Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques

By Kundan Krishna, Sopan Khosla, Jeffrey P. Bigham, Zachary C. Lipton. 2020

147 citations recorded by the source corpus.

Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout. In this paper, we introduce the first complete pipelines to leverage deep summarization models to generate these notes based on transcripts of conversations between physicians and patients. After exploring a spectrum of methods across the extractive-abstractive spectrum, we propose Cluster2Sent, an algorithm that (i) extracts important utterances relevant to each summary section; (ii) clusters together related utterances; and then (iii) generates one summary sentence per cluster. Cluster2Sent outperforms its purely abstractive counterpart by 8 ROUGE-1 points, and produces significantly more factual and coherent sentences as assessed by expert human evaluators. For reproducibility, we demonstrate similar benefits on the publicly available AMI dataset. Our results speak to the benefits of structuring summaries into sections and annotating supporting evidence when constructing summarization corpora.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing

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Citations in the corpus

Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text (2022, background): “For example, Krishna et al. (2021) investigate the generation of doctors’ notes from conversations and analyze the performance in the presence of speech recognition errors.”
Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation (2022, unknown): “, 2016b; Alsentzer and Kim, 2018), and with abstractive neural methods (Zhang et al., 2018; Liu et al., 2019; MacAvaney et al., 2019; Zhang et al., 2020; Enarvi et al., 2020; Joshi et al., 2020; Krishna et al., 2021; Chintagunta et al., 2021; Yim and Yetisgen-Yildiz, 2021; Moramarco et al., 2021; Zh”
SummScreen: A Dataset for Abstractive Screenplay Summarization (2021, background): “Relevant datasets have been studied for medical dialogues (Joshi et al., 2020; Krishna et al., 2021),”