Chapter 4 E-health services and applications: A technological paradigm shift
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K. Aditya Shastry
Abstract
Internet of Things (IoT) technology has brought significant advancements to the field of e-health services and applications. By leveraging IoT devices with sensors and wireless communication capabilities, healthcare providers now have access to innovative solutions for remote monitoring, diagnostics, and treatment. This revolution in the healthcare industry has led to improved patient outcomes and cost reductions. One of the notable advantages of IoT-enabled e-health services is the ability to collect and analyze data in real-time. Wearable devices equipped with IoT technology allow for continuous monitoring of vital signs, enabling early detection of abnormalities and prompt medical interventions. Additionally, telemedicine platforms facilitate remote consultations, eliminating geographical barriers and providing access to healthcare expertise from anywhere in the world. Moreover, smart home systems offer effective management of chronic conditions, promoting independent living and improving patient’s health. However, the widespread adoption of IoT-powered e-health solutions additionally reveal issues that must to be addressed. Data privacy and security are major concerns due to the sensitive nature of personal health information. It is crucial for the healthcare industry to establish secure and interoperable standards to protect patient information from unlawful access and breaches. Furthermore, the complexity of integrating various IoT devices and platforms poses interoperability challenges that must be overcome for seamless communication and data exchange. This work presents a comprehensive assessment of the current technological developments in IoT for e-health applications. It examines various case studies to illustrate the different applications of e-health services and presents their motivations, challenges, and future trends. By exploring these aspects, the research seeks to contribute to the advancement and understanding of IoT-driven e-health solutions, paving the way for a more connected and efficient healthcare ecosystem.
Abstract
Internet of Things (IoT) technology has brought significant advancements to the field of e-health services and applications. By leveraging IoT devices with sensors and wireless communication capabilities, healthcare providers now have access to innovative solutions for remote monitoring, diagnostics, and treatment. This revolution in the healthcare industry has led to improved patient outcomes and cost reductions. One of the notable advantages of IoT-enabled e-health services is the ability to collect and analyze data in real-time. Wearable devices equipped with IoT technology allow for continuous monitoring of vital signs, enabling early detection of abnormalities and prompt medical interventions. Additionally, telemedicine platforms facilitate remote consultations, eliminating geographical barriers and providing access to healthcare expertise from anywhere in the world. Moreover, smart home systems offer effective management of chronic conditions, promoting independent living and improving patient’s health. However, the widespread adoption of IoT-powered e-health solutions additionally reveal issues that must to be addressed. Data privacy and security are major concerns due to the sensitive nature of personal health information. It is crucial for the healthcare industry to establish secure and interoperable standards to protect patient information from unlawful access and breaches. Furthermore, the complexity of integrating various IoT devices and platforms poses interoperability challenges that must be overcome for seamless communication and data exchange. This work presents a comprehensive assessment of the current technological developments in IoT for e-health applications. It examines various case studies to illustrate the different applications of e-health services and presents their motivations, challenges, and future trends. By exploring these aspects, the research seeks to contribute to the advancement and understanding of IoT-driven e-health solutions, paving the way for a more connected and efficient healthcare ecosystem.
Chapters in this book
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XIII
- Chapter 1 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 1
- Chapter 2 Introduction to industry’s fourth revolution and its impacts on healthcare 33
- Chapter 3 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 67
- Chapter 4 E-health services and applications: A technological paradigm shift 101
- Chapter 5 Breaking down walls: The influence of virtual reality on accessible healthcare delivery 129
- Chapter 6 Digital twins and dietary health technologies: Applying the capability approach 165
- Chapter 7 Big Data analytics in healthcare system: A systematic review approach 185
- Chapter 8 Machine learning models for cost-effective healthcare delivery systems: A global perspective 199
- Chapter 9 Machine learning models for cost-effective healthcare delivery systems 245
- Chapter 10 Enhancing biomedical signal processing with machine learning: A comprehensive review 277
- Chapter 11 Data-driven AI for information retrieval of biomedical images 307
- Index 331
Chapters in this book
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XIII
- Chapter 1 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 1
- Chapter 2 Introduction to industry’s fourth revolution and its impacts on healthcare 33
- Chapter 3 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 67
- Chapter 4 E-health services and applications: A technological paradigm shift 101
- Chapter 5 Breaking down walls: The influence of virtual reality on accessible healthcare delivery 129
- Chapter 6 Digital twins and dietary health technologies: Applying the capability approach 165
- Chapter 7 Big Data analytics in healthcare system: A systematic review approach 185
- Chapter 8 Machine learning models for cost-effective healthcare delivery systems: A global perspective 199
- Chapter 9 Machine learning models for cost-effective healthcare delivery systems 245
- Chapter 10 Enhancing biomedical signal processing with machine learning: A comprehensive review 277
- Chapter 11 Data-driven AI for information retrieval of biomedical images 307
- Index 331
