Clinical Data Warehousing

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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


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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


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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


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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


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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


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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)


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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


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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



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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



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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


Drive Clinical Innovation

Empower Clinical Teams

Maximize Data Value

Enable Collaboration

Simplify Outsourcing

Accelerate Regulatory Queries


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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


Value

Time

Clinical Data Warehouse Traditional Approach

Data Collation

and Searching

Opportunity

for Innovation


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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



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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


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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)


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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


H l h S i J l I M h


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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


SponsorsStudyTeams

ImmediateKnowledge

H lth S i J l I 1 M h 2013


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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


Health Sciences Journal Issue 1 March 2013


18/31


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



19/31


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)



20/31


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




21/31


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




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.




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




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



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)



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


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



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




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




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



30/31

p 30

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