In-depth study of internet-enhanced healthcare services Complete and thorough survey of the most promising e-health technologies Presents numerous real world examples Emphasis on international health-informatics topics, such as better access of states / countries to modern e-health technologies developed by leading centers
The internet of things (IoT) has had a major impact on academic and industrial fields. Applying these technologies to healthcare systems reduces medical costs while enriching the patient-centric approach to medicine, allowing for better overall healthcare proficiency. However, usage of IoT in healthcare is still suffering from significant challenges with respect to the cost and accuracy of medical sensors, non-standard IoT system architectures, assorted wearable devices, the huge volume of generated data, and interoperability issues. Incorporating the Internet of Things in Healthcare Applications and Wearable Devices is an essential publication that examines existing challenges and provides solutions for building smart healthcare systems with the latest IoT-enabled technology and addresses how IoT improves the proficiency of healthcare with respect to wireless sensor networks. While highlighting topics including mobility management, sensor integration, and data analytics, this book is ideally designed for computer scientists, bioinformatics analysts, doctors, nurses, hospital executives, medical students, IT specialists, software developers, computer engineers, industry professionals, academicians, researchers, and students seeking current research on how these emerging wireless technologies improve efficiency within the healthcare domain.
This book aims to provide a detailed understanding of IoMT-supported applications while engaging premium smart computing methods and improved algorithms in the field of computer science. It contains thirteen chapters discussing various applications under the umbrella of the Internet of Medical Things. These applications geared towards IoMT cloud analysis, machine learning, computer vision and deep learning have enabled the evaluation of the proposed solutions.
This book explores potentially disruptive and transformative healthcare-specific use cases made possible by the latest developments in Internet of Things (IoT) technology and Cyber-Physical Systems (CPS). Healthcare data can be subjected to a range of different investigations in order to extract highly useful and usable intelligence for the automation of traditionally manual tasks. In addition, next-generation healthcare applications can be enhanced by integrating the latest knowledge discovery and dissemination tools. These sophisticated, smart healthcare applications are possible thanks to a growing ecosystem of healthcare sensors and actuators, new ad hoc and application-specific sensor and actuator networks, and advances in data capture, processing, storage, and mining. Such applications also take advantage of state-of-the-art machine and deep learning algorithms, major strides in artificial and ambient intelligence, and rapid improvements in the stability and maturity of mobile, social, and edge computing models.
Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks. - Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning - Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics - Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies
In 1996, the Institute of Medicine (IOM) released its report Telemedicine: A Guide to Assessing Telecommunications for Health Care. In that report, the IOM Committee on Evaluating Clinical Applications of Telemedicine found telemedicine is similar in most respects to other technologies for which better evidence of effectiveness is also being demanded. Telemedicine, however, has some special characteristics-shared with information technologies generally-that warrant particular notice from evaluators and decision makers. Since that time, attention to telehealth has continued to grow in both the public and private sectors. Peer-reviewed journals and professional societies are devoted to telehealth, the federal government provides grant funding to promote the use of telehealth, and the private technology industry continues to develop new applications for telehealth. However, barriers remain to the use of telehealth modalities, including issues related to reimbursement, licensure, workforce, and costs. Also, some areas of telehealth have developed a stronger evidence base than others. The Health Resources and Service Administration (HRSA) sponsored the IOM in holding a workshop in Washington, DC, on August 8-9 2012, to examine how the use of telehealth technology can fit into the U.S. health care system. HRSA asked the IOM to focus on the potential for telehealth to serve geographically isolated individuals and extend the reach of scarce resources while also emphasizing the quality and value in the delivery of health care services. This workshop summary discusses the evolution of telehealth since 1996, including the increasing role of the private sector, policies that have promoted or delayed the use of telehealth, and consumer acceptance of telehealth. The Role of Telehealth in an Evolving Health Care Environment: Workshop Summary discusses the current evidence base for telehealth, including available data and gaps in data; discuss how technological developments, including mobile telehealth, electronic intensive care units, remote monitoring, social networking, and wearable devices, in conjunction with the push for electronic health records, is changing the delivery of health care in rural and urban environments. This report also summarizes actions that the U.S. Department of Health and Human Services (HHS) can undertake to further the use of telehealth to improve health care outcomes while controlling costs in the current health care environment.
This comprehensive book focuses on better big-data security for healthcare organizations. Following an extensive introduction to the Internet of Things (IoT) in healthcare including challenging topics and scenarios, it offers an in-depth analysis of medical body area networks with the 5th generation of IoT communication technology along with its nanotechnology. It also describes a novel strategic framework and computationally intelligent model to measure possible security vulnerabilities in the context of e-health. Moreover, the book addresses healthcare systems that handle large volumes of data driven by patients’ records and health/personal information, including big-data-based knowledge management systems to support clinical decisions. Several of the issues faced in storing/processing big data are presented along with the available tools, technologies and algorithms to deal with those problems as well as a case study in healthcare analytics. Addressing trust, privacy, and security issues as well as the IoT and big-data challenges, the book highlights the advances in the field to guide engineers developing different IoT devices and evaluating the performance of different IoT techniques. Additionally, it explores the impact of such technologies on public, private, community, and hybrid scenarios in healthcare. This book offers professionals, scientists and engineers the latest technologies, techniques, and strategies for IoT and big data.
The digital transformation of healthcare delivery is in full swing. Health monitoring is increasingly becoming more effective, efficient, and timely through mobile devices that are now widely available. This, as well as wireless technology, is essential to assessing, diagnosing, and treating medical ailments. However, systems and applications that boost wellness must be properly designed and regulated in order to protect the patient and provide the best care. Optimizing Health Monitoring Systems With Wireless Technology is an essential publication that focuses on critical issues related to the design, development, and deployment of wireless technology solutions for healthcare and wellness. Highlighting a broad range of topics including solution evaluation, privacy and security, and policy and regulation, this book is ideally designed for clinicians, hospital directors, hospital managers, consultants, health IT developers, healthcare providers, engineers, software developers, policymakers, researchers, academicians, and students.
This book presents a hands on approach to the digital health innovation and entrepreneurship roadmap for digital health entrepreneurs and medical professionals who are dissatisfied with the existing literature on or are contemplating getting involved in digital health entrepreneurship. Topics covered include regulatory affairs featuring detailed guidance on the legal environment, protecting digital health intellectual property in software, hardware and business processes, financing a digital health start up, cybersecurity best practice, and digital health business model testing for desirability, feasibility, and viability. Digital Health Entrepreneurship is directed to clinicians and other digital health entrepreneurs and stresses an interdisciplinary approach to product development, deployment, dissemination and implementation. It therefore provides an ideal resource for medical professionals across a broad range of disciplines seeking a greater understanding of digital health innovation and entrepreneurship.
This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.
