Compensating for Uncertainty Biases in Health Risk Judgments

描述（由申请人提供）：关于医疗干预结果的准确统计数据通常令人困惑，不完整或患者无法获得。如果没有这些信息，患者会感到沮丧，知情同意的精神也会受到阻碍。即使有相关数据，也可能无法正确传达。执业医师可能无法提供准确的建议，例如，测试结果的概率影响。因此，来自医学测试的假阳性结果导致不必要的焦虑，而假阴性结果导致不必要的延迟。此外，一些医生推荐他们喜欢的治疗方法，而不是花费时间让病人成为知情的决策者。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。风险沟通不畅可能导致患者做出错误的选择。医生和他们的患者将受益于软件工具，这些软件工具可以满足临床决策支持领域的两个需求：（1）以可理解和准确的方式帮助解释医学测试结果的含义，以及（2）帮助决定治疗方案。相关信息通常以结果频率或概率的形式进行编码，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，数据通常存在抽样误差。第二个复杂的问题是，人类被大量的认知错觉所困扰，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个软件需求的技术与以前的尝试在两个主要方面不同。首先，我们明确表示使用强大的贝叶斯方法（也称为贝叶斯敏感性分析）的频率和概率的不确定性，这是不精确概率理论的一部分。这些方法使我们能够在面临不确定性时提出切实可行的建议。例如，当将信息添加到评估中以针对个人进行个性化评估时，如果子组的样本量小得多，则概率的不确定性可能会扩大。其次，我们利用心理测量学的发现来补偿频率和概率感知中的认知错觉。虽然统计学上的不识数通常被归因于心理偏见和误解，但我们相信，最近的研究令人信服地表明，许多误解和沟通失败是由医疗统计数据的错误呈现造成的。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，并以适当的方式传达风险，以便其含义易于理解。 公共卫生相关性：关于测试结果和医疗干预结果的统计数据通常令人困惑，因为频率的不精确性很难传达，而且因为对不确定性的多种认知错觉。医生和他们的患者将受益于设计良好的软件工具，这些工具可以补偿这些问题，以（1）以可理解和准确的方式解释医学测试结果的含义，以及（2）帮助决定通常具有复杂的概率结果阵列的治疗方案。

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.