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Title:
Optimizing the First Response to Sepsis: An Electronic Health Record-based Markov Decision Process Model for Personalizing Acute Care for Deteriorating Patients

Abstract:
Sepsis is considered a medical emergency where delays in initial treatment are associated with increased morbidity and mortality. It is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis affects more than 1.7 million Americans each year, causing approximately 270,000 deaths annually. One in three hospitalized patient deaths are associated with sepsis. In 2019, the total cost of sepsis care for inpatient admission and skilled nursing facility admission was estimated at more than $62 billion. Sepsis is a significant healthcare challenge, where the lack of a gold standard for diagnosis causes inconsistencies in categorizing sepsis phenotypes and accurately capturing patients’ trajectories, which evolve stochastically over time. This makes treatment decision making and early intervention difficult. We integrate electronic health record (EHR) data with clinical expertise to develop a continuous-time Markov decision process model of the natural history of sepsis. We use this model to better understand the stochastic nature of patients’ health trajectories and determine the optimal treatment policy to minimize mortality and morbidity. Specifically, the optimal health states for first anti-infective and first fluid are identified. We formulate this as a stopping problem in which the patient leaves the system when he or she receives the first treatment (intervention) and receives a lump sum reward. Our objective is to find the optimal first intervention for health states to minimize expected mortality and morbidity. We explore the effect of the complex trade-offs associated with the intervention costs and patient disposition costs which are subjective and difficult to estimate. Our model captures the natural progression along sepsis trajectory using a clinically defined treatment delayed population. The model translates observations of patient health as defined by vitals and laboratory results recorded during hospitalization in the EHR to capture the complex evolution of sepsis within a patient population. This framework provides key insights into sepsis patients’ stochastic trajectories and informs clinical decision making associated with caring for these patients as their health dynamically evolves.

Bio:
Julie Simmons Ivy 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 also received her M.S. in Industrial and Systems Engineering with a focus on Operations Research at Georgia Tech. She is a President of the Health Systems Engineering Alliance (HSEA) Board of Directors. 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 has made an impact on how researchers and practitioners address complex societal issues, such as health disparities, public health preparedness, hunger relief, student performance, and personalized medical decision-making and has been funded by AHRQ, CDC, NSF, Clinton Health Access Initiative, and the UNC Cancer Center.