Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations

Abstract

Summaries generated from medical conversations can improve recall and understanding of care plans for patients and reduce documentation burden for doctors. Recent advancements in automatic speech recognition (ASR) and natural language understanding (NLU) offer potential solutions to generate these summaries automatically, but rigorous quantitative baselines for benchmarking research in this domain are lacking. In this paper, we bridge this gap for two tasks: classifying utterances from medical conversations according to (i) the SOAP section and (ii) the speaker role. Both are fundamental building blocks along the path towards an end-to-end, automated SOAP note for medical conversations. We provide details on a dataset that contains human and ASR transcriptions of medical conversations and corresponding machine learning optimized SOAP notes. We then present a systematic analysis in which we adapt an existing deep learning architecture to the two aforementioned tasks. The results suggest that modelling context in a hierarchical manner, which captures both word and utterance level context, yields substantial improvements on both classification tasks. Additionally, we develop and analyze a modular method for adapting our model to ASR output.

Weakly Supervised Medication Regimen Extraction from Medical Conversations

“…Moreover, our method can be generalized to any number of attributes while all these methods would require a separate model for each attribute. Tasks: Understanding doctor-patient conversations is starting to receive attention recently (Rajkomar et al, 2019;Schloss and Konam, 2020). Selvaraj and Konam (2019) performs MR extraction by framing the problem as a generative question answering task.…”

“…Increased data management work is also correlated with increased doctor burnout (Kumar, 2016). Information extracted from medical conversations can also aid doctors in their documentation work (Rajkomar et al, 2019;Schloss and Konam, 2020), allow them to spend more face time with the patients, and build better relationships.…”