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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
Innovation in healthcare is currently a "hot" topic. Innovation allows us to think differently, to take risks and to develop ideas that are far better than existing solutions. Currently, there is no single book that covers all topics related to microelectronics, sensors, data, system integration and healthcare technology assessment in one reference. This book aims to critically evaluate current state-of-the-art technologies and provide readers with insights into developing new solutions. With contributions from a fully international team of experts across electrical engineering and biomedical fields, the book discusses how advances in sensing technology, computer science, communications systems and proteomics/genomics are influencing healthcare technology today.
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Seitenzahl: 471
Veröffentlichungsjahr: 2020
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Table of Contents
Cover
Engineering and Technology for Healthcare
Copyright
Dedication
List of Contributors
Introduction
References
Introduction
References
Chapter 1: Maximizing the Value of Engineering and Technology Research in Healthcare: Development‐Focused Health Technology Assessment
1.1 Introduction
1.2 What Is HTA?
1.3 What Is Development‐Focused HTA?
1.4 Illustration of Features of Development‐Focused HTA
1.5 Activities of Development‐Focused HTA
1.6 Analytical Methods of Development‐Focused HTA
1.7 What Are the Challenges in the Development and Assessment of Medical Devices?
1.8 The Contribution of DF‐HTA in the Development and Translation of Medical Devices
1.9 Conclusion
References
Chapter 2: Contactless Radar Sensing for Health Monitoring
2.1 Introduction: Healthcare Provision and Radar Technology
2.2 Radar and Radar Data Fundamentals
2.3 Radar Technology in Use for Health Care
2.4 Conclusion and Outstanding Challenges
2.5 Future Trends
References
Chapter 3: Pervasive Sensing: Macro to Nanoscale
3.1 Introduction
3.2 The Anatomy of a Human Skin
3.3 Characterization of Human Tissue
3.4 Tissue Sample Preparation
3.5 Measurement Apparatus
3.6 Simulating the Human Skin
3.7 Networking and Communication Mechanisms for Body‐Centric Wireless Nano‐Networks
3.8 Concluding Remarks
References
Chapter 4: Biointegrated Implantable Brain Devices
4.1 Background
4.2 Neural Device Interfaces
4.3 Implant Tissue Biointegration
4.4 MRI Compatibility of the Neural Devices
4.5 Conclusion
References
Chapter 5: Machine Learning for Decision Making in Healthcare
5.1 Introduction
5.2 Data Description
5.3 Proposed Methodology
5.4 Results
5.5 Analysis and Discussion
5.6 Conclusions
References
Chapter 6: Information Retrieval from Electronic Health Records
6.1 Introduction
6.2 Methodology
6.3 Results and Analysis
6.4 Conclusion
References
Chapter 7: Energy Harvesting for Wearable and Portable Devices
7.1 Introduction
7.2 Energy Harvesting Techniques
7.3 Conclusions
References
Chapter 8: Wireless Control for Life‐Critical Actions
8.1 Introduction
8.2 Wireless Control for Healthcare
8.3 Technical Requirements
8.4 Design Aspects
8.5 Co‐Design System Model
8.6 Conclusions
References
Chapter 9: Role of D2D Communications in Mobile Health Applications: Security Threats and Requirements
9.1 Introduction
9.2 D2D Scenarios for Mobile Health Applications
9.3 D2D Security Requirements and Standardization
9.4 Existing Solutions
9.5 Conclusion
References
Chapter 10: Automated Diagnosis of Skin Cancer for Healthcare: Highlights and Procedures
10.1 Introduction
10.2 Framework of Computer‐Aided Skin Cancer Classification Systems
10.3 Conclusion
Acknowledgment
References
Conclusions
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1
Key Differences between Use‐Focused and Development‐Focused HTA.
Table 1.2
Examples of information flows represented by arrows in Figure 1.1
Table 1.3
Analytical methods by framework aspect
Table 1.4
Evidence generation methods to support development‐focused HTA
Chapter 3
Table 3.1
Measured material parameters using the THz‐TDS technique.
Table 3.2
Fitting statistics Of the proposed model with respect to the measurem...
Chapter 5
Table 5.1
Number of Samples Generated after the Feature Extraction from the Seg...
Table 5.2
Algorithms‐Specific Important Hyperparameters and Their Values Used i...
Table 5.3
Performance Comparison of Machine Learning Algorithms for Different W...
Table 5.4
Performance of the Best Performing K‐NN Classifier against Different ...
Table 5.5
Class‐Specific Empirical Results via Confusion Matrix for the Best‐Pe...
Table 5.6
Features Combinations with the Best Performance for Different Window ...
Chapter 7
Table 7.1
Power Consumption of Electronic Devices Vullers et al. (2009b)
Table 7.2
Comparison of different PV Technologies (Polman et al. 2016)
Table 7.3
The Value of Figure of Merit (ZT) of the Bi‐Te‐Based Material and the ...
Table 7.4
Transmitter Power Restrictions
Table 7.5
Overview of Alternative Sources of Energy to Replace Batteries
Chapter 8
Table 8.1
Technical Requirements of Real‐Time Wireless Communication and Contro...
Table 8.2
Independent Design vs Co‐Design
Chapter 10
Table 10.1
Clinical ABCDE Rule Criteria for Diagnosing Malignant Melanoma apart ...
Table 10.2
ABCD Score Calculation for Diagnosing Malignant Melanoma
Table 10.3
Some of the commonly used metrics in evaluating the classifier's per...
List of Illustrations
Chapter 1
Figure 1.1 Activities of development‐focused HTA.
Chapter 2
Figure 2.1 In‐home sketch scenario of radar‐based sensing technologies for h...
Figure 2.2 Example of radar data format in the three main domains of range (...
Figure 2.3 Information domains of radar data used for human activity classif...
Figure 2.4 Common high‐level framework for classification, with an example o...
Figure 2.5 Example of spectrograms for six human activities recorded by rada...
Figure 2.6 Spectrogram for a continuous sequence of six activities performed...
Figure 2.7 Example of spectrograms for four different walking gait performed...
Figure 2.8 Example of spectrogram for a person sitting on a chair at approxi...
Figure 2.9 Radar simulation environment to design for paradigm shift.
Figure 2.10 Radar in a suite of sensors for ambient in‐home patient monitori...
Chapter 3
Figure 3.1 Illustration of BCWNs and the associated components. (a) The full...
Figure 3.2 Categories of nano‐scale communications.
Figure 3.3 Microscopic image of a real human skin illustrating the two defin...
Figure 3.4 Cross‐section of skin sample (a) Longitudinal section of the hair...
Figure 3.5 Measured THz pulses through air, TPX, and a human tissue sample. ...
Figure 3.6 Measured refractive index as a function of frequency. The refract...
Figure 3.7 Measured absorption coefficient using THz‐TDS. The alpha value is...
Figure 3.8 Collagen sample prepared for the study. Source: Chopra et al. (20...
Figure 3.9 Schematic diagram of a THz‐TDS system operating in the transmissi...
Figure 3.10 Numerical skin model (based on CST Microwave Studio
TM
) represent...
Figure 3.11 Correlation level fitting of eq. (3.5) and eq. (3.6), used to ca...
Figure 3.12 Envisioned architecture for nano‐healthcare.
Figure 3.13 System model for in‐vivo cooperative communication at terahertz ...
Chapter 4
Figure 4.1 Diagram of Sagittal View of a Rat Brain Indicating Regions Which ...
Figure 4.2 Examples of Different Neural Recording Systems.
Figure 4.3 Schematic Illustration of the Key Parameters in Neuroscience Rese...
Figure 4.4 Failure Modes of Implantable Neural Devices and Duration.
Figure 4.5 Comparison of Young's Modulus between Commonly Used Materials for...
Figure 4.6 The Complex MR Environment Can Interact with an Implant in Many D...
Chapter 5
Figure 5.1 Data Collected in Hydrated and Dehydrated State in Sitting Postur...
Figure 5.2 Key Steps of the Methodology Used for the Development of Hydratio...
Figure 5.3 BITalino Kit Used for the Data Collection of GSR
Figure 5.4 Performance Comparison of Different Algorithms in Different Body ...
Figure 5.5 Class‐Specific Performance of the K‐NN Based Best Models in Each ...
Figure 5.6 Comparison of the Performance of K‐NN for a Different Combination...
Chapter 6
Figure 6.1 Basic LSI Technique.
Figure 6.2 Parallel LSI.
Figure 6.3 Distributed LSI.
Figure 6.4 Distribution of 2500 documents in Five Clusters Using the Balance...
Figure 6.5 Comparison between LSI, Multi‐Thread LSI and DLSI in Terms of Com...
Figure 6.6 Post‐Processing Comparison between LSI and DLSI.
Figure 6.7 Performance Evaluation for DLSI on a Large‐Scale Database (100,00...
Figure 6.8 Accuracy vs. K Value.
Figure 6.9 Time vs. K Value.
Figure 6.10 Comparison Results (Recall and Precision) between LSI and DLSI....
Chapter 7
Figure 7.1 Schematic diagram of PV cell. Reproduced with permission from Gha...
Figure 7.2 Equivalent circuit representation of an (a) Ideal PV cell, and (b...
Figure 7.3 Flexible PV cell for wearable applications.
Figure 7.4 PV cells used in different wearable applications. These applicati...
Figure 7.5 (a) Inverse piezoelectric effect, (b) Direct piezoelectric effect...
Figure 7.6 Piezoelectric energy could be harvested from(a) Shoulder (Granstr...
Figure 7.7 Thermoelectric Generator Made of n‐type and p‐type Semiconductor ...
Figure 7.8 Block Diagram of an RF Energy Harvesting System.
Chapter 8
Figure 8.1 Real‐Time Wireless Control System for Healthcare.
Figure 8.2 Real‐Time Wireless Control System. Source: Chang, B., Zhao, G., Z...
Figure 8.3 State Updates for Different QoS.
Figure 8.4 Control Cost Comparison.
Figure 8.5 Transmission Energy Consumption Comparison.
Chapter 9
Figure 9.1 D2D communication use cases and scenarios based on the coverage a...
Chapter 10
Figure 10.1 Confusion matrix of binary classification model
Figure 10.2 Dermoscopy Image CAD System Block Diagram
Guide
Cover
Table of Contents
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Engineering and Technology for Healthcare
Edited by
Muhammad Ali Imran
University of Glasgow
Rami Ghannam
University of Glasgow
Qammer H. Abbasi
University of Glasgow
Copyright
This edition first published 2021
© 2021 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Muhammad Ali Imran, Rami Ghannam and Qammer H. Abbasi to be identified as the authors of this work has been asserted in accordance with law.
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Library of Congress Cataloging‐in‐Publication Data
Names: Imran, Muhammad Ali, editor. | Ghannam, Rami, editor. | Abbasi,
Qammer H., editor.
Title: Engineering and technology for healthcare / [edited by] Muhammad Ali
Imran, Rami Ghannam, Qammer H. Abbasi.
Description: Hoboken, NJ : Wiley‐IEEE, 2020. | Includes bibliographical
references and index.
Identifiers: LCCN 2020025470 (print) | LCCN 2020025471 (ebook) | ISBN
9781119644248 (hardback) | ISBN 9781119644224 (adobe pdf) | ISBN
9781119644286 (epub)
Subjects: MESH: Biomedical Technology | Biomedical Engineering | Technology
Assessment, Biomedical
Classification: LCC R855.3 (print) | LCC R855.3 (ebook) | NLM W 82 | DDC
610.28–dc23
LC record available at https://lccn.loc.gov/2020025470
LC ebook record available at https://lccn.loc.gov/2020025471
Cover Design: Wiley
Cover Image: © CI Photos/Shutterstock
Dedication
To our parents
List of Contributors
Rami Ghannam,
University of Glasgow
Glasgow
UK
Muhammad A. Imran
University of Glasgow
Glasgow
UK
Qammer H. Abbasi
University of Glasgow
Glasgow
UK
You Hou
University of Glasgow
Glasgow
UK
Yuchi Liu
University of Glasgow
Glasgow
UK
Yidi Xiao
University of Glasgow
Glasgow
UK
Guodong Zhao
University of Glasgow
Glasgow
UK
Francesco Fioranelli
Technical University of Delft
Netherlands
Julien Le Kernec
University of Glasgow
Glasgow
UK
Ahmed Zoha
University of Glasgow
Glasgow
UK
Janet Bouttell
University of Glasgow
Glasgow
UK
Eleanor Grieve
University of Glasgow
Glasgow
UK
Neil Hawkins
University of Glasgow
Glasgow
UK
Naem Ramzan
University of West of Scotland
UK
Introduction
Along with Medicine and Law, Engineering is one of the oldest professions in the world. While there is debate regarding the exact definition of engineering, the word engineer derives itself from the Latin word ingenium, which means ingenuity Wall [2010]. In fact, engineering involves the application of science and technology to develop new products, tools, services or processes that can benefit society Crawley et al. [2007]. According to Theodore Von Krmn: “Scientists discover the world that exists; engineers create the world that never was” Mackay [1991]. Consequently, engineers are problem solvers and are responsible for creating the healthcare products that we see today.
During the past two hundred and fifty years, the field of engineering has witnessed several waves of innovation, which have depended on the world's techno‐economic paradigm shifts Perez [2010]. Each of the these overlapping waves are approximately 50 years in duration and are also known as the Kwaves. Thanks to the use of iron, waterpower and mechanical constructions, the first of these innovation waves started with the industrial revolution in 18th Century Britain de Graaff and Kolmos [2014]. Now, as we approach the 21st century and the sixth innovation wave, engineers are shifting their interests from the fields of physics, electronics and communications to the interdisciplinary fields of biology and information technology.
Thus, during the past two decades the healthcare industry has seen a rapid transformation. In fact, medical technologies have evolved since the development of the bifocal lens in the early 18th century by Benjamin Franklin. Today, engineers are transforming these contact lenses into healthcare platforms that can monitor vital human signs Yuan et al. [2020]. Consequently, the healthcare field is continuously being reshaped through advances in sensors, robotics, microelectronics, big data and artificial intelligence.
Innovation in healthcare is now a widely researched topic. It is currently a “hot” topic, since it is desperately needed. Without doubt, innovation allows us to think differently, to take risks and to develop ideas that are far better than existing solutions. In this book, we aim to highlight the research that engineers have been engaged in for developing the next generation of healthcare technologies.
Currently, there is no book that covers all topics related to microelectronics, sensors, data, system integration and healthcare technology assessment in one reference. This book aims to critically evaluate current state‐of‐the‐art technologies and will provide readers with insights into developing new solutions.
The book discusses how advances in sensing technology, computer science, communications systems and proteomics/genomics are influencing healthcare technology.
Our book is highly beneficial for healthcare executives, managers, technologists, data scientists, clinicians, engineers and industry professionals to help them identify realistic and cost‐effective concepts uniquely tailored to support specific healthcare challenges. Moreover, researchers, professors, doctorate and postgraduate students would also benefit from this book, as it would enable them to identify open issues and classify their research based on existing literature. In fact, these academics need to ensure that their curricula are constantly being revised and updated according to the previously mentioned innovation waves Ahmad et al., Magjarevic et al. [2010], Xeni et al..
Additionally, our book aims to provide in‐depth knowledge to stakeholders, regulators, institutional actors, research agencies on the latest developments in this field, which serves as an aid to making the right choices in prioritizing funding resources for the next generation of healthcare technologies. The first chapter deals with Healthcare Technology Assessment (HTA). This chapter focuses on three main topics. The first aims to provide an explanation of the principles of HTA and its familiar role in determining coverage of health care provision. The second involves outlining the challenges of health technology assessment for medical devices. An outline of the main categories of devices will be presented (large capital items, point of care devices, diagnostics, implantables and telehealth) and the difficulties associated with evaluating each of these types of devices. Challenges include licensing and regulation, incremental improvement, evidence generation, short lifespan, workflow, behavioural and other contextual factors and indirect health benefit. Finally, the authors will mention the contribution of HTA in the development and translation of medical devices. They will set out the role of HTA in identifying needs, assessing the potential of technologies in development, aiding design and tailoring evidence generation activities. The chapter will also be Illustrated with appropriate case studies.
Chapter 2 deals with contactless RF sensing, which has recently gained plenty of interest in the domain of healthcare and assisted living due to its capability to monitor several parameters related to the health and well‐being of people. This ranges from respiration and heartbeat to gait and mobility, to activity patterns and behaviour. The main advantage of RF sensing is its contactless monitoring capability. Consequently, no sensors need to be worn by the person monitored and no optical images need to be taken via conventional cameras, which can raise problems of privacy especially in private homes. The aim of this chapter is to provide an overview of the most recent different RF technologies for healthcare, including active and passive radar and wireless channel information.
Chapter 3 discusses recent advances in Pervasive Sensing. Here, the vision of nanoscale networking attempts to achieve the functionality and performance of the Internet with the exceptions that node size is measured in cubic nanometres and channels are physically separated by up to hundreds or thousands of nanometres. In addition, these nano‐nodes are assumed to be self‐powered, mobile and rapidly deployable in and around a specific target. Nevertheless, downscaling the principles of traditional electromagnetic networks to the nanoscale introduces several challenges, both in terms of device technologies and communication solutions. This chapter will shed light on the basic principles of nano‐electromagnetic communication in the Terahertz frequency region in the nanoscale dimension.
Moreover, chapter 4 is concerned with providing recent advances in Microelectronics for Brain Implants. This Chapter discusses advances in diagnosis, monitoring, management and treatment of neurological disorders. It will be two parts: first we will discuss our approaches for in vitro diagnostics include lab‐on‐chip progresses for neurodegenerative diseases such as Alzheimers and Parkinsons diseases. Secondly, we will review our in‐vivo implantable medical devices for different applications include treatments of epilepsy and spinal cord. We will conclude this chapter from different perspective including sensing, communications and energy harvesting.
Chapter 5 describes the rationale for using machine learning (ML) techniques for decision making in the healthcare industry. In human physiology, hydration is essential for the proper functioning of multiple systems. Hydration is responsible for controlling various biological reactions by acting as a solvent, a reaction medium, a reactant and a reaction product. Water is the major component of the human body, making it critical for thermoregulation, cell volumes and even for joint lubrication. This chapter will deal with applying machine learning techniques on data collected from a controlled environment for detection of skin hydration levels.
In chapter 6, the authors describe how machine learning techniques can revolutionize medical diagnosis. Single Nucleotide Polymorphisms (SNPs) are one of the most important sources of human genome variability and ML has the potential to predict SNPs, which can enable the diagnosis and prognosis of several human diseases. To separate the affected samples from the normal ones, various techniques have been applied on SNPs. Achieving high classification accuracy in such a high‐dimensional space is crucial for successful diagnosis and treatment. In this work, we propose an accurate hybrid feature selection method for detecting the most informative SNPs and selecting an optimal SNP subset.
Chapter 7 provides an overview of the energy harvesting techniques that can be used to power wearable and portable devices. Power harvesting or generation is still a big challenge in biomedical devices. Conventionally, these devices have relied on batteries, which are disadvantageous due to their size, lifespan and hazardous nature. It is therefore important to investigate alternative methods to ensure that medical devices are battery‐free and/or are self‐powered. There are various methods that are currently being investigated, which include Photovoltaic cells (PV), Piezoelectric Generators (PEG), Thermoelectric Cells (TEG) and Radio Frequency techniques. This chapter aims to provide the latest trends in each of these energy harvesting methods and offer recommendations for future applications.
Chapter 8 is concerned with how data can be transferred from in‐vivo to in‐vitro Due to losses in human tissue, reliable data transfer through skin is a major challenge. In this chapter, a novel concept of cooperative communication for in‐vivo nano network will be presented to enhance the communication among these devices. The effect on the system outage probability performance will be conducted for various parameters including relay placement, number of relays, transmit power, bandwidth and carrier frequency.
Chapter 9 aims to provide a rationale for Wireless Control. In this chapter, we will discuss the real‐time wireless control of life‐critical actions, which is one of the essential features to enable many healthcare applications, e.g., remote diagnosis and surgery. In particular, we will introduce the basics of wireless control systems and discuss the fundamental design capabilities needed to realize real‐time wireless control, with primary emphasis given to communicationcontrol co‐design. The goal is to provide integrated solutions for life‐critical actions in healthcare.
Similarly, chapter 10 deals with how life‐critical communications networks can be developed. It is noteworthy to mention that device‐to‐device (D2D) communication is regarded as a promising solution to improve the spectrum utilization of cellular systems. This is due to the direct link between nearby devices, which can be established on the same time/frequency resources (cellular resources) over a short distance. Consequently, D2D communication can be a potential candidate for mobile (M)‐health applications. However, due to their intrinsically open nature, D2D communication is vulnerable to security attacks. In this chapter, we will highlight the security requirements of D2D communication to make them applicable to M‐health scenarios. In addition, we will investigate the standardization efforts for secure D2D communications.
Finally, concluding remarks are provided in the final chapter of the book, where we discuss the challenges and opportunities ahead for healthcare devices and systems.
References
2019 Wasim Ahmad, Rami Ghannam, and Muhammad Imran. Course design for achieving the graduate attributes of the 21st century uk engineer. In Poster presented at Advance HE STEM Teaching and Learning Conference, Birmingham, UK. 30‐31 Jan 2019.
2007 Edward Crawley, Johan Malmqvist, Soren Ostlund, Doris Brodeur, and Kristina Edstrom. Rethinking engineering education. The CDIO Approach, 302:60–62, 2007.
2014 Erik de Graaff and Anette Kolmos. Innovation and research on engineering education. In Handbook of research on educational communications and technology, pages 565–571. Springer, 2014.
1991 Alan L Mackay. A dictionary of scientific quotations. CRC Press, 1991.
2010 Ratko Magjarevic, Igor Lackovic, Zhivko Bliznakov, and Nicolas Pallikarakis. Challenges of the biomedical engineering education in europe. In 2010 annual international conference of the IEEE engineering in medicine and biology, pages 2959–2962. IEEE, 2010.
2010 Carlota Perez. Technological revolutions and techno‐economic paradigms. Cambridge journal of economics, 34(1):185–202, 2010.
2010 Kevin Wall. Engineering: Issues, challenges and opportunities for development. UNESCO, 2010.
2019 Nikolas Xeni, Rami Ghannam, Fikru Udama, Vihar Georgiev, and Asen Asenov. Semiconductor device visualization using tcad software: Case study for biomedical applications. In Poster presented at IEEE UKCAS 2019, London, UK. 6 Dec 2019.
2020 Mengyao Yuan, Rupam Das, Rami Ghannam, Yinhao Wang, Julien Reboud, Roland Fromme, Farshad Moradi, and Hadi Heidari. Electronic contact lens: A platform for wireless health monitoring applications.
Advanced Intelligent Systems
, page 1900190, 2020.
Introduction
Along with Medicine and Law, Engineering is one of the oldest professions in the world. While there is debate regarding the exact definition of engineering, the word engineer derives itself from the Latin word ingenium, which means ingenuity Wall [2010]. In fact, engineering involves the application of science and technology to develop new products, tools, services or processes that can benefit society Crawley et al. [2007]. According to Theodore Von Krmn: “Scientists discover the world that exists; engineers create the world that never was” Mackay [1991]. Consequently, engineers are problem solvers and are responsible for creating the healthcare products that we see today.
During the past two hundred and fifty years, the field of engineering has witnessed several waves of innovation, which have depended on the world's techno‐economic paradigm shifts Perez [2010]. Each of the these overlapping waves are approximately 50 years in duration and are also known as the Kwaves. Thanks to the use of iron, waterpower and mechanical constructions, the first of these innovation waves started with the industrial revolution in 18th Century Britain de Graaff and Kolmos [2014]. Now, as we approach the 21st century and the sixth innovation wave, engineers are shifting their interests from the fields of physics, electronics and communications to the interdisciplinary fields of biology and information technology.
Thus, during the past two decades the healthcare industry has seen a rapid transformation. In fact, medical technologies have evolved since the development of the bifocal lens in the early 18th century by Benjamin Franklin. Today, engineers are transforming these contact lenses into healthcare platforms that can monitor vital human signs Yuan et al. [2020]. Consequently, the healthcare field is continuously being reshaped through advances in sensors, robotics, microelectronics, big data and artificial intelligence.
Innovation in healthcare is now a widely researched topic. It is currently a “hot” topic, since it is desperately needed. Without doubt, innovation allows us to think differently, to take risks and to develop ideas that are far better than existing solutions. In this book, we aim to highlight the research that engineers have been engaged in for developing the next generation of healthcare technologies.
Currently, there is no book that covers all topics related to microelectronics, sensors, data, system integration and healthcare technology assessment in one reference. This book aims to critically evaluate current state‐of‐the‐art technologies and will provide readers with insights into developing new solutions.
The book discusses how advances in sensing technology, computer science, communications systems and proteomics/genomics are influencing healthcare technology.
Our book is highly beneficial for healthcare executives, managers, technologists, data scientists, clinicians, engineers and industry professionals to help them identify realistic and cost‐effective concepts uniquely tailored to support specific healthcare challenges. Moreover, researchers, professors, doctorate and postgraduate students would also benefit from this book, as it would enable them to identify open issues and classify their research based on existing literature. In fact, these academics need to ensure that their curricula are constantly being revised and updated according to the previously mentioned innovation waves Ahmad et al., Magjarevic et al. [2010], Xeni et al..
Additionally, our book aims to provide in‐depth knowledge to stakeholders, regulators, institutional actors, research agencies on the latest developments in this field, which serves as an aid to making the right choices in prioritizing funding resources for the next generation of healthcare technologies. The first chapter deals with Healthcare Technology Assessment (HTA). This chapter focuses on three main topics. The first aims to provide an explanation of the principles of HTA and its familiar role in determining coverage of health care provision. The second involves outlining the challenges of health technology assessment for medical devices. An outline of the main categories of devices will be presented (large capital items, point of care devices, diagnostics, implantables and telehealth) and the difficulties associated with evaluating each of these types of devices. Challenges include licensing and regulation, incremental improvement, evidence generation, short lifespan, workflow, behavioural and other contextual factors and indirect health benefit. Finally, the authors will mention the contribution of HTA in the development and translation of medical devices. They will set out the role of HTA in identifying needs, assessing the potential of technologies in development, aiding design and tailoring evidence generation activities. The chapter will also be Illustrated with appropriate case studies.
Chapter 2 deals with contactless RF sensing, which has recently gained plenty of interest in the domain of healthcare and assisted living due to its capability to monitor several parameters related to the health and well‐being of people. This ranges from respiration and heartbeat to gait and mobility, to activity patterns and behaviour. The main advantage of RF sensing is its contactless monitoring capability. Consequently, no sensors need to be worn by the person monitored and no optical images need to be taken via conventional cameras, which can raise problems of privacy especially in private homes. The aim of this chapter is to provide an overview of the most recent different RF technologies for healthcare, including active and passive radar and wireless channel information.
Chapter 3 discusses recent advances in Pervasive Sensing. Here, the vision of nanoscale networking attempts to achieve the functionality and performance of the Internet with the exceptions that node size is measured in cubic nanometres and channels are physically separated by up to hundreds or thousands of nanometres. In addition, these nano‐nodes are assumed to be self‐powered, mobile and rapidly deployable in and around a specific target. Nevertheless, downscaling the principles of traditional electromagnetic networks to the nanoscale introduces several challenges, both in terms of device technologies and communication solutions. This chapter will shed light on the basic principles of nano‐electromagnetic communication in the Terahertz frequency region in the nanoscale dimension.
Moreover, chapter 4 is concerned with providing recent advances in Microelectronics for Brain Implants. This Chapter discusses advances in diagnosis, monitoring, management and treatment of neurological disorders. It will be two parts: first we will discuss our approaches for in vitro diagnostics include lab‐on‐chip progresses for neurodegenerative diseases such as Alzheimers and Parkinsons diseases. Secondly, we will review our in‐vivo implantable medical devices for different applications include treatments of epilepsy and spinal cord. We will conclude this chapter from different perspective including sensing, communications and energy harvesting.
Chapter 5 describes the rationale for using machine learning (ML) techniques for decision making in the healthcare industry. In human physiology, hydration is essential for the proper functioning of multiple systems. Hydration is responsible for controlling various biological reactions by acting as a solvent, a reaction medium, a reactant and a reaction product. Water is the major component of the human body, making it critical for thermoregulation, cell volumes and even for joint lubrication. This chapter will deal with applying machine learning techniques on data collected from a controlled environment for detection of skin hydration levels.
In chapter 6, the authors describe how machine learning techniques can revolutionize medical diagnosis. Single Nucleotide Polymorphisms (SNPs) are one of the most important sources of human genome variability and ML has the potential to predict SNPs, which can enable the diagnosis and prognosis of several human diseases. To separate the affected samples from the normal ones, various techniques have been applied on SNPs. Achieving high classification accuracy in such a high‐dimensional space is crucial for successful diagnosis and treatment. In this work, we propose an accurate hybrid feature selection method for detecting the most informative SNPs and selecting an optimal SNP subset.
Chapter 7 provides an overview of the energy harvesting techniques that can be used to power wearable and portable devices. Power harvesting or generation is still a big challenge in biomedical devices. Conventionally, these devices have relied on batteries, which are disadvantageous due to their size, lifespan and hazardous nature. It is therefore important to investigate alternative methods to ensure that medical devices are battery‐free and/or are self‐powered. There are various methods that are currently being investigated, which include Photovoltaic cells (PV), Piezoelectric Generators (PEG), Thermoelectric Cells (TEG) and Radio Frequency techniques. This chapter aims to provide the latest trends in each of these energy harvesting methods and offer recommendations for future applications.
Chapter 8 is concerned with how data can be transferred from in‐vivo to in‐vitro Due to losses in human tissue, reliable data transfer through skin is a major challenge. In this chapter, a novel concept of cooperative communication for in‐vivo nano network will be presented to enhance the communication among these devices. The effect on the system outage probability performance will be conducted for various parameters including relay placement, number of relays, transmit power, bandwidth and carrier frequency.
Chapter 9 aims to provide a rationale for Wireless Control. In this chapter, we will discuss the real‐time wireless control of life‐critical actions, which is one of the essential features to enable many healthcare applications, e.g., remote diagnosis and surgery. In particular, we will introduce the basics of wireless control systems and discuss the fundamental design capabilities needed to realize real‐time wireless control, with primary emphasis given to communicationcontrol co‐design. The goal is to provide integrated solutions for life‐critical actions in healthcare.
Similarly, chapter 10 deals with how life‐critical communications networks can be developed. It is noteworthy to mention that device‐to‐device (D2D) communication is regarded as a promising solution to improve the spectrum utilization of cellular systems. This is due to the direct link between nearby devices, which can be established on the same time/frequency resources (cellular resources) over a short distance. Consequently, D2D communication can be a potential candidate for mobile (M)‐health applications. However, due to their intrinsically open nature, D2D communication is vulnerable to security attacks. In this chapter, we will highlight the security requirements of D2D communication to make them applicable to M‐health scenarios. In addition, we will investigate the standardization efforts for secure D2D communications.
Finally, concluding remarks are provided in the final chapter of the book, where we discuss the challenges and opportunities ahead for healthcare devices and systems.
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Chapter 1Maximizing the Value of Engineering and Technology Research in Healthcare: Development‐Focused Health Technology Assessment
Janet Boutell, Neil Hawkins and Eleanor Grieve
Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK
This chapter focuses on three main topics. The first aims to provide an explanation of the principles of health technology assessment (HTA) and its familiar role in determining coverage of healthcare provision. Second, we discuss the growing contribution of HTA in the development and translation of medical devices introducing what we term “development‐focused HTA”(DF‐HTA). We set out the role of DF‐HTA in identifying needs, assessing the potential of technologies in development, aiding design, and tailoring evidence generation activities. Finally, we outline the challenges of development and assessment presented by medical devices distinguishing large capital items, point of care devices, diagnostics, implantables, and digital devices. Each category of device has its own set of challenges for developers and HTA analysts alike. Challenges include a complex licensing and regulation environment, short lifespan and incremental improvement, difficulties in generating clinical evidence, the importance of contextual factors (e.g., how the device will be used and by whom), patient and clinician acceptance, and the indirect health benefit from diagnostic devices.
1.1 Introduction
Advancements in engineering and technology have the potential to revolutionise patient care and medical research. However, resources available for research and development and for healthcare provision are limited, so it is essential that any funds invested are spent on those projects that are both likely to succeed and likely to make a difference to patients' health. Health Technology Assessment (HTA) is a multi‐disciplinary approach that studies the medical, social, ethical, and economic implications of development, diffusion, and use of health technology (INAHTA.ORG 2019). HTA has been most widely used by public payers or reimbursement agencies when a technology (such as a pharmaceutical or a medical device) is ready for market. However, there is increasing recognition that HTA undertaken at an earlier stage in the development of a health technology can aid investors and developers to focus their resources on technologies that are likely to succeed as well as identifying those that are likely to fail (IJzerman et al. 2017). We term this earlier form of HTA, “development‐focused HTA” (DF‐HTA) and the more familiar form of HTA “use‐focused HTA.”
Health technology is a broad term that encompasses drugs, medical procedures, tests, and service configuration. Medical devices form a sub‐set of health technology. The diverse sub‐set includes large, expensive, capital equipment such as the Da Vinci robotic surgery platform (INTUITIVE.COM 2019) and small consumable items such as sticking plasters. There are some common challenges for developers of all categories of medical device. In particular, the licensing and regulatory environment is highly complex and differs according to the jurisdiction where the device will be used. Evidence generation is also particularly challenging for many kinds of medical devices as different decision‐makers require different levels of evidence. For devices with short lifespans, when it is common for different versions to be developed sequentially with incremental improvements, it is difficult to know which version of the device the evidence relates to. Items like the robotic surgery platform are subject to the “learning curve” effect, as surgeons need an initial training period to improve their competence before the clinical effectiveness of the new equipment can reasonably be compared with prior standards of care. Diagnostic tests form an important sub‐category of medical devices. Evidence generation for diagnostics is challenging because any health outcome resulting from the use of the diagnostic is indirect rather than direct. In order for there to be an improvement in health, the diagnostic test needs to change the diagnostic or treatment pathway so that the patient is treated sooner or more effectively. Not only is any health gain indirect, it also depends upon the behavior of the clinician and the patient. A test may indicate that treatment B is more appropriate for the patient, but if the patient and/or the clinician prefer treatment A, the test cost has been wasted and the patient's health is not improved. The value proposition for many devices is also contextually dependent. By this we mean that the device may add value in some places but not others, depending on factors such as what the current treatment and diagnostic pathways are; staffing levels; capacity and workflow; and, what other capital equipment is in place.
The numerous challenges facing developers of medical technologies in general, and medical devices in particular, have led to a recognized problem in translating research from bench to bedside. One response to this has been the growth of translational research bodies charged with supporting developers and bridging the translation gap. Two notable contributors to the DF‐HTA literature are the Center for Translational Molecular Medicine (LYGATURE.ORG 2019), based in the Netherlands and MATCH UK (MATCH.AC.UK 2018), a collaboration between several UK universities. This growing literature demonstrates how the various challenges of medical device development can begin to be addressed at an early stage of development using the methods of DF‐HTA.
The aims of this chapter are to explain what HTA is and how it has been used to determine the coverage of healthcare provision; to explain what DF‐HTA is and how it differs from use‐focused HTA; to set out the challenges in the development and assessment of medical devices; and to illustrate the contributions of DF‐HTA in the development and translation of medical devices through a number of case studies.
1.2 What Is HTA?
Healthcare resources are limited in every setting, and decision‐makers are faced with difficult choices about which technologies should be adopted and used within their service. The definition of HTA given in the introduction (INAHTA.ORG 2019) was
HTA is a multi‐disciplinary approach which studies the medical, social, ethical and economic implications of development, diffusion and use of health technology.
Technology in HTA is widely defined and includes drugs, devices, health services, and systems. As the study of these various aspects of health technologies, HTA is well‐placed to inform decision‐makers as they make resource allocation decisions. Indeed, the role of HTA to inform decision‐makers is included in the World Health Organisation (WHO.INT 2019) definition of HTA:
the systematic evaluation of properties, effects and/or impacts of health technologies and interventions. It covers both the direct, intended consequences of technologies and interventions and their indirect, unintended consequences. The approach is used to inform policy and decision‐making in health care, especially on how best to allocate limited funds to health interventions and technologies.
An ongoing project to reach a consensus definition of HTA proposed a definition that includes the important additional factors of a systematic and transparent process.
a multidisciplinary process that uses explicit and scientifically robust methods to assess the value of using a health technology at different points in its lifecycle. The process is comparative, systematic, transparent and involves multiple stakeholders. The purpose is to inform health policy and decision‐making to promote an efficient, sustainable, equitable and high‐quality health system.
Health Technology Assessment, as a discipline, first developed in the United States when Congress requested Technology Assessment of health technologies in the mid 1970s (Stevens et al. 2003), and the term is now internationally used. The adoption of this term gained popularity in wealthier countries that prioritized the evaluation and improvement of health care. HTA draws on Evidence Based Medicine (EBM). EBM developed from the publication in 1972 of Archie Cochrane's “Effectiveness and Efficiency” (Cochrane 1972) and is now championed by the international organization, the Cochrane Collaboration (Stevens et al. 2003). Evidence synthesis methods such as systematic review and meta‐analysis are core to HTA and draw heavily on guidance developed by the Cochrane Collaboration. These methods often form the basis for the clinical effectiveness estimates in cost‐effectiveness analysis and health economic modelling.
The components of HTA vary according to the particular decision‐maker, but many forms of HTA start with the definition of a decision problem to address. Analysts may find it useful to use a structure to help them define the decision problem. A popular structure is PICO, which stands for Population, Intervention, Comparator, and Outcome. The intervention is the technology to be assessed, and the comparator is the current standard of care in that disease area. Once the decision problem has been defined, the next step is synthesis of the clinical evidence, using techniques such as systematic review and meta‐analysis. Once the evidence on clinical effectiveness has been assembled and issues regarding evidence quality and generalisability addressed, cost‐effectiveness can be considered. Finally, other considerations such as legality and ethics may be addressed (Eddy 2009).
HTA informs a variety of healthcare decision‐makers, ranging from national healthcare providers like the National Health Service in the UK, to regional health authorities (for example, in Spain and Canada) and local providers such as hospitals. Insurance companies and commercial healthcare providers also need to make decisions about coverage and reimbursement. HTA agencies may be established within, or supported by, the decision‐maker as with the National Institute for Health and Care Excellence (NICE) in the UK or may be external bodies such as the Institute for Clinical and Economic Review (ICER) in the United States, which is funded primarily by not for profit organizations (ICER.ORG 2019) and provides advice for guidance. Some agencies have a strong emphasis on cost‐utility analysis (for example, UK, Netherlands, Canada) and some have acknowledged a financial limit to the amount they consider acceptable to pay for each year in full health delivered by a health technology.
Decisions supported by HTA include two broad categories: allocation of a set budget over a number of healthcare areas and decisions about individual technologies or programs. In the first category, the decisions involve which programs to include in a package of Universal Health Coverage (for example, maternity care, vaccination programmes) or decisions about prioritization within a research budget. The aim of the HTA would be to allocate the budget according to agreed criteria of effectiveness, value for money, and other considerations, perhaps equity. The second category includes assessment of individual technologies, such as pharmaceuticals, to determine whether they should be adopted. Again, they would be likely to be assessed against pre‐established criteria relating to evidence base, need, value for money, and equity issues. Medical devices and surgical procedures could also be assessed in this way. HTA may also be used to determine whether a technology in current use should be excluded from reimbursement or coverage. This is known as “disinvestment.” There is growing interest in how HTA could be used before or during the development of a technology to inform a broader set of decisions. This form of HTA, which we have termed “development‐focused HTA,” is the subject of the next section.
1.3 What Is Development‐Focused HTA?
Development‐Focused HTA (DF‐HTA) is concerned with whether and how a technology should be developed. It is contrasted in this section with Use‐focused HTA (described in the previous section), which compares the clinical benefit that an available technology is likely to deliver to the cost of the technology and makes a recommendation to a decision‐maker based on an assessment of opportunity cost and other local criteria. DF‐HTA differs in that the technology is under development, perhaps still at the concept stage. DF‐HTA aims to inform the developers of the technology about a wider range of questions, including how the technology should be designed, used, and/or priced. DF‐HTA is a relatively young but expanding field. We believe that the tools of DF‐HTA could be usefully employed to evaluate technologies in development and ensure that only those that are likely to succeed continue to be developed as well as to prioritize research expenditure on new health technologies. This form of HTA, used to inform developers of health technologies, has been termed “early HTA” in the academic literature (Ijzerman and Steuten 2011). We prefer to use the label “development‐focused HTA” as it is the audience, rather than the timing of the HTA, which drives many of the differences. Table 1.1 sets out key differences between use‐focused and development‐focused HTA, and these are explained in the paragraphs that follow using an example of home brain monitoring in epilepsy patients.
Table 1.1 Key Differences between Use‐Focused and Development‐Focused HTA.
Feature
Use‐focused HTA
Development‐focused HTA
Target audience
Reimbursement agencies, insurers, clinicians & patients
Technology developers, investors, and public sector funders
Specific decisions HTA designed to inform
One off Binary ‐ accept/reject Optimising guidelines Price revisions Reimbursement decisions Budget allocation
Broad range including: Go/no‐go decisions Technology design Trial design Research prioritization Reimbursement strategy prioritization
Available evidence
Evidence‐base more developed
Evidence‐base fluid, may be limited
Timing
Close to and post‐approval
Repeated
Pre‐ and during development
Underlying user objective
Maximize health
Maximize financial and/or societal return on investment
Core decision rule
Reimburse when value meets established criteria
Continue development if project has (most) potential to deliver financial/societal return on investment
Clinical decision space
Targeted at specific decision‐makers, indications, comparator and patient groups defined by local practice and licensing
Potentially multiple jurisdictions, indications, comparators, funders, user, groups, thresholds (test cut‐off), levels of test performance, and positions in pathway
Business model
Fixed, reimbursement by payer/insurer
Broad, not yet defined
Resources for analysis
Committed ‐ limited number of technologies reviewed
Often constrained
Stance of analysis
Normative
Positive
Burden of Proof
Established standard of evidence
Evidence credible to the development team
1.4 Illustration of Features of Development‐Focused HTA
Digital health technologies have the potential to improve patient health through improved diagnosis and/or ongoing monitoring of health conditions. They may also reduce cost by accelerating diagnosis and/or reducing hospital admissions. The following paragraphs contrast a use‐focused HTA exercise and a development‐focused exercise concerning a home‐based brain monitoring device (HBM) for epilepsy patients. Epilepsy is diagnosed using electroencephalography (EEG), but because the standard routine of EEG is relatively short, it has only 20‐56% sensitivity (Breteler 2012). HBM could increase the sensitivity of diagnosis to in excess of 90 percent by increasing the period of observation and adding a detection algorithm (Breteler 2012).
1.4.1 Use‐Focused HTA
The timing of any use‐focused HTA would be after the device was licensed when it was available for purchase. The audience for a use‐focused HTA of HBM would generally be a national decision‐maker (perhaps a ministry of health or a national reimbursement agency) but could potentially be a healthcare provider at a more local level, such as a hospital. The underlying objective of either of these decision‐makers would be to maximize health given the budget at their disposal. The specific decisions to be informed would be whether to purchase the monitoring system and potentially, in which populations it should be used. Although the level of evidence required for a device to be licensed is not as well‐defined as the evidence required for a pharmaceutical, the available evidence should include evidence of safety and performance. The analysts undertaking the use‐focused HTA may find sufficient evidence of clinical utility or may flag up that some additional evidence is required prior to any decision being made. The price of the system would be known (although it may potentially be open to negotiation). The decision space in this exercise would be relatively narrow as the jurisdiction and disease are both fixed. It would probably be necessary to consider different sub‐groups of the population where the effectiveness of HBM may vary and possibly different positions in the diagnostic pathway. A use‐focused HTA of a diagnostic technology may involve modelling the impact of the technology (HBM) on health outcomes and resource use over a long time‐horizon. This analysis would produce an estimate of the clinical efficacy and cost‐effectiveness of the technology, which could then be used to inform the one‐off decision about whether or not to purchase. This decision would be informed by the decision‐makers' underlying decision rule in order to decide whether or not to implement the program. If a national decision‐making body commissioned the HTA exercise, the resources available for analysis are likely to have been adequate to undertake a comprehensive analysis. However, for diagnostic technologies and other devices, use‐focused HTA is sometimes undertaken by smaller, local healthcare services, and they may need to undertake a less comprehensive review to reflect the resources they have available.
1.4.2 Development‐Focused HTA
By way of contrast, the timing of a development‐focused HTA may precede the discovery research for a digital health application or may be undertaken when there is a prototype available but there are decisions to be made about whether it is worthwhile to continue the development or to prioritize its implementation. The target audience for the HTA analysis may be a public or charitable research funder allocating funds across a portfolio of projects or a commercial developer/investor. The underlying objective of these two groups would potentially differ with public or charitable research funders looking to maximize health given the budget at their disposal and commercial developers/investors looking to maximize financial return on investment. The timing of the assessment would determine the available evidence, but this is unlikely to be large‐scale technology‐specific evidence even at the prototype stage. Evidence is more likely to come from bench studies, similar technologies, or assumptions informed by input from experts. In contrast to use‐focused HTA, the decision space in this exercise may be very wide. There may be scope to use HBM in many different geographical areas, populations, and diseases. As in use‐focused HTA, the analysis may involve modelling the health and cost impact of HBM, but a number of plausible scenarios may be modelled incorporating evidence and assumptions as described above. Modelling would be an iterative process, revised a number of times, reflecting evidence generated and with increasing sophistication as the decision‐space became narrower through the development process. Rather than informing a single decision, the analysis informs ongoing discussions. Even if the analysis showed that the technology in the current form in the selected scenarios did not look promising, this may not mean that it should be abandoned ‐ it may instead indicate that other settings are preferable or an improved design is required.
