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PaniniQA: Enhancing Patient Education Through Interactive Question Answering

Paper · 2023 · Pengshan Cai, Zonghai Yao, Fei Liu et al.

Summary

Patients leaving hospital often struggle to grasp or remember their discharge instructions, and their misreadings usually go uncorrected. PaniniQA addresses this with a chatbot that quizzes patients about their own instructions. It follows the Prompt–Evaluate–Expand–Repeat (P.E.E.R.) cycle from Dialogic Reading.

The authors compare two ways of generating questions:

  • Direct: zero-shot prompting of GPT-3.5 (text-davinci-003) on the instruction text.
  • Enhanced: the same model, steered toward salient medical events and the correlational or causal links between them, such as a procedure and the condition it treats.

To support event extraction, they built a new expert-annotated dataset of events and relations over MIMIC-III discharge instructions. A ChatGPT-based module then judges each patient answer as correct, partially correct, or wrong, and replies with confirmation or clarification.

Automatic, GPT-4-based and human evaluations show that event-guided questions outperform directly generated ones. The feedback is mostly accurate, although the LLM still sometimes hallucinates. The work points to conversational quizzing as a practical complement to static patient portals.

Key points

  • Motivation: patients may immediately forget up to 80% of discharge information.
  • Models and resources: questions come from text-davinci-003, and answers are checked by gpt-3.5-turbo. The new event/relation annotations are built on MIMIC-III, and data and code are on GitHub (pengshancai/PaniniQA).
  • GPT-4 evaluation: generated questions scored around 4 or higher, and Q(Enhanced) beat Q(Direct). Answer feedback averaged 4.14 for Correctness and 4.01 for Education Potential.
  • Heuristic review: an MD student assessed 192 chatbot responses from 30 conversations. 86.4% were judged correct and 74.1% educationally useful.
  • Privacy: API calls were limited to synthetic discharge notes, each checked by physicians.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of Massachusetts AmherstAmherst Center · United States106 works · 66,692 citations🎓Northeastern UniversityBoston · United States57 works · 6,533 citations🎓Emory UniversityAtlanta · United States36 works · 39,779 citations🎓University of Massachusetts LowellLowell · United States15 works · 3,942 citations🎓University of MinnesotaMinneapolis · United States28 works · 2,184 citations🎓University of Massachusetts Chan Medical SchoolWorcester · United States2 works · 27 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Biochemistry, Genetics and Molecular Biology · 234 worksMolecular Biology · 231 worksMedicine · 241 worksHealth Informatics · 184 worksHealth Professions · 69 worksGeneral Health Professions · 14 works

🧭 Topics 1Research subjects and languages the work is about.Question answering · 441 works

Cite

Cite as 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.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00616

@article{cai2023paniniqa,
  title = {{PaniniQA: Enhancing Patient Education Through Interactive Question Answering}},
  author = {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},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2023},
  doi = {10.1162/tacl\_a\_00616},
  url = {https://doi.org/10.1162/tacl_a_00616}
}

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