# PaniniQA: Enhancing Patient Education Through Interactive Question Answering

arXiv: https://arxiv.org/abs/2308.03253 · HTML: https://mlsanity.com/paper/2308.03253/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 75 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 30 of 38)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)
- [Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics](https://mlsanity.com/paper/2104.13346/) (2021)
- [Unsupervised Commonsense Question Answering with Self-Talk](https://mlsanity.com/paper/2004.05483/) (2020)
- [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1809.00732/) (2018)
- [Can Language Models be Biomedical Knowledge Bases?](https://mlsanity.com/paper/2109.07154/) (2021)
- [Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension](https://mlsanity.com/paper/2203.13947/) (2022)
- [Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method](https://mlsanity.com/paper/2305.13412/) (2023)
- [Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering](https://mlsanity.com/paper/2004.11892/) (2020)
- [What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization](https://mlsanity.com/paper/2105.00816/) (2021)
- [Improving Neural Question Generation using Answer Separation](https://mlsanity.com/paper/1809.02393/) (2018)
- [Learning to Revise References for Faithful Summarization](https://mlsanity.com/paper/2204.10290/) (2022)
- [CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering](https://mlsanity.com/paper/2010.16021/) (2020)
- [Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset](https://mlsanity.com/paper/2005.00574/) (2020)
- [It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books](https://mlsanity.com/paper/2109.03423/) (2021)
- [What Question Answering can Learn from Trivia Nerds](https://mlsanity.com/paper/1910.14464/) (2019)
- [MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score](https://mlsanity.com/paper/2210.05875/) (2022)

## Built on by (most cited first, 21 of 21)

- [NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes](https://mlsanity.com/paper/2310.15959/) (2023)
- [README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP](https://mlsanity.com/paper/2312.15561/) (2023)
- [MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback](https://mlsanity.com/paper/2410.13191/) (2024)
- [Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects](https://mlsanity.com/paper/2312.04578/) (2023)
- [SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs](https://mlsanity.com/paper/2402.10979/) (2024)
- [Improving Summarization with Human Edits](https://mlsanity.com/paper/2310.05857/) (2023)
- [DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge](https://mlsanity.com/paper/2509.07188/) (2025)
- [MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning](https://mlsanity.com/paper/2507.07419/) (2025)
- [EHRTutor: Enhancing Patient Understanding of Discharge Instructions](https://mlsanity.com/paper/2310.19212/) (2023)
- [ChatThero: An LLM-Supported Chatbot for Behavior Change and Therapeutic Support in Addiction Recovery](https://mlsanity.com/paper/2508.20996/) (2025)
- [Chatbot To Help Patients Understand Their Health](https://mlsanity.com/paper/2509.05818/) (2025)
- [Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study](https://mlsanity.com/paper/2401.09637/) (2024)
- [A Neuro-Symbolic Approach to Monitoring Salt Content in Food](https://mlsanity.com/paper/2404.01182/) (2024)
- [QOG:Question and Options Generation based on Language Model](https://mlsanity.com/paper/2406.12381/) (2024)
- [Conversational Assistants to support Heart Failure Patients: comparing a Neurosymbolic Architecture with ChatGPT](https://mlsanity.com/paper/2504.17753/) (2025)
- [ChatCLIDS: Simulating Persuasive AI Dialogues to Promote Closed-Loop Insulin Adoption in Type 1 Diabetes Care](https://mlsanity.com/paper/2509.00891/) (2025)
- [Chatting Up Attachment: Using LLMs to Predict Adult Bonds](https://mlsanity.com/paper/2409.00347/) (2024)
- [Towards conversational assistants for health applications: using ChatGPT to generate conversations about heart failure](https://mlsanity.com/paper/2505.03675/) (2025)
- [Rethinking Patient Education as Multi-turn Multi-modal Interaction](https://mlsanity.com/paper/2604.14656/) (2026)
- [MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education](https://mlsanity.com/paper/2607.21570/) (2026)
- [Clinical Intent Extraction: A FHIR-Aligned Representation and the CIRCA Benchmark](https://mlsanity.com/paper/2609.29479/) (2026)

## Abstract (arXiv)

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. 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 and human evaluation results demonstrate our PaniniQA is capable of improving patients' mastery of their medical instructions through effective interactions