Research

Research Funders

Affiliated with

At MJ LAB, we work to inform public health policy and decision-making to improve outcomes on a population level.

We are driven by three primary goals. First, we conduct simulation modeling and informatics research on population-based health policies. Second, we investigate how human decision-making impacts healthcare systems to understand why many health policies fail or produce unintended outcomes. Finally, we use data science to explore the root causes of public health issues, developing methods to connect models with quantitative data and bridging gaps across various methodological domains.

In all these efforts, we collaborate closely with subject matter experts to ensure our approaches are well-informed by the nuances of each complex health problem.

Our Research Methods:

Systems Thinking and Simulation Modeling

• System dynamics: from qualitative participatory methods to quantitative modeling, including cohort and agent-based approaches
• Check out a collection of our online model interfaces here

Cost-Effectiveness Analysis

• Using model-based simulations to assess policy and intervention cost-effectiveness

Optimization

• Applying optimization techniques to improve intervention efficiency and resource allocation

Advanced Statistical Analysis and Data Science

• Employing advanced statistical methods and data science to uncover patterns, predict outcomes, and derive actionable insights from complex datasets

Generative AI

• Enhancing approaches above by leveraging recent developments in generative AI