Data Mining and Analytics in Healthcare Management: Applications and Tools (International Series in Operations Research & Management Science, 341) - Hardcover

Olson, David L.; Araz, Özgür M.

Hardcover

ISBN 10: 3031281128 ISBN 13: 9783031281129

Publisher: Springer, 2023

Synopsis

This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today’s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management.

Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.

About this title

About the Author

David L. Olson is the James & H.K. Stuart Professor in MIS and Chancellor’s Professor at the University of Nebraska-Lincoln, USA. He has published research in over 200 refereed journal articles and has authored over 40 books. He has served as associate editor of a number of journals and made hundreds of presentations at international and national conferences on research topics. He is a member of the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He was a Lowry Mays endowed Professor at Texas A&M University from 1999 to 2001. He was named Best Enterprise Information Systems Educator by the IFIP in 2006. He is a Fellow of the Decision Sciences Institute.

Dr. Özgür Araz is the Ron and Carol Cope Professor and Professor of Supply Chain Management and Analytics at the University of Nebraska-Lincoln, USA. His research interests include systems simulation, business analytics, healthcare operations and public health informatics. His research has been supported by the NIH, Veterans Engineering Resource Center (VERC), HDR company, Boys Town of Nebraska, Nebraska Medicine and the University of Nebraska. Before joining the College of Business at UNL, he served at the College of Public Health at the University of Nebraska Medical Center (UNMC). He received his Ph.D. in Industrial Engineering from Arizona State University and was a postdoctoral research fellow at the Center for Computational Biology and Bioinformatics of the University of Texas at Austin. He is an editorial advisory board member of the Transportation Research Part E and also serves as associate editor for Decision Sciences and IISE Transactions on Healthcare Systems Engineering. He is the Public Health Informatics Area Editor for the journal Health Systems. He is also a faculty fellow of the Nebraska Governance and Technology Center and Daugherty Water for Food Global Institute.

From the Back Cover

This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today’s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management.

Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.

Edition details

• Publisher: Springer
• Publication date: 2023
• Language: English
• ISBN 10: 3031281128
• ISBN 13: 9783031281129
• Binding: Hardcover
• Number of pages: 201