Personalized Clinical Note Generation from Doctor-Patient Conversations

TL;DR

This paper introduces a new method for generating personalized clinical notes from doctor-patient conversations, effectively modeling physician styles and preferences, and supporting new physicians with limited data.

Contribution

It presents a novel technique for personalized clinical note generation that adapts to new physicians with minimal data without retraining the model.

Findings

Improved ROUGE-2 scores for key clinical note sections

Outperforms baseline models in note quality

Supports new physicians with limited data

Abstract

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%.