Scientific Reports· 2026Q1
A mixed-methods evaluation of a language model-powered breast cancer note augmentation tool for patients
- 9citations
- Q1SCImago
- 2026year
Short summary
A patient-facing tool using GPT-4 to augment clinical notes with simplified text, definitions, FAQs, and key takeaways increased participant confidence by 29% and comprehension scores by 17% compared to a control group (n=200).
AI-generated from the title and abstract; the full text is not read.
Key points
- Patient-facing LLM tool simplifies clinical notes, defines terms, provides FAQs, and extracts key info.
- Augmented notes increased participant confidence by 29% (2.83 vs. 2.18, p<0.001).
- Comprehension scores improved by 17% with augmented notes (0.63 vs. 0.54, p<0.001).
- Qualitative feedback highlighted ease of use and better understanding, alongside LLM concerns.
- Clinician error analysis indicated mostly minor errors related to assumed context.
AI-generated from the title and abstract; the full text is not read.
Abstract
Large language models (LLMs) have the potential to make clinical notes more understandable to patients, but tools must be tested with patients pre-deployment. We developed a patient-facing tool that uses an LLM (GPT-4-031) to generate “augmentations” to notes: simplified note text, defined medical terminology, frequently asked questions, extracted key information, and to-do items. We evaluated the tool with 200 healthy female participants and 8 notes. Compared to control, participants with access to augmentations had greater self-reported confidence (2.83 ± 0.05 augmentations, 2.18 ± 0.07 control, p<0.001) and scored higher on comprehension questions (0.63 ± 0.02 augmentations, 0.54 ± 0.04 control, p<0.001). We conducted interviews with 7 participants with a history of breast cancer and their own notes who described ease of use and perceived improvements in note understanding, but also reported concerns around LLMs. Three clinicians conducted an error analysis and found most errors to be minor and related to assumed context.
The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗
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Field: Health Informatics
Health InformaticsMedicine