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.

Healthcare management involves many decisions. Physicians typically have a variety of potential treatments to select from. There are also important decisions involved in resource planning, such as identifying hospital capacity and hiring requirements. Healthcare decisions discussed in the book include:

• Selection of patient treatment

• Design of facility capacity

• Utilization of resources

• Personnel management

• Analysis of hospital stay

• Annual disease progression

• Comorbidity patterns

• Patient survival rates

• Disease-specific analysis

• Overbooking analysis

The book begins with a discussion of healthcare issues, discussed knowledge management, and then focused on analytic tools useful in healthcare management.

Title: Data Mining and Analytics in Healthcare Management: Applications and Tools

Author: David L. Olson, Özgür M. Araz

Publisher: Springer

Year: 2023

Edition: – Edition

Language: English

Pages: 195

Ebook: PDF

File size: 6 MB

ISBN Number: 3031281128, 9783031281129

CBID: CBM336