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<p>The use of certain artificial intelligence (AI) tools may improve hospital operational efficiency, in particular in overcrowded emergency departments (ED). Here, we conduct a prospective, quasi experimental study evaluating an AI model predicting hospital admission risk, alternately displaying and hiding its outputs to clinicians in 2 week blocks over 11 months in 2023. Among 54,394 eligible ED visits, the AI tool does not change the number of ED patients discharged per day but reduces median ED length of stay by 12 min without increasing 72 h bounceback visits. Model performance remained stable (AUC 0.80–0.82), and hospitalist clinicians reported greater perceived usefulness than ED clinicians. These findings show that integrating a low burden AI prediction tool into ED workflows can improve operational efficiency. The study was registered on Clinicaltrials.gov (NCT05683899).</p>

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Artificial intelligence for predicting hospital admissions from the emergency department: a prospective, quasi-experimental study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • Alexander J. Ryu,
  • Shant Ayanian,
  • Ray Qian,
  • Riddhi S. Parikh,
  • Sagar B. Dugani,
  • Karen M. Fischer,
  • Heather A. Heaton,
  • Jens P. Boyum,
  • Benjamin J. Hinton,
  • Donna K. Lawson,
  • M. Caroline Burton

摘要

The use of certain artificial intelligence (AI) tools may improve hospital operational efficiency, in particular in overcrowded emergency departments (ED). Here, we conduct a prospective, quasi experimental study evaluating an AI model predicting hospital admission risk, alternately displaying and hiding its outputs to clinicians in 2 week blocks over 11 months in 2023. Among 54,394 eligible ED visits, the AI tool does not change the number of ED patients discharged per day but reduces median ED length of stay by 12 min without increasing 72 h bounceback visits. Model performance remained stable (AUC 0.80–0.82), and hospitalist clinicians reported greater perceived usefulness than ED clinicians. These findings show that integrating a low burden AI prediction tool into ED workflows can improve operational efficiency. The study was registered on Clinicaltrials.gov (NCT05683899).