Artificial Intelligence for Healthcare: Interdisciplinary Partnerships for Analytics-Driven Improvements in a Post-COVID World - Hardcover

Über die Autorinnen und Autoren

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.