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Digital Transformation in Healthcare in Post-Covid19 Times discusses recent advances in patient care and offers critical comparative insights into their application across multiple domains in healthcare. By showcasing key problems, best practices and emerging challenges, the book offers a state-of-art review of opportunities and prospects in the process of delivering smart sustainable healthcare services. Topics discussed include healthcare challenges in the post-COVID-19 era, enabling technologies for digital transformation, value driven approaches to the delivery of patient centric top-quality health services, and analytics and enhanced decision making. In addition, the book updates knowledge on best practices for training towards digital transformation and sustainable health. This is a valuable resource for healthcare professionals, medical doctors, researchers, graduate students and members of the biomedical field who are interested in learning more about the use of emerging technologies in healthcare.

Active Learning for Digital Transformation in Healthcare Education, Training and Research discusses the potential of advanced training of health professionals as a contributing factor to improve treatment outcomes. By reading this book, professionals who deal with patients with low health literacy will be prepared to promote better access to digital tools, understand the habits of users of health services, and empower engagement. The book contains a set of techniques and instruments associated with health literacy, communication skills and personal development that will enable their application in good daily practices and assist healthcare professionals to promote digital transformation to patients. This is a valuable resource for researchers, graduate students and healthcare professionals who are interested in learning more about how they can be an effective agent of change in healthcare.

Next Generation eHealth: Applied Data Science, Machine Learning and Extreme Computational Intelligence discusses the emergence, the impact, and the potential of sophisticated computational capabilities in healthcare. This book provides useful therapeutic targets to improve diagnosis, therapies, and prognosis of diseases, as well as helping with the establishment of better and more efficient next-generation medicine and medical systems. Machine learning as a field greatly contributes to next-generation medical research with the goal of improving medicine practices and medical Systems. As a contributing factor to better health outcomes, this book highlights the need for advanced training of professionals from various health areas, clinicians, educators, and social professionals who deal with patients. Content illustrates current issues and future promises as they pertain to all stakeholders, including informaticians, professionals in diagnostics, key industry experts in biotech, pharma, administrators, clinicians, patients, educators, students, health professionals, social scientists and legislators, health providers, advocacy groups, and more. With a focus on machine learning, deep learning, and neural networks, this volume communicates in an integrated, fresh, and novel way the impact of data science and computational intelligence to diverse audiences.