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Compensating for Uncertainty Biases in Health Risk Judgments

补偿健康风险判断中的不确定性偏差

基本信息

批准号：
7926647
负责人：
SCOTT D FERSON
金额：
$ 162.78万
依托单位：
APPLIED BIOMATHEMATICS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-30 至 2013-09-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7926647
关键词：
Accounting Address Affect Algorithms Anxiety Bayesian Analysis Benefits and Risks Binding Bypass Case-Control Studies Clinical Clinical Data Cognitive Communication Complex Computer Simulation Computer software Coronary heart disease Counseling Data Data Set Decision Making Disease Face Failure Frequencies Health Human Illusions Individual Informed Consent Institution Internet Intervention Judgment Lead Light Logistic Regressions Measurement Measures Medical Methods Nature Optical Illusions Outcome Output Paper Patients Perception Physicians Probability Professional counselor Psyche structure Research Risk Risk Factors Sample Size Sampling Errors Software Tools Solutions Subgroup Surveys Techniques Technology Test Result Testing Time Uncertainty base clinical decision-making data format design experience improved preference public health relevance risk perception simulation statistics theories

项目摘要

DESCRIPTION (provided by applicant): Accurate statistical data about medical intervention outcomes are often confusing, incomplete or inaccessible to patients. Without such information, patients are frustrated, and the spirit of informed consent is thwarted. Even when relevant data are available, they may not be correctly communicated. Practicing physicians may not provide accurate counsel about, for example, the probabilistic implications of test results. Consequently, false positive results from medical tests result in needless anxiety, and false negatives result in needless delay. Moreover, some physicians recommend treatments they prefer instead of undertaking the time-consuming effort to make the patient an informed decision maker. Physicians and medical counselors themselves are often confused about the implications of complex numerical information. Poor communication of risks can lead to patients making poor choices. Physicians and their patients will benefit from software tools that answer two needs within the broad field of clinical decision support: (1) helping explain the meaning of the results of a medical test in a way that is understandable and accurate, and (2) helping to decide among treatment options. The relevant information is usually encoded in terms of outcome frequencies or probabilities, but there are two serious complicating issues. The first issue is that there is usually uncertainty about the data arising from sampling error due to limited sample sizes, random measurement error, and a variety of systematic measurement biases. The second complicating issue is that humans are beset by a host of cognitive illusions that confuse their perception of frequency and probability information. Our proposed technology to address these two software needs differs from previous attempts in two main ways. First, we explicitly represent the uncertainty about frequencies and probabilities using robust Bayes methods (also known as Bayesian sensitivity analysis) which are part of the theory of imprecise probabilities. These methods allow us to generate practical advice in the face of uncertainty. For instance, when information is added to an assessment to personalize it for an individual, the uncertainty about a probability might widen if sample sizes are much smaller for subgroups. Second, we make use of findings in psychometry to compensate for cognitive illusions in the perception of frequencies and probabilities. Although statistical innumeracy is often attributed to mental biases and misperceptions, we believe that recent research is convincing that many of the misunderstandings and failures to communicate are caused by flawed presentation of medical statistics. Clinical and other evidence suggest that data formats strongly affect interpretability. Our techniques will compensate for cognitive biases and convey risks in a proper light so that their implications are easily understood. PUBLIC HEALTH RELEVANCE: Statistical data about test results and medical intervention outcomes are often confusing because imprecision about frequencies is hard to convey and because of multiple cognitive illusions about uncertainty. Physicians and their patients will benefit from well designed software tools that compensate for these problems to (1) explain the meaning of the results of a medical test in a way that is understandable and accurate, and (2) help to decide among treatment options that often have complex arrays of probabilistic outcomes.

描述(由申请人提供)：有关医疗干预结果的准确统计数据往往令人困惑、不完整或患者无法获得。没有这样的信息，患者会感到沮丧，知情同意的精神也会受挫。即使有相关数据，也可能无法正确传达。例如，执业医生可能不会就测试结果的概率含义提供准确的建议。因此，医学检查的假阳性结果会导致不必要的焦虑，而假阴性会导致不必要的延误。此外，一些医生建议他们更喜欢的治疗方法，而不是承担耗时的努力，使患者成为知情的决策者。医生和医疗顾问自己经常对复杂的数字信息的含义感到困惑。风险沟通不力可能导致患者做出糟糕的选择。医生和他们的患者将受益于满足临床决策支持广泛领域中两个需求的软件工具：(1)帮助以易于理解和准确的方式解释医学测试结果的意义，以及(2)帮助在治疗方案中做出决定。相关信息通常根据结果频率或概率进行编码，但有两个严重的复杂问题。第一个问题是，由于样本量有限、随机测量误差和各种系统测量偏差，采样误差通常会导致数据存在不确定性。第二个复杂的问题是，人类受到一系列认知错觉的困扰，这些错觉混淆了他们对频率和概率信息的感知。我们提出的解决这两种软件需求的技术在两个主要方面与以前的尝试不同。首先，我们使用不精确概率理论中的稳健贝叶斯方法(也称为贝叶斯灵敏度分析)来显式地表示关于频率和概率的不确定性。这些方法使我们能够在面对不确定性时提出实用的建议。例如，当信息被添加到评估中以使其针对个人进行个性化时，如果子组的样本大小要小得多，则概率的不确定性可能会扩大。其次，我们利用心理测量学中的发现来补偿对频率和概率的感知中的认知错觉。虽然统计上的失误常常归因于心理偏见和误解，但我们相信，最近的研究令人信服，许多误解和沟通失败是由医疗统计数据的缺陷陈述造成的。临床和其他证据表明，数据格式强烈影响可解释性。我们的技术将弥补认知偏差，并以适当的方式传递风险，以便很容易理解它们的含义。公共卫生相关性：关于检测结果和医疗干预结果的统计数据往往令人困惑，因为很难传达关于频率的不精确，以及对不确定性的多重认知错觉。医生和他们的患者将受益于精心设计的软件工具，这些工具可以弥补这些问题：(1)以一种易于理解和准确的方式解释医学测试结果的意义，以及(2)帮助在通常具有复杂概率结果数组的治疗方案中做出决定。