Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare (Paperback)

Adeyemi Abel Ajibesin

ISBN 13: 9798337300825

Published by IGI Global, 2025

About this Item

Paperback. Healthcare systems face the challenge of delivering high-quality care while efficiently managing costs and resources. Traditional methods of performance evaluation often fall short when addressing the complex and diverse nature of healthcare operations. Data envelopment analysis (DEA) has been used to measure the efficiency of healthcare providers, but its linear, deterministic nature limits its adaptability to dynamic environments. In contrast, machine learning (ML) can handle complex, non-linear relationships and high-dimensional data, offering deeper insights and predictive capabilities. The synergy between DEA and ML presents an opportunity to overcome these limitations and drive more effective performance optimization. It leads to efficiency assessments through predictive analytics and improved resource allocation with data-driven insights and optimizing clinical pathways and decision support systems for better patient outcomes. Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare explores the integration of DEA and ML to enhance performance optimization in healthcare, improving efficiency, care quality, and resource management. It examines theoretical foundations, methodological innovations, and practical applications, providing a comprehensive resource with a key focus on development of algorithms to address challenges in healthcare optimization. Covering topics such as healthcare equipment manufacturing, human augmentation, and robotic surgery, this book is an excellent resource for hospital administrators, clinical managers, clinical decision-makers, policymakers, public health officials, professionals, researchers, scholars, academics, and more. "The objective of this book is to advance the field of healthcare performance optimization by exploring the integration of Data Envelopment Analysis (DEA) and Machine Learning (ML). The book will also explore advancements in data preprocessing, model interpretability, and the development of customized algorithms for healthcare applications"-- Provided by publisher. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9798337300825

Bibliographic Details

Title: Synergizing Data Envelopment Analysis and ...

Publisher: IGI Global

Publication Date: 2025

Binding: Paperback

Condition: new

About this title

Synopsis:

Healthcare systems face the challenge of delivering high-quality care while efficiently managing costs and resources. Traditional methods of performance evaluation often fall short when addressing the complex and diverse nature of healthcare operations. Data envelopment analysis (DEA) has been used to measure the efficiency of healthcare providers, but its linear, deterministic nature limits its adaptability to dynamic environments. In contrast, machine learning (ML) can handle complex, non-linear relationships and high-dimensional data, offering deeper insights and predictive capabilities. The synergy between DEA and ML presents an opportunity to overcome these limitations and drive more effective performance optimization. It leads to efficiency assessments through predictive analytics and improved resource allocation with data-driven insights and optimizing clinical pathways and decision support systems for better patient outcomes. Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare explores the integration of DEA and ML to enhance performance optimization in healthcare, improving efficiency, care quality, and resource management. It examines theoretical foundations, methodological innovations, and practical applications, providing a comprehensive resource with a key focus on development of algorithms to address challenges in healthcare optimization. Covering topics such as healthcare equipment manufacturing, human augmentation, and robotic surgery, this book is an excellent resource for hospital administrators, clinical managers, clinical decision-makers, policymakers, public health officials, professionals, researchers, scholars, academics, and more.