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Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study
Augmentations were evaluated for errors by clinicians, and we found misleading errors occur, with errors more common in real donated notes than synthetic notes, illustrating the importance of carefully written clinical notes.
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes
The large amount of time clinicians spend sifting through patient notes and documenting in electronic health records (EHRs) is a leading cause of clinician burnout.
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