Figure 1. Convention model of telemedicine system: which connect rural and urban area by using various wireless technologies
According to a survey by World Health Organization (WHO), an abysmally more number of people living in rural area have access to specialist care and advice in the developing countries. This opens up the possibility of using information and communication technology effectively in the 21st century. Telemedicine is currently being used to bridge the physical distances between patients in remote areas and medical specialists around the world. The recent advances in broadband technology by facilitating anytime, anywhere access. It is not required doctor to be present physically at the hospital center. In this paper, the authors present an overview of traditional telemedicine system, cloud computing based framework for telemedicine. They clearly indicate the architecture of telemedicine cloud and discuss the applicability of recent wireless technologies in large-scale telemedicine systems.
According to Bauer and Ringel, (1999), telemedicine is the combined use of telecommunications and computer technologies to improve the efficiency and effectiveness of health care services by liberating care givers from traditional constraints of place and time and by empowering consumers to make informed choices in a competitive market place. Many of the existing telemedicine solutions are centralized, i.e. health workers reports the patient details from the remote area are received at the hospital center and the doctors needs to be present there to provide consolation (Mediintera), (Connected Health care) and (Belgium-Hf). However, the recent advances in the ICT add a new dimension to telemedicine by facilitating anytime, anywhere computing. Now, the specialist located anywhere in the world could use his mobile device or PC to access the patient report via internet and provide required advice. VAST/WIMAX/WIFI could be used to provide connectivity to the rural areas. Thus, there is exciting opportunity of using internet to bring a shared object repository for collaboration between specialists located anywhere and anytime with health workers and patients located in the rural area. (Figueredo and Dias, 2004) and (Mikael, Thomas, Jonathan and Shankar, 2005) describe telemedicine for home care and patient monitoring, where in mobile phones can interact with electro medical devices like patient monitoring and then transmit the signals via internet to the hospital. According to (Carlos, Fernando, Jorge, Armando and Giovanni, 2010) the problem of patient's vital data collection, distribution and processing. It suggests using sensor node to collect the patient information electronically and transfers to the specialists through cloud. They have used advances in information and communication technology to facilitate multisite collaboration among doctors using images, video conferencing (Telemedicine on the Grid project) and (Sathyamurthy, 2007). For the others like (Berti, Benkner, Fenner, Fingberg Lonsdale, Middleton and Surridge, 2003) and (GEMSS Project) have used grid technology, dynamic discovery of doctors to serve the requests from health workers. According to (Buyya, Rajkumar, Yeo, Chee, Venugopal and Srikumar, 2008) and (Foster, Ian, Zaho, Young, Raicy, Loan and Shiyong, 2008) there is a specialized subset of utility computing, named cloud computing. The resources available in this cloud are configured to receive, store, process and distribute the information. (Gerg, Padma, Dennis, Lindal and Harold, 2007) describe cloud is a pool of virtualized computer resources and it supports for SAN (Storage Area Network). Thus, in this paper the authors present an overview of cloud computing, traditional telemedicine system and Road towards vision of large scale telemedicine system. Further they discuses, a framework for telemedicine system using cloud computing, a Service-oriented architecture and its Implementation.
Several studies have demonstrated that the limited access to patient-related information during decisionmaking and the ineffective communication among patient care team members are proximal causes of medical errors in health care (Ackerman, Michel, Craft, Ferrante, Kratz, Mandil and Sapci, 2002), (Leape, 1994) and (Reason, 1990). Thus, pervasive and ubiquitous access to health data is considered essential for the proper diagnosis and treatment procedure. Cloud computing is a model for enabling convenient, ondemand network access to a shared group of configurable computing resources (like networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of four essential characteristics. These major characteristics of cloud computing can be summarized into the following (George, 2009).
A consumer can unilaterally obtain access to computing capabilities, such as server computing time and/or network storage, as need automatically without requiring human interaction with each service provider.
Resources are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g. smart phones).
The providers computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. Examples of resources include storage, processing, memory, network bandwidth and virtual machines.
Resources can be rapidly and elastically provisioned, in some cases automatically, to quick scale out and rapidly released to quickly scale.
Given the characteristics of cloud computing and the flexibility of the services that can be developed, a major benefit is the agility that improves with users being able to rapidly and inexpensively re-provision technological infrastructure resources. Device and location independence enable users to access systems using a web browser regardless of their location or what device they are using (e.g. mobile phones). Multi-tenancy enables sharing of resources and costs across a large pool of users thus allowing for centralization of infrastructure in locations with lower costs. Reliability improves through the use of multiple redundant sites, which makes cloud computing suitable for business continuity and disaster recovery. Security typically improves due to centralization of data and increase security focused resources. Sustainability comes about through improved resource utilization, more efficient systems. A number of cloud computing platforms are already available for pervasive management of user data, either free e.g. (iCloud), and (Drop Box) or commercial e.g. (GoGrid) and (Amazon AWS). The majority of them however, do not provide to developers, the ability to create their own applications and incorporate cloud computing functionality apart from Amazon AWS.
Telemedicine is composed of the Greek word tele meaning 'far' or 'at a distance' and the word 'medicine' (Sunitha and Vidya, 2008). Telemedicine infrastructure is set up at big hospital and connectivity is established with different parts of the country using VSAT or other means.
In Figure 1 a telemedicine unit is created in a local hospital in these rural areas where doctors and patients can directly interact with specialists at urban hospital. The telemedicine unit is equipped with different kinds of vital parameter measuring devices. The doctor at the rural area receives the data from measuring devices and uploads into desktop PC. Then he transfers the data to the urban hospital via VSAT or WiMAX. As soon as a report is received, a specialist at the big hospital would go through the report and send back comments and advice. The patient can be waiting at the other end and receive the reports. Similarly the remote telemedicine units are small in number and distributed across the country. So, you have to use mobile telemedicine units. It works like telemedicine unit with an in built infrastructure.
These limitations have given a roadmap to develop large scale telemedicine. In the next section we see the vision of large scale telemedicine system.
Figure 1. Convention model of telemedicine system: which connect rural and urban area by using various wireless technologies
The vision of large scale telemedicine is illustrated in the Figure 2.
It comprises of multiple telemedicine hospitals in urban areas as well as international, mobile medical specialists and rural hospitals/mobile units/clinics forming a large virtual enterprise. It must support mobility at both the patients end and the specialists end. Support for mobility at patients end will result in increased penetration. In addition, since small mobile devices can be used for this purpose, it is cost effective. Due to these factors, the scale of operation of the system increases. Mobility at the end of a specialist also has several key advantages. One is flexibility-a specialist need not always be at hospital or central server waiting for reports to be received. Another advantage is improved availability- a report will be delivered on to the handled of a specialist, who may be located, anywhere, rather than the central server, so that he or she can immediately attend to it.
Figure 2. Telemedicine: A giant virtual cloud enterprise
The concept of utilizing cloud computing in the context of health information management is relatively new, but is considered to have great potential (Shimrat, 2009). The work in (Avila, Trefethen, Brady, Glesson and Goodman, 2008) proposes a framework design based on cloud computing concepts, Microsoft technologies existing middleware and image toolkits to process colorectal cancer images. Moreover, the work in (Yunpeng, Yang and Qian, 2008) proposes a solution based on wireless web access, where mobile devices use processing power of cloud to parser HTML components of a web page. (Cloud Computing for Beginners), provides a model where security as service on the cloud to protect mobile applications. The work in (Christian, Robert, Martin and Andreas, 2008) proposes architecture for a distributed data store based on public cloud storage infrastructure, protected by rights management techniques. Finally, focusing on endpoints, it is not just browsers that are accessing cloud services, but also apps, sensors, voice and video terminals and at the extreme distributed high performance servers can use it. The main purpose of the work is to provide seamless and consistent communication flow between rural clinics and urban hospitals, international hospitals using devices like PCs, Mobiles, Wireless telemedicine tool kit and Tablet PCs. In the next section we examine the proposed framework for telemedicine using cloud computing.
Figure 3 shows the high-level view of telemedicine using cloud computing for developing and deploying the health care applications in the rural areas.
Telemedicine offers rural hospitals and healthcare organizations a variety of on-demand services on clouds rather than owning standalone applications on local servers. The main components of cloud computing services are the platform frontend interface that communicate directly with users and allows the management of storage content.
In Figure 3 doctors from rural areas (i.e. rural clinics, doctors with mobile, mobile telemedicine unit and telemedicine kit) gather the necessary data regarding the patients manually, through sensors and RFID technology. The data gathered may contain images, MRI scan report, X-ray, ECG and other relevant data. The data are stored in and sent through the web application server to the telemedicine cloud with the help of internet (Wi-Fi, Wi-Max, GPRS, and leased line).
Figure 3. High-level view of telemedicine system using cloud computing to connect rural and urban areas
The next operation takes place in the telemedicine cloud. Telemedicine cloud is an automated virtualized system which provides infrastructure, compatible platform and software as a service. It stores the data sent by the doctors from the rural areas. The cloud administrator, by default, is capable of assessing the clients' situation and decides the priority and the destination (national and international hospitals and the specialists in the concerned pathological condition). The cloud administrator sends the data to the data center to be stored and simultaneously sends it to the suitable destination. The data center in the telemedicine cloud would be constantly supervised by a monitoring agent.
The specialists in the destination receive the data, analyze them and prepare a report. This report would be sent to the telemedicine cloud. The administrator of the cloud would send the report to the concerned doctors in the rural area. The doctors, depending on the report from the specialists, decide the future course of action regarding the patient.
There are several practical advantages with this framework, such as:
Thus, this framework is support for anytime anywhere computing. In the next section we will discuss serviceoriented telemedicine cloud.
In the Telemedicine cloud, Cloud Administrator processes the requests to provide rural area doctors with the corresponding services. These services are developed with the concept of utility computing, virtualization and service oriented architectures (e.g. web applications and data servers). The cloud architecture could be different in different contexts. For example, a four layered architecture is explained in (Foster, Ian, Zaho, Young, Raicy, Loan and Shiyong, 2008) to compare cloud computing with grid computing. Alternatively, service oriented architecture, called Aneka, and is introduced to enable developers to build .NET applications with the support of application programming interfaces (API s) and multiple programming models (Vecchiola, Chu and Buyya, 2009). According to (Buyya, Yeo, Venugopal, Broberg and Brandic, 2009) Presents architecture for market oriented clouds, and (Huang, Su, Sun, Zhang, Guo, Xu, Jiang and Zhu, 2010) proposes an architecture for web delivered business services. This paper focuses on the layered architecture of telemedicine cloud. This architecture is commonly used to demonstrate the effectiveness of the cloud computing model in terms of meeting the rural community doctor's requirements.
Generally a cloud computing is a large scale distributed network system implemented based on a number of servers in data centers. The cloud services are generally classified based on a layer concept shown in Figure 4.
Figure 4. Layered Architecture of Telemedicine cloud
In the upper layers of this paradigm, Infrastructure as a service (IaaS), Platform as a service (PaaS), and Software as a service (SaaS) are stacked.
This layer provides the hardware facility and infrastructure for telemedicine cloud. In data center layer, a number of servers are linked with high-speed networks to provide services for clients (Rural area doctors). Typically, data centers are built in less populated places, with high power supply stability and a low risk of disaster.
IaaS is built on top of the data center layer. IaaS enables the provision of storage, hardware, servers and networking components. Infrastructure can be expanded or shrunk dynamically as needed. The examples of IaaS are Amazon EC2 (Elastic Cloud Computing) and S3 (Simple Storage Service).
PaaS offers an advanced integrated environment for building, testing and deploying client (Rural area doctors) applications. PaaS should be consisting of Cloud Operating system and Cloud middleware (Orange Scape, Wolf PaaS). The examples of PaaS are Google App Engine, Microsoft Azure, and Amazon Map Reduce/Simple Storage Service.
SaaS supports a software distribution with specific requirements. In this layer, the users can access an application and information remotely via the Internet. Sales force is one of the pioneers in providing this service model. Microsoft's Live Mesh also allows sharing files and folders across multiple devices simultaneously.
This section the authors discuss the main features of the telemedicine cloud. The prevalent functionality of the application is to provide medical experts and patients with any (PC'S, Mobiles, laptops, Telemedicine kit and mobile telemedicine unit) user interface for managing healthcare information. The latter interprets into storing, querying and retrieving medical images, patient health records and patient-related medical data (e.g., biosignals). The data may reside at a distributed Cloud Storage facility, initially uploaded/stored by medical personnel through a Web application server. There are number of platforms are already available for pervasive management of user's data like iCloud, GoGrid, Amazon AWS and Drop Box. The majority of them however, do not provide to developers, the ability to create their own applications and incorporate Cloud Computing functionality, apart from Amazon AWS. In our Telemedicine cloud, we are going to be utilizing Amazon AWS. This telemedicine cloud supports the following functionality.
The main application allows users to retrieve, modify and upload medical content (medical images, patient health records and biosignals) utilizing Web Services (Leonard, Sam and David, 2009). The content resides remotely into the distributed storage elements but access is presented to the user as the resources are located locally in the device.
Information regarding patient's status, related biosignals and image content can be displayed and managed through the application's interface.
User is authenticated at the Cloud Computing Service with SHA1 (US Security Algorithm 1) hashing for message authentication and SSL (Rescorla, 2000) for encrypted data communication.
Instead of building your applications on fixed and rigid infrastructures, Cloud Architectures provide a new way to build applications on on-demand infrastructures. The telemedicine cloud has been realized in Linux Red Hat and uses Amazon S3 cloud to store the data. The object schemes are represented in XML. The composite object consisting of the patient health record is prepared by applying XSLT to the XML source files. In order to prove the system's usability, some initial experiments evaluating the system's performance have been conducted and the screen shots are shown in the Figure-5(a) (b) (c) (d) (e).
From the Figure 5(a) and (b) shows that, the doctor in the rural clinics maintains the Patient Health Record with different parameters (like name, id, sex, age and address). The doctor, examines the patient, and enters patient data into the health record. Figure 5( c) shows the telemedicine cloud. From the Figure 5(d), the rural area doctor uploads the patient data (like images, ECG reports, X-ray etc...) into the telemedicine cloud. After that, the telemedicine cloud transfers the data to the Specialists in the urban hospital. From the Figure 5(e), the specialist downloads the data and analyzes them and prepares a report. The report is send to the rural area doctor through telemedicine cloud. Experiments concern the time needed to transmit data to the Amazon S3 Cloud storage service. Due to the fact that textual data like a patient's health record or a biosignal sequence do not consist of large data files and do not require high bandwidth, the presented results involve the transmission of medical images. The transmission times are displayed in Table 1. As indicated, the wireless network infrastructure that has been utilized a WLAN.
Figure 5(a). Screen shots of telemedicine cloud using Laptop
Figure 5(b). Screen shots of telemedicine cloud using Laptop
Figure 5(c). Screen shots of telemedicine cloud using Laptop
Figure 5(d). Screen shots of telemedicine cloud using Laptop
Figure 5(e). Screen shots of telemedicine cloud using Laptop
Table 1. Transmission time of medical images using Amazon S3 cloud service and WLAN network type
The performance of WLAN networks can be easily biased by traffic and other network conditions, since commercial networks have been utilized in both cases. Also, the response time of the Amazon S3 Cloud service can play an important role on the total transmission time. However, the acquired results can be considered as indicative since the experiments reflect a real case scenario where the specific service and commercial wireless networks are utilized in order to transmit medical data. In addition, the time needed to decode and present the specific images used in the experiments has been measured.
In this paper the authors have seen that, using a Telemedicine cloud along with a service-oriented architecture makes the telemedicine system scalable and robust. The telemedicine cloud can be seen as a national and international-level distributed database of patient medical records. This data can be used for carrying out large-scale simulations for medical research. Besides, by integrating health insurance, blood banks, ambulance, rural clinics etc… into the telemedicine cloud, we can have full-fledged health cloud spanning across the country that can provide a whole lot of medical services. Such a health cloud will be of immense help to the developing nations and this forms part of our future research.