Thomas Kannampallil, PhD

Assistant Professor of Anesthesiology
School of Medicine

Associate Chief Research Information Officer, School of Medicine

Personal Website

Thomas Kannampallil, PhD, is focused on integrating cognitive, behavioral, and computational informatics techniques for developing health information technology solutions in the areas of clinical decision support, clinical reasoning, and clinical workflow.


Thomas Kannampallil, PhD, is an Assistant Professor in the Department of Anesthesiology at Washington University School of Medicine in St. Louis and the Associate Chief Research Information Officer for the School of Medicine. Dr. Kannampallil’s research interests lies at the intersection of computer science and cognitive science and focuses on applying cognitive, behavioral and computational techniques for studying clinical behaviors in various contexts for studying medication errors, clinical decision support, HIT usability, interactive communication, and clinical workflow. In addition, he specializes in developing scalable infrastructure for informatics research projects including development of registries, cohort identification and longitudinal tracking. His recent research has focused on the development and evaluation of multiple clinical decision rules for evaluating acute care opioid use, medication ordering errors, and chronic diseases. His research has been supported by a combination of grants and contracts by AHRQ, NCATS, NLM and CMS. Previously, Dr. Kannampallil was the Director of Primary Care Informatics at the University of Illinois; prior to that he was the Assistant Director for the Center for Cognitive Informatics in Medicine and Public Health at the New York Academy of Medicine. Dr. Kannampallil’s research has been published in leading clinical and informatics journals. He has served as a special issue editor for the Journal of Biomedical Informatics (Cognitive informatics methods for interactive clinical systems) and a co-editor for a graduate textbook on Human Computer Interaction.

Research Interests

  • Clinical decision support applications for tracking, monitoring, and evaluating EHR-based activities such as medication/lab orders, decision-making for chronic care, and opioid management
  • Tracking and analysis of medical errors in a variety of situations including medication orders, transitions of care, and clinical decision-making and evaluating its impact on clinical outcomes and patient safety
  • Use of cognitive and human factors approaches for identifying behavioral, collaborative and workflow challenges in the design and use of health information technology

Lab Members

  • Ruixuan Dai, PhD Student in Computer Science, Washington University in St. Louis, McKelvey School of Engineering
  • Ethan Pfeifer, MD Student, Washington University in St. Louis, School of Medicine
  • Rongrong Dai, PhD Candidate in Materials Science, Washington University in St. Louis, McKelvey School of Engineering