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

医疗专业人员决策的生物学和行为基础

• 批准号: 8049915
• 负责人: P Read Montague
• 金额: $ 0.18万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2010-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8049915
• 关键词: Adopted; Adoption; Algorithms; Attenuated; Behavior Therapy; Behavioral; Belief; Biological; Clinical; Collection; Corpus striatum structure; Data; Decision Making; Diagnosis; Diagnostic; Environment; Equilibrium; Failure; Feedback; Frequencies; Health; Health Care Costs; Human; Imagery; Inferior; Inferior frontal gyrus; Lead; Learning; Lobule; Magnetic Resonance; Measures; Medical; Modeling; Monitor; Outcome; Overweight; Parietal; Patients; Performance; Phase; Physicians; Population; Positioning Attribute; Process; Recommendation; Simulate; Social Conditions; Sorting - Cell Movement; Source; Systematic Bias; Test Result; Testing; Time; Training; Treatment Efficacy; Treatment Failure; Treatment outcome; Update; Use of New Techniques; Ventral Striatum; Work; abstracting; base; clinical decision-making; cohort; comparative effectiveness; effective intervention; effectiveness research; experience; improved; neural patterning; neuroimaging; relating to nervous system; research study; response; success

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.

描述（由申请人提供）：对于医疗决策的基本机制，或者实际上在类似的高维环境中做出的任何决策，人们知之甚少。我们认为，这些环境与围绕专业知识的社会条件相结合，容易导致重大决策偏差，特别是确认偏差和锚定，这可能在一定程度上导致 CER 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，再加上神经数据的收集，将使我们能够了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这些偏见。 我们将使用行为和功能磁共振成像分析来检查医疗专业人员评估的计算和机械基础。此外，我们将使用定量模型来探索该人群决策中涉及的计算机制。此外，我们将应用实时功能磁共振成像反馈的新技术来进行行为矫正。 确认偏差、成功追逐和锚定是医生错误更新的潜在原因。我们的初步数据显示，表现不佳的受试者医生倾向于快速得出结论，然后忽略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。为此，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模型，2）在给定额外的外部先验信息（例如先前的诊断）的情况下估计医生的信念形成和信息搜索模型；识别特别擅长这一过程的医生的行为和神经标记；并评估简单行为干预对结果的功效。 我们的初步工作表明，临床决策任务中的高绩效者表现出明显不同的神经激活。我们相信，通过向医生提供有关其神经反应以及治疗结果的反馈，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：人们对医疗决策的基本机制知之甚少。我们认为，这种多维环境与围绕专业知识的社会条件相结合，会导致重大的决策偏差，这在一定程度上可以解释 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，并结合神经影像数据，以便更好地理解助长这些偏见的基本机制。