Evidence map›Paper›PMID 41350380›Full record

ArticleScientific reports2025

Enhancing patient participation in emergency department through patient-friendly clinical notes generated by large language models.

Sung-In Kim, Joonyoung Park, Taewan Kim, Woosuk Seo, Taerim Kim, Won Chul Cha, Hwajung Hong

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Trial
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sung-In KimDepartment of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
Joonyoung ParkDepartment of Industrial Design, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Taewan KimTwelveLabs, Seoul, Republic of Korea.
Woosuk SeoDepartment of Emergency Medicine, Yale School of Medicine, Connecticut, USA.
Taerim KimDepartment of Emergency Medicine, Samsung Medical Center, Seoul, Republic of Korea.
Won Chul ChaDepartment of Emergency Medicine, Samsung Medical Center, Seoul, Republic of Korea.
Hwajung HongDepartment of Industrial Design, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea. hwajung@kaist.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patient-centered care (PCC) emphasizes providing patients with clear information to support active participation in medical decision-making. However, the fast-paced nature of emergency departments (ED), coupled with communication barriers and varying health literacy, limits effective patient engagement. While large language models (LLMs) have shown potential in generating patient-friendly documents, their use in ED settings remains underexplored. This study aimed to develop LLM-generated patient-friendly clinical notes (PFCNs) that transform clinical notes into plain language, and to evaluate whether PFCNs could enhance patient participation in ED consultations. In this study, a total of 120 PFCNs were generated and evaluated, receiving high understandability ratings from both 10 clinicians and 20 patients (PEMAT score: 87.2%). Patients who used PFCNs during simulated ED consultations reported significantly higher participation levels compared to prior ED experiences (PPQ, P < 0.05). Qualitative data showed that PFCNs supported understanding, question preparation, emotional reassurance and improved relationships with clinicians, though concerns about hallucinations and integration into clinical workflows remained. These findings suggested that PFCNs generated by LLMs show promise for enhancing patient participation in ED consultations. Future work should address accuracy and explore real-world integration to support safe and effective deployment.

Indexed as

Emergency Service, HospitalLanguagePatient ParticipationAdultAgedFemaleHealth LiteracyHumansLarge Language ModelsMaleMiddle AgedPatient-Centered CareCommunicationEmergency medicineLarge language modelPatient-centered carePatient participation

Identifiers

PMID41350380
PMCPMC12796464

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.