PaniniQA: Enhancing Patient Education Through Interactive Question Answering-Reference-Cited by-同舟云学术

PaniniQA: Enhancing Patient Education Through Interactive Question Answering

Published:2023 Issue: Volume:11 Page:1518-1536
ISSN:2307-387X
Container-title:Transactions of the Association for Computational Linguistics
language:en
Short-container-title:

Author:

Cai Pengshan¹,Yao Zonghai²,Liu Fei³,Wang Dakuo⁴,Reilly Meghan⁵,Zhou Huixue⁶,Li Lingxi⁷,Cao Yi⁸,Kapoor Alok⁹,Bajracharya Adarsha¹⁰,Berlowitz Dan¹¹,Yu Hong¹²¹³¹⁴

Affiliation:

1. University of Massachusetts, Amherst, USA pengshancai@umass.edu

2. University of Massachusetts, Amherst, USA zonghaiyao@umass.edu

3. Emory University, USA fei.liu@emory.edu

4. Northeastern University, USA d.wang@neu.edu

5. UMass Chan Medical School, USA Meghan.Reilly@umassmed.edu

6. University of Minnesota, USA zhou1742@umn.edu

7. University of Massachusetts, Amherst, USA lingxili@umass.edu

8. University of Massachusetts, Amherst, USA yicao@umass.edu

9. UMass Chan Medical School, USA Alok.Kapoor@umassmemorial.org

10. UMass Chan Medical School, USA adarsha.Bajracharya@umassmemorial.org

11. University of Massachusetts, Lowell, USA Dan_Berlowitz@uml.edu

12. University of Massachusetts, Amherst, USA

13. UMass Chan Medical School, USA

14. University of Massachusetts, Lowell, USA hongyu@umass.edu

Abstract

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

Publisher

MIT Press

Subject

Artificial Intelligence,Computer Science Applications,Linguistics and Language,Human-Computer Interaction,Communication

Link

https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl_a_00616/2196857/tacl_a_00616.pdf

Reference64 articles.

1. Towards generating personalized hospitalization summaries;Acharya,2018

2. What’s in a summary? Laying the groundwork for advances in hospital-course summarization;Adams,2021

3. Learning to revise references for faithful summarization;Adams,2022

4. On the opportunities and risks of foundation models;Bommasani;arXiv preprint arXiv:2108.07258,2022

5. What question answering can learn from trivia nerds;Boyd-Graber,2020