Healthcare and technology are at a convergence point where significant changes are poised to take place. The vast and complex requirements of medical record keeping, coupled with stringent patient privacy laws, create an incredibly unwieldy maze of health data needs. While the past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities that have enabled quantities of data to be accumulated, processed, and shared around the globe. Transforming Healthcare with Big Data and AI examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field. This book is the product of the Transforming Healthcare with Data conference, held at the University of Southern California. Many speakers and digital healthcare industry leaders contributed multidisciplinary expertise to chapters in this work. Authors' backgrounds range from data scientists, healthcare experts, university professors, and digital healthcare entrepreneurs. If you have an understanding of data technologies and are interested in the future of Big Data and A.I. in healthcare, this book will provide a wealth of insights into the new landscape of healthcare.

Healthcare and technology are at a convergence point where significant changes are poised to take place. The vast and complex requirements of medical record keeping, coupled with stringent patient privacy laws, create an incredibly unwieldy maze of health data needs. While the past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities that have enabled quantities of data to be accumulated, processed, and shared around the globe. Transforming Healthcare with Big Data and AI examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field. This book is the product of the Transforming Healthcare with Data conference, held at the University of Southern California. Many speakers and digital healthcare industry leaders contributed multidisciplinary expertise to chapters in this work. Authors’ backgrounds range from data scientists, healthcare experts, university professors, and digital healthcare entrepreneurs. If you have an understanding of data technologies and are interested in the future of Big Data and A.I. in healthcare, this book will provide a wealth of insights into the new landscape of healthcare.

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.

The book Digital Health Transformation with Blockchain and Artificial Intelligence covers the global digital revolution in the field of healthcare sector. The population has been overcoming the COVID-19 period; therefore, we need to establish intelligent digital healthcare systems using various emerging technologies like Blockchain and Artificial Intelligence. Internet of Medical Things is the technological revolution that has included the element of "smartness" in the healthcare industry and also identifying, monitoring, and informing service providers about the patient’s clinical information with faster delivery of care services. This book highlights the important issues i.e. (a) How Internet of things can be integrated with the healthcare ecosystem for better diagnostics, monitoring, and treatment of the patients, (b) Artificial Intelligence for predictive and preventive healthcare systems, (c) Blockchain for managing healthcare data to provide transparency, security, and distributed storage, and (d) Effective remote diagnostics and telemedicine approach for developing smart care. The book encompasses chapters belong to the blockchain, Artificial Intelligence, and Big health data technologies. Features: Blockchain and internet of things in healthcare systems Secure Digital Health Data Management in Internet of Things Public Perception towards AI-Driven Healthcare Security, privacy issues and challenges in adoption of smart digital healthcare Big data analytics and Internet of things in the pandemic era Clinical challenges for digital health revolution Artificial intelligence for advanced healthcare Future Trajectory of Healthcare with Artificial Intelligence 9 Parkinson disease pre-diagnosis using smart technologies Emerging technologies to combat the COVID-19 Machine Learning and Internet of Things in Digital Health Transformation Effective Remote Healthcare and Telemedicine Approaches Legal implication of blockchain technology in public health This Book on "Digital Health Transformation with Blockchain and Artificial Intelligence" aims at promoting and facilitating exchanges of research knowledge and findings across different disciplines on the design and investigation of secured healthcare data analytics. It can also be used as a textbook for a Masters course in security and biomedical engineering. This book will also present new methods for the medical data analytics, blockchain technology, and diagnosis of different diseases to improve the quality of life in general, and better integration into digital healthcare.

Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.

The book, Transformation in Healthcare with Emerging Technologies, presents healthcare industrial revolution based on service aggregation and virtualisation that can transform the healthcare sector with the aid of technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Bigdata and Blockchain. These technologies offer fast communication between doctors and patients, protected transactions, safe data storage and analysis, immutable data records, transparent data flow service, transaction validation process, and secure data exchanges between organizations. Features: • Discusses the Integration of AI, IoT, big data and blockchain in healthcare industry • Highlights the security and privacy aspect of AI, IoT, big data and blockchain in healthcare industry • Talks about challenges and issues of AI, IoT, big data and blockchain in healthcare industry • Includes several case studies It is primarily aimed at graduates and researchers in computer science and IT who are doing collaborative research with the medical industry. Industry professionals will also find it useful.

This book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others. This will include topics such as Artificial Intelligence of Thing (AIOT), Explainable Artificial Intelligence (XAI), Distributed learning, Blockchain of Internet of Things (BIOT), Cybersecurity, and Internet of (Medical) Things (IoTs). Healthcare providers will learn how to leverage Big Data analytics and AI as methodology for accurate analysis based on their clinical data repositories and clinical decision support. The capacity to recognize patterns and transform large amounts of data into usable information for precision medicine assists healthcare professionals in achieving these objectives. Intelligent Health has the potential to monitor patients at risk with underlying conditions and track their progress during therapy. Some of the greatest challenges in using these technologies are based on legal and ethical concerns of using medical data and adequately representing and servicing disparate patient populations. One major potential benefit of this technology is to make health systems more sustainable and standardized. Privacy and data security, establishing protocols, appropriate governance, and improving technologies will be among the crucial priorities for Digital Transformation in Healthcare.