At MJ Lab, we follow three goals. First, we conduct simulation modeling and informatics research for various population-based health policies, focusing on health outcomes and cost-effectiveness. In our modeling research—drawn on theories of optimization and strategy—we analyze the impacts of large-scale policies for prevention, screening, and treatment. We have developed models for opioids, obesity, post-traumatic stress disorder, and depression. We are currently working with the FDA on two U01 grants, developing an opioid systems model to inform opioid policies at the FDA and other government agencies.
Second, we focus our research on mechanisms that connect human decision-making to health care systems, because that is where many important policy-resistant problems lie. In particular, we aim to understand how and why many health policies fail to produce lasting results or worse, create results counter to their goals.
Third, we use data science approaches to understand the underlying causes of public health problems and develop methods to rigorously connect models with quantitative data. The growing complexity of health care issues, combined with the ubiquity of large amounts of data, requires increasingly sophisticated analytical methods. We complement our phenomenological research with methodological contributions that build bridges across methodological and application domains.