Focuses on differences in health and health care as linked to important social factors. This title includes six articles looking at racial disparities on a variety of topics such as: knowledge of hepatitis C Virus; health services received and patients' experiences in seeking health care; use of CAM services; and, more.

There is widespread acceptance that much of the developed world faces a potential pensions and welfare crisis as a result of declining birth rates and an ageing population. However, there is considerable uncertainty about the specifics of demographic forecasting and this has significant implications for public finances. Uncertain Demographics and Fiscal Sustainability addresses the economic consequences of uncertainty and, with particular reference to European economies, explores the impact of demographic risks on public finances, including pension systems, health care and old-age care expenditures. Covering a spectrum of theoretical and empirical approaches, different types of computational models are used to demonstrate not only the magnitudes of the uncertainties involved but also how these can be addressed through policy initiatives. The book is divided into four parts covering demographic, measurement, policy and methodological issues. Each part is followed by a discussion essay that draws out key elements and identifies common themes.

Within two volumes, more than 400 signed entries and their associated bibliographies and recommended readings authoritatively cover issues in both the historical and contemporary context of health services research.

Healthcare Management takes a look at international perspectives in healthcare management and the way regional priorities, national income, and social factors are crucial to effective healthcare services. Readers are provided the skills to address issues and solve problems as a healthcare manager by understanding and appreciating the complex interrelationships of global health provision. The book compares and contrasts different healthcare systems, examining the role of policymaking, health financing, healthcare beyond hospitals, leadership, risk management, and quality. A range of international case studies provide the opportunity to see how different theories work in practice. This comprehensive book is suitable for students and professionals undertaking healthcare management courses.

This collection of critical ideas relating organization science to operations and accomplishments in the health care environment provides a thematic guide for leaders, practitioners, academics and administrators. It pulls in a broad cross-section of perspectives on the important linkage of scholarship and practice with a solid global perspective.

Introduces various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges facing society Handbook of Real-World Applications in Modeling and Simulation provides a thorough explanation of modeling and simulation in the most useful, current, and predominant applied areas of transportation, homeland security, medicine, operational research, military science, and business modeling. Offering a cutting-edge and accessible presentation, this book discusses how and why the presented domains have become leading applications of modeling and simulation techniques. Contributions from leading academics and researchers integrate modeling and simulation theories, methods, and data to analyze challenges that involve technological and social issues. The book begins with an introduction that explains why modeling and simulation is a reliable analysis assessment tool for complex systems problems. Subsequent chapters provide an orientation to various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges across real-world applied domains. Additionally, the handbook: Provides a practical one-stop reference on modeling and simulation and contains an accessible introduction to key concepts and techniques Introduces, trains, and prepares readers from statistics, mathematics, engineering, computer science, economics, and business to use modeling and simulation in their studies and research Features case studies that are representative of fundamental areas of multidisciplinary studies and provides a concise look at the key concepts of modeling and simulation Contains a collection of original ideas on modeling and simulation to help academics and practitioners develop a multifunctional perspective Self-contained chapters offer a comprehensive approach to explaining each respective domain and include sections that explore the related history, theory, modeling paradigms, and case studies. Key terms and techniques are clearly outlined, and exercise sets allow readers to test their comprehension of the presented material. Handbook of Real-World Applications in Modeling and Simulation is an essential reference for academics and practitioners in the areas of operations research, business, management science, engineering, statistics, mathematics, and computer science. The handbook is also a suitable supplement for courses on modeling and simulation at the graduate level.

Nurses have a unique role in redefining the way we view partnerships in healthcare— Transitioning from individualized to family-focused care is not only advocated by the Institute of Medicine; it’s becoming a way of life. Families want their perspectives and choices for their loved ones to be heard.

OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application; emphasizes validating and evaluating predictive models; provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare; highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.

The goal of this proposed Research Topic is to encourage researchers in this field from different jurisdictions to use this as a platform to share their experiences in conducting studies designed in a patient-centric approach using RWD, their encountered areas of concern during the process, and the anticipated impact of their studies.