W. Alton Russell, PhD
Assistant Professor
- alton.russell@mcgill.ca
- twitter.com/altonrus
- 2001 McGill College Avenue, Montreal, QC H3A 1G1
- Our lab sits within the McGill Clinical and Health Informatics Research Group on the 11th floor of 2001 McGill College Avenue. Visitors must arrange for someone to let them into the lab or visit to the reception area at the 12th floor.
Alton joined the McGill School of Population and Global Health as an Assistant Professor in 2022. As a researcher, Alton has developed decision analytic models and data-driven analyses for multiple areas of health policy and clinical practice, including blood donation and transfusion, managing pediatric kidney disease, opioid use disorder and overdose, and gastroenterology. Alton is also:
- Associated investigator, Research Institute of the McGill University Health Centre (RI-MUHC)
- Researcher, McGill Quantitative Life Sciences program
- Scientific advisor, COVID-19 Immunity Task Force
- Member, Group for Research in Decision Analysis (GERAD)
You can view his CV here.
- Data-driven decision analysis
- Health policy modeling
- Health technology assessment
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Postdoc, Mass General Hospital Institute for Technology Assessment, 2021
Harvard Medical School
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PhD, Management Science and Engineering, 2021
Stanford University
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MSc, Management Science and Engineering, 2018
Stanford University
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BSc, Industrial Engineering (Health Systems Engineering concentration), 2014
North Carolina State University
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BSc, Interdisciplinary Studies (Global Health and Sustainability concentration), 2014
North Carolina State University
D3Mod Lab
The Data-Driven Decision Modeling Lab–or D3Mod Lab–aims to enable the efficient, effective, and equitable use of finite healthcare resources. We use do so by developing, assessing, and applying traditional decision modeling methods (mathematical modeling, simulation, optimization) together with data-driven methods (machine learning, Bayesian statistics). Our work informs challenging decisions in health policy and medicine. We are part of the McGill Clinical and Health Informatics Research Group and the McGill School of Population and Global Health in Montreal, Quebec, Canada.
Lab members
Yuan Yu
Postdoc
Small area estimation, Bayesian hierarchical modeling, Sampling methods,
survey studies, Bayesian applications, statistical and machine learning
Chen-Yang Su
PhD student (rotation)
Clinical decision-making, Healthcare applications of AI and machine
learning, Health economics and economic evaluation
Huzbah Jahagirdar
MSc student
Health economics, Health policy modeling, Applied machine learning and data
science
Wanjin (Jennifer) Li
MSc student
Health technology assessment, Economic evaluation, Public health data
science
Matthew Knight
MSc student
Clinical decision-making, Public health data science, Disease surveillance
Melina Thibault
MSc student
Health informatics/digital health, Health policy modeling, Non-communicable disease epidemiology
Teaching
Alton teaches the following courses at McGill:
Research
The Decision Modeling Lab’s research informs health policy and clinical decisions through data-driven modeling and analysis. We use methods from decision science, optimization, epidemiology, health economics, and machine learning to enable the efficient, effective, and equitable utilization of resources. We collaborate with stakeholders in medicine and health policy to maximize our impact on policy and practice while extending the state of the art in data-driven decision modeling.
A major area of focus is data-driven decision analytic modeling, which integrates individual-level data into models that compare health intervention or policy options. Traditionally, decision analyses either model an ‘average’ patient or a relatively homogeneous cohort of synthetic individuals, extracting values from the literature or expert opinion to characterize the impacted population and estimate the impact of policy alternatives. This assumes risks and costs are not distributed across the population and interventions' treatment effects are homogeneous. Our lab is developing methdos to directly integrate individual-level data to reflect the true heterogeneity in patient populations and capture differences in expected outcomes under different policy alternatives. This enables more accurate estimation of the trade-offs involved with an intervention and allows us to look at the distributional impact of interventions to reveal potential inequities.
Current projects
Currently funded projects include:
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Analyzing the operational impact of a patient portal with propensity score matching
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Assessing the public health value of blood donor data with Bayesian modeling
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Developing an individualized approach to managing risk of iron deficiency in repeat blood donors using machine learning, optimization, and simulation
Pending projects
Several research projects that are pending funding decisions will use methods from epidemiology, machine learning, health economics and mathematical modeling to inform decisions in health policy and clinical practice. Prospective group members should send an email to Alton with their CV so he can determine whether any of the pending projects are aligned with your interests.
Open science research philospohy
Our group works hard to produce research that is informative, rigorous, transparent, and reproducible. The Decision Modeling Lab Manual describes our approach to open research and dissemination.
Thank you to our funders
Research in the decision modeling lab is supported by grants from:
