areas.
This time, we expand that lens to another important disease,
diabetes, where stakeholders are seeking much deeper knowledge of
treatment outcomes in patient subgroups. RWE is transforming a
broad understanding with real insights into differential cohort
responses, based on powerful clinical and even genomic data to
evaluate benefits and risks. We also take a broader look at trends in
RWE and spotlight ongoing advances in real-world data (RWD),
methodologies and RWE applications.
We have focused this edition around these three topics
• RWE research reveals new insights into more effective ways of
researching diabetes, assessing outcomes and understanding
the implications for broader care provision. Although the quantity
of diabetes-related patient data is significant, gaps in the
completeness of datasets have impeded researchers. Now, new
mixed methods approaches such as we describe in Germany, and
analytic innovations including the IMS CORE Diabetes Model,
make research for this critical condition easier to conduct with
increased confidence and scientific rigor. A UK analysis of utility
values provides a basis for improving diabetes modeling and a recent
study in Canada shows how RWE analysis can pinpoint the resource
drivers requiring policy and clinical practice changes. This is a hopeful
time in diabetes.
• We have observed several important trends that could shape
the way companies create or use RWE, which will be of
importance to our industry moving forward. New ways of
thinking about RWD strategies are emerging, leading us to
propose a disease-centric framework to help guide those efforts.
We also comment on how involving commercial colleagues in
RWE is driving substantial value for companies that enable this
approach. And we look forward to seeing continued
collaborations with external stakeholders, namely payers, and in
new geographies, specifically Asia Pacific.
• Advancements continue to derive more value from RWE,
including improved data sourcing, methodologies and
stakeholder engagement. Predictive modeling is increasing RWE
accuracy with demonstrated benefits in risk stratification. We are
seeing leaders leverage the richness of Scandinavian data to
enable new disease-level insights. RWE also continues to support
value demonstration, such as showing the impact of adherence on
mortality, readmission risk and costs in ACS. And it is helping
companies move ‘beyond the pill’ by creating even more value
through enabling care management services.
At IMS Health, we are committed to providing insights to help advance
health and improve patient outcomes across all care settings globally.
We hope you find this edition particularly useful in your RWE journey.
AccessPoint is published twice yearly by the
IMS Health Real-World Evidence (RWE) Solutions and
Health Economics & Outcomes Research (HEOR) team.
VOLUME 5, ISSUE 9. PUbLISHEd NOVEMbER 2014.
IMS HEALTH210 Pentonville Road, London N1 9JY, UK
Tel: +44 (0) 20 3075 4800 • www.imshealth.com/rwe
RWEinfo@imshealth.com
©2014 IMS Health Incorporated and its affiliates.
All rights reserved.
Trademarks are registered in the United States
and in various other countries.
Jon Resnick
Vice President and General Manager
Real-World Evidence Solutions, IMS Health
Jresnick@imshealth.com

VOLUME 5, ISSUE 9 • NOVEMBER 2014
RWE driving deeper insights in diabetes
Major validation upholds relevance of IMS CORE diabetes Model 5
diabetes complexities drive resource consumption in Canada 15
Identifying reference utility values for economic models in diabetes 40
A collaborative foundation for new diabetes insights in Germany 45
demonstrating external validity of the IMS CORE diabetes Model 50
Perspectives and trends in RWE
Enabling disease-specific RWE through fit-for-purpose RWd 6
A roadmap for increasing RWE use in payer decisions 10
Finding the true potential of RWE through scientific-commercial collaboration 20
Preparing for RWE in Asia Pacific 36
Advances in RWD, methodology and RWE applications
Improving outcomes through predictive modeling 26
Holistic real-world data brings a new view of patients and diseases 32
Evaluating disease burden, unmet need and QoL in a chronic inflammatory disorder 56
demonstrating the impact of non-adherence to antiplatelet therapy in ACS 60
Modeling disease management above the brand with RWE 63
nEWs
2 PARTNERSHIP ENRICHES SCANDINAVIAN DATASETS
3 RESEARCH INFORMS POLICY PRIORITIES
4 FORUMS ACCELERATE RWE USE
5 IMS CDM CONFIRMS CONTEMPORARY RELEVANCE
PROJECt FOCUs
56 CHRONIC INFLAMMATORY DISORDER
Evaluating patient-reported outcomes
60 ACUTE CORONARY SYNDROME
Demonstrating the impact of non-adherence
63 RWE-BASED DISEASE MANAGEMENT
Informing the value of treatments
IMs RWEs & hEOR OVERVIEW
66 ENABLING YOUR REAL-WORLD SUCCESS
Solutions, locations and expertise
ACCESSPOINT • VOLUME 5 ISSUE 9 PAGE 1

nEWs SCANDINAVIAN RWE COLLABORATION
Partnership linkage of unique, Norwegian biobank data opens up groundbreaking
research potential with global impact
IMS Health/Lifandis AS elevate real-world insights
with enriched Scandinavian datasets
The strategic collaboration with IMS Health allows researchers to look
at a broader set of data in Norway as well as Sweden and other
Scandinavian markets through IMS Health’s existing real-world solutions
assets. Clients will now be able to benefit from the Lifandis integrated
partnership in addition to IMS Health’s other information assets,
scientific capabilities and involvement in research projects.
ESTABLISHED EXCELLENCE WITH GLOBAL IMPACT
This development enriches an already distinctive offering that allows
healthcare researchers to develop globally and locally relevant insights
into populations, diseases and treatment experience.
The ability of the IMS Health and Lifandis team to create holistic views
across settings of care over time enables Scandinavian-based affiliates
and global headquarters to answer meaningful and challenging
research questions, based on • Long-term study reviews for anonymous patients across
settings of care • Difficult-to-get patient attributes for more meaningful
treatment journeys • Information to determine the economic value of different
outcomes measures • Analytics to support research from epidemiology to
comparative effectiveness
TOWARDS A REALNTIME UNDERSTANDING
The extension of IMS Health’s RWE capabilities in Northern Europe marks
another important step in helping healthcare decision makers identify,
link and interpret real-world outcomes in near real time.
For further information on the IMS Health/Lifandis AS approach to
RWE and the exciting opportunities for integration of complex datasets
in the Scandinavian region, please email Patrik Sobocki at
Psobocki@se.imshealth.com or Christian Jonasson at cj@lifandis.com
FIGURE 1: LEGISLATION, CONSENT ANd A PERSONAL Id CREATE
POTENTIAL FOR HIGH QUALITY, COMPREHENSIVE dATASETS
Further expanding IMS Health’s distinctive and growing
real-world evidence capabilities in Northern Europe, the
company has announced a collaboration with Lifandis AS, an
independent company that works closely with the HUNT
Research Centre in Norway. The agreement combines IMS
Health’s Pygargus extraction methodology with access to
the HUNT biobank and databank, as well as other
Norwegian biobanks and health registries, enabling the
creation of significantly enhanced real-world datasets.
Underscoring the rising importance of Scandinavia as a rich
hub for RWE, this linkage affords one of the most holistic
patient-level views imaginable with potential for
unprecedented insights of both local and global relevance.
RICH SETTING FOR REALNWORLD DATA
Scandinavia is unrivalled in opportunities to generate RWE given its
well-structured public healthcare, long established high-quality
electronic medical records (EMR) and mature regulatory research
framework. In a first-of-its kind RWE approach, IMS Health brings the
most complete, integrated view of patient-level care through
anonymous EMR data along with national and disease-specific registers.
The new collaboration with Lifandis in Norway extends application of the
IMS Health Pygargus patented extraction methodology, first launched in
Sweden, to the HUNT biobank and databank, recognized by international
researchers for its value in personalized medicine (biomarker Id and
validation, disease etiology, patient subgroup stratification), epidemiology
(RWE, post-marketing studies, burden of disease, comparison of treatment
outcomes), drug discovery (target identification, target validation) and
clinical trial optimization. Containing unique patient data from 125,000
anonymous individuals, with more than 25 years of follow-up, and
covering 6,000 distinct variables, the Nord-Trøndelag Health (HUNT) Study
is one of the largest population-based health studies ever performed.1
UNIQUE FOUNDATION FOR TAILORED RESEARCH
Lifandis was founded to drive partnership between Norwegian biobanks,
academia and industry, and the company has also established a strong
foothold within register-based epidemiology. Its heritage includes
recruitment of at least 1.4 million Norwegians, around 30% of the
population, into consent-based research biobanks based on population-based
studies, with an additional 25-30 million samples in clinical
biobanks. Legislation, broad consent and the existence of a personal
identification number opens up the opportunity to build high-quality
and comprehensive datasets with access to more than 40 healthcare and
disease-specific registries, hospital and primary care EMRs and separate
endpoint registries with validated outcomes (Figure 1).
Importantly, while affording direct insights from Scandinavia, the data
can also inform scientific research to support global decisions across a
range of disease areas.
HUNT Biobank
HUNT Databank
Healthcare
Registr
Registries
ies
Electronic Medical
Records
Endpoint
ies
Registr
Registries
P
ersonal ID
Personal Da
tabank
Archival issue
samples
1 Krokstad S, et al. Cohort Profile:The HUNT Study, Norway. Int. J Epidemiol. 2013
Aug; 42(4): 968-77
PAGE 2 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

nEWs EMERGING HEALTHCARE TRENDS
Research from IMS Health informs opportunities for harnessing trends to achieve
the triple aim of US health reform
Study reveals ten dynamics for policy prioritization
in US managed care
At a time of tremendous flux in the US healthcare system, a
new report, underpinned by IMS Health research, has
identified potential for strategies to achieve the triple aim
of health reform (improved care, improved health and
reduced cost) leveraging the top emerging healthcare
trends. The findings provide real-world insights into key
policy priorities for healthcare stakeholders.
The report, “Ahead of the Curve: Top 10 Emerging Health Care Trends
– Implications for Patients, Providers, Payers and Pharmaceuticals”
was developed under the direction of the American Managed Care
Pharmacy (AMCP) Foundation, in collaboration with Pfizer, Inc. The
Foundation is a research, education and philanthropic organization
established in 1990 with the goal of advancing collective knowledge and
insights into major issues associated with the practice of pharmacy in
managed healthcare settings.
In seeking to help stakeholders proactively prepare for the impact of
changes in the US healthcare marketplace, the collaborative project was
designed to systematically identify and assess current and emerging
trends impacting healthcare delivery and MCP practices.
Reflecting a strong focus on partnering with stakeholders to improve
patient outcomes and advance healthcare globally, the research was
conducted by IMS Health on behalf of the Foundation, along with
development of the report itself. The company has established excellence
in generating scientifically credible real-world evidence that drives
powerful insights for more efficient decision making. The process
employed was designed to add scientific rigor by drawing on secondary
research evidence in addition to key opinion leaders’ insights. It was
systematic and replicable and drew upon the cross-functional expertise and
knowledge base of team members from multiple practice areas.
The six-month program of research followed a two-part methodology in
which distilled information from a targeted literature review was
analyzed by an advisory panel of healthcare thought leaders from
academia, industry, managed care, government and patient advocacy.
The panel was engaged to validate, identify and prioritize trends and
provide insight into implications across healthcare stakeholders. This
process included participation in a full-day, facilitated discussion and
trends assessment.
TOP TEN TRENDS DRIVING POLICY PRIORITIES
The top ten trends identified for their impact over the next five years are
1. Migration from fee-for-service to new provider payment models
that better align incentives for cost control and high-quality patient care
2. Consolidation of healthcare stakeholders, fueling standardization
of decisions and opportunities to evolve patient care practices
3. Widespread use of data and analytics in patient care, providing
novel opportunities for improving care effectiveness and efficiency
4. Increased utilization and spending for specialty medicines,
burdening payers and manufacturers to develop novel approaches
to formulary design and pricing practices that ensure patient access
5. Medicaid expansion, shifting a larger portion of economic risk to
payers and providers and driving creation of new models for care
delivery and tactics to improve efficiency
6. Migration to a value-oriented healthcare marketplace, reflecting
new approaches to balancing care quality and cost
7. Growth and performance of accountable care organizations, with
long-term success requiring investments in data structure and
analytics and willingness to evolve new models of care
8. Greater patient engagement through technology, which will
empower patients and providers to enhance practices for managing
and coordinating healthcare
9. Increasing patient cost-sharing, to curtail costs and incentivize
patient involvement
10. Healthcare everywhere through new tools and mobile
applications, with new avenues for patient engagement and
new healthcare delivery roles as wellbeing becomes a
community-wide effort
A NEED FOR NOVEL SOLUTIONS
Overall, the report suggests an advance towards a system of patient-centric
holistic care over the next five years, with shared accountability
across stakeholders and value being the core currency of the healthcare
marketplace – changes that are expected to translate into improved
patient outcomes. In preparation, stakeholders will need to move
beyond conventional practices and generate novel solutions that
improve patient metrics and tracking, enhance patient engagement and
find the balance between driving accountability, curtailing costs and
incentivizing. Specifically, this will involve
• Providers becoming increasingly accountable for driving care
efficiency. This may require a fundamental shift from conventional
care approaches. To support the transition, providers can leverage
healthcare technologies and the expansion of patient data to drive
quality in patient care and improve care processes. • Payers designing and implementing new payment models that
share risk and drive accountability across stakeholders and
populations with varying needs and requirements. They should
increasingly leverage technology tools, patient data and health care
analytics to better engage patients and track provider performance. • Pharmaceutical companies experiencing increased demand for
proof of value and real-world effectiveness data beyond trial-based
safety and efficacy, and being asked to share the risk for supporting
improved patient outcomes. They can prepare by investing in
evidence-generation capabilities that move beyond clinical trials to
leverage real-world data from provider and payer organizations.
The report concludes that while the path forward will vary by
stakeholder, all players in the US healthcare system will need to place
the patient center stage and consider their role in supporting long-term
improvements in patient health in a more holistic manner.
For further information, the report is available to download from the
Foundation’s website at www.amcpfoundation.org

nEWs RWE DEBATE
Experts gather with IMS Health to accelerate the application of real-world evidence for
maximum utility in healthcare decision making
Stakeholders unite to improve collaboration
in realizing RWE potential
Alongside greater demand for real-world evidence and
increasing recognition of its value across the healthcare
spectrum, there are clear signs that many stakeholders still
struggle to act on its potential. Its appropriate use can
deliver benefits to all, but more open dialogue and
enhanced collaboration between relevant stakeholders is
needed. Together with other partners, IMS Health works to
help all constituent groups achieve the common goal of
advancing healthcare.
As part of the company's commitment to accelerating the application
of RWE in pricing and market access decisions, two recent initiatives in
the US and UK have broken new ground in connecting perspectives and
broadening thinking about key issues for the current use of RWE and
solutions for realizing its true value.
US: REALNWORLD EVIDENCE LEADERSHIP SYMPOSIUM
A first-of-its-kind event, the Real-World Evidence Leadership Symposium
was held on 4 November 2014.
Co-sponsored through a thought leadership partnership between
IMS Health and Johns Hopkins Center for drug Safety & Effectiveness
in baltimore, Md, “Realizing the full potential of real-world evidence
to support pricing and reimbursement decisions”, offered a forum for
invited payers, pharmaceutical executives and academicians to engage
in frank and constructive discussion on how payers and life sciences
companies were using RWE and to look for pragmatic opportunities to
maximize its utility in pricing and reimbursement decisions. A key focus
was to explore potential collaborations between pharma and payers in
RWE generation.
Under the Chairmanship of dr. Lou Garrison, Professor and Associate
director in the Pharmaceutical Outcomes Research and Policy Program,
department of Pharmacy, at the University of Washington in Seattle, the
debate was structured into three sessions
1. Review of illustrative use cases showing effective and ineffective
use of RWE, to demonstrate opportunities and limitations facing its
broader application
2. Facilitated payer panel to discuss payer views on the role of RWE
in decision making and requirements for further use
3. Discussion and proposed solutions as a starting point for action
to identify potential for united efforts to increase the value of RWE
shaping the RWE opportunity
Reactions to the symposium from both speakers and participants
underscored its value in highlighting opportunities for making RWE
more core to pricing and market access decisions, whilst also capturing
a need for life sciences companies to hear directly from payers that their
RWE can have impact in order to increase their confidence in its use.
The key discussion points and actionable outputs from the symposium
are being taken forward for further exploration in post-forum research,
the findings of which will form the basis of an authoritative white paper
to further the discussion and serve as a catalyst for more collaborative
generation and use of RWE in the future.
UK: DECISION MAKING USING REALNWORLD DATA
Pushing forward the RWE conversation in the UK, the first IMS Health
Decision Making Using Real-World Data Conference, “Understanding the
changing landscape of patient data: Informed decision making in the
UK healthcare market”, was held on 30 September, 2014. The event
was organized in response to a request from IMS Health clients to learn
more about RWE best practice in the UK and its use by other players in
the healthcare arena. bringing together life sciences industry leaders
with a variety of healthcare stakeholders, the conference afforded a
unique opportunity to explore, through open debate, the ways that real-world
data should be utilized for healthcare decision making in the UK.
The event and panel discussion were chaired by Professor Sir Alasdair
breckenridge, former Chairman of the UK Medicines and Healthcare
Products Regulatory Agency (MHRA) who brought a deep understanding
of pharmaceutical regulators, their goals and requirements.
Broadening thinking on optimizing use of RWE
The presentations offered a variety of perspectives and cross-sectional
view of decision making. Speakers included dr Sarah Gardner, Associate
director of R&d at the National Institute for Health and Care Excellence
(NICE); Kevin V. blake, Scientific Administrator, best Evidence
development Office, at the European Medicines Agency (EMA); Skip
Olson, Global Head of HEOR Excellence at Novartis; and Professor Liam
Smeeth, Professor of Clinical Epidemiology and Head of the department
of Non-communicable disease Epidemiology at the London School of
Hygiene and Tropical Medicine. IMS Health was represented by dr. Patrik
Sobocki who shared the company’s view of RWE and vision for its use.
Among the topics covered by the panel of guest speakers were • Real-world data and the changing policy landscape • EMA use of best evidence in regulatory decision making • Leadership in RWE: An industry perspective • Leveraging patient-centric data and generating evidence across the
product lifecycle • Confounding, its impact and how it can be managed to maximize
the benefit of RWE
The speakers discussed how effectively RWE is used in their sectors
currently, how they believe it should be used to help decision making
and how they see the landscape changing in the future.
Feedback from both speakers and attendees was extremely positive and
there are plans to develop and expand the "Decision Making Using Real-
World Data" conference for 2015.

nEWs IMS CORE DIABETES MODEL VALIDATION
IMS CORE diabetes Model demonstrates continued credibility as the leading tool for
policy and reimbursement strategy in diabetes
Major validation upholds relevance of
IMS CORE diabetes Model
The IMS CORE diabetes Model (CdM) is a well-published
and validated simulation model that predicts long-term
health outcomes and costs in type 1 and type 2 diabetes.
For those developing policy and implementing decisions
informed by CdM analyses, confirmation that the model
remains contemporary and validated is essential. Findings
from a new validation to recent diabetes outcome studies1
reaffirm the model’s suitability to support policy decisions
for improving diabetes management.
disease simulation models are increasingly being applied to inform a
wide range of issues in healthcare decision making. Their ability to
project long-term outcomes and costs on the basis of short-term study
data is particularly relevant in a chronic condition like diabetes, given
its progressive course, associated complications and high and growing
economic burden.
The market-leading CdM is designed to assess the lifetime health
outcomes and economic consequences of interventions in diabetes, and
comprises 17 interdependent sub-models that simulate the major
complications of the disease. It allows estimation of direct and indirect
costs; adjusts for quality of life; and enables users to perform both cost-effectiveness
and cost utility analyses. It is routinely used to inform
reimbursement decisions, public health issues, clinical trial design and
optimal patient management strategies.
ROBUST VALIDATION PEDIGREE
Validation to external studies has been an intrinsic part of the CdM’s
development process. In a major evaluation in 2004, its operational
predictive validity was demonstrated against 66 clinical endpoints from
11 epidemiological and clinical studies. Evolution of the model also
reflects its strong links with the Mount Hood Challenge, a recognized
biennial forum for comparing the structure and performance of diabetes
health economic models with data from clinical trials (see Insights on
page 50).
RECENT ENHANCEMENTS
An ongoing commitment to ensuring that the CdM remains the best
available tool for economic evaluations in diabetes has seen the model
undergo a series of significant updates in recent years. These include • Ability to model individual anonymous patient-level data • Incorporation of treat-to-target efficacy data for HbA1c • Inclusion of a detailed hypoglycemia sub-model • Expansion of variables for probabilistic sensitivity analysis • Addition of UKPdS 68 and 82 risk equations
ENSURING CONTEMPORARY RELEVANCE
To ensure the CdM’s continued relevance and accuracy following these
enhancements, the aim of the latest validation study, published in 2014,
was to examine the validity of the updated model to results from recent
major long-term and short-term diabetes outcome studies. Particular
emphasis was placed on cardiovascular (CV) risk.
Independent researchers with unrestricted access to the CdM and its
source code worked with IMS Health to verify (ensure the model is coded
as intended and free from errors) and externally validate (quantify how
well outcomes observed in the real world are predicted) the model. In
total 121 validation simulations were performed, stratified by study follow-up
duration, study endpoints, year of publications and diabetes type.
goodness of fit
A number of statistical measures of goodness-of-fit were used, including • Testing of null hypothesis of no difference between the
annualized event rates (observed vs. predicted) and relative risk
reduction across all validation endpoints • Assessment of whether the confidence intervals for the number of
events predicted by the model and those reported in the
validation studies overlapped • Evaluation of goodness-of-fit between simulated and observed
endpoints for trials, endpoints, treatment arm, and date of study
using the mean absolute percentage error (MAPE) and the root
mean square percentage error (RMSPE) • Scatterplots of observed vs. predicted endpoints along with the
coefficient of determination (R2)
Impact of choice of CV risk equations
The CdM currently uses, amongst others, CV risk equations derived from
the United Kingdom Prospective diabetes Study Outcomes Model
(UKPdS68) but, given the increasing choice of equations that is
emerging, assessing the continued relevance of UKPdS68 is essential.
As part of the validation exercise, the absolute level of risk and relative
risk reduction was compared for 12 CV disease risk equations developed
specifically for T2dM patients.
RESULTS
At conventional levels of statistical significance, the study found that
the CdM fitted the contemporary validation data well, supporting the
model as a credible tool for predicting the absolute number of clinical
events in dCCT- and UKPdS-like populations.
Underscoring the significance of these results, Professor Phil McEwan of
Swansea University, the lead researcher of the study, emphasized that
"Organizations developing policy and implementing decisions informed by
CDM require the reassurance that the model and its results are current and
validated. This study helps to demonstrate that the model is a validated tool
for predicting major diabetes outcomes and consequently is potentially
suitable for supporting policy decisions relating to disease management in
diabetes."
A copy of the full validation study is available to download online at:
http://www.valueinhealthjournal.com/article/S1098-3015(14)01928-7/pdf
For further information on the IMS CORE diabetes Model, please
email Mark Lamotte at Mlamotte@be.imshealth.com
1 McEwan P, Foos V, Palmer JL, Lamotte MD, Lloyd A, Grant D. Validation of the IMS
CORE Diabetes Model. Value in Health, 2014; 17: 714-724

InsIghts DISEASE-SPECIFIC RWE
The author
Enabling disease-specific RWE
through fit-for-purpose RWD
Increased stakeholder demand and the greater supply of
electronic real-world data are expanding the application of
real-world evidence across the product lifecycle. The most
successful organizations are developing RWE platforms,
capabilities and analytical methodologies focused on
therapeutic areas. Increasingly, understanding how the
characteristics of a particular disease area can influence the
availability and use of real-world data for evidence generation is
important in setting strategies that create differentiation.
Rob Kotchie, M.CHEM, MSC
is Vice President, RWE Solutions, IMS Health
Rkotchie@imshealth.com

DISEASENDRIVEN DETERMINANTS OF RWE
In seeking to inform the ease and extent of RWE
development in a particular therapeutic class, IMS Health
has identified five key characteristics of a disease area
that have influenced the evolution of RWD development
to date
1. Routine capture of clinical measures
2. Nature of the critical endpoint
3. Number of treatment settings
4. Length of follow-up
5. Available sample size
By assessing each disease area against these five
characteristics it is possible to identify the specific factors
limiting an expansion of RWD use and the levers that can
be engaged to accelerate future adoption. This point is
illustrated in Figure 2 and discussed below for the
projected top five therapy areas in 2017.
Oncology: Complex patient subgroups
For oncology, a disease area that is often more amenable
to RWE research due to the nature of the critical endpoint
and frequent short length of required patient follow-up,
analysis can be often limited by the complexity of patient
subgroups and the need to capture detailed information
on disease staging, therapy sequencing, role of surgery
and patient biomarker status.
These challenges are now being overcome to a degree
by healthcare stakeholders working together to link
important rich clinical information with genomic and
proteomic data, increasing the value and uses of RWD in
this area.
For example, RWD is increasingly being leveraged in
oncology to facilitate pricing and reimbursement of
therapies by use, enabling a mechanism for greater
alignment between manufacturers and healthcare payers
and providers on the value and costs of treatment in a
specific indication or patient population.
continued on next page
A framework for reference in key disease areas
Globally, intensified pressure to obtain better value for
healthcare spending has elevated the importance of
real-world evidence (RWE) as an enabler of improved
healthcare decision making. Increased stakeholder
demand and the greater supply of electronic real-world
data (RWD) are expanding its application across the
product lifecycle as companies become attuned to the
insights it can deliver.
Leading life sciences organizations are now using RWE to
support clinical development, improve launch
performance and drive better commercial results. The
most successful are moving beyond a product-specific,
study-based approach to develop RWE platforms,
capabilities and analytical methodologies focused on a
single or set of therapy areas to drive sustained value
across their franchises.
As these trends continue, the ability to compare and
understand how the characteristics of a particular disease
area can influence the availability and use of RWD is an
important step in setting focused and relevant RWE
strategies that create differentiation and drive
achievement of commercial goals. This article offers a
framework for assessing RWD availability by therapy area
to guide internal decision making.
NUANCED CHALLENGES FOR RWE RESEARCH
By 2017, IMS Health estimates that the largest therapeutic
classes in the developed markets will include a
combination of both traditional primary care and
specialized areas, led by oncology, diabetes, anti-TNFs,
pain and asthma/COPD (Figure 1). Each of these disease
areas presents markedly different patient populations,
unmet medical need, standards of care and disease
outcomes, leading to a nuanced set of challenges for
RWE research.
20 = 71% market
value by
2017
TOP

InsIghts DISEASE-SPECIFIC RWE
Diabetes: Extended timeframe and multiple
care settings
In diabetes the generation and application of RWE, either
by researchers to support burden of disease, comparative
effectiveness or safety research or by commercial
functions for forecasting or sales and marketing purposes,
is often hindered by the need to track patients over long
periods of time and across multiple settings of care. In
other words, in order to infer the effects of a diabetes
intervention on delaying the worsening of a secondary
condition (eg, renal disease) or a reduction in a related
complication (eg, microvascular or macrovascular events)
patients must be followed over several years. This
includes tracking their admissions and discharge to and
from hospital, and across multiple treatment centers.
Hence, to fully assess the comparative effectiveness of a
diabetes intervention in the real-world setting requires
linking one or more datasets across both ambulatory and
specialist treatment settings, and/or combining a closed
database of medical and pharmacy claims with EMR data
to provide meaningful clinical data on outcomes and
confounding factors such as Body Mass Index and HbA1c.
Despite the proliferation of data in a primary care disease
like diabetes, the challenge is in bringing it together in a
meaningful way that will increase the usability of
diabetes RWD.
Anti-tnFs/Pain: Patient-reported endpoints
In the case of anti-TNFs or therapies to treat pain, RWE
research is often limited by the lack of routine capture of
patient-reported endpoints in clinical practice. While
disease-specific instruments that are used to assess a
patient’s response to therapy are systematically applied in
clinical trials, they are typically either not routinely
recorded in clinical practice or the data is stored in
unstructured clinical notes making it challenging and
time consuming to extract, analyze and interpret.
Asthma/COPD: Routine tests and acute events
Similarly, in other chronic disease areas such as
asthma/COPD, research can be restricted by the lack of
routine capturing of test results used to assess the long-term
deterioration of the disease (eg, spirometry
measures such as FEV1) or detailed descriptions of acute
episodic events, such as admission to hospital for a major
COPD exacerbation, or the documentation of rescue
medication use for a mild to moderate exacerbation.
FIGURE 1: LEAdING THERAPEUTIC CLASSES IN 2017 WILL INCLUdE PRIMARY CARE ANd SPECIALIST AREAS
Oncology
Diabetes
Anti-TNFs
Pain
Asthma/COPD
Other CNS Drugs
Hypertension
Immunostimulants
HIV Antivirals
Dermatology
Antibiotics
Cholesterol
Anti-Epileptics
Immunosuppressants
Antipsychotics
Antiulcerants
Antidepressants
Antivirals excluding HIV
ADHD
Interferons
Developed Markets Sales in 2017 (LC$)
$74-84Bn
$34-39Bn
$32-37Bn
$31-36Bn
$31-36Bn
$26-31Bn
$23-26Bn
$22-25Bn
$22-25Bn
$22-25Bn
$18-21Bn
$16-19Bn
$15-18Bn
$15-18Bn
$13-16Bn
$12-14Bn
$10-12Bn
$8-10Bn
$7-9Bn
$6-8Bn
Source: Rickwood S, Kleinrock M, Nunez-Gaviria M. The global use of medicines: Outlook to 2017.
IMS Institute for Healthcare Informatics, 2013 Nov.
Others
29%
Top 20
Classes
71%

Levers
Supplementation
Supplementation NLP
Linkage
Linkage retention modeling
Pooling
FIGURE 2: FRAMEWORK FOR dETERMINING CHALLENGES OF RWE GENERATION bY dISEASE
Abundant
Hard
Single
Short
Infrequent
Soft
Multi
Long
Large Small
Oncology Diabetes Anti-TNF Pain Asthma/COPD
Routine capture
of clinical
measures
Nature of
the critical
endpoint
Number of
treatment
settings
Length of
follow up
Available
sample size
LEVERAGING PROGRESS TO REALIZE VALUE
Growing need and rapidly expanding applications of RWE
are driving the development of innovative techniques to
link, supplement and pool data sources for deeper and
more meaningful research in this area.
The deployment of data encryption engines and greater
collaboration between key players is enabling ever
increasing scope to link anonymous information across
datasets and settings of care, while preserving patient
confidentiality and appropriate use.
Innovative techniques are now available to supplement
secondary data from the electronic health record through
novel primary data collection from physician and/or
patients at the point of care (‘over the top’ data collection),
and deploy Natural Language Processing (NLP) to extract
additional rich information from clinical notes in a HIPAA-compliant
manner.
These developments are providing life science researchers
with unprecedented access to comprehensive disease area
real-world datasets spanning multiple sources and settings
of care - with sufficient sample size and patient follow-up
to power an expanded set of RWE applications.
As companies look to maximize the value of RWE in their
organization, a focus on understanding the specific needs
and challenges for evidence generation presented by
disease areas of interest will be a key step to leveraging
the progress being made and realizing its full potential
across their franchises.
Understanding how the characteristics of a disease area can influence
availability and use of real-world data for evidence generation is
increasingly important. “
”

InsIghts RWE ROADMAP
The authors
A roadmap for increasing
RWE use in payer decisions
Real-world evidence has been part of healthcare for more than
30 years. Despite this, its application to really improve the
efficiency of healthcare delivery remains uneven and siloed.
Some of the greatest opportunities lie within the realms of
collaborative and partnership initiatives between stakeholders,
especially payers.
Marla Kessler, MBA
is Vice President, IMS Consulting Group
Mkessler@imscg.com
Ragnar Linder, MSC
is Principal, RWE Solutions & HEOR, IMS Health
Rlinder@se.imshealth.com

Bridging the gap between promise and reality
Real-world evidence has been part of healthcare for over
30 years, applied at varying levels by regulators, clinicians,
payers and manufacturers to inform decisions, build
programs and improve health. IMS Health has documented
more than 100 case studies where RWE has actively
influenced product labeling, price, access and use.1
Despite this, the application of RWE to really improve the
efficiency of healthcare delivery remains uneven and
siloed. Does this suggest a lack of comprehensive, quality
data? Are healthcare professionals, policy makers and
other key stakeholders waiting for better tools? Are the
skills sets to link and analyze data not widely accessible?
In fact the evidence suggests that the ability to produce
RWE is expanding, and rather quickly. However, the gap
between the exponential increase in RWE sources and
the capacity to harness these effectively is also growing.
Our research suggests that this widening gap between
the promise and reality is due to three critical – but
manageable – barriers.
GROWING VOLUME BUT UNREALIZED POTENTIAL
The quantity and importance of RWE has expanded
tremendously in recent years (Figure 1). RWE is generated
and applied throughout the lifecycle of pharmaceuticals
and other medical interventions to demonstrate
effectiveness, safety and value. It can be used for
population health management, for example in
identifying significant health factors by geography or
demographics for the design and evaluation of
interventions to improve health. It can enable better
understanding and characterization of disease
epidemiology, treatment paradigm and associated
resource utilization. It can inform quality of care
assessment, point of care decision guides and
translational research projects. And it can also serve to
assess a drug’s performance outside the randomized
controlled trial (RCT) setting and describe any shifts in
practice once the drug is approved and used.
" " " " "
FIGURE 1: THERE HAS bEEN AN EXPLOSION OF REAL-WORLd dATA FOR ANALYSIS
of payer respondents had no confidence in
the economic evidence provided by pharma 44%

FIGURE 2: CASE STUdY bREAdTH ANd VOLUME dEMONSTRATE EXISTING RWE dEMANd
22
21
16
11
10
9
4
3 3
2
Italy USA UK Sweden Canada Spain Netherlands France Germany Denmark
Label Launch access Ongoing access Price Use
Source: Hughes B, Kessler M. RWE market impact on medicines: A lens for pharma. IMS Health AccessPoint, 2013; 3(6): 12-17
25
20
15
10
5
0
Number of case studies
While RCT data is still regarded as being top of the
evidence hierarchy, there has been an increased use of
approaches that assess patient outcomes and follow all
the care and interventions they receive. Real-world data
(RWD) is now being used to complement RCT
information, providing valuable evidence of the way
pharmaceuticals are being used in practice and in many
populations, which cannot be gained from RCTs.
The breadth and volume of demand for RWE by payers
across markets is shown in Figure 2, based on research
conducted in 2013.1 In addition, payers are involved in a
plethora of RWE activities, building RWD for commercial
purposes (eg, Humana, Lifandis), collaborating more
broadly with other payers (eg, Health Care Cost Institute),
or simply using their own data for internal assessments.
Clearly, payers have not ‘opted-out’ of RWE. And yet
examples of them accepting industry-generated RWE or
working collaboratively with pharma to generate RWE are
few. These two key players may often be on opposite sides
of a negotiating table but opportunities exist for
partnerships that could potentially improve the entire
healthcare system. While current examples do provide
hope for a more collaborative future, they also force a more
fundamental question: what are the barriers to greater use
of RWE by payers and their willingness to work with
pharma and other stakeholders to broaden its application
in pricing, reimbursement and access decision?
SOME IDENTIFIED BARRIERS
In reviewing this issue with many payers and pharma
executives and in published literature, conferences and
other forums, barriers emerge in three key areas: data
and technology; science; and collaboration. While not
exhaustive or quantified, the challenges discussed
below within these areas provide a view of the
roadblocks being encountered.
Data and technology barriers
• Data infrastructure
While fully adjudicated claims data is structured with
fewer and more consistently defined variables, the
volume of it is expanding even as it is increasingly
linked with laboratory records, medical records, patient
social media and now genomic data, stretching the
bounds of healthcare informatics. All players in the
healthcare system seek more clinical and patient
outcomes information but now appear to be drowning
in vast amounts of data without it being sufficiently
complete for effective decision making. A study from
the Health Research Institute (HRI) in the US2 notes that
payers themselves believe they lack an adequate data
infrastructure to apply RWE in areas such as outcomes-based
contracting. And although the related
technology is growing and scalable, it is too expensive
and time consuming for most stakeholders to realize
its full potential at this time.

• Data extraction and linkage
Many payers have built distinctive capabilities in
understanding claims-related data but clinical data
requires a different set of expertise. The magnitude of
the challenge is just as great for pharma although its
nature is different. Companies may have acquired
substantial data and even technology integration
solutions but the data sits in functional and geographic
silos using new and old technologies, making it
challenging to link let alone analyze.
Even in a country like Sweden, where almost all patient
data can be tied to a consistent national social security
number, linkage is possible but not immediate.
• Data programming and processing
Speed is critical. However, a well-constructed research
study involving intensive SAS programming can take
months to conduct, extended by delays in gaining
answers to questions, with knock-on implications for
the timeliness of the insights delivered.
scientific barriers
• Lack of consistent RWD methodologies
The insights to be gained from RWD are substantial,
but the growing availability of data highlights
important methodological challenges. Even at a basic
level, questions can arise. For example, what defines a
diabetic patient? Is it based on medications taken, a
recorded diagnosis code, or an actual laboratory or
series of laboratory results?
Not every patient record contains all that information
or even some of it. This quickly leads to more complex
challenges: when should data matching be
deterministic versus probabilistic? When is it
acceptable to impute missing values? How will these
decisions bias the results? How can advanced analytics,
including predictive analytics, improve the quality of
and confidence in RWE? The expertise to deal with this
exists, but not always in-house. Furthermore, payers
can be skeptical of data because there is no easy way
of ensuring that the deployed methodologies are
sufficiently robust.
• Absence of standardized measures
The current lack of consensus around many key
measures means that even issues such as how
long a patient needs to demonstrate an outcome
before a treatment is deemed cost-effective, are not
universally agreed.
The variation in approaches can significantly impact
study results. Exploring methods used to score
physician spending patterns (cost profiling), a measure
frequently assessed by payers, a Rand Health research
study showed that even slight changes in attribution
rules can dramatically change the characterization of
physician performance. For example, “Between 17 and
61 percent of physicians would be assigned to a
different cost category if an attribution rule other than
the most common rule were used.”3
Collaboration barriers
• Lack of trust
This is perhaps the elephant in the room that everyone
is willing to talk about. While payers and pharma
should be aligned around patient outcomes, economic
incentives are more complex. The previously
referenced HRI study found that 44% of payer
respondents had no confidence in the economic
evidence provided by pharma.2 Fewer than 1 in 10
were very confident in using pharma-generated
information to evaluate a drug’s comparative
effectiveness.
For data holders, the need to protect patient privacy
and the integrity of the data being used has created
many hurdles to access. Even straightforward protocols
can take months to approve if each proposal is
evaluated individually.
• Lack of imperative
While some payers see their data as entirely adequate
to support comparative effectiveness and other
analysis, others are not even sure the analysis is
required to achieve their goals. If the main objectives
are managing unit costs of treatments, payers have
other mechanisms such as rebates, formulary design
and traditional analysis of claims data, which they may
find easier to use.
In parallel, many pharma companies can be risk averse
to generating RWE with a payer without fully
understanding what will be said and how it will
be used.
Some of the greatest opportunities for achieving the goal of improved
efficiency in healthcare lie within the realms of collaborative and
partnership initiatives between stakeholders, to ensure implementation. “
”

SOME POTENTIAL SOLUTIONS
None of the barriers referenced are insurmountable.
Indeed, interesting examples are already emerging of
innovative solutions on the path towards greater use of
RWE in pricing and reimbursement decisions.
• Evolution of methodologies and technology-enabled
analytics
This edition of AccessPoint alone spotlights the area of
predictive modeling where novel methodologies are
driving a new generation of applications in RWE (see
article on page 26). In these areas, researchers are
taking advantage of improved data and computing
power to run analytics that otherwise would have been
too time-consuming, if not impossible, to conduct.
• Richer data sources
Not every research question must rely on locally-sourced
data. In countries such as Scandinavia, more
than two decades of rich patient-level data exists
electronically. Technologies such as the IMS Pygargus
Customized eXtraction Program facilitate linkage
between the various sources by extracting the desired
data from an electronic medical record (EMR) to build
databases of EMR and register data. A 2014
retrospective cohort study linked national Swedish
mandatory registries to EMR data from outpatient
urology clinics to study prostate cancer (PC) patients.
The use of this approach provided a unique
understanding of the clinical course of PC that can
inform treatment and research across developed
markets – not only in Sweden.4
• Collaborations
Organizations such as the Healthcare Cost Institute
(HCCI) have been established with the goal of pooling
data (in this case, from US payers) and increasing its
quality. In reality, the value of cooperation between
stakeholders in different parts of the system – payers,
providers and pharma – will be critical, not only in
improving data sources but also in increasing buy-in to
and application of the insights from them. This check-and-
balance will enable stakeholders to put the patient
at the center of RWE and provide care that actually
improves outcomes.
In addition, it can enable a movement away from
different parties running analytics to stakeholders
working together to solve problems. For example,
RWE can support efforts to improve decision making,
adherence and efficient care delivery, where the
focus goes beyond analytics and ultimately to better
patient care.
• third-party involvement
The involvement of independent, objective third
parties can increase confidence in the underlying data
as well as the resulting analysis. It can also be an
important enabler of packaged analytics where data
can be used for a variety of applications within a
spectrum of pre-approved uses. A trusted third party
can deliver that protection. In addition, for data
providers interested in commercializing their data, a
third party can enable the full value potential of that
data to be captured across a range of research goals
involving many different types of organizations.
FULFILLING THE PROMISE
The importance of RWE is continuing to grow along with
its ability to inform critical decisions for payers, pharma
companies and other healthcare stakeholders. However,
the full impact of its potential has yet to be realized. This
article has considered some of the barriers to wider use
of RWE and proposed some solutions to address them.
Some of the greatest opportunities for achieving the
goal of improved efficiency in healthcare lie within the
realms of collaborative and partnership initiatives
between stakeholders, to ensure implementation.
Only then can we provide the best care for patients
and improve outcomes.
1 Hughes B, Kessler M. RWE market impact on medicines: A lens for pharma. IMS Health AccessPoint, 2013; 3(6): 12-17
2 Health Research Institute/PWC. Unleashing value: The changing payment landscape for the US pharmaceutical industry. May, 2012
3 Mehrotra D, Adams JL, Thomas WJ, McGlynn EA. Is physician cost profiling ready for prime time? Research Brief, Rand Health, 2010
4 Banefelt J, Liede A, Mesterton J, Stålhammar J, Hernandez RK, Sobocki P, Persson BE. Survival and clinical metastases among prostate cancer patients
treated with androgen deprivation therapy in Sweden. Cancer Epidemiology, 2014, Aug; 38(4): 442-7. doi: 10.1016/j.canep.2014.04.007. Epub 2014
May 27.

InsIghts PRIMARY CARE UTILIZATION IN CANADA
Diabetes complexities
drive resource
consumption in Canada
The authors
According to the OECD, Canada currently ranks 27 out of 34
member countries in the number of physicians per 1,000
persons.1 Around 15% of Canadians report either being unable to
access a primary care doctor or choosing not to do so.2 A new
IMS Health analysis of EMR data reveals diabetes as the main
consumer of GP resource among chronic conditions in Canada,
with key insights for improvement initiatives.
Sergey Mokin, MSC, MBA
is Consultant, CES, IMS Brogan
SMokin@ca.imsbrogan.com
Richard Borrelli, B. COMM, MBA
is Principal, CES, IMS Brogan
Rborrelli@ca.imshealth.com
Michael Sung, MSC, MBA
is Consultant, CES, IMS Brogan
Msung@ca.imsbrogan.com

A case study of EMR data in diabetes
LEVERAGING REALNWORLD EVIDENCE
Findings from the 2013 National Physician Survey in Canada
indicate that 64% of family physicians and 59% of specialists
now utilize electronic medical records (EMR) in their
practices.3 The improved availability of EMR data makes it a
powerful source of real-world evidence to better understand
demands on the healthcare system. In seeking to evaluate
primary care utilization in the country, a study was
conducted using Canadian data from the IMS Evidence 360
EMR database. This provided access to a panel of around 500
general practitioners (GPs) and specialists covering more
than 500,000 anonymous patients as a sample of the
Canadian population in major chronic indications.
Objectives
The cross-sectional EMR study had three key objectives
1. Identify medical conditions that are the highest
consumers of physicians’ time in Canada, measured in
visits per patient per year
2. Describe the contributing factors for the medical
condition associated with the most frequent visits per
patient per year
3. Propose areas of high potential impact for further
investigation and intervention
Methodology
A cohort of all patients with at least one physician visit
recorded during the study period of June 2013–May 2014
was extracted from the EMR dataset. The overall
concentration of patient visits and average visits per
patient was then determined across different diagnosed
conditions. These conditions were prioritized based on
the average visits per patient, and statistical significance
calculated to identify the top consumer of physicians’
time for both the acute and chronic conditions.
STUDY FINDINGS
Primary care system utilization overview
In the study period, a total of 122,296 unique patients
recorded visits to physicians in the EMR database. The
concentration of visits showed that 10% of patients were
responsible for nearly 40% of primary care visits (Figure 1).
FIGURE 1: 10% OF PATIENTS ACCOUNTEd FOR 40% OF PRIMARY
CARE VISITS
Frequency of visits Vs. Number of patients
concentration curve
100
80
60
40
20
0
0 10 20 30 40 50 60 70 80 90 100
% Patients
% Visits
Among the patients with chronic conditions, those with
diabetes made more repeat visits to a physician, as
indicated by the significantly higher average number of
visits per patient (2.6 per year) compared to other chronic
diseases (Table 1A). Among the acute conditions (which
were not studied further), patients with diseases of the
respiratory system had the highest average number of
visits per year (1.6 per patient) over the study period
(Table 1B). The further analysis focused on diabetes given
its chronic status and the significantly larger portion of
year-to-year healthcare spending on this condition.
TAbLE 1A: CHRONIC CONdITIONS
Medical Condition
Diabetes mellitus
Mental health disorders
Hypertension & other heart diseases
Chronic musculoskeletal system & connective tissue disorders
Chronic diseases of the respiratory system
Patients
2765
5901
4764
9263
3970
Visits
7205
11425
8270
13906
5319
Visits per patient
2.61
1.94
1.74
1.50
1.34
p-value*
<0.001
<0.001
0.066
<0.001
TAbLE 1b: ACUTE CONdITIONS
Medical Condition
Acute diseases of the respiratory system
Diseases of the urinary system (cystitis)
Family planning, contraceptive advice, advice on sterilization or abortion
Immunization (all types)
Acute musculoskeletal system & connective tissue disorders
Diarrhea, gastroenteritis, viral gastroenteritis
Patients
15706
5155
3820
4702
1970
2205
Visits
25083
6609
4844
5627
2354
2522
Visits per patient
1.60
1.28
1.27
1.20
1.19
1.14
p-value*
<0.001
0.92
<0.001
0.31
<0.001
Note: ICD-9 Code 078 containing other diseases due to virus was excluded due to potential for multiple viral infections to be captured under this single code
*p-value for the
Wilcoxon rank sum
test measures the
significance of the
difference in
visits/patient
between each
medical condition
and the next highest
medical condition

More than 70% of patients were treated with metformin.
However, multiple classes of anti-diabetic medications
were used to manage the disease, with DPP-IV inhibitors
and sulphonylureas being the next two most frequently
prescribed (Table 2). Diabetic patients were also likely to
be taking medications for cholesterol and triglyceride
control as well as for hypertension or other cardiovascular
conditions (Table 3). The type and prevalence of
concomitances were consistent with an older and mostly
overweight patient population.
Of patients whose med lab test results were available and
who had been treated with an anti-diabetic, distribution
analysis of their most recent HbA1c and fasting glucose
levels (Figure 4) showed that 51% did not meet the
HbA1c control threshold and 60% were out of control
based on the fasting glucose threshold.
Patients on metformin alone were compared with those
who had metformin plus at least one other anti-diabetic
in the study period. There was a statistically significant
relationship between the medication regimen (metformin
vs. metformin plus other) and achieved control state (in
control vs. out of control) within the study period (Table 4).
Fasting glucose and HbA1c levels were significantly
higher for patients treated with metformin and another
anti-diabetic in the study period. These patients also had a
significantly higher number of GP visits (Table 5). However,
further studies are required to determine the link between
the medications prescribed and control of diabetes.
FIGURE 3: bMI dISTRIbUTION OF dIAbETIC PATIENTS (N=1697)
0.4%
17.7%
30.8%
51.0%
<18.50 18.50-24.99 25.00-29.99 >30.00
Resource use contributors in diabetes
To determine potential contributors to the high level of
resource use in diabetes, data on its associated
demographics, co-morbidities/concomitances and lab
tests was extracted and analyzed. All diabetic patients
were identified in the cohort on the basis of having at least
one ICD-9 diagnosis code 250 or at least one prescription
for an anti-diabetic described by the ATC code A10.
Body Mass Index (BMI), HbA1c and fasting glucose levels
were analyzed for the diabetic cohorts based on the latest
available result within the study period. Patients with
fasting glucose >6.9 mmol/L or HbA1c >7% were further
segmented as ‘out of control’. Those treated with a
metformin product alone for the entire study period and
those who received metformin plus another anti-diabetic
class in the study period were also segmented. Statistical
tests were conducted to determine if observed differences
between patient segments were statistically significant.
Patients
A total of 4,390 diabetic patients recorded physician visits
in the EMR dataset over the study period. More males
(55%) than females (45%) were observed among these
patients, which is representative of the Canadian diabetic
population (54% males vs. 46% females).4 The majority
(73%) were over 50 years of age (Figure 2). Of the 1,697
patients with measurable BMI, more than 50% were
classified as obese (BMI >30.00) and another 30% as
overweight (BMI 25.00–29.99) (Figure 3).
60.0
50.0
40.0
30.0
20.0
10.0
0.0
BMI
% Patients
FIGURE 2: AGE dISTRIbUTION OF dIAbETIC PATIENTS (N=4390)
30.0
25.0
20.0
15.0
10.0
5.0
0.0
0.1% 0.7%
4.1%
6.6%
15.3%
25.5%
23.4%
16.1%
8.2%
0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90
Age Range
% Patients
The findings of the study utilizing EMR data identify diabetes as the
primary consumer of GP resource among chronic conditions in Canada. “ ”

TAbLE 2: dIAbETES TREATMENT LANdSCAPE
Type
Anti-diabetic
Class
Metformin
DPP-IV Inhibitor
Sulphonylurea
Human insulins and analogues
Other anti-diabetics
Total treated patients
No. of Patients
1514
624
619
212
135
2094
% Patients
72.3%
29.8%
29.6%
10.1%
6.4%
100.0%
Note: Patients treated with multiple product classes would be counted multiple times,
once within each row corresponding to each product class prescribed
TAbLE 3: TOP dIAbETES CONCOMITANCES
Indication
Anti-hyperlipidemia
Cardiovascular
Gastrointestinal
Cardiovascular
Cardiovascular
Cardiovascular
Cardiovascular
FIGURE 4: dISTRIbUTION OF dIAbETIC PATIENTS bY HbA1C ANd FASTING GLUCOSE LEVEL
Treatment type
Cholesterol & triglyceride
regulating preparations
Ace inhibitors
Antiulcerants
Calcium antagonists
Angiotensin II antagonists
Beta blocker agents
Diuretics
Control level
HbA1c: >= 7% --> Out of control (51%)
Fasting glucose: >6.9 mmol/L --> Out of control (60%)
7-<8
8-<9
9-<10
10-<11
11-<12
12-<13
13-<14
HbA1c (%) & Fasting glucose (mmol/L)
HbA1c Fasting glucose
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
Patient Distribution Between Test Levels (%)
2-<3
3-<4
4-<5
5-<6
6-<7
14-<15
15-<16
16-<17
No. of Patients
17-<18
1500
743
525
478
459
446
413
18-<19
19-<20
% Patients
34.1%
16.9%
11.9%
10.9%
10.4%
10.1%
9.4%
20+
*Refers to a treatment with metformin in
combination with any other anti-diabetic
in the study period
TAbLE 4: PEARSON CHI-SQUAREd TESTS FOR INdEPENdENCE bETWEEN TREATMENT TYPE ANd
CLINICAL OUTCOMES bY FASTING GLUCOSE ANd HbA1C TEST RESULTS
Fasting glucose level
In control
Out of control
Total
p-value
HbA1c
In control
Out of control
Total
p-value
Metformin
213
148
361
<0.001
Metformin
289
134
423
<0.001
Metformin plus other*
89
204
293
120
238
358
Total
302
352
654
Total
409
372
781
TAbLE 5: NON-PARAMETRIC TESTS FOR SIGNIFICANT dIFFERENCE IN OUTCOMES (MEASUREd bY FASTING
GLUCOSE ANd HbA1C TEST RESULTS) ANd VISITS TO A PHYSICIAN
Fasting glucose (mmol/L)
HbA1c (%)
Visits
Metformin
7.08
6.88
2.46
8.59
7.96
3.42
p-value
<0.001
<0.001
<0.001

IMPLICATIONS FOR FUTURE INTERVENTIONS
It has been estimated that by 2020 around 10.8% of the
Canadian population will be diagnosed with diabetes, a
57% increase over a 10-year period. In addition, 22.6% of
the population will be classified as pre-diabetic and at risk
of developing diabetes in the future.5 This could
significantly increase the financial burden to Canadian
healthcare; direct medical costs are projected to reach
CN$3.8 billion by 2020 (37% growth since 2010), with
about 5% attributed to GP and specialist visits.5
The findings of the study utilizing EMR data identify
diabetes as the primary consumer of GP resource among
chronic conditions in Canada. With 80% of diabetic
patients classified as being either overweight or obese
there is a clear need for weight management programs
and lifestyle counseling.
Many diabetics are also often treated for co-morbidities
with antihypertensive, gastrointestinal or hyperlipidemia
medications. This is indicative of a more complex patient,
leading to greater demands on a primary care physician
in managing these interrelated conditions.
Despite the availability of multiple treatment choices,
more than half of the diabetic patients in the study cohort
failed to achieve control of their most recent HbA1c
levels. Although the study was not designed to evaluate
the drivers of diabetes control, further investigation into
the real-world effectiveness of various therapies is
encouraged. The results could potentially inform
treatment choices, resulting in a more efficient allocation
of resources.
A further observation from the study is that treatment
complexity, as indicated by a drug regimen including
metformin plus other, is associated with poorer
HbA1c/glucose-level control and an increased demand
for physician time. Thus, patients who were unable to
achieve target control and required more complex
treatment regimens consumed a higher number of
primary care visits. This implies that maintaining better
control of patients during earlier treatment phases can
reduce the additional resource required for more
advanced diabetes care.
Finally, the study findings point to four key areas with
high potential impact for intervention to improve the
real-world management of diabetes in primary care
1. Controlling weight
2. Efficiently managing the challenges of treating a
patient for multiple conditions
3. Evaluating and identifying the most appropriate and
effective medications per patient
4. Achieving and maintaining effective early control
of diabetes.
The study findings point to four key areas with high potential impact to
improve the management of diabetes in primary care. “ ”
1 OECD Health Statistics 2014 : How does Canada compare? Available at: http://www.oecd.org/els/health-systems/Briefing-Note-CANADA-2014.pdf.
Accessed 6 October, 2014
2 Statistics Canada, Community Health Survey 2012. Available at http://www.statcan.gc.ca/pub/82-625-x/2013001/article/11832-eng.htm.
Accessed 6 October, 2014
3 2013 National Physician Survey. The College of Family Physicians of Canada, Canadian Medical Association, The Royal College of Physicians and
Surgeons of Canada. Available at: http://nationalphysiciansurvey.ca/wp-content/uploads/2013/10/2013-National-ENr.pdf. Accessed 6 October, 2014
4 Statistics Canada. Data for 2013. Available at: http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/health53a-eng.htm.
Accessed 6 October 2014
5 Canadian Diabetes Association, Diabetes Québec, 2011. Diabetes: Canada at the tipping point. Charting a new path. Available at:
http://www.diabetes.ca/CDA/media/documents/publications-and-newsletters/advocacy-reports/canada-at-the-tipping-point-english.pdf.
Accessed 6 October 2014

InsIghts SCIENTIFIC-COMMERCIAL RWE SUPPORT
The authors
Finding the true potential of
RWE through scientific-commercial
collaboration
A recent report from IMS Health demonstrates the value that
real-world evidence delivers throughout the pharmaceutical lifecycle
and proposes the more active engagement of commercial teams in
RWE – both in terms of leadership and consumption. This article
summarizes key highlights of that research and presents a framework
for increasing scientific-commercial collaboration in support of RWE.
Marla Kessler, MBA
is Vice President, IMS Consulting Group
Mkessler@imscg.com
Amanda McDonell, MSC
is Senior Consultant,
RWE Solutions & HEOR, IMS Health
Amcdonell@uk.imshealth.com
Ben Hughes, PHD, MBA, MRES, MSC
is Vice President, RWE Solutions,
IMS Health
Bhughes@uk.imshealth.com

Realizing a US$1 billion opportunity through
scientific-commercial collaboration
STEPPING UP TO UNTAPPED RWE POTENTIAL
The IMS Health report1 shows how a few leading
companies pursue RWE as a capability, implementing
RWE platforms that move beyond narrow, study-based
approaches to create sustained value across the product
lifecycle and disease franchises. By following this
approach, a top-10 pharmaco could derive US$1 billion in
value from RWE.
For commercial teams the expanding applications of RWE
come at just the right time, when their stakeholders are
demanding ever more support of a product’s value
proposition just as they and others are producing
evidence of its performance in real-life settings.
In parallel, commercial teams appreciate the
shortcomings of traditional approaches to gaining market
insights but feel they lack ready alternatives. Primary
market research is inherently limited in sample size and
depth of insight, as well as being time intensive. It can
also be inaccurate and thus an inconsistent indicator of
actual behavior. There is a growing need for more time-efficient,
fact-based research.
FOUR GOLDEN PRINCIPLES FOR TRANSFORMATION
Leading companies have recognized these challenges
and taken steps to address them. Their experiences
suggest Four Golden Principles of using RWE to transform
performance, with direct implications for commercial teams.
1. RWE capabilities converge in a platform
Leaders approach platform investments in information,
technology and analytics tools with a plan to support a
range of uses – both scientific and commercial. In these
companies, commercial teams can respond rapidly to
queries about product use and evolving treatment
paradigms rather than having to wait a year to answer the
most fundamental questions.
Leaders think carefully about the platform capabilities
they should buy versus build, and how best to balance
the benefits of centralization (economies of skill) with the
benefits of embedding capabilities within the business
unit (responsiveness to business needs) (Figure 1).
FIGURE 1: CAPAbILITIES LAYER IN AN RWE PLATFORM
RWE capabilities stack
Channels for
dissemination & engagement
CoEs for scientic
commercial analytics
Technology-enabled
tools analytics
Information, networks
data linkage
Business specic setup/build
Partially consolidated capabilities/build
Consolidated capabilities/buy
The necessary layers of capabilities are
• Information, networks and data linkage
Increasingly, technology is enabling managed access
to new information with consent. Leaders develop
relationships with healthcare stakeholders to access
specific data sources relevant to their research needs.
They are able to link datasets, comply with privacy
laws, use technologies that anonymize data at source,
or integrate routine databases with traditional
prospective data. The result is a rich end-to-end view
of patient journeys.
• technology-enabled tools and analytics
Leaders provide users with direct access to data insights
through user-friendly interfaces. Pre-defined, validated
queries under scientific leadership facilitate simple
requests. This flexibility, coupled with high-performance
architecture, reduces time to insight. It
does not replace experienced scientific and statistical
staff, but rather ensures their focus on value-added
instead of routine tasks.
5%brand growth via RWE-enabled marketing
20% launch improvement via patient pool segmentation
3-month acceleration of market access submissions
25-90%cost savings versus primary research
INCLUDING
$1bn

FIGURE 2: PLATFORM dEPLOYMENT TO FUNCTIONS
RD HEOR Medical Safety Market Access Commercial
t Translational research
t Drug pathways
t Target population/
product prole
t Trial simulation/
recruitment
t Pragmatic clinical
trials (pRCTs)
t HEOR productivity
(speed quality)
t Local burden of
illness/disease/costs
Analytics CoE Analytics CoE Analytics CoE Analytics CoE
Data discovery interrogation tools
t Drug utilization/
monitoring
t Risk management
t AE/signal detection
t Rapid FDA/EMA
responses
t Speed to market
(dossier, CED1)
t New pricing mechanisms
t Formulary simulation
t Ongoing value
dierentiation
1 CED: Coverage with
Evidence Development
Technology-enabled tools analytics
Information, networks data linkage
RWE-enabled insights also have potential to accelerate drug development (eg, by improving target selection) which has not been accounted for in this assessment.
• Centers of Excellence (CoEs) for scientific and
commercial analytics
Leaders standardize analytics across markets and data
sources, pooling analysts in a flexible and scalable
service capacity. The continued tendency to manage
scientific and commercial CoEs separately allows
economies of skill where possible but also the
development of deep analytical methods specific to a
therapeutic area (TA) or function.
t RWE-enabled marketing
(eg, undertreated)
t Launch/promotion
planning via
physician-patient
segmentation
t Forecasting
t Engagement services
(eg, adherence)
Insights reporting tools
• Channels for dissemination and engagement
Leaders formalize the use of RWE across global and
local channels to engage stakeholders. This ranges
from global branding programs promoting the overall
credibility of RWE platforms to locally deployed
initiatives for improving RWE capabilities within
medical and pricing market access teams.
Internally, on-demand RWE insights are being
embedded into operational processes across functions.
Thus, the broader organization – including scientific and
commercial functions - can benefit from RWE-enabled
insights tailored to their research interests or operational
needs, as illustrated in Figure 2.
2. narrow precedes broad
Leaders focus on select TAs and markets to ensure their
investments generate differential value. Commercial
teams are often responsible for the overall franchise
performance, best positioning them to understand evidence
needs and priorities.
Companies need to funnel their investment into a
‘must-win’ TA. In our experience, they can only be
distinctive in areas of internal expertise and
products/treatments that give them credibility and
real-world experience with stakeholders. Many emerging
leaders have elected to use RWE in one or two TAs where
there is a strong pipeline and in-market portfolio, and
within mission-critical markets (to include the US and up
to three to five additional markets worldwide).
Even today, no one has full RWE-platform capabilities
across multiple TAs and geographies. However, companies
have had successes in single TAs or with single market
FIGURE 3: AdVANCEd PLATFORM STRATEGIES bY THERAPEUTIC AREA
ANd GEOGRAPHICAL SCOPE
B A A
TA
Multi C C
D
J
TA Single D
H G
Company Evolution US Multi-market
X
Therapy area (TA) scope
Market coverage
Target platform scope
(ongoing build)
Current platform
scope
PAGE 22 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS HEOR

approaches that they have expanded over time, as shown
by the migration of individual platforms in Figure 3.
Many will debate this view, given the desire to drive
distinctive capabilities simultaneously in all key TAs,
markets and functions. In reality, it takes several years to
develop the necessary capabilities and deliver value,
which is easier to do when those involved are aligned by
common data and/or challenges, often defined by TA.
Companies outlining a transformation agenda must set
the right expectations. There is no silver bullet; success
requires a multi-year effort of continuous improvement.
3. Commercial leads the charge
HEOR and other scientific colleagues are sometimes
critical of commercial-driven RWE, as the speed to insight
is contrary to their experience of time-intensive study
design and implementation. Yet platform-based RWE
capabilities will help them deliver more and better
research publications with greater scientific and market
impact. Commercial teams must champion the overall
platform to broaden RWE’s application and value for
many reasons – including their unique ability to secure
resources – while HEOR continues to lead the
development and implementation of scientifically
rigorous studies.
The need for commercial to take the lead in this
traditionally scientific domain is not immediately obvious.
However, leaders realize that scientific can be the data
custodian and user of RWE for protocol-driven studies
while commercial can be given appropriate access to
drive strategic decisions. Strong governance, allowing
nominated individuals outside scientific access to data
insights, enables scale in RWE investments.
The largest immediate financial value of RWE is in
supporting about-to-launch and launched products,
areas where commercial drives decision making. Many
decisions related to labeling and identifying target
patients, contracting and pricing strategies, and launch
planning are transformed by RWE, requiring commercial
to be close to RWE strategy. Ultimately, only franchise
leaders can really champion the longer-term investment
in their patients and key markets.
How can commercial initiate its leadership role in a
pragmatic way? More product teams are now sharing
their priorities across functions and mapping their current
and pending evidence plans against them. One company
reoriented several expensive prospective studies to build
a platform capability linking key information sets for
required insights. Thus, longer-term evidence planning
and commercial’s ability to remove organizational barriers
is an emerging vehicle for RWE leadership.
4. speed is a goal
Leaders seek speed to insight and can perform end-to-end
scientific studies in weeks. In their vision of on-demand
insights, quality and speed are harmonious, not
trade-offs. With better, timelier information, commercial
teams can become more nimble and work more effectively
with their customers.
Platform-based RWE capabilities challenge the paradigm
that robust, scientific-led insights require significant time.
With existing data agreements in place and pre-defined
analytics established, analyses can start almost immediately.
In companies where RWE delivery teams have a customer
service mindset (at least three to our knowledge), full
scientific studies using platform-enabled analytics have
been completed in less than a month, rather than up to a year.
FIGURE 4: VALUE CAPTURE FROM RWE ACROSS LIFECYCLE FOR A TOP-10 PHARMACO
Development Launch In-market
Initial pricing
market access*
US$100m
Launch planning
tracking
US$150m
Productivity cost savings
US$100m
Clinical development*
US$100-200m
Safety value
demonstration
US$200-600m
Commercial
US$200-300m
* Selected operational opportunities only; excludes increased RD pipeline throughput and better pricing
spend eectiveness

Insights from RWE can provide commercial teams with
feedback on market changes and the impact of their
actions within weeks. Leaders realize such speed only
matters if there is willingness to act on these insights
promptly. This could mean changing sales call plans,
reprioritizing physician targets, altering or dropping
promotional plans and even engaging with payers more
frequently or differently. RWE leaders make this more real-time
information available, adopt more dynamic
marketing plans, and empower key account managers
and others to leverage the new knowledge.
SOURCES OF THE US$1 BILLION RWE
OPPORTUNITY
The experience of companies living the Four Golden
Principles demonstrates the significant value RWE can
deliver at different stages of the pharmaceutical lifecycle.
Our research identified six main areas of value capture:
clinical development; initial pricing market access;
launch planning tracking; safety value demonstration;
commercial spend effectiveness; and overall productivity
cost savings. As shown in Figure 4, most of the value is
likely to come after product launch.
Examples of impact
t 5% brand growth via RWE-enabled marketing
t 20-50% improved promotion via physician–patient segments
t Better forecasting via disease progression models
t Formulary improvement from Tier-3 to -2
t Avoidance of label changes
t 2-week responses to FDA/3rd party journal publications
t 20% launch improvement via patient pool segmentation
t Rapid adjustment of messaging/resource allocation at launch
t 3-month acceleration of market access submissions
t Payment by use/indication, more eective price negotiations
(not quantied)
t Conditional access via coverage with evidence development
t 25-90% cost saving versus primary market research
t Doubling of impact factor of publications1
t 30% improvement in trial enrolment
t Reduction in strategic trial design aws
t Better product prole design (not quantied)
FIGURE 5: CASE STUdIES OF RWE IMPACT ACROSS OPPORTUNITY AREAS
Commercial spend eectiveness
US$200-300m
Safety value demonstration
US$100 m
(upside)
US$100-500m
(downside avoidance)
Launch planning tracking
US$150m
Initial pricing market access*
US$100m
Productivity cost savings
US$100m
Clinical development*
US$100-200m
Traditional focus Leaders’ additional focus
1 Hruby GW, et al. J Am Med Inform Assoc, 2013; 20: 563-567
* Selected operational opportunities only; excludes increased RD pipeline throughput and better pricing

The opportunity for RWE to add value is substantial but commercial needs
to step up and take accountability for implementing “ RWE capabilities. ”
In companies without RWE platform capabilities, the roles
of scientific and commercial are compartmentalized:
scientific teams are asked for studies to support specific
ad hoc arguments without long-term strategic input,
while commercial teams face increasing scrutiny of their
products but are often unarmed with the evidence to
defend them. Leaders have built RWE capabilities that
span both functions, enabling immediate and strategic
evidence generation.
Diving deeper into the buckets of RWE value, the research
sought to provide more information about the value drivers
and financial magnitude. Case studies enabled a richer
understanding. While RWE can help increase revenues, it
can also avoid downside risk as well as unnecessary costs.
Of particular interest were areas where leaders think
beyond traditional RWE applications (Figure 5).
IMPLICATIONS FOR SCIENTIFIC AND
COMMERCIAL COLLABORATION
The involvement of commercial does not diminish the
role of HEOR and other scientific and medical teams.
Rather, it should be complementary, serving to focus on
removing roadblocks to broader commitment for RWE
and increasing its overall application to demonstrate the
value of a franchise.
At the same time, scientific teams should champion the
treatment of RWE as a capability instead of a series of
studies to increase their overall effectiveness and
productivity. With the right RWE information and tools,
these teams can focus on the highest-value analytics
rather than lower value activities such as ad hoc data
sourcing and protocol development. Just as commercial
teams will need to generate, analyze and apply insights
more frequently, scientific colleagues will have to
integrate more seamlessly into the faster pace of decision
making enabled by systematic application of RWE.
Best practice example
A leading company provides an intriguing lens into best
practice. It began its RWE journey by creating an integrated
evidence platform in response to value and safety
demonstration challenges. When the FDA questioned the
appropriate use of its blockbuster oncology product, up to
US$500m of revenue was placed at risk due to potential
label changes. By developing the broadest RWE platform at
the time, the company enabled a variety of insights to
inform discussions with a multitude of stakeholders,
successfully responding to the FDA challenge.
Having experienced the power of RWE insights, the
company continued to invest beyond value and safety
demonstration. Commercial leaders acquainted with RWE
capabilities started to systematically lever detailed patient
pathways to understand product use, identify patterns of
under-diagnosis and under-treatment, and shape highly
targeted marketing campaigns. These campaigns nearly
doubled sales growth. Over time, RWE became the
company’s currency and competitive advantage for
engaging health systems, with granular forecasting and
disease progression models levered by a series of medical
center partners for their own service planning. For the
first time in the industry it effectively developed a closed-loop
system, using insights to engage and improve
patient pathways.
SIGNIFICANT ADDED VALUE
The opportunity for RWE to add value is thus substantial,
especially for in-market products. As the principal
organizational owners of these products, commercial
needs to step up and take accountability for implementing
RWE capabilities. Working collaboratively and cross-functionally
with scientific will ensure that investment in
RWE spans the interests of both respective functions.
1 Hughes B, Kessler M, McDonell A. Breaking New Ground with RWE: How Some Pharmacos are Poised to Realize a $1 Billion Opportunity.
A White Paper from IMS Health. August 2014.

InsIghts PREDICTIVE MODELING
The author
Improving outcomes
through predictive modeling
Predictive modeling involves assigning values to new or unseen
data. With growing promise across a wide range of fields, it is
increasingly being applied in various healthcare settings both to
reduce costs and drive quality improvements. However, while its
potential contribution is substantial, even exciting, applications
involving its use are not widespread and demonstrable evidence
on effectiveness is limited.
John Rigg, PHD
is director Predictive Analytics, RWE Solutions, IMS Health
John.rigg@uk.imshealth.com

Potential and challenges for developing successful models
Referencing real-world cases studies that have emerged,
this article discusses ways in which predictive modeling is
currently being used, considers the potential for
innovations from machine learning to extend its value
and accuracy, and highlights the challenges to
developing a successful predictive modeling application.
DIVERSE APPLICATIONS IN PRIMARY CARE
The scope of predictive modeling applications is wide
ranging, with models used to stratify risk both at a
population and patient level. At the population level, risk
stratification is routinely employed by payers/
commissioners to understand resource need and help
shape service delivery. Typically, this involves estimates of
disease prevalence, including age-demographic
adjustments. These models will likely become
increasingly advanced, helping to quantify the depth of
clinical need and define the type and scope of service.
At patient level, the applications principally focus on
identifying patients at high risk of particular events such
as unplanned hospital (re)admission, or the onset of a
chronic disease such as diabetes. High-risk patients are
then targeted with an intervention aimed at mitigating
the event.
1. Reducing hospitalizations
Identifying patients at greatest risk of unplanned hospital
readmission is currently by far the most widespread use
of predictive modeling in primary care.1 Readmissions
within thirty days of discharge are common, costly and
hazardous. Moreover, many readmissions are considered
avoidable.2 Reducing them is thus a major focus in
virtually all healthcare systems.3,4,5 It has certainly
captivated policymakers as a goal that can both improve
quality and reduce healthcare costs, seen in the US, for
example, with powerful incentives in the Patient
Protection and Affordable Care Act penalizing hospitals
that have higher-than-expected readmission rates.5
Heart failure has been a particular target, being one of the
most common reasons for hospitalization in the
developed world and accounting for the highest thirty-day
readmission rates.3
Parkland Health Hospital System: Informing CHF and
expanded disease areas
One example of a successful program is Parkland Health
Hospital System in Dallas, Texas. In 2009, Parkland began
analyzing electronic medical records (EMR) with the aim
of using predictive modeling to identify patients at high
risk of hospital readmission. The initial focus was on
congestive heart failure (CHF). Today, case managers and
other frontline providers receive details of high-risk
patients on a near real-time basis, information that is used
to prioritize workflow and allocate scarce resources to
support those most in need. Interventions are both
hospital- and community-based.6
Evaluation of the program identified a reduction in thirty-day
all-cause readmission rates from 26.2% to 21.2%.7 As
observed in an editorial by McAlister, “This effect size was
achieved even though the programme was only offered to
approximately a quarter of discharged patients, was only
deployed on weekdays (weekend discharges actually exhibit
the highest rate of readmissions) and despite the fact that
only a minority of readmissions may be truly preventable.”3
Given the observed fall in readmissions and costs for CHF
patients at Parkland, the program has been expanded to
patients with diabetes, acute myocardial infarction and
pneumonia. Preliminary data suggests similar success
with readmission rates in these conditions.6
NorthShore University HealthSystem: Supporting
hospital and primary care
Positive results have also been achieved through the use
of an effective predictive model at NorthShore University
HealthSystem in Chicago. Reports stratifying inpatients by
high, medium or low risk of readmission in 30 days are
provided to health system hospitalists on a daily basis and
scores noted as a value in every inpatient EMR.
26% 21% reduction in
re-admission rates

These have proved so useful that reports are also now
sent to the system’s primary care physicians listing their
patients with a high risk of readmission. The program has
seen a reduction in readmissions from 35% to 28%
among high-risk patients.8
Despite these successes, recent reviews reveal little
systematic evidence on what works in terms of community-based
alternatives to hospital admissions.4,5,9 However, there
is evidence to suggest some impact of particular initiatives
in targeted populations, such as education with self-management
in asthma, and specialist heart failure
interventions. Moreover, certain types of interventions, such
as post-discharge telephone calls, have also been identified
as effective.5 Beyond that, most other interventions appear
to have no effect in reducing emergency admissions in a
wide range of patients. There is a clear need to better
understand what works and for whom.
Interventions to reduce emergency admissions take place
within a complex environment where the nature and
structure of existing care services, individual professional
attitudes, patient and family preferences, and general
attitudes to risk management can affect their
implementation. While some interventions fail to reduce
admissions, they may have other beneficial effects, such as
reducing length of stay or improving the experience of care.4
2. Mitigating risk
NorthShore University HealthSystem: Predictive
modeling in hypertension
NorthShore is a pioneer in the use of various risk stratification
applications. One success story involves predictive modeling
to identify undiagnosed patients with hypertension (HTN).10
Although many patients with HTN are actively managed,
the condition is often overlooked. The risk stratification is
based on three screening algorithms, developed using
established HTN diagnosis guidelines, to identify patients
with consistently elevated blood pressure readings and
exclude those with only intermittent elevations. Patients
are considered at risk for undiagnosed HTN if they meet
the criteria of any of the three algorithms. The screening
tool was built using outpatient data from the NorthShore
data warehouse and the model has an accuracy rate
(Predictive Positive Value) of approximately 50%.
Veterans Health Administration (VHA): Population-wide
risk scores
The VHA has also invested heavily in risk stratification
applications, covering its entire primary care population.11
This includes models that output a patient’s percentile
scores associated with risk of hospitalization and
mortality. Updated weekly to reflect changes in individual
clinical status, the models rely on six data domains pulled
from the VHA’s extensive data platform: demographics;
diagnoses (inpatient and outpatient); vital signs;
medications; laboratory results; and prior use of health
services. Risk scores can be accessed on-line by each care
team, alongside other information such as active
diagnoses, recent visits to primary care and enrollment in
care management programs. They can also be rendered
as high-resolution geospatial maps to assist managers
with program planning and determining where new sites
for service delivery might be located.
While it is too early to determine whether the risk scores
help improve outcomes, the VHA suggests that based on
the frequency of access, healthcare providers are finding
them worthwhile. In addition, testimonials from clinicians
and care managers indicate that the scores are more
useful than clinical reminders, since each score takes into
account the patient’s unique needs and allows staff
members to focus on what is most likely to improve
future outcomes on an individual basis.
The VHA has also implemented a system for early
detection and management of chronic kidney disease,
including risk-based clinical EMR reminders which play an
important part in the effectiveness of the program.12
DEVELOPING AND APPLYING A PREDICTIVE MODEL
An outline of the main stages associated with developing,
validating and operationalizing a typical predicting
modeling application is shown in Figure 1 (page 30) and
described below.
1. Cohort creation from raw input data
In the initial stage, patient cohorts are created from the
input data. There are generally two: one cohort for model
development, the other for validation. A common
practice is to randomly split the data approximately two-thirds
and one-third between development and
validation cohorts respectively.
2. Algorithm development
In the second stage, the predictive model is estimated on
the development sample using an appropriate statistical
method such as regression analysis. The model is then used
to identify at-risk patient profiles and key predictors/
characteristics are described and clinically verified.
3. Algorithm validation
It is important that model development and validation
are carried out on separate data. This enables
independent assessment of its performance, ensuring it is
not ‘overfitting’ (where a model may accurately describe
data upon which it is estimated but poorly describe new
or unseen data). Thus, the third stage involves detailed
evaluation of model performance using a variety of
metrics. In the case of hospital readmission modeling,
for example, the metrics may include the number of

readmitted patients correctly predicted to be readmitted,
the number of readmitted patients incorrectly predicted
not to be readmitted, and the Area Under Curve (AUC; a
summary measure of model accuracy).
4. Model operationalization
Finally, the model is operationalized to identify high-risk
patients. This can be done in a number of ways, from
paper-based forms used by clinicians to a host of
technological solutions. One option is a real-time clinical
alert/screening tool that generates a pop-up on the EMR
system each time a high-risk patient presents.
THE PROMISE OF MACHINE LEARNING
To-date, risk models in primary care have almost
exclusively employed standard statistical methods, such
as regression analysis. State-of-the-art techniques from
the field of machine learning13 are particularly effective at
producing robust predictions in complex settings.
Machine learning, sometimes referred to as predictive
analytics, encompasses the fields of artificial intelligence
and data mining and is designed to identify complex, often
subtle, patterns in data – detecting ‘signal’ from ‘noise’.
Examples in healthcare include identifying genotype-phenotype
associations predictive of disease risk14 and the
automatic extraction of accurate diagnostic information
from medical images, such as MRI scans and X-rays.15
Next generation predictive modeling applications
Innovative methods from machine learning are likely to
play an important part in some of the next generation of
predictive modeling applications in primary care in
several ways
1. First, they can help improve the accuracy of current
applications, such as readmission modeling. The benefit
is likely to be most notable where many potential risk
factors are modeled simultaneously (eg, where data is
combined from multiple care settings).
2. Second, risk models almost always simply identify
high-risk patients for the event in question. Where
patients identified are eligible for a range of
interventions, models need to go beyond current
practice and determine which patients are most likely
to benefit from which intervention. Given the
considerable heterogeneity in patient response to
different interventions, predictive modeling
applications potentially could be far more effective by
unpicking this heterogeneity.
In statistical modeling and machine learning, this is an
area known as uplift modeling which relates to the
expected ‘uplift’ (improvement in outcome) if a patient
were to receive an alternative intervention compared to
a benchmark. While standard statistical approaches
(such as regression analysis) are not designed to
optimize this problem, recent innovations in machine
learning have demonstrated remarkable effectiveness in
this area16,17,18 and can thus help ensure that the patients
likely to benefit most from an intervention will receive it.
3. Third, the journey towards precision medicine will be
facilitated by physician decision support tools designed
to show expected/predicted outcomes associated with
different treatment choices, based on detailed patient-level
demographic, treatment history, biomarker and
other information. Machine learning can help provide
robust evidence for complex disease states. The
EuResist project19 is a key case in point, involving
management of treatment for a complex disease (HIV)
and highly-effective machine learning algorithms.
Uplift modeling is important here, too, since
personalized medicine means the right patient
receiving the right treatment which in turn involves
analysis of expected outcomes for different treatments.
4. Finally, machine learning can help identify patients with
conditions that often slip beneath the radar in primary
care practice, such as those with rare or early stage
diseases. Machine learning comes into its own if there
are multiple idiosyncratic factors that determine
whether the condition is present; these are
circumstances calling for specialist, purpose-built
methods for detecting ‘signal from noise’.
Clinical Vigilance for Sepsis: Sophisticated early detection
Sophisticated screening tools are more common in
secondary rather than primary care. Clinical Vigilance for
Sepsis is a sophisticated early detection system for sepsis,20
a disease that often goes undetected until too late and
where early detection depends on multiple patient-specific
factors. There are several applications to detect sepsis, many
with a high rate of false-positive signals, but a particularly
effective solution uses machine learning techniques,
including natural language processing of unstructured data.
The system has more than one hundred variables derived
from real-time telemetry data from bedside machines,
structured data (such as medical codes and other numeric
values) and unstructured data (such as doctor’s notes and
operative reports). When it detects that a patient is
heading towards sepsis, a text is sent to the doctor or
nurse. Hospitals using the tool are able to spot sepsis
earlier, which translates into quicker administration of
antibiotics and a shorter stay in the hospital. For a 300-
bed hospital, an average of US$2 million in direct savings
is estimated as measured by length of stay.

FIGURE 1: dEVELOPING ANd APPLYING A PREdICTIVE MOdEL
Raw input data
2 3 4
Algorithm development
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FH
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LFZQSFEJDUPSTDIBSBDUFSJTUJDT
$SFBUFUXPDPIPSUT
Algorithm validation
6TJOHWBMJEBUJPOEBUB

tWBMVBUFBMHPSJUINVTJOH
TFQBSBUFWBMJEBUJPODPIPSU
t$PNQVUFBDDVSBDZPG
BMHPSJUIN
FH
tNumber of actual
diagnosed patients
correctly predicted to
have the diagnosis
tNumber of actual
diagnosed patients
incorrectly predicted
not to have the diagnosis
tAUC (summary measure of
model accuracy)
1
OVERCOMING CHALLENGES TO SUCCESSFUL
APPLICATION
Developing and implementing a successful predictive
modeling application is not without its challenges. Some of
the key barriers, with guiding principles, are outlined below.
• Understand effectiveness of proposed intervention
Often, the purpose of the modeling is to identify
patients who may be ‘best-placed’ to receive a given
intervention, such as a visit from a community
nurse, to help avoid hospital readmission. While the
models themselves may be adept at stratifying
patients by risk, the program as a whole will not be
effective unless the interventions work – a more
difficult challenge than many may have imagined in
areas such as reducing readmissions. Thus, the
success of predictive modeling will be judged as
part of the broader success of risk stratification
initiatives and there is a long way to go to fully
understand the determinants of effective
interventions in many settings.
• Ensure sound methodology and full buy-in
A predictive modeling application must be built and
validated with input from all relevant stakeholders,
including end-users, clinicians and statisticians. This is
Development cohort
5XPUIJSETPGEBUB
Validation cohort
0OFUIJSEPGEBUB
Algorithm
operationalization
t0QUJPOTJODMVEFSFBMUJNF
DMJOJDBMBMFSUTTDSFFOJOH
UPPM
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essential to ensure the solution is both accurate and
fit for purpose. Accuracy is far more likely to be
achieved if the solution is developed with all relevant
clinical knowledge, captures the specific
circumstances of the local setting and is constructed
according to sound methodological principles.
End-users, whether clinicians or health executives,
must buy in to the solution, so it is imperative that
their requirements are integrated to ensure it is fit for
purpose. All this may seem nothing more than
common sense, but so often applications fail due to
lack of adherence to these basic principles.
• Maintain ongoing relevance
A challenge that has not yet come to the fore is how to
ensure solutions remain contemporary and relevant.
Circumstances change, from treatment regimens to
data feeds, and applications must capture these
developments. Too frequently they are launched with
insufficient consideration to post-launch evaluation
and maintenance, and even less to post-launch model
optimization. Data generated post-launch can, and
should, be used to recalibrate models. This can make a
huge difference, especially if a key data component
was sparsely populated pre-launch (eg, details on a
new and/or infrequently discharged treatment).

HIGH TRANSFORMATIONAL POTENTIAL
With the knowledge to overcome the challenges it
presents, predictive modeling has a part to play in
improving the quality, efficiency and effectiveness of
primary care. Successful applications, albeit currently
confined to isolated pockets of best practice, illustrate the
potential for transformational innovation. Important
applications include physician decision support tools,
screening tools and early warning systems.
Standard statistical methods are perfectly adequate for the
models underpinning many of these applications. However,
innovations from the world of machine learning are likely to
be pivotal to many of the next generation of predictive
modeling applications, especially where personalized
medicine involves complex disease states and conditions
(such as many rare diseases) are difficult to detect.
Predictive modeling has a part to play in improving the quality,
efficiency and effectiveness of primary care with the potential for
transformational innovation. “
”
1 Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, Kripalani S. Risk prediction models for hospital readmission: A systematic
review. JAMA, 2011; 306(15):1688-1698. doi:10.1001/jama.2011.1515
2 Van Walraven C, Bennett C, Jennings A, Austin PC, Forster AJ. Proportion of hospital readmissions deemed avoidable: A systematic review. CMAJ,
2011; 183: E391-402
3 McAlister FA. Decreasing readmissions: It can be done but one size does not fit all. BMJ Qual Saf, 2013; 22: 975-976. Epub 4 September 2013
doi:10.1136/bmjqs-2013-002407
4 Purdy S. Avoiding hospital admissions. What does the research evidence say? The King’s Fund, 2010, December. ISBN: 978 1 85717 607 0
5 Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: A systematic review, 2011, Oct 18; 155(8):
520-8. doi: 10.7326/0003-4819-155-8-201110180-00008.
6 AHRQ. Hospital uses data analytics and predictive modeling to identify and allocate scarce resources to high-risk patients, leading to fewer
readmissions. Available at: https://innovations.ahrq.gov/profiles/hospital-uses-data-analytics-and-predictive-modeling-identify-and-allocate-scarce-resources.
Accessed 21 October, 2014.
7 Amarasingham R, Patel PC, Toto K, Nelson LL, Swanson TS, Moore BJ, et al. Allocating scarce resources in real-time to reduce heart failure admissions:
A prospective, controlled study. BMJ Quality Saf, 2013; 22(12): 1-8
8 Van Dyke M. Predictive analytics: Pinpointing how best to allocate patient resources. Healthcare Financial Management Association, 1 March, 2013.
Available at http://www.hfma.org/Content.aspx?id=16069. Accessed 27 October, 2014
9 Bardsley M, Steventon A, Smith J, Dixon J. Evaluating integrated and community-based care. How do we know what works? Nuffield Trust, 2013,
June. Available at: http://www.nuffieldtrust.org.uk/sites/files/nuffield/publication/evaluation_summary_final.pdf. Accessed 21 October, 2014
10 Rakotz MK, Ewigman BG, Sarav M, Ross RE, Robicsek A, Konchak CW, et al. A technology-based quality innovation to identify undiagnosed
hypertension among active primary care patients. Ann Fam Med, 2014; 12(4):352-358. doi: 10.1370/afm.1665.
11 Fihn D, Francis J, Clancy C, Nielson C, Nelson K, Rumsfeld J, Cullen T, Bates J, Graham GL. Insights from advanced analytics at the Veterans Health
Administration. Health Affairs, 2014; 33(7): 1203-1211
12 Patel TG, Pogach LM, Barth RH. CKD screening and management in the Veterans Health Administration: The impact of system organization and an
innovative electronic record. Am J Kidney Dis, 2009; 53(3 Suppl 3): S78-S85
13 Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: Data mining, inference, and prediction. Springer Series in Statistics 2009 (2nd
Edition), New York: Springer
14 Gondro C, van der Werf J, Hayes B. Genome-wide association studies and genomic prediction. Methods in Molecular Biology. New York: Humana
Press. 2013; 1019. Springer Protocols.
15 Criminisi A, Shotton J. Decision forests for computer vision and medical image analysis. 2013. London: Springer-Verlag
16 Kuusisto F, Santos Costa V, Nassif H, Burnside E, Page D, Shavlik J. Support vector machines for differential prediction. Computer Science, 2014;
8725: 50-65
17 Sołtys M, Jaroszewicz S, Rzepakowski P. Ensemble methods for uplift modeling. Data Min Knowl Disc, 2014 September. DOI 10.1007/s10618-014-0383-9.
18 Radcliffe NJ, Surry PD. Real-world uplift modelling with significance-based uplift trees. Portrait Technical Report. 2011 TR-2011-1, Stochastic solutions
19 EUResist. http://www.euresist.org
20 Woodie A. Fighting sepsis with real-time analytics. Datanami. 2014, March. Available at:
http://www.datanami.com/2014/03/04/fighting_sepsis_with_real-time_analytics/ Accessed 21 October, 2014

InsIghts
SCANDINAVIAN REAL-WORLD DATA
Holistic real-world data
brings a new view of patients
and diseases
A promise of increasingly rich, anonymous clinical data is the
ability to understand more about the way in which modern
healthcare affects the patient experience in the real world, based
on all the relevant evidence to make informed and confident
decisions. In reality, this goal remains a vision of the future in
many cases. We are, however, seeing breakthrough research
opportunities using unparalleled Scandinavian healthcare data
and linking critical variables for the first time ever across the
entire patient journey.
Ragnar Linder, MSC
is Principal, RWE Solutions HEOR, IMS Health
Rlinder@se.imshealth.com
Mats Rosenlund, PHD, MPH
is Principal, RWE Solutions HEOR, IMS Health
Mrosenlund@se.imshealth.com
The authors

Gateway to breakthrough outcomes research globally
WHAT MAKES SCANDINAVIA SO UNIQUE?
Scandinavia has a long history of electronic medical
records (EMRs) and the collection of healthcare and
socioeconomic data in registries. These EMRs provide
broad coverage of the population but are uniquely rich in
clinical depth for many diseases and patient populations.
According to a recent IMS Health study of real-world
evidence (RWE) impact1 and an assessment of progress
towards value-based healthcare by Soderland, et al,2 the
Nordic countries, Sweden in particular, have reached the
highest maturity for the supply and use of RWE. This
reflects their well-structured public healthcare system,
legal and ethical frameworks, and universal
implementation of EMRs since the 1990s.
Today, both primary and hospital care in Scandinavia
have 100 percent EMR coverage. In many cases, national
health and medical care or social services registries have
been collecting data since the 1950s. Further, there are
more than 100 national disease-specific registries
available, including in Sweden the National Diabetes
Register, the National Cataract Register, the Stroke
Registry and the National Rheumatology Register.
Available clinical data includes co-morbidity (ICD-10
diagnoses), demographic information, hospitalizations,
laboratory results, risk factors and measures, prescriptions
and mortality. The inclusion of socioeconomic factors
(eg, educational level, long-term sick leave, early
retirement) further enrich the dataset, with decades-long
longitudinal data enabling insights around disease
progression and long-term outcomes (Figure 1).
Finally, there is the possibility to link this rich data through
technology advances such as the Pygargus methodology
(Customized eXtraction Program) and a mature legal and
ethical framework. Linkage is also possible due to the
longstanding use of unique personal Social Security
numbers, which eliminates many of the patient-matching
issues that afflict linked datasets in other areas.
FIGURE 1: SCANdINAVIAN REAL-WORLd dATA IS UNPARALLELEd IN dEPTH ANd LONGITUdINAL AVAILAbILITY
Longitudinal history of EMR data for 20+ years
Mortality data • Date of mortality
Rx data • Medication files (type,
strength, days supply) • Treatment switch • Patient co-pay • Total cost
Risk factors, measures • BMI, smoking status • Blood pressure • Tumor staging • Biomarkers • Performance status
Patient info • Age, gender, vitals • Date of diagnosis • Co-morbid conditions • Socioeconomic data
hCP contact • Healthcare visit • Type of HCP • Referrals
Lab results • Tests performed
(HbA1c, lipids, etc) • Test results
interpretation
hOLIstIC
PAtIEnt
VIEW
100% 100+ 20+
EMR coverage disease-specific registries years of data

InsIghts SCANDINAVIAN REAL-WORLD DATA
The common set-up of healthcare, health information and
classification systems in Scandinavia ensures the quality
and consistency of data variables. The legal and ethical
frameworks for research purposes enable linkage of EMR
with public health registers within any given therapeutic
area. And the strength of academic hubs throughout the
region provides potential for extensive scientific
collaboration.
SCANDINAVIAN DATA SUPPORTS GLOBAL
RESEARCH WITH REALNWORLD INSIGHT
Opportunities to apply these insights are significant in
Scandinavian markets, where RWE is already in formal use
for HTA processes. However, the real potential for
transformation in healthcare will come from applying this
information to answer questions that affect healthcare
decisions globally.
In collaboration with research specialists and academics,
companies are experiencing firsthand the unique nature
of outcomes research based on Scandinavian real-world
data. They are constructing entire patient journeys
through the healthcare system, analyzing resource
utilization and assessing the outcomes of interventions in
a wide range of leading disease areas.
Robust, comprehensive databases built on data from
Scandinavian EMR registries have supported global
research with real-world insight addressing diverse
questions around comparative effectiveness,
pharmacovigilance, epidemiology and clinical outcome.
DRIVING LANDMARK RESEARCH IN PRIMARY CARE
A practical example of the process and impact of
combining primary care EMR and national register data is
a study in chronic obstructive pulmonary disease (COPD).
This observational retrospective epidemiological analysis,
conducted in Sweden, generated data from patients with
COPD during the first decade of the 21st century. COPD is
the fourth leading cause of mortality, affects 1 in 4 adults
aged over 35 years, and is increasing in prevalence
worldwide. The implications of the findings for managing
the disease were thus considerable.
The study was made possible by identifying COPD
patients in primary care EMRs and linking them to register
data from some of Sweden’s national healthcare registers.
This enabled the creation of a unique and detailed
database of more than 20,000 patients. The aim was to
describe the COPD population, the disease characteristics
and approaches to management, based on more than 11
years of longitudinal data. Emphasis was placed on
prevalence, incidence, exacerbations, co-morbidities and
mortality. The study also compared the outcomes and
safety of modern COPD treatments.
Published in leading clinical journals and presented at
major international conferences, this landmark study
brought a new understanding to recent COPD
epidemiology. Mapping out the longitudinal management
of patients with the disease, it revealed changes in care
over the last decade, the importance of ‘specialist’
competence in primary care, and the impact of therapy
on exacerbations and pneumonia-related mortality.
CHARTING DISEASE PROGRESSION IN
SECONDARY CARE
In secondary care, real-world data can be used to study
disease progression, from early symptoms and the
patient’s first contact with healthcare through to their
death. Unique population-based studies can be
performed by linking hospital EMR data with disease-specific
registers and national healthcare and
socioeconomic registers.
An example published recently by Banefelt, et al, in
Cancer Epidemiology3 studied the incidence of
metastases and clinical course of prostate cancer patients
who were without confirmed metastasis when initiated
on androgen deprivation therapy (ADT). In this study,
EMR data from outpatient urology clinics was linked with
national mandatory registers to capture medical and
demographic data. The unique ability to examine PSA
values recorded over time provided important insights
into risk determinants of metastasis and death, for
advancing optimal management of the disease.
INFORMING OUTCOMES IN SMALL POPULATIONS
The large datasets that exist in Scandinavia also make it
possible to stratify populations and study different
cohorts, which is especially valuable in rare diseases and
small patient groups. A recent study of T-cell lymphomas
by Ellin, et al, illustrates this point.4
We can track the entire patient journey and make really deep dives.
By following social security numbers we can study the connection between
early diagnosis and improved therapy and care. We can also understand how
care develops and see which factors drive improvements in treatment results.
Bo Lidman, Co-founder of Pygargus, and Principal, IMS RWE Solutions HEOR
“
”

The use of unique personal identification numbers in combination with a legal
framework that allows linking of registers and access to longitudinal data
make Scandinavia unique for RWE research. It enables a deeper understanding
than it is possible to get in any other region.
Lars-Åke Levin, Professor of Health Economics, Linköping University, Sweden
“
Lymphoma is the most common form of blood cancer,
principally comprising Hodgkin lymphoma and non-
Hodgkin lymphoma. In turn, these are subdivided into
about 50 different histological subtypes. In Sweden,
peripheral T-cell lymphomas have an annual incidence of
only slightly more than one hundred cases per year.
Typically aggressive, they have a poor outcome with the
treatments that are currently available.
Due to the very low incidence of peripheral T-cell
lymphomas, population-based studies of these
conditions are scarce or evaluate small cohorts. In their
study, Ellin and colleagues assessed a cohort of 755
patients with peripheral T-cell lymphomas based on the
entire population of Sweden over a period of 10 years.
This represented the largest population-based material
reported for this type of disease. The analysis included
frequency, factors of prognostic impact, therapeutic
response to radiation, effects of chemotherapy with or
without addition of etoposide, and autologous stem
cell transplantation.
The data obtained in this study provides important
information on outcome in an unselected cohort of
patients, bringing the potential to aid development of
new treatment regimens and also serve as a baseline for
comparison of novel therapies and their outcomes.
”
PROGRESSING VALUENBASED HEALTHCARE
GLOBALLY
The use of EMRs and registries has proved valuable for
quality improvements in healthcare by enhancing health
outcomes and reducing costs. Going forward, there is
potential to maximize the value of existing resources and
ensure more effective, efficient and insightful outcomes
research to improve patient care. Based on the available
data and experience, the Scandinavian countries can lead
the transformation to value-based healthcare globally.
1 Hughes B, Kessler M. RWE market impact on medicines: A lens for pharma. IMS Health AccessPoint, 2013; 3(6): 12-17
2 Soderland N, Kent J, Lawyer P, Larsson S. Progress towards value-based health care, Boston Consulting Group, BCG, June 2012
3 Banefelt J, Liede A, Mesterton J, Stålhammar J, Hernandez RK, Sobocki P, Persson B-E. Survival and clinical metastases among prostate cancer
patients treated with androgen deprivation therapy in Sweden. Cancer Epidemiology, 2014 Aug; 38(4): 442-7. doi: 10.1016/j.canep.2014.04.007. Epub
2014 May 27.
4 Ellin F, Landström J, Jerkeman M, Relander T. Real world data on prognostic factors and treatment in peripheral T-cell lymphomas: A study from the
Swedish Lymphoma Registry. Blood, 2014; 124(10): 1570-7

InsIghts
The authors
IMS HEALTH SYMPOSIUM
Preparing for real-world
evidence in Asia Pacific
Real-world evidence is increasingly informing healthcare
decisions in Western markets. Technology advances have
increased the quantity of electronic data, enabled linking of
disparate datasets, and powered more sophisticated analytic
applications of the data. This has opened the window onto the
full patient journey with visibility across each care setting where
valuable clinical information is collected. However, the situation
is very different in Asia Pacific.
Jovan Willford, MBA
is Senior Principal, RWE Solutions, IMS Health
Jovan.Willford@us.imshealth.com
Joe Caputo, BSC
is Regional Principal, RWE Solutions HEOR, IMS Health
Jcaputo@sg.imshealth.com

Challenges and novel approaches
The dynamics that have significantly advanced the use of
RWE in North America and the EU are still relatively
nascent in Asia Pacific, meaning that RWE has not
developed to the same extent. These dynamics include
• Regulatory and reimbursement requirements
The level of scrutiny afforded by formalized HTA and
regulatory processes is historically lower in Asia Pacific
than in Western markets although this is rapidly
changing
• Data accessibility The majority of healthcare data is
owned by government with limited or no access to
private organizations
• Data quality There are issues over data
integrity/reliability and resulting complexities for
research methodology
• Infrastructure Data sources are fragmented due to
lack of data standardization and integration, partly
driven by historical lower demand/need for
sophisticated RWE solutions
Not only is Asia Pacific very different from North America
and EU, but also there is clear diversity among the region’s
markets themselves with respect to the application of
RWE. Figure 1 illustrates some of the key drivers of RWE
use within the region, highlighting the need for tailored
responses according to market conditions.
As an example, a high degree of formalized evidence is
incorporated into reimbursement and access Cum
decisions in
Korea, whereas E
in China no such formal requirement
exists to date. However, when considering fundamentals
such as overall market, prescribing and disease
prevalence, it is clear that the sheer size of China relative
to other markets means that it cannot be ignored
completely from a RWE-development perspective.
One of the key consequences of this diversity is that local
healthcare stakeholders have not yet fully embraced RWE
as an evidence source even though they have a growing
need for more evidence to allocate scarce resources. This
becomes especially critical given the stated aims of many
governments across the region to provide universal
coverage – particularly within the context of improving
access to general medicine services in response to
growing needs for primary healthcare in these markets.
Today, two key drivers of change offer light at the end of
the tunnel. Firstly, is the range of data sources that are
emerging across Asia Pacific and China. Although
fragmented, the ability to access government-owned
claims data in several markets and hospital-based
FIGURE 1: HETEROGENEITY OF RWE ACROSS THE ASIA-PACIFIC REGION: ILLUSTRATIVE dRIVERS ANd MARKET COMPARISON
Formalized evidence
in access
Value with commercial
decision makers
Accessibility of
su!cient data
Standards, methods,
legal structures in place
Compelling market
fundamentals
Low
Low
Low
Low
High
High
High
High
Low High
India China Japan Taiwan Australia South Korea
Industry needs to push for greater accessibility to government-owned
data sources, particularly in markets where such data exists and is being
used for reimbursement and access decision making. “
”

InsIghts IMS HEALTH SYMPOSIUM
information systems and private electronic medical
records (EMRs) in others brings promise for applications
of RWE. Secondly, market leaders on the global RWE stage
have been increasingly looking to Asia as their RWE
capabilities mature in developed markets. Consequently,
innovative pricing schemes, responsiveness to growing
demands for HTA, and exploration of novel mechanisms
to generate relevant data are accelerating in the region.
NEW PATIENTNCENTRIC APPROACHES
As the industry seeks to overcome the barriers and
challenges presented within Asia Pacific and China, there
is a need to adopt new and innovative approaches by
leveraging opportunities for RWE generation through
patient-centric programs. By their very nature large scale,
such programs generate significant amounts of locally-relevant
RWE. Traditionally, they have been run in
isolation with data locked down and results consigned to
archives once the original study question has been
analyzed and reported. Yet they have much greater
potential to yield data that could modify clinical practice
when analyzed beyond the scope of their original intent,
particularly if the data could be supplemented with pre-existing
or concurrent datasets such as EMR or health
insurance claims.
Through this type of innovation and integration –
expanding purpose-collected patient-level data by linking
and applying it more broadly – the full potential can be
unlocked through multiple concurrent uses. These might
include outcomes research, clinical practice audit feedback,
treatment guideline validation, risk-share agreement
design and implementation, or even as a physician tool in
informing individual patient treatment management.
EMERGING APPLICATIONS
Certainly the use of patient-centric programs represents
one way of overcoming lack of data across Asia Pacific
and China. Further evidence generation methods that are
growing in the region include retrospective analysis of
existing databases via protocol-ized research,
partnerships and sponsorship of patient registries, and
primary observational research. In Asia, in particular, the
use of registries has doubled every two years since 2004.
One such example is China Cardiometabolic Registries, an
evidence-based research program to improve
understanding of unmet medical need and clinical
outcomes in cardiovascular (CV) and metabolic diseases
in the country. It is currently following over 25,000
patients with type-2 diabetes and CV risk factors. 1
Aside from traditional applications of RWE, is the
increasing application of RWE across the product lifecycle.
A very strong trend emerging from the US and EU is the
convergence of clinical and commercial evidence
requirements, and the need to leverage RWE for a wide
variety of internal stakeholders at various points in the
product development cycle.
As RWE infrastructure grows in the region so, too, will the
potential applications, in particular the opportunity for
more scientifically-robust health economics and
outcomes research studies. Local examples include
Avastin (bevacizumab, Chugai Seiyaku) in Japan, which
gained a 5% ‘premium’ during re-pricing negotiations
through post-marketing evidence of prolonged overall
survival in lung cancer in 2012, and Bayer’s use of Japan
Medical Data Center (JMDC) data in Japan to support
removal of a label warning for Adalat (nifedipine).
IMPERATIVES FOR PROGRESS
Looking ahead to the future of RWE in Asia Pacific, there
are clear pointers for the industry as a whole, as well as
individual companies and other stakeholders, in moving
the RWE agenda forward. Key amongst these is the need
to leverage existing data, including linking
complementary datasets and investing in emergent data
sources. Industry needs to push for greater accessibility to
government-owned data sources, particularly in markets
where such data exists and is being used for
reimbursement and access decision making.
The linkage of datasets is emerging as a useful and valid
research technique that can expand the application of
individual databases, as demonstrated in a recent
retrospective observational cohort study in Japan.2
Innovative data sourcing requires integration across
fragmented sources, which in itself presents an
opportunity for industry, manifested through collaboration
with IT systems providers, use of social media or app-driven
data, or other innovative initiatives that can help to fulfill
the requirements of multiple stakeholders.
Regardless of data considerations, there is the need to
develop innovative approaches to research objectives, to
move away from singular research studies towards
broader value studies meeting a variety of needs across
stakeholder groups. Last, but by no means least, is the
need to embed RWE and especially HEOR capabilities,
which are critical to leverage existing and evolving data
sources to conduct high-quality outcomes research
specific to local settings.
The APAC nations are already making rapid progress in learning from the
triumphs and failures of other regions as well as from each other – and
that must continue. “
”

THE EVOLVING LANDSCAPE FOR RWE IN ASIA
PACIFIC: AN INDUSTRY PERSPECTIVE
IntERVIEW WIth AnDREW EggLEstOn,
B. PhARM, M.MED.sC
Andrew Eggleston is Regional (JAPAC) Market Access
Director at AbbVie. A trained pharmacist and clinical
epidemiologist, he has worked in the pharmaceutical
industry for 26 years including roles at Pharmacia Upjohn
and at Johnson Johnson in Health Economics, Outcomes
Research and Policy in Australia, Canada, Belgium and Asia.
He also worked in the medical device industry for a number
of years as Senior Director Health Economics and
Reimbursement, Asia Pacific at Medtronic and was most
recently Head of Market Access Asia at Sanofi. Here he shares
his perspective on the changing landscape for RWE in
Asia Pacific.
Q: What do you see as the key differences between
RWE in APAC versus the Us EU?
AE: The differences that exist are due to a shorter
evolution time in APAC. The drivers for the use of
RWE in the region have not been in forceful existence
for as long as they have in North America and EU.
Specifically, I would say data hungry reimbursement
and regulatory systems as well as ex-government
health insurers. These drive lower demand, although
this is changing. Hence, the key differences are in the
smaller number of RWE sources and the less
elaborate infrastructures supporting this data.
Q: People often talk about the diversity of the Asian
markets. Is this truly a barrier or can it be also an
opportunity for pharma?
AE: Diversity in culture, political form, health system and
economic development all suggest that a simple
adoption strategy from North America and EU for
RWE will fail badly. There is a need for locally-generated
evidence that can be used for local
decision support. This is an opportunity.
Q: the limitations of physical data are clear, but how
limiting is the lack of scientific and commercial
expertise in the region?
AE: There is no real lack of scientific or commercial
expertise per se in APAC. I sense it is more an under-development
of RWE and health system-specific
experience and education. The APAC nations are
already making rapid progress in learning from the
triumphs and failures of other regions as well as from
each other – and that must continue. In addition,
they should actively seek intellectual investment and
skills transfer from RWE-sophisticated jurisdictions.
Q: Patient-centric programs offer significant
potential for RWE generation. Why do you think
they have been under-used so far?
AE: I see three principal reasons: first is a lack of
awareness of the potential, possibly deriving from a
reluctance to think and work outside of comfortable
boundaries of existing practice; second is technology
– real-time use of very large and structurally complex
datasets in a user-friendly way demands a lot of
processing power and we haven’t had this until
relatively recently; thirdly, historically RWE has not
been highly regarded for various reasons, usually
because of the inability to eliminate or account for
systematic bias in datasets. This in turn was partly
driven by a lack of computing power to drive
corrective algorithms – but that has changed
recently, too. So the ‘status’ if you like of RWE has
improved as a basis of decision-informing evidence
in recent times.
Q: What could be the potential benefits of an
integrated approach to RWE?
AE: Efficiency mostly. Generating and accessing patient
data is an expensive exercise so we need to maximize
the opportunities this data offers. An integrated
approach helps with that. As to investment, this
covers finance, expertise development, infrastructure
building and, to a degree, decision-making process
realignments. If done well the impact on patients
would be faster improvements in standards of care
and health outcomes, physicians should experience
an improvement in their ability to make good
therapeutic decisions. The payoff for industry is a
better, earlier understanding of product strengths
and weaknesses, the best places for therapies in the
treatment pathway, and a fast, valid way of clarifying
the value of interventions in specific situations.
This article draws on presentations from the IMS Health
Symposium, “Real-world evidence in Asia-Pacific:
Are we ready? Challenges and novel approaches to
generating evidence in the absence of real-world
data”, held during the ISPOR 6th Asia-Pacific Meeting in
Beijing, China, in September 2014. Chair: Jovan
Willford, MBA, Senior Principal, RWE Solutions,
IMS Health. Speakers: Andrew Eggleston,
B. Pharm, M. Med Sc, Regional (JAPAC) Market Access
Director, AbbVie, and Joe Caputo, BSc, Regional
Principal, RWE Solutions HEOR, IMS Health.
1 China Metabolic Registers. Available at: http://www.ccmregistry.org/ Accessed 17 October, 2014
2 Shimizu E, Kawahara K. Assessment of medical information databases to estimate patient numbers. Jpn J Pharmacepidemiol, 2014; 19(1): 1-11

InsIghts UTILITY VALUES IN DIABETES MODELING
Identifying reference utility
values for economic models
in diabetes
Diabetes is a long-term condition associated with excess mortality
and substantial morbidity due to its range of fatal and non-fatal
complications. Health-related quality of life is severely impacted in
diabetes patients, who report significantly lower health state utility
scores compared to the non-diabetic population and experience
reduced quality-adjusted life expectancy. This article identifies a
preferred set of values for assessing utility consistent with the NICE
reference case.
The authors
Adam Lloyd, MPHIL
is Senior Principal, RWE Solutions HEOR, IMS Health
Alloyd@uk.imshealth.com
Amélie Beaudet, MSC
is Senior Consultant, RWE Solutions HEOR, IMS Health
Abeaudet@ch.imshealth.com

Challenges of systematic utility data selection
Approach
Publications for five computer models simulating
long-term outcomes in T2DM were appraised to identify
diabetic complications impacting patient utility
1. IMS CORE Diabetes Model10
2. UK Prospective Diabetes Study (UKPDS)
Outcomes Model11
3. Cardiff Diabetes Model12
4. Sheffield Diabetes Model13
5. CDC and Prevention/Research Triangle Institute
Type 2 Diabetes Model13
Health states used in these models were considered relevant
if they described microvascular or macrovascular
complications of T2DM or direct consequences of treatment
(such as hypoglycemia), or were related to excess body
weight (Table 1). Where available, data was also extracted on
the utility value for patients without specific complications.
TAbLE 1: COMPLICATIONS OF TYPE 2 dIAbETES IdENTIFIEd dURING
MOdEL REVIEW
Angina
Cataract
Diabetic retinopathy
Excess weight*
Foot ulcer
Heart failure
Hypoglycemia
Macular edema
T2DM Complications
Microalbuminuria/protenuria
Myocardial infarction
Neuropathy
Peripheral vascular disease
Renal dialysis
Renal transplant
Stroke
Vision loss
*Defined as presence vs. absence of obesity or increased BMI
Articles reporting utility values for diabetic complications
were identified in MEDLINE; Medline In-Process; Embase;
EconLIT; and the National Health Service Economic
Evaluation Database, from the earliest available date to
May, 2012. Only those containing original data, reporting
a study performed in adults, and written in English were
considered. The shortlist was then reviewed to identify
papers meeting the NICE reference case criteria. As NICE
recommends utilities estimated from a sample of the
general population, those using the EQ-5D were
considered more appropriate than the EQ-VAS.8,14
Globally, there are around 382 million people with
diabetes; by 2035 this number is set to reach 592 million.1
In the top five European countries,i approximately 6-12%
of all healthcare spending is attributable to the disease.2
There is thus an imperative public health and economic
need to ensure efficient allocation of resources for more
effective diabetes management.
The National Institute for Health and Care Excellence
(NICE) in England and Wales is a highly respected health
technology assessment authority. Consistent with the
NHS goal of maximizing health gain from limited
resources, NICE has defined appropriate methods for
assessing the effectiveness and cost-effectiveness of
medical technologies. This ‘reference case’ recommends
the use of quality-adjusted life-years and measurement of
health states through the EuroQol five-dimensional (EQ-
5D) valuation questionnaire.3,4,5,6
The EQ-5D comprises a descriptive system and the EuroQol
visual analogue scale (EQ-VAS). The former is a self-administered
questionnaire addressing five key areas:
mobility; self-care; usual activities; pain/discomfort; and
anxiety/depression. The EQ-VAS is a vertical visual analogue
scale on which respondents rate their current health state
from ‘best imaginable’ (100) to ‘worst imaginable’ (0).7
The descriptive system allows EQ-5D questionnaire index
values to be generated using scores from a set of
preference weights measured on a sample from the
general population. The index value can thus be seen as a
societal valuation of the patient’s health state rather than
the patient’s own assessment and is therefore preferable
from an economic perspective.
DEFINING UTILITY VALUES FOR ECONOMIC
MODELING
A key element of economic analysis in type 2 diabetes
mellitus (T2DM) is the need to evaluate the impact of
multiple, wide-ranging complications. The choice of
methodology for assessing utility can significantly affect
predicted values and thus the outcome of economic
evaluation.8,9 A study was therefore carried out to identify a
set of utility values consistent with the NICE reference case,
based on extensive review of the published literature.
i France, Germany, Italy, Spain and UK
382m 592m 6-12%
2014 2035 of EU5 healthcare spend
DIABETES

Studies consistent with the reference case were
considered for the preferred input set and the following
attributes extracted
• Country in which each study took place
• Year of publication
• Sample size and recruitment method
• Patient demographic characteristics
• Statistical methods
• Measures of precision reported
When the diabetic complication had no index values
that met the NICE reference case, an alternative source
was sought.
The preferred input set was created on the basis of
several criteria
• If only one measure meeting NICE criteria was
available, this was accepted
• When more than one estimate was identified, studies
reporting the marginal impact of T2DM complications
relative to a baseline ‘no complication’ state were
preferred over those reporting disutilities alone.
Consideration was given to synthesizing health utility
values if identified studies were sufficiently
homogeneous.4
• When an article provided utility values using multiple
statistical models, the best-fitting model or the one
preferred by the authors was selected
• Where possible, disutility estimates calculated from
statistical models were selected, otherwise the
difference between patients with and without the
specific complication was presented.
If these criteria were insufficient to identify a preferred
measure, the study most precisely matching the
definition of the preferred complication was selected.
For measures of utility in the preferred set, the 95%
confidence intervals were extracted if available; otherwise
they were estimated around each point value using
reported mean and sample size values, assuming a
normal distribution.
Results
A total of 16,574 records were identified from which 339
full-text articles were retrieved. Of these, 61 included
utility values assessed against NICE reference criteria
(Figure 1). Among the 21 studies considered for the
preferred set, 19 met the NICE criteria, reporting utility
values for all complications apart from renal transplant; in
this case, two studies using alternative methodology were
considered for inclusion.
Index value estimates for T2DM without complications
ranged from 0.711 to 0.940.15,16 The utility decrement
associated with complications ranged from 0.014 (minor
hypoglycemia)15 to 0.28 (amputation).17
Discussion
The preferred set of values for modeling T2DM
complications is shown in Table 2, alongside 95%
confidence intervals. Uncertainty around the point
estimate was important. In the case of every
complication, the interval overlapped with that of the
‘T2DM without complication health state’, reflecting the
expectation of amputation.
Most values for the preferred reference set were extracted
from Clarke, et al, due to its large sample size, T2DM-specific
nature, methodological quality and use of EQ-5D
in a UK population.17
FIGURE 1: FLOW OF STUdY SELECTION FOR PREFERREd SET OF UTILITY VALUES
Records identified
through database
searching (n=19,195)
Additional records
identified through other
sources (n=0)
Records after duplicates
removed (n=16,574)
Abstracts screened
(n=16,574 )
Abstracts excluded
(n=16,235)
Full-text articles assessed
for eligibility (n=339)
Full-text articles
excluded (n=278)
Reasons for exclusion
- Not presenting utility
values
- Presenting only utility
values associated with
specific intervention
- Not presenting utility
values associated with
diabetes complications
Studies assessed against
NICE criteria (n=61)
Full-text articles
excluded (n=40)
- Health states not
valuated by patents
- Not using societal
valuation algorithm
- Not using EQ-5D
Studies considered for
preferred set (n=21);
- Meeting NICE criteria
(n=19)
- Reporting values
where no available
studies met NICE
criteria (n=2)

2. Uncertainty around disutility associated with each
complication
Nearly all studies report measures of uncertainty.
However, assumptions were required to estimate
confidence intervals which may not reflect the full range
of uncertainty in the underlying studies. Most of the
selected utility values were adjusted for age, sex, and
presence of multiple complications. However, values from
Wasserfallen, et al,19 and Kiberd and Jindal20 were not.
Unadjusted disutility values should be interpreted with
caution and sensitivity analyses performed to test the
uncertainty around these values, especially for the most
frequent diabetes complications.
3. heterogeneity across identified studies
Variations in study country, study size and type, and
inclusion of diabetes type provided limited justification
for pooling disutility estimates.
4. Use of different value sets
All index values in this analysis were based on a UK value
set, with the exception of cataracts. None of the articles
reported the values with two different value sets.
However, while there may be a difference in mean
calculated index values, a study by Sakthong, et al,23
comparing EQ-5D index scores in a Thai T2DM sample,
found that UK, US and Japanese value sets showed
equivalent psychometric properties.
Range of candidate
CI, confidence interval;
T2DM, type 2 diabetes
mellitus.
* Estimated from the
standard error values
provided.
† Estimated from the
interquartile range
values provided.
‡ Disutilities converted
into annual values.
Sourcing predominantly from a single study was
beneficial for retaining internal consistency although in
some cases the sample size was relatively small.
When values were not reported by Clarke, et al, utilities
presented in Bagust and Beale18 were selected. While not
including UK patients, the large sample size, use of EQ-5D,
and robust methodology made it a suitable alternative.
Values for health states not selected from these articles
were sourced from the references shown in Table 2.
DIFFICULTIES IN DERIVING THE PROPOSED
REFERENCE SET
Deriving a set of utilities from multiple heterogeneous
studies highlighted a number of challenges and
uncertainties including
1. Variability in baseline utility value
The choice of baseline utility value (BUV) influences
predicted cost-effectiveness considerably. In this case,
the 0.785 value obtained by Clarke, et al, was
considered most appropriate, being in the range of
identified values for T2DM without complications.
Ideally, BUV should be obtained through a meta-analysis
if there is sufficient data meeting the NICE
recommendation, and sensitivity analyses performed
using the limits of the confidence interval.
TAbLE 2: PREFERREd UTILITY VALUES FOR MOdELING T2dM COMPLICATIONS
Parameter
T2DM without complication
Myocardial infarction
Ischemic heart disease
Heart failure
Stroke
Severe vision loss
Amputation event
Peripheral vascular disease
Proteinuria
Neuropathy
Active ulcer
Excess BMI (each unit above 25 kg/m2)
Hemodialysis
Peritoneal dialysis
Renal transplant
Cataract
Moderate nonproliferative background diabetic retinopathy
Moderate macular edema
Vision-threatening diabetic retinopathy
Major hypoglycemia event
Minor hypoglycemia event
Proposed
reference
Clarke, et al.17
Clarke, et al.17
Clarke, et al.17
Clarke, et al.17
Clarke, et al.17
Clarke, et al.17
Clarke, et al.17
Bagust and Beale18
Bagust and Beale18
Bagust and Beale18
Bagust and Beale18
Bagust and Beale18
Wasserfallen, et al.19
Wasserfallen, et al.19
Kiberd and Jindal20
Lee, et al.21
Fenwick, et al.22
Fenwick, et al.22
Fenwick, et al.22
Currie, et al.15
Currie, et al.15
Proposed
utility value
0.785
–0.055
–0.090
–0.108
–0.164
–0.074
–0.280
–0.061
–0.048
–0.084
–0.170
–0.006
–0.164
–0.204
0.762
–0.016
–0.040
–0.040
–0.070
–0.047
–0.014
95% CI
0.681–0.889
–0.067 to –0.042
–0.126 to –0.054
–0.169 to –0.048
–0.222 to –0.105
–0.124 to –0.025
–0.389 to –0.170
–0.090 to –0.032*
–0.091 to –0.005*
–0.111 to –0.057*
–0.207 to –0.133*
–0.008 to –0.004*
–0.274 to –0.054*
–0.342 to –0.066*
0.658–0.866
–0.031 to –0.001*
–0.066 to –0.014†
–0.066 to –0.014†
–0.099 to –0.041†
–0.012‡
–0.004‡
values
0.690–0.940
–0.059 to –0.007
–0.090 to –0.027
–0.108 to –0.051
–0.164 to –0.070
–0.070 to –0.012
–0.280 to –0.063
–0.186 to –0.061
One reference identified
–0.247 to –0.050
–0.206 to –0.016
–0.006 to –0.002
0.762–0.820
–0.070 to –0.012
–0.020–0.005‡
–0.031 to –0.001‡

Furthermore, the variation in estimated EQ-5D
questionnaire index values was found to have a marginal
impact on the projected incremental cost utilization ratio.
Nevertheless, when creating a utility value set for
modeling, it would be preferable to select values using
the same EQ-5D questionnaire value set or convert the
original data using the appropriate EQ-5D questionnaire
value set.
5. Variation in utility over time
Utility can vary over time, both among different
subpopulations as well as in line with evolving medical
practice. The two main studies contributing to the
proposed reference set, while still the best sources
identified, reported on data collected some years ago.
However, unless there has been recent progress in
treating a particular complication, it is preferable to select
publications on the basis of methodological strength
rather than on whether they are contemporary.
6. Changing impact of clinical events on hRQoL over
course of disease
To allow for this uncertainty, it may be appropriate to vary
the disutility values associated with a complication
depending on time since onset. In the present case, Clarke,
et al, reported the relationship between health state utilities
and clinical events occurring in the previous year or prior to
the previous year. The authors did not find a greater
disutility during the year of the event versus the disutility for
events that occurred during the previous years.
COMMON INPUT FOR ECONOMIC EVALUATION
Despite inherent challenges and uncertainties, the
preferred reference set was derived in a transparent way
and may serve as a common input for evaluating different
technologies for diabetes in line with the NICE reference
case. Although the specific characteristics of novel
interventions may justify additional or different values
from those presented, the dataset may provide future
economic evaluations with a starting point for such
considerations. The relevance of the identified utility value
set should be considered before it is applied to a modeling
study, and appropriate sensitivity analyses conducted.
The learnings from this methodological approach may also
serve to inform similar economic evaluations in other countries.
Consistency in the use of statistical models and reporting
would improve comparability of utility-related research. In
addition, further research surrounding appropriate
estimation of utility values for patients experiencing
several complications would improve the current
evidence base. This is likely to be of increasing importance
for T2DM patients with advanced disease given the typical
development of additional complications over time.
Further details of the review and analysis can be found in
the original study publication on which this article is based.
Beaudet A, Clegg J, Thuresson PO, Lloyd A, McEwan P. Review
of Utility Values for Economic Modeling in Type 2 Diabetes.
Value in Health, 2014; 17: 462-70. Available at
www.sciencedirect.com
1 IDF Diabetes Atlas, 6th Edition. Available at
www.idf.org/diabetesatlas/introduction. Accessed 16 October, 2014
2 Zhang P, Zhang X, Brown J, Vistisen D, Sicree R, Shaw J, et al. Global
healthcare expenditure on diabetes for 2010 and 2030. Diabetes Res
Clin Pract. 2010 Mar;87(3):293-301
3 NICE Guide to the methods of technology appraisal, 2013. Available at:
http://www.nice.org.uk/article/PMG9/chapter/Foreword. Accessed 16
October, 2014
4 Papaioannou D, Brazier J, Paisley S. TSD 9: The identification, review and
synthesis of health state utility values from the literature. Decision
Support Unit, School of Health and Related Research. Sheffield, UK:
University of Sheffield, 2010
5 Brazier J, Longworth L. TSD 8: An introduction to the measurement and
valuation of health for NICE submissions. Sheffield, UK: Decision
Support Unit, ScHARR, University of Sheffield, 2011
6 National Institute for Health and Care Excellence. Single technology
appraisal (STA), Specification for manufacturer/sponsor submission of
evidence. Rotterdam, The Netherlands: NICE, 2012.
7 Rabin R, Oemar M, Oppe M. EQ-5D-3L User Guide (Version 4.0).
Rotterdam, The Netherlands: EuroQol Group, 2011
8 Conner-Spady B, Suarez-Almazor ME. Variation in the estimation of
quality-adjusted life-years by different preference-based instruments.
Med Care 2003; 41: 791-801
9 Kopec JA, Willison KD. A comparative review of four preference
weighted measures of health-related quality of life. J Clin Epidemiol
2003; 56: 317-25
10 Palmer AJ, Roze S, Valentine WJ, et al. The CORE Diabetes Model:
projecting long-term clinical outcomes, costs and cost-effectiveness of
interventions in diabetes mellitus (types 1 and 2) to support clinical and
reimbursement decision-making. Curr Med Res Opin, 2004; 20
(Suppl. 1): S5-26
11 Clarke PM, Gray AM, Briggs A, et al. A model to estimate the lifetime
health outcomes of patients with type 2 diabetes: the United Kingdom
Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68).
Diabetologia, 2004; 47: 1747-59
12 McEwan P, Peters JR, Bergenheim K, Currie CJ. Evaluation of the costs
and outcomes from changes in risk factors in type 2 diabetes using the
Cardiff stochastic simulation cost-utility model (DiabForecaster). Curr
Med Res Opin, 2006; 22: 121-9
13 Mount Hood 4 Modeling Group. Computer modeling of diabetes and
its complications: A report on the Fourth Mount Hood Challenge
Meeting. Diabetes Care, 2007; 30: 1638-46
14 Lung TW, Hayes AJ, Hayen A, et al. A meta-analysis of health state
valuations for people with diabetes: explaining the variation across
methods and implications for economic evaluation. Qual Life Res,
2011; 20: 1669-78
15 Currie CJ, Morgan CL, Poole CD, et al. Multivariate models of health
related utility and the fear of hypoglycaemia in people with diabetes.
Curr Med Res Opin, 2006; 22: 1523-34
16 Smith DH, Johnson ES, Russell A, et al. Lower visual acuity predicts worse
utility values among patients with type 2 diabetes. Qual Life Res, 2008; 17:
1277-84
17 Clarke P, Gray A, Holman R. Estimating utility values for health states of type
2 diabetic patients using the EQ-5D (UKPDS 62). Med Decis Making, 2002;
22: 340-9
18 Bagust A, Beale S. Modelling EuroQol health-related utility values for
diabetic complications from CODE-2 data. Health Econ, 2005; 14: 217-30
19Wasserfallen JB, Halabi G, Saudan P, et al. Quality of life on chronic
dialysis: Comparison between haemodialysis and peritoneal dialysis.
Nephrol Dial Transplant, 2004 ;19: 1594-9
20 Kiberd BA, Jindal KK. Screening to prevent renal failure in insulin
dependent diabetic patients: An economic evaluation. BMJ, 1995; 311:
1595-9
21 Lee WJ, Song KH, Noh JH, et al. Health-related quality of life using the
EuroQol 5D questionnaire in Korean patients with type 2 diabetes. J
Korean Med Sci, 2012; 27: 255-60
22 Fenwick EK, Xie J, Ratcliffe J, et al. The impact of diabetic retinopathy
and diabetic macular edema on health-related quality of life in type 1
and type 2 diabetes. Invest Ophthalmol Vis Sci, 2012; 53: 677-84
23 Sakthong P, Charoenvisuthiwongs R, Shabunthom R. A comparison of
EQ-5D index scores using the UK, US, and Japan preference weights in a
Thai sample with type 2 diabetes. Health Qual Life Outcomes, 2008; 6: 1

InsIghts MIXED METHODS REGISTRY CREATION
The authors
A collaborative foundation
for new diabetes insights
in Germany
Researchers conducting analytics and epidemiological studies
using electronic medical record databases frequently find
themselves short of critical variables. The value from data
collected through a mixed methods registry like DIAREG spans
scientific and commercial applications and creates new
potential for exploring relationships between perspectives,
actions and outcomes.
Joshua Hiller, MBA
is Senior Principal, RWE Solutions, IMS Health
jhiller@imshealth.com
Laura Garcia Alvarez, PHD
is Senior Consultant, RWE Solutions, IMS Health
LGarciaAlvarez@uk.imshealth.com

Enhanced insights from a mixed methods approach
Researchers conducting analytics and epidemiological
studies using electronic medical record (EMR) databases
frequently find themselves short of critical variables,
potentially limiting the breadth of research they can
perform. Although widely available EMR databases such
as The Health Improvement Network (THIN), IMS® Disease
Analyzer, and the Clinical Practice Research Datalink
(CPRD) contain a great deal of longitudinal primary care
data, it is often the case that certain types of information
are missing – either because an EMR field has not been
completed or because a particular field does not exist
within the database.
In particular, behavioral detail such as reasons for changing
therapy or the physician’s perspective of important clinical
characteristics are rarely part of a structured health record
and thus are not contained in mainstream EMR databases.
Typically, researchers must then decide whether to sacrifice
the breadth of variables captured, and hence limit the
study scope, or use a purely prospective design and
sacrifice time and cost to implement an extended
prospective observational study.
LEVERAGING MIXED METHODS FOR A
COMPREHENSIVE RESOURCE
To address these challenges, IMS Health, in partnership
with AstraZeneca, has developed an innovative registry
(DIAREG) of patients with type 2 diabetes mellitus
(T2DM). AstraZeneca is committed to demonstrating the
efficacy and benefit of its medicines in a real-world
setting, especially in terms of patient-relevant outcomes.
The registry is based on the complementary methods of
retrospective and prospective data collection, thereby
overcoming the individual limitations of each, enabling
the creation of a rich data resource for observational
research in this area.
IDENTIFYING REQUIREMENTS
Work on DIAREG began in 2012. Understanding the key
requirements for a comprehensive prospective disease
registry, IMS® Disease Analyzer in Germany was selected
as the core data backbone, being representative with
input from physicians in general practice as well as
diabetologists,i and validated with a documented
history of application in published scientific studies.
Initial analysis of data variables confirmed that Disease
Analyzer contained rich information on population
characteristics (eg, demographics, medical history) and
treatment patterns (eg, diagnosis, prescriptions, co-medications,
co-morbid conditions) in diabetes patients.
However, while some data existed for certain diabetes-relevant
clinical parameters, such as HbA1c and body
mass index (BMI), this was often recorded less frequently
or sometimes not at all. Furthermore, other clinical
outcomes (eg, cardiovascular events, hypoglycemic
episodes, hospitalizations), physician behavior (eg, drivers
of therapy decision, reasons for dose or treatment
modification) and patient-reported outcomes (PRO)
(eg, general quality of life, disease-specific quality of life
or treatment satisfaction), were not captured as
structured data within the patient record at all.
As a result of this initial analysis, a set of 27 variables were
identified for their potential research value if collected, to
enhance the available EMR resource.
DIAREG IS BORN
The identified need for an ‘enhanced’ EMR registry took
the next stage of development down two separate paths
– technical and ethical – to achieve an optimal solution.
technical implementation
To facilitate technical implementation of the registry,
IMS Health worked closely with the EMR software vendor
responsible for collecting the data underpinning Disease
Analyzer. Together, they designed and created the
capability for a retrieve form data capture window (or
‘pop up’) to be triggered in the physician office during the
patient visit, based on a set of criteria available within the
patient EMR (eg, diagnosis code, existence of prior anti-diabetic
treatment, etc). Every time an eligible patient was
identified through the trigger, the physician completed
an electronic case report form (eCRF) in the ‘pop-up’
window to provide the required additional clinical data.
1,071
patients
DIAREG
i Becher H, Kostev K, Schröder-Bernhardi D. Validity and
representativeness of the Disease Analyzer patient database for use in
pharmacoepidemiological and pharmacoeconomic studies. Int J Clin
Pharmacol Ther, 2009; 47: 617-626
22%
changed therapy

FIGURE 1: CUSTOMIZEd EMR ANd REGISTRY dATA COHORT
IMS Disease Analyzer
Patient characteristics of
interest programmed into
EMR database to trigger eCRF
eCRF pop-up
PRO
Double hash algorithm applied for data anonymization
Since patient EMR was used as the basis for including or
excluding a patient from the registry, the potential impact
of subjective selection was reduced. Consecutive new
patients continued to be triggered for inclusion in the
registry until the physician reached a pre-defined cap, thus
providing a framework for random sample selection. Data
collected from the retrieve form data capture window eCRF
is currently being linked back to the EMR using a hash
de-id process that removes protected health information
(PHI) prior to extraction to the IMS Health database.
In addition to the enhanced clinical data collection, a
second phase of the registry build involved the
introduction of PROs to provide a further layer of
information. These are collected via paper-based
questionnaires handed to patients at the physician site
where they are filled in and returned for entry into an
electronic database. An additional hash algorithm has
been deployed for one-way linkage of the PRO data to
the EMR and eCRF (Figure 1).
Ethical implementation
From an ethical perspective, it was essential to ensure
that the registry was developed in accordance with sound
observational research practices. To that end, a Scientific
Advisory Board was created to provide guidance on the
methods for site identification, eCRF review, inclusion of
PROs, use of patient informed consent, and submissions
for ethics approval. The Committee is made up of six
independent academic researchers and physicians who
have no affiliation with either AstraZeneca or IMS Health.
Patients participating in the registry have given informed
consent for inclusion of their information from EMR, as
well as the eCRF and PRO questionnaire. The registry
protocol was reviewed and approved by the Ethics
Committee, Nordrhein, Germany (Ethikkommission der
Ärztekammer Nordrhein) under the name of DIAREG.
Enhanced Disease
Cohort
EMR and PRO linked to disease-specic data at
patient level creating enhanced patient record
UNIQUELY GRANULAR OBSERVATIONAL
RESEARCH
As of September 2014, DIAREG has been collecting data
for more than 18 months. The registry currently contains
eCRF questionnaires, with comprehensive, longitudinal
data variables, for 1,071 diabetes patients, enabling
granular observational research. A subset analysis of
these patients (n=824) shows that 77% were enrolled by
GPs, the remainder being recruited by diabetologists.
Based on data from half of the cohort, average length of
time with T2DM is 12.3 years (median 11 years). Twenty-two
percent of patients (n=181/824) in the registry have
experienced a change to their anti-diabetes therapy at
least once within the last year, mostly by the GP (57%) but
also by diabetologists, who were responsible for 35% of
therapy changes. For 152 patients (84% of the therapy
modification population), this took the form of a dose
adjustment to their existing therapy, mainly due to
insufficient control of HbA1c (Figure 2). A change of drug
was recorded for 60 patients (33%) for the same reason.
Overall, doctors have reported high expectations of HbA1c
reduction when deciding on a new treatment regimen.
A total of 475 patients (58%) self-monitored their blood
glucose levels, with 30% checking their blood sugar more
than twice a day. Visits to other specialists were recorded
for 43% of 824 patients, the most frequently visited being
ophthalmologists (57%) for diagnosis of retinopathies.
Of the 824 patients in the subset, 43 experienced at least
one hypoglycemic event, four of whom required
hospitalization (Figure 3).
ENABLING EVIDENCENBASED CONNECTIONS
The data captured in DIAREG enables researchers to
identify and explore associations across measures that
have not been collected before in a sustainable and
integrated manner.

FIGURE 2: MOST THERAPY AdJUSTMENTS ARE dUE TO POOR HbA1C CONTROL
Number of patients with
at least one dose adjustment
Reason for therapy adjustment
16% (n=29)
84%
(n=152)
39
9
13
10
22
16
2
23
112
0 20 40 60 80 100 120
Other
Co-medication
Weight gain
Hypoglycemic events
Microvascular complications
Macrovascular complications
Change of substance combination
Insucient HbA1c reduction
Source: Disease registries including Patient Reported Outcomes - IMS® DIAREG
Patient request
By allowing comparison of clinical parameters at a
patient level, it provides evidence of associations from a
real-world setting that previously could only be
identified anecdotally or through market research.
As an example, the capture of BMI and HbA1c
No Yes
measurements without DIAREG was recorded in 61.9%
and 42.3% of the population respectively. With DIAREG,
the capture of these critical lab measurements increases
to 83.3% and 77.6% respectively (Figure 4).
FIGURE 3: PATIENTS EXPERIENCING A HYPOGLYCEMIC EVENT
Number of patients having a hypoglycemic event
1
1
3
3
4
2
2
6
7
12
9
15
13
0 2 4 6 8 10 12 14 16
4 or more 3 2 1
Number of events per patient
N= 824 Patients, of which 43 had at least 1 hypoglycemic event as reported in DIAREG
Type of
hypoglycemic event
17
Hypoglycemia requiring
hospitalization
Hypoglycemia
requiring assistance
Blood sugar 70 mg/dl
measured by patient
Hypoglycemia with
glucose consumption

FIGURE 4: dIAREG ENAbLES INCREASEd CAPTURE OF CRITICAL MEASUREMENTS
61.9%
57.0%
42.3%
41.0%
15.7%
HbA1c
Height/Weight
Blood pressure
% 0 10 20 30 40 50 60 70 80 90 100
Already in DA Update in DIAREG Missing
DIAREG: n=407 patients
29.0%
FIGURE 5: CATEGORIES OF dATA ENHANCEd THROUGH A MIXEd METHOdS APPROACH
Information in IMS® Disease Analyzer
Documented type of diabetes
Therapy duration at the treating physician
Disease-relevant parameters (eg, HbA1c, blood glucose, weight/BMI, blood pressure)
–
–
–
Diabetes-related complications
Referral to hospital
Referral to specialists
Referral to rehabilitation
Patient education
–
–
Prior to implementation of DIAREG, real-world
information on the proportion of patients checking blood
sugar, the reason for modifying treatment, the number
and type of hypoglycemic events, diagnosis for specialist
visits or quantity of lab measurements captured was non-existent.
Figure 5 outlines categories of data enhanced
through the mixed methods approach.
EXTENDED VALUE WITH MULTIPLE APPLICATIONS
The value from data collected through a mixed methods
registry like DIAREG spans scientific and commercial
applications. For researchers, the depth of detail from the
16.7%
22.4%
14.0%
Information in IMS® DIAREG
Confirmation of type 2 diabetes diagnosis
Start/duration of type 2 diabetes
Complete documentation of all disease-relevant parameters
Frequency and severity of hypoglycemias
Treatment goals (related to symptoms, laboratory parameters and complications)
Reasons for change of therapy and treatment goals associated with the change
Complete documentation of all diabetes-related complications
All stays in hospital with reasons for hospitalization, diagnosis at discharge and hospital days
All specialist consultations with diagnosis
All rehabilitation measures with diagnosis
All educational activities
Frequency of blood glucose self monitoring
Physician's estimate of the patient's therapy adherence
comprehensive patient record allows retrospective analysis
using measures that are not available in other datasets. For
brand teams, the behavioral information from physicians
and patients, such as reasons for switch and quality of life,
creates new potential for exploring relationships between
perspectives, actions and outcomes.
The IMS® DIAREG registry is open to other collaborations.
For further information please email
Jhiller@imshealth.com

InsIghts IMS CORE DIABETES MODEL AT MOUNT HOOD
Demonstrating external
validity of the IMS CORE
Diabetes Model
The Mount Hood Challenges provide an opportunity to understand
the performance of the IMS CORE Diabetes Model against a series of
external validation tests and inform its continued development.
A comparison of the model’s results with published studies across
each of the areas covered at the 2014 meeting demonstrates how
well it is equipped to address those issues.
The authors
Mark Lamotte, MD
is Senior Principal, RWE Solutions HEOR, IMS Health
Mlamotte@be.imshealth.com
Volker Foos, MSC
is Senior Consultant, RWE Solutions HEOR, IMS Health
Vfoos@ch.imsheath.com

The 2014 Mount Hood Challenge
In June 2014, members of the IMS CORE Diabetes Model
(CDM) team participated in the 7th Mount Hood
Challenge at Stanford University in Palo Alto, CA. Focused
on economic aspects of diabetes and its complications,
this biennial event is an important forum for comparing
different health economic diabetes simulation models
(DSM), particularly in terms of structure and performance.
The goal is to advance the science of economic modeling
in diabetes and increase its validity and relevance to real-world
decision making. Attendees include leading
diabetes modeling groups, academics, industry
representatives and payers with an interest in the
economics of diabetes.
A particular theme of the 2014 Challenge was how to
generalize DSM for different populations and over time,
specifically exploring the ability of existing models to
adjust for risk differentials arising from ethnic and
socioeconomic variability and any secular improvements
in diabetes care. Participants were tasked with three
‘challenges’ to compare model projections to real-world
or clinical trial outcomes and explain and discuss
observed differences
1. Replication of key endpoints from the Action for Health
Diabetes (Look AHEAD) study
2. Prediction of mortality following first myocardial
infarction (MI) or stroke
3. Variation in event rates due to ethnicity
TAKING THE CHALLENGE
The CDM is a simulation model that predicts the long-term
clinical and economic outcomes of diabetes mellitus
type 1 (T1DM) and type 2 (T2DM). It is the leading tool for
policy analysis and reimbursement strategy in the disease
and is regularly updated and revalidated (see News
section, page 5). The Mount Hood Challenges provide an
opportunity to understand its performance against a
series of external validation tests and inform the
continued development and enhancement of its
functionalities. Although findings from the challenges
themselves cannot be reported here, comparing CDM
results with published studies across each of the areas
they covered demonstrates how well the model is
equipped to address these issues.
Challenge 1: Look AhEAD validation
The LOOK AHEAD study1 included 5,145 overweight or
obese T2DM patients to participate in an intensive
lifestyle intervention (ILI) promoting weight loss through
decreased caloric intake and increased physical activity
(intervention group) or to receive diabetes support and
education (DSE) (control group). Patients in the ILI group
experienced greater weight loss (8.6% vs. 0.7%) and
reductions in HbA1c (0.6% points vs. 0.1% points) at one
year. The study concluded that weight loss did not reduce
the rate of cardiovascular (CV) events in overweight or
obese adults with T2DM.
External validation of DSM against studies that primarily
focused on weight reduction is of interest particularly
following a recent review criticizing the degree by which
models translate weight improvements into benefits.2 The
authors assert that their fundamental assumptions
relating to weight effects are too strong and unsupported
by the literature.
Like many models targeted in the review and
participating in the Mount Hood Challenge, the CDM
applies UKPDS risk equations3,4 that incorporate BMI as a
risk factor for heart failure. In fact, no other micro- or
macrovascular complication risk is affected by weight. The
overall magnitude of weight-changes on clinical
outcomes in DSM is therefore modest when compared to
the impact of other risk factors such as HbA1c, lipids or
blood pressure.
For example, a CDM lifetime analysis utilizing UKPDS 68
equations3 in a ACCORD-like population to explore the
benefits of reducing BMI from 32 to 31 kg/m2 translates
into 0.008 life years gained and 0.004 QALYs gained with
no substantial improvements projected for any endpoint
except a 4.5% relative reduction of heart failure (HF)
incidence (16.1% vs. 16.8%).
The external validation of the LOOK AHEAD trial across
several DSM was relevant to further inform this research
question and help eliminate doubts that models
overestimate the clinical effects associated with weight
changes.
The goal of the Mount Hood Challenge is to advance the science of
economic modeling in diabetes and increase its validity and relevance
to real-world decision making. “
”

Challenge 2: Mortality validation
It is important to understand how DSM are capable of
predicting mortality across populations that were not
used for the construction of their inherent risk equations
(external validation). This challenge examined their ability
to predict mortality vs. trends observed in 20,836 people
with T2DM from the Swedish National Diabetes Register.
The CDM utilizes risk equations from the UKPDS3,4 to
predict mortality following major diabetes-related
complications. It has recently undergone a single ‘all-cause
mortality’ (ACM) validation against various
contemporary outcome studies (UKPDS, ACCORD,
ADVANCE, VADT, ASPEN)5,6,7,8,9 which has shown a below
average fit with an R2-statistic of 0.651. This compares to
an overall R2-statistic of 0.90 as obtained in the 2014 CDM
revalidation exercise including 112 microvascular,
macrovascular and mortality validation endpoints.10
Lack of fit in the ACM validation was associated with a
model over-estimation of ACM when compared to
contemporary outcome studies (ACCORD, ADVANCE, and
VADT). It is generally understood that these studies
reported low mortality incidence, likely because patients
were managed under controlled clinical trial (RCT)
conditions. CDM mortality predictions were therefore
contrasted against several external datasets that were
more eligible to represent real-world conditions
• Charlson Co-morbidity Index (CCI)
The CCI11 is widely utilized to measure burden of disease
and predict mortality in various disease subgroups,
including cancer, renal disease, liver disease and
diabetes. Its ability to predict mortality risk has been
validated extensively. The CCI was applied to predict
10-year mortality risk for diabetes patients aged 50, 60,
70 and 80 years. Risk scores were generated for four
different co-morbidity levels: no complications (NC); MI;
MI and stroke (MI+S); MI+S and CHF (MI+S+CHF); and
MI+S+CHF and end-stage renal disease (MI+S+CHF+ESRD).
CCI mortality scores were compared to corresponding
10-year ACM predictions from the CDM.
• UK general Practice Research Database (gPRD)
The CDM was validated to data from a retrospective
cohort study12 from the UK General Practice Research
Database (GPRD). The study compared ACM across five
glucose-lowering regimens: metformin monotherapy;
sulfonylurea monotherapy; insulin monotherapy;
metformin plus sulfonylurea combination therapy; and
insulin plus metformin combination therapy.
• Administrative dataset from Western Australia (WA)
Predictions from an online life expectancy calculator
that applies mortality risk equations derived from
13,884 WA hospital and mortality records13 were
compared to respective predictions from the CDM.
The coefficient of determination (R2) goodness-of-fit
measure was evaluated separately for the individual
exercises. R2 scores of 0.76, 0.82 and 0.99 were obtained
when the CDM was compared to predictions from the
CCI, GPRD and WA life expectancy calculator. Overall R2,
including all mortality and life expectancy validation
outcomes, amounted to 0.88 which supports the CDM as
a credible tool for predicting mortality in real-world
settings. Individual outcomes for the four validation
settings are shown in Figure 1.
This validation exercise outlines the observed discrepancy
when the CDM is compared to data from RCTs or to data
from non-controlled, real-world observations. As the
overall intention of DSM is to predict the implications of
new technologies in clinical practice, this emphasizes the
need to compare against evidence from real-world
settings when assessing the validity of DSM.
Challenge 3: Ethnicity differences
Economic evaluations of health interventions need to
consider differences in healthcare expenditures across
subgroups.14 Accounting for the heterogeneity of the
treated population to identify subgroups is important to
expose variation in incremental cost-effectiveness ratios
(ICERs); a given intervention may be cost-effective for
some subgroups but not for others.
Ethnicity can considerably influence the incidence of
diabetes-related complications which may be caused by
genetic, socioeconomic and socio-cultural factors.15 It is
thus important to understand the extent to which DSM
consider ethnic differences in the modeling. Several
studies have reported on ethnic differences in rates of
diabetes-related complications.16,17,18,19 The aim of the
third challenge was to understand the degree to which
these differences are incorporated into the various DSM
and affect predicted outcomes.
The goal for the modeling groups was to estimate
survival, costs and diabetes-related outcomes for an
average T2DM profile and all ethnic groups considered in
the respective models. The CDM considers ethnicity
adjustments for a number of micro- and macrovascular
complications including renal disease, eye disease,
neuropathy, and coronary heart disease. Details of the
ethnicity adjustments applied in the CDM, together with
respective literature sources, are reported in Table 1.
In this respect, a comparison of ethnic adjustments in the
CDM to a systematic review20 documenting a similar trend
of ethnicity-related risk associations for renal disease, eye
disease, CV disease and mortality, is revealing.
Consolidating the evidence of 51 articles reporting data
on ethnic differences in mortality and diabetes-related
complications, the review quantified the number of
identified studies (see Table 2) that reported higher (red),
lower (blue) or equal (green) risk for several ethnicity
groups versus a white reference population.
Studies were classified in different groups to distinguish
those that reported significant risk associations with
ethnicity after adjustment of confounders (eg, age,
duration of diabetes and sex) and those that further
adjusted for additional risk factors (eg socioeconomic
status, smoking). Although the CDM applies data from
different sources, comparability to the review
demonstrates that its ethnicity adjustments are
contemporary and qualified to differentiate ethnicity-related
differences in the natural history of diabetes.

FIGURE 1: ALL CAUSE MORTALITY VALIdATION WITH THE CdM AGAINST EXTERNAL SOURCES
CDM vs. outcome studies
ACCORD GL
ADVANCE
ASPEN PP
ASPEN SI
VADT
UKPDS 33
Study CDM
CDM vs. prediction from CCI
UKPDS 33
UKPDS 80 - SU
UKPDS 80 - MET
ACCORD BP
UKPDS 80 - SU
UKPDS 80 - MET
ACCORD BP
ACCORD GL
ADVANCE
ASPEN PP
ASPEN SI
VADT
40 50 60 70 80 50 60 70 80 50 60 70 80 50 60 70 80 50 60 70 80
No Complication MI MI + Stroke MI + Stroke
Study CDM
CDM vs. retrospective cohort study from GPRD
GPRD MET GPRD SU GPRD MET + SU GPRD Insulin GPRD Insulin + MET
Study CDM
CDM validation against predictions from WA life expectancy calculator
years)
(Expectancy Life Male
Male
Male
Female
Male
65
65-84
85+
65
85+ Post MI Post Stroke
Study CDM
Source: IMS CORE Diabetes Model analysis
1200
1000
800
600
400
200
0
1000
800
600
400
200
0
+ CHF
MI + Stroke
+ CHF + ESRD
Number of deaths in
study population
during trial follow-up
Number of deaths
in 10 years per
1000 patients
3500
3000
2500
2000
1500
1000
500
0
Number of deaths in
study population
during trial follow-up
12
10
8
6
4
2
0
Female
65
Female
65-84
Female
85+
Female
65-84
Female
85+
Male
65
Male
65-84

TAbLE 1: SUMMARY OF OddS RATIOS APPLIEd TO ETHNIC AdJUSTMENTS IN THE CdM
WHO = Ethnicity specific
life table data from World
health organization
(http://apps.who.int/gho
/data/view.main.61780)
adjusted (reduced) for all
diabetes-related causes
of death that are tracked
in the CDM;
UK 68 RE=UKPDS 68 risk
equations;
equations;
engine;
T1DM= Type 1 diabetes;
T2DM= Type 2 diabetes;
x = no race adjustment
Microvascular
Eye disease
BDR onset
BDR to PDR
PDR to SVL
ME to SVL
Renal disease
MAU onset
MAU to GRP
GRP to ESRD
ESRD death
post HD
post PD
post RT
Neuropathy
Macrovascular
MI
Stroke
IHD
CHF
Mortality
Event fatality
Long-term diabetes related mortality
Non diabetes-specific mortality
Type
T2DM
T2DM
T2DM
T2DM
T2DM
T1DM T2DM
T1DM T2DM
T1DM T2DM
T1DM T2DM
T1DM T2DM
T1DM T2DM
T1DM T2DM
T1DM T2DM
T1DM T2DM
T1DM T2DM
T1DM T2DM
T1DM T2DM
T1DM T2DM
White
x
x
x
x
x
x
x
x
112
112
112
x
x
x
x
x
x
x
WHO30
Black
1.0321
3.7721
122
2.323
1.38211
4.425
4.425
0.7226
0.5626
1.0026
0.5327
UK 68 RE3
UK 82 RE4
UK 56 RE29
x
x
x
x
x
WHO30
Hispanic
1.0321
3.6321
122
2.323
124
2.525
2.525
x
x
x
128
x
x
x
x
x
x
WHO30
Am. Indian
1.0321
11
122
123
1.04824
1.525
1.525
x
x
x
0.2927
x
x
x
x
x
x
WHO30
Asian/Pacific
Islander
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
WHO30,19
Indian
x
x
x
x
x
x
x
x
x
x
x
UK 82 RE4
x
x
x
UK 82 RE
x
x
TAbLE 2: SUMMARY FINdINGS ON NUMbER OF STUdIES REPORTING EITHER LOWER, EQUAL OR HIGHER RISK ASSOCIATEd
WITH ETHNICITIES VERSUS WHITE POPULATION IN US UK
Higher risk (average)
Lower risk (average)
Equal risk (average)
Retinopathy
Black
Hispanic
Asian
Nephropathy ESRD
Black
Hispanic
Asian
CV complications
Black
Hispanic
Asian
Neuropathy
Black
Hispanic
Asian
Mortality
Black
Hispanic
Asian
Adjusted for confounders
(age, duration, sex)
higher
1
1
2
1
4
1
lower
0
0
0
equal
0
0
1
1
2
2
1
1
1
US
additional risk factors
higher
1
3
2
1
1
1
lower
1
1
0
1
3
3
equal
3
0
1
1
2
1
1
1
3
UK
(age, duration, sex)
higher
0
0
lower
0
0
1
2
equal
1
0
1
1
additional risk factors
higher
0
0
1
lower
0
1
1
equal
0
0
1
1

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Mount Hood Modeling Group.

PROJECt FOCUs CHRONIC INFLAMMATORY DISORDER
Evaluating disease burden,
unmet need and quality of life in
a chronic inflammatory disorder
Primary data collection, with a focus on patient-reported outcomes, provides an evidence
base for improved disease awareness and more efficient treatment on a pan-European scale
Chronic inflammatory disorders (CIDs) place a
high and growing demand on healthcare
resources with significant socioeconomic
implications, both in terms of cost and lost
productivity.
Encompassing a wide range of diverse long-term
conditions, including Crohn’s disease, COPD, rheumatoid
arthritis, multiple sclerosis and ulcerative colitis, they affect
millions of people worldwide, often causing a devastating
impact on health-related quality of life (HRQoL).
When measuring HRQoL, selecting the right patient-reported
outcome (PRO) tool is key to capturing the
expected outcome and understanding disparities among
different patient types. The decision-making process and
tool administration benefit greatly from the involvement of
an expert team to both define the optimal administration
time sequence as well as ensure correct study analysis.
DETERMINING HRQOL IN A DEBILITATING CID
With in-house PRO experts in markets around the world,
IMS Health has extensive experience in selecting and
generating PROs to answer specific research questions,
using validated and ad-hoc questionnaires, and in analyzing
and interpreting patient responses in a meaningful way.
Recognizing the value of these capabilities, a company
developing a new drug for a debilitating CID approached
IMS Health for help in designing and executing a survey
examining disease impact on patient HRQoL, and
determining unmet clinical need and patient satisfaction
with current treatments. A particular complexity of the
study was the number of participant countries and sites,
drawing on IMS Health’s demonstrated strengths in the top-level
and local project management of these types of projects.
study design
Following a series of internal and external discussions,
IMS Health experts in the CID area from four representative
European countries identified a retrospective medical chart
review as the best and most efficient way to collect the
The authors
núria Lara, MD, MsC
is Senior Principal, RWE Solutions HEOR,
IMS Health
Nlara@es.imshealth.com
Mark Lynam, MsC
is Senior Consultant, RWE Solutions HEOR,
IMS Health
Mlynam@es.imshealth.com
required clinical data. Eleven countries were selected for
inclusion in the study, which was designed as shown in
Figure 1.
FIGURE 1: STUdY dESIGN
12 months 12 months
2-year limited
chart review
(T-24)
Limited
chart review
completed
by physician
if available
1-year
mandatory
chart review
(T-12)
Chart review
completed
by physician
screening and
enrolment
Patient informed
consent
Index date (t0)
Patient
characteristics
completed by
physician
survey
completed by
patients

FIGURE 2: THE STUdY FOLLOWEd A PHASEd ANd STRUCTUREd APPROACH
Project
kick-off
study
protocol
CRF
site
screening
selection
study implementation
Combining the knowledge of both global and local
IMS Health experts in each of the participant countries, the
study followed a phased and structured approach (Figure 2),
commencing with clarification of the objectives, alignment
with stakeholder expectations, and definition of rules and
communication pathways. Regular, scheduled meetings
with the company and local country teams were a key part
of the overall process.
study protocol and CRF development
IMS Health began by preparing a study protocol and CRF in
conjunction with the company and CID experts, ensuring clear
case definition in a set of unambiguous inclusion/exclusion
criteria. The most appropriate validated PROs were identified
and administered (following translation into local language)
via short ad-hoc questionnaires to study patients. These
consisted of
• EQ-5D generic HRQoL questionnaire to allow
comparison of results to other disease populations as
well as the general population
• Disease-specific hRQoL questionnaire to enable small
differences between patients in the study to be
distinguished
• tsQM generic questionnaire to evaluate treatment
satisfaction related to side effects, effectiveness,
convenience and global satisfaction
• Disease-specific work productivity and activity
questionnaire to measure work- and activity-related
impact of CID on patients
• Ad-hoc questions on patient knowledge of disease to
determine patients’ understanding of their disease,
including possible progression, complications and available
treatments
• Ad-hoc questions on disease activity to understand
patients’ assessment of current disease activity and collect
information on treatment preferences.
Ethics
Committees
contracting
Data
collection
site
managment
Analysis
reporting
The licensing of proprietary PROs was handled by IMS Health,
along with translation of the validated and ad-hoc
questionnaires to the relevant language of each study country.
The PROs were translated in accordance with the authors’
instructions following pilot testing with a small sample of
patients. The master versions of the protocol and CRF were
prepared in English and translated to local languages or
adapted in English as required for Ethics Committee (EC)
presentations or study execution.
site screening and selection
Site selection is a key part of any clinical or observational
study given the role of successful patient recruitment in its
overall success. In this case, the company was keen to
maintain a clear distance from the sites included in order to
avoid creating bias and therefore requested IMS Health to
select all participant sites. Investigators from 11 different
countries were chosen and invited to take part, using the
IMS Health internal database of physicians known to have
an interest in contributing to observational research.
Potential sites were screened using a questionnaire
reflective of the study protocol to confirm availability of
evaluable patients, level of interest in the study and
ability to use an online electronic CRF (eCRF). Site
selection was conducted in the local language by
IMS Health local teams in the interests of efficiency and
to maximize the rate of acceptance.
Ethics Committees and contracting
The EC process can be time consuming and its efficient
management by local experts who understand the culture,
language and procedure, is key for an expedited and
successful outcome.
In this study, IMS Health local regulatory experts presented
the study to national and/or local ECs in accordance with
legislation. The same experts also managed contracts with
the study centers – important for ensuring conformity with
local requirements and sensitivities.

PROJECt FOCUs CHRONIC INFLAMMATORY DISORDER
Data collection
Optimal collection of data was enabled through the use of
a user-friendly eCRF developed by IMS Health for all study
countries (Figure 3). This was intuitive and allowed
physicians and study monitors to easily navigate from
screen to screen.
FIGURE 3: SAMPLE SCREENS FROM THE eCRF
Customized tracking tools and downloadable Excel reports
were built into the eCRF (Figure 4).
FIGURE 4: TRACKING TOOL ANd EXCEL dOWNLOAdS FROM THE eCRF

site management
When planning site management and monitoring, it is
important to be aware that the needs of observational
studies are very different from those of randomized clinical
trials (RCTs). All too often there is a tendency to over
complicate this element in observational research as
though it was a RCT, resulting in an unnecessarily
protracted and expensive process. In this case, once each
site had received EC approval and a signed contract was in
place, the local CRAs were able to train investigators and
their teams.
Based on previous successful experiences, and in
agreement with the company, IMS Health conducted
telephonic training with each site. This not only allowed the
sites to be trained at the most convenient time for them,
even at very short notice, but also provided study teams
with the chance to ask more questions than they would be
able (or want) to in a group training call.
During the patient recruitment period, regular follow-up
calls were made to the sites by IMS Health to motivate
patient inclusion, resolve queries and check for unreported
serious adverse events. Queries were managed on an
ongoing basis to maximize data quality and avoid
unnecessary delays in study closure, facilitated by the
integration of a query management system in the eCRF.
A number of on-site close-out visits (SCVs) were made for
source data verification. As an important cost driver of
primary data collection, limiting SCVs to a randomly
selected percentage of sites is recommended.
Analysis and reporting
Once all the data had been entered into the eCRF, it was
cleaned and the database locked for analysis. Paper PROs
completed by patients were collected on an ongoing basis
and entered into a database designed by IMS Health.
Reported diseases and co-morbidities were also coded.
As a final study step, IMS Health ran the study analysis and
report in accordance with the approved Statistical Analysis
Plan, and documented the results in a statistical report.
REALNWORLD EVIDENCE THAT IMPACTS PATIENTS’
TREATMENT
The findings of the study provided detailed insights into the
impact of the CID on patients’ HRQoL and evidenced the
unmet clinical need. They also served to drive awareness of
the societal impact of the CID and support the company’s
efforts to demonstrate the benefits of an early treatment
intervention.
The observational study was successfully executed in
11 European countries. The results have already formed the
basis of several poster presentations at international
conferences and a further publication is being prepared for
presentation in the most important international
conference for the CID in question before the end of 2014.
Scientifically important conclusions have been drawn from
the study with the potential for the evidence generated to
influence clinical practice in the future.

PROJECt FOCUs ACUTE CORONARY SYNDROME
demonstrating the impact of
non-adherence to antiplatelet therapy
in acute coronary syndrome
The linkage of real-world data on medication adherence, resource use and mortality from
multiple sources can reveal untapped potential for lower costs and improved patient outcomes
Adherence to medical therapies (the extent to
which patients take their medicines as prescribed)
is a primary determinant of treatment success,1
with significant health and cost implications.
Importantly, at a time of increased emphasis on
improving quality and efficiency in healthcare, it
is also a key modifier of health system
effectiveness.1
Non-adherence to medication has been found to reduce
the potential benefit shown in clinical trials by as much as
50%.2 It is a particular problem in cardiovascular disease,
reportedly occurring in more than 60% of patients.3
According to a Cochrane review of adherence-improving
programs, identifying effective ways of helping people
follow medical treatments could have a far greater impact
on health than any treatment itself.4 Understanding the
prevalence and clinical and economic impact of
non-adherence is essential to achieving this goal.
ADHERENCE IN ACUTE CORONARY SYNDROME
Acute coronary syndrome (ACS) refers to a range of
emergency cardiac conditions that are triggered by a
sudden reduction of blood flow to the heart. Current
practice guidelines for ACS recommend the administration
of antiplatelet therapy (AT) both during and after
hospital discharge.5,6 Non-adherence to AT has been
associated with higher risk of thrombosis, myocardial
infarction and mortality.7
To date, real-world evidence on the relationship between
adherence with AT post-discharge and patient outcomes
has been restricted to data from self-reported surveys on
medication adherence, and administrative data from two
integrated healthcare systems.8,9,10 In addition to their lack
of generalizability, these studies have been limited by lack
of access to data on inpatient prescriptions for AT,
preventing determination of long-term patient persistence
from the point of initial treatment within the hospital. In
addition, no study has reported on the relationship
between AT adherence and healthcare costs.
The author
Jerrold hill, PhD, Ms
is Director, RWE Solutions HEOR, IMS Health
J.hill@us.imshealth.com
DOCUMENTING ANTIPLATELET TREATMENT
ADHERENCE POSTNDISCHARGE
Against this background, and to better understand the
mortality, readmission risk and cost implications of failure
to adhere to AT therapy, IMS Health undertook a study to
document AT adherence following discharge from
hospitalization for ACS. The study also sought to estimate
the relationship between adherence post-discharge and
the following outcomes
• Rate of hospital readmission within 30 days and one year • Mortality within one year post-discharge (and time to
death) • Healthcare costs at one year post-discharge
study data
Patients hospitalized for ACS between July 2009 and July
2012 were identified from IMS Comprehensive Disease
Records (CDRs) for ACS. CDRs links anonymous patient-level
data from four separate IMS Health databases
1. PharMetrics Plus™ health plan claims
2. Hospital Charge Data Master (CDM)
3. Ambulatory Electronic Medical Record (EMR) data
4. Mortality data derived from the Social Security Death
Index (SSDI)
The linkage allows a comprehensive set of measures not
available in a single data source. It is made possible via a
unique patient ID, created through the IMS Health patented
and HIPAA-compliant encryption methodology. This enables
deterministic matching of patients across databases.
Three sources of data from the ACS CDRs were used for the
study: more than 500,000 patients from PharMetrics Plus and
410,000 from IMS Hospital Charge Master were linked to 130
million individuals with mortality data derived from the SSDI.

To be included in the study, patients were required to
• Have an ACS diagnosis and hospitalization in
PharMetrics Plus and CDM • Be enrolled in PharMetrics Plus six months prior to index
hospitalization for ACS with 12 months of follow-up data
post-index or until death within one year post-discharge • Have data on death status in IMS mortality data • Be prescribed AT at index ACS hospitalization • Be alive more than one month post-discharge
The prerequisite for patients to survive at least one month
was necessary because the date of death in the database
is recorded as calendar month and year – not calendar
day. As a result, the relationship between period of
non-adherence and date of death in the first month was
unknown. Mortality risk was therefore evaluated among
patients for whom there was at least one month of adherence
data prior to data on death in the subsequent month.
Methodology
Patients with no diagnosis of ACS 180 days prior to hospital
admission and with confirmed inpatient AT therapy were
followed until the earlier of 360 days post-discharge or date
of death. Adherence was measured by
• time to first fill post-discharge Four dichotomous
variables measured whether the patient filled a
prescription within 30 days, 60 days, 90 days, or within
one year post-discharge. Indicators of early fill dates
after discharge are valuable since risk of mortality and
readmission is highest in the months immediately after
discharge
• Proportion of days covered (PDC) from days’ supply
on Rx claims This is measured as the percent of days
one year post-discharge or up to the time of the event
of interest (death or readmission) or censure from sample.
• PDC at each month post-discharge for use as the
adherence measure in Extended Cox models with time
varying adherence
Unadjusted estimates of study outcomes covered four
categories of adherence: 0%; 1-39%; 40-79%; ≥80%.
Adjusted estimates controlled for patient demographics,
hospital characteristics, ACS hospital interventions and
cardiovascular risk factors, using logistic regression
(mortality, readmission), Cox Proportional Hazards and
extended Cox (mortality, readmission) and a Generalized
Linear Model (costs).
FIGURE 1: TIME TO FIRST ANTIPLATELET FILL
Only 50% of patients !lled an AT
prescription at 30 days post-discharge
Filled Rx for AT
within 1 year
67.0%
Filled Rx for AT
within 90 days
62.8%
Filled Rx for AT
within 60 days
59.9%
Filled Rx for AT
within 30 days
50.1%
PRINCIPAL STUDY RESULTS
The analysis showed that of the 2,994 patients selected for
analysis, only 50% filled an AT prescription at 30 days
post-discharge and only 67% by the end of year one
(Figure 1). Patients who filled for AT within 30 days had a
lower readmission rate (7.4%) compared to those with no
fill (14.2%). The greater benefits associated with first AT fill
reflected the higher risk of death and readmission in the
months immediately following index discharge.
After adjusting for patient characteristics, high adherence
to AT (80%) after hospital discharge for ACS was linked
to significantly lower mortality E
(70%), with death
increasing markedly as adherence fell below 80%
(Figure 2).
FIGURE 2: AdHERENCE ANd MORTALITY ONE YEAR POST-dISCHARGE continued on next page
Adjusted results show that the impact of adherence
on mortality is markedly higher when over 80%
COXPH Results
Mortality increases markedly as adherence
0%
1% - 40%
40% - 80%
≥80%
falls below 80% (unadjusted)
4.1%
4.5%
3.1%
0.7%
1% - 40%
40% - 80%
≥80%
(CI: 0.583 - 1.674 )
(CI: 0.512 - 1.418 )
(CI: 0.166 - 0.503)
Extended COX Results
Time varying
adherence
0.289
0.319
(CI: 0.187 - 0.544)
Mortality rate Hazards ratio
Reference group for Cox models is patients not lling a script for AT post-discharge. P0.001 for all hazards ratios.
0.988
0.852

PROJECt FOCUs
ACUTE CORONARY SYNDROME
Adherence of ≥80% was also associated with substantially
lower readmission rates (Figure 3) and lower total
healthcare costs at one-year post-hospital discharge.
NEW INSIGHTS FOR IMPROVED OUTCOMES AND
REDUCED COSTS
This study provides new and important evidence-based
insights into the impact of AT adherence on mortality,
readmission risk and costs in ACS. The results reveal
significant opportunities to reduce healthcare costs as well
as improve clinical outcomes in ACS through efforts to
increase AT adherence. In addition, the study demonstrates
the value of linking data across large administrative
databases. Specifically, the study showed
• The potential to link adherence, healthcare cost/use
and mortality data from multiple healthcare
administrative data sources to enhance research
capabilities, using data sources such as
IMS Comprehensive Disease Records for ACS.
• Valuable applications from linking data across multiple
administrative data sources including potential
preclusion of costly primary data collection; addition
of mortality data similar to this study; and linkage of
large patient populations to extend geographic and
provider coverage, thus increasing confidence to
generalize study results.
FIGURE 3: ADHERENCE AND HOSPITAL READMISSION ONE YEAR
POST-DISCHARGE (UNADJUSTED)
0%
1%- 40%
40% - 80%
≥80%
Readmission rate
Adherence - PDC
P0.0001 for 30 day and 1-year readmissions
44.8%
39.2%
40.8%
16.9%
Acknowledgement
The outstanding scientific merit of this study was recognized with an ISPOR Research Presentation Podium Award following
oral presentation of the findings at the ISPOR 19th Annual International Meeting in Montreal, May 31–June 4, 2014.*
Dr Hill gratefully acknowledges the contribution of his co-authors.
*Hill J, Pokras S, Makin C, Schabert VF, Nelson M, Foody J. Non-adherence to antiplatelet therapy after hospitalization for acute
coronary syndrome (ACS) increases readmissions, mortality, healthcare use and costs. Value in Health, 2014; 17: A8
1 World Health Organization. Adherence to long-term therapies: Evidence for action. Geneva: WHO, 2003
2 Cherry SB, Benner JS, Hussein MA, Tang SS, Nichol MB. The clinical and economic burden of nonadherence with antihypertensive and lipid-lowering
therapy in hypertensive patients. Value in Health, 2009, Jun; 12(4): 489-97
3 Kravitz RL, Hays RD, Sherbourne CD, DiMatteo MR, Rogers WH, Ordway L, Greenfield S. Recall of recommendations and adherence to advice among
patients with chronic medical conditions. Arch Intern Med, 1993, Aug 23; 153(16): 1869-78
4 Haynes RB, Ackloo E, Sahota N, McDonald HP, Yao X. Interventions for enhancing medication adherence. Cochrane Database Syst Rev 2008;
(CD000011)
5 Jneid H, Anderson JL, Scott Wright R, Adams CD, Bridges CR, Casey DE, et al . 2012 ACCF/AHA Focused update of the guidelines for management of
patients with unstable angina/non-ST elevation myocardial infarction. (Updating the 2007 Guideline and Replacing the 2011 Focused Update): A Report
of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation, 2012; 176: 875-910
6 O'Gara PT, Kushner FG, Ascheim DD, Casey DE, Jr, Chung MK, de Lemos JA, et al. 2013 ACCF/AHA Guideline for the management of ST elevation
myocardial infarction. A report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines.
Circulation, 2013; 127: 529-555
7 Jarvie JL, Foody JM. Predictors of early discontinuation of dual-antiplatelet therapy: Room for improvement. Circulation, 2010; 22: 946-948
8 Valgimigli M, Campo G, Arcozzi C, Malagutti P, Carletti R, Ferrari F, et al. Two-year clinical follow-up after sirolimus-eluting versus bare-metal stent
implantation assisted by systematic glycoprotein IIb/IIIa Inhibitor Infusion in patients with myocardial infarction: Results from the STRATEGY study.
J Am Coll Cardiol, 2007; 50: 138-145
9 Ho PM, Peterson ED, Wang L, Magid DJ, Fihn SD, Larsen GC, et al. Incidence of death and acute myocardial infarction associated with stopping
clopidogrel after acute coronary syndrome. JAMA, 2008; 299: 532-539
10 Ho PM, Tsai TT, Wang TY, Shetterly SM, Clarke CL, Go AS, et al. Adverse events after stopping clopidogrel in post–acute coronary syndrome patients:
Insights from a large integrated healthcare delivery system. Circ Cardiovasc Qual Outcomes, 2010; 3: 303-308

PROJECt FOCUs RWE-BASED DISEASE MANAGEMENT
Modeling disease management above the
brand with real-world evidence
Insights from RWE can illustrate the value of treatments alongside the full suite of care
activities to inform and enable comprehensive disease management
To operate in a value-oriented healthcare
marketplace, decision makers require new
approaches to disease management that reduce
costs while supporting high-quality patient
outcomes. However, with advances in medical
science and the rising number of novel alternative
therapeutic interventions, disease management
is becoming more complex. New data and tools
are needed to understand how individual
treatment decisions fit into the overall
management of a disease to help drive care
efficiency in a comprehensive manner.
The tremendous growth of real-world evidence is creating
opportunities to transform the decision-making
landscape. Data-driven insights are increasingly required
to inform treatment choices and resource allocation for
patient care at a broader level, based on a complete
picture of disease management options. Manufacturers
can play a role in supporting patient health at a holistic
level by expanding their above-the-brand offerings and
aligning with external stakeholders seeking to improve
patient outcomes.
RWE-based disease management tools can assist healthcare
payers and providers in their efforts to understand the value
of new treatments alongside the full suite of patient care
activities. For one leading pharmaceutical organization, the
development of such a tool in support of its portfolio has
paved the way for evidence-based discussions with
decision makers on the potential of different disease
management programs to promote efficiency
improvements and advance patient health.
BUILDING A VALUE PROPOSITION ABOVE THE BRAND
The company was seeking to position itself as a partner
with several external customers who bear financial risk of
disease management decisions. To help meet this objective,
IMS Health was asked to design and develop a tool for the
The authors
Julie Munakata, Ms
is Senior Principal, RWE Solutions HEOR
IMS Health
jmunakata@us.imshealth.com
stacey Kowal, Ms
is Director, RWE Solutions HEOR
IMS Health
skowal@us.imshealth.com
Cheryl Ferrufino
is Senior Consultant, RWE Solutions HEOR
IMS Health
cferrufino@us.imshealth.com
Beth Wehler, MPh
is Consultant, RWE Solutions HEO
IMS Health
bwehler@us.imshealth.com
company’s customer-facing field team to address questions
surrounding pharmaceutical and healthcare cost, utilization
patterns and the impact of disease management
alternatives (Figure 1).
MODELING DISEASE MANAGEMENT LEVERS AND
TOTAL HEALTHCARE COST
IMS Health began by collecting cost and utilization data for
patients in three different disease areas from a custom
RWE platform that integrates the IMS National Prescription
Audit (NPA™) database with the IMS PharMetrics Plus
database. For each disease, fully-adjudicated pharmacy and
medical claims for patients meeting key inclusion and
exclusion criteria were combined to create a longitudinal
cohort with a pre-index of 180 days and a follow-up of
360 days. Results of cohort analyses represented total
direct medical costs and can be interpreted as the cost of
care to a fiscal risk-bearing stakeholder in the US, including
healthcare payers and providers.

PROJECt FOCUs
RWE-BASED DISEASE MANAGEMENT
FIGURE 1: THE TOTAL HEALTHCARE COST TOOL AddRESSES KEY PAYER ANd PROVIdER QUESTIONS
Questions
answered by
RWE-based
insights from the
total healthcare
cost tool
• What are the national/
regional benchmarks for
the total cost of care
across diseases?
• Where are the
opportunities to make
a meaningful impact
on total cost patterns?
• How do treatment
options help to shift
the cost curve?
Actionable
options with
the highest
impact on
fiscal savings
Using the output on health service utilization and direct
medical costs from the custom RWE platform, IMS Health
developed a total healthcare cost tool to support the
company’s discussions on direct medical cost trends and
the potential of concepts proven useful in disease
management to change these trends. The concepts
were derived from the medical literature to identify
’levers’ that could shift total costs, including patient
and physician behaviors, treatments/interventions and
healthcare quality initiatives.
The model compares the current, RWE-based costs of
disease to the projected costs after applying disease
management levers, with savings estimated by cohort
analyses in the RWE platform. All costs and disease trends
are based on a one-year time horizon. The levers focus on
Key questions
among payers
providers facing
a high fiscal
burden
• How much am I
spending to provide
healthcare and how do
my costs compare to
others?
• How do I best allocate
limited resources to
meet population needs?
• How can new treatment
alternatives or treatment
approaches change cost
trends?
broad disease concepts as well as above-the-brand
treatment concepts that dovetail with brand-level
propositions for the company’s product portfolio.
Developed in MS Excel, the tool includes three distinct
disease modules with nationally- and regionally-representative
benchmark data presented in a user-friendly
format. The model enables ad hoc explorations of current
cost trends and the impact of potential actions (levers)
using benchmark or custom data inputs. For each disease,
model inputs and results can be dynamically viewed across
changes in patient age, geographies (national, regional),
cost types (allowable medical costs, patient out-of-pocket
payments), care settings (inpatient, outpatient, pharmacy)
and pre-defined patient sub-groups (all diagnosed patients,
newly diagnosed patients, treated or untreated patients).
FIGURE 2A: HYPOTHETICAL IMPACT OF ALTERNATIVE dISEASE MANAGEMENT ACTIVITIES IN HEART dISEASE
- CHANGE IN TOTAL HEALTHCARE COSTS
Relative savings from change in ‘lever’ Current costs
-20.00% -15.00% -10.00% -5.00% 0.00%
Change in total healthcare costs
Reducing the risk
of major bleeds
Reducing the risk
of ischemic bleeds
Closing the
treatment gap
Improved management
of diabetes co-morbidity
Reducing the likelihood
of readminssion

FIGURE 2b: HYPOTHETICAL IMPACT OF ALTERNATIVE dISEASE MANAGEMENT ACTIVITIES IN HEART dISEASE
- CURRENT ANd FUTURE HEALTHCARE COSTS
Improved
management
of diabetes
co-morbidity
Absolute savings from
change in ‘lever’
Closing
the
treatment
gap
Reducing
the
likelihood of
readmission
Reducing
the risk
of ischemic
stroke
Reducing
the risk
of major
bleeds
Current healthcare costs
Future healthcare costs
$500m
$450m
$400m
$350m
$300m
$250m
$200m
$150m
$100m
$50m
$0m
FIELDNREADY TOOL WITH REALNTIME UPDATES
The field-ready tool presents real-time updates of
projected impact based on external customer inputs for
costs, population characteristics and potential changes in
disease management activities. For example, a payer
could compare the likely impact of several new disease
management efforts in heart disease to help inform
future decisions on resource allocation. Figures 2A and 2B
present a scenario evaluating the impact of a hypothetical
25% improvement from baseline lever values, based on
national benchmark data for one million covered lives.
Examples of levers modeled for heart disease are shown
in Table 1.
NOVEL DYNAMIC SUPPORT FOR INTERACTIVE
CUSTOMER DISCUSSIONS
Through working with IMS Health, the company has
gained a novel and dynamic tool that leverages RWE to
move beyond the currently published evidence,
presenting nationally representative benchmark data
with the ability to quickly view differences across age
groups, geographies, cost types and care settings. The
tool facilitates and informs interactive engagement with
external customers on several levels. Firstly, by
quantifying the magnitude of costs across diseases and
highlighting the value of addressing avoidable outcomes
or improving symptom management, it supports the
foundation of the value proposition for the company’s
portfolio of products in the disease area. Secondly, by
identifying challenges in disease management at a more
macro level, it helps the field team transition to
discussions around the benefits of the company’s above-the-
brand offerings, thereby also serving as a pathway to
its suite of resources for improving population health.
TAbLE 1: SELECTEd LEVERS MOdELEd FOR HEART
dISEASE MANAGEMENT
Lever
Closing the treatment gap
Changing the likelihood of
readmission
Reducing the risk of ischemic
stroke
Improving the management of
diabetes co-morbidity
Reducing the risk of major bleeds
Benefits
Closing the anticoagulant treatment gap in
treatment-eligible patients (CHADS2 score of
2) who are not contraindicated for
treatment can improve disease management
and reduce avoidable costs
Improving inpatient care and post-hospitalization
treatment can reduce the
likelihood of readmission and associated costs
Effective treatment and disease management
could potentially reduce the risk of ischemic
stroke and stroke-related healthcare costs
Care activities and education efforts can help
reduce the risk of contracting diabetes and
reducing any avoidable, excess diabetes-related
costs (effective treatment) in patients
with heart disease
Effective disease management with
treatments with a low (or reduced) risk of
avoidable treatment-related bleeding events
can reduce healthcare costs

IMs hEALth RWE sOLUtIOns hEOR OVERVIEW
Enabling your real-world success
IMS Health has a globally unique and powerful RWE approach to understand
patient outcomes and support successful market access. It is built off a long history
in HEOR, scientific methodologies, real-world data and cross-stakeholder collaborations.
Our approach is designed to enable your success
• Largest global teamwith HEOR, epidemiology, drug safety and RWE expertise based across 18 countries
• strong scientific voice reflected in over 2,600 publications
• Established success in generating and communicating RWE to advance stakeholder engagement,
including guideline development, compliance program management and managed entry agreements
• Market leadership in developing and adapting robust economic models
• Most advanced RWE capabilities and technology including data sourcing, warehousing,
integration, curation and protection, driving powerful scientific and commercial insights
• Broadest and deepest collection of scientifically validated, anonymous patient-level data assets,
enabling therapy area, market-specific and global insights
Leadership and innovation across the RWE and HEOR spectrum
Technology-Enabled
IMS HEALTH
REAL-WORLD
EVIDENCE
SOLUTIONS
HEOR
Analytics
Services
Dat
Da
at ata oWo World aeR
Real-l- -and Engagement
D
d ld rl or Wo
Real-World Data
IMS LifeLinkTM – the broadest and deepest
collection of scientifically validated,
anonymous patient-level data assets
• Health plan claims
• PharMetrics PlusTM
• Longitudinal Rx
• Electronic medical records
• Hospital disease
• Custom data sourcing
Technology-Enabled Analytics
Analytic tools that leverage powerful
technologies to deliver scientific and
commercial insights efficiently
• data integration linking
• Evidence platform development
• data warehouse/data marts
• User interface sophisticated
analytics library
• IMS Evidence 360™
Services and Engagement
Generating scientifically and commercially
relevant insights using novel patient-centric metrics
Outcomes Research,
Epidemiology drug Safety
• Evidence generation
• PRO, QoL studies
• Late-phase studies
• Mixed methods
• database studies, CER
• drug utilization studies
• Comparative safety
outcomes studies
Strategic Support
• Corporate, franchise and product
RWE strategy development
• Evidence plans aligned with
commercial priorities
• dynamic marketing plans and
outcomes-based commercialization
• Stakeholder engagement
• RWE training/organizational
readiness
Market-Level Engagement
• Patient journeys, market
landscapes and forecasts
• Incentive compensation
approaches
• Performance management
approaches
• Targeted media and patient
(where allowed) engagement
models
Health Economic Modeling
Market Access
• Health economic evaluations
• Global models local
adaptations
• budget impact
• Indirect comparisons
• Value dossiers
• HTA readiness
• Value communication

IMs hEALth RWE sOLUtIOns hEOR LOCATIONS
Global scope, local expertise
IMS RWE Solutions HEOR experts are located in 18 countries worldwide and they
have published on projects completed in more than 50 countries on all continents.
Your primary contacts
Jon Resnick, Vice President and General Manager
One IMS drive, Plymouth Meeting, PA 19462, USA • Tel: +1 610 834 0800 • Jresnick@imshealth.com
Dr. Jacco Keja, Senior Principal
210 Pentonville Road, London N1 9JY, UK • Tel: +31 (0) 631 693 939 • Jkeja@nl.imshealth.com
Dr. Patrik Sobocki, Senior Principal
Sveavägen 155, SE-113 46 Stockholm, Sweden • Tel: +46 (0) 8 508 999 95 • Psobocki@se.imshealth.com
IMS RWE Solutions HEOR key office locations
ASIA PACIFIC
REGIONAL HEADQUARTERS
8 Cross Street #21-01/02/03
Singapore 048424
Tel: +65 6412 7365
EUROPE
210 Pentonville Road
London N1 9JY
United Kingdom
Tel: +44 (0) 20 3075 4800
JAPAN
Toranomon Towers
4-1-28 Toranomon
Minato-ku
Tokyo 105-0001
Japan
Tel: +81 3 5425 9541
LATIN AMERICA
Insurgentes Sur # 2375
5th Floor, Col. Tizapan
México City d.F. - C.P. 01090
México
Tel: +52 55 5089 5205
NORTH AMERICA
11 Waterview boulevard
Parsippany, NJ 07054
USA
Tel: +1 973 316 4000
AUSTRALIA
Level 5, Charter Grove
29-57 Christie Street
St Leonards, NSW 2065
Australia
Telephone: +61 2 9805 6800
BELGIUM
Medialaan 38
1800 Vilvoorde
belgium
Tel: +32 2 627 3211
CANADA
16720 Route Transcanadienne
Kirkland, Québec H9H 5M3
Canada
Tel: +1 514 428 6000
CHINA
7/F Central Tower
China Overseas Plaza
Jianguomenwai Avenue,
Chaoyang district
beijing 100001
China
Tel: +86 10 8567 4414
FRANCE
29ème Etage
Tour Ariane
5-7 Place de la Pyramide
92088 La défense Cedex
France
Tel: +33 1 41 35 1000
GERMANY
Erika-Mann-Str. 5
80636 München
Germany
Tel: +49 89 457912 6400
ITALY
Viale Certosa 2
20155 Milano
Italy
Tel: +39 02 69 78 6721
SOUTH KOREA
9F Handok building
735 Yeoksam1-dong
Kangnam-ku Seoul
135-755
S. Korea
Tel: +82 2 3459 7307
SPAIN
dr Ferran, 25-27
08034 barcelona
Spain
Tel: +34 93 749 63 00
SWEDEN
Sveavägen 155/Plan9
11346 Stockholm
Sweden
Tel: +46 8 508 842 00
SWITZERLAND
Theaterstr. 4
4051 basle
Switzerland
Tel: +41 61 204 5071
Tel: +44 (0) 20 3075 4800
TAIWAN
18/F 216 Tun Hwa South Road
Sec 2
Taipei 10669
Taiwan
ROC
Tel: +886 2 2376 1836
UNITED KINGDOM
London N1 9JY
United Kingdom
Tel: +44 (0) 20 3075 4800
UNITED STATES
8280 Willow Oaks
Corporate drive, Suite 775
Fairfax, Virginia 22031
USA
Tel: +1 (703) 992 1025
One IMS drive
Plymouth Meeting
PA 19462
USA
Tel: +1 610 834 0800
For further information, email RWEinfo@imshealth.com or visit www.imshealth.com/rwe

IMs hEALth RWE sOLUtIOns hEOR EXPERTISE
Expertise in depth
The IMS Health RWE Solutions HEOR team brings unrivalled experience and specialist
knowledge from industry, consulting, government and academia globally, and includes
leading scientists in epidemiology, drug safety and risk management. With proven expertise
in all key therapy areas, we have a track record of helping clients meet the growing demands
of an increasingly complex pharmaceutical landscape.
Our senior team
Jean-Marc Aubert, M.Eng, MsC
• Jean-Marc Aubert is a Senior Principal, supporting healthcare providers, health authorities and payers.
• Jean-Marc has extensive pharmaceutical experience ranging from real-world effectiveness and the regulatory process to
sales force, marketing effectiveness and brand performance. His background includes roles as a partner heading business
development in the healthcare sector at Jalma, as deputy director at CNAMTS (French National Health Insurance Fund for
Salaried Workers) and as Chief of Staff of the State Secretary for Health Insurance.
• An expert in the French healthcare system, market access, commercial effectiveness, RWE and HEOR, Jean-Marc holds a
Master’s degree in Engineering and a Master of Science degree, both from École Polytechnique, France; a Specialist
Postgraduate diploma in Statistics and Economics from École Nationale de la Statistique et de l'Administration
Économique (ENSAE); and a Specialist Postgraduate diploma in Economics (dEA) from École des Hautes Études en
Sciences Sociales (EHESS), France.
Yumiko Asukai, MsC
• Yumi Asukai is a Principal, specializing in the development of economic models across the product lifecycle and the
interpretation of model outputs for strategic market access and value demonstration. Her expertise in this field spans
from early strategic modeling through to global core cost-utility models.
• Yumi’s background includes roles at Fourth Hurdle Consulting and in healthcare and business consulting in San Francisco
and Tokyo, where she focused on comparative studies of health policies between Japan and the US complemented by
analyses of primary data. Yumi has worked extensively in the cardiovascular, oncology and respiratory disease areas and
she is part of a global modeling taskforce for COPd composed of academic and industry members.
• Yumi holds a Master's degree in Health Policy, Planning and Financing from the London School of Hygiene Tropical
Medicine and the London School of Economics, and a bachelor's degree in Political Science from Stanford University.
Karin Berger, MBA
• Karin berger is a Principal, with a focus on RWE, PROs and cost-effectiveness evaluation analyses at a national and
international level.
• Formerly Managing director of MERG (Medical Economics Research Group), an independent German organization
providing health economics services to the pharmaceutical industry, university hospitals and European Commission,
Karin has more than 15 years experience in the health economics arena. She lectures at several universities, has published
extensively in peer-reviewed journals, and regularly presents at economic and medical conferences around the world.
• Karin graduated as diplom-Kaufmann (German MbA equivalent) from the bayreuth University, Germany, with a special
focus on health economics.
Christopher M. Blanchette, PhD, MsC, MBA, MA
• Dr. Christopher Blanchette is a Principal, experienced in leading clinical and health services research programs
and teams in the pharmaceutical industry and consultancy. He is skilled in the use of healthcare databases to
assess clinical and economic outcomes, with a particular focus on chronic diseases.
• Prior to joining IMS Health, Chris was Associate Dean for Research Public Engagement, Director of Data
Sciences Business Analytics and an Associate Professor of Public Health Sciences, College of Health
Human Services, University of North Carolina. He was also a Research Health Scientist at the W.G. (Bill)
Heffner VA Medical Center, Salisbury, NC.
• Chris is a peer reviewer for the AHRQ and PCORI, editor-in-chief for Drugs in Context, and editorial board
member for Journal of Medical Economics and Current Medical Research and Opinion. He holds a PhD in
Pharmaceutical Health Services Research with a concentration in Pharmacoepidemiology and a Master of
Science degree in Epidemiology from the University of Maryland; an MBA in Pharmaceutical Healthcare
Marketing from Saint Joseph’s University; and a Master of Arts degree in Medical Sociology from the University
of North Carolina.

Ian Bonzani, PhD, BsC
• dr. Ian bonzani is a Principal, leveraging his scientific background and consulting experience to help clients create and
implement franchise strategies in the pricing and market access and RWE space. He manages large-scale RWE
engagements across stakeholders, functions and geographies.
• With a background that includes roles in the IMS Consulting Group in Europe, Ian has expertise in RWE strategy, franchise
evidence generation strategy, real-world data sourcing, and data marts/technology platforms. He has been involved in a
wide range of global projects, including RWE planning, large-scale data mart creation and implementation, collaborative
engagement models, and disruptive evidence generation.
• Ian holds a Phd in Regenerative Medicine from Imperial College London (Marshall Scholar) and a bachelor of Science
degree in biomedical Engineering from Worcester Polytechnic Institute, Worcester, Massachusetts.
Richard Borrelli, MBA
• Richard borrelli is a Principal, leading a team in Canada supporting evidence-based solutions for healthcare stakeholders.
He is recognized for his expertise in leveraging longitudinal patient-level data to better understand and quantify real-world
treatment pathways, and heads development of innovative research protocols involving EMR supplemented with
patient and physician feedback.
• Richard has extensive experience leveraging Canadian pharmacy and claims data to evaluate patient utilization of
medicines. These insights have informed decision making for market access, health economic, sales and marketing
divisions of pharmaceutical companies, as well as payer organizations in the country. Richard also utilizes Canadian EMR
to describe indirect and direct burden of illness while evaluating patient real-world outcomes.
• Richard holds an MbA (with distinction) from deGroote School of business, McMaster University, and a bachelor of
Commerce degree from the University of Toronto.
nevzeta Bosnic, BA
• Nevzeta bosnic is a Principal, focused on managing projects to meet the broad spectrum of client needs in the Canadian
pharmaceutical market.
• Formerly director of Economic Consulting at brogan Inc, Nev has led many strategic consulting, policy and data analyses
for pharmaceutical clients, government bodies and academic institutions in Canada. She has extensive knowledge of
public and private drug plans across the country and in-depth expertise and experience on the drug reimbursement process.
• Nev holds a bachelor’s degree in business Economics from the School of Economics and business at the University of
Sarajevo, bosnia-Herzegovina.
Chakkarin Burudpakdee, PhARM. D
• dr. Chakkarin burudpakdee is a Principal, with extensive experience in HEOR and strategic consulting, including product
value development and communication, market entry strategies and lifecycle management plans. He has led teams in
observational research, economic modeling, patient and provider surveys, systematic reviews and meta-analyses.
• Prior to joining IMS Health, Chakkarin was VP, Evidence development at MKTXS, where he built and oversaw scientific
direction of the HEOR department and developed relationships with academic institutions around the world that
provided access to patient-level data for observational research. He began his career as a clinical analyst at ValueMedics
Research LLC.
• Chakkarin holds a Pharm.d from Philadelphia College of Pharmacy and Science, now University of the Sciences in
Philadelphia, and is a Research Assistant Professor in the College of Health and Human Services, University of North
Carolina at Charlotte.
Joe Caputo, BsC
• Joe Caputo is Regional Principal, leveraging more than 20 years experience in the pharmaceutical sector to help clients
address the challenges of global reimbursement and market access throughout the drug development program in the
Asia-Pacific region. He has led numerous projects involving payer research, value dossiers, local market access models
and HTA submissions.
• Joe's background includes industry roles in drug development, sales and marketing, and UK and global health outcomes,
as well as consulting in health economics. He has wide-ranging knowledge of the drug development process at both
local and international level and a unique understanding of evidence gaps in light of reimbursement and market
access requirements.
• Joe holds a bachelor's degree in Applied Statistics and Operational Research from Sheffield Hallam University, UK.
Adam Collier, MsC
• Adam Collier is a Senior Principal, with responsibility for consulting and data related to IMS Health patient-level data
assets in the UK. He has 18 years commercial experience in the UK and European healthcare industry.
• Adam’s background spans pharmaceuticals, consulting and healthcare provision, allowing an unusually broad view of
the challenges inherent across the healthcare arena. He spent nine years at GlaxoSmithKline in roles within customer and
trading strategy, commercial analysis and European marketing, and two years at Accenture, where he also completed a
secondment to the Medicines Healthcare Products Regulatory Agency (MHRA) to work on their patient data asset
GPRd (now CPRd). Prior to joining IMS Health, he spent several years with a private healthcare provider.
• Adam holds a Master’s degree in Chemistry from the University of Oxford.

neil Corner
• Neil Corner is a Leader, RWE Solutions, supporting government, academics and the pharmaceutical industry in
understanding and delivering health outcomes data, with a focus on mHealth, integrated patient data and EMRs,
including the creation of interactive electronic patient registries.
• Neil has 27 years experience in the pharmaceutical industry in the UK, US, EMEA and Canada, 16 of which were spent at
Janssen Pharmaceuticals, including the post of Global Commercial Leader. Prior to joining IMS Health, where his roles
have included international franchise lead for patient and medical data, Neil led Helix Healthcare, a division of Quintiles.
• Neil is the author of several publications on EMR data validation, RWE in the Canadian market and Customer Relationship
Management. His research and development activities currently focus on the innovative design and construction of
integrated health data ecosystems to create outcomes in the world of big data.
Bruce Crawford, MA, MPh, BsC
• bruce Crawford is a Senior Principal, with over 20 years consulting experience and expertise in prospective study design,
patient-reported outcome evaluations, cost-effectiveness analyses and reimbursement.
• Over the past 13 years, bruce has worked on projects throughout Asia and Japan. He was previously Managing director
Asia and Senior VP at Adelphi, prior to which he was Operations director at Mapi Values. He has worked in managed care
and for a major CRO as a health economist, and been involved in research and training with the US FdA, the Japanese
PMdA, and the Thailand FdA and National List of Essential drugs committee.
• bruce has written and lectured on pharmacoeconomic and outcomes research methodologies and impacts on study
validity, and recently held appointments as Adjunct Project Professor of HTA and Public Policy at Tokyo University
Graduate School of Public Policy and as Adjunct Instructor at Kyoto University, School of Medicine and Public Health,
dept. of Pharmacoepidemiology. He holds a Master of Arts degree in Economics and a bachelor of Science degree in
Mathematics and Economics from the University of New Hampshire, and an MPH, specializing in Epidemiology and
biostatistics, from Tufts University School of Medicine.
Mitch DeKoven, MhsA
• Mitch deKoven is a Principal, leading teams in a variety of projects, including value development plans, retrospective
database studies and observational surveys.
• Prior to joining IMS Health, Mitch was an Associate director of Reimbursement and Market Access at ValueMedics
Research LLC. His previous roles include Manager of Reimbursement Services at United bioSource Corporation’s Center for
Pricing Reimbursement, Consultant with CHPS Consulting, and Program Manager of the Center for Cancer and blood
disorders Children’s National Medical Center in Washington, dC, a position he held after completing an administrative
fellowship with the Johns Hopkins Health System.
• A past president of the board of directors of the Lupus Foundation of America Greater Washington Chapter, Mitch serves
on six editorial advisory boards and is a peer reviewer for a number of international healthcare journals. He has also
authored several articles. Mitch holds an MHSA from the University of Michigan School of Public Health and a bachelor’s
degree in Spanish from Washington University in St. Louis.
Frank-Ulrich Fricke, PhD, MsC
• dr. Frank-Ulrich Fricke is a Principal at IMS Health and Professor for Health Economics, Georg-Simon-Ohm University of
Applied Sciences, Nuremberg in Germany, with a focus on health economic evaluations, market access strategies and
health policy.
• Formerly a Managing director of Fricke Pirk GmbH, and previously Head of Health Economics at Novartis
Pharmaceuticals, Frank-Ulrich has conducted health economic evaluations across a wide range of therapeutic areas,
developing a wealth of experience in pricing, health affairs and health policy. As a co-founder of the NIG 21 association, he
has forged strong relationships with health economists, physicians and related researchers working in the German
healthcare system.
• Frank-Ulrich holds a Phd in Economics from the bayreuth University, and an MbA equivalent from the Christian-Albrechts-
University, Kiel.
Joshua hiller, MBA
• Joshua Hiller is a Senior Principal, supporting the strategic planning and development of IMS Health capabilities for
data sourcing, integration, analytics and studies. He is also currently serving as Alliance director in the company’s
collaboration with AstraZeneca for the advancement of RWE.
• during a career that includes roles in market analytics, government and healthcare consulting in both the US and UK,
Joshua has led a wide range of projects for clients in the pharmaceutical and biotech sector as well as industry
associations. He has extensive experience in pharmaceutical pricing, contracting, market landscape development,
supply management, cross border trade, lifecycle management, competitive defense, generics market drivers and
account management, with expertise across US and European markets.
• Joshua holds an MbA (beta Gamma Sigma) from Columbia business School, New York, and a bachelor of Science
degree in Mathematics from James Madison University, Virginia.

Benjamin hughes, PhD, MBA, MREs, MsC
• dr. ben Hughes is a Vice President, leading development of the company’s RWE strategy and offering. He has helped
many clients in the pharmaceutical industry to articulate and implement their RWE strategies through definition of
RWE vision, business cases for RWE investments, capability roadmaps, partnerships, brand evidence reviews, HEOR
function design, RWE training programs and related clinical IT strategies.
• Previously head of the European RWE service line at McKinsey Co, ben has extensive experience advising healthcare
stakeholders on health informatics and RWE-related topics. This includes work on France’s electronic health record
strategy, EMR adoption strategy for governments across Europe and Asia, data releases to support the UK’s
transparency agenda, and the development of payer health analytics and RWE capabilities across countries in Europe.
• A widely published author on health informatics, ben holds a Phd in Medical Informatics from ESAdE barcelona, an MbA
from HEC Paris, and Masters’ degrees in Research from ESAdE barcelona and in Physics from University College, London.
Kjel A. Johnson, PhARM.D, BCPs, FCCP, FAMCP
• dr. Kjel Johnson is a Vice President, focused on developing the company’s oncology data, analytics and informatics
business across key geographies.
• Kjel was previously Senior Vice President of Strategy and business development at Magellan Pharmacy Services/ICORE
Healthcare, developing comprehensive specialty management strategies and services for payers. A co-founder of ICORE,
he has significant expertise in outsourcing and turn-around strategies, outcomes measures and cost reduction strategies
gained during a career that includes senior roles at deloitte Consulting, UPMC HealthPlan and Coventry Healthcare.
• Principle investigator on more than 40 clinical trials, Kjel has authored over 50 papers and founded Managed Care
Oncology. He lectures at the University of North Carolina and is a Fellow of the Academy of Managed Care Pharmacists
and the American College of Clinical Pharmacy. Kjel holds degrees from the University of Minnesota and he completed
a post-doctoral fellowship at St. Paul Ramsey Medical Center, Minnesota. He is board Certified in Pharmacotherapy.
Jacco Keja, PhD
• dr. Jacco Keja is a Senior Principal, drawing on deep expertise in global market access, operational and strategic
pricing, and health economics and outcomes research.
• Jacco’s background includes four years as global head of pricing, reimbursement, health outcomes and market access
consulting services at a large clinical research organization and more than 13 years experience in the pharmaceutical industry,
including senior-level international and global roles in strategic marketing, pricing and reimbursement and health economics.
• Jacco holds a Phd in biology (Neurophysiology) from Vrije Universiteit in Amsterdam, a Master's degree in Medical
biology, and an undergraduate degree in biology, both from Utrecht. He is also visiting Professor at the Institute of
Health Policy Management at Erasmus University, Rotterdam.
tim Kelly, MsC, Bs
• Tim Kelly is a Vice President, with responsibility for the company’s RWE data assets and data architecture backbone, and
for overseeing platform delivery infrastructure and engagements to ensure at-scale,
high-quality data mart deployment. He also leads the client services team supporting data and technology applications.
• Tim’s background includes two decades of life-science experience managing large-scale data warehousing, technology,
and analytic applications and engagements. He has worked with many clients in the pharmaceutical and biotech sectors,
leveraging deep expertise in information management and modeling, commercial operations and analytics, advanced
analytics, business intelligence, data warehousing and longitudinal analytics.
• Tim holds a Master’s degree in Management Science from Temple University, Philadelphia and a bachelor’s degree in
Quantitative business Analysis from Penn State University.
Marla Kessler, MBA
• Marla Kessler is a Vice President, heads overall marketing efforts for IMS Health RWE Solutions and is an active leader of
global RWE projects. She helps clients develop commercial strategies for products and portfolios, define evidence plans to
support them, and coordinate implementation to ensure successful execution.
• Marla has 15 years strategic and business line experience gained through previous leadership roles at McKinsey
Company and Pfizer. during her career at IMS Health she has designed and led RWE boot camps to help clients build
capabilities in this area across the broader organization, and also developed thought leadership in RWE. This includes co-authoring
a major IMS Health benchmarking study exploring variations in RWE supply and demand across the
pharmaceutical industry’s top markets.
• Marla holds an MbA from duke University’s Fuqua School of business in durham, North Carolina.
Joseph Kim, PhD, MPh
• dr. Joe Kim is a Senior Principal, providing scientific direction in the design and analysis of observational studies across a
wide range of projects.
• A trained epidemiologist and statistician, Joe has over 20 years experience in population-based research in the US and Europe.
He was previously Senior director in benefit-Risk Management at Quintiles assisting in the development of pharmacovigilance
systems, risk management plans and benefit-risk evaluation reports, and in the design of post-authorization safety studies.
Prior to this, worked in epidemiology at Roche and Amgen.
• For the last 10 years, Joe has taught pharmacoepidemiology and pharmacovigilance at the London School of Hygiene
Tropical Medicine, and more recently on the MPH program at the French School of Public Health in Paris. He holds a Phd in
Epidemiology from the University of Minnesota, and an MPH from the Graduate School of Public Health, San diego.

Rob Kotchie, M.ChEM, MsC
• Rob Kotchie is a Vice President, with a focus on bringing innovative solutions to clients through strategic alliances,
collaborations and the deployment of novel technology.
• Previously with ZS Associates, Rob has more than 10 years consulting experience, specializing in the synthesis and
application of RWE to facilitate market access, drug uptake and the responsible use of medicines. In his former role as
Chief of Staff at IMS Health, he supported all operational and management activities related to execution of the
company’s strategy, and played an integral role in its 2013 dividend recapitalizations and initial public offering in 2014.
• Rob has particular expertise in the areas of oncology, respiratory, cardiovascular and CNS and has published more than 30
peer-reviewed journal articles and poster presentations. He holds a first class honors degree in Chemistry from the
University of Oxford and an MSC in International Health Policy from the London School of Economics.
Mark Lamotte, MD
• dr. Mark Lamotte is a Senior Principal, responsible for project management and quality assurance within his team, and for
leadership of health economic modeling.
• A medical doctor specialized in cardiology, Mark spent six years in clinical practice before joining Rhône-Poulenc Rorer as
Cardiovascular Medical Advisor and later becoming Project Manager and Scientific director at the belgian research organization,
HEdM. He has worked on over 300 cardiovascular, pulmonary, diabetes, urology and oncology projects, incorporating expert
interviews, patient record review, modeling and report writing. Many of these projects have resulted in peer-reviewed publications.
• Mark holds an Md from the Free University of brussels (Vrije Univeristeit brussel, belgium) and is fluent in dutch, French,
English and Spanish.
Claude Le Pen, PhD
• dr. Claude Le Pen is a member of the strategic committee of IMS Health and Professor of Health Economics at
Paris-dauphine University, providing expert economic advisory services to the consulting practice.
• A renowned economist, leading academic and respected public commentator, Claude has served as an appointed
senior member of several state commissions in the French Ministry of Health and is an expert for a number of
parliamentary bodies, bringing a unique perspective and unparalleled insights into the economic evaluation of
pharmaceutical technologies at the highest level.
• Claude studied business Administration in HEC business School in Paris and holds a Phd in Economics from
Panthéon-Sorbonne University.
Bo Lidman, MsC
• bo Lidman is a Principal, with more than 30 years experience in the life sciences industry.
• bo’s background spans roles in marketing, sales and business development in both start-up companies and pharmaceutical
organizations, including Upjohn Ab and Merck Sharpe dohme Ab. He served as the General Manager of Profdoc Ab, CPC Ab
and Peritide Ab before co-founding the Nordic-based consultancy and research organization, Pygargus Ab, specializing in real-world
evidence. He was most recently responsible for developing the company’s IT platform and EMR extraction methodology.
• bo holds a Pol. Mag. (Master’s equivalent) and bachelor’s degree in business and Economics from the Institute of
Philosophy in Marketing and Economics, Uppsala University, Sweden.
Ragnar Linder, MsC
• Ragnar Linder is a Principal, with more than 25 years experience in pharmaceutical marketing, sales and business development.
• Co-founder of the Nordic-based consultancy and research organization, Pygargus Ab, Ragnar has worked in various senior
level industry roles. These include General Manager of Amgen Nordic Ab, director of International Marketing at
Aventis/Hoechst Marion Roussel, and Head of Sales Marketing at Hoechst Pharmaceuticals Ab. He has also served on the
board of directors for several CRO and biotech companies.
• Ragnar holds a Master of Science degree in Chemical Engineering from the Royal Institute of Technology in Sweden.
Adam Lloyd, M.PhIL, BA
• Adam Lloyd is a Senior Principal, with a focus on economic modeling and the global application of economic tools to
support the needs of local markets.
• A co-founder and former director of Fourth Hurdle, and previously Senior Manager of Global Health Outcomes at
GlaxoWellcome, Adam has extensive experience leading economic evaluations of pre-launched and marketed products,
developing submissions to NICE and the SMC, decision-analytic and Markov modeling, and in the use of health economics
in reimbursement and marketing in continental Europe.
• Adam holds a Master's degree in Economics and a bachelor's degree (Hons) in Philosophy, Politics and Economics from
the University of Oxford.
Frédérique Maurel, Ms, MPh
• Frédérique Maurel is a Principal, with a focus on observational research and health economics studies.
• A skilled consultant and project manager, Frédérique has extensive experience in the economic evaluation of medical
technologies gained in roles at ANdEM, Medicoeconomie, and AREMIS Consultants.
• Frédérique holds a Master’s degree in Economics – equivalent to an MS – and completed a post-graduate degree
equivalent to an MPH with a specialization in Health Economics at the University of Paris-dauphine (Paris IX) as well as a
degree in Industrial Strategies at the Pantheon-Sorbonne University (Paris I).

Joan McCormick, MBA
• Joan McCormick is a Principal, leading a team providing strategic advice to companies with new products coming to
market and ongoing consultation on the rules for existing drugs post launch.
• Formerly Head of Price Regulation Consulting at brogan Inc, Joan has supported many major pharmaceutical companies
with the preparation of pricing submissions to the Patented Medicine Prices Review board (PMPRb), gaining extensive
insights into the operation of the Canadian pharmaceutical market.
• Joan holds an MbA from the University of Ottawa, Canada and a bachelor’s degree in Life Sciences from Queen’s University
in Kingston, Canada.
Lisa stockwell Morris, PhD, RPh
• dr. Lisa Morris is a Vice President, with global responsibility for LifeLink, the company’s suite of patient-centered insights,
and for developing patient-centered information capabilities within the US, EMEA, and Asia Pacific.
• Lisa has many years experience in applying market research tools to answer a wide range of business questions, delivering
customized solutions using LRx, medical and pharmacy claims data, EMRs and other clinically-rich secondary information
sources. Previously Associate director for Health Outcomes Assessment at Wyeth-Ayerst Research, where she incorporated
health outcomes and economic information into drug development plans, Lisa has also held roles as a senior manager in
the Outcomes Research group at diversified Pharmaceutical Services (dPS) and United Healthcare Corporation (UHC),
managing all aspects of customized health services research projects.
• A registered pharmacist, Lisa holds a doctorate in Pharmacoeconomics with an emphasis on Marketing from the
University of South Carolina, where she also received a bachelor’s degree in Pharmacy.
Julie Munakata, Ms
• Julie Munakata is a Senior Principal, with a focus on global economic modeling, value development planning, and survey
data analysis.
• An accomplished researcher and author of more than 25 original articles, Julie has extensive experience in managing
clinical trials, health economic studies and decision analytic modeling work, gained in senior roles at ValueMedics
Research LLC, the VA Health Economics Resource Center and Stanford Center for Primary Care Outcomes Research, and
Wyeth Pharmaceuticals.
• Julie holds an a Master's degree in Health Policy and Management from the Harvard School of Public Health and a
bachelor’s degree in Psychobiology from the University of California, Los Angeles.
stefan Plantör, PhD, MBA, MsC
• dr. Stefan Plantör is a Principal, with a focus on AMNOG-related projects, including benefit dossiers, as well as reference
price management, health economic evaluations and health policy analyses.
• Stefan’s background includes roles as a researcher and five years experience in the pharmaceutical industry. He has also
served as a board member of ProGenerika, the German pharmaceutical association. Over the course of his career, Stefan
has broadened his expertise to include data analyses and decision analytic modeling, authored a number of publications
in international journals and presented his research at major congresses.
• Stefan holds a Phd in biology from the University of Tübingen, an MbA in International Marketing from the European
business School, Reutlingen and an a Master's degree in Microbiology from the Eberhardt-Karls-University (Tübingen).
Antonella Porta, MsC
• Antonella Porta is a Principal, with a focus on the RWE Solutions Quality Management Program. She brings 15 years of
management experience in quality assurance, compliance and risk management in the pharmaceutical industry and
highly regulated fast-moving consumer goods (FMCG) sector.
• during the course of her career, Antonella has held leadership roles in operational quality, quality systems, remediation
programs, auditing and compliance. She was most recently Quality Compliance director at Shire, heading the global
Local Operating Companies’ quality team. Antonella began her management career at Procter Gamble as Regional Head
of External Operation Quality, progressing with roles of increasing responsibility to become latterly Global Head of the
Microbiological Risk Management Program.
• Antonella holds a Master’s degree in Industrial Chemistry from Federico II University in Naples and is currently studying for
an MbA at Warwick University, UK.
Emile schokker, MBA, MsC
• Emile Schokker is a Senior Principal, with nearly 20 years of international pharmaceutical and consulting experience
including expertise in launch, brand and portfolio strategy, commercial model redesign and post-merger integration.
• Prior to joining IMS Health, Emile was a global senior expert at McKinsey’s global benchmarking service line in belgium,
where he previously served as an associate principal responsible for leading strategic engagements at board and senior
management level. He has also worked in leadership roles at Unaxis/Oerlikon in Switzerland, Arthur d. Little in the
Netherlands, and Unilever in various international locations.
• Emile holds an MbA from IMd in Lausanne and a Master of Science degree in Applied Physics from the delft University of
Technology, the Netherlands.

Jon Resnick, MBA
• Jon Resnick is a Vice President and General Manager, leading the company’s global RWE HEOR business, including the
development of RWE strategy, offerings, collaborations and foundational technologies to meet the RWE needs of
healthcare stakeholders.
• A former Legislative Research Assistant in Washington dC and member of the Professional Health and Social Security staff
for the US Senate Committee on Finance, Jon has 10 years consulting experience at IMS. He was most recently responsible
for leading the European management consulting team and global HEOR business teams of 300 colleagues, advising
clients on a wide range of strategic, pricing and market access issues.
• Jon holds an MbA from the Kellogg School of Management, Northwestern University, with majors in Management and
Strategy, Finance, Health Industry Management, and biotechnology.
Mats Rosenlund, PhD, MPh
• dr. Mats Rosenlund is a Principal, with long experience in epidemiology, outcomes research and health economics from
academia, the pharmaceutical industry and consultancy.
• Prior to joining IMS Health, Mats was director of Health Economics at OptumInsight and director of Epidemiology and
Health Outcomes at GSK. He was previously a researcher at the Karolinska Institute, a Public Health Official at the
Karolinska Hospital, and completed two post-doctoral periods in Italy and Sweden. An affiliated researcher at the Center
for Pharmacoepidemiology, Clinical Epidemiology Unit, Karolinska Institutet, he has authored more than 20 peer-reviewed
articles.
• Mats holds a Phd in Epidemiology, a Master’s degree in Public Health and a bachelor’s degree in Environmental Health
from Karolinska Institutet. He has also completed university training in health economics in belgium and the UK.
Daniel simpson, M.BIOChEM
• daniel Simpson is a Senior Principal, with responsibility for diabetes portfolios and involvement in the UK CObIC initiative,
focused on moving healthcare commissioning towards patient-based outcome measures. He also takes a leadership
position on commercial analytics.
• daniel has more than 18 years experience in healthcare and pharmaceutical markets. Over the course of his career he has
worked for all the top 10 pharmaceutical companies and healthcare systems in major markets, delivering insights from
patient-level data to support improved decision making on resource allocation. He previously worked in the
healthcare/pharmaceutical strategy divisions for both Accenture and the Monitor Group.
• Published in a series of conference posters and papers, dan holds a Master’s degree in biochemistry from St Anne’s
College, University of Oxford.
Patrik sobocki, PhD, MsC
• dr. Patrik Sobocki is a Senior Principal, with more than 14 years experience in RWE, HEOR and market access.
• Patrik’s background spans academia, consulting and the life-science industry within RWE and HEOR, including
international management responsibilities in various senior roles. He was most recently a partner at the Nordic-based
consultancy and research organization, Pygargus Ab, where he worked with the company’s unique methodology for
generating population-based RWE based on anonymous patient-level data from EMR and health registers.
• Patrik has conducted numerous health economics projects, outcomes research and epidemiology studies and published
more than 40 articles in international peer-reviewed journals. He holds a Phd in Health Economics from the Karolinska
Institutet, a Master’s degree in Economics and business Administration from the Stockholm School of Economics, and an
Associate Professorship at the Karolinska Institutet.
núria Lara surinach, MD, MsC
• dr. Núria Lara is a Senior Principal, with a focus on the design and coordination of local and international observational
and patient-reported outcomes studies.
• A former practicing GP and clinical researcher, Núria’s experience spans roles in outcomes research at the Institute of
Public Health in barcelona and in Catalan Health Authorities, and consulting positions within the pharmaceutical and
medical device industries focusing on medical regulatory and pricing affairs, pharmacoeconomics and market access strategies.
• Núria holds an Md (specializing in Family and Community Medicine in barcelona), and a Master’s degree in Public Health
from the London School of Hygiene and Tropical Medicine and London School of Economics.
Patrick svarvar, PhD
• dr. Patrick Svarvar is a Principal, with more than 15 years pharmaceutical industry experience in HEOR and related
functional areas.
• Patrick’s background includes Swedish/Nordic affiliate roles in HEOR and pricing at Pfizer and Schering-Plough as well as
global HEOR roles at AstraZeneca, Pfizer and Merck. Most recently, he was Executive director and Franchise Leader, Global
Health Outcomes at Merck. Prior to moving into the industry, he worked for a number of years in health economics and
health services research at the Swedish Institute for Health Economics (IHE).
• Patrick has been involved in a wide range of HEOR study types across multiple therapeutic categories. He is also experienced in
market access, health technology assessment, clinical development strategies processes, strategic pricing reimbursement,
marketing, and payer market research. He holds a Phd in business Administration/Economics from Lund University.

Marc tapies, MBA
• Marc Tapies is a Principal, with a focus on market access, health economics and outcomes research.
• Prior to his current role at IMS Health, Marc worked within the company’s wholesaler data business in Spain where he was
responsible for wholesaler panel management, design and quality control of data production and the development of
new offerings based on wholesaler data. As Engagement Manager in the IMS Consulting team in Spain, he led projects in
the areas of commercial effectiveness, market analysis and portfolio management in the pharmaceutical industry. Marc
previously worked for 5 years in the areas of supply chain management and information technologies.
• Marc holds an MbA from IESE business School and a degree in Industrial Engineering from Universitat Politècnica de
Catalunya, Spain.
Massoud toussi, MD, PhD, MsC, MBA
• dr. Massoud Toussi is a Principal and Medical director, applying his expertise to assure the quality of outcomes research
and pharmacovigilance. He is also the representative of IMS Health in ENCePP.
• Previously head of Global Clinical Research Operations at Cegedim, Massoud has also worked with the French High
Authority for Health (HAS) and various CROs as Project Lead, Scientific Manager and Operations director. His experience
includes drug safety reporting, natural language processing, database linkage and drug utilization studies.
• Massoud holds an Md from Mashad University in Iran, a Master's degree in Medical Informatics and Communication
Technology from Paris VI, a Phd in Medical Informatics from Paris XIII University, and an executive MbA from a joint
program of Universities of Paris-dauphine and Quebec à Montreal.
Arnaud troubat, PhARM.D, MBA, MhEM
• dr. Arnaud Troubat is a Principal, with extensive consulting experience and special expertise in the development of
registration dossiers and market access strategies across a large number of therapeutic areas.
• A pharmacist by training, Arnaud began his career at the French pharmaceutical industry association (LEEM). He then
spent a number of years in the pharmaceutical affairs department at ICI, leading regulatory work on registration
submissions and reimbursement strategies, before subsequently moving into consulting. Most recently he was director at
Carré-Castan Consultants, managing a research team.
• Arnaud holds a doctor of Pharmacy degree and an MbA from IAE Paris and a Master’s degree in Health Economics and
Management from Paris-dauphine University.
Rolin Wade, RPh, Ms
• Ron Wade is a Principal and a recognized expert in the applications and limitations of using large retrospective datasets
and late-phase datasets for health economics and outcomes research.
• Prior to joining IMS Health, Ron served as a Healthcare Executive and Principal Investigator with Cerner Research and as a
Research director at HealthCore. He also has experience generating evidence to support value messages to managed
care, government payers and public health associations, gained in leadership roles within the pharmaceutical industry.
• A widely published author with expertise in many therapy areas, Ron lectures at colleges of pharmacy and he has had
leadership roles with the American College of Clinical Pharmacy and the Academy of Managed Care Pharmacy. He is a
licensed pharmacist and holds a Master's degree in Pharmaceutical Sciences from the University of the Pacific, California
and a bachelor of Science degree in Pharmacy.
Jovan Willford, MBA
• Jovan Willford is a Senior Principal, supporting growth strategy, offering development and commercialization of RWE
solutions in the Asia-Pacific region.
• Jovan’s background includes more than 10 years strategic advisory experience across payers, providers, life science
organizations and technology companies, including several cross-industry collaborations to advance quality and value of
care delivery.
• Jovan holds an MbA from the Kellogg School of Management, Northwestern University, with majors in Management and
Strategy, Managerial Economics and International business, and an undergraduate degree from the University of Notre
dame with majors in Marketing and Philosophy.
Ashley Woolmore, D.CLIn PsYCh, MBA
• dr. Ashley Woolmore is a Senior Principal, with a focus on developing innovative approaches to help clients reinforce
differentiation through the integration of real-world data into strategic decision making. He has 20 years experience in the
life sciences and healthcare sector.
• Ashley leverages a uniquely diverse background in clinical, healthcare system management and life sciences strategy
consulting in senior advisory roles to support clients across developed and emerging markets on a wide set of healthcare
system issues. His expertise includes strategy development, healthcare analytics, RWE for strategic insight, population
health management applications, and differentiated market access approaches.
• A thought leader with a particular interest in opportunities arising from convergence between the life sciences industry
and broader healthcare system, Ashley holds a doctorate in Clinical Psychology from the University of Oxford, an MbA in
Strategy from HEC in Paris, and a bachelor of Science (Hons) degree in Natural Sciences and Psychology.

IMs hEALth REAL-WORLD EVIDEnCE sOLUtIOns AnD hEALth ECOnOMICs OUtCOMEs REsEARCh
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ABOUt IMs hEALth
IMS Health is a leading global information and technology services company providing clients in the healthcare industry
with comprehensive solutions to measure and improve their performance. By applying sophisticated analytics and
proprietary application suites hosted on the IMS One intelligent cloud, the company connects more than 10 petabytes of
complex healthcare data on diseases, treatments, costs and outcomes to help its clients run their operations more
efficiently. Drawing on information from 100,000 suppliers, and on insights from more than 45 billion healthcare
transactions processed annually, IMS Health’s approximately 9,500 employees drive results for healthcare clients globally.
Customers include pharmaceutical, consumer health and medical device manufacturers and distributors, providers,
payers, government agencies, policymakers, researchers and the financial community.
Additional information is available at www.imshealth.com
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