Health Data Analytics And Informatics
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With the advent of big data analytics and informatics, and its associated technologies, a rapid growth in career opportunities, coupled with continual technological innovation, has led to an increasing need for qualified professionals who are able to analyse big data and manage technology and information in the healthcare industry. This book is designed to help you meet that need by providing you with a detailed overview of topics and to explore areas of interest pertaining to data analytics and informatics in the healthcare and biomedical ecosystem. The main topics covered in this book includ...
With the advent of big data analytics and informatics, and its associated technologies, a rapid growth in career opportunities, coupled with continual technological innovation, has led to an increasing need for qualified professionals who are able to analyse big data and manage technology and information in the healthcare industry. This book is designed to help you meet that need by providing you with a detailed overview of topics and to explore areas of interest pertaining to data analytics and informatics in the healthcare and biomedical ecosystem. The main topics covered in this book include: data analytics and healthcare informatics; artificial intelligence and machine learning technologies; biomedical sensors and trackers; digital clinical trials; high-definition medicine; precision medicine; connected health; how data analytics and informatics can transform healthcare; reflections and perspectives for the future. The book will appeal to people who are interested in the ways in which health data and technology can be used to enhance the quality of health care. Healthcare informatics is a multi-disciplinary field suited for innovators, healthcare workers, and non-healthcare professionals, united in the goal of improving the quality of health care. Healthcare informatics involves the acquisition, storage and retrieval of healthcare information and aims to ensure the availability of critical data enable the making of sound policies and programmatic decisions to improve patient care across interactions with the health system. Big Data analytics covers collection, manipulation, and analyses of massive, diverse data sets that contain a variety of data types such as electronic health records (EHRs) to reveal hidden patterns, cryptic correlations, and other intuitions on a Big Data infrastructure Due to its effectiveness, Big Data analytics is widely used in various fields. Biomedical big data analytics is in the initial adoption phase, and many healthcare organisations want to implement big data analytics to obtain its benefits. To succeed, big data analytics in the healthcare and biomedical ecosystem needs to be packaged so it is menu-driven, user-friendly, real-time and transparent. Artificial intelligence (AI) and Big Data analytics are seen as novel tools in the planning of health services as well as identifying and monitoring health problems in individuals and populations. Automation's major benefit is that it helps medical personnel in processing large amounts of patient's data, especially when taking into consideration that medical personnel are often overwhelmed by a series of healthcare tasks. There is potential in delivering more targeted, wide-reaching, and cost-efficient healthcare by exploiting current big data trends and technologies. The slow pace of innovation in the healthcare industry reflects challenges that are unique to healthcare in implementing and applying sophisticated big data analytics tools, and this points to the need for federal or government policy to emphasise interoperability of health data and prioritise payment reforms that will encourage providers to develop data analytics capabilities. Salient guidelines and important implications for practitioners and implementers of big data analytics systems that can assist with successful adoption of big data analytics systems in the healthcare system need to be developed. Describing the crucial factors that are required for understanding is important prior to creating a strategy for the acceptance of big data analytics in the healthcare industry, particularly in low- and middle-income countries, where the industry requires filling the gap of big data analytics adoption.
