The biological and behavioral bases of decision-making in medical professionals

描述(由申请人提供)：对医疗决策的基本机制知之甚少，或者实际上对类似类型的高维环境中的任何决策都知之甚少。我们认为，这些环境，再加上围绕专业知识的社会条件，导致了重大的决策偏差，特别是确认偏差和锚定，这可能在一定程度上导致CER的采用缓慢。我们认为，在受控实验条件下研究医生的决策过程，结合神经数据的收集，将使我们能够理解助长这些偏见的基本机制，并导致更好的培训和行为干预来纠正它们。 我们将同时使用行为分析和功能磁共振分析来检查医疗专业人员估值的计算和机制基础。此外，我们将使用定量模型来探索在这一群体中参与决策的计算机制。此外，我们将应用新技术，使用实时功能磁共振反馈进行行为矫正。 确认偏差、追求成功和锚定是导致医生更新错误的潜在原因。我们的初步数据显示，我们表现不佳的学科医生有一种倾向，即迅速得出结论，然后忽视可能使他们的信念失效的治疗失败。这种确认偏差导致我们的受试者形成了次优的治疗算法。在这一目标中，我们将：1)在医学和非医学背景下的受控实验环境中估计医生学习的模型；2)估计给定额外外部先验信息(如先前的诊断)的医生的信念形成和信息搜索的模型；识别特别擅长这一过程的医生的行为和神经标记物；以及评估简单行为干预对结果的效果。 我们的初步工作表明，在我们的临床决策任务中，表现出色的人表现出明显不同的神经激活。我们相信，通过向医生反馈他们的神经反应以及他们的治疗结果，我们将能够提高他们正确学习最佳治疗算法的能力。 公共卫生相关性：对医疗决策的基本机制知之甚少。我们认为，这样的多维环境，再加上围绕专业知识的社会条件，导致了重大的决策偏差，这可能在一定程度上解释了CER采用缓慢的原因。我们将在受控实验条件下研究医生的决策过程，并结合神经成像数据，以便更好地理解助长这些偏见的基本机制。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.