7/29/2019 Clinical Data Warehousing
1/31
Health SciencesJournal
ISSUE 1 MARCH 2013
A resource dedicated to the convergence o the lie sciences
and healthcare industries
Clinical Data
WarehousingHow does the theory
translate into practice?
An industry perspective
The Clinical Data Warehouse
a New Mission-Critical HubJonathan Palmer, Oracle Health Sciences
A Clinical Data Warehouse Solution
to Improve Operational EfcienciesColin Burns, ICON Clinical Research
A Dynamic Platorm or Data Integration,
Standardization and ManagementBrooks Fowler and Nareen Katta, AbbVie
Clinical Research Innovation through
Shared Clinical Data WarehousingJerry Whaley, Pfzer
7/29/2019 Clinical Data Warehousing
2/31
Health SciencesJournal Issue 1 March 2013
p 2
Contents
Foreword 3
The Clinical Data Warehouse a New Mission-Critical Hub 5
Jonathan Palmer Senior Director or Clinical Warehousing and Analytics, Oracle Health Sciences
A Clinical Data Warehouse Solution to Improve Operational Efciencies 13
Colin Burns Senior Director o Global Data and Technologies, ICON Clinical Research
A Dynamic Platorm or Data Integration, Standardization and Management 18
Brooks Fowler Global Head o Data Sciences, AbbVie
Nareen Katta Senior Manager o Data Sciences, AbbVie
Clinical Research Innovation through Shared Clinical Data Warehousing 24
Jerry Whaley Senior Director o Development Business Technology, Pzer
7/29/2019 Clinical Data Warehousing
3/31
Health SciencesJournal Issue 1 March 2013
p 3
ForewordThe lie sciences industry is changing The common driver
or this change is the need to improve overall eciency and
cost-eectiveness In response, organizations are rigorously
seeking ways to extract maximum value rom their assets
and become more operationally and economically ecient
Driven by ever increasing cost constraints, regulations and
competition, organizations look to create new business models
through acquisitions, global outsourcing and collaborative
partnerships The need to better manage the lie sciences
industrys key asset, data, has become extremely important
Maximizing the value o data can only be achieved through
improved data management, standardization, storage, and
accessibility which ideally should be universally available to
everyone across an organization and its partners
In response to these challenges, clinical data warehousing is
evolving rom a purely in-house solution to an essential tool
or harnessing and maximizing potential rom lie sciences
industry data It is becoming a business-critical platorm that
supports decisions across the clinical trial portolio, is central
to collaboration, and undamental to business survival
This issue o the Health Sciences Journal explores clinical
data warehousing rom various industry perspectives, with
particular ocus on meeting business needs, implementation,
governance, challenges and solutions, and associated benets
Jonathan Palmer, a senior director or clinical warehousing and
analytics at Oracle Health Sciences, provides insights into this
area by dening a clinical data warehouse and describing the
drivers or implementation, importance o data standardization,
necessary requirements or successul implementation, and
how changing trends in the lie sciences and technological
industries have impacted clinical data warehousing
Increased outsourcing, partnering, and globalization across the
lie sciences industry has also created the need to improve
overall working eciency and communication between
partners and service providers Colin Burns, a senior director
at ICON Clinical Research, provides his views on the role oclinical data warehouses in contract research organizations
(CROs) Specically, how they have been used to convert
technical data into a usable ormat accessible to all users and
how this is subsequently used to inorm operational decisions
In contrast, Brooks Fowler, global head o data sciences,
and Nareen Katta, senior manager data sciences, at
AbbVie, provide a pharmaceutical view point on clinical data
warehouses They highlight some specic industry and
business challenges that led AbbVie to consider a clinical
data warehouse solution The article explains how AbbVie
has used its clinical data warehouse to integrate, standardize,
and manage data eectively with a phased implementation,
including key challenges and benets to users
7/29/2019 Clinical Data Warehousing
4/31
Health SciencesJournal Issue 1 March 2013
p 4
Foreword (continued)
Pzer, the worlds largest research-based pharmaceutical
company, has responded to changing trends in the lie
sciences industry by re-assessing its clinical trials operational
model, particularly with regard to IT inrastructure Pzer is
paving the way or a radical approach to data management
in the clinical trials space Jerry Whaley, senior director o
development business technology at Pzer, describes the
companys vision to build a new clinical data warehousing
platorm that can be shared by companies across the industry
The premise is to reocus company resources on scientic
discovery or better healthcare, rather than just on the
management o clinical trial data Pzers vision is to nd other
companies with similar mindsets with whom such a platorm
can be shared or mutual gain
7/29/2019 Clinical Data Warehousing
5/31
Health SciencesJournal Issue 1 March 2013
p 5
The Clinical Data Warehouse a NewMission-Critical Hub Jonathan Palmer Oracle Health Sciences
Jonathan Palmer is senior director or
clinical warehousing and analytics at
Oracle Health Sciences.
Jonathan frst joined Oracle in 1997
where he has had various roles across
business development, consulting,
and product development. Since 2008,
Jonathan has been involved in defning new product
strategy targeted at developing innovative solutions or
the lie sciences industry.
Demands or clinical warehousing are increasing dramaticallyFrom being viewed as a data gathering tool, a clinical data
warehouse is now moving into a new phase o becoming
a business-critical platorm Such a platorm can support all
clinical decisions across the clinical trial portolio, be central
to collaboration, and undamental to the survival and agility
o the business To ully comprehend the meaning, applicability
and relevance o a clinical data warehouse, and how it can
potentially benet a company, we must understand how
it is dierent rom a typical data warehouse, what has drivenits need, and how its adoption can be maximized to drive
clinical development
Traditional warehousing versus clinical
data warehousing
Traditional business data warehousing is a well-established
IT discipline, the primary ocus o which is oten to deliver
decision support capabilities to drive productivity and
eciency gains across a business Typical examples can behorizontally ocused, such as in inventory management, or in
industries such as banking, telecommunications or retail These
warehouses are based on well-dened structures, data sources
and goals For example, a warehouse ocused on inventory
management allows a business to manage stock, assess sales,
and answer well-dened questions to drive ecient stock
management in response to sales activity
7/29/2019 Clinical Data Warehousing
6/31
Health SciencesJournal Issue 1 March 2013
p 6
In contrast, the clinical trials segment o the lie sciences
industry is unique in its needs based on the variability o its
data structures driven by trial design The primary ocus o
a clinical data warehouse is to acilitate extraction o value
rom clinical data This can aid study design, prove ecacy
and saety o new products, and support regulatory queries
Productivity and eciency gains, whilst important, are rarely
the key ocus o a clinical data warehouse
Traditional warehouses are less ocused on regulatory
compliance, and the need or ull traceability o data lie
cycles is oten less relevant and compared with the high
demand or such capabilities rom the lie sciences industry
As with all warehouses, a key eature o a clinical data
warehouse is that it should allow a company to store and
release value rom data assets to drive better decisions For
example, it may enable a pharmaceutical company to realize
value rom the vast amount o clinical data generated rom a
single trial, all trials in a particular program, a therapy area or,
indeed, all the trials in the company The ability to standardize,
pool, analyze, explore and mine data across large and disparate
data sets has previously been challenging or the lie sciences
industry Whilst a traditional warehouse uses only a nite set
o data sources, there are potentially hundreds o data sources
in the clinical space, each with a dierent structure, variability
and requency
Drivers or change
Historically, data have been managed and stored in distinct
silos in a unction-centric model or example, data or the
clinical data management, biostatistics or saety groups
Whilst synergies exist across these groups, oten data in silos
cannot be accessed by other teams This oten creates data
lag, as data must be requested rom one group to another, and
manually handed over Transparency and availability o data
across the breadth o the organization has been a challenge
As the industry has evolved, there has been an increasing
need to access, combine and share data across multiple
inormation domains Further, there is the need to eectively
align clinical and administrative data to provide a complete
picture o study conduct, rom an operational, saety and
regulatory perspective
There have been a number o key changes in the industry
including:
1) Industry consolidation
2) Cost constraints
3) Globalization, outsourcing and virtual technology
4) Aggressive generic drug replacements
5) Increased scrutiny by governmental
and regulatory bodies
The Clinical Data Warehouse a New Mission-Critical Hub (continued)
7/29/2019 Clinical Data Warehousing
7/31
Health SciencesJournal Issue 1 March 2013
p 7
The Clinical Data Warehouse a New Mission-Critical Hub (continued)
These drivers or change have ocused the attention o
senior management on the need or greater data visibility,
transparency and availability Inormation silos have become
barriers to clinical innovation, and organizations are increasingly
seeing clinical data warehousing as a platorm to support more
agile clinical research, and as an essential base to maximize
the clinical portolio
Figure 1: A clinical data warehouse to enable
collaboration through shared visibility
Clinical data warehousing is becoming ever more important
Acting as a central hub or inormation storage, collation
and archiving, a clinical warehouse is essential to delivering
a consistent, single view o the data assets across an
organization It is essential that all team members, both
internal and external, have access to a consistent view o
the data to drive clinical research (Figure 1)
Standardization and master data management
Standardization is undamental to combining and sharing data
Whilst most industries are based on well-dened standards,
the clinical trial industry has struggled to agree on a universal
standard due to the broad nature o trial data Standards
organizations such as the Clinical Data Interchange Standards
Consortium (CDISC) and Health Level Seven International
(HL7) have made signicant contributions over recent years
Complete adoption o standards across the clinical portolio
is now recognized as being undamental to enabling ecient
process design, and extensive usage o standard sotware
tools or analysis, exploring, and mining without signicant
reliance on trial-centric programming resources Submitting
standardized clinical trial data to regulatory authorities (or
example, the FDA or EMA) allows analysis and review o
submissions using standardized methodologies and tools,
which streamlines processes and regulatory reviews,
potentially resulting in aster drug approval timelines As with
all standards, they are constantly evolving and expanding, and
must make provision or data which cannot be standardized
Variation is typically ocused around ecacy data, and
platorms are available to support this variability
7/29/2019 Clinical Data Warehousing
8/31
Health SciencesJournal Issue 1 March 2013
p 8
Another consideration is management o master data, which
typically relates to process-centric entities (or example,
address details o principal investigators or trial sites) I
master data are not recorded and managed correctly, critical
contextual inormation may be lost, leading to expensive
manual data cleansing and sourcing to complete the ormal
regulatory submission Master data management, although
well-known in general data warehousing, is a relatively new
issue in the clinical trials data space
O course the problem is, as soon as you nd one
data inconsistency, then everything is gonethe whole
structure is compromized, and the research is at risk.
Evolving rom clinical data management systems
(CDMSs), to clinical data warehouses
Historically CDMSs have been used to capture case report
orm (CRF) data and support cleaning cycles Over the years
the role o the CDMS has changed Organizations typically
sought to import as much data as needed into CDMSs to
support new product submission Laboratory data were
oten imported into CDMSs, which evolved into an idealistic
single source or pseudo data warehouse However, asclinical trials became more complex and data source variability
became broader and more diverse, it became impractical
to load all data into the CDMS Conversely, the evolution
o CDMS systems to electronic data capture (EDC) moved
the ocus rom manual data processing to site-based data
capture Whilst succeeding in accelerating CRF data capture,
the need to manage non-eCRF data sources remained
unaddressed Today many organizations pass the burden o
data consolidation and integration onto data management
programmers and/or biostatistics programmers, to manually
combine a le-based data source with the eCRF-based data
This process is labor intensive, requires specialist programming
skills, and can result in quality issues i data standards are not
leveraged eectively
Using CDMS systems as a pseudo data warehouse,
augmented by le-based approaches, inherently ragments the
data For a single drug trial this approach may be adequate as
it is possible to navigate through the dierent les and obtain
the required data or drug approval However, to view all trials
in a therapy area (or example, diabetes) and to explore across
related trials becomes a complex undertaking as data arestored in separate les, requiring specialist programming skills
to access and analyze
So has the industry survived? Sure. Companies have
got new drugs to the market, but are oten missing an
opportunity to exploit huge inherent value embedded
in their vast data stores. Why? Because in many cases
the data are locked away, in a secret vault that only the
biostatistician or statistical programmer has access to.
As organizations have undergone this system evolution,
it has become increasingly apparent that orce-tting all
data into data capture systems (CDMS or EDC) is not a viable
solution Furthermore, le-based stores are inherently dicult
to search and mine across This reinorces the role o the
clinical data warehouse as a purpose-built relational data
store, which can fex, expand and scale to meet the varied
needs o clinical trials
The Clinical Data Warehouse a New Mission-Critical Hub (continued)
7/29/2019 Clinical Data Warehousing
9/31
Health SciencesJournal Issue 1 March 2013
p 9
Globalization and outsourcing a new paradigm
The lie sciences industry is continually under increasing
pressure to become leaner and reduce healthcare payers
costs, whilst ultimately increasing end value to shareholders
Ongoing industry consolidation is partly driven by the need
to be more ecient and the need to nd ways to expand
portolios, and deend against aggressive competition
Twenty years ago the industry was incredibly cash rich
and blockbuster-centric. Weve seen over recent years
massive consolidation through mergers and acquisitions,
huge patent expiries, and healthcare payer budget cuts.
Clearly the industry needs to be a lot more agile and
innovative.
The world is becoming smaller through more integration,
virtualization and collaboration Enabling technologies, like the
Internet, have opened up new, previously unavailable business
models Workorces are becoming more geographically
distributed across dierent time and language zones so as
to lower costs To meet the demands that these changes
bring, the role o the clinical warehouse becomes ever more
important The clinical warehouse is evolving rom an internal
knowledge base into a hub or leveraging new business
models For contract research organizations (CROs), clinical
warehouses had previously been irrelevant due to the study-
centric processing model However, many CROs are now
ocusing on building warehouses that can act as a centralized
and standardized platorm on which they can add tools or
extracting value rom data This allows CROs to dierentiate
themselves rom their competitors, rom commodity vendor
to strategic partner Similarly, as pharmaceutical companies
look to reduce internal costs and optimize processes, they
are increasingly leveraging global service providers and using
clinical data warehouses as an integration and collaboration
platorm to enable ull service, and hybrid (using both internal
and outsourced resources), outsourcing
Challenges in implementing a clinical data warehouse
The challenges acing clinical data warehouse implementation
are largely determined by the managements view o
the implementing company Forward-thinking senior
management view a clinical data warehouse as being
undamental to progression and essential or collaborative
innovation ie the ability to adapt, be agile, acquire
organizations, outsource, and generally be more ecient,
cost-eective and competitive
Demonstrating a clear return on investment (ROI) is always
challenging as clinical data warehouses are oten multi-year
programs with abstract, cross-unctional concepts To be
successul, a clinical data warehouse requires continuous
senior management commitment and sponsorship
It is important to build a broad, holistic picture or the
organization rather than at a departmental level Buy-in solely
rom one or two department heads is rarely sucient
However, transition rom batch-centric data preparation and
programming to better solutions, more in keeping with the
dynamic nature o the industry, requires a key company
visionary, with the gravitas to communicate the overall
benets o a clinical data warehouse to the wider company
The Clinical Data Warehouse a New Mission-Critical Hub (continued)
7/29/2019 Clinical Data Warehousing
10/31
Health SciencesJournal Issue 1 March 2013
p 10
The implementation phase o a clinical data warehouse can
be challenging This phase requires essential specialist skills in
areas such as process design, data standardization, modeling,
and system integration However, once the clinical data
warehouse is in place, ewer specialist skills are required
Key emphasis must also be placed on management o
process design and change, user training, adoption, and cross-
departmental co-ordination to ensuring that investment in the
clinical data warehouse is maximized A continuous program o
monitoring and driving user adoption, streamlining data fow,
and extending and enhancing use cases and tools-sets or
data exploration, visualization and analysis are key to ongoing
return These projects do not stop when they go live butevolve with continuous improvement and adaptation (Figure 2)
Figure 2: Schematic depicting the general capabilities
o a clinical data warehouse
Identiying use cases
For a clinical warehouse project to be successul it is essential
that clear and specic use cases are dened beore project
initiation A use case can be analogized to breaking up a
big problem into bite-sized pieces, targeted at delivering
specic business benet These could be ocused on reducing
data handos, standardizing data, accelerating statistical
analysis, or simpliying medical and saety review Without
use cases to drive project goals and ROI, such projects can
become complex IT architecture programs with poorly dened
endpoints delivering little business value at completion Focus
on specic use cases allows a clear understanding o the likely
process changes and an understanding o the potential benets
o changing these processes
Once use cases have been identied, the right platorm can
be selected, along with associated technology and consulting
services to assist companies in delivery
Phased delivery is one o the key things. Due to the
many interdependencies o the components in these
projects some companies adopt a Big Bang approach
o trying to do everything at once, but these oten ail
to deliver as return on investment is too long. It is critical
to implement use cases that give incremental return.
A phased delivery, rather than trying to deliver universally on
everything, is advisable Dening specic use cases at the
start, and phasing the implementation over several stages,
allows the realization o tangible benets, while successully
managing stakeholder expectations
The Clinical Data Warehouse a New Mission-Critical Hub (continued)
Drive Clinical Innovation
Empower Clinical Teams
Maximize Data Value
Enable Collaboration
Simplify Outsourcing
Accelerate Regulatory Queries
7/29/2019 Clinical Data Warehousing
11/31
Health SciencesJournal Issue 1 March 2013
p 11
Extracting ull potential
It is imperative to create a learning organization which can
evolve, extend and expand, to extract ongoing value rom a
clinical data warehouse
There must be a conscious eort towards creating alignment
between IT and business units o an organization The clinical
data warehouse platorm should be an enabling platorm
rom which the business can gain signicant, repeatable
value, or example improving response time to regulators,
identiying potential new therapies, or moving towards
individualized medicine This is the ultimate vision or clinical
data warehouses
Modeling and simulation is another area rom which value can
be extracted using a clinical data warehouse For example,
simulations may infuence trial design in terms o identiying
appropriate recruitment populations or helping to design
ecacy parameters Providing these groups with rich, collated,
standardized data via a clinical data warehouse can enable
them to eectively and precisely predict outcomes in various
models
A good clinical warehouse can truly fip the 80:20 ruleor a modeling/simulation analyst. Instead o spending
80% o the day searching and cleaning data or analysis,
and only 20% on analysis, they can leverage the data
warehouse, nd their data quickly, and spend 80% o the
day analyzing to accelerate research.
The ability to easily mine and explore legacy data can also
uncover hidden value, or example, a previously abandoned
drug in one disease could be investigated or use in another
disease Likewise, mining and exploring data acquired through
mergers and acquisitions could be used to augment current
company data, thereby increasing data value (Figure 3)
Figure 3: Optimizing collation and search leads
to increase opportunity or innovation
The Clinical Data Warehouse a New Mission-Critical Hub (continued)
Value
Time
Clinical Data Warehouse Traditional Approach
Data Collation
and Searching
Opportunity
for Innovation
7/29/2019 Clinical Data Warehousing
12/31
Health SciencesJournal Issue 1 March 2013
p 12
Emerging technologies and big data
There is increasing recognition o a need or improved
management o big data in the clinical space, and ecient
aggregation and integration with core clinical data will be key
to successul clinical warehouses in the uture
Big data are unstructured compared with traditional (ully
structured) or clinical (mostly structured) data ormats They
can be obtained rom sources or ormats such as social
media and include real world use o prescription and over-
the-counter drugs In this scenario, patients may use social
networks to relate their drug experiences, or example, in
terms o saety or adverse eects It is critical or the drug
manuacturer to be able to mine these data, recognize potentialissues arising, and address or manage them eectively, thus
providing pharmacovigilance insights on a marketed drug
Other key sources o big data will come rom advances
in patient genomic proling, as well as wearable medical
monitoring technologies Combining such huge data sources
with well curated clinical trial data will be essential to delivering
individualized or personalized medicine An approach taken
by IT vendors to address big data is to design technologies
that combine both sotware and hardware to support large-scale data sources and data warehouses, or example, Oracle
Exadata As the lie sciences and healthcare industries
converge around delivery o individualized medicine, the
need or such database machines will be critical to delivering
targeted treatments By exploiting such high perormance data
management platorms the industry will transition rom drug-
driven clinical trials to patient-driven
Final thoughts
In addition to, and as a consequence o, the overall move o
the lie sciences and healthcare industry to become more
cost-eective, ecient and competitive, it must improve the
overall value realized rom its key asset, data A clinical data
warehouse provides a solution or lie sciences companies to
better access, mine, explore and use data across their trials
and portolios Furthermore, clinical data warehouses can
be viewed as an essential tool or speed o access to data
when considering globalization, outsourcing and merger and
acquisition activity Most importantly it can be viewed as a
platorm or accelerating clinical innovation
Clinical data warehouses represent an exciting area o currentdevelopment and oer the potential to shape the way we
utilize and manage clinical data They will continue to evolve
and, with the convergence o the lie sciences and healthcare
industry, will become a necessity in order to eciently drive
value rom data and ultimately accelerate development o
new therapies
The Clinical Data Warehouse a New Mission-Critical Hub (continued)
7/29/2019 Clinical Data Warehousing
13/31
Health SciencesJournal Issue 1 March 2013
p 13
A Clinical Data Warehouse Solution to ImproveOperational Eciencies Colin Burns ICON Clinical Research
Colin Burns is senior director o
global data and technologies at
ICON. He leads the development
o ICONs enterprise clinical data
warehousing capability, the data
arm o ICONIK Monitoring
service, which optimizes the clinical
trial execution strategy or each trial
to help clients manage risk and improve efciency.
Colin has more than 15 years o lie science and healthscience experience.
The changing trends in the lie sciences industry in terms o
outsourcing, partnering and globalization have created the
need to improve overall working eciency and communication
between partners and service providers Contract research
organizations (CROs), such as ICON, understand the intense
pressure to reduce cost and timelines or drug development
while ensuring data quality Accordingly, ICON is among
the eCROs to have emerged that have the inormatics
capabilities to re-aggregate clinical trial data in a time and
cost-eective manner
Increasingly, CROs now have a stake in clinical drug
development through risk sharing with pharmaceutical
companies and involvement at every step o the process
Clearly, this new model involves handling larger volumes
o data rom a vast number o dierent sponsors and in
dierent ormats For ICON, the implementation o a clinical
data warehouse was an obvious solution to manage these
large volumes o disparate data Through the identication
o a specic business case and denition o precise project
parameters, the company has succeeded in implementing a
clinical data warehouse that delivers operational eciency
and, consequently, competitive advantage
7/29/2019 Clinical Data Warehousing
14/31
Health SciencesJournal Issue 1 March 2013
p 14
Business challenges/unmet needs leading to a clinical
data warehouse
The CROs main business challenge, in terms o data, is
the increasing volume and variety o sponsors/clients that it
typically engages with CROs typically handle large volumes
o disparate data rom dierent sources presented in various
ormats One challenge is how to eectively and eciently
access and manage disparate data while maximizing value,
increasing usability and maintaining data integrity ICON
realized the limitations o previous approaches to data handling
which were unworkable and unsustainable over the long term
It identied an urgent need to move away rom traditional and
ad hocpractices, to a more harmonized approach to clinical
trial data handling ICON also realized the need to convert
data, which may have been overly technically or scientically
ocused, to a more accessible and usable orm or operations
teams This would enable CRO clinical trial study teams and
sponsors to more eectively access, analyze and use data
on a real-time basis (compared to previous batch-centric
approaches) Thus, a plan was dened to implement and
establish a clinical data warehouse
We [ICON] knew what we needed to do and we wentabout doing that in a very ocused and directed way.
A CRO approach or implementing a clinical
data warehouse
ICONs approach to implementing a clinical data warehouse
was specically directed and purposed to meet the need as
identied by its business case; to make large volumes o data
usable and empower study teams to proactively manage
their studies based on more insightul data The solution had
to be scalable, thus handle increasing volumes o data, and
implementation and deployment had to be rapid in a specied
time rame in keeping with typical CRO project turnarounds
In addition, to ensure these targets were met, the clinical data
warehouse would include only new clinical trial study data
Legacy data and associated legacy data conversions were
not incorporated in the clinical data warehouse an approach
that is suited to CROs typically contracted scope o services
within the trial execution phase
We [ICON] took a very purposeul and directed
approach. We wanted to do something quick; we
wanted to do something manageable.
A general approach when implementing a clinical data
warehouse is to deploy a range o technologies targeting
various layers o data management rom data input and
governance to data output and reporting, publication,
visualization and collaboration ICON contracted with Oracle
to supply key enabling products/technologies to orm the basis
o the clinical data warehouse platorm or data custody,governance, and export to sponsors In addition, third-party
providers were contracted to supply supplementary tools or
data access and visualization Together all o the interlinked
technologies ultimately allow clinical study teams to gain
access to and insights on trial data
Finally, and most importantly, implementation o a clinical data
warehouse requires support and buy-in rom key stakeholders,
A Clinical Data Warehouse Solution to Improve Operational Efciencies (continued)
7/29/2019 Clinical Data Warehousing
15/31
Health SciencesJournal Issue 1 March 2013
p 15
particularly executive personnel, who can catalyze the speed
o the overall project For ICON, there was both executive and
board support rom the project outset as one o the companys
strategic goals was to improve its inormation and inormatics
capability A clinical data warehouse is directly associated with
this goal, hence implementation was unanimously supported
and deployment was successul
Implementation challenges, solutions and
standardization
ICONs clinical data warehouse implementation was divided
into a number o manageable phases, to acquire, centralize,
standardize and visualize data and subsequently, to address
associated operational deployment and change management
challenges The challenges o rapid deployment were
overcome by dening simplied, achievable use cases and
having appropriate milestones by which to assess progress
There have been technical challenges, particularly during early
implementation ICON was one o the rst to adopt this clinical
data warehouse strategy and consequently experienced pain
points associated with being pioneers These have lessened
over the years as other organizations have adopted thestrategy and product solutions have been identied to address
these early challenges
Data standardization has been a cornerstone to the success
o ICONs clinical data warehouse and was an important
consideration even beore use case identication and project
initiation The CRO approach to standardization is dierent
rom a pharmaceutical company based on the nature o CRO
activities CROs typically engage with numerous, diverse
sponsors, each with their pre-dened set o requirements in
terms o a clinical trial, thus a CRO must set its own internal
standards ICON has achieved this through the development o
a comprehensive clinical data standards hub, built o Clinical
Data Interchange Standards Consortium (CDISC) standards
and the study data tabulation model (SDTM), which drives
operational data review, data visualization and data delivery
A clinical data warehouse as part o an overall
integrated inormation platorm ICONIK
In July 2010, a year ater signing the initial contract with their
selected clinical data warehouse provider (Oracle), ICON
launched its clinical data warehouse platorm The clinical
data warehouse is a key part o ICONs overall integrated
inormation platorm ICONIK
For us [ICON] ICONIK is about trying to be very
transparent with our client in terms o how we are
running their studies, trying to drive operational
eciencies and improve quality, and being proactive in
terms o what we do in the conduct o our studies.
ICONIK is a powerul integrated inormation platorm that
consolidates, standardizes and visualizes both operational
and clinical data, to provide a single holistic view o all study
inormation to both sponsor and CRO teams It oers near
real-time access to clinical trial perormance metrics, critical
saety and ecacy data, and the ability to analyze these
data in novel ways The ICONIK inormation platorm and
associated operational processes improve data quality and
A Clinical Data Warehouse Solution to Improve Operational Efciencies (continued)
H l h S i J l I M h
7/29/2019 Clinical Data Warehousing
16/31
Health SciencesJournal Issue 1 March 2013
p 16
subject saety while yielding signicant operational eciency
gains (Figure 1)
Oracle Lie Sciences Data Hub powers the clinical data
warehouse and alongside an operational metrics data
warehouse, ICON has the key components in place to drive
operational activities such as assessing study easibility, trial
start-up, subject enrollment, subject retention, and saety
ICONIKs integrated inormation platorm provides our
levels o knowledge to the sponsor and study teams:
Operational eciency automated processes to
gather and surace inormation
Transparency increased transparency to clientsthroughout the development process
Visibility accurate and detailed inormation on site
perormance and risk
Quality increased ocus on data integrity and control
o clinical data
The ICONIK integrated inormation platorm and associated
operational processes has enabled the company to
revolutionize management o clinical trials, such as improving
study planning and design by access to historical and
operational clinical data to guide protocol development and
provide quicker evaluation o site easibility, aster identication
o delays or potential diculties in site start-up, and the ability
to obtain insights into patient eligibility and screening ailures to
improve retention rates, among others
Figure 1: ICONIKs integrated inormation platorm
provides our levels o knowledge to the sponsor
and study teams
Today, ICONs enhanced Sponsor Reporting Services oers
a number o key benets or the optimized visualization o
data rom the clinical data warehouse The enhanced Sponsor
Reporting Services provide a single source or study team
members to access study inormation in a consistent manner
across the liecycle o a study Using clinical data rom
electronic data capture (EDC), interactive voice response (IVR),
eDiary or Central Laboratory, a study or a program o studies
can be instantly evaluated rom a scientic, saety and quality
perspective Any operation, rom the detection o a saety
signal to the data quality analysis o a solitary site, can be
perormed in a ew clicks
A Clinical Data Warehouse Solution to Improve Operational Efciencies (continued)
SponsorsStudyTeams
ImmediateKnowledge
H lth S i J l I 1 M h 2013
7/29/2019 Clinical Data Warehousing
17/31
Health SciencesJournal Issue 1 March 2013
p 17
Main users o the clinical data warehouse, benefts
and impact
The main users o the clinical data warehouse are the end
consumers (ie the clinical trial study teams, clinical data
teams, the medical monitors, study start-up teams, quality
assurance teams, who are the main beneciaries o the clinical
data warehouse) rather than data programmers Such end-
users may not necessarily log on, but do consume all data,
insights and analyses o study perormance and status and
use these to inorm operational decisions
One o the best examples to illustrate this is the ICONIK
Monitoring service, where the centralized monitoring team
routinely uses holistic scientic data analysis, together withclinical research associate site knowledge to direct central
and site monitoring activities The centralized monitoring team
has access to and continually reviews real-time investigator
perormance and risk metrics, all o which are predictive o
overall investigator perormance and compliance Investigators
with abnormal behavior patterns are tracked and analyzed
centrally in order to evaluate the need or site-specic action
and ensure a ocused approach to monitoring Study teams
are managing monitoring resources in a fexible and intelligentway, employing resources as and when they are required
based on the demands o the study
We [ICON] have 8,500 employees in the company and our
view is that the clinical data warehouse is the oundation
rom which we get the data that all o the teams consume;
without the clinical data warehouse and operational data
warehouse they would not have access to this.
Conclusion
A clinical data warehouse oers a data-handling solution or
CROs as it enables the centralization and governance o clinical
data which ultimately acilitates the publication o data in a
usable ormat For ICON, the main ocus o the clinical data
warehouse was to convert previously technical data to a more
usable and understandable orm or clinical trial study teams
and sponsors, which could then drive operational decisions
In other words, operational eciency was at the heart o the
decision-making process
ICON believes that its implementation o a clinical data
warehouse has dierentiated it rom other CRO competitors
The companys pioneering eorts to implement ICONIKhas acilitated access to useul, comprehensive real-time
data or its study teams and sponsors, giving the company a
competitive advantage over other CROs
As the company looks to the uture, the aim is to continue to
ocus on operational activity and identiy niche areas within
this space on which to deliver added value to its sponsors/
clients ICON has successully implemented and deployed
its clinical data warehousing solution and 3 years rom initial
deployment o ICONIK, the company has already realized its
main aim, which was to ensure accessibility to operationally
useul data The clinical data warehouse will continue to evolve
and deliver benets and eciencies
A Clinical Data Warehouse Solution to Improve Operational Efciencies (continued)
Health Sciences Journal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
18/31
Health SciencesJournal Issue 1 March 2013
p 18
A Dynamic Platorm or Data Integration,Standardization and Management Brooks Fowler and Nareen Katta AbbVie
Brooks Fowler is the global head
o data sciences at AbbVie. Brooks
is specifcally accountable or data
management operations, clinical
inormatics and clinical sample
management operations. Brooks
began his career in pharma with G.D.
Searle in 2000. He joined AbbVie in
2003 as a section manager o clinical data management.
Over the course o the last nine years, Brooks and the
AbbVie team have designed and implemented enterprise
solutions or EDC, ePRO and IRT.
Nareen Katta is the senior manager,
data sciences at AbbVie. Nareen is
specifcally accountable or managing
the companys EDC system and
clinical databases, including design
and defnition, data integrations,
standardization and ETL operations.
To eectively compete in the current economic climate and,
in the ace o changing trends in commerce, the lie sciences
industry has had to evolve and become more cost-eective,
ecient and responsive There is increased emphasis on
optimizing the clinical trial process and enabling maximum use
o data, the industrys key asset To this eect, pharmaceutical
companies such as AbbVie are continually searching or ways
to maximize value rom data Better analyses o clinical trial
data and optimization o operational aspects (or example,
administrative and nancial) o each trial can improve both
cycle time and eciency
It is about cultivating previously unused data whether
its clinical or operational, and putting it to good use.
In addition, AbbVie recognizes the need or better data
management, achieved through better IT solutions Indeed,
although IT is not a core competency o the lie sciences
industry, there is a high demand or up-to-date IT inrastructure
and solutions Thus, an IT provider or specialist company builds
and implements the IT inrastructure while the pharmaceutical
company uses this inrastructure to consolidate, mine, and
explore data, thereby inorming clinical and operational decisions
with reduced need or specialist skills internally
Health Sciences Journal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
19/31
Health SciencesJournal Issue 1 March 2013
p 19
Addressing industry needs and business drivers
AbbVie identied a number o unmet needs that led to a
clinical data warehouse as a solution These included the:
1) Absence o an archiving solution in the companys currentand legacy clinical data management systems (CDMSs)
there was no unctional system rom which archived clinical
data could be accessed on demand, as there was no
archival acility in the previous CDMS or clinical data rom
the companys own trials, or data inherited rom mergers
and acquisitions
2) Use o numerous and varied data entry systems, thus
data were disparate rather than standardized making
analysis challenging3) Use o data management systems with a xed
structure restricted data integration as data had
to be in a certain ormat
4) Inability to perorm cross-study analyses the companys
vision was to create a system that allowed all users, and
not just specialists like statisticians, to conduct ad hoc
analyses and be able to visualize data, thereby maximizing
the value o data
AbbVie needed to address key business drivers, including
minimizing the number o manual steps required to access
data It was imperative to identiy the right IT tools or the
right unctions thereby allowing near real-time data access in
a consolidated, accessible and eortless manner In addition,AbbVie required a solution that was dynamic and allowed
upgrading and switching o systems as new versions or
peripheral applications became available Thus, a solution that
could readily evolve with minimal disruptions was needed
A fexible clinical data warehouse presented the most
suitable solution based on the act that it does not have a
pre-dened data structure As a result, it was possible to
integrate data rom any data structure, or example other
clinical and operational systems, and subsequently make
these data conorm to AbbVies structure templates, that are
source system agnostic within the warehouse, with minimal
disruption Furthermore, with a clinical data warehouse it is
possible to build integrated data access layers These enable
non-specialist users to readily access data and perorm
cross-trial data analysis
A Dynamic Platorm or Data Integration, Standardization and Management (continued)
Health Sciences Journal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
20/31
Health SciencesJournal Issue 1 March 2013
p 20
A phased approach to clinical data warehouse
implementation
Once the unmet needs and business drivers had been
dened, the company scored and ranked business issues
via internal interviews to highlight possible approaches or
nding a solution The approaches were categorized as
process, application or inrastructure changes Through
this screening, application emerged as the most commonly
requested change The company responded to this by
replacing its existing data management system with a clinical
data warehouse AbbVie wanted its solution to serve as an
end-to-end CDMS with both data warehousing and data
management capabilities; the warehousing aspect or data
aggregation, standardization and reporting and the CDMS or
data cleaning, blinding and medical coding requirements
We [AbbVie] were looking or, not only a clinical data
warehouse and repository, but a ull blown clinical data
management system.
The clinical data warehouse was deployed over two phases
Phase 1 involved assessment and implementation o core
unctionality, as determined by a cross-unctional group
in workshop settings, while Phase 2 involved the addition
o tools and urther renement Prior to Phase 1, AbbVie
perormed a proo-o-concept test to assess the core
unctionality, process change and use cases, thereby re-
conrming the suitability o a clinical data warehouse solution
To dene how processes and the business, as a whole, were
likely to change as a result o a clinical data warehouse, the
company extrapolated and mapped the nal project outcomes
to the base requirements This exercise dened the end-user
interaction with the new ramework and highlighted areas
that would need urther development in order to optimizeunctionality There were various integration processes during
Phase 1, or example with electronic data capture (EDC) and
Laboratory Inormation Management (LIMS) systems, making
it possible to amalgamate and consolidate data with the
core system To ensure continued accessibility o data to the
statistics teams, the extraction methodology or pulling clinical
data rom the clinical database to the analysis database was
redesigned to t the clinical data warehouse For example,
a previous storage acility o metadata was repurposed orthe clinical data warehouse and enabled the company to
begin processing some studies through the warehouse In
addition, a metadata driven study setup utility, a parameter-
driven edit check engine to enable discrepancy management
and integration with coding solutions (or example, Oracle
Thesaurus Management System) used to standardize medical
encoding terminology across studies, were developed With
core unctionality achieved, the company was able to deploy
its clinical data warehouse at the end o Phase 1
In Phase 2, there was additional integration o tools to enable
the users to more extensively use the system For example,
using metadata, AbbVie created a tool to allow users to
identiy a new study and search or similar studies rom legacy
data In addition, there was integration with additional data
A Dynamic Platorm or Data Integration, Standardization and Management (continued)
Health SciencesJournal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
21/31
Health Sciences Journal Issue 1 March 2013
p 21
sources like AbbVies Phase 1 management system, as well
as bi-directional integration with the EDC system to enable
discrepancy management with sites, thereby enabling end-
to-end data fow Reporting and data browsing tools were
added to urther simpliy user interaction and access o theclinical data warehouse Finally, Phase 2 also involved process
automation
The clinical data warehouse is currently in the early stage
o rollout and is thereore restricted to use by the global
data management and statistics divisions; however, the
company envisages urther roll-out and expansion o the user
community in the uture as reporting and visualization tools are
added to the platorm
We [AbbVie] are utilizing the tool across our global
data management and statistics sites. We are making
sure that the use o the system and the implementation
is geographically dispersed rather than ocusing it here
[Chicago] at our single headquarter oce.
AbbVie expects to process all o its studies through the clinical
data warehouse once it is ully scaled up Thus, the overall
intention is that every global site will utilize the new system,
increasing operational eciencies and cost-eectiveness
Key challenges and data standardization
AbbVies main challenge with implementing a clinical data
warehouse has been the act that the new clinical data
warehouse ramework is a complete paradigm shit A
clinical data warehouse is an entirely novel undertaking and
completely dierent to the companys previous experience It
was challenging to ully comprehend the capabilities and select
appropriate tools to be integrated onto the technology platorm
However, the IT provider was instrumental in this endeavor and
provided the guidance and expertise needed to manage the
process change
To be able to translate this [a clinical data warehouse]
into a uture vision and be able to execute it was achallenge. It is a technology ramework, not just a
business process.
Implementing a clinical data warehouse was a major IT
initiative and to ensure its success it was important to improve
both the IT and business inrastructures, including but not
limited to process and resource development, which were
likely to impact the project
The volume o technology and process integrations requiredalso presented a challenge One o the drivers or implementing
a clinical data warehouse was to reduce the amount o manual
activities Automation o manual tasks required an assessment
o both present systems/processes and uture clinical data
warehouse environment capabilities/processes Based on
these, tasks or automation were identied
A Dynamic Platorm or Data Integration, Standardization and Management (continued)
Health SciencesJournal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
22/31
p 22
Stakeholder buy-in was less o a challenge Through specic
use cases and business cases there was clear demonstration
o improved eciencies, drug saety implications o data
integration (in terms o having aggregated data or regulatory
queries and analyses), and the cost justication which willbecome increasingly evident over time as more clinical trial
data are collected and reduction in manual eort maniests
more broadly Due to these elements, unanimous support
rom project sponsors was won
The clinical data warehouse has enabled AbbVie to implement
the Clinical Data Interchange Standards Consortium (CDISC)
Study Data Tabulation Model (SDTM) standards The company
required fexibility to allow incorporation o data rom various
sources, but to also have industry-recognized standards To do
this, a number o tools were added to the platorm to convert
native standards to CDISC STDM ormats, which were then
accessible to users In addition, a data governance team was
built to manage these processes
Benefts to users
AbbVie has realized a number o benets rom its clinical
data warehouse solution including the ability to extract valuerom metadata and legacy data For example, using legacy
data, programmers are able to more eciently design uture
trials and processes In addition, a single inormation hub
has allowed the use o one or two key reporting visualization
systems which provide data in readily usable ormats to end
users Previously there had been numerous, dierent reporting
and visualization tools providing data in diverse ormats
Sample management logistics is another potential benet and
may allow sample tracking rom origination and collection to
process end within a robust warehousing environment
A clinical data warehouse also provides a central repository or
storing data inherited rom mergers and acquisitions It provides
an open but secure ramework onto which acquired data can be
archived, mined and standardized, as required Clinical data rom
three legacy CDMS applications inherited rom mergers and
acquisitions have been archived to date
An additional benet, though not specically identied by the
company when dening the use cases, has been the ability to
lock clinical trial databases more quickly The ability to manageblinding and un-blinding o sensitive clinical data in the clinical
data warehouse contributes to urther reduction in the cycle
time For example, in previous systems, blinding data were
added once the database had been locked and this was time
consuming With a clinical data warehouse, sensitive data can
be uploaded and stored in a secure/non-accessible area long
beore database lock Thus, on database lock un-blinding can
potentially be perormed instantaneously as all data are already
on the system
With regard to being able to lock databases quicker
and being able to change and increase the requency
o data rereshers into our [AbbVie] system we have
realized eciencies but I think it will be a while
beore we completely realize the eciencies that
are associated with the new system.
A Dynamic Platorm or Data Integration, Standardization and Management (continued)
Health SciencesJournal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
23/31
p 23
Furthermore, the system allows direct linkage to AbbVies
EDC tool, thereby making it easier to access data with less
administrative burden
The overall response rom both stakeholders and users has
been positive thus ar However, urther time is required
to ully appreciate the eciencies and benets o the new
system, particularly with regard to uture enhancements
Some o the expected benets include system portability,
reduced manual eort related to data cleaning and data
loading, and or aggregating data or cross-study analysis
Looking towards the uture
As the company looks towards the uture, the intention isto replace current processes (or example, various reporting
tools) with the new system Improved data archiving will allow
storing o original data in a well-controlled environment with
subsequent standardization and reporting in a readily usable
ormat This will provide an ad hocanalysis capability within
the drug development process making it possible to assess,
or example, whether anything was missed in the initial
analysis, whether there were any saety implications o note,
and whether a drug mechanism currently under investigationhad been previously tested; all o which will be used to drive
uture decisions
AbbVie is also looking to capture large-volume data,
particularly rom its post-marketing registry trials which
typically involve a large number o patients, into the clinical
data warehouse Future plans include partnering with health
outcomes organizations and utilizing electronic medical recorddata and claims data rom these organizations to maneuver
the structure o these data into a more usable ormat or both
internal personnel and health outcomes teams
In summary, AbbVies implementation o a new clinical
data warehouse (integrated with clinical data management
capabilities) has provided a platorm that enables data
integration, standardization and management The company
has ocused on automating its data fow rom collection to
analysis to minimize manual steps, thereby decreasing sources
o error and increasing operational eciency Clinical data
warehouse implementation has been successul using a
two-phased approach The uture user community is
predicted to increase as data stored in the clinical data
warehouse becomes more integrated and accessible The
clinical data warehouse is able to store both production and
legacy data, allowing standardization, exploration, mining
and analyses o these data to inorm uture decisions The
company views its clinical data warehouse as a dynamic,
evolving platorm that will eventually replace most o its
current technologies and systems
A Dynamic Platorm or Data Integration, Standardization and Management (continued)
Health SciencesJournal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
24/31
p 24
Clinical Research Innovation through SharedClinical Data Warehousing Jerry Whaley Pfzer
Jerry Whaley is senior director o
development business technology
at Pfzer and is involved in the
implementation o Pfzers clinical
data warehousing solution.
Jerry began his Pfzer career in 2001,
as director, development inormatics
Ann Arbor site head. Beore joining Pfzer, Jerry was an
SAS programmer and supervisor at the Upjohn Company
and a systems analyst, developer, project leader andmanager at Parke-Davis. Prior to returning to Pfzer Jerry
was vice president at Advanced Systems Development
with responsibilities or business development and client
implementation management.
Increasing partnerships between pharmaceutical and
biotechnology companies and/or service providers is a key
emerging trend in the lie sciences industry Specically
in clinical trial management, there has been an overall
re-assessment o what constitutes competitive advantage
with regard to data capture and management The questions
being asked include: does a custom-developed electronic
data management platorm really provide competitive
advantage? Could using a standard platorm help minimize
the many issues caused by variability o data? Where is it
best to ocus pharmaceutical company resources?
Unsurprisingly, ocus is all on data. I, as an industry,
we can holistically understand our data better and
more in-depth, so not just as Pzer-specic data
but, or example, healthcare as a whole, then thats
advantageous as it allows us to better analyze it.
Pzers clinical trials operational model has evolved over the
years The initial model progressed rom conducting inhouse
trials to outsourcing trials to 17 unctional service providers
This has now evolved to the companys current position o
having two alliance partners (ICON and Parexel), or contract
research organizations (CROs) This change has been driven
by a business need to be more cost-eective and manage
resources more eciently This model enables the company
to leverage the CROs expertise in execution o clinical trials
Health SciencesJournal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
25/31
p 25
and allows Pzers role to evolve, into a more oversight
role Pzers oversight input on clinical trials also requires
expertise and skills that need to be acquired over time The
consequence o this model is that it rees company resources
to ocus on analysis o trial data, rather than preparationo data Such an undertaking requires a standardized data
warehousing solution or data receipt, aggregation, access,
and analysis
Pzers intention is to create a road map to dene and
standardize processes or data integration and data sharing
based on a communal data warehousing solution In addition
to enabling interactions with CROs, the visionary view or
this type o clinical data warehousing is that it could also
acilitate uture interactions with multiple partners including
other pharmaceutical or biotechnology companies, regulatory
authorities, and companies absorbed through mergers
and acquisitions
Pfzers Clinical Aggregation Layer (CAL) solution
Pzers vision is to create a cloud technology platorm which
acilitates ecient clinical trial operation or industry peers,
to minimize duplication o eort in tool development, anddrive process eciency to accelerate new drug research
Company owned data handling tools and applications may not
necessarily provide competitive advantage but do increase
costs In Pzers view, as long as individual company data
are secure and protected and there is appropriate legal and
regulatory approval, data can be stored and processed rom a
central platorm that is located externally to Pzer, providing an
opportunity or sharing technology across the industry
Pzers data warehousing solution, known as the Clinical
Aggregation Layer (CAL), consists o three core components
(Figure 1A and 1B):
1) A clinical and scientic data warehouse (CSDW) to
manage, aggregate, and analyze clinical trial data
2) An operational data warehouse (ODW) or trial
perormance metrics
3) A custom-developed trial master le (TMF) tool to keep
a comprehensive record o all clinical trial activities
Data are loaded into CAL through various mechanisms,
depending on data type and source (or example, data
exchange adapters, secure le transer protocols, etc) such
mechanisms being based on industry standards Data stored
in a metadata repository, are also uploaded into CAL Pzer
captures and maintains these metadata It is imperative that
input data are correctly reerenced and indexed or such a
solution to be eective
We [Pzer] see this as sowing the seed o an industryinrastructure, thats our vision. This is not just a Pzer
solution; we are trying to seed this solution with
partners such as Oracle and Accenture.
Clinical Research Innovation through Shared Clinical Data Warehousing (continued)
Health SciencesJournal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
26/31
p 26
Figure 1A: Pfzers long-term technology vision
The CAL solution can be potentially both cost-eective and
innovative and could drive the development o new tools
available or a majority o users Collective innovation may
also result rom being able to conduct in-depth analysis usingshared data that are readily accessible rom a standardized
IT inrastructure A shared platorm may also be used to
leverage trial data more broadly or example, companies
conducting trials in a single disease area could, in theory, share
placebo data i patient recruitment criteria were similar thereby
reducing costs or the placebo arm o a trial
This model is possible due to sucient evolution o data
standards, services, technology and IT inrastructure The
convergence o technology and business needs, tighter
business models (with regard to eciency and cost), and
stringent regulatory processes and requirements haveurther reinorced the premise o such a solution
Figure 1B: Components needed or the new
Pfzer platorm
One version of the truth
Two Guiding Principles
Exchanged data based ondened data standards
Analysis and Review Tools
Contextual Data
Reference Data Management (RDM)
Operational DataWarehouse
Clinical ScienticData Warehouse
Trial Master File
Secure Data Exchange
Integrated repositoryfor data required to
track and manage study
and developmentprogram execution
Integrated repositoryof analysis-ready
data from our partners,
Pzer, and relevantexternal sources
Authoritative source
of Essential Documents
Clinical Research Innovation through Shared Clinical Data Warehousing (continued)
ICONSystems
FutureAcquisitions/Future Partners
(PXL/ICON/FSP)On-going Studies:
[OC/RDC/TMS]
Aggregatereporting
Visualization Data mining
Pzer owned andoperated systems
InformationeXchange Hub
(IXH)
Clinical Aggregation
Layer
ParexelSystems
Pzer RunStudies
Others
ProjectMgmt
ClinicalSupply
SafetyOperational Data
Warehouse(ODW)
Trial Master File(TMF)
Clinical ScienticData Warehouse
(CSDW)
Study OperationalReports
Programme/portfoliomilestones
Partnership
Scorecard
Contextual Reference Data Module (RDM)
Accenture/Oracle
Health SciencesJournal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
27/31
p 27
The willingness o industry peers to be participants in such a
warehousing solution is yet to be realized To this eect, Pzer
is actively engaging in discussion with peer companies to
gauge interest Initial indications appear positive
Approach to implementing a shared clinical
data warehouse
The most evident dierentiator o Pzers clinical data
warehousing solution is its accessibility o technology across
the breadth o the lie sciences industry Pzer is actively
avoiding customization o its data warehousing solution, and is
making every eort to maintain it as an o-the-shel solution
to allow or broad applicability
We [Pzer] are trying to stay rigid to the act that these
are commercially available, o-the-shel solutions Do
not Pzerize them; Do not customize them avoid this as
much as possible. This approach allows reusability, ease
o implementation and ease o support long-term.
To urther ensure this, Pzer requently engages in discussions
with its IT provider to ensure that tools and applications
remain generic, enabling easy upgrades, processing and most
o all, seamless partnering with external parties Although
outsourcing operational unctions (or example, execution o
clinical trials) and using shared data warehousing platorms
and technologies is not a new concept, it was unprecedented
or large companies like Pzer The long-term aspiration is to
externalize most processes and maximize the use o external
expertise to drive Pzers healthcare goals
Implementation o the Pzer solution necessitates a phased-
approach to make this complex and challenging undertaking
a more manageable and viable prospect Future releases
include additional unctionality within the CSDW, across other
therapy area and clinical trial teams, ollowed by ODW-relatedoperational data
Stakeholders have readily championed and supported this
project rom its genesis The CAL solutions goal is to be
instrumental in increasing Pzers operational eciency and
re-ocusing resources towards accelerating clinical research
Use cases supporting a clinical data warehousing
solution and potential users
A number o identied use cases supported the need or the
CAL solution The most immediate was associated with the
new operational model o increased clinical trial externalization
This required consolidation o data to a single location to
allow Pzer easy access to trial-related inormation and data
In addition, the need to consolidate data gained through
mergers and acquisitions Pzer identied the need to improve
the ability to explore, analyze and mine both clinical trial and
operational data so as to maximize its value From a regulatoryand compliance perspective, the availability o all trial data in a
single location could acilitate a quicker response to queries
Clinical Research Innovation through Shared Clinical Data Warehousing (continued)
Health SciencesJournal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
28/31
p 28
Presently, the two critical primary users o the CSDW
component o the CAL solution are clinicians and statisticians
Users can explore, analyze, and mine clinical trial data on a
single platorm and, as a result o data standardization, can
leverage a broad range o tools to drive research Other usersare the companys partners and service providers who upload
trial-related data which Pzer can use or data analysis, as well
as monitoring trial progress The ODW provides operational
metrics which can be used to drive decisions (or example,
what geographical region may be suited to a trial in a particular
disease area) In order to remain compliant with regulatory
requirements, the TMF solution provides denitive proo and
record o all clinical trial activities Such actors contribute to
cost-benets, better time eciency and management (throughstandardization) and, increased data value realization
In addition, the CAL solution could simpliy the role o industry
regulators and auditors For example, where previously there
were dierent processes/systems or each company, with the
CAL solution there is a single system to understand Thus,
processes such as auditing/inspecting could become more
ecient based on the reduction o industry systems an auditor
would need to be amiliar with
This model can acilitate a more progressive and
ecient lie sciences industry in that regulators have
only one inrastructure and set o applications that they
need to understand, audit and ensure compliance.
Planned trial throughput via the CAL solution
Moving ahead, Pzers aim is to route as many clinical trials
through the new operational model as possible Typically the
company runs approximately 800 trials in a given year the
intention is to transer a proportion o trials (approximately 100)
to the new model by the end o year one and to accelerate
throughput to approximately 300 in year two The long-
term vision is to decommission all legacy processes and
applications
Although there is a company-wide eort to transer clinical
trials to the new system, there is recognition that this must be
done in a controlled manner to maintain data integrity To this
eect, the Clinical Data Interchange Standards Consortium
(CDISC) and Study Data Tabulation Model (SDTM) have
played important roles In addition, standardization acilitates
amalgamation o data ollowing mergers and acquisitions
an activity requently associated with Pzer
Clinical Research Innovation through Shared Clinical Data Warehousing (continued)
Health SciencesJournal Issue 1 March 2013
7/29/2019 Clinical Data Warehousing
29/31
p 29
Standardization is an important piece o our [Pzer]
strategy because without it, aggregating these data
would have a lot less impact and a lot less value.
The past, the present, and looking to the uture
In the past, Pzer attempted to build and implement its
own in-house data warehousing solution but this proved
challenging Nonetheless, many lessons were learned rom
early eorts which have infuenced the CAL solution including
the value in using a generic, robust, commercial o-the-shel
technology tool-set acceptable to other industry peers as a
shared data warehousing platorm
We [Pzer] elt we chose our tools wisely; it wasimportant to choose a scalable and industry leading
tool-set that others would embrace.
The present implementation o the CAL solution has not
been without challenges Some o these include converting
ingrained legacy business processes to new processes and
systems, availability o required stakeholders/personnel
or making implementation-related decisions and meeting
stringent deadlines and, as with all ambitious projects,
managing budgets eectively
Looking to the uture, Pzers view is that this is merely
the beginning o the journey For the company, the CAL
solution provides an innovation bed or managing, analyzing,
accessing, exploring and extracting maximum value rom data,
whether legacy or newly generated In addition, it provides asimple and standardized means o collaborating with multiple
partners Overall, the vision is that this joint data warehousing
solution, which is a novel concept in the clinical trial space,
will enhance innovation both rom a scientic and technical
perspective To achieve this vision, Pzer has leveraged the
expertise o global IT service providers, including Oracle, to
provide it with a clinical data warehousing solution that acts as
an integration and collaboration platorm to enable ull-service,
hybrid outsourcing, as well as to support internal processes
Clinical Research Innovation through Shared Clinical Data Warehousing (continued)
7/29/2019 Clinical Data Warehousing
30/31
p 30
STAY CONNECTEDTOORACLE HEALTH SCIENCES.
Join the Health Sciences Social Media Hub.
Follow Oracle Health Sciences thought leaders through
our social media channels Whether you join us on
Facebook, ollow us on Twitter, or become part o our
new Health Sciences Forum on LinkedIn, youll see howOracle Health Sciences is leading the conversation
Like us on Facebook:
acebookcom/OracleHealthSciences
Follow us on Twitter:
twittercom/OracleHealthSci
Join our LinkedIn group:
Health Sciences Forum
http://www.oracle.com/go/?&Src=7490940&Act=647&pcode=WWHS11110364MPP083http://www.oracle.com/go/?&Src=7490940&Act=647&pcode=WWHS11110364MPP083http://www.oracle.com/go/?&Src=7490940&Act=648&pcode=WWHS11110364MPP083http://www.oracle.com/go/?&Src=7490940&Act=648&pcode=WWHS11110364MPP083http://www.oracle.com/go/?&Src=7490940&Act=649&pcode=WWHS11110364MPP083http://www.oracle.com/go/?&Src=7490940&Act=649&pcode=WWHS11110364MPP083http://www.oracle.com/go/?&Src=7490940&Act=649&pcode=WWHS11110364MPP083http://www.oracle.com/go/?&Src=7490940&Act=648&pcode=WWHS11110364MPP083http://www.oracle.com/go/?&Src=7490940&Act=647&pcode=WWHS11110364MPP0837/29/2019 Clinical Data Warehousing
31/31
This publicati on was produced b y Oracle
Editorial support was provided by ApotheCom
Copyright 2013, Oracle and/or its afliates.
All rights reserved. Oracle and Java areregistered trademarks o Oracle and/or its
afliates. Other names may be trademarks
o their respective owners.
Oracle Corporation
World Headquarters
500 Oracle ParkwayRedwood Shores, CA 94065
U.S.A
Worldwide Inquiries:
Phone: +1.800.633.0643oracle.com/healthsciences
Printed on recycled material.
http://www.oracle.com/us/industries/health-sciences/overview/index.htmlhttp://www.oracle.com/us/industries/health-sciences/overview/index.htmlhttp://www.oracle.com/us/industries/health-sciences/overview/index.htmlhttp://www.oracle.com/us/industries/health-sciences/overview/index.htmlhttp://www.oracle.com/us/industries/health-sciences/overview/index.htmlhttp://www.oracle.com/us/industries/health-sciences/overview/index.htmlhttp://www.oracle.com/us/industries/health-sciences/overview/index.htmlhttp://www.oracle.com/us/industries/health-sciences/overview/index.htmlhttp://www.oracle.com/us/industries/health-sciences/overview/index.htmlhttp://www.oracle.com/us/industries/health-sciences/overview/index.htmlTop Related