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\"This new volume explores how the merging of interactive multimedia with artificial intelligence has created new and advanced tools in healthcare. It looks at how the latest technologies (artificial intelligence, deep learning, machine learning, big data, IoT, smart device, etc.) help to manage health data, diagnose health issues, monitor treatment, predict pandemic diseases, and more. The book covers several important applications of multimedia in healthcare, including for data visualization purposes, for computer vision for elder healthcare monitoring, for detection of lung nodules, for health monitoring and management systems using machine learning techniques, and for fusion applications in medical image processing. The book goes into detail on the various methods and techniques for supporting multimedia systems for e-healthcare. The chapter authors discuss using data mining and machine learning techniques in the context of COVID-19 diagnosis and prediction, in detecting knee osteoarthritis using texture descriptor algorithms, in applying algorithms in fetal ECG enhancement using blockchain for wearable internet of things in healthcare, and more. A chapter also reviews how doctors can make good use of genomics and genetic data through advanced technology. The book concludes with discussions of open issues, challenges, and future research directions for using intelligent interactive multimedia in healthcare. Key features Provides an in-depth understanding of emerging technologies and integration of artificial intelligence, deep learning, big data, IoT in healthcare Details specific applications for the use of AI, big data, and IoT in healthcare Discusses how AI technology can help in formulating protective measures for COVID-19 and other diseases Includes case studies Intelligent Interactive Multimedia Systems for e-Healthcare Applications will be valuable to undergraduate and graduate students planning their careers in either industry or research and to software engineers for using multimedia with artificial intelligence, deep learning, big data, and IoT for healthcare applications\"-- Provided by publisher.

This book explores the ethical governance of Artificial Intelligence (AI) & Machine Learning (ML) in healthcare. AI/ML usage in healthcare as well as our daily lives is not new. However, the direct, and oftentimes long-term effects of current technologies, in addition to the onset of future innovations, have caused much debate about the safety of AI/ML. On the one hand, AI/ML has the potential to provide effective and efficient care to patients, and this sways the argument in favor of continuing to use AI/ML; but on the other hand, the dangers (including unforeseen future consequences of the further development of the technology) leads to vehement disagreement with further AI/ML usage. Due to its potential for beneficial outcomes, the book opts to push for ethical AI/ML to be developed and examines various areas in healthcare, such as big data analytics and clinical decision-making, to uncover and discuss the importance of developing ethical governance for AI/ML in this setting.

\"Implementing new information technologies into the healthcare sector can provide alternatives to managing patients' health records, systems, and improving the quality of care received. This book provides an overview of IoT technologies related to the healthcare field and covers the main advantages and disadvantages along with industry case studies\"-- Provided by publisher.

Real-life examples of how to apply intelligence in the healthcare industry through innovative analytics Healthcare analytics offers intelligence for making better healthcare decisions. Identifying patterns and correlations contained in complex health data, analytics has applications in hospital management, patient records, diagnosis, operating and treatment costs, and more. Helping healthcare managers operate more efficiently and effectively. Transforming Healthcare Analytics: The Quest for Healthy Intelligence shares real-world use cases of a healthcare company that leverages people, process, and advanced analytics technology to deliver exemplary results. This book illustrates how healthcare professionals can transform the healthcare industry through analytics. Practical examples of modern techniques and technology show how unified analytics with data management can deliver insight-driven decisions. The authors—a data management and analytics specialist and a healthcare finance executive—share their unique perspectives on modernizing data and analytics platforms to alleviate the complexity of the healthcare, distributing capabilities and analytics to key stakeholders, equipping healthcare organizations with intelligence to prepare for the future, and more. This book: * Explores innovative technologies to overcome data complexity in healthcare * Highlights how analytics can help with healthcare market analysis to gain competitive advantage * Provides strategies for building a strong foundation for healthcare intelligence * Examines managing data and analytics from end-to-end, from diagnosis, to treatment, to provider payment * Discusses the future of technology and focus areas in the healthcare industry Transforming Healthcare Analytics: The Quest for Healthy Intelligence is an important source of information for CFO's, CIO, CTO, healthcare managers, data scientists, statisticians, and financial analysts at healthcare institutions.

\"Artificial Intelligence (AI) in general and machine learning (ML) and deep learning (DL) in particular and related digital technologies are a couple of fledging paradigms that the next generation healthcare services are sprouting towards. These digital technologies can transform various aspects of healthcare, leveraging advances in computing and communication power. With a new spectrum of business opportunities, AI-powered healthcare services would improve the lives of patients, their families, and societies. However, the application of AI in the healthcare field requires special attention given the direct implication with human life and well-being. Rapid progress in AI leads to the possibility of exploiting healthcare data for designing practical tools for automated diagnosis of chronic diseases such as dementia and diabetes. This book highlights the current research trends in applying AI models in various disease diagnoses and prognoses to provide enhanced healthcare solutions. The primary audience of the book will be postgraduate students and researchers in the broad domain of healthcare technologies\"-- Provided by publisher.

Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Acknowledgments -- Disclaimer -- Foreword -- Chapter 1 Introduction -- Purpose of This Book -- Health Data Defined -- Healthcare Challenges and Focus Areas -- Audience for This Book -- How to Read This Book -- State of Healthcare -- Health Technology -- Health Research -- Medical Procedures -- Growth in Healthcare -- Healthcare Data -- Multifaceted and Siloed Data -- Unstructured Data -- Strict Regulations -- Value of Analytics -- Chapter 2 People -- The Ability to Produce Results -- What Types of Roles? -- What Job Roles Do You Need? -- What Capabilities Do You Need? -- Organizational Structures for your Company -- Roles to Execute your Data and Analytics Strategy -- Challenges to Getting the Right People -- The Ability to Consume Results -- The \"Real Analytical Employee Cost\" to an Organization -- Building a Resource Library -- Conclusion -- Chapter 3 Process -- What is Culture? -- What is Design Thinking? -- What is Lean? -- What is Agile? -- Design Thinking, Lean, and Agile Definitions -- Creating a Data Management and Analytics Process Framework -- Changing the Analytics Journey -- What is the current process? -- Chapter 4 Technology -- Status Quo -- In-Database Processing -- Cost of In-Database Processing -- In-Memory Processing -- Need for Speed -- Benefits with In-Memory Processing -- How to Get Started -- Requirements -- Cost of In-Memory Processing -- Open Source Technology -- Hadoop -- Spark -- Data Preparation and Integration -- In-Stream Processing -- Prescriptive Analytics -- Interactive Insights -- Python -- R -- Open Source Best Practices -- Chapter 5 Unifying People, Process, and Technology -- People Use Case - Delivering Primary Care Predictive Risk Model -- Process Use Case - Rate Realization Model.

\"This book provides applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles that various techniques such as machine learning, fuzzy logic, and statistical mathematics play in the advancements of smart healthcare systems. The book presents the basics as well as the advanced concepts to help beginners, as well as industry professionals get up to speed on the latest developments in healthcare systems. The book will examine descriptive, predictive, and social network techniques, as well as provide a discussion on analytical tools and the important role they play in finding solutions to problems in healthcare systems\"-- Provided by publisher.

Many promising technological innovations in health and social care are characterized by nonadoption or abandonment by individuals or by failed attempts to scale up locally, spread distantly, or sustain the innovation long term at the organization or system level. Our objective was to produce an evidence-based, theory-informed, and pragmatic framework to help predict and evaluate the success of a technology-supported health or social care program. The study had 2 parallel components: (1) secondary research (hermeneutic systematic review) to identify key domains, and (2) empirical case studies of technology implementation to explore, test, and refine these domains. We studied 6 technology-supported programs-video outpatient consultations, global positioning system tracking for cognitive impairment, pendant alarm services, remote biomarker monitoring for heart failure, care organizing software, and integrated case management via data sharing-using longitudinal ethnography and action research for up to 3 years across more than 20 organizations. Data were collected at micro level (individual technology users), meso level (organizational processes and systems), and macro level (national policy and wider context). Analysis and synthesis was aided by sociotechnically informed theories of individual, organizational, and system change. The draft framework was shared with colleagues who were introducing or evaluating other technology-supported health or care programs and refined in response to feedback. The literature review identified 28 previous technology implementation frameworks, of which 14 had taken a dynamic systems approach (including 2 integrative reviews of previous work). Our empirical dataset consisted of over 400 hours of ethnographic observation, 165 semistructured interviews, and 200 documents. The final nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework included questions in 7 domains: the condition or illness, the technology, the value proposition, the adopter system (comprising professional staff, patient, and lay caregivers), the organization(s), the wider (institutional and societal) context, and the interaction and mutual adaptation between all these domains over time. Our empirical case studies raised a variety of challenges across all 7 domains, each classified as simple (straightforward, predictable, few components), complicated (multiple interacting components or issues), or complex (dynamic, unpredictable, not easily disaggregated into constituent components). Programs characterized by complicatedness proved difficult but not impossible to implement. Those characterized by complexity in multiple NASSS domains rarely, if ever, became mainstreamed. The framework showed promise when applied (both prospectively and retrospectively) to other programs. Subject to further empirical testing, NASSS could be applied across a range of technological innovations in health and social care. It has several potential uses: (1) to inform the design of a new technology; (2) to identify technological solutions that (perhaps despite policy or industry enthusiasm) have a limited chance of achieving large-scale, sustained adoption; (3) to plan the implementation, scale-up, or rollout of a technology program; and (4) to explain and learn from program failures.

\"The main aim of Healthcare 4.0: Health Informatics and Precision Data Management is to improve the services given by the healthcare industry and to bring meaningful patient outcomes, Informatics involved by applying the data, information and knowledge in the healthcare domain. The precise focus of this handbook will be on the potential applications and use of data informatics in area of healthcare, including clinical trials, tailored ailment data, patient and ailment record characterization and health records management\"-- Provided by publisher.

A team of RAND Corporation researchers projected in 2005 that rapid adoption of health information technology (IT) could save the United States more than $81 billion annually. Seven years later the empirical data on the technology's impact on health care efficiency and safety are mixed, and annual health care expenditures in the United States have grown by $800 billion. In our view, the disappointing performance of health IT to date can be largely attributed to several factors: sluggish adoption of health IT systems, coupled with the choice of systems that are neither interoperable nor easy to use; and the failure of health care providers and institutions to reengineer care processes to reap the full benefits of health IT. We believe that the original promise of health IT can be met if the systems are redesigned to address these flaws by creating more-standardized systems that are easier to use, are truly interoperable, and afford patients more access to and control over their health data. Providers must do their part by reengineering care processes to take full advantage of efficiencies offered by health IT, in the context of redesigned payment models that favor value over volume. [PUBLICATION ABSTRACT]