Improving Security, Privacy, and Connectivity Among Telemedicine Platforms


Book Description

The digital transformation of the health sector consistently presents unique challenges. As technologies like artificial intelligence, big data, and telemedicine rapidly evolve, healthcare systems need to keep up with advancements and data protection. This rapid evolution, compounded by the complexities of managing patient data and ensuring cybersecurity, creates a daunting task for healthcare providers and policymakers. The COVID-19 pandemic has also highlighted the urgent need for digital solutions, amplifying the pressure on an already strained sector. Improving Security, Privacy, and Connectivity Among Telemedicine Platforms is a comprehensive guide to navigating the digital revolution in healthcare. It offers insights into identifying vital digital technologies and understanding their impact on the Health Value Chain. Through an analysis of empirical evidence, this book provides a roadmap for effectively managing change, transition, and digital value creation in healthcare. With a focus on business sustainability, change management, and cybersecurity, it equips scholars, researchers, and practitioners with the tools needed to thrive in a rapidly evolving digital landscape.




The New Era of Precision Medicine


Book Description

The New Era of Precision Medicine: What it Means for Patients and the Future of Healthcare highlights the aspects of precision medicine in different specialties and offers an understanding of how a biological background integrates into clinical guidelines, the therapeutic efficacy of interventions, and disease prevention. The book explains how precision healthcare differs among countries, as well as how there is a collaboration among many laboratories to share resources and knowledge to advance the field across the globe. The book also discusses the cultural differences and cultural sensitivity that may be involved in the precision medicine approach. Finally, with regard to safety and quality outcomes, the book presents a range of current and possible future concerns related to those outcomes. Precision medicine is the new standard of quality healthcare delivery. It aims to optimize patient safety and clinical outcomes, enhance the efficacy of therapeutic interventions, and facilitate disease prevention, offering a way to customize patient care, decision-making, and clinical practice.




Artificial Intelligence in Healthcare


Book Description

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




Artificial Intelligence in Medicine


Book Description

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.




Improving Diagnosis in Health Care


Book Description

Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€"has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.




The Ethics of Biomedical Big Data


Book Description

This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. ‘Biomedical Big Data’ refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understanding of the ethical conundrums posed by biomedical Big Data, and shows how practitioners and policy-makers can address these issues going forward.




Beyond the HIPAA Privacy Rule


Book Description

In the realm of health care, privacy protections are needed to preserve patients' dignity and prevent possible harms. Ten years ago, to address these concerns as well as set guidelines for ethical health research, Congress called for a set of federal standards now known as the HIPAA Privacy Rule. In its 2009 report, Beyond the HIPAA Privacy Rule: Enhancing Privacy, Improving Health Through Research, the Institute of Medicine's Committee on Health Research and the Privacy of Health Information concludes that the HIPAA Privacy Rule does not protect privacy as well as it should, and that it impedes important health research.




Relieving Pain in America


Book Description

Chronic pain costs the nation up to $635 billion each year in medical treatment and lost productivity. The 2010 Patient Protection and Affordable Care Act required the Department of Health and Human Services (HHS) to enlist the Institute of Medicine (IOM) in examining pain as a public health problem. In this report, the IOM offers a blueprint for action in transforming prevention, care, education, and research, with the goal of providing relief for people with pain in America. To reach the vast multitude of people with various types of pain, the nation must adopt a population-level prevention and management strategy. The IOM recommends that HHS develop a comprehensive plan with specific goals, actions, and timeframes. Better data are needed to help shape efforts, especially on the groups of people currently underdiagnosed and undertreated, and the IOM encourages federal and state agencies and private organizations to accelerate the collection of data on pain incidence, prevalence, and treatments. Because pain varies from patient to patient, healthcare providers should increasingly aim at tailoring pain care to each person's experience, and self-management of pain should be promoted. In addition, because there are major gaps in knowledge about pain across health care and society alike, the IOM recommends that federal agencies and other stakeholders redesign education programs to bridge these gaps. Pain is a major driver for visits to physicians, a major reason for taking medications, a major cause of disability, and a key factor in quality of life and productivity. Given the burden of pain in human lives, dollars, and social consequences, relieving pain should be a national priority.




AI and education


Book Description

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]