A retrieval-plus-fine-tuning pipeline for generating SOAP clinical summaries from doctor-patient conversations outperforms zero-shot GPT-4 models on a 20-conversation simulated test set.
WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models
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abstract
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.
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CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations
A retrieval-plus-fine-tuning pipeline for generating SOAP clinical summaries from doctor-patient conversations outperforms zero-shot GPT-4 models on a 20-conversation simulated test set.