Chapter 8 Machine learning models for cost-effective healthcare delivery systems: A global perspective
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Sukhmani Kaur Thethi
Abstract
This book chapter provides a comprehensive overview of the utilization of machine learning (ML) models in cost-effective healthcare delivery systems across various countries, including developed, developing, and underdeveloped nations. By harnessing the power of data and advanced analytics, machine learning models offer promising solutions to optimize resource allocation, enhance patient outcomes, and streamline healthcare operations. The chapter explores the diverse applications of machine learning models in healthcare delivery systems. Predictive analytics aids in patient risk stratification, enabling proactive interventions and preventive measures. Resource allocation optimization models enhance operational efficiency by analyzing historical data and predicting future demand for healthcare resources. Fraud detection algorithms help identify and prevent healthcare fraud, minimizing financial losses. Personalized treatment recommendation systems leverage patient data, genetics, and treatment outcomes to deliver tailored interventions, reducing unnecessary treatments and associated costs. Demand forecasting models optimize pharmaceutical supply chain management, ensuring adequate inventory levels and minimizing waste. Remote monitoring systems, powered by machine learning, enable early detection of patient deterioration, preventing costly hospital admissions. While developed nations may focus on integrating electronic health records and centralized patient identity systems, developing and underdeveloped countries face unique challenges. The chapter addresses these challenges by proposing strategies that include leveraging existing government schemes, employing mobile and voicebased interfaces, and utilizing community health workers. Partnerships between governments, healthcare organizations, technology companies, and research institutions are crucial to drive the implementation of Artificial Intelligence (AI) solutions in healthcare systems. Ethical considerations, like privacy, fairness, and transparency, are emphasized throughout the chapter. The effective incorporation of ML models necessitates cooperation among healthcare practitioners, data analysts, decision-makers, and concerned parties, guaranteeing the ethical and fair utilization of AI within the healthcare sector. Hence, this chapter underscores the global relevance of ML models in cost-efficient healthcare delivery systems. When tailoring the methods and remedies to the unique circumstances of individual countries, policymakers and healthcare experts can leverage the capabilities of AI to enhance healthcare accessibility, elevate patient results, and streamline resource distribution within a wide array of healthcare settings.
Abstract
This book chapter provides a comprehensive overview of the utilization of machine learning (ML) models in cost-effective healthcare delivery systems across various countries, including developed, developing, and underdeveloped nations. By harnessing the power of data and advanced analytics, machine learning models offer promising solutions to optimize resource allocation, enhance patient outcomes, and streamline healthcare operations. The chapter explores the diverse applications of machine learning models in healthcare delivery systems. Predictive analytics aids in patient risk stratification, enabling proactive interventions and preventive measures. Resource allocation optimization models enhance operational efficiency by analyzing historical data and predicting future demand for healthcare resources. Fraud detection algorithms help identify and prevent healthcare fraud, minimizing financial losses. Personalized treatment recommendation systems leverage patient data, genetics, and treatment outcomes to deliver tailored interventions, reducing unnecessary treatments and associated costs. Demand forecasting models optimize pharmaceutical supply chain management, ensuring adequate inventory levels and minimizing waste. Remote monitoring systems, powered by machine learning, enable early detection of patient deterioration, preventing costly hospital admissions. While developed nations may focus on integrating electronic health records and centralized patient identity systems, developing and underdeveloped countries face unique challenges. The chapter addresses these challenges by proposing strategies that include leveraging existing government schemes, employing mobile and voicebased interfaces, and utilizing community health workers. Partnerships between governments, healthcare organizations, technology companies, and research institutions are crucial to drive the implementation of Artificial Intelligence (AI) solutions in healthcare systems. Ethical considerations, like privacy, fairness, and transparency, are emphasized throughout the chapter. The effective incorporation of ML models necessitates cooperation among healthcare practitioners, data analysts, decision-makers, and concerned parties, guaranteeing the ethical and fair utilization of AI within the healthcare sector. Hence, this chapter underscores the global relevance of ML models in cost-efficient healthcare delivery systems. When tailoring the methods and remedies to the unique circumstances of individual countries, policymakers and healthcare experts can leverage the capabilities of AI to enhance healthcare accessibility, elevate patient results, and streamline resource distribution within a wide array of healthcare settings.
Chapters in this book
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XIII
- Chapter 1 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 1
- Chapter 2 Introduction to industry’s fourth revolution and its impacts on healthcare 33
- Chapter 3 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 67
- Chapter 4 E-health services and applications: A technological paradigm shift 101
- Chapter 5 Breaking down walls: The influence of virtual reality on accessible healthcare delivery 129
- Chapter 6 Digital twins and dietary health technologies: Applying the capability approach 165
- Chapter 7 Big Data analytics in healthcare system: A systematic review approach 185
- Chapter 8 Machine learning models for cost-effective healthcare delivery systems: A global perspective 199
- Chapter 9 Machine learning models for cost-effective healthcare delivery systems 245
- Chapter 10 Enhancing biomedical signal processing with machine learning: A comprehensive review 277
- Chapter 11 Data-driven AI for information retrieval of biomedical images 307
- Index 331
Chapters in this book
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XIII
- Chapter 1 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 1
- Chapter 2 Introduction to industry’s fourth revolution and its impacts on healthcare 33
- Chapter 3 The Fourth Industrial Revolution: A paradigm shift in healthcare delivery and management 67
- Chapter 4 E-health services and applications: A technological paradigm shift 101
- Chapter 5 Breaking down walls: The influence of virtual reality on accessible healthcare delivery 129
- Chapter 6 Digital twins and dietary health technologies: Applying the capability approach 165
- Chapter 7 Big Data analytics in healthcare system: A systematic review approach 185
- Chapter 8 Machine learning models for cost-effective healthcare delivery systems: A global perspective 199
- Chapter 9 Machine learning models for cost-effective healthcare delivery systems 245
- Chapter 10 Enhancing biomedical signal processing with machine learning: A comprehensive review 277
- Chapter 11 Data-driven AI for information retrieval of biomedical images 307
- Index 331
