Generation of Patient After-Visit Summaries to Support Physicians. Cai, P., Liu, F., Bajracharya, A., Sills, J., Kapoor, A., Liu, W., Berlowitz, D., Levy, D., Pradhan, R., & Yu, H. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6234–6247, Gyeongju, Republic of Korea, October, 2022. International Committee on Computational Linguistics. ![Generation of Patient After-Visit Summaries to Support Physicians [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex An after-visit summary (AVS) is a summary note given to patients after their clinical visit. It recaps what happened during their clinical visit and guides patients' disease self-management. Studies have shown that a majority of patients found after-visit summaries useful. However, many physicians face excessive workloads and do not have time to write clear and informative summaries. In this paper, we study the problem of automatic generation of after-visit summaries and examine whether those summaries can convey the gist of clinical visits. We report our findings on a new clinical dataset that contains a large number of electronic health record (EHR) notes and their associated summaries. Our results suggest that generation of lay language after-visit summaries remains a challenging task. Crucially, we introduce a feedback mechanism that alerts physicians when an automatic summary fails to capture the important details of the clinical notes or when it contains hallucinated facts that are potentially detrimental to the summary quality. Automatic and human evaluation demonstrates the effectiveness of our approach in providing writing feedback and supporting physicians.
@inproceedings{cai_generation_2022,
address = {Gyeongju, Republic of Korea},
title = {Generation of {Patient} {After}-{Visit} {Summaries} to {Support} {Physicians}},
url = {https://aclanthology.org/2022.coling-1.544},
abstract = {An after-visit summary (AVS) is a summary note given to patients after their clinical visit. It recaps what happened during their clinical visit and guides patients' disease self-management. Studies have shown that a majority of patients found after-visit summaries useful. However, many physicians face excessive workloads and do not have time to write clear and informative summaries. In this paper, we study the problem of automatic generation of after-visit summaries and examine whether those summaries can convey the gist of clinical visits. We report our findings on a new clinical dataset that contains a large number of electronic health record (EHR) notes and their associated summaries. Our results suggest that generation of lay language after-visit summaries remains a challenging task. Crucially, we introduce a feedback mechanism that alerts physicians when an automatic summary fails to capture the important details of the clinical notes or when it contains hallucinated facts that are potentially detrimental to the summary quality. Automatic and human evaluation demonstrates the effectiveness of our approach in providing writing feedback and supporting physicians.},
urldate = {2022-12-18},
booktitle = {Proceedings of the 29th {International} {Conference} on {Computational} {Linguistics}},
publisher = {International Committee on Computational Linguistics},
author = {Cai, Pengshan and Liu, Fei and Bajracharya, Adarsha and Sills, Joe and Kapoor, Alok and Liu, Weisong and Berlowitz, Dan and Levy, David and Pradhan, Richeek and Yu, Hong},
month = oct,
year = {2022},
pages = {6234--6247},
}
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In this paper, we study the problem of automatic generation of after-visit summaries and examine whether those summaries can convey the gist of clinical visits. We report our findings on a new clinical dataset that contains a large number of electronic health record (EHR) notes and their associated summaries. Our results suggest that generation of lay language after-visit summaries remains a challenging task. Crucially, we introduce a feedback mechanism that alerts physicians when an automatic summary fails to capture the important details of the clinical notes or when it contains hallucinated facts that are potentially detrimental to the summary quality. 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