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Despite diverse efforts to mine various modalities of medical data, theconversations between physicians and patients at the time of care remain anuntapped source of insights. In this paper, we leverage this data to extractstructured information that might assist physicians with post-visitdocumentation in electronic health records, potentially lightening the clericalburden. In this exploratory study, we describe a new dataset consisting ofconversation transcripts, post-visit summaries, corresponding supportingevidence (in the transcript), and structured labels. We focus on the tasks ofrecognizing relevant diagnoses and abnormalities in the review of organ systems(RoS). One methodological challenge is that the conversations are long (around1500 words), making it difficult for modern deep-learning models to use them asinput. To address this challenge, we extract noteworthy utterances---parts ofthe conversation likely to be cited as evidence supporting some summarysentence. We find that by first filtering for (predicted) noteworthyutterances, we can significantly boost predictive performance for recognizingboth diagnoses and RoS abnormalities.

Extracting Structured Data from Physician-Patient Conversations By Predicting Noteworthy Utterances