Personalized Clinical Note Generation from Doctor-Patient Conversations
This addresses the problem of automating clinical note generation for physicians, reducing their documentation burden, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackles generating personalized clinical notes from doctor-patient conversations by modeling implicit physician styles and enabling enrollment of new physicians with limited data, resulting in ROUGE-2 score improvements of 13.8% for History of Present Illness, 88.6% for Physical Examination, and 50.8% for Assessment & Plan sections.
In this work, we present a novel technique to improve the quality of draft clinical notes for physicians. This technique is concentrated on the ability to model implicit physician conversation styles and note preferences. We also introduce a novel technique for the enrollment of new physicians when a limited number of clinical notes paired with conversations are available for that physician, without the need to re-train a model to support them. We show that our technique outperforms the baseline model by improving the ROUGE-2 score of the History of Present Illness section by 13.8%, the Physical Examination section by 88.6%, and the Assessment & Plan section by 50.8%.