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User-Driven Healthcare: Concepts, Methodologies, Tools, and Applications provides a global discussion on the practice of user-driven learning in healthcare and connected disciplines and its influence on learning through clinical problem solving. This book brings together different perspectives for researchers and practitioners to develop a comprehensive framework of user-driven healthcare.
The world of medical technologies is undergoing a sea change in the domain of consumer culture. Having a grasp on what appeals to consumers and how consumers are making purchasing decisions is essential to the success of any organization that thrives by offering a product or service. As such, it is vital to examine the consumer-centered aspects of medical technological developments that have a patient-centered focus and allow patients to take part in their own personal health and wellness. Consumer-Driven Technologies in Healthcare: Breakthroughs in Research and Practice is a critical source of academic knowledge on the use of smartphones and other technological devices for cancer therapy, fitness and wellness, chronic disease monitoring, and other areas. The tracking of these items using technology has allowed consumers to take control of their own healthcare. Highlighting a range of pertinent topics such as clinical decision support systems, patient engagement, and electronic health records, this publication is an ideal reference source for doctors, nurse practitioners, hospital administrators, medical professionals, IT professionals, academicians, and researchers interested in advancing medical practice through technology.
"This book provides comprehensive coverage and understanding of clinical problem solving in healthcare, especially user-driven healthcare, using concerted experiential learning in conversations between multiple users and stakeholders, primarily patients, health professionals, and other actors in a care giving collaborative network across a web interface"--
"This book explores various individual user-driven strategies that assist in solving multiple clinical system problems in healthcare, using social networking to improve their healthcare outcomes"--Provided by publisher.
Provides a global discussion of the practice of user driven learning in healthcare and connected disciplines and its influence on learning through clinical problem solving. This book brings together different perspectives for researchers and practitioners to develop a comprehensive framework of user-driven healthcare.
Healthcare information systems are crucial to the effective and efficient delivery of healthcare. Healthcare Information Systems: Challenges of the New Millennium reports on the implementation of medical information systems.
Chaplin, medical director at Continental Rehabilitation Hospital in San Diego, and Terninko, a QFD trainer and consultant, explain how Quality Function Deployment (QFD) can help health care organizations adapt to the changing competitive environment by listening to customer needs and designing initiatives that will satisfy customers. Well over 100
Annotation The purpose of Creating Knowledge Based Healthcare Organizations is to bring together some high quality concepts closely related to how knowledge management can be utilised in healthcare.
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data