Physicians write notes about patients. In doing so, they reveal much about themselves. Using data from 129,228 emergency room visits, we train a model to identify notes written by fatigued physicians -- those who worked 5 or more of the prior 7 days. In a hold-out set, the model accurately identifies notes written by these high-workload physicians, and also flags notes written in other high-fatigue settings: on overnight shifts, and after high patient volumes. Model predictions also correlate with worse decision-making on at least one important metric: yield of testing for heart attack is 18% lower with each standard deviation increase in model-predicted fatigue. Finally, the model indicates that notes written about Black and Hispanic patients have 12% and 21% higher predicted fatigue than Whites -- larger than overnight vs. daytime differences. These results have an important implication for large language models (LLMs). Our model indicates that fatigued doctors write more predictable notes. Perhaps unsurprisingly, because word prediction is the core of how LLMs work, we find that LLM-written notes have 17% higher predicted fatigue than real physicians' notes. This indicates that LLMs may introduce distortions in generated text that are not yet fully understood.

在对129,228次急诊室就诊记录的数据进行分析后，我们训练了一个模型，用于识别疲劳医生所写的医疗记录，并将其与其他疲劳状态下的医疗记录进行对比。模型的预测结果与至少一个重要指标的糟糕决策相关，即心脏病测试的结果，每个标准差增加，疲劳预测值降低18%，最终发现模型指示涉及黑人和西班牙裔患者的记录比白人的预测疲劳值高出12%和21%，这一差距大于夜间和白天之间的差异。我们的研究结果对于大型语言模型(LLMs)具有重要的影响，我们的模型指示疲劳医生所写的记录更加可预测。并且，不出奇地，因为词语预测是LLMs运作的核心，我们发现LLMs所生成的记录的疲劳预测值比真实医生的记录高出17%，这表明LLMs可能引入了尚未完全理解的文本失真。

临床记录揭示医生疲劳