Dr Jean-François Ethier is a clinician scientist and an associate professor in the Department of Medicine and the Department of Computer Science at the Université de Sherbrooke. He also practices as a general internal medicine attending physician at the Sherbrooke University Health Centre.
Dr. Ethier leads the technological development the Health Data Research Network (hdrn.ca) and is the scientific co-director of the Groupe de recherche interdisciplinaire en informatique de la santé (GRIIS.ca). His research program is also developed in collaboration with French colleagues through his position as an associate researcher at INSERM. This led to the creation of the French-Canadian Ensemble network for rare diseases which he co-leads with Dr. Anita Burgun.
Jean-François Ethier’s work focuses on learning health systems. In particular, it centres on methods to enable secure, ethical and efficient health data usage for care delivery, research activities (clinical trials, observational studies, real world evidence projects) and knowledge translation through decision support systems and audit-feedback tools (e.g.
the ReflexD tool) where citizens play active roles.
Placing the citizen at the center of the system has profound implications on how data access processes should be structured and how data should be meshed to fully represent a citizen’s journey inside and outside the traditional health system.
A key component of this work is the development of ontologies and biomedical terminologies. Ontologies allow the representation of knowledge and information through which various data models and heterogeneous databases can be accessed together as a unified data network.
Jean-François Ethier participated to the TRANSFoRm project in Europe (a project funded by the European Community under the FP7 program). TRANSFoRm has created a prototype of a learning health system to support primary health care and services. This work served as the foundation of the Plateforme apprenante pour recherche en santé et les services sociaux (
PARS3), an open source, distributed and decentralized toolkit to enable safe data usage (including distributed analysis) without requiring bulk data pooling.
