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

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.

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Publication Details

Journal
npj Digital Medicine
Published
2024-12-19
DOI
https://doi.org/10.1038/s41746-024-01375-3
Citations
4
Primary Topic
Emergency and Acute Care Studies
Type
article
Field-Weighted Citation Impact
2.76
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article

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

Elaine Sui, Abhinav Kumar, Zhenzhen Weng, Jesutofunmi A. Omiye et al.
4 citations
npj Digital Medicine
Emergency and Acute Care Studies
2.76
article

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

Elaine Sui, Abhinav Kumar, Zhenzhen Weng, Jesutofunmi A. Omiye, Jason Fries, Maria Xenochristou, Nicholas Scoulios, Ryan Ribeira, Hayden L. Hofmann, Ali Mottaghi, Lawrence V. Hofmann, Maja Artandi, Serena Yeung, Malathi Srinivasan, Ananya Ganesh, Wui Ip, Debadutta Dash, Elyse Ruan
article en
4 citations

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.

npj Digital MedicineVol. 7(1)
Palo Alto University (US), Stanford Health Care (US), Chan Zuckerberg Initiative (United States) (US), Stanford Medicine (US), Stanford University (US)
Openalex Percentile: Top 9%
Emergency and Acute Care Studies
2.76
DOIOpen Access PDFOpenAlex
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