@article{cai-etal-2023-paniniqa,
title = "{P}anini{QA}: Enhancing Patient Education Through Interactive Question Answering",
author = "Cai, Pengshan and
Yao, Zonghai and
Liu, Fei and
Wang, Dakuo and
Reilly, Meghan and
Zhou, Huixue and
Li, Lingxi and
Cao, Yi and
Kapoor, Alok and
Bajracharya, Adarsha and
Berlowitz, Dan and
Yu, Hong",
journal = "Transactions of the Association for Computational Linguistics",
volume = "11",
year = "2023",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2023.tacl-1.86",
doi = "10.1162/tacl_a_00616",
pages = "1518--1536",
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",
}
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<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</abstract>
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%0 Journal Article
%T PaniniQA: Enhancing Patient Education Through Interactive Question Answering
%A Cai, Pengshan
%A Yao, Zonghai
%A Liu, Fei
%A Wang, Dakuo
%A Reilly, Meghan
%A Zhou, Huixue
%A Li, Lingxi
%A Cao, Yi
%A Kapoor, Alok
%A Bajracharya, Adarsha
%A Berlowitz, Dan
%A Yu, Hong
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F cai-etal-2023-paniniqa
%X 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
%R 10.1162/tacl_a_00616
%U https://aclanthology.org/2023.tacl-1.86
%U https://doi.org/10.1162/tacl_a_00616
%P 1518-1536
Markdown (Informal)
[PaniniQA: Enhancing Patient Education Through Interactive Question Answering](https://aclanthology.org/2023.tacl-1.86) (Cai et al., TACL 2023)
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.