Abstract

Decision making to satisfy the basic human needs of health, food, and education is complex. This involves thinking about the systems that drive the satisfaction of these basic needs and how they are connected. We will frame this in the context of systems thinking and decision making particularly under conditions of uncertainty. We will discuss how the goals of the decision maker help define what it means to make a “good” decision. Further, we consider how we can identify strategies in such a way that solutions are personalized to the needs of the individual. We will explore how we can use data and a systems thinking approach to inform decision making with the goal of improving decision quality.

Julie Simmons Ivy, Ph.D. is a Professor in the Edward P. Fitts Department of Industrial and Systems Engineering and Fitts Faculty Fellow in Health Systems Engineering.  She previously spent several years on the faculty of the Stephen M. Ross School of Business at the University of Michigan.  She received her B.S. and Ph.D. in Industrial and Operations Engineering at the University of Michigan.  She received her M.S. in Industrial and Systems Engineering at Georgia Tech. She is an active member of the Institute of Operations Research and Management Science (INFORMS), Dr. Ivy served as the 2007 Chair (President) of the INFORMS Health Applications Society and the 2012 – 13 President for the INFORMS Minority Issues Forum. Her research interests are mathematical modeling of stochastic dynamic systems with emphasis on statistics and decision analysis as applied to health care, public health, and humanitarian logistics. This research seeks to impact how researchers and practitioners address complex societal issues, such as health disparities, public health preparedness, hunger relief, student performance, and personalized medical decision-making.

Maria Mayorga, Ph.D. is a Professor of Personalized Medicine in the Edward P. Fitts Department of Industrial and Systems Engineering. Prior to joining the NC State faculty, she was on the faculty at Clemson University, Department of Industrial Engineering for seven years. Her goal is to address fundamental research barriers in moving from estimates of efficacy to estimates of the effectiveness of interventions or policies by explicitly considering individual patient preferences when the underlying patient population is heterogeneous. She is also interested in optimally allocating resources in Emergency Medical Service systems. To achieve these goals, Dr. Mayorga creates analytical models of health systems that incorporate patient-level data. She uses techniques such as simulation, dynamic programming, applied probability, queuing theory and mathematical programming. She employs multiple sources of secondary data and a mixed methods approach to enable predictions of health outcomes at levels for which it is difficult to conduct studies in practice. This research is inherently interdisciplinary and is thus facilitated via collaborations with health services researchers such as epidemiologists, economists, and medical doctors. She received the distinguished National Science Foundation CAREER Award for her work to incorporate patient choice into predictive models of health outcomes.