NSERC CREATE in Responsible Health and Healthcare Data Science

NSERC-CRSNGThe medical sector generates large amounts of structured and unstructured data on a dailybasis, including 1) health-related data such as medical images, laboratoryresults, questionnaire scores, treatment details and 2) healthcare-related data such as hospital admissions/transfers/discharges, demographics, disease registries, billing accounts, appointments, and schedules. Clinical environments are arguablyamong the sectors producingthe largest quantityof data yet one where their potential remains relativelyuntamed from a Big Data and artificial intelligence perspective. There are several reasons for this, first and foremost being restrictive access rules to complywith confidentialityand privacylaws and policies. There is also a recognized shortage of highly-qualified personnel in data science, a problem exacerbated in the health and healthcare sector where dataexperts must possess professional skills and knowledge beyond what is typicallyrequired for the management, analysis and exploitation of non-confidential data. Health and healthcare data scientists and engineers, for instance, must master encryption and de-identification techniques, implement system architectures that will ensure privacybydesign, perform non-disclosive data analyses, and develop bias-free tools that can be intuitivelyused bypatients, clinicians and decision-makers. Furthermore, theymust be acutelyaware of the Ethical, Legal and Social Implications (ELSI) of their work, and act responsiblyto maintain public trust. In thiscontext, we propose to create an innovative training program at the interface of data sciences, medicine, public health, law, ethics and policy. The proposed training program, at the forefront of new challenges related to the use of artificial intelligence in the health sector, is novel bydesign in this era of rapidlychanging social and digital environments. Canada, with its large provincial healthcare systems and federated IT systems, is well-positioned to become a leader in responsible health and healthcare data science, provided it can relyon a workforce capable of addressing associated challenges.

Co-applicants : Louis Archambault, Nadia Lahrichi, Isabel Fortier, France Légaré, Catherine Régis, Louis-Martin Rousseau and John Kildea.

Organisations: MSSS, INESSS, Imagia Cybernetics, Greybox Solutions Inc, McGill Medical Physics Unit, NRC, ICES, Population Data BC, IVADO, INSPQ, École Polytechnique Montréal, McGill.