Frontiers of medical decision-making in the modern age of data analytics

Abstract

Recent decades have seen considerable advances in developing Industrial Engineering/Operations Research (IE/OR) models for improving decision-making in healthcare. These approaches span the full range of descriptive, predictive, and prescriptive models for supporting patients' and clinicians' decision-making. The pervasive use of information technology to collect and store electronic health records, insurance claims, genomic information, and other observational data has opened new doors for developing, validating, and applying these types of data-driven IE/OR models. This article describes opportunities at the frontier of medical decision-making, emphasizing the intersection of medicine, data analytics, and operations research. Many of the examples covered intersect the fields of statistics, machine learning, and artificial intelligence. A series of motivating examples illustrate the possibilities and some promising future research directions.

Keywords:

• Data analytics
• predictive models
• machine learning
• artificial intelligence
• optimization
• healthcare
• medical decision making

Acknowledgments

The author is grateful for the feedback from two anonymous reviewers and the editor-in-chief, Yu Ding, whose reviews helped improve this manuscript. The author also benefited from feedback on an early draft from three PhD students: Daniel Felipe Otero-Leon, Erkin Ötleş, and Kevin Smith.

Additional information

Notes on contributors

Brian T. Denton

Brian Denton is Stephen M. Pollock Professor and Chair of the Department of Industrial and Operations Engineering at the University of Michigan. His research interests include data-driven sequential decision making and optimization under uncertainty with applications to healthcare and industrial systems. Before joining the University of Michigan, he worked at IBM, Mayo Clinic, and North Carolina State University. He is a fellow of IISE and INFORMS, past Chair of the INFORMS Health Applications Section, and past-President of INFORMS.