The RHHDS program, at the interface of computer science, medicine, public health, law and ethics, aims at training highly qualified personnel in a critical sector: data science in heath and healthcare. These specialists will tackle problems across the entire data lifecycle, from capture to analysis, in both the public and private sector.
how ELSI-related matters influence, among others, the concept of privacy across jurisdictions in Canada and abroad, and how it impacts the data flow and the implementation of IT solutions in health and healthcare.
and implement, using project management and engineering best practices, IT infrastructures and solutions respecting the FAIR data principles and leveraging current and emerging standards.
bias-free and interpretable data-driven tools for patients, clinicians and policy/decision makers, in close collaboration with all stakeholders.
these tools using efficient knowledge and technology transfer strategies and evaluate their impact in the heatlcare sector, including from an ethical, legal and social perspective.
Communication skills in the context of health are much broader than the ability to efficiently deliver a presentation or writing a scientific paper. Results from data-driven health research can potentially cause harm (e.g. discrimination or stigmatization) to individuals or populations (e.g. drug users, First Nations) if they are not associated with an appropriate communication plan. Reaching vulnerable populations is another challenge in the context of public health. Efficient communications between the natural science and engineering sector on one hand, and the health sector on the other are also non-trivial and require appropriate skills. Therefore, the training program will include workshops on non-discriminatory communication, intersectoral communications, perception by the community, and public communication campaigns, in order to expose trainees to the social implications of their work. Internships within ministries and public bodies, where communications are a core activity, will also reinforce these skills in trainees.
Graduate research projects are often conducted in relative isolation, as reducing external dependencies in general shortens the time to graduation. In this training program, trainees will evolve in large ecosystems (e.g. health institutions, ministries, public agencies) where external dependencies and collaborative work are the norm. It is therefore critical that trainees develop management skills to coordinate the efforts, distribute the workload, and deliver on schedule. Trainees will have access to the PULSAR ecosystem at Université Laval (infrastructure and expertise), an environment where project management is central (e.g. Agile development, sprints, scrums). Workshops on project management tools and methods (e.g. JIRA, Agile, code versioning) will be offered twice a year. Trainees will also become familiar with Research Data Management plans as these documents are becoming a mandatory requirement for several funding organizations and are considered an essential ingredient of excellence in research.
Knowledge and technology transfer (KTT) is often neglected or outright dismissed in graduate research projects for various reasons: lack of time, bad planning or disconnection with partners (e.g. industrial, medical, public sector). KTT is nonetheless essential to maximize the impact of research, and is a central component of the training program aimed at improving the job readiness of candidates. KTT professional skills will be fostered in trainees by engaging them from the beginning of their projects with end-users, from patients and clinicians to policy makers. Trainees will be embedded as much as possible in facilities and organizations during their projects to favor fruitful professional interactions. Understanding the needs and expectation of clinical end-users, for instance, is crucial in the medical sector to foster the adoption of new technologies. Activities organized by the Canada Research Chair in Shared Decision Making and Knowledge Translation (France Légaré) will constitute tremendous opportunities for trainees to learn from patient partners and KTT specialists. The Canada Research Chair in Collaborative Culture in Health Law and Policy (Catherine Régis) is also an outstanding training environment that will be leveraged to cover relevant topics for traineee. Finally, trainees will benefit from PIs having an outstanding track-record of technology transfers towards the private sector.
Ethical and legal questions transcend the entire training program as they are fundamental in any endeavour aimed at responsibly exploiting health data. All trainees will complete online tutorials on research ethics by the Ministère de la Santé et des Services sociaux (MSSS) du Québec (Level 1 and 3) as well as the TCPS 2: CORE (Course on Research Ethics), including recent modules on First Nations, Inuit and Métis Peoples and Multi-Jurisdictional Research. Trainees will also be directly involved in obtaining ethics approval for their project. The program will also seize several training opportunities by various organizations such as MSSS (Journée d’étude des comités d’éthique de la recherche), Institut d'éthique appliquée (IDÉA) at Université Laval and International observatory on the societal impacts of AI and digital technology. The program can count on experts in legal and privacy matters in the context of health, and seminars will be organized on topics such as PIPEDA, the European GDPR, the US Common Rule or the status and expected changes of the legislation in Canada (federally and provincially). Given the current transformation of the data landscape from ethical and legal perspectives, these training activities are timely and extremely relevant. Designing software and large scale IT systems to comply with legal and ethical requirements commands an acute knowledge and understanding of these dimensions.
Featured project
Prostate cancer is the second most frequent cancer and the fifth leading cause of cancer death among men. To improve patient outcomes, treatment must be personalized based on accurate prognosis. Nomograms already exist to identify patients at low risk for recurrence based on preoperative clinical information, but these tools do not use patients’ medical images.
