Home

Rafael Aroutiunian

Academic title(s): 

Assistant Professor

Rafael Aroutiunian
Location: 
Jewish General Hospital
Degree(s): 

MD, CCFP (EM)

Medical School: 

Université de Montréal

Residency Training: 

McGill University - Family Medicine (CCFP)

Fellowship Training: 

McGill University - Emergency Medicine (CCFP-EM)

Areas of interest: 

Healthcare Informatics

Artificial Intelligence & Machine Learning

Telehealth / Telemedicine

Data Science

EMR/EHR Design and Implementation

Information Security

Pre-Hospital Emergency Medicine / EMS / Advanced Paramedics Program

Selected publications: 

Grant, L., Diagne, M., Aroutiunian, R. et al. Machine learning outperforms the Canadian Triage and Acuity Scale (CTAS) in predicting need for early critical care. Can J Emerg Med 27, 43–52 (2025). https://doi.org/10.1007/s43678-024-00807-z

Grant, L., Xue, X., Vajihi, Z., Azuelos, A., Rosenthal, S., Hopkins, D., Aroutiunian, R., . . . Afilalo, M. (2020). LO32: Artificial intelligence to predict disposition to improve flow in the emergency department. CJEM, 22(S1), S18-S19. doi:10.1017/cem.2020.88

Back to top