Generation of Patient After-Visit Summaries to Support Physicians

Pengshan Cai, Fei Liu, Adarsha Bajracharya, Joe Sills, Alok Kapoor, Weisong Liu, Dan Berlowitz, David Levy, Richeek Pradhan, Hong Yu


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.
Anthology ID:
2022.coling-1.544
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6234–6247
Language:
URL:
https://aclanthology.org/2022.coling-1.544
DOI:
Bibkey:
Cite (ACL):
Pengshan Cai, Fei Liu, Adarsha Bajracharya, Joe Sills, Alok Kapoor, Weisong Liu, Dan Berlowitz, David Levy, Richeek Pradhan, and Hong Yu. 2022. Generation of Patient After-Visit Summaries to Support Physicians. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6234–6247, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Generation of Patient After-Visit Summaries to Support Physicians (Cai et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.544.pdf
Code
 pengshancai/avs_gen