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

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.