Decision-making problems in healthcare are inherently complex. The complexity arises due to time-sensitivity of decisions, resource limitations, uncertainty in health trajectories, and biases in data and human behavior, among other factors. Further, healthcare decisions exhibit dependencies. For example, decisions about screening can impact the diagnosis. Delays and inequities in the diagnosis may impact which populations are medically underserved.
Our research lab focuses utilizes decision analysis, stochastic modeling, statistical learning and Operations Research to analyze, evaluate, and inform clinical decision problems, particularly in ambulatory setting, under uncertainty.