  Generation of automated clinical notes have been posited as a strategy to
mitigate physician burnout. In particular, an automated narrative summary of a
patient's hospital stay could supplement the hospital course section of the
discharge summary that inpatient physicians document in electronic health
record (EHR) systems. In the current study, we developed and evaluated an
automated method for summarizing the hospital course section using
encoder-decoder sequence-to-sequence transformer models. We fine tuned BERT and
BART models and optimized for factuality through constraining beam search,
which we trained and tested using EHR data from patients admitted to the
neurology unit of an academic medical center. The approach demonstrated good
ROUGE scores with an R-2 of 13.76. In a blind evaluation, two board-certified
physicians rated 62% of the automated summaries as meeting the standard of
care, which suggests the method may be useful clinically. To our knowledge,
this study is among the first to demonstrate an automated method for generating
a discharge summary hospital course that approaches a quality level of what a
physician would write.
