Adarsha Bajracharya


2023

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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
Transactions of the Association for Computational Linguistics, Volume 11

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

2022

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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
Proceedings of the 29th International Conference on Computational Linguistics

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.