Little is known about the basic mechanisms of medical decision-making, or in fact any decision making in similar sorts of high-dimensional environment. We propose that these environments, paired with the social conditions around expertise lend themselves to significant decision biases, particularly confirmation-bias and anchoring, which may in part contribute to slow CER adoption. We feel that studying decision-making processes in doctors under controlled experimental conditions, paired with the collection of neural data will allow us to understand the fundamental mechanisms fueling these biases and lead to better training and behavioral interventions to correct them. We will be using both behavioral and fMRI analysis to examine the computational and mechanistic underpinnings of valuation in medical professionals. In addition we will be using quantitative models to explore the computational mechanisms involved in decision making in this population. Further, we will be applying new techniques using real-time fMRI feedback for behavioral modification. Confirmation-bias, success chasing, and anchoring are among the potential causes for faulty updating in physicians. Our preliminary data show that our low-performing subject physicians had a tendency to come to a conclusion quickly and then ignore treatment failures that might invalidate their beliefs. This confirmation-bias led to our subjects forming suboptimal treatment algorithms. In this aim, we will: 1) estimate models of physician learning in a controlled experimental setting in both medical and non-medical contexts, and 2) estimate models of belief formation and information search in physicians given extra external prior information, such as a previous diagnosis;identify the behavioral and neural markers of physicians who are particularly adept at this process;and assess the efficacy of a simple behavioral intervention on outcomes. Our preliminary work showed that high-performers in our clinical decision-making task showed distinctly different neural activations. We believe that by giving physicians feedback on their neural responses as well as their treatment outcomes we will be able to improve their ability to correctly learn the optimal treatment algorithm.
Little is known about the basic mechanisms of medical decision-making. We propose that such multidimensional environments, paired with the social conditions around expertise, lend themselves to significant decision biases, which in part may explain slow CER adoption. We will study decision-making processes in physicians under controlled experimental conditions, paired with the neuroimaging data, to allow for better understanding of the fundamental mechanisms fueling these biases.
