Department of Surgery and Cancer, Imperial College London, London, UK.
Cogn Res Princ Implic. 2022 Jul 27;7(1):70. doi: 10.1186/s41235-022-00421-6.
Evidence-based algorithms can improve both lay and professional judgements and decisions, yet they remain underutilised. Research on advice taking established that humans tend to discount advice-especially when it contradicts their own judgement ("egocentric advice discounting")-but this can be mitigated by knowledge about the advisor's past performance. Advice discounting has typically been investigated using tasks with outcomes of low importance (e.g. general knowledge questions) and students as participants. Using the judge-advisor framework, we tested whether the principles of advice discounting apply in the clinical domain. We used realistic patient scenarios, algorithmic advice from a validated cancer risk calculator, and general practitioners (GPs) as participants. GPs could update their risk estimates after receiving algorithmic advice. Half of them received information about the algorithm's derivation, validation, and accuracy. We measured weight of advice and found that, on average, GPs weighed their estimates and the algorithm equally-but not always: they retained their initial estimates 29% of the time, and fully updated them 27% of the time. Updating did not depend on whether GPs were informed about the algorithm. We found a weak negative quadratic relationship between estimate updating and advice distance: although GPs integrate algorithmic advice on average, they may somewhat discount it, if it is very different from their own estimate. These results present a more complex picture than simple egocentric discounting of advice. They cast a more optimistic view of advice taking, where experts weigh algorithmic advice and their own judgement equally and move towards the advice even when it contradicts their own initial estimates.
循证算法可以提高非专业人士和专业人士的判断和决策能力，但这些算法的应用仍然不足。关于接受建议的研究表明，人们往往会低估建议——尤其是当建议与自己的判断相矛盾时（“自我中心的建议折扣”）——但这可以通过了解顾问过去的表现来缓解。建议折扣通常是通过具有低重要性的结果（例如一般知识问题）和学生作为参与者的任务来研究的。使用法官-顾问框架，我们测试了建议折扣的原则是否适用于临床领域。我们使用了现实的患者情况、经过验证的癌症风险计算器的算法建议和全科医生（GP）作为参与者。GP 可以在收到算法建议后更新他们的风险估计。其中一半人收到了有关算法推导、验证和准确性的信息。我们测量了建议的权重，发现平均而言，GP 平等地权衡了他们的估计和算法——但并非总是如此：他们保留初始估计值的时间占 29%，完全更新的时间占 27%。更新不取决于 GP 是否被告知算法。我们发现估计值更新与建议距离之间存在微弱的负二次关系：尽管 GP 平均整合算法建议，但如果建议与他们自己的估计值相差很大，他们可能会对其进行一定程度的折扣。这些结果呈现出比简单的自我中心建议折扣更复杂的情况。它们对接受建议的看法更为乐观，即专家平等地权衡算法建议和他们自己的判断，并在建议与他们的初始估计值相矛盾时向建议靠拢。
