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Artificial Intelligence for Healthcare: Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World New Edition
Author(s): Sze-chuan Suen (Editor), David Scheinker (Editor), Eva Enns (Editor)
Publisher: Cambridge University Press
Publication Date: 5 May 2022
Edition: New
Language: English
Print length: 204 pages
ISBN-10: 1108836739
ISBN-13: 9781108836739
Book Description
Healthcare has recently seen numerous exciting applications of artificial intelligence, industrial engineering, and operations research. This book, designed to be accessible to a diverse audience, provides an overview of interdisciplinary research partnerships that leverage AI, IE, and OR to tackle societal and operational problems in healthcare. The topics are drawn from a wide variety of disciplines, ranging from optimizing the location of AEDs for cardiac arrests to data mining for facilitating patient flow through a hospital. These applications highlight how engineering has contributed to medical knowledge, health system operations, and behavioral health. Chapter authors include medical doctors, policy-makers, social scientists, and engineers. Each chapter begins with a summary of the health care problem and engineering method. In these examples, researchers in public health, medicine, and social science as well as engineers will find a path to start interdisciplinary collaborations in health applications of AI/IE/OR.
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Book Description
Overviews of interdisciplinary research partnerships applying AI, IE, and OR to societal and operational problems in healthcare settings.
About the Author
Sze-chuan Suen is an assistant professor in the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California. She received her PhD in the department of Management Science and Engineering from Stanford University in 2016. Her research interests include developing applied mathematical models to identify epidemiological trends and evaluating health policies to support informed decision-making. Her work in health policy modeling draws from a variety of techniques, including simulation, dynamic systems modeling, Markov decision processes, cost-effectiveness analysis, and decision analysis. Her previous work has examined the optimal management of tuberculosis, HIV, and chronic diseases.
David Scheinker is a Clinical Associate Professor of Pediatrics in the Stanford School of Medicine and the Executive Director of Systems Design and Collaborative Research at the Stanford Lucile Packard Children’s Hospital. He is the Founder and Director of SURF Stanford Medicine (surf.stanford.edu), a group that brings together students and faculty from the university with physicians, nurses, and administrators from the hospitals to improve the quality of care using operations research methodology. His research focuses on applications of operations research in healthcare. Previously, he was a Joint Research Fellow at The MIT Sloan School of Management and Massachusetts General Hospital.
Eva Enns is an Associate Professor in the Division of Health Policy and Management at the University of Minnesota School of Public Health. She received her PhD in Electrical Engineering from Stanford University in 2012 and her dissertation was awarded the Decision Sciences Institute Elwood S. Buffa Doctoral Dissertation Award in 2013. In her research, she applies engineering concepts, including simulation modelling, optimization, cost-effectiveness analysis, and resource allocation, to help inform policies for the prevention and treatment of infectious diseases. Specific application areas include HIV, sexually transmitted infections, antimicrobial resistance, and most recently COVID-19.
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