  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.
