PaniniQA: Enhancing Patient Education Through Interactive Question Answering
Pengshan Cai, Zonghai Yao, Fei Liu, Dakuo Wang, Meghan Reilly, Huixue Zhou, Lingxi Li, Yi Cao, Alok Kapoor, Adarsha Bajracharya, Dan Berlowitz, Hong Yu
Abstract
A patient portal allows discharged patients to access their personalized discharge instructions in electronic health records (EHRs). However, many patients have difficulty understanding or memorizing their discharge instructions (Zhao et al., 2017). In this paper, we present PaniniQA, a patient-centric interactive question answering system designed to help patients understand their discharge instructions. PaniniQA first identifies important clinical content from patients’ discharge instructions and then formulates patient-specific educational questions. In addition, PaniniQA is also equipped with answer verification functionality to provide timely feedback to correct patients’ misunderstandings. Our comprehensive automatic & human evaluation results demonstrate our PaniniQA is capable of improving patients’ mastery of their medical instructions through effective interactions.1- Anthology ID:
- 2023.tacl-1.86
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 11
- Month:
- Year:
- 2023
- Address:
- Cambridge, MA
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 1518–1536
- Language:
- URL:
- https://aclanthology.org/2023.tacl-1.86
- DOI:
- 10.1162/tacl_a_00616
- Cite (ACL):
- Pengshan Cai, Zonghai Yao, Fei Liu, Dakuo Wang, Meghan Reilly, Huixue Zhou, Lingxi Li, Yi Cao, Alok Kapoor, Adarsha Bajracharya, Dan Berlowitz, and Hong Yu. 2023. PaniniQA: Enhancing Patient Education Through Interactive Question Answering. Transactions of the Association for Computational Linguistics, 11:1518–1536.
- Cite (Informal):
- PaniniQA: Enhancing Patient Education Through Interactive Question Answering (Cai et al., TACL 2023)
- PDF:
- https://preview.aclanthology.org/ijclclp-past-ingestion/2023.tacl-1.86.pdf