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This title demystifies 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 title demystifies 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.
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Autorenporträt
Nilmini Wickramasinghe is the Professor and Optus Chair of Digital Health at La Trobe University. In addition, she is the inaugural Professor and Director of Health Informatics Management at Epworth HealthCare. She also holds honorary research professor positions at the Peter MacCallum Cancer Centre, Murdoch Children's Research Institute (MCRI), and Northern Health. After completing ¿ve degrees at the University of Melbourne, she earned a PhD at Case Western Reserve University, Cleveland, Ohio, USA, and later completed the executive education at Harvard Business School, Harvard University, Cambridge, Massachusetts, USA, in value-based healthcare. For over 25 years, Professor Wickramasinghe has been actively researching and teaching within the health informatics/digital health domain in the United States, Germany and Australia, with a particular focus on designing, developing and deploying suitable models, strategies and techniques grounded in various management principles to facilitate the implementation and adoption of technology solutions to e¿ect superior, value-based patient-centric care delivery. Professor Wickramasinghe collaborates with leading scholars at various premier healthcare organizations and universities throughout Australasia, the United States and Europe and is well published, with more than 400 referred scholarly articles, more than 15 books, numerous book chapters, an encyclopaedia and a well-established funded research track record securing over $25M in funding from grants in the United States, Australia, Germany and China as a chief investigator. She holds a patent around analytics solutions for managing healthcare data and is the editor-in-chief of the International Journal of Networking and Virtual Organisations (www.inderscience.com/ijnvo) as well as the editor of the Springer book series Healthcare Delivery in the Information Age. In 2020, she was awarded the prestigious Alexander von Humboldt award for outstanding contribution to digital health, the ¿rst time this honour has been bestowed to someone in the discipline of digital health. Freimut Bodendorf earned a degree in computer science at the School of Engineering, University of Erlangen-Nuremberg. He also earned a PhD in information systems. Subsequently, he was head of an IS Department at the University Hospital and Medical School at the University of Freiburg, Germany; professor at the Postgraduate School of Engineering in Nuremberg, Germany; and head of the Department of Computer Science and Information Systems at the University of Fribourg, Switzerland. He also is the head of the research group Management Intelligence Systems of the Institute of Information Systems at the University of Erlangen-Nuremberg. He is a faculty member of the School of Business and Economics as well as the School of Engineering and the School of Natural Sciences. Recently he was appointed to be a Research Fellow of the Fraunhofer Institute IIS, the largest institute in Germany. His scienti¿c work focuses on business intelligence and digital health, including advanced data analytics, responsible arti¿cial intelligence, intelligent assistance, data sharing and federated learning ecosystems. His research projects investigate and create solutions in the ¿elds of digital transformation in healthcare and digital support of individual wellness. Mathias Kraus is an Assistant Professor for Data Analytics at the Institute for Information Systems, FAU Erlangen-Nürnberg, where he also heads the White-Box AI research group. Prior to this appointment, he was a research assistant at ETH Zurich and the University of Freiburg. In his current role, he develops advances in data analytics with a focus on transparency and reliability in machine learning models. He has made several contributions to the scienti¿c community through his work, which has been published in leading information systems and operations research journals and at prestigious computer science conferences.
Inhaltsangabe
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.
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.
Rezensionen
"I have had the pleasure of being a contributing author to one of Professor Wickramasinghe's previous books on digital health. Once again, she and her co-editors have assembled a roster of domain experts that cover currently relevant topics in the rapidly changing field of digital health. I appreciate how this book weaves in the areas of technical, management, clinical, and human factor considerations into the delivery of healthcare today. As a practicing clinician, I understand how analytic and AI technologies will play an increasingly critical role in effecting value-based care outcomes that are more precise and bespoke to the individual patient. I recommend this book as a critical read to all stakeholders who seek a greater understanding of just how technology plays an increasingly pertinent role in the delivery of care now and into the future."
Duane F. Wisk, DO, MPH, FACOEM, Managing Partner, GlobalMed Physicians
"AI is the tool that promises to change everything-with good reason. But without the intelligent analytics discussed in this groundbreaking book, it could just be the source of confusion and error. The two dimensions are critical to realizing its promise."
Albert J. Weatherhead III, Professorship of Management, Dean and Professor, Department of Banking and Finance, Weatherhead School of Management, USA
"Using data driven approaches in providing highly reliable patient care is the right thing to do. As technologies have advanced, Wickramasinghe et al. provide a glimpse into the management, technical, clinical and human factors associated with the critically important topics of applying analytics, artificial intelligence and machine learning to healthcare. Developing patient centered and clinician derived approaches to improving care models using descriptive, diagnostic, predictive and prescriptive analytics is the right approach, and the authors are expertly leading the readers to expedite their journey to improving healthcare."
Jonathan Schaffer, MD, MBA, Managing Director, eCleveland Clinic, Information Technology Division of Cleveland Clinic, USA
