Nilmini Wickramasinghe – författare

Healthcare and knowledge management is the need of the era; this book investigates various challenges faced by practitioners in this area. It also covers the work to be done in the healthcare sector and the use of different computing techniques for better insight and decision-making.

Healthcare and Knowledge Management for Society 5.0: Trends, Issues, and Innovations showcases the benefits of computing techniques used for knowledge management in the field of healthcare in the futuristic perspective of having a human-centric society 5.0. The book includes topics related to the use of technologies like artificial intelligence, machine learning, deep learning, Internet of Things, blockchain, and sensors for effective healthcare and management. Case studies are included for easy comprehension and the book covers the most up-to-date research in the field. The use of techniques like artificial intelligence in the field of knowledge management is also discussed.

This book is intended for researchers and academicians to explore new ideas, techniques, and tools. Researchers working in interdisciplinary research can also find many interesting topics which will pave the way for a new arena in healthcare and knowledge management.

Healthcare and knowledge management is the need of the era; this book investigates various challenges faced by practitioners in this area. It also covers the work to be done in the healthcare sector and the use of different computing techniques for better insight and decision-making.

Healthcare and Knowledge Management for Society 5.0: Trends, Issues, and Innovations showcases the benefits of computing techniques used for knowledge management in the field of healthcare in the futuristic perspective of having a human-centric society 5.0. The book includes topics related to the use of technologies like artificial intelligence, machine learning, deep learning, Internet of Things, blockchain, and sensors for effective healthcare and management. Case studies are included for easy comprehension and the book covers the most up-to-date research in the field. The use of techniques like artificial intelligence in the field of knowledge management is also discussed.

This book is intended for researchers and academicians to explore new ideas, techniques, and tools. Researchers working in interdisciplinary research can also find many interesting topics which will pave the way for a new arena in healthcare and knowledge management.

This title demystifies artificial intelligence (AI) and analytics, upskilling individuals (healthcare professionals, hospital managers, consultants, researchers, students, and the population at large) around analytics and AI as it applies to healthcare.

This book shows how the tools, techniques, technologies, and tactics around analytics and AI can be best leveraged and utilised to realise a healthcare value proposition of better quality, better access and high value for everyone every day, everywhere. The book presents a triumvirate approach including technical, business and medical aspects of data and analytics and by so doing takes a responsible approach to this key area.

This work serves to introduce the critical issues in AI and analytics for healthcare to students, practitioners, and researchers.

This title demystifies artificial intelligence (AI) and analytics, upskilling individuals (healthcare professionals, hospital managers, consultants, researchers, students, and the population at large) around analytics and AI as it applies to healthcare.

This book shows how the tools, techniques, technologies, and tactics around analytics and AI can be best leveraged and utilised to realise a healthcare value proposition of better quality, better access and high value for everyone every day, everywhere. The book presents a triumvirate approach including technical, business and medical aspects of data and analytics and by so doing takes a responsible approach to this key area.

This work serves to introduce the critical issues in AI and analytics for healthcare to students, practitioners, and researchers.

Healthcare systems globally are grappling with how best to implement effective and efficient patient-centred care while simultaneously trying to contain runaway costs and provide high quality. This book explores the essential enabling role of digital health, taking a socio-technical perspective and looking at the key facets of technology, people and process in turn.

This book examines the opportunities of key digital health components, demystifying digital health and demonstrating how to use its key precepts effectively. The book presents evidence and anecdotes from stakeholders around the world, demonstrating the global relevance and the ability of digital health to uplift and upskill care delivery as it is applied commercially. Bridging academic theory and practice, this is a functional and accessible text for all digital health stakeholders.

The text introduces critical issues and is suitable reading for students, practitioners and researchers in digital health and all healthcare-related domains.

Healthcare systems globally are grappling with how best to implement effective and efficient patient-centred care while simultaneously trying to contain runaway costs and provide high quality. This book explores the essential enabling role of digital health, taking a socio-technical perspective and looking at the key facets of technology, people and process in turn.

This book examines the opportunities of key digital health components, demystifying digital health and demonstrating how to use its key precepts effectively. The book presents evidence and anecdotes from stakeholders around the world, demonstrating the global relevance and the ability of digital health to uplift and upskill care delivery as it is applied commercially. Bridging academic theory and practice, this is a functional and accessible text for all digital health stakeholders.

The text introduces critical issues and is suitable reading for students, practitioners and researchers in digital health and all healthcare-related domains.

This book centres on the topic of digital twins for superior healthcare decision support, as access is enabled to large volumes of multi-dimensional data such as patient’s electronic medical records, medical scans, and data. The reader learns about the possibility of a digital representation of analogous clinical cases built from data-driven models to represent and present relevant information and germane knowledge in context.

Together with cutting-edge technologies, authors share the ability of data-driven models to offer more efficient clinical decision support. The authors take a three-prong approach in the study of digital twins, the positive contributions made in other industries, the different types of applications, and the numerous benefits offered. Artificial Intelligence (AI) techniques, such as Machine Learning (ML) and Deep Learning (DL) algorithms are discussed in the context of digital twins in healthcare applications. By looking at how digital twins reduce workflow challenges, provide fast and precise diagnosis, therefore support superior clinical decision making. Importantly, the editors identify critical success issues including co-design and research, for the design, development, and deployment of suitable digital twins.

This book is written for the healthcare audience, professionals, physicians, medical administrators, managers, and the IT practitioner. It would also serve as a useful reference for the senior level undergraduate students and graduate students in health informatics and public health.

This book centres on the topic of digital twins for superior healthcare decision support, as access is enabled to large volumes of multi-dimensional data such as patient’s electronic medical records, medical scans, and data. The reader learns about the possibility of a digital representation of analogous clinical cases built from data-driven models to represent and present relevant information and germane knowledge in context.

Together with cutting-edge technologies, authors share the ability of data-driven models to offer more efficient clinical decision support. The authors take a three-prong approach in the study of digital twins, the positive contributions made in other industries, the different types of applications, and the numerous benefits offered. Artificial Intelligence (AI) techniques, such as Machine Learning (ML) and Deep Learning (DL) algorithms are discussed in the context of digital twins in healthcare applications. By looking at how digital twins reduce workflow challenges, provide fast and precise diagnosis, therefore support superior clinical decision making. Importantly, the editors identify critical success issues including co-design and research, for the design, development, and deployment of suitable digital twins.

This book is written for the healthcare audience, professionals, physicians, medical administrators, managers, and the IT practitioner. It would also serve as a useful reference for the senior level undergraduate students and graduate students in health informatics and public health.