Following each patient visit, physicians must draft detailed clinical summaries called SOAP notes . Moreover, with electronic health records, these notes must be digitized. For all the benefits of this documentation the process remains onerous , contributing to increasing physician burnout. In a parallel development, patients increasingly record audio from their visits (with consent), often through dedicated apps. In this paper, we present the first study to evaluate complete pipelines for leveraging these transcripts to train machine learning model to  generate these notes . We first describe a unique dataset of patient visit records, consisting of transcripts , paired SOAP notes, and annotations marking noteworthy utterances that support each summary sentence. We decompose the problem into extractive and abstractive subtasks, exploring a spectrum of approaches according to how much they demand from each component.  Our best performing method first (i) extracts noteworthy utterances via multi-label classification assigns them to summary section(s); (ii) clusters noteworthy utterances on a per-section basis; and (iii) generates the summary sentences by conditioning on the corresponding cluster and the subsection of the SOAP sentence to be generated. Compared to an end-to-end approach that generates the full SOAP note from the full conversation, our approach improves by 7 ROUGE-1 points . Oracle experiments indicate that fixing our generative capabilities, improvements in extraction alone could provide (up to) a further 9 ROUGE point gain .
Following each patient visit, physicians must draft a detailed clinical summary called a SOAP note . Moreover, with electronic health records, these notes must be digitized. Despite the benefits of this documentation , their creation remains an onerous process , contributing to increasing physician burnout. In this paper, we present the first study to evaluate complete pipelines to train summarization models to generate these notes from conversations between physicians and patients. We benefit from a dataset that, along with transcripts and paired SOAP notes, consists of annotations marking noteworthy utterances that support each summary sentence. We decompose the problem into extractive and abstractive subtasks, exploring a spectrum of approaches according to how much they demand from each component. We observe that the performance improves constantly as the extractive subtask is made more complex - an observation that we also replicate on the well-known AMI meeting summarization dataset. Our best performing method first (i) extracts noteworthy utterances via multi-label classification , assigning each to summary section(s); (ii) clusters noteworthy utterances on a per-section basis; and (iii) generates the summary sentences by conditioning on the corresponding cluster and the subsection of the SOAP sentence to be generated. Compared to an end-to-end approach that generates the full SOAP note from the full conversation, our approach improves by around 8 ROUGE-1 points .
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detailed clinical summaries called SOAP notes
a detailed clinical summary called a SOAP note
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For all
Despite
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the process remains onerous
, their creation remains an onerous process
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a parallel development, patients increasingly record audio from their visits (with consent), often through dedicated apps. In
D
for leveraging these transcripts to train machine learning model
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train summarization models to
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. We first describe a unique dataset of patient visit records, consisting of transcripts ,
from conversations between physicians and patients. We benefit from a dataset that, along with transcripts and
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and
consists of
A
We observe that the performance improves constantly as the extractive subtask is made more complex - an observation that we also replicate on the well-known AMI meeting summarization dataset.
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assigns them
, assigning each
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7
around 8
D
. Oracle experiments indicate that fixing our generative capabilities, improvements in extraction alone could provide (up to) a further 9 ROUGE point gain