Evidence map›Paper›PMID 41758148›Full record

ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2026

ED-Explain: Personalized Video Instructions for Patients Discharged from the Emergency Department.

Luyang Luo, Emma Chen, Xiaoman Zhang, Julian Nicolas Acosta, Boyang Tom Jin, Fatma Gunturkun, Christian Rose, Carl Preiksaitis, Brian Suffoletto, Pranav Rajpurkar and 1 more

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In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Luyang LuoDepartment of Biomedical Informatics, Harvard Medical School, USA.
Emma ChenDepartment of Biomedical Informatics, Harvard Medical School, USA.
Xiaoman ZhangDepartment of Biomedical Informatics, Harvard Medical School, USA.
Julian Nicolas AcostaDepartment of Biomedical Informatics, Harvard Medical School, USA.
Boyang Tom JinDepartment of Computer Science, Stanford University, USA.
Fatma GunturkunDepartment of Emergency Medicine, Stanford University School of Medicine, USA.
Christian RoseDepartment of Emergency Medicine, Stanford University School of Medicine, USA.
Carl PreiksaitisDepartment of Emergency Medicine, Stanford University School of Medicine, USA.
Brian SuffolettoDepartment of Emergency Medicine, Stanford University School of Medicine, USA.
Pranav RajpurkarDepartment of Biomedical Informatics, Harvard Medical School, USA.
David A KimDepartment of Biomedical Informatics, Harvard Medical School, USA.

Funding

Integrating Acute and Ambulatory Care with Post-Discharge Monitoring and Machine LearningR01HL172794 · NHLBI · STANFORD UNIVERSITY · PI David Andrew Kim, Pranav Rajpurkar · 2024 to 2026
$2.3M
NHLBI NIH HHS R01 HL172794
6 · The paper itself

Abstract

This paper presents ED-Explain, an integrated, AI-driven system that transforms emergency department (ED) discharge instructions and electronic health records into personalized video presentations featuring a virtual healthcare provider. By leveraging multimodal ED data, large language models, and video generation, we aimed to produce accessible discharge instructions tailored to patients' ED visits. Four board-certified Emergency Medicine physicians reviewed 39 pairs of original and ED-Explain-produced discharge instructions. AI video summaries received significantly higher (p<0.001) average ratings (1-5) of completeness (4.1 vs. 3.1), correctness (3.9 vs. 3.5) and patient accessibility (3.4 vs. 2.9). Physicians expressed reservations about 13.3% of ED-Explain's discharge instructions for patient viewing, and only 4.6% of ED-Explain's instructions were found inappropriate for use with patients. Physician feedback suggests that AI-enhanced video discharge summaries have potential to improve communication of discharge information to ED patients, though patient-centered evaluation is needed. This work contributes to the growing field of AIassisted healthcare communication and offers insights into the potential for AI to improve physician-patient communication and patient self-efficacy.

Indexed as

Emergency Service, HospitalPatient DischargePatient Education as TopicArtificial IntelligenceComputational BiologyElectronic Health RecordsEmergency Room VisitsHumansVideo Recording

Identifiers

PMID41758148
PMCPMC12952664

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.