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DIAGNOSTICITY AND HEURISTICS IN MEDICAL DECISION MAKING

医疗决策中的诊断性和启发式

基本信息

批准号：
3373741
负责人：
Fredric M. Wolf
金额：
$ 13.2万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
1985
资助国家：
美国
起止时间：
1985-09-30 至 1989-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/3373741
关键词：
computer assisted diagnosis computer assisted medical decision making computer simulation data collection methodology /evaluation diagnosis quality /standard judgment medical education physicians

项目摘要

The focus of this research is on increasing the understanding of the cognitive processes involved in information acquisition and clinical decision making in medicine. This will be investigated by using theories of probability (specifically, Bayes' theorem) and heuristics (cognitive strategies) and experimental procedures previously developed in these areas. The primary aim of this research is to examine the way in which physicians seek diagnostically relevant information and the relationship of information selection to the selection of a most likely diagnosis. The specific objectives of this research are (1) to examine the effects of how a problem is framed on the selection of diagnostic information in patient cases, (2) to investigate the cognitive heuristics used in the selection of diagnostic information, (3) to study the threshold value of information that influences the selection of diagnostic information, (4)\to examine the relationship between selection of information and a diagnosis, and (5) to identify educational strategies for training medical students and physicians to (a) select optimally diagnostic information for the systematic evaluation of disease candidates in the differential diagnosis list and (b) become aware of potential biases in diagnostic evaluation that may result from the selection of non-optimal information. A series of experiments involving medical students, beginning house officers, and practicing physicians will be conducted to study these issues. In addition, a computerized simulation model of the information acquisition, evaluation, and diagnostic process in medical decision making will be developed. The data will be analyzed by comparing physicians' selections of diagnostic data and most likely diagnosis for patient cases to optimal decisions based on the competing hypotheses heuristic and Bayes' theorem. These optimal decisions dictate that each piece of diagnostic data be evaluated across all competing diseases in the differential diagnosis list. Both framing effects and threshold effects of the problem will be studied to determine how they influence physicians' judgments and cognitive strategies. Results of these experiments will have significant implications in training medical students and physicians in information management and problem solving. Insight into efficient heuristics (strategies) for collecting and evaluating information will be obtained. Non-optimal strategies and biases in judgments that may result will also be identified.

这项研究的重点是增加对这一问题的认识信息获取与临床中的认知过程医学上的决策。这将用理论来研究。概率(特别是贝叶斯定理)和启发式(认知策略)和以前在这些方面开发的实验程序区域。这项研究的主要目的是检查医生寻求与诊断相关的信息和从信息选择到选择最有可能的诊断。这项研究的具体目标是：(1)考察中的诊断信息选择问题是如何框定的患者案例，(2)调查认知启发式在诊断信息的选择，(3)研究诊断信息的阈值影响诊断信息选择的信息，(4) 检查信息选择和诊断之间的关系，以及(5)确定培养医学生的教育策略以及医生(A)为患者选择最佳的诊断信息鉴别诊断中对候选疾病的系统评价列出和(B)意识到诊断评估中的潜在偏差可能是由于选择了非最佳信息造成的。一系列参与实验的有医科学生、新入职的内务官员和执业医师将对这些问题进行研究。在……里面此外，还建立了信息采集的计算机模拟模型，医疗决策中的评估和诊断过程将是发展起来的。数据将通过比较医生的选择进行分析患者病例的诊断数据和最有可能的诊断达到最佳基于竞争假设、启发式和贝叶斯定理的决策。这些最优决策规定了每条诊断数据在鉴别诊断中对所有竞争疾病进行评估单子。问题的框架效应和阈值效应都将是研究以确定它们如何影响医生的判断和认知战略。这些实验的结果将对训练有重要的意义医学生和医生在信息管理中存在的问题解决问题。洞察有效的启发式(策略)收集和将获得评估信息。非最优策略和偏差在判决中，可能的结果也将被确定。