Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes

• URL: http://arxiv.org/abs/2406.02826v1
• Date: Wed, 5 Jun 2024 00:11:20 GMT
• Title: Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes
• Authors: Yu-Wen Chen, Julia Hirschberg,
• Abstract summary: We investigate the performance of state-of-the-art doctor-patient conversation generative summarization models on the out-of-domain data. We analyzed the limitations and strengths of the fine-tuning language model-based methods and GPTs on both configurations.
• Score: 12.628782042255395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Summarizing medical conversations poses unique challenges due to the specialized domain and the difficulty of collecting in-domain training data. In this study, we investigate the performance of state-of-the-art doctor-patient conversation generative summarization models on the out-of-domain data. We divide the summarization model of doctor-patient conversation into two configurations: (1) a general model, without specifying subjective (S), objective (O), and assessment (A) and plan (P) notes; (2) a SOAP-oriented model that generates a summary with SOAP sections. We analyzed the limitations and strengths of the fine-tuning language model-based methods and GPTs on both configurations. We also conducted a Linguistic Inquiry and Word Count analysis to compare the SOAP notes from different datasets. The results exhibit a strong correlation for reference notes across different datasets, indicating that format mismatch (i.e., discrepancies in word distribution) is not the main cause of performance decline on out-of-domain data. Lastly, a detailed analysis of SOAP notes is included to provide insights into missing information and hallucinations introduced by the models.

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