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Features of statistical and operational research methods and tools being used to improve the care industry With a focus on cutting-edge approaches to the quickly growing field of care, care Analytics: From Data to Knowledge to care Improvement provides an integrated and comprehensive on recent research advancements in data-driven care analytics in an effort to provide more personalized and smarter care services. Emphasizing data and care analytics from an operational management and statistical perspective, the book details how analytical methods and tools can be utilized to enhance care quality and operational efficiency. Organized into two main sections, Part I features biomedical and informatics and specifically addresses the analytics of genomic and proteomic data; physiological signals from patient-monitoring systems; data uncertainty in clinical laboratory tests; predictive modeling; disease modeling for sepsis; and the design of cyber infrastructures for early prediction of epidemic events. Part II focuses on care delivery systems, including system advances for transforming clinic workflow and patient care; macro analysis of patient flow distribution; intensive care units; primary care; demand and resource allocation; mathematical models for predicting patient readmission and postoperative outcome; physician–patient interactions; insurance cls; and the role of social media in care. care Analytics: From Data to Knowledge to care Improvement also features: • Contributions from well-known international experts who shed light on new approaches in this growing area • Discussions on contemporary methods and techniques to address the handling of rich and large-scale care data as well as the overall optimization of care system operations • Numerous real-world examples and case studies that emphasize the vast potential of statistical and operational research tools and techniques to address the big data environment within the care industry • Plentiful applications that showcase analytical methods and tools tailored for successful care systems modeling and improvement The book is an ideal reference for academics and practitioners in operations research, management science, applied mathematics, statistics, business, industrial and systems engineering, care systems, and economics. care Analytics: From Data to Knowledge to care Improvement is also appropriate for graduate-level courses typically offered within operations research, industrial engineering, business, and public departments. HUI YANG, PhD, is Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University. His research interests include sensor-based modeling and analysis of complex systems for process monitoring/control; system diagnostics/ prognostics; quality improvement; and performance optimization with special focus on nonlinear stochastic dynamics and the resulting chaotic, recurrence, self-organizing behaviors. EVA K. LEE, PhD, is Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology, Director of the Center for Operations Research in Medicine and Care, and Distinguished Scholar in System, Systems Institute at both Emory University School of Medicine and Georgia Institute of Technology. Her research interests include -risk prediction; early disease prediction and diagnosis; optimal strategies and drug delivery; care outcome analysis and prediction; public and medical preparedness; large-scale care/medical decision analysis and quality improvement; clinical translational science; and business intelligence and organization transformation.