This document proposes an adaptive cloud architecture for telemedicine systems. It discusses using distributed databases and cloud computing to store and process large amounts of medical data from telemedicine systems in a scalable way. This architecture would allow data to be analyzed to discover new medical information and correlations. The system would need to comply with privacy and security regulations when handling sensitive patient medical data in the cloud.

1. Adaptive Services with Cloud Architecture for
Telemedicine
Ábel Garai
Department of Information Technology
University of Debrecen
Debrecen, Hungary
garai.abel@ inf.unideb.hu
István Péntek
Department of Information Technology,
University of Debrecen
Debrecen, Hungary
pentek.istvan @ inf.unideb.hu
Abstract—Telemedicine embraces telecommunication and
information technologies in order to serve health care at remote
locations outside of the premises of medical institutions. These
systems produce and process dynamically growing amount of
data to keep the pace with this exponential process. Moreover,
they need to support real time processing in some cases which
requires a dynamically scalable and fault tolerant architecture.
Based on the large amount of data we propose for these
telemedicine systems an extensible architecture which uses
distributed and disjoint databases as an anonymized storage in
order to support data analytics. Utilizing services, these methods
could help us to create a system which could derive new medical
information from the collected data that could be used to find
possible treatments for illnesses and discover hitherto unknown
correlations between different risk factors, thus involving
cognitive aspects in our outlined solution. The future of adaptive
telemedicine systems should be based on the successful
application of distributed data processing with services in order
to produce high value-added structured information flows and
innovative knowledge base.
Keywords—Telemedicine Systems, Cloud Computing
Architecture, Distributed Data Storage, Web service
I. INTRODUCTION
Access to high standard health services remain an
overwhelming challenge for the human civilization. Modern
innovative telemedicine systems and solutions with cloud
computing architecture integrate related latest emerging
information technology achievements with refined state-of-the-
art scientific cognitive approach and mindset. Notwithstanding
that telemedicine nowadays still tends to be an extraordinary
discipline within human sciences, as time goes by, it may be
the solely available health-care method for many patients
within less than a century, as the proportion of medical care
personal within the western hemisphere is dramatically
declining. Modern telemedicine captures complex bulk data:
continuous measurement entries, images, continuously growing
medical records. The incoming volume of information from
telemedicine systems is significant and is foreseen to expand
exponentially. In order to be able to handle the exponential data
growth a scalable and fault-tolerant infrastructure with
cognitive, smart business intelligence is to be applied where
information security and privacy are also reliably solved.
Ubiquitous Computing, where every device is connected to
same network is a very promising direction for the future of the
adaptive services. Telemedicine systems have to involve these
different devices into the telemedicine processes and to use
their capabilities in order to give better services to patients.
Collection and sharing information are priorities for the future
medical treatment, this will shape the telemedicine community
of the future. Our vision is an integration of the different
solutions into one single framework, including all prominent
telemedicine-solutions, smart phone apps and the features of
smart devices (like smart bracelets and smart watches). From
community's point of view is the individual patient's add-in
highly valuable: affected individuals applying smart health-
care tools (e.g. offline blood-pressure or cardio-meter
producing dataflow to be uploaded to the telemedicine system's
server at a designated time) supply the basis for community
knowledge-base and thereby historic and statistical data flaws
(forming workable time series for the analytical modules). The
collected information is representative for the diseases of the
given community. These data flows and knowledge databases
represent the basis for further scientific experiments and leads
to intelligent and preventive (and finally cognitive) information
systems.
Medical care systems are dramatically changing all over the
western civilization. As time elapses, less medical and health
care personal are reachable for the developed countries' aging
population. New ideas have since long time arisen, and now
technology reaches maturity in order to better serve humanity's
needs.
As telemedicine solutions tend to produce expanding
volumes of critical and sensible data, a new technology has to
keep up with the foreseeable pace. Security, accessibility,
reliability, scalability, performance and availability are critical
factors in health care and questions to be addressed by the
selected architectural design supporting the designated
telemedicine solution.
978-1-4673-8129-1/15/$31.00 ©2015 IEEE

2. II. CLOUD ARCHITECTURE FOR TELEMEDICINE
A. Cloud Architecture
The next dominant keyword is Cloud computing, which
came into reality in the 2010s for a wide range of organizations
and individuals. Cloud computing architecture beyond its core
benefits implies a brand new mindset for all its users: the
offered solutions are delivered on the basis of utility model.
Users, organizations, developers bear the price-list, SLAs and
GT&Cs in mind, and the cloud-provider manages all necessary
technical aspects to reach its commitments, even when client
requirements are changing.
Telemedicine systems are creating bulk data and processed
for numerous different purposes. Analysis of available
telemedicine information and the possibility of prediction for
reliable prognoses are future benefits of the related fresh
technologies, allowing to find the synergies between intelligent
systems and human wisdom. They could combine the human
and the IT-factor, allowing to augment the mentally captured
telemedicine information of the telemedicine staff with the
information and conclusion of the connected
infocommunicational systems.
As the quantity of produced, stored and processed data of
telemedicine systems is hardly predictable in the medium term,
a flexible architecture is demanded. The boom of data
generated by telemedicine systems requires scalable, flexible
and secure architecture. Cloud architecture offers the best
attributes regarding scalability. No other contemporary
architecture can respond to the short-term varying processing
and data-storing capacity requirements of a telemedicine
system as the cloud architecture. Cloud architecture is the best
choice to deliver the flexibility both on storage and processing
capacity needed for telemedicine systems.
Private cloud systems, where the data-center is also located
physically within the given country of operation of the
telemedicine system, allow the telemedicine systems to be
streamlined with the legal prerequisites stipulated by the laws
of the given country. Only the private cloud architectural
solution offers the needed conditions for an efficient
telemedicine system: it is secure, as private cloud may only be
contacted by the explicitly dedicated users. Privacy is best
guaranteed within this choice among cloud architectural
solutions, as above the general authentication routines the data-
center and therefore the data itself remains physically within
the given country. Taking the different type of cloud
architectures into consideration, this private cloud serves best
the telemedicine systems.
Availability is another dimension to be addressed just at the
drafting of the designated architectural solution. As cloud
architecture is perceived by the user or by the running system
as a service, an SLA regarding the availability of the cloud
architecture can be defined, measured and enforced. Therefore
the cloud architecture offers the best solution beside other
architectural options (e.g. regarding a private server of a
medical institution the availability is not prescribed and when
the server fails, the recovery procedure means downtime from
the systems’ and users’ point of view).
B. Environment specific rules
As contrary to both telemedicine and cloud computing
technology, where services are ready to be offered globally, in
medical care systems national and regional (e.g. EU)
regulations are in force. This implies special considerations
regarding data storage and data manipulation in cloud
computing architecture.
There are distinct ethical and security considerations
regarding sensible medical data. Who should access it? Should
dedicated cloud computing servers be placed within the
country of the data source’s origin? May the data leave the
borders of the country? If so, should be the data-owner be
informed? Should the information be anonymized? First of all,
who is the data owner: the medical institution running the
telemedicine system, the patient, or the ministry of health of
the applicable country? A complex clinical solution offering
telemedicine connectivity provided by an international leading
ICT company has been by the authors of this article assessed,
and found, that it complies with the prerequisites of cloud
architecture readiness. The solution offers both integration and
embedding in cloud architecture. The mentioned clinical
software solution is in operation among others at the Clinic of
the University of Debrecen, so the cooperation between the
Department of Information Technology and Clinic is promising
for the development of cognitive telemedicine information
solutions.
Naturally, several questions are arrived. Who should have
access to the data? Should access granted for the sensible
medical data to insurance companies, employers, Ministry of
Defense? May the collected data (most likely in anonymized
form) internationally used for global human research
programs? What are the long term pros and cons regarding the
global sharing of medical information from the individual's and
from the entire humanity's aspect?
Telemedicine solutions with cloud computing delivers
health care services also to places where immediate personal
emergency care is unavailable, e.g. in offshore wind farms and
on ocean tankers [2]. These brand new technical solutions
enrich the variety of life-saving treatment options for people
working at remote hazardous locations or in a case of a natural
disaster.
There are various positive aspects of a reliable telemedicine
solution and cloud computing infrastructure for the individual,
community and also for the medical workers [6]:
Lower probability of complications: Telemedicine
systems continue monitoring when patient is at home
after he is discharged from the acute care health
institution. All necessary medical information is
available real time for health care professionals at the
right place at the right time to take necessary decisions.
Reduction in travel: patients living in rural areas
thousands of kilometers away from medical centers
have the opportunity to gain medical care and treatment
on their own premises. Therefore neither the patient, nor
the doctor need to travel, saving time, money and
carbon dioxide emission.

3. Enhancement of health systems: telemedicine systems
deliver extensive and sophisticated information on
patients, so health care personal can make faster and
more reliable decisions.
As with all new emerging technologies, there are several
open issues with telemedicine. For example, what measures to
be taken, when the internet-connection is blocked?
Mainframe infrastructure and solutions responded to the
needs of critical mass data and transaction processing for
decades, until now, when emerging technologies, i.e. Cloud
Computing, reached maturity. Besides the fulfillment of the
requirements of the telemedicine industry (e.g. massive
throughput capacity in parallel with security, stability and
accessibility), the new targeted Cloud Computing
Infrastructure should be ready to integrate bulk legacy data and
data structure. It is to be assessed and tested, how the new
target Cloud Computing Infrastructure integrates the content of
complex legacy data banks [8]. Cloud architecture may offer
better and more effective architecture for telemedicine systems
than previous and legacy system architectures. The scalability
and massive computing capability of cloud computing and
cloud architecture can offer better foundation stone for the
needs of massive data manipulation, store and processing than
other available solutions. During the previous decades
multinational companies relying upon their mainframe
computing capabilities for decades searched for cheaper and
more flexible platform solutions for running their mission-
critical systems. Client-Server and Unix/Linux-based solutions
prevailed in the 2000s. The client-server and Unix-based
architectures shifting from mainframe enabled companies to
lower their related IT-budget, however both client-server and
Unix solutions could not provide the same high performance
transaction processing and security capabilities as mainframes.
As mature cloud architectures and reliable cloud computing
solutions were launched by leading global ICT-providers
within the previous decade, cloud architecture promises the
best suitable solution for telemedicine systems, enabling a
good mix of cost efficiency, data processing and storing
capacity.
The future telemedicine systems offer multi-platform
availability: information are accessed by stand-alone systems,
personal computers and handhelds such as tablets and
smartphones. From this perspective the telemedicine systems
relying upon cloud computing architecture offer a complete
and integrated IaaS for patients, health organizations and for
medical staff. The three keywords regarding telemedicine
platforms are: availability, security and user-friendliness [1].
C. IoT and Smart Devices
The Internet of Things (IoT) [14] is the network of physical
objects like smart devices. Devices embedded with electronics,
software, sensors and connectivity to able to exchange data
with the world. The IoT can be very useful part of a
telemedicine system if the devices able to transfer
measurement data into it. Most of the smart devices can share
their location information too by GPS sensor, GSM
triangulation technique or IP address geolocation. The
treatment by geolocation could be more rapid and effective for
example the emergency assistance could arrive sooner. IoT is
also expected to generate large amounts of data – for example
from various location or measurement data. This is aggregated
very quickly and our telemedicine system have to keep up with
this growing, so our architecture have to be prepared to handle
and process large amount of data. To store the collected
information by internet of things the database have to be write
intensive because in one second thousands of devices send
small piece of information, the database's responsibility to
handle this properly. Besides that the database have to be write
intensive, our telemedicine system affect many of the records
while processing our query. Based on these the databases in
our telemedicine cloud environment have to be read- and write-
intensive at the same time. Our purpose is to design a private
cloud architecture that can work with structured and
unstructured big data and the individual private cloud
environments share information with each other considering
privacy.
Nowadays smart devices are forming a very promising
area. Thousands of people have at least one smart device to
track free time activities like running, swimming, cycling, etc.
The devices could transfer or share the collected information
over the network. These devices collect information about
location, path, blood pressure, burned calories or calculate fluid
needs after the activity. Our telemedicine system can use this
information to follow up the patients' health or analyze the
changes from the collected information. Our proposed adaptive
telemedicine system could store, analyze and process the
personalized historical data. Data mining algorithm could
cluster the personalized data by age, weight, sex or any other
attribute to provide filtered data for medical researches.
III. OUR PRIVATE CLOUD PROPOSAL
As we mentioned the telemedicine environments produce
large amount of data, the telemedicine industry count on
exponentially data growth in the next few years. Based on the
wide range of the medical examination and these examination
results we can recognize that data structure is very variable.
Examination result can be images, medical records as text or
simple numbers. Considering the data structure and the amount
of data the best solution to store these information in a system
which is capable to store more petabytes of unstructured data
besides that the system process the information almost real
time. It follows that the system must be distributed, scalable
with huge storage capacity. Moreover, the system must warrant
the information is in secure. This means that the collected,
prepared and transformed information have to be available all
the time even if a system error occurs. Because of the
information is the most valuable component in our
telemedicine environment this component have to been
protected as much as possible. To protect data the environment
have to support data duplication and/or replication. The
architecture design have to take into account this.
There are two main trends to protect data and speed up
querying in cloud architectures, data duplication and data
replication. Data duplication is when a record stored more than
once in separated databases, the data stays in the same domain.
The other similar approach is when the data travel outside from
the domain and replicated in other physical cloud system. All

4. of these increase overhead on create, update and delete
operations, each replicated and duplicated databases needed to
be done the operation in order to maintain the consistency.
These techniques have a huge advantage namely the data can
be found in several places, so the read time decreases and the
data processing could be more faster. This has the consequence
that the distributed querying and processing can improve
performance and the single-site failure does not affect
performance of the telemedicine system. However distributed
databases could be very complex and difficult to maintain their
integrity. Replicated or duplicated databases much more
expensive because of the redundant physical storage.
Replicated data or databases between private clouds could help
information sharing. With our proposal, this approach every
cloud environment can provide same answer for questions
because they have the same information. Share information
with separated private clouds raises the question of information
security and the privacy. Some of regulations require the data
anonymization because of the user’s privacy.
The private cloud architecture is the most suitable
architecture to operate a telemedicine system because
organizations can build private cloud numerous ways.
Organization could build its private cloud system with own-,
rented- or hosted hardware environment. If the organization
use rented or hosted hardware items the environment will be
dynamically scalable without buying new expensive hardware
items. Naturally, the transferred data stream needs to be
encrypted. The own hardware with dedicated internet line is the
most secure way to implement our proposed environment. In
this case the hardware owned by the organization and have full
control over the telemedicine system, but the organization have
to spend a lot of money to obtain the appropriate hardware
elements. We recommend to implement a telemedicine system
based on private cloud with own hardware elements to
guarantee the lowest security risk and reach the maximum
control over the whole environment.
The other very important criteria against the telemedicine
systems is privacy. The system produce, store, process and
supplies very sensitive information about the patient, their
illnesses and their medical records. In most countries, the
access to the information is highly regulated. In some cases,
organizations do not share information with others to avoid
information leak. Considering privacy and different
regulations, the system must keep data security in mind.
Telemedicine system has to work as a separated island with
own data storage and custom services. Among these
circumstances, the best architecture to operate a telemedicine
system is a self-managed private cloud.
Our telemedicine system use an own Content Distribution
Network (CDN) in order to keep the separated private cloud
environments synchronized. Applying of this approach every
connected network can work with the same data set all the
time. The idea based on our colleagues previous work which
realized and used in our University [16][19][20]. The created
extensible architecture is a good starting point to derive and
evolve to our Content Distribution Network where querying is
a basic need when our telemedicine system process several
tasks for its services. Our CDN works like a network service,
CDN manage the access rights to the shared information
between separated private cloud environments, but our content
distribution network is not responsible for the data
anonymization. Anonymization has to be handled inside
private cloud environments. An intelligent CDN based on
separated private cloud environments, CDN share the public
information between individual environments only after
authentication and authorization.
Our proposed telemedicine private cloud architecture
handles web-, database- and CDN requests through load
balancers. Every individual environment has a special
monitoring and security subsystem, where every request is
monitored and the users are authenticated (e.g.: basic
authentication, OAuth, etc). Organizations can define their user
groups and roles. The environment gives three basic user
groups: smart tools, medical workers and network users. Only
users with appropriate rights can reach information or call
remote services or procedures from other environments. Users
from medical workers group can browse the recorded data and
use services from the cloud. Smart tools have rights to record
the collected data through an endpoint where the data will be
transformed.
There are various techniques to transform received
unstructured or weakly structured information to structured
information. The most effective way to store and work with
telemedicine data is to use a business intelligence subsystem.
This business intelligent (BI) system can be used to support
faster computing in a distributed environment. In a
telemedicine system the BI subsystem could make the data
standardization, detect and correct the inaccurate data, check
inappropriate value (e.g. empty or null). Telemedicine systems
already capture some metadata (e.g. GPS coordinates) that the
BI has to handle correctly and are able to process this
dimension of the data. With using BI, the telemedicine system
is suitable for building a data warehouse. Data warehouse
could be connected over the individual private clouds to
implement the information as a service.
Telemedicine systems have to produce results almost real
time. To meet this criteria the system must process large
amount of data as fast as possible. The best way to speed up the
result generation is the parallel computing. Instead of working
with a single node, a distributed system can use more than one
node to prepare the expected result. MapReduce programming
model [10] is very useful to processing and generating large
data sets with distributed and parallel algorithms on clusters.
Private cloud architecture with distributed data processing
is an island system with custom, individual and large data set.
Separated telemedicine system which works in closed
environment carry the risk that are unable to provide a solution
to the specified tasks because the system can't reach the
relevant information from the database. To overcome this
problem, these systems should be connected or prepare them
for the information sharing. Every individual private cloud can
share own information as a service with other connected cloud.
Information sharing helps the island systems to prepare more

5. Fig. 1. Proposed solution for telemedicine cloud architecture
accurate result using information from other individual
systems. Another advantage of connect the independent
systems that increases the availability due to the geographically
separation.
The community collaborated telemedicine system collect
information about patient, patient health status, different types
of illnesses besides the personal information anonymization.
Our proposed system uses the collected information to
calculate the best medical treatment for every patient with
different types of their illnesses. Our telemedicine system could
use as a decision support system that operates disjoint
databases from different private clouds. Every individual
private cloud database contains information for the regional
illnesses, average age or age related problems based on data
collected from the community or current region. The collected
information can be clustered by data mining procedures, for
example telemedicine system can provide useful information
about blood pressure problems in women when they are
menopause. To prepare the result for blood pressure problem,
telemedicine system uses the information from data warehouse.
With Hadoop [9] MapReduce procedure filter the available
information, the filtered information could be clustered by age
and sex [11]. Filtered and clustered data could be perfect input
for a cognitive algorithm to create a conclusion or recommend
appropriate treatment. For the regionally rare illnesses the
telemedicine system could collect and filter information from
other systems wherein the examined disease more often. Our
system could be adaptive if the individual private clouds are
connected. In this case the system could adopt the relevant
information from other sources [12].
Today, the smart tools can spread very quickly. The
Ubiquitous Computing [13] is a promising concept for the
future. This new concept means in the software engineering
and in the computer science that computing is made to appear
anywhere and everywhere. Every smart device can be part of
the telemedicine system as a worker role. The main problem to
use smart devices in the telemedicine systems is the
information security. Anonymization of the sensitive
information is a solution for this issue.
Although today remote telemedicine (patient) sensors still
tend to submit the collected data or datastream into the
designated data-center for analysis, among the smart devices
there is a rising tendency of interpreting the majority of the
collected data in itself and then to send the results of the
interpretation to the data-center.
The proposed private cloud architecture gives room for
intra-cognitive sensor-bridging and inter-cognitive sensor-
sharing communications. This is the suitable category of
Cognitive Infocommunications for the enhanced telemedicine
systems allowing doctors to assess remotely patients’
physiological, psychological and neural state. The cloud
architecture provides the link between the cloud architectural
solution for telemedicine systems and the Cognitive
Infocommunications, as information on a human patient is
directed to the doctor using the telemedicine cognitive
subsystem, while the data is captured by medical sensors. As
telemedicine surgery systems gain ground, the drafted cloud
computing architecture links the human doctor with the remote
surgery machine, concluding an intra-cognitive sensor-sharing
cognitive infocommunication [18].

6. The architecture for telemedicine system has only one
serious limitation. This architecture is useful if the environment
use smart tools with IoT capability and the medical
environment produce large amount of data. This architecture
with huge storage capacity, distributed processing and
cognitive algorithm is over engineered in a small medical
office with some computer. However this architecture with its
cognitive services could be a smart tool to help doctors make
more reliable diagnosis or plan for treatments. Nowadays we
can say that medical science uses more smart tools to collect
information about patients than earlier, and people use more
tools in their everyday life which can help them to participate
in the proposed architecture in the near future.
IV. CONCLUSION AND FURTHER DIRECTION
Our proposed telemedicine architecture based on big data
collected from communities whose members use smart devices
or wear specialized medical tools. Our cloud environments are
connected over intelligent content distribution network. The
designed telemedicine system places great emphasis on data
safety and privacy. We believe that the future of the
telemedicine systems depends on the community and the
Internet of Things [15] rather than using separated and closed
island systems. For the future, an important question is whether
the smart devices can evolve dynamically to help the
community based on telemedicine systems? We believe, that
modern telemedicine systems relying upon cloud architecture
will bring science, medical profession and human community’s
value.
Cognitive infocommunication is elemental part of
telemedicine systems, as it extends the possibilities and
capabilities of the overall medical care system, bringing the
human and the machine even closer. Data is evaluated by both
computer systems and human doctors, and input is given by the
human patient and digital sensors of the telemedicine systems
and smart devices connected to the private cloud computing
architecture. The flexibility, scalability, availability and
security of the private cloud architecture for telemedicine
systems gives unprecedented room for establishing augmented
interdisciplinary wisdom-base enhancing the human talent with
capabilities of digital systems. The reason and justification for
choosing the drafted private cloud architecture for telemedicine
systems is the following: it provides the optimal constellation
of scalability, privacy, security, availability and flexibility.
The application of cloud architecture for telemedicine
systems solves several dedicated so far unsolved issues: the
cloud architecture provides real-time scalability, allowing the
needed - and just the needed - data-storing and processing
capabilities of the running telemedicine system. When the
short-term requirement for new capacities arises, it is smoothly
and quickly satisfied through the flexible scalability of the
cloud architecture. Other architectural solutions - e.g. dedicated
server farms - do not have the capability for responding to the
operational needs of the actual telemedicine system and its
users. The private cloud architecture solves several questions:
how to fit to the sudden changes of storage- and processing
capacity of the running system, how to provide secure storage
and necessary high level privacy for sensible personal data
subject to legal regulation. No other architecture as the
described private cloud system responds better to the emerging
needs of the telemedicine systems.
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