A renowned expert in AI-based clinical decision support and patient safety innovations.
Thomas Kannampallil, PhD
Associate Professor of Anesthesiology
Associate Professor of Computer Science and Engineering
Thomas Kannampallil, PhD is an Associate Professor in the Washington University School of Medicine’s (WUSM) Department of Anesthesiology (WUDA) and the Institute for Informatics, Data Science, and Biostatistics (I2DB). His research interests lie at the intersection of computer science, cognitive science, and clinical informatics and is focused on translational efforts to implement clinical decision support to improve clinical cognition and patient safety. He has extensive experience in the design, implementation, and evaluation of novel AI-based technology in healthcare settings, and in using electronic health record (EHR) data to develop patient phenotypes for a variety of projects. His research is currently funded by several NIH grants from the National Library of Medicine (NLM), National Institute for Aging(NIA) and Agency for Healthcare Research and Quality (AHRQ). Most of his current research efforts are on developing artificial intelligence (AI)-based tools and evaluating them using pragmatic clinical trials. He is also an Associate Editor for the Journal of Biomedical Informatics and serve on several national technical expert panels on health information technology.
- Implementing AI at the point-of-care and pragmatic challenges – how can we translate AI applications to be effectively used at the point of care
- Using audit logs to predict phenotypes of clinician behaviors and assessing impact on clinician workload and burnout
- Clinical trial(s) of voice-based AI applications for depression and anxiety
- Secure messaging in the clinical work environment – how messaging within the EHR affects clinician workload, interruptions, and errors.
View publications and research interests on Research Profiles »
- King, C.R., Gregory, S., Fritz, B.A., Budelier, T.P., Ben Abdallah, A., Kronzer, A., Helsten, D.L., Torres, B., McKinnon, S., Goswami, S., Mehta, D., Higo, O., Kerby, P., Henrichs, B., Wildres, T.S., Politi, M.C., Abraham, J., Avidan, M.S. & Kannampallil, T.G. (2023). Impact of an Interaoperative Telemedicine Program on Perioperative Quality Metrics: The ACTFAST-3 Randomized Clinical Trial, JAMA Network Open, 6(9), pp. e2332517-e2332517.
- Lou, S.S., Baratta, L., Lew, D., Avidan, M.S. & Kannampallil, T.G. (2023). Comparison of Anesthesia Workload Estimated from Electronic Health Record Documentation vs Billed Relative Value Units: A Cross-sectional Study, JAMA Network Open, 6(8), pp. e2328514-e2328514.
- Kannampallil, T.G., Ajilore, O.A., Lv, N., Smyth, J.M., Wittels, N.E., Ronneberg, C.R., Kumar, V., Xiao, L., Dosala, S., Barve, A., Zhang, A., Tan, K.C., Cao, K.K., Patel, C.R., Gerber, B.S., Johnson, J.A., Kringle, E.A., & Ma, J. (2023). Effects of a Virtual Voice-based Coach Delivering Problem-Solving Treatment on Emotional Distress and Brain Function: A Pilot RCT in Depression and Anxiety, Translational Psychiatry, 13(1), pp. 166.
- Kim, S., Warner, B.C., Lew, D., Lou, S.S., & Kannampallil, T.G. (2024, Accepted), Measuring Cognitive Effort using Tabular Transformer-based Language Models of EHR-based Audit Log Action Sequences, Journal of the American Medical Informatics Association (JAMIA)
- National Multi-Institutional Validation of a Surgical Transfusion Risk Prediction Model
Lou, S. S., Liu, Y., Cohen, M. E., Ko, C. Y., Hall, B. L. & Kannampallil, T., Jan 1 2024, In: Journal of the American College of Surgeons. 238, 1, p. 99-105 7 p. - Lou, S.S., **Liu, H., Harford, D., Lu, C. & Kannampallil, T.G. (2023). Characterizing the Macrostructure of EHR Work Using Raw Audit Logs: An Unsupervised Action Embeddings Approach, Journal of the American Medical Informatics Association (JAMIA), 30(3), pp. 539-544.
- Lou, S.S., Lew, D., Harford, D., Lu, C., Evanoff, B.A., Duncan, J.G., & Kannampallil, T.G. (2022). Temporal associations between EHR-derived workload, burnout, and errors: a prospective cohort study, Journal of General Internal Medicine, 37(9), pp. 2165-2172.
- Secure Messaging and Telephone Use for Clinician-to-Clinician Communication
Lou, S. S., Lew, D., Baratta, L. R., Eiden, E., Sinsky, C. A. & Kannampallil, T., Jun 20 2024, In: JAMA Network Open. 7, 6, p. e2417781 - *Lou, S.S., Liu, H., Lu, C., Wildes, T.S., Hall, B.L., & Kannampallil, T.G. (2022). Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders, Anesthesiology, 137(1), pp. 55-66.
- Unlocking inpatient workload insights with electronic health record event logs Burden, M., Keniston, A., Pell, J., Yu, A., Dyrbye, L. & Kannampallil, T., 2024, (Accepted/In press) In: Journal of hospital medicine.