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.

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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.

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

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

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].

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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