Bøker i SpringerBriefs in Service Science-serien

Addressing the entire care chain, this book presents the outcomes of advanced research on healthcare operations management based on real-world data and practices in China. It includes hands-on methods and applications in this interdisciplinary research field, which combines healthcare service, operations management, industrial engineering and information technology.The content is divided into three parts, reflecting the entire care chain. The first part discusses the pre-hospital service stage and explores resource deployment problems in emergency medical service, such as ambulance allocation. The second part focuses on inpatient care services, including staffing and task allocation among nurses and doctors based on multi-project management under uncertainties. In addition, a highly promising diagnosis approach is proposed and a specific algorithm is derived on the basis of real-world datasets which can improve the diagnosis accuracy remarkably. In turn, the third part considers the post-hospital service stage, which most often takes place at community hospitals, and provides a quantitative evaluation and optimization of scheduling for tasks and team members for home care services.The book is intended for a broad audience, including students, researchers and practitioners working in various areas of healthcare management, service management, and operations management.

By incorporating the latest advancement in complex system modeling and simulation into the service system research, this book makes a valuable contribution to this field that will lead service innovation and service management toward the digital twin and metaverse. It covers important topics such as computational experiments and parallel execution of a parallel service system, the modeling of artificial service systems, semi-parallel service systems, parallel service, and digital twin/metaverse. It also provides a unified framework for realizing a parallel service system that demonstrates the capabilities or potentials of adopting digital twin and metaverse.In addition, the book contains numerous solutions to real-world problems, through which both academic readers and practitioners will gain new perspectives on service systems, and learn how to model a parallel service system or how to use the model to analyze and understand the behaviors of the system. For academic readers, it sheds light on a new research direction within the service science/engineering domain made possible by the latest technologies. For practitioners, with the help of methods such as Agent-based Modeling and Simulation, the book will enable them to enhance their skills in designing or analyzing a service system.

This book introduces the most advanced and recent theoretical research on innovative priority mechanisms in service settings. It covers cutting-edge topics on service innovations such as line-sitting, service-position-trading, referral priority programs, queue-scalping, distance-based priority, and dynamic priority policy. It also contains a variety of practical examples and applications which help managers to make better decisions and to develop a coherent business strategy.This book appeals to a wide readership, from academics and Ph.D. students who are interested in priority mechanisms, to service managers and researchers in the service industry.This is an open access book.

This book reviews the development of physics-based modeling and sensor-based data fusion for optimizing medical decision making in connection with spatiotemporal cardiovascular disease processes. To improve cardiac care services and patients¿ quality of life, it is very important to detect heart diseases early and optimize medical decision making. This book introduces recent research advances in machine learning, physics-based modeling, and simulation optimization to fully exploit medical data and promote the data-driven and simulation-guided diagnosis and treatment of heart disease. Specifically, it focuses on three major topics: computer modeling of cardiovascular systems, physiological signal processing for disease diagnostics and prognostics, and simulation optimization in medical decision making. It provides a comprehensive overview of recent advances in personalized cardiac modeling by integrating physics-based knowledge of the cardiovascular system with machine learning and multi-source medical data. It also discusses the state-of-the-art in electrocardiogram (ECG) signal processing for the identification of disease-altered cardiac dynamics. Lastly, it introduces readers to the early steps of optimal decision making based on the integration of sensor-based learning and simulation optimization in the context of cardiac surgeries. This book will be of interest to researchers and scholars in the fields of biomedical engineering, systems engineering and operations research, as well as professionals working in the medical sciences.