Learning health systems to support data intensive research: a Canadian perspective
Speaker: Jean-François Ethier, Université de Sherbrooke
Bio
Jean-François Ethier is associate researcher at Unit 1138 Cordeliers Research Center at INSERM and Paris Descartes University, and at the Research Center of the University Hospital of Sherbrooke. He is a specialist in general internal medicine.
Jean-François Ethier’s work focuses on learning health systems. In particular, it centres on methods of access to health data, research systems and clinical decision aid tools where citizens play active roles. He develops theoretical approaches and concrete tools so that information and research systems can communicate with each other.
His research influences how biomedical data warehouses are structured. It also allows to combine a variety of health data that is stored in computer systems that operate differently.
Jean-François Ethier develops ontologies and biomedical terminologies. These integrate diverse and heterogeneous databases within a unified data network. These solutions facilitate the flow of information between science and clinical practice. They propel health research while supporting health care professionals who make many clinical decisions every day.
Jean-François Ethier participated in the development of the TRANSFoRm project in Europe (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.
Abstract
The learning health system (LHS) approach is increasingly regarded as an optimal paradigm to foster concrete health improvements for the population. AI plays a significant role to offer new insights on health data, yet difficulties in accessing data currently curtails its potential. By placing it in the context of learning health systems, it is possible to foster data access securely and ethically while ensuring the evaluation of anticipated benefits for care delivery. Canada is currently implementing structures at the national, provincial and regional domains to facilitate this. The presentation will therefore briefly present how AI can fit in the LHS paradigm, explore challenges regarding this integration and discuss Canadian organisations supporting it, like the Health Data Research Network Canada.