Lars Grant
Assistant Professor
MD, PhD
- The development and use of artificial intelligence in Emergency Medicine.
- Automated data collection and use in the Emergency Department.
- Emergency Department Triage.
- Emergency Department operations in the context of the COVID-19 pandemic and COVID-19 patient registries.
- The appropriate use of antibiotics for urinary infections in geriatric Emergency Department patients.
Dr. Grant completed a PhD in theoretical physics at Harvard University and completed medical school and residency at McGill University.
My primary research focus is currently the development of an Emergency Department Artificial Intelligence Flow Assistant. The central question here is: Can machine learning and artificial intelligence methods genuinely improve care? A related question that I am also studying is: What is the value of machine learning methods in the development of generalizable clinical decision rules and risk stratification tools?
In the context of the current COVID-19 pandemic, I am also carrying out research designed to assess the value of modifications to usual Emergency Department operations in response to the pandemic.
Funded by CIHR, NSERC and SSHRC
Grant L, Joo P, Nemnom MJ, Thiruganasambandamoorthy V. Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data. Intern Emerg Med. 2021 Nov 3. doi: 10.1007/s11739-021-02873-y. Epub ahead of print. PMID: 34734350.
Grant, L., Xue, X., Vajihi, Z., Azuelos, A., Rosenthal, S., Hopkins, D., . . . 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