Evidence map›Paper›PMID 39702364›Full record

ArticleNPJ digital medicine2024

Hospitalization prediction from the emergency department using computer vision AI with short patient video clips.

Wui Ip, Maria Xenochristou, Elaine Sui, Elyse Ruan, Ryan Ribeira, Debadutta Dash, Malathi Srinivasan, Maja Artandi, Jesutofunmi A Omiye, Nicholas Scoulios and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2024. 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. Article
  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

18 authors.

Wui Ip *Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA. wui@stanford.edu.
Maria Xenochristou *Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
Elaine Sui *Department of Computer Science, Stanford University, Palo Alto, CA, USA.
Elyse RuanDigital Health Care Integration, Stanford Health Care, Palo Alto, CA, USA.
Ryan RibeiraDepartment of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Debadutta DashDepartment of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Malathi SrinivasanDepartment of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Maja ArtandiDepartment of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Jesutofunmi A OmiyeDepartment of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
Nicholas ScouliosDepartment of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Hayden L HofmannDepartment of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
Ali MottaghiDepartment of Electrical Engineering, Stanford University, Palo Alto, CA, USA.
Zhenzhen WengInstitute for Computational & Mathematical Engineering, Stanford University, Palo Alto, CA, USA.
Abhinav KumarDepartment of Computer Science, Stanford University, Palo Alto, CA, USA.
Ananya GaneshDepartment of Computer Science, Stanford University, Palo Alto, CA, USA.
Jason FriesStanford Center for Biomedical Informatics Research, Palo Alto, CA, USA.
Serena Yeung-LevyDepartment of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
Lawrence V HofmannDigital Health Care Integration, Stanford Health Care, Palo Alto, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we investigate the performance of computer vision AI algorithms in predicting patient disposition from the emergency department (ED) using short video clips. Clinicians often use "eye-balling" or clinical gestalt to aid in triage, based on brief observations. We hypothesize that AI can similarly use patient appearance for disposition prediction. Data were collected from adult patients at an academic ED, with mobile phone videos capturing patients performing simple tasks. Our AI algorithm, using video alone, showed better performance in predicting hospital admissions (AUROC = 0.693 [95% CI 0.689, 0.696]) compared to models using triage clinical data (AUROC = 0.678 [95% CI 0.668, 0.687]). Combining video and triage data achieved the highest predictive performance (AUROC = 0.714 [95% CI 0.709, 0.719]). This study demonstrates the potential of video AI algorithms to support ED triage and alleviate healthcare capacity strains during periods of high demand.

Identifiers

PMID39702364
PMCPMC11659512

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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.