Doctor decision making with time inconsistent patients.

Non-adherence to treatments is prevalent. The aim of this paper is to model how doctors should adapt their medical treatment decisions if non-adherence is due to present-bias in the patient population, and to test the predictions of this model in a lab experiment. Under certain conditions, a rational doctor should adapt to non-adherence by choosing a treatment all patients complete (though less effective) when the probability of a patient being present-biased is sufficiently large. This is explored in a lab experiment where we test whether students in the doctor role adapt their behaviour as they learn about the distribution of non-adherence (due to present bias) in the patient population over the rounds of the experiment. We test the model prediction when we align individual incentives with the goal of maximising overall patient welfare. The results show that, on average, participants adapt to non-adherence as they learn about the probability of non-adherence (due to present-bias). However, a proportion of participants do not adapt to the optimal choice. The rate of adaptation was similar for the first 5 rounds under both individual incentives and salary. However, participants continued to adapt after round 5 under individual incentives whilst adaptation plateaued under salary. The adaptation to non-adherence may indicate that adherence can be improved by providing doctors with information about the probability of non-adherence (due to present-bias) in their patients.