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The first textbook on public health intelligence presents in depth the key concepts, methods, and objectives of this increasingly important competency. It systematically reviews types of evidence and data that comprise intelligence, effective techniques for assessment, analysis, and interpretation, and the role of this knowledge in quality health service delivery. The book’s learner-centered approach gives readers interactive context for mastering the processes of gathering and working with intelligence as well as its uses in informing public health decision-making. And its pragmatic framework will help establish standards for training, practice, and policy, leading to continued improvements in population health. This path-breaking resource: Offers a comprehensive, up-to-date introduction to public health intelligence, a core area of public health competency. Is suitable for both graduates’ and healthcare professionals’ training and development for national and international contexts. Helps readers apply theory to real-life scenarios, from multi-professional perspectives. Features activities, case studies, and discussion tasks for easy reader engagement. Anticipates and examines emerging developments in the field. Public Health Intelligence - Issues of Measure and Method is bedrock reading for postgraduate and advanced undergraduate students in public health, global health, health policy, health service management, nursing, medicine, statistics, epidemiology, quantitative methods, health intelligence, health inequality, and other allied healthcare fields. It is also a salient text for public health practitioners and health policymakers. "This book is a 'must-read' for students contemplating a career in Public Health or for anyone who is already in practice. The breadth of chapters from respected authors provide a detailed overview and critique of issues related to public health intelligence. A key strength of the book is that it is written with both students and practitioners in mind." Gurch Randhawa, PhD, FFPH, Professor of Diversity in Public Health & Director, Institute for Health Research, University of Bedfordshire, UK
Health surveillance and intelligence play an important role in modern health systems as more data must be collected and analyzed. It is crucial that this data is interpreted and analyzed effectively and efficiently in order to assist with diagnoses and predictions. Diagnostic Applications of Health Intelligence and Surveillance Systems is an essential reference book that examines recent studies that are driving artificial intelligence in the health sector and helping practitioners to predict and diagnose diseases. Chapters within the book focus on health intelligence and how health surveillance data can be made useful and meaningful. Covering topics that include computational intelligence, data analytics, mobile health, and neural networks, this book is crucial for healthcare practitioners, IT specialists, academicians, researchers, and students.
A NEW AND ESSENTIAL RESOURCE FOR THE PRACTICE OF EPIDEMIOLOGY AND PUBLIC HEALTH The CDC Field Epidemiology Manual is a definitive guide to investigating acute public health events on the ground and in real time. Assembled and written by experts from the Centers for Disease Control and Prevention as well as other leading public health agencies, it offers current and field-tested guidance for every stage of an outbreak investigation -- from identification to intervention and other core considerations along the way. Modeled after Michael Gregg's seminal book Field Epidemiology, this CDC manual ushers investigators through the core elements of field work, including many of the challenges inherent to outbreaks: working with multiple state and federal agencies or multinational organizations; legal considerations; and effective utilization of an incident-management approach. Additional coverage includes: � Updated guidance for new tools in field investigations, including the latest technologies for data collection and incorporating data from geographic information systems (GIS) � Tips for investigations in unique settings, including healthcare and community-congregate sites � Advice for responding to different types of outbreaks, including acute enteric disease; suspected biologic or toxic agents; and outbreaks of violence, suicide, and other forms of injury For the ever-changing public health landscape, The CDC Field Epidemiology Manual offers a new, authoritative resource for effective outbreak response to acute and emerging threats. *** Oxford University Press will donate a portion of the proceeds from this book to the CDC Foundation, an independent nonprofit and the sole entity created by Congress to mobilize philanthropic and private-sector resources to support the Centers for Disease Control and Prevention's critical health protection work. To learn more about the CDC Foundation, visit www.cdcfoundation.org.
This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an example—a truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions.
This book aims to highlight the latest achievements in epidemiological surveillance and internet interventions based on monitoring online communications and interactions on the web. It presents the state of the art and the advances in the field of online disease surveillance and intervention. The edited volume contains extended and revised versions of selected papers presented at the International World Wide Web and Population Health Intelligence (W3PHI) workshop series along with some invited chapters and presents an overview of the issues, challenges, and potentials in the field, along with the new research results. The book provides information for a wide range of scientists, researchers, graduate students, industry professionals, national and international public health agencies, and NGOs interested in the theory and practice of computational models of web-based public health intelligence.
Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. - Summarizes AI advances for use in mental health practice - Includes advances in AI based decision-making and consultation - Describes AI applications for assessment and treatment - Details AI advances in robots for clinical settings - Provides empirical data on clinical efficacy - Explores practical issues of use in clinical settings
Public Health Surveillance (PHS) is of primary importance in this era of emerging health threats like Ebola, MERS-CoV, influenza, natural and man-made disasters, and non-communicable diseases. Transforming Public Health Surveillance is a forward-looking, topical, and up-to-date overview of the issues and solutions facing PHS. It describes the realities of the gaps and impediments to efficient and effective PHS, while presenting a vision for its possibilities and promises in the 21st century. The book gives a roadmap to the goal of public health information being available, when it is needed and where it is needed. Led by Professor Scott McNabb, a leader in the field, an international team of the top-notch public health experts from academia, government, and non-governmental organizations provides the most complete and current update on this core area of public health practice in a decade in 32 chapters. This includes the key roles PHS plays in achieving the global health security agenda and health equity. The authors provide a global perspective for students and professionals in public health. Seven scenarios lay out an aid to understand the context for the lessons of the book, and a comprehensive glossary, questions, bullet points, and learning objectives make this book an excellent tool in the classroom.
This book describes the principal conceptual, methodological, and empirical developments stemming from PAHO and WHO's institutional efforts in public health, which have entailed the broad and committed participation of the Member States. It provides and overview of the status of Essential Public Health Functions (EPHF) in 41 countries and territories of the Americas, based on self-evaluation exercises performed by health authorities to measure their performance.
A Science Friday pick for book of the year, 2019 One of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.
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