Longitudinal Cognitive Trajectory Modelling and Phenotyping with Multiple Features using Health Administrative Data
This project aims to develop and apply both model-based and algorithm-based statistical and machine learning methods to longitudinal trajectory clustering of multiple repeatedly measured features of cognitive decline.
Lead: Kuan Liu
Affiliates: Geoffrey M. Anderson
Bayesian sensitivity analyses for time-dependent unmeasured confounding
The strongly ignorable treatment assignment assumption (also known as no unmeasured confounding) is an untestable causal assumption which requires a sufficiently large set of covariates being measured to ensure that subjects are exchangeable across the observed exposure given measured covariates […]
Lead: Kuan Liu
Evaluation of rapid virtual care scale-up during the Covid-19 Pandemic: (DECISION)
This research explores the impact of the shift to virtual care at the level operations and professional and institutional identity in four distinct clinical sites in UHN and SickKids.
Lead: Robert Paul
Affiliates: Maria Athina (Tina) Martimianakis
Investigating regional trends in electronic cigarette consumption in Canada and identifying geographical characteristics associated with high consumption
This study investigates the prevalence of e-cigarette consumption and neighbourhood/community characteristics across Canada.
Lead: Kuan Liu
Affiliates: Aya Mitani
Consideration of Trade-offs Regarding COVID-19 Containment Measures in the United States: Implications for Canada
This study attempts to find if stimulus packages can be deemed cost-effective and whether shutdown measures are too restrictive resulting in massive economic shock to individuals.
Lead: Mayvis Rebeira
Affiliates: Eric Nauenberg
Data Science in Context: Understanding the Work of Health Data Scientists
This research study is a qualitative content analysis of health data science curricula and an observational study of the work of health data scientists.
Lead: Aviv Shachak
Improving outcomes and efficiency by reducing unnecessary invasive angiograms using AI-based clinical decision support
CREATE (CentRe for digital hEalth and dATa sciEnce) has developed an AI model that can help reduce the number of unnecessary angiograms and now working to develop a clinical decision support tool for use at the point of care.
Lead: Jeremy Petch
Affiliates: Laura C. Rosella
Liquid Gold: Exploring the social landscape of expanding use of immune globulin in Canada
This project aims to evaluate the use of Ig products from production, evaluation, dissemination and use in the healthcare system. The goal of the project is to generate a better understanding of its use and generate policy-level insights into how best to achieve the equitable and cost-effective allocation of Ig products in the Canadian healthcare system.
Lead: Kelly Holloway
Developing a Pan-Canadian Risk-Assessment Tool to Proactively Assess Hospital Safeguards for Controlled Substances
A Pan-Canadian risk assessment tool is being developed for hospitals to identify, safeguard, and estimate the cost of drug diversion in hospitals.
Lead: Patricia Trbovich
Improving Surgical Safety with the Operating Room Black Box
NYGH has implemented the Operating Room Black Box (ORBB), which creates an audio/video record of surgical procedures over time to rigorously study how errors come about, and how they can be prevented.
Lead: Patricia Trbovich
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Communications
Marielle Boutin
Email Address: ihpme.communications@utoronto.ca