RWD is increasingly accessed for decision-making but often in the absence of a systematic process for ensuring that it is fit for purpose. A T-shaped approach which acknowledges the need to build a portfolio of broad and deep data assets allows maximum value to be derived from RWD and the type of analyses that were previously possible only through observational studies and primary research.

1. T-shaped guided RWD portfolios
RWD is increasingly accessed for decision making but often in
the absence of a systematic process for ensuring that it is fit
for purpose. A T-shaped approach which acknowledges the
need to build a portfolio of broad and deep data assets allows
maximum value to be derived from RWD and the type of
analyses that were previously possible only through
observational studies and primary research.
PAGE 60 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS
INSIGHTS RWE PLATFORM DEVELOPERS
The authors
Ashley Woolmore, D.ClIN.PSYCH, mbA
is Senior Principal, RWE Solutions, IMS Health
Awoolmore@imscg.com
Daniel Simpson, m.bIOCHEm
is Senior Principal, RWE Solutions, IMS Health
Dsimpson@imscg.com

2. ACCESSPOINT • VOLUME 5 • ISSUE 10 PAGE 61
continued on next page
Addressing the trade-off between breadth and depth
RWD and the evidence that evolves from it is
increasingly being used to support critical
decisions in R&D, medical, drug safety and
market access. It allows companies to make
choices, engage with healthcare stakeholders
and demonstrate the value of their medicines,
based on millions of patient healthcare encounters.
In recognition of its growing importance and in addition to
collecting data through primary observational research,
companies have been moving quickly to acquire ‘off-the-
shelf’ RWD datasets which exist as a byproduct of medical
transactions captured from everyday clinical practice. This
RWD includes longitudinal prescription data, integrated
claims data, physician panels, patient registries and EMRs.
Companies have rightly started to migrate away from
prospective data collection to address their research needs.
But as they enter the world of secondary data acquisition,
many have been too linear and transactional in purchasing
RWD without a clear view of how a particular type of data or
collection of datasets can be used across the organization
and into the future. This less than efficient approach to
RWD reflects errors in five key areas:
1. Lack of a comprehensive approach for assessing data
needs, categorizing research questions and navigating
data sources, leading to disconnected purchases
2. Settling for incomplete datasets, which may be readily
available but not always the best solution and often the
tip of the iceberg in terms of what could be achieved
3. Poor prioritization of investments in datasets due to
their complex attributes and caveats, which make it
difficult to determine their relative value in addressing
specific research questions and drive purchases that are
not fit for purpose, are sub-scale or lack the necessary
precision
4. Failure to anticipate long-term data needs and the lead
time to acquire suitable data
5. Pursuit of a higher burden of proof than is required
or even possible in situations when a directional answer
may be sufficient and more cost-effective
Recognizing a need for trade-offs
Unlike customized studies, which are intended to address a
specific business issue, ‘off-the-shelf’ RWD datasets exist
for reasons that are entirely independent of the industry’s
research requirements and are thus ill-designed to meet
many of them. No single one contains all the necessary
information to answer a company’s questions regarding
patients’ real-world treatment experience.
A fundamental characteristic of RWD is that it is extremely
rare for any dataset to include all four key elements,
namely: clinically rich, high-quality, longitudinal data, with
sufficiently large numbers of patients. In practice, there is
always a compromise between breadth and depth and that a
collection of datasets will be needed.
Research is also limited by the ready availability of really
high-quality data assets. The world of data collection,
especially electronic, is still quite nascent. Although some
centers have prioritized the development of their
information infrastructure and use it to help improve
clinical care, others are less sophisticated in collecting and
managing the quality of their data. The information being
sought may not be captured or readily available in the
countries, timeframe or format required. And the issues of
coding can further complicate the usability of the data (see
article in this issue of AccessPoint on page 64).
To make the best use of RWD, each business question must
be matched to the most appropriate data source available,
based on the line of enquiry and the particular
characteristics of the dataset. In this context ‘available’
does not automatically mean commercially available;
included in that definition are sources that need to be
accessed using different models. This will mean accessing
multiple sources, accepting that the match will never be
perfect and that trade-offs will have to be made.
In order to build an efficient RWD strategy, companies
must invest in creating a portfolio of data assets that are
sufficiently specific to their research foci but also offer
broad utility to meet the nuanced needs of different
functions across the organization. Each element must be
carefully scrutinized to ensure the investment is
cost-effective and the data acquisition fit for purpose.
To make the best use of RWD, each business question must be
matched to the most appropriate data source available, based on the
line of enquiry and the particular characteristics of the dataset.
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3. PAGE 62 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS
INSIGHTS RWE PLATFORM DEVELOPERS
A well-organized group of datasets will serve a company in
the short, medium and long term, positioning them to
answer questions around population characteristics, clinical
practices, product effectiveness, comparative effectiveness
and disease characteristics, as well as conduct deep
scientific investigations. The identification, analysis and
matching of sources to research questions is referred to here
as a nascent capability of ‘data navigation’.
T-shaped dichotomy
Longstanding experience of working with pharmaceutical
companies and witnessing some of the common mistakes
that are made in addressing RWD requirements, illustrates
the value of differentiating business questions and
systematically assessing which data sources will address
them. This approach puts companies on the right path to
building a comprehensive and flexible portfolio of
RWD assets.
Some questions (eg, around epidemiology, treatment
patterns, clinical care or product use in the patient
population) necessarily warrant a broad, comprehensive
view of a large group of patients. However, the constraint of
taking a broader view is the lack of clinical depth. Breadth
can be achieved – RWD sources such as disease registries1
or
national databases can span broad, near population-level
cohorts – but only with minimal clinical information about
each individual patient.
The opposite also holds true in that deep, clinically rich data
can be obtained (from modules of specialized EMRs, for
example) but only for a defined and relatively finite patient
population, typically with coverage of between 5-10% of a
total market population.
A good portfolio of RWD assets adheres to the
T-shaped principle
Adhering to the T-shaped principle considers both
representativeness and clinical depth, combining data of
both types to form a T-shaped guided RWD portfolio that
meets all the information requirements for a market or
disease area. The ‘broad and thin’ datasets form the bar of
the T while the ‘deep and detailed’ datasets form the stem
(Figure 1).
‘T-shaped’ is thus a way of guiding a company’s future
strategy around RWD as it identifies what questions are
being asked and which are the right sources (off-the-shelf
or otherwise) that will help deliver answers to those key
questions. It allows companies to adopt a more concise,
segmented and intelligent use of RWD based on
acceptance that there are different formats of the data
which, when used in the right way, can deliver a more
complete, focused answer.
Rangeofdatatypes
Deep, flexible
therapy-area-specific data
Complete disease views can be created through
novel approaches to supplement data including:
eCRF, ePRO, NLP and rapid custom sourcing/linkage
DEEP
BROAD
Nationally relevant databases
Augmented datasets
Population coverage
Biobanks, labs, registries
Clinically rich, deep data for a
discrete population of patients
typically with coverage of 5-10%
of a total market population.
Figure 1: T-shaped principle reflects the need to consider both breadth and depth when accessing the most appropriate fact base
Source: ImS Health

4. ACCESSPOINT • VOLUME 5 • ISSUE 10 PAGE 63
Companies considering how they should acquire or procure
their set of RWD assets should thus know what questions
they want to ask, then construct a portfolio with a choiceful
selection of stems and bars that enables them to answer all
of their questions. In doing so, they should bear in mind
their evolving needs, the potential of different data access
models and the broader context of RWD’s value in the
organization’s investment in insight generation.
Within such a portfolio, the different types of sources need
to be analyzed in an integrated way. Bar data reveals the
nuances and variations across the breadth of clinical
settings. It is essential to the interpretation of the data
obtained through ‘stems’ but providing knowledge of the
specificities and perhaps idiosyncrasies of their particular
patient populations and clinical settings. Furthermore,
multiple stems need to be considered together, in order to
achieve sufficient cohort size or to provide a diverse group
of patients.
best practice
Companies that succeed in accessing the right RWD will:
• Understand the limitations of each RWD source and the
need to make trade-offs
• Spend time matching datasets to current and
anticipated needs
• Avoid a fragmented approach to acquiring RWD
• Adopt a portfolio mindset that leverages a combination
of datasets to achieve the best answer
• Plan to ensure that the highest priority diseases areas are
appropriately supported
• Anticipate future evidence needs and start building
access now
• Focus on enhancing available data
• Be open to new access models beyond ‘data purchase’
A step towards connectivity
In moving forward with RWD, the T-shaped approach also
paves the way for a new era of connectivity. The link that
exists between ‘stem’ data and the clinical setting provides
an environment where it is possible not only to observe
but also, through the connection to that environment
(either virtually or otherwise), to contextualize those
observations, supported by qualitative narrative from
those who work there.
For example, moving from merely analyzing data from a
group of primary care practices in a region to interpreting
those analyses, potentially with a view to proposing an
appropriate public health intervention to change the way in
which care is organized or educate patients in a better way.
This can only be achieved because of the additional context
obtained through narrative from known individuals and the
ability to engage with them and their actions in that system.
Conclusion
In a world where no single RWD dataset can fulfill all their
research and information requirements, companies need to
prioritize their business questions and map them to the
appropriate RWD source. With the proper sourcing strategy
and a long-term view of data needs and availability, the
shortcomings of any single source can be overcome and a
company’s information needs met. Constructing a
T-shaped guided RWD portfolio allows maximum value to
be derived from RWD sources and enables the type of deep
analyses that previously have only been possible through
observational studies and primary data collection.
By following the rule of T-shaped data, companies will be
enabled to make the necessary trade-off decisions when
acquiring RWD and operate within a more flexible model
where the data is constantly refreshed and available on
demand for multiple types of users. Those that successfully
construct a T-shaped guided portfolio of RWD assets will
have a cost-effective system capable of answering a wide
range of business questions now and in the future. They will
be able to understand clinical practices, analyze how
patients interact with the healthcare system, perform broad
evaluations at the product level and explore a disease area in
depth. With faster speed to insight they will improve
decision making across the organization and throughout the
product lifecycle. To realize full value from their RWD
investment, they will need to underpin their strategy with
the right infrastructure and organizational principles for
working within this new model successfully.
1
Defined here as databases created and maintained by health systems/governments for population management and care planning rather
than databases created by manufacturers or researchers as part of non-observational studies used to test the safety or efficacy of a drug in a
given real-world context.
Constructing a T-shaped guided RWD portfolio allows maximum value to be
derived from RWD sources and enables deep analyses that previously have
only been possible through observational studies and primary data collection.
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