Healthcare Research, Informatics
Causal Inference, Machine Learning, Statistics
Relevant Projects:

National Institutes of Health / National Heart, Lung, and Blood Institute


American Thoracic Society, Members in Transition and Training Committee; AcademyHealth; Society for Critical Cre Medicine

Andrew J. Admon, MD, MPH, MSc

Assistant Professor

Department of Internal Medicine

Assistant Professor of Internal Medicine, Medical School and Assistant Professor o f Epidemiology, School of Public Health

I am a pulmonary and critical care physician who is passionate about improving critical care delivery by applying advanced methods for causal inference to observational data. My prior work has leveraged real-world data clinical and administrative data to study the epidemiology of critical illness, the organization of critical care, and health care financing.

My current work leverages real-world clinical data to understand whether and how care team fragmentation (transitions of physicians and other providers while a patient is still hospitalized) influences clinical outcomes like survival and recovery. Answering these questions correctly requires methods that are attentive to the complex causal structure underlying the relationship, depicted here. It features time-varying exposures (A), confounders (L), and mediators (M), all of which can influence clinical outcomes (Y). Arrows in the figure identify directional (i.e., causal) relationships between variables.