The Path to Implementation of Al for Prehospital Stroke Triage
Bridging Predictive Models with Real-World Emergency Care
Translating artificial intelligence (Al) into clinical practice remains a major challenge, even for high-priority areas such as stroke detection. StrokePath is an early-stage decision support tool that uses routine Emergency Medical Systems (EMS) data to assist providers in stroke triage, designed to enhance clinical judgment while fitting existing workflows. This presentation will share our dual focus (1) developing and validating predictive models, and (2) conducting early implementation studies with stakeholder engagement and workflow integration, to ensure StrokePath evolves as a practical, real-world tool. The approach may also inform the translation of other Al-based clinical decision support systems.
Date: Tuesday, October 14, 2025
Time: Noon-1:00 p.m.
Location: Center for Translational Research and Education, 2160 S. First Avenue, Room 152A, Maywood, IL 60153
Speakers
- Michael Saban is a PhD student in Computer Science at Loyola University Chicago and a Graduate Research Assistant in the Department of Health Informatics and Data Science. His research integrates state-of-the-art Artificial Intelligence with implementation science to advance scalable clinical decision support tools.
- Daniel Heiferman, MD is a cerebrovascular neurosurgeon at Endeavor Health with a research program centered on advancing stroke care. His research focuses on advancing stroke care through clinical trials, endovascular innovation, and the application of artificial intelligence to improve diagnosis and triage.
- Paula de la Pena, PhD RN is a neurocritical care nurse with specialized expertise in stroke care and a currentTL 1 Postdoctoral Fellow in the Marcella Niehoff School of Nursing, where her research is focused on optimizing recovery and long-term outcomes in stroke survivors.
- Samie Tootooni, PhD is Assistant Professor in the department of Health Informatics and Data Science and Associate Director of the CHOIR. He leads a multidisciplinary lab focused on translational Al for clinical decision support.
Bridging Predictive Models with Real-World Emergency Care
Translating artificial intelligence (Al) into clinical practice remains a major challenge, even for high-priority areas such as stroke detection. StrokePath is an early-stage decision support tool that uses routine Emergency Medical Systems (EMS) data to assist providers in stroke triage, designed to enhance clinical judgment while fitting existing workflows. This presentation will share our dual focus (1) developing and validating predictive models, and (2) conducting early implementation studies with stakeholder engagement and workflow integration, to ensure StrokePath evolves as a practical, real-world tool. The approach may also inform the translation of other Al-based clinical decision support systems.
Date: Tuesday, October 14, 2025
Time: Noon-1:00 p.m.
Location: Center for Translational Research and Education, 2160 S. First Avenue, Room 152A, Maywood, IL 60153
Speakers
- Michael Saban is a PhD student in Computer Science at Loyola University Chicago and a Graduate Research Assistant in the Department of Health Informatics and Data Science. His research integrates state-of-the-art Artificial Intelligence with implementation science to advance scalable clinical decision support tools.
- Daniel Heiferman, MD is a cerebrovascular neurosurgeon at Endeavor Health with a research program centered on advancing stroke care. His research focuses on advancing stroke care through clinical trials, endovascular innovation, and the application of artificial intelligence to improve diagnosis and triage.
- Paula de la Pena, PhD RN is a neurocritical care nurse with specialized expertise in stroke care and a currentTL 1 Postdoctoral Fellow in the Marcella Niehoff School of Nursing, where her research is focused on optimizing recovery and long-term outcomes in stroke survivors.
- Samie Tootooni, PhD is Assistant Professor in the department of Health Informatics and Data Science and Associate Director of the CHOIR. He leads a multidisciplinary lab focused on translational Al for clinical decision support.