Digital Economy and Innovative Practices in Healthcare Services

In this chapter, the authors analyze the impact of the new ICT-driven economic paradigm—the digital economy—on healthcare services. The increasing adoption of ICT in healthcare has been very fruitful and has led to the innovative approach to healthcare practice commonly known as e-health. Here the authors first propose a framework, consisting of six elements, whose mutual interaction outlines the structure and the dynamics of the digital economy. Then, a classification scheme of services is presented, which considers their characteristics and their delivery modes; this scheme supports understanding the way in which the adoption of ICT impacts healthcare services. Finally, an overall explanatory outline is constructed that allows one to analyze and understand the origins, implications, and future perspectives of the changes that ICT has brought to healthcare services. Examples of e-health applications are traced back to the building blocks of the framework, isolating the impact of each driver on their structure, configuration, and delivery modes.

Evaluation of Quality of Context Information in U-Health Smart Homes

Ubiquitous Health (U-Health) smart homes are intelligent spaces capable of observing and correctly recognizing the activities and health statuses of their inhabitants (context) to provide the appropriate support to achieve an overall sense of health and well-being in their inhabitants’ daily lives. With the intrinsic heterogeneity and large number of sources of context information, aggregating and reasoning on low-quality raw sensed data may result in conflicting and erroneous evaluations of situations, affecting directly the reliability of the U-Health systems. In this environment, the evaluation and verification of Quality of Context (QoC) information plays a central role in improving the consistency and correctness of context-aware U-Health applications. Therefore, the objective of this chapter is to highlight the impact of QoC on the correct behavior of U-Health systems, and introduce and analyze the existing approaches of modeling, evaluating, and using QoC to improve its context-aware decision-making support.

mHealth

The mHealth field focuses on the use of mobile technologies to support hospital care, healthy behavior, patient monitoring, and educational awareness. It is a new field that is developing rapidly, with thousands of mHealth applications developed within the last two years alone. In this chapter, the authors discuss the current state of, and the opportunities and challenges within, the mHealth field. They also introduce the term Mobile Social Networking Healthcare (MSN-Healthcare), which they define as follows: “The use of mobile health applications that incorporate social networking tools to promote healthy behaviors and awareness among patient groups and communities.” This concept has not been introduced in previous literature. This chapter is organized as follows: 1) introduction and background of mHealth; 2) opportunities for the implementation of mHealth in relation to chronic disease management, the education of health professionals, the needs of health professionals, and the decision-making process for patients and clinicians; 3) challenges concerning implementation and usability, information needs, and interactions with clinical work; 4) current application uses; and 5) future trends and conclusion.

Body Sensors and Healthcare Monitoring

The aging population and the high expectations towards quality of life in our society lead to the need of more efficient and affordable medical systems and monitoring solutions. The development of wireless Body Sensor Networks (BSNs) offers a platform to establish such a healthcare monitoring systems. However, BSNs in the healthcare domain operate under conflicting requirements. These are the maintenance of the desired reliability and message latency of data transmissions (i.e. quality of service), while simultaneously maximizing battery lifetime of individual body sensors. In doing so, the characteristics of the entire system, especially the Medium Access Control (MAC) layer, have to be considered. For this reason, this chapter aims for the optimization of the MAC layer by using energy-saving techniques for BSNs. The fact that the IEEE 802.15.4 MAC does not fully satisfy BSNs requirements highlights the need for the design of new scalable MAC solutions, which guarantee low-power consumption to the maximum number of body sensors in high density areas (i.e., in saturation conditions). In order to emphasize IEEE 802.15.4 MAC limitations, this chapter presents a detailed overview of this de facto standard for Wireless Sensor Networks (WSNs), which serves as a link for the introduction and description of the here proposed Distributed Queuing (DQ) MAC protocol for BSN scenarios. Within this framework, an extensive DQ MAC energy-consumption analysis in saturation conditions is presented to be able to evaluate its performance in relation to IEEE 802.5.4 MAC in highly dense BSNs. The obtained results show that the proposed scheme outperforms IEEE 802.15.4 MAC in average energy consumption per information bit, thus providing a better overall performance that scales appropriately to BSNs under high traffic conditions. These benefits are obtained by eliminating back-off periods and collisions in data packet transmissions, while minimizing the control overhead.

System Design and Data Fusion in Body Sensor Networks

Body Sensor Networks (BSNs) are formed by the equipped or transplanted sensors in the human body, which can sense the physiology and environment parameters. As a novel e-health technology, BSNs promote the deployment of innovative healthcare monitoring applications. In the past few years, most of the related research works have focused on sensor design, signal processing, and communication protocol. This chapter addresses the problem of system design and data fusion technology over a bandwidth and energy constrained body sensor network. Compared with the traditional sensor network, miniaturization and low-power are more important to meet the requirements to facilitate wearing and long-running operation. As there are strong correlations between sensory data collected from different sensors, data fusion is employed to reduce the redundant data and the load in body sensor networks. To accomplish the complex task, more than one kind of node must be equipped or transplanted to monitor multi-targets, which makes the fusion process become sophisticated. In this chapter, a new BSNs system is designed to complete online diagnosis function. Based on the principle of data fusion in BSNs, we measure and investigate its performance in the efficiency of saving energy. Furthermore, the authors discuss the detection and rectification of errors in sensory data. Then a data evaluation method based on Bayesian estimation is proposed. Finally, the authors verify the performance of the designed system and the validity of the proposed data evaluation method. The chapter is concluded by identifying some open research issues on this topic.

RFID in E-Health

Electronic healthcare or E-Health promises to offer better care at lower cost. This is critical as the cost of healthcare continues to increase and as the population ages. Radio Frequency Identification (RFID) technology is one form of wireless technology that will be part of the E-Health environment. RFID provides the ability to identify, track, and monitor patients and staff members. This enables better resource allocation, reduction of medical errors, and increased independence for patients. One part of E-Health is the Electronic Medical Record (EMR). New developments in RFID technology now enable the storage of all or part of the EMR on an RFID tag that remains with the patient. This chapter investigates the use of RFID in E-Health, how RFID can be used to store the EMR, and the security and privacy risks associated with using RFID to store the EMR.

Distributed Monitoring and Supervising System for E-Health Applications

The system presented in this chapter is mainly destined to offer support and to monitor chronic and elderly patients. In accordance with the new tendencies in the field, it integrates innovative components for data acquisition systems, Web-based virtual instrumentation, personalized user interfaces, and relational data in a complex, modular, flexible, and opened structure. Compared with other similar integrated communication systems, which are based on Wi-Fi technology, the presented one has as distinctive features: small dimensions, low power consumption, and a considerable autonomy. A large set of experiments and the corresponding results illustrates the functionality of the configurable virtual web instrument principle materialized in the E-Health Monitoring and Supervising System (EMSS) that has many possible applications. As an example, a cheap, easy to use, and personalized support destined to improve the quality of life for subjects suffering from chronic diseases or elderly patients was chosen. The implementation of the complete application included a model for gesture recognition, which allows the classification and assessment of the characteristics of the subject’s movement, highlighting even small progresses of the monitored patients.

Designing the E-Health Message

The tremendous growth in the use of Web 2.0 technologies, interactive computer technologies, electronic records, and mobile devices for delivery of e-health necessitates attention to design. Designing e-health requires consideration of research, including best practices embodied in design principles. This chapter reviews key background information, including central definitions, concepts, and research, followed by a presentation of 9 key considerations that are recommended for guiding the design of e-health messages. An illustrative case example demonstrates how a typology that codifies design principles gave rise to a research tool that permits the evaluation of health care websites. The case example underscores the important role of findings from research evaluations in creating a feedback loop for designers, permitting research to inform refinements in design. Overall, the 9 key considerations suggest a new paradigm for design, while also giving rise to corresponding recommendations for future research to support evolution in the field of e-health.

Smart Clothing for Health Care

The use of wearable technologies in medicine and health care has become of important in order to considerably improve benefits for patients and health service providers. Within telemedicine, biomedical clothing plays a crucial role. The main technology advances and the research of the Textile and Paper Materials Research Unit (UMTP) and of the Assisted Living Computing and Telecommunications Laboratory (ALLab) teams, in the area, will be addressed. Issues that remain unsolved will be presented. The chapter presents an overview of the key concepts for telemedicine and the role of textile electrodes and their integration in smart clothing. The development of software algorithms that specifically handle signals that are collected using biomedical clothing, integrating resiliency and a proper set of alarms, is presented and discussed in the context of classical biomedical signal processing. Finally, biomedical clothing design will be discussed in social, psychological, and esthetical contexts.

Clinical-Pull Approach to Telemedicine Implementation Policies using Health Informatics in the Developing World

Telemedicine could effectively aid hospital referral systems in bringing specialized care to rural communities. South Africa has identified telemedicine as part of its primary health care strategic plan, but similar to many other developing countries, the successful implementation of telemedicine programs is a daunting challenge. One of the contributing factors is the insufficient evidence that telemedicine is a cost-effective alternative. Furthermore, many telemedicine services are implemented without a thorough needs assessment. Throughout this chapter, the authors investigate the use of medical informatics in quantitative telemedicine needs assessments. A framework is introduced to direct implementation policies towards a proven clinical need rather than pushing technology into practise. This clinical-pull strategy aims to reduce the amount of failed projects, by providing decision support to implement appropriate technologies that have the potential to contribute towards better quality healthcare.