Healthcare Analytics: From Data To Knowledge To Healthcare Improvement

With practical and realworld examples from data mining to statistics to service science to decision making, this is the first mathematically rigorous treatment of healthcare analytics. Appropriate for practitioners, researchers, and students alike, this book reflects the rapid advances in health information technologies that have led to big data environments. While the various applications and approaches to extracting meaningful information from rich healthcare datasets remains limited, this book details how analytical methods and tools can be utilized to enable and assist in enhanced operational efficiency. Specifically, data mining and statistical learnin methods are used to achieve data-driven modeling and analysis; simulation and modeling techniques are utilized to illustrate disease modeling, epidemic simulation, and healthcare system simulation and optimization; service science methods are applied to healthcare operational management, resource allocation, logistics, financial policy, and quality improvement; and lastly medical decision making is discussed to showcase the various approaches to patient management, disease screening, diagnosis, monitoring, and treatment. Topical coverage includes: an introduction to healthcare; healthcare information technology; biostatistics; nonlinear dynamics in healthcare; biomedical simulation and modeling; healthcare systems simulation; simulation optimization in healthcare; personalized healthcare; healthcare logistics; patient scheduling and resource allocation in dynamic healthcare settings; healthcare organizational management and market analysis; chronic disease screening policy and treatment optimization; real-time patient monitoring, diagnostics, and prognostics; and healthcare quality assessment and improvement.

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 HealthCare, and Distinguished Scholar in Health System, Health Systems Institute at both Emory University School of Medicine and Georgia Institute of Technology. Her research interests include health-risk prediction; early disease prediction and diagnosis; optimal treatment strategies and drug delivery; healthcare outcome analysis and treatment prediction; public health and medical preparedness; large-scale healthcare/medical decision analysis and quality improvement; clinical translational science; and business intelligence and organization transformation.