Dan Poenaru

Membre académique associé

Professeur, McGill University, Département de chirurgie pédiatrique

Biographie

Dan Poenaru est professeur de chirurgie pédiatrique à l’Université McGill et chercheur principal à l’Institut de recherche du Centre universitaire de santé McGill, à Montréal. Il est titulaire d’une maîtrise en éducation aux professions de la santé et en développement international, et d’un doctorat en stratégie et gestion de la santé.

Chercheur financé par le Fonds de recherche du Québec - Santé (FRQS) et les Instituts de recherche en santé du Canada (IRSC) dans le domaine des soins chirurgicaux centrés sur le patient, il est également chef du laboratoire CommiSur de l’Université McGill, directeur de la bourse Jean-Martin-Laberge en chirurgie pédiatrique mondiale et membre fondateur de l’Initiative mondiale pour la chirurgie infantile (GICS).

Ses domaines d’intérêt actuels sont la communication chirurgicale et l’enseignement médical assistés par la technologie, y compris l’IA, la réalité virtuelle et les dispositifs de santé numériques, les soins chirurgicaux centrés sur le patient et le développement de la capacité de recherche chirurgicale mondiale.

Étudiants actuels

Aylin Erman

Maîtrise recherche - McGill University

Superviseur⋅e principal⋅e :

Jackie Cheung

aylin.erman@mila.quebec

Hakima Laribi

Doctorat - Université de Sherbrooke

Co-superviseur⋅e :

Martin Vallières

hakima.laribi@mila.quebec

Site web

Github

Jean Marie Tshimula

Postdoctorat - McGill University

jeanmarie.tshimula@mila.quebec

Mohsen Amoei

Doctorat - McGill University

mohsen.amoei@mila.quebec

Olivier Lefebvre

Doctorat - Université de Sherbrooke

Co-superviseur⋅e :

Martin Vallières

olivier.lefebvre@mila.quebec

Github

Publications

"Your child needs surgery": A survey-based evaluation of simulated expert consent conversations by key stakeholders.

Zoe Atsaidis

Stephan Robitaille

Elena Guadagno

Jeffrey Wiseman

Sherif Emil

Dan Poenaru

2023-01-01

Journal of Pediatric Surgery (publié)

doi.org

A debriefing tool to acquire non-technical skills in trauma courses

Fabio Botelho

Jason M. Harley

Natalie Yanchar

Simone Abib

Ilana Bank

Dan Poenaru

2022-11-07

Surgery Open Science (publié)

doi.org

The use of artificial intelligence and virtual reality in doctor-patient risk communication: A scoping review.

Ryan Antel

Samira Abbasgholizadeh-Rahimi

Elena Guadagno

Jason M. Harley

Dan Poenaru

2022-10-01

Patient Education and Counseling (publié)

doi.org

Application of Artificial Intelligence in Shared Decision Making: Scoping Review

Samira Abbasgholizadeh-Rahimi

Michelle Cwintal

Yuhui Huang

Pooria Ghadiri

Roland Grad

Dan Poenaru

Genevieve Gore

Hervé Tchala Vignon Zomahoun

France Légaré

Pierre Pluye

Background Artificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared… (voir plus) decision making (SDM). However, there is no comprehensive mapping of how AI may be used for SDM. Objective We aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM. Methods We performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed. Results The search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions. Conclusions Evidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients’ values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings.

2022-08-01

JMIR Medical Informatics (publié)

doi.org

Exploring the roles of artificial intelligence in surgical education: A scoping review

Elif Bilgic

Andrew Gorgy

Alison Yang

Michelle Cwintal

Hamed Ranjbar

Kalin Kahla

Dheeksha Reddy

Kexin Li