Dimensions of Intelligent Analytics for Smart Digital Health Solutions

ISBN-13: 9781032699721 / Angielski / Miękka / 2024 / 424 str.

Opis

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.

Recenzje

"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

Spis treści

PART 1: Techincal Considerations  1. Medical Image Processing  2. Smart Wearables in Healthcare  3. Causal AI in Personalized Healthcare  4. Interpretable AI in Healthcare  PART 2: 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 & Analytics  8. AI approaches for Managing Preventive Care in Digital Health Ecosystems  9. Competitive Intelligence in Healthcare  PART 3: Clinical Applications  10. Machine Learning for Healthcare Applications: Possibilities and Barriers  11. Systematic Review of Prediction Models for Chronic Opioid Use Following Surgery  12. Addressing Challenges in the Emergency Department with Analytics  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  PART 4: 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  18. Facilitating a Shared Meaning of AI/ML Findings Amongst Key Healthcare Stakeholders: The Role of Analytic Translators

O autorze

Freimut Bodendorf

Professor Freimut Bodendorf graduated from the University of Erlangen-Nuremberg (School of Engineering) with a degree in Computer Science. He obtained his doctor degree (Ph.D.) 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 in Switzerland. Actually, he is head of the research group Management Intelligence Systems of the Institute of Information Systems at the University of Erlangen-Nuremberg. He is faculty member of the School Business and Economics as well as the School of Engineering and the School of Natural Sciences. Recently he was appointed to be Research Fellow of the Fraunhofer Institute IIS, the largest institute in Germany. His scientific work focuses on Business Intelligence and Digital Health including advanced data analytics, responsible artificial intelligence, intelligent assistance, data sharing and federated learning ecosystems. Research projects investigate and create solutions in the fields of digital transformation in healthcare and digital support of individual wellness.

 

Mathias Kraus

Mathias Kraus is 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 scientific community through his work, which has been published in leading information systems and operations research journals and at prestigious computer science conferences.

 

Nilmini Wickramasinghe

As of August 1, Professor Wickramasinghe became the Professor and Optus Chair of Digital Health at La Trobe University. In addition, she is the inaugural Professor – Director Health Informatics Management at Epworth HealthCare. She also holds honorary research professor positions at the Peter MacCallum Cancer Centre, Murdoch research Children’s Institute (MCRI) and Northern Health.  After completing 5 degrees at the University of Melbourne, she completed PhD studies at Case Western Reserve University, Cleveland, OH, USA and later completed executive education at Harvard Business School, Harvard University, Cambridge, MA, 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 US, 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 effect superior, value-based patient centric care delivery. Professor Wickramasinghe collaborates with leading scholars at various premier healthcare organizations and universities throughout Australasia, US 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 US, Australia, Germany and China as a chief investigator. She holds a patent around analytics solution for managing healthcare data  and is the editor-in-chief of Intl. J 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 first time this honour has been bestowed to someone in the discipline of Digital Health.