ArticleJournal of the American College of Emergency Physicians open2025
National Cost Savings From Use of Artificial Intelligence Guided Echocardiography in the Assessment of Intermediate-Risk Patients With Syncope in the Emergency Department.
Article in Journal of the American College of Emergency Physicians open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
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Who cites it
2 citing papers in PubMed.
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- Feasibility of a multimodal AI-based clinical assessment platform in emergency care: an exploratory pilot study.Frontiers in digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Our primary objective was to estimate the realistic impact of an artificial intelligence (AI)-based trans-thoracic echocardiogram (TTE)-first strategy on the annual national cost savings among eligible adult emergency department (ED) patients presenting with syncope in the United States. Our secondary outcomes were the estimated reduction in avoidable ED bed hours and comprehensive TTE studies. Methods: Using publicly available estimates for inputs such as the size of the adult ED syncope population, typical disposition and risk stratification proportions, and frequency of comprehensive TTE studies, we created a model and ran 1000 trials of a Monte Carlo simulation. Using this simulation, we modeled the national annual cost savings and potential bed hours averted through the impact of avoiding comprehensive TTE studies. We report the descriptive statistics modeling the distribution of all endpoints. Results: An AI-assisted TTE-first strategy was estimated to save a mean (±SD) of $815 million (±$260 million) by avoiding 468,000 (±141,000) comprehensive TTE studies resulting in 12,500,000 (±4,600,000) bed hours saved. Conclusion: If adopted widely, an AI-based TTE-first strategy applied to eligible ED patients presenting with syncope could yield substantial benefits by averting avoidable comprehensive TTE studies and saving bed hours.
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Registered trials
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