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Current Fellows

 Stefanie Ames, MD

Dr. Ames is a second-year pediatric critical care fellow. She attended medical school at Southern Illinois University School of Medicine and completed her pediatric residency at the University of Utah. Mentored by Dr. Jeremy Kahn, her research interests include evaluation of sepsis mandates and health policy on pediatric patient outcomes. 

 Idris Evans, MD

Dr. Evans is a clinical instructor of Pediatric Critical Care Medicine. He graduated from the Drexel University College of Medicine, and did both his Pediatrics residency and Pediatric Critical Care Medicine fellowship at the Children’s Hospital of Pittsburgh of UPMC. He is mentored by Drs. Christopher Seymour, Joseph Carcillo, and Derek Angus. He is focused on leveraging data from the electronic health record to identify pediatric sepsis phenotypes.

 Andrew Prout, MD

Dr. Prout is in the Department of Critical Care Medicine's Pediatric Critical Care Medicine Fellowship Program. He attended medical school at Wayne State University and completed his residency in Pediatrics at University of Michigan Mott Children's Hospital. His research focuses on the epidemiology and risk factors for development of sepsis and septic shock in children, with a focus on comorbid conditions, indwelling devices, and immunosuppression, as well as late outcomes after septic shock in chronically ill children. Mentored by Dr. Sachin Yende, he is currently performing a retrospective cohort analysis to define these risk factors and evaluate long-term outcomes in this population.

 Joo Yoon, MD

Dr. Yoon is a clinical instructor of Pulmonary, Allergy, and Critical Care Medicine. He graduated from Catholic University of Korea, and trained in Internal Medicine at the New York Medical College, New York, before completing Pulmonary and Critical Care Medicine fellowship at the University of Pittsburgh Medical Center. Mentored by Drs. Gilles Clermont and Michael Pinsky, he has been focusing on signal processing to predict various instabilities among critically-ill patients with complex modeling using machine learning algorithms.