PaniniQA: Enhancing Patient Education Through Interactive Question Answering
Pengshan Cai, Zonghai Yao, Fei Liu, Dakuo Wang, M.H. Reilly, Huixue Zhou, Lingxi Li, Yi Cao, Alok Kapoor, Adarsha S. Bajracharya, Dan R. Berlowitz, Hong Qing Yu · Transactions of the Association for Computational Linguistics · 2023
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