The diversity of operational (or so-called "real-life") data sources promises to improve capabilities to support not only research, but also policy, management, and clinical practice. Realizing this promise requires the combined use of data from all information sources for a more detailed description of population health and the clinical status of individual patients. New insights from data integrated by various algorithms are increasingly becoming part of everyday patient care. Meaningful use of real-world data is necessary to implement the "learning health system," which will be characterized by continuous improvement through the acquisition of knowledge from such integrated data.

At this conference on April 4, Nathalie de Marcellis-Warin, full professor at Polytechnique Montréal and CEO of CIRANO, Stefan Schulz, professor at the University of Medicine of Graz and Jean Noel Nikiema, researcher at CReSP and assistant professor at the School of Public Health of the University of Montreal will address the practical issues and challenges of the social acceptability of sharing and using data for new needs driven by artificial intelligence algorithms. They will then examine why data standards are important for the creation of artificial intelligence algorithms for health care and life sciences.