Abstract: In the market for medical care, physicians often face uncertainty about how a newly diagnosed patient will respond to available treatments. I design a framework to analyze how price and promotion influence the learning process as the patient and physician jointly search for the most effective treatment. The dynamic model I employ accommodates large choice sets and permits learning to be correlated within clusters of choices. Applying this model to depression care, I ask how the design of a health insurance plan, including the required patient out-of-pocket costs by drug, might interact with the physician’s learning process. In the data, patient costs largely correspond to the drug’s wholesale cost. In contrast, I design a new drug pricing schedule that lowers the patient cost for those drugs that the model suggests are best to sample early in the search process. By using these price incentives to redirect the search process, I find physicians identify the optimal treatment faster, leading to lower overall costs, improved adherence, and ultimately better patient health.