Ongoing projects

This project is centered on an examination of the process of preparing a patient-centered media and social media strategy that provides patients with useful information about the Opal patient portal and how they can make the most of it.

A patient portal is an extension of an electronic medical record system that is accessible to patients. Although patient portals have been around for many years, they have had poor adoption in Canada. This is due in large part to the desire of provinces to invest in large centralized electronic medical record systems and the complexity of implementing such systems. But patients are demanding access to their medical data and do not wish to wait for complex centralized systems to be implemented.

Therefore, in this research project, we will expand and evaluate the Opal patient portal, previously developed and implemented at the McGill University Health Centre, to function as a multi-institutional patient portal using a novel asynchronous data federation infrastructure.

This project involves two components: (1) preparation of Opal for the caregiver functionality in which patients will be able to share some or all of their medical data with their caregivers, and (2) general content preparation for Opal.

This research project is focused on preparing a pilot project for the donation of radiotherapy data by radiotherapy patients using the Opal patient portal.

This project is investigating ways in which patients can share their data and it will put in place the infrastructure for a demonstrative project.

This project involves an examination of the regulatory privacy and confidentiality compliance requirements for the use of a patient portal in various Canadian provinces.

Romina also works as a member of the quality assurance team, the market research team, and assisted with deploying Opal in numerous clinics within the Cedars Cancer Centre.

Process efficacy and robustness are crucial to assure productivity and predictability in pharmaceutical manufacturing. Medicago’s vaccine manufacturing technology uses plants for production and our aim is to develop a system capable of predicting and monitoring plant’s fitness for production early in the process, from plant seedling to harvest of producing leaves.

To this end, we must identify the factors that regulate the production level for each plant. We plan to measure a great deal of molecules, called metabolites, to determine the optimal conditions for the plants to generate the maximal amount of each product. Since the quantity of measurements is large, we will use machine learning to design an artificial intelligence capable of understanding and identifying the potentially highly complex patterns of metabolites and/or biomarkers that are correlated with the optimal production.

The discovery of antimicrobial agents was one of the great triumphs of the 20th century. The sobering news is that antibiotic resistance was part of the process as well. If nothing is done by 2050, antimicrobial resistance  (AMR) will cost $100 trillion with 10M people/year expected to die (https://amr-review.org). Factors driving AMR extend beyond human healthcare with implications in veterinary medicine, agriculture and the environment (the One Health approach). New and improved approaches for tackling AMR include better surveillance; rational drug use, different business model for generating antibiotics, innovation at all levels and most importantly a global approach.

This transnational team grant proposal is tasked to apply new machine learning approaches for modelling AMR for faster diagnosis, better surveillance and prediction of resistance emergence. Specifically, we will develop machine learning implementation that can orient the selection of treatments by assessing the level of resistance, provide rational for the generation of novel antibiotics, and assist in the surveillance of human and livestock AMR around the globe.

To achieve this, we have assembled a transnational team (Canada, China, Finland, France) with complementary skills with demonstrated expertise in machine learning applied to both genomics and metabolomics and AMR domain experts. Our transnational team has all the elements to be highly impactful and to continue collaborating well past the JPIAMR funding period.

The complementarity of our expertise will help us to tackle the challenges ahead and ensure our continued success. 

An exception drug is a drug that is not usually covered by the public drug insurance plan (RPAM). The measures implemented at the RAMQ for exceptional drugs allow the entire population to obtain coverage for certain drugs if they are used in compliance with the indications recognized by the Institut national d'excellence en santé et services sociaux (INESSS). Exception drugs are now a large and constantly increasing part of total spending on prescription drugs.

For the RPAM, one of the ways to control this increase is to reimburse these drugs according to pre-established rules. Currently, the system automatically processes around 20% of requests while the rest are directed to a case-by-case analysis, which generates delays.

This project is to help the business sector respond more quickly to requests for approval of exception drugs. A tool will be developed based on 15 years of data collected by the current system, and will aim to increase the number of requests processed automatically.

Data sharing is often limited by privacy issues. This is very common in particular for health datasets, given the inherent sensitivity of this type of data. When sharing of the original dataset is not possible, one method that can be used is to generate a synthetic dataset, which contains as much statistical information as possible from the original dataset, but which provides data on false individuals in order to protect the confidentiality of respondents.

This project is interested in rigorously measuring the confidentiality protection offered by a synthetic dataset. We will carefully examine some measures proposed in the literature, to understand their guarantees and the differences and similarities between them in order to identify the measure (s) that would be the most relevant for the sharing of synthetic data.

Data sharing is often limited by privacy issues. This is very common in particular for health datasets, given the inherent sensitivity of this type of data. When sharing of the original dataset is not possible, one method that can be used is to generate a synthetic dataset, which contains as much statistical information as possible from the original dataset, but which provides data on false individuals in order to protect the confidentiality of respondents. One way to ensure that these synthetic data effectively protect respondents is to use differential confidentiality, a rigorous measure of disclosure risk. 
This project is interested in how to analyze these synthetic datasets to obtain valid statistical results, as traditional methods of inference need to be modified to account for the variability added by the generation of the synthetic dataset.