WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models
This addresses the problem of automating clinical documentation for healthcare professionals, though it is incremental as it builds on existing large language models.
The paper tackled automatic clinical note generation from doctor-patient conversations, achieving high performance with two approaches: fine-tuning a pre-trained language model and using few-shot in-context learning with GPT-4, which ranked second and first in the MEDIQA-Chat 2023 shared task and was preferred as often as human-written notes in expert evaluation.
This paper describes our submission to the MEDIQA-Chat 2023 shared task for automatic clinical note generation from doctor-patient conversations. We report results for two approaches: the first fine-tunes a pre-trained language model (PLM) on the shared task data, and the second uses few-shot in-context learning (ICL) with a large language model (LLM). Both achieve high performance as measured by automatic metrics (e.g. ROUGE, BERTScore) and ranked second and first, respectively, of all submissions to the shared task. Expert human scrutiny indicates that notes generated via the ICL-based approach with GPT-4 are preferred about as often as human-written notes, making it a promising path toward automated note generation from doctor-patient conversations.