ArticleJournal of Korean medical science2024
Patient-Friendly Discharge Summaries in Korea Based on ChatGPT: Software Development and Validation.
Article in Journal of Korean medical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- Artificial intelligence in rheumatology: a cross-sectional Scopus-based analysis.Rheumatology international · 2026Article
- Integrating Generative AI Into Patient-Centered Clinical Decision Support: Viewpoint on Research and Practice Considerations.Journal of medical Internet research · 2026Article
- Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.Patient education and counseling · 2026Article
- Effectiveness of large language models in preoperative and discharge education: a systematic review based on an evaluation framework.NPJ digital medicine · 2026Article
- ED-Explain: Personalized Video Instructions for Patients Discharged from the Emergency Department.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM): 2025 Updates.Korean journal of radiology · 2025Review
- A Bilingual On-Premises AI Agent for Clinical Drafting: Implementation Report of Seamless Electronic Health Records Integration in the Y-KNOT Project.JMIR medical informatics · 2025Article
- One template does not fit all: where next to improve hospital discharge communication to primary care?Primary health care research & development · 2025Article
- Generative artificial intelligence in cardiovascular specialty care: a scoping review.BMC nursing · 2025Article
- Evaluation of a large language model to simplify discharge summaries and provide cardiological lifestyle recommendations.Communications medicine · 2025Article
- Generative AI-Based Nursing Diagnosis and Documentation Recommendation Using Virtual Patient Electronic Nursing Record Data.Healthcare informatics research · 2025Article
- Using ChatGPT for writing hospital inpatient discharge summaries - perspectives from an inpatient infectious diseases service.BMC health services research · 2025Article
- Using Large Language Models to Extract Core Injury Information From Emergency Department Notes.Journal of Korean medical science · 2024Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundAlthough discharge summaries in patient-friendly language can enhance patient comprehension and satisfaction, they can also increase medical staff workload. Using a large language model, we developed and validated software that generates a patient-friendly discharge summary.
methodsWe developed and tested the software using 100 discharge summary documents, 50 for patients with myocardial infarction and 50 for patients treated in the Department of General Surgery. For each document, three new summaries were generated using three different prompting methods (Zero-shot, One-shot, and Few-shot) and graded using a 5-point Likert Scale regarding factuality, comprehensiveness, usability, ease, and fluency. We compared the effects of different prompting methods and assessed the relationship between input length and output quality.
resultsThe mean overall scores differed across prompting methods (4.19 ± 0.36 in Few-shot, 4.11 ± 0.36 in One-shot, and 3.73 ± 0.44 in Zero-shot;
conclusionLarge-language models utilizing Few-shot prompts generally produce acceptable discharge summaries without significant misinformation. Our research highlights the potential of such models in creating patient-friendly discharge summaries for Korean patients to support patient-centered care.
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