Full Description
This title demystifies artificial intelligence (AI) and analytics, upskilling individuals (healthcare professionals, hospital managers, consultants, researchers, students, and the population at large) around analytics and AI as it applies to healthcare.
This book shows how the tools, techniques, technologies, and tactics around analytics and AI can be best leveraged and utilised to realise a healthcare value proposition of better quality, better access and high value for everyone every day, everywhere. The book presents a triumvirate approach including technical, business and medical aspects of data and analytics and by so doing takes a responsible approach to this key area.
This work serves to introduce the critical issues in AI and analytics for healthcare to students, practitioners, and researchers.
Contents
Section I: Technical Considerations. 1. Medical Image Processing. 2. Smart Wearables in Healthcare. 3. Causal AI in Personalised Healthcare. 4. Interpretable AI in Healthcare. Section II: Management Perspectives. 5. Data Ownership and Emerging Data Governance Models in Healthcare. 6. Privacy-Preserving Roadmap for Medical Data-Sharing Systems. 7. A Comparative Review of Descriptive Process Models in Healthcare Operations Management and Analytics. 8. AI Approaches for Managing Preventive Care in Digital Health Ecosystems. 9. Competitive Intelligence in Healthcare. Section III: Clinical Applications. 10. Machine Learning for Healthcare Applications: Possibilities and Barriers. 11. A Systematic Review of Prediction Models for Chronic Opioid Use Following Surgery. 12. Addressing Challenges in the Emergency Department with Analytics and AI. 13. Using Simulators to Assist with Mental Health Issues: The Impact of a Sailing Simulator on People with ADHD. 14. A Possible Blockchain Architecture for Healthcare: Insights from Catena-X. Section IV: Human Factors. 15. Implications and Considerations of AI for the Healthcare Workforce: A Theoretical Perspective. 16. Unplanned Readmission Risks for Comorbid Patients of Diabetes: An Action Design Research Paradigm Data-Driven Decision Support. 17. Establishing a Digital Twin Architecture for Superior Falls Risk Prediction Using a Bayesian Network Model. 18. Facilitating a Shared Meaning of AI/ML Findings amongst Key Healthcare Stakeholders: The Role of Analytic Translators.
