背景:实践中的医学诊断通过连续的反馈循环与研究联系在一起:对确诊病例的研究塑造了我们对疾病的理解,塑造未来的诊断实践。不考虑不完善和复杂的诊断过程,在该过程中,某些病例更有可能被正确诊断(或完全诊断),反馈回路可能无意中加剧未来的诊断错误和偏见。
方法:如果关于疾病病因的误导性证据鼓励自我延续的系统性错误,则会发生反馈回路失败,影响未来的诊断和病人护理。本文定义了医疗诊断中反馈回路故障的场景。
方法:通过模拟案例,我们描述了疾病的发病率,介绍,当观察数据被总结为诊断错误引起的偏见时,风险因素可能会被误解。第四模拟扩展到进行性疾病。
结果:当严重的疾病更容易被诊断时,不太严重的病例无法诊断,越来越导致对疾病表现的患病率和异质性的低估。观察到的人口统计学群体之间的发病率和症状差异可能是由风险差异驱动的。介绍,诊断过程本身,或者这些的组合。我们提出了对风险因素和代表性的看法如何驱动诊断的可能性。患者组之间不同的诊断率可以反馈越来越大的诊断错误和诊断和治疗时机的差异。
结论:过去的数据和未来的医疗实践之间的反馈循环似乎是有益的。然而,在看似合理的情况下,执行不当的反馈回路会降低护理水平。基于诊断个体的观察数据的直接总结可能会产生误导,特别是那些影响诊断过程本身的症状和危险因素。
结论:关于疾病的现有证据可以(并且应该)影响诊断过程。如果有偏见的“证据”鼓励诊断错误,则可能会发生反馈回路故障,导致未来证据库的错误。当轻度病例与重度病例或人口统计组之间的诊断准确性有所不同时,关于疾病患病率和表现的错误结论将导致没有特别考虑这种变异性。在诊断过程中使用人口统计特征应谨慎合理,特别是避免潜在的认知偏见和过度矫正。
BACKGROUND: Medical diagnosis in practice connects to research through continuous feedback loops: Studies of diagnosed cases shape our understanding of disease, which shapes future diagnostic practice. Without accounting for an imperfect and complex diagnostic process in which some cases are more likely to be diagnosed correctly (or diagnosed at all), the feedback loop can inadvertently exacerbate future diagnostic errors and biases.
METHODS: A feedback loop failure occurs if misleading evidence about disease etiology encourages systematic errors that self-perpetuate, compromising future diagnoses and patient care. This article defines scenarios for feedback loop failure in medical diagnosis.
METHODS: Through simulated cases, we characterize how disease incidence, presentation, and risk factors can be misunderstood when observational data are summarized naive to biases arising from diagnostic error. A fourth simulation extends to a progressive disease.
RESULTS: When severe cases of a disease are diagnosed more readily, less severe cases go undiagnosed, increasingly leading to underestimation of the prevalence and heterogeneity of the disease presentation. Observed differences in incidence and symptoms between demographic groups may be driven by differences in risk, presentation, the diagnostic process itself, or a combination of these. We suggested how perceptions about risk factors and representativeness may drive the likelihood of diagnosis. Differing diagnosis rates between patient groups can feed back to increasingly greater diagnostic errors and disparities in the timing of diagnosis and treatment.
CONCLUSIONS: A feedback loop between past data and future medical practice may seem obviously beneficial. However, under plausible scenarios, poorly implemented feedback loops can degrade care. Direct summaries from observational data based on diagnosed individuals may be misleading, especially concerning those symptoms and risk factors that influence the diagnostic process itself.
CONCLUSIONS: Current evidence about a disease can (and should) influence the diagnostic process. A feedback loop failure may occur if biased \"evidence\" encourages diagnostic errors, leading to future errors in the evidence base.When diagnostic accuracy varies for mild versus severe cases or between demographic groups, incorrect conclusions about disease prevalence and presentation will result without specifically accounting for such variability.Use of demographic characteristics in the diagnostic process should be done with careful justification, in particular avoiding potential cognitive biases and overcorrection.