This document introduces the concept of a cloud-based clinical trial management system (CTMS) and discusses its potential advantages over traditional legacy CTMS systems. It defines cloud computing and explains how a cloud-based CTMS would work and be architected. Key benefits highlighted include scalability, elasticity, lower costs through a utility pricing model, and faster innovation cycles through a multi-tenant architecture. The document argues that a cloud-based CTMS could transform how clinical trials are conducted by enabling sponsors to instantly expand global trials. It concludes by noting that developing a successful cloud-based CTMS requires an accurate data model as the foundation.

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Chris Merriam-Leith
Cloud based Clinical Trial Management Systems
Introduction
So what exactly is a “Cloud based Clinical Trial Management System?” This might be the first question that comes to mind.
To answer this question will require some efforts on my part for two reasons; first, the concept of a fully functional Cloud
based Clinical Trial Management Systems is only theoretical at this time, but I do have a working prototype, and second the
definition of cloud computing is still evolving and changing. The reason there is no precise definition is because of the speed
at which the technology has been advancing, and it’s been very challenging keeping pace with the changes. I’m not sure if
there is any single expert who can provide a specific authoritative definition that won’t be outdated by next week. Therefore, I
will do my best to discuss the important concepts of cloud computing, and then attempt to frame these concepts within the
context of a Clinical Trial Management System (CTMS) and the Regulatory Affairs field.
Most of the information that I will provide in this paper will be original material or designs, and or my professional opinions,
combined with researched content and references to external sources when applicable. My goals in writing this paper are
twofold; first to discuss the merits of this new technology, and second to attempt to make some projections about how it may
be applied in the future within industry. Hopefully by the end of this paper, the reader will have a much better understanding
of cloud based computing and why a Cloud based Clinical Trial Management System (CTMS) has the potential to improve
how clinical studies can be managed.
Cloud Computing Defined
A cloud based platform is architecturally very large. You should envision the cloud as a massively huge single computing
system that has many implementations of different applications running inside. The cloud is much like your desktop computer
from the perspective that it is a self contained system, and it has the ability to host many software applications that you load
into it. Of course, the biggest difference is the size and scale of the cloud. Cloud based platforms are hosted in data centers
such as those pictured in figures 1 and 2 below. Installed in these data centers there are thousands of smaller computers
networked together to function as one single virtual computer. If you have ever watched the movie “The Matrix”, then you
have a good idea of what the real cloud looks like. The Matrix is the cloud, except there is no Agent Smith, and I am not as
dapper as Keanu Reeves.
Figure 1 Figure 2
Inside a Data Center
1
Google Cloud Data Center in Goose Creek, South Carolina
2
Before I delve deeper into the details of describing how a Cloud based Clinical Trial Management System (CTMS) can be
used, it will be important for me to first explain the differences between the traditional legacy technologies and the new cloud
based computing platforms.
1
Ecofabulous Website, http://www.ecofabulous.com/DEVDeploy/DEVDeploy/category/ecolifestyle/media/
2
Data Center Knowledge Website, http://www.datacenterknowledge.com/google-data-center-faq-part-2/

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Legacy CTMS vs. Cloud based CTMS
Legacy Clinical Trial Management System (CTMS) technology has been hosted on an application server that typically resides
within a company’s internal data center and utilizes the company’s internal infrastructure. This traditional infrastructure model
requires a significant amount of resources that includes capital components such as office space, large investments in
hardware and cooling systems, networking with high bandwidth capacity, complicated software stacks and expensive
database technologies. To help manage it all, there is also usually a fully staffed IT department required as well. Historically
this traditional infrastructure model has been very costly to install, maintain and support. Other limitations include, lack of
scalability, difficulty extending access outside of the companies firewall to other stakeholders, upgrading of client software is
intermittent and could cause other problems to develop within the application or operational environment.
A new Cloud based CTMS could challenge this traditional model and has the potential to transform and become the next
generation of CTMS technology. Cloud computing is a major paradigm shift in how business applications of the future will be
developed and delivered. The customers of a Cloud based CTMS will share, consume, and access platform resources as a
service, and pay for only those resources that they use. Furthermore, a Cloud based CTMS Company could employ a utility
based computing model, which is comparable to how traditional utilities such as electricity and telephone services are
consumed and paid for.
Future of Cloud based CTMS Technology
Cloud computing is the next major computing advancement that promises to transform traditional clinical study management
into something much more powerful. A Cloud based CTMS has the potential to change the landscape of clinical trials
management and to revolutionize how companies will conduct and perform clinical studies. Cloud computing in particular
offers a sponsor the ability to dynamically expand their capability to perform large multi-phase studies on an integrated global
scale. This new capability will be unlike anything that we have been able to accomplish in the past using legacy clinical study
technologies. Cloud based CTMS will equip sponsors with the powerful ability to capture, and then to both push and pull data
much more efficiently from remote study sites and between stakeholders, and then to the FDA when needed. This will be
attainable because of the unique scalability and elastic characteristics of cloud technology.
Benefits of a Cloud based Clinical Trial Management System (CTMS)
Some of the unique characteristics and tangible benefits of a cloud based CTMS are the following:3
1. CTMS users have the ability to rapidly and inexpensively provision infrastructure resources
2. CTMS applications have instant, real time, on demand scalability via dynamic provisioning of platform resources
3. Independence of CTMS cloud location enables users to access applications via the internet using a web browser from any
location globally
4. CTMS applications will have greater elasticity, which is the ability to expand and contract processing resources as needed
5. Infrastructure can be indirectly accessed via the internet from any location globally
6. Infrastructure is provided by a third-party and does not need to be purchased or leased
7. Multi-tenancy enables sharing of resources and costs across a large pool of users
8. Costs are reduced and capital expenditure is transformed into operational expenditure at a much lower level
9. Pricing on a utility computing basis is instantly scalable based on the demand of usage
10. Fewer IT resources are required
11. Reliability improves as a result of the use of multiple redundant sites which makes cloud computing very suitable for business
continuity and disaster recovery
12. Security is often equally comparable or even better than with traditional systems
13. Maintenance and support of cloud computing applications is easier since the changes reach the clients instantly
All of the above characteristics represent significant differences between a cloud based CTMS platform and the more
traditional legacy infrastructure model.4
All of these differences translate into huge cost savings and significant technological
simplicity. I could easily write a volume of content on each of the above topics, but for purposes of brevity, I will restrict my
discussion on these topics. Although, I do want to emphasize one of the more compelling differences about this new
technology and that is, that a Cloud based CTMS has a very powerful capability to instantly scale on a global basis. This has
the potential to have a very profound impact on how clinical studies can and will be performed in the future. Therefore, I will
elaborate more specifically on this topic in much greater detail below.
3
Understanding infrastructure in the cloud, http://inukonda.wordpress.com/2009/06/04/understanding-infrastructure-in-the-cloud/
4
Wikipedia Website, http://en.wikipedia.org/wiki/Cloud_computing

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Figure 3
Multi-tenant Cloud based Clinical Trial Management System 5
Cloud based Clinical Trial Management System
Figure 3, above is a conceptual depiction of what a Cloud based CTMS implementation would look like. Recognize that this is
a somewhat simplified representation. In the cloud there would be multiple instances of the CTMS. In the above diagram
there are only 8 instances and the master version shown in the center. Understand that there are no physical constraints on
the cloud and that it is easy to replicate hundreds or even thousands of instances of the CTMS. Conceivably, there could be
one or more instances created for every sponsor who is conducting a clinical study on a global basis. This is the virtual world
of computer science where the only constraint is time.
It doesn’t matter if Sponsor 1 is a small company with only 10 users compared to Sponsor 2 who may be a large company
with 1500 users. The cloud is both scalable and elastic and therefore it doesn’t matter if the sponsor is large or small or
anything in between, since the resources of the cloud are allocated dynamically based on need. Also because we are
speaking about a virtual environment existing within the same virtual domain as the internet, the breadth of the cloud is
global. Like the internet, in the cloud, geographic boundaries as we know them have no meaning. The users of the CTMS
can be located anywhere in the world as long as there is an access point to the internet. There is no need for any type of
centralization or concentration of resources in order to support the infrastructure. This means that clinical studies will have no
physical constraints limiting the boundaries such as needing to be on the same network, or using the same software, or even
being in the same geographical locations or time zones.
Standards will be much easier to establish and maintain since essentially everyone can be working within the same cloud
universe. It will be much easier to create and adopt a global data standard like ICH, since once the initial design of the data
structures are implemented on the cloud then all sponsors could have immediate access to them. Just this small
advancement by itself would be a monumental breakthrough for the drug development industry. This would be the equivalent
of having everyone in the world being able to speak in the exact same language with 100% fluency. The cost savings alone
for just standardizing the data structures would be into the hundreds of millions of dollars. This would also dramatically speed
up the time involved in transforming data from its native legacy design into something completely foreign and mediocre such
as the CDISC standard in order to submit to the FDA.
5
Visio Diagram, created by Chris Merriam-Leith

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Scalability and Elasticity of a Cloud based Clinical Trial Management System (CTMS)
Study sponsors will be more easily able to expand or contract study site locations by simply extending access of the CTMS
via the dynamic scalability of the cloud itself. Furthermore, the elasticity of the cloud will provide the sponsor with a system
that can dynamically increase or decrease the application or processing resources needed during periods of peak load usage
and then likewise dynamically contract the resources when workload demands drop. There will be no need to purchase or
install any new hardware or software to accomplish this task.6
To provide an example, let’s assume that a sponsor is running a large multi-phase clinical study in the U.S. that includes 100
different sites locations. Now let’s assume that for whatever reason the sponsor decides that they need to expand the
number of sites by 50. A decision is made that instead of adding these sites within the U.S. the sponsor has located a CRO in
Australia who has 50 sites available and ready to go. The sponsor could add these 50 new sites to the Cloud based CTMS all
within the amount of time it takes to issue 50 new user ids and passwords (i.e. less than 1 hour). The scalability and elasticity
of the cloud is immediate. This differs significantly from the traditional legacy model, including website based systems
because there is no need for increased hardware, or software, or any deployment time or resources.
You may be saying “so what, big deal” some websites can do this already because they usually have extra capacity available
to expand. Yes, this is true, but now let me give you an even more dramatic example. Let’s now assume that the above
sponsor decides that they need to again increase the number of sites by a total of 350 for this study. But this time they need
50 sites in Japan, 100 sites in Europe, 25 sites in New Zealand, and 100 sites in China. No problem, the cloud is capable of
providing this instant scalability and elasticity and the only constraint is that this time it might take a little bit longer, say like 3
hours instead of only just 1 hour to configure the user ids and passwords. Again, no hardware, or software is needed and
more importantly no deployment time or infrastructure outlay costs. I could continue to scale this cloud example indefinitely if
necessary to make my point, but I will stop here since the traditional model will eventually reach a point of failure because of
the constraints of scalability in this type of comparison.
Utility Pricing Model
There are other benefits to the scalability and elasticity of the cloud. Let’s look 6 months forward into the future to see what
the impact of scaling down will have on the hypothetical clinical study. At this later date when the sponsor closes down study
sites because the trial is ending, then the only actions necessary for the sponsor to perform on the Cloud based CTMS is to
just shut down access. As a result, all the application and processing resources will be automatically released and scaled
down. This is also a good example of how the utility pricing model will function. Just like the electricity in your home, if you
have all your appliances and lights turned on, your electricity meter will be spinning and charging your account. As you start
to shut lights off, the meter will start to turn slower and likewise at then end of the month your bill will decrease. At any point
in time you have the ability to use as much or as little electricity as you need. This is how the Utility Pricing Model works for a
Cloud based CTMS.
Multi-tenant Computing and Economies of Scale
The benefits of a Cloud based CTMS that were listed in the previous sections can be realized as a result of the significant
economies of scale that are created by the sharing of the cloud resources. Another term this is known by is a “Multi-tenant”
computing model. The multi-tenant architecture enables the fundamental economic benefits of shared resources. Cloud
computing costs keep dropping as a result of better economics of multi-tenant solutions and high capacity utilization.7
In a
multi-tenant environment, the costs model decrease as the usage grows, letting the service provider invest in new
innovations and the customers overall success. There is no way to resist this very powerful economic force. Furthermore,
who would want to resist, the more that demand grows, the cheaper the supply becomes.
The Multi-tenant model will also provide a much faster way to deploy Cloud based CTMS application improvements. Within
the multi-tenant Cloud based CTMS, new features can be implemented across all instances overnight. This is because there
is only one current version of the Master CTMS. Within a multi-tenant cloud solution the innovation is continuous,
incremental, and globally available.8
Conceptually what this means is that when the master version of the CTMS has been
changed, all other instances would receive the same updates at the same time overnight. This keeps all instances of the
CTMS in sync and current. The other significant value to this is that this would make data exchange between sponsors and
the FDA seamless, because the underlying data structures will be identical.
6
Cloud: Elasticity is more important than Scalability http://inukonda.wordpress.com/2009/06/05/cloud-elasticity-is-more-important-than-scalability/
7
Cloud-hosted collaboration: multi-tenant or dedicated? http://blogs.zdnet.com/forrester/?p=356
8
Cloud-hosted collaboration: multi-tenant or dedicated? http://blogs.zdnet.com/forrester/?p=356

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Development of a Cloud based Clinical Trial Management System (CTMS)
After reading up to this point, I hope that you are excited and asking yourself the question of how someone would build a
Cloud based CTMS. I always like to use the analogy of an architect who is building a house to describe the components
needed for the development of a Cloud based CTMS. The key to developing a successful Cloud based CTMS is having an
accurate and well defined data model for the clinical study domain. The data model is the foundation for a system of this type.
It is at this layer that the specific design of the system captures and models the clinical trial processes. The importance of the
data model cannot be over emphasized and it is at this level where a majority of the design efforts should be focused since
this is the equivalent to building the foundation of a house.
If I were going to hire an architect to build me a house, there are certain things that would need to be planned in advance,
and then designed, and then certain things built in a very specific order. Like building a house, a Cloud based CTMS requires
that there be a solid foundation to build upon. The data model is the foundation of a Cloud based CTMS. In fact, when you
view a blue print of a data model for a Cloud based CTMS it looks strikingly similar to the blue prints for the foundation of a
house. Before delving deeper into this topic I would like to articulate some philosophical principles that I believe are very
important to the success of any systems development activities. Some of this philosophy has to do with perspectives and
authority.
Regulatory Affairs - Subject Matter Experts
A business user is an individual who works within a certain area or department and who understands a business process or
business domain area well enough to answer questions from people from other business groups. The business users who
are experts within a specific domain are known as Subject Matter Experts (SME’s). This term is most commonly used to
describe the people who help to explain business processes to IT personnel that are trying to build a technology system or
solution to streamline a business process.9
Those of us who are in the Regulatory Affairs profession would be called
Regulatory Affairs - Subject Matter Experts (RA-SME’s).
As a Regulatory Affairs – Subject Matter Expert, it is important that whenever you are participating in a systems development
project, that your domain knowledge be correctly translated into recognizable and identifiable concepts that will be used to
define the clinical data standards and processes. If you do not recognize the concepts because they have been translated
into esoteric codes that can only be understood by IT folks, or they have been abstracted to the point of non-recognition, then
this is a clear indication that the design efforts have gone significantly off course and things are not correct.
As the SME, it is incumbent on you to halt the design efforts and force IT back on track. I can not begin to over emphasize
the importance of making sure that your domain knowledge is correctly translated in a way that is both accurate and results in
meaningful systems design requirements that you as the SME can still recognize and understand. This is the number one
issue where I see projects failing on a company to company basis. Hopefully, I will be able to demonstrate some of these
core principles in the remaining sections of the paper.
Important Regulations for Cloud based Clinical Trial Management Systems
It is important for those of us who are in the Regulator Affairs profession to have a better understanding of the computer
systems and electronic records that we use within clinical studies. This is important because there are some very strict
regulations within the Code of Federal Regulations that govern the use of computer systems and process controls. Some of
the key regulations that we need to be more aware of are:
 The Health Insurance Portability and Accountability Act (HIPAA)
 21 CFR Part 11 – Electronic Records and Electronic Signatures
 HHS 45 CFR 170 – Electronic Health Records
Regardless of whether the system is a cloud based clinical trial management system or the legacy technology that has been
around for the last 10 to 15 years. These computer systems and processes if not managed properly can expose a company
to some very serious regulatory consequences if the FDA finds violations with any of these regulations.
All of the above regulations deal with topics related to such things as privacy of individual identifiable health information,
security of data, authenticity and integrity of data, electronic and digital signatures (there are differences), audit and validation
controls as well as many other complex regulatory topics. In fact, the above regulations contain some of the most complex
regulatory requirements that can be found in the Code of Federal Regulations regarding procedures and controls within
clinical environments. Perhaps this is why these regulations are some of the most frequently misunderstood and
misinterpreted regulations found within the industry.
9
About.com Website, Definition of Subject Matter Expert http://management.about.com/cs/adminaccounting/g/subjmatrexp.htm

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It is not uncommon to discover that most Regulatory Affairs Professionals do not have a very good understanding of these
regulations. This lack of knowledge increases the regulatory risks for companies, because it exposes them to some
potentially very serious regulatory violations. The FDA in the past has not been very strict when enforcing these regulations,
but over the last few years they have started to become much more aggressive on this front, and with the recent enactment
of HHS 45 CFR 170, it looks like the FDA is going to get even tougher on this topic. I would strongly encourage all Regulatory
Affairs professionals to dive deeper into these regulations and understand them inside and out, so that you are able to assist
your company with adhering to them.
Clinical Study Conceptual Domain
A concept may be described as a thing which is recognized as being an independent object within our clinical study domain
and for which an instance of its existence can be uniquely identified. Concepts should be thought of as “noun” type objects
that exist in the clinical study domain such as a person or an organization, or places, locations or sites, or things, processes
or events.10
The table below contains a list of the concepts that I have identified that exist within a clinical study domain. I thought that it
would be useful to define some of the high level system requirements that would exist for the development of a Cloud based
CTMS. The emphasis of importance from the below table should be placed on the proper identification of as many of the
relevant concepts from the domain as possible. The domain by definition will establish the boundaries of the functionality that
the Cloud based Clinical Trial Management System will satisfy.
These concepts will ultimately come to represent those objects that will be encompassed within a Domain Model and an ER-
Diagram for a Cloud based Clinical Trial Management System. I will define and provide examples of both a Domain Model
and an ER-Diagram further below.
Figure 4
Domain Conceptual Objects
Persons or Organization Places / Locations
or Sites
Things Processes Events
Subject IRB Study IRB Annual Review Adverse Event
Investigator Study Site Contract IRB Materials Tracking Disbursement
Clinical Research Associate (CRA) CRO Study Schedule Site Monitoring Contract Payment
Regional Monitor Sub-Protocol Site Protocol Protocol Material Tracking Subject Visit
Sponsor Protocol Milestone
Site Contact FDA Site Visit
FDA Protocol Deviation
IRB Member
Definition of Domain Model
A domain model in software engineering can be thought of as a conceptual model of a system which describes the various
entities involved in that system and their relationships. An important benefit of a domain model is that it describes and
constrains the system scope. The domain model can be effectively used to verify and validate the understanding of the
subject matter domain among various subject matter experts (SMEs) and the stakeholders of a project. It is especially helpful
as a communication tool when used as focal point between technical and business teams. The domain model is created in
order to document the key concepts, and the vocabulary of the domain and system being modeled.11
In our example, the
domain is a Clinical Study, and the system is a Cloud based Clinical Trial Management System (CTMS).
Once the concepts for a clinical study domain have been identified, then the next step would be to define the relationships
that exist between the concepts. For purpose of this paper I will not describe all of the relationships that exist within the
domain, but I will demonstrate the process of interpretation so that it is clearly understood how these relationships are
identified and then how they should be defined in the model. It is also important to explain the real world meaning and the
purposes for the relationships. In practice this process should be as easy and as intuitive as just describing what we already
know from our domain knowledge perspective.
First, it should be very obvious from the concepts listed above, that these objects are real world concepts that a Regulatory
Affairs Subject Matter Expert (SME), should already easily recognize. This is a very important principle that needs to be
clearly understood and put into real world practice when developing any type of clinical system or data standards. For the
purposes of modeling, these objects are known as domain concepts.12
Please see the domain model depicted in figure 5
below.
10
Agile Requirements Modeling, http://www.agilemodeling.com/essays/agileRequirements.htm
11
Wikipedia Website, Domain Model Definition, http://en.wikipedia.org/wiki/Domain_model
12
Agile/Evolutionary Data Modeling: From Domain Modeling to Physical Modeling, http://www.agiledata.org/essays/agileDataModeling.html

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Figure 5
Domain Model
Cloud based - Clinical Trial Management System
Interpretation and Translation of Domain Model
The above domain model includes all of the previously identified domain concepts from figure 4 above. I have also identified
most of the relationships that exist between the concepts. In the model, the domain concepts are represented by the
rectangles and include their corresponding names. The relationships are represented by the lines with the arrows and include
some verbiage that helps to define the relationship.
Some important things to understand about the model are:
 This model does not necessarily include all possible relationships that can or may exist. The fact is, that it doesn’t need too, what is most important
is that this model attempts to identify the most relevant relationships. It is important to keep in sight that the purpose of this model is to help define
the domain scope and to be used as a communications tool. If it satisfies these requirements, then it has accomplished its goal.
 This model may not contain all possible domain concepts. This is also ok, as long as it at least includes the concepts that define the scope for the
business domain that we are trying to model.
 This model may not be 100% accurate, since we do not have perfect information about the domain. This is also an acceptable deficiency, since it
is not always possible to have complete information. We have to start somewhere, and if we are always fearful that we may not capture everything
100% correctly then we risk getting bogged down with something called, “Analysis Paralysis Syndrome” and then we will never make any
progress.
In software development, analysis paralysis typically manifests itself through exceedingly long phases of project planning, requirements gathering, program
design or data modeling resulting in little or no extra value created by those steps. When extended over too long of a timeframe, such processes tend to
emphasize the bureaucratic aspect of the software project, while detracting from its value-creating purpose.
13
You may notice that I have color coded some of the domain concepts blue and some of the relationships red. The reason I
have done this is so that I can focus specifically on these objects to demonstrate how they get translated into the next stages
of design for the Cloud based CTMS. First, I want to demonstrate how the domain model should be read. I have highlighted
the Subject, Subject Visits, and Adverse Events concepts. To translate them into English, just read them out loud starting
with the concept that is pointing in the direction that the arrow is pointing, “A Subject has Visits”, and “A Subject may
experience Adverse Events”. That’s all there is to it. The only other question that needs to be confirmed is, are these
statements true? The answer should be “Yes”, otherwise the model would be considered wrong.
13
Wikipedia Website, http://en.wikipedia.org/wiki/Analysis_paralysis

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Entity Relationship Model Diagram (ERD)
In software engineering an Entity Relationship Model is a diagrammatic representation of entities, attributes and relationships
for a Domain Model. Entity Relationship Modeling is a database modeling method used to produce a physical blue print of a
database system. This modeling process is used in the development of relational database systems as well as cloud based
systems. The diagram created by this process is called an Entity-Relationship Diagram, or an ER-Diagram, or just an ERD.14
Figure 6
Entity Relationship Model Diagram
Cloud based - Clinical Trial Management System
The ER-Diagram will identify all major entities within the system and their important attributes as well as the relationships
between the entities. Basically, the ER-Diagram takes the concepts from the Domain Model and now defines them in more
concrete terms with greater details related to the concept. It is at this stage that we convert the abstract Concepts and
Relationships into Entities and Relationship to complete the ER-Diagram.
It is at this point in the modeling process that the objective is simply just to identify all of the detailed attributes that will be
tracked for the business concept that is being modeled. In figure 6 above for the “Subject” entity we have identified about 20
attributes (data fields) that will be used to record data. These are the pieces of data that are required by a Cloud based
CTMS for tracking the information about the Subject. Some of the attributes / fields that are tracked in the subject entity
include the First Name, Last Name, Social Security Number, Date of Birth, Address, Phone, and others.
These fields represent the basic type of data that you would expect would be needed by a CTMS in order for the system to
manage the business processes. It is all really very straight forward when you stay focused on the objectives and you view
the requirements from the perspective of a Regulatory Affairs – Subject Matter Expert (SME).
14
Wikipedia Website, http://en.wikipedia.org/wiki/Entity-relationship_model

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Conclusion
In this paper I have introduced you to a new an emerging technology known as Cloud Computing. A cloud computing
platform is a large single computing system that has many different applications running inside. Cloud computing is a major
paradigm shift in how business applications of the future will be developed and delivered. Cloud computing technology is
ideal for implementing a Clinical Trial Management System (CTMS). A Cloud based CTMS has the potential to change the
landscape of clinical trials management and to revolutionize how companies will conduct and perform clinical studies.
Cloud computing in particular offers a sponsor the ability to dynamically expand their capability to perform large multi-phase
studies on an integrated global scale. A Cloud based Clinical Trial Management System (CTMS) has some unique
characteristics and provides significant benefits compared to traditional legacy technology. One of the more compelling
differences about this new technology is that a Cloud based CTMS has a very powerful capability to instantly scale on a
global basis. This has the potential to have a very profound impact on how clinical studies can and will be performed in the
future.
There are very few if any physical constraints on a Cloud based CTMS and it is very easy to replicate hundreds or even
thousands of instances on the cloud. A Cloud based CTMS is both highly scalable and highly elastic and therefore it doesn’t
matter if a sponsor is large or small or anything in between, since the resources of the cloud are allocated dynamically based
on need. The breadth of a Cloud based CTMS is global and geographic boundaries as we know them have no meaning. The
users of the CTMS can be located anywhere in the world as long as there is an access point to the internet. Just like the
electricity in your home, at any point in time you have the ability to use as much or as little electricity as you need. This is how
the Utility Pricing Model works for a Cloud based CTMS.
The benefits of a Cloud based CTMS can be realized as a result of the significant economies of scale that are created by the
sharing of the cloud resources. In a multi-tenant environment, the costs model decrease as the usage grows, letting the
service provider invest in new innovations and the customers overall success. The multi-tenant model will also provide a
much faster way to deploy Cloud based CTMS application improvements. Within the multi-tenant Cloud based CTMS, new
features can be implemented across all instances overnight. This is a significant technological advantage compared to the
legacy technology. The key to developing a successful Cloud based CTMS is having an accurate and well defined data
model for the clinical study domain. The importance of the data model cannot be over emphasized and it is at this level where
a majority of the design efforts should be focused since this is the equivalent to building the foundation of a house.
The business users who are experts within a specific domain are known as Subject Matter Experts (SME). Those of us who
are in the Regulatory Affairs profession are called Regulatory Affairs - Subject Matter Experts. It is important for those of us
who are in the Regulator Affairs profession to have a better understanding of the computer systems and electronic records
that we use within clinical studies. The regulation 21 CFR Part 11 is related to such topics as privacy of individual identifiable
health information, security of data, authenticity and integrity of data, electronic and digital signatures, audit and validation
controls as well as many other complex regulatory topics. The FDA in the past has not been very strict when enforcing these
regulations, but over the last few years they have started to become much more aggressive on this front, and with the recent
enactment of HHS 45 CFR 170, it looks like the FDA is going to get even tougher on this topic.
The prospects of a Cloud based Clinical Trial Management System are very exciting. I intend to continue my development
efforts and hopefully some of the future projections that I made in this paper will come to be realized. Its hard to say exactly
where technology will take us in the coming years, but one thing is certain, technology teaches us that things can always be
done better, faster, cheaper, different, or even perhaps they wont need to be done at all. You never know what to expect.
Hopefully now that you have reached the end of this paper, you have learned something new and perhaps maybe you may
have even been a little inspired to build something yourself out on the cloud. Why not, the cloud is endless, just like human
ingenuity.