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Handbook of Healthcare Operations Management
From the Preface: Collectively, the chapters in this book address application domains including inpatient and outpatient services, public health networks, supply chain management, and resource constrained settings in developing countries. Many of the chapters provide specific examples or case studies illustrating the applications of operations research methods across the globe, including Africa, Australia, Belgium, Canada, the United Kingdom, and the United States. Chapters 1-4 review operations research methods that are most commonly applied to health care operations management including: queuing, simulation, and mathematical programming. Chapters 5-7 address challenges related to inpatient...
Artificial Intelligence for Healthcare
This overview of interdisciplinary research partnerships applying AI, IE, and OR in societal and operational problems in a variety of healthcare settings highlights how engineering has contributed to medical knowledge, health system operations, and behavioral health. Chapter authors include doctors, policy-makers, social scientists, and engineers.
Handbook of Healthcare System Scheduling
This edited volume captures and communicates the best thinking on how to improve healthcare by improving the delivery of services -- providing care when and where it is needed most -- through application of state-of-the-art scheduling systems. Over 12 chapters, the authors cover aspects of setting appointments, allocating healthcare resources, and planning to ensure that capacity matches needs for care. A central theme of the book is increasing healthcare efficiency so that both the cost of care is reduced and more patients have access to care. This can be accomplished through reduction of idle time, lessening the time needed to provide services and matching resources to the needs where they...
Markov Decision Processes in Practice
This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach. The book is divided into six parts. Part 1 is devoted to the state-of-the-art theoretical foundation of MDP, including approximate methods such as policy improvement, successive approximation and infinite state spaces as well as an instructive chapter on Approximate Dynamic Programming. It then continues with five parts of specific and non-exhaustive application areas. Part 2 covers MDP healthcare appl...
Challenges of Healthcare Systems in the Era of COVID-19
In light of the Covid 19 pandemic and its impact on healthcare systems, this book examines health care innovations and service management models and discusses significant reforms and organizational and managerial changes in the healthcare systems of countries in the Balkans and Eastern Europe. It features contributions that shed new light on the impact of the pandemic on healthcare organizations and the lessons that can be drawn from this crisis for management practice. The book covers topics such as best practices in healthcare delivery, healthcare management, process and product innovation, digitization and information technologies, healthcare governance, collaborative healthcare experiences and networks, healthcare financing, and healthcare policy reform. It is aimed at scholars and practitioners in healthcare, as well as anyone interested in innovation in healthcare services and management.
Handbook of Healthcare Delivery Systems
With rapidly rising healthcare costs directly impacting the economy and quality of life, resolving improvement challenges in areas such as safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity has become paramount. Using a system engineering perspective, Handbook of Healthcare Delivery Systems offers theoretical foundation
Handbook of Matching and Weighting Adjustments for Causal Inference
An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.
Decision Analytics and Optimization in Disease Prevention and Treatment
A systematic review of the most current decision models and techniques for disease prevention and treatment Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource of the most current decision models and techniques for disease prevention and treatment. With contributions from leading experts in the field, this important resource presents information on the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology. Designed to be accessible, in each chapter the text presents one decision problem with the related methodology to showcase the vast applicability of operation...
Machine Learning, Optimization, and Data Science
This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
