SynthesisJournal of medical systems2025
A Meta-Analysis of the Diagnostic Test Accuracy of Artificial Intelligence for Predicting Emergency Department Revisits.
Synthesis in Journal of medical systems, 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
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.
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.
Who cites it
2 citing papers in PubMed.
- Explainable AI for mental health emergency returns: integrating large language models with predictive modeling.JAMIA open · 2026Article
- Deep learning to predict emergency department revisit using static and dynamic features (Deep Revisit): development and validation study.BioData mining · 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
3 authors.
Funding
Abstract
The revisit of the emergency department (ED) is a key indicator of emergency care quality. Various strategies have been proposed to reduce ED revisits, including the use of artificial intelligence (AI) models for prediction. However, AI model performance varies significantly, and its true predictive capability remains unclear. To address these gaps, the primary purpose of this study is to evaluate the performance of AI in predicting ED revisits through a meta-analysis. Specifically, this study aims to (1) Quantitatively assess the predictive performance of AI in ED revisit prediction and (2) Identify covariates contributing to between-study heterogeneity. A systematic search was conducted on December 31, 2024, across multiple electronic databases, including Scopus, SpringerLink, ScienceDirect, PubMed, Wiley, Sage, and Google Scholar, to identify relevant studies meeting the following criteria: (1) Utilized machine learning, deep learning, or artificial intelligence techniques to predict patient return visits to the ED, (2) Written in English, and (3) Peer-reviewed. Diagnostic accuracy was assessed using pooled sensitivity, specificity, and area under receiver operating characteristic curve (AUROC), while subgroup analysis explored factors contributing to heterogeneity. This meta-analysis included 20 articles, comprising 27 AI models. The summary estimates for ED revisit prediction were as follows: (1) Sensitivity: 0.56 (95% Confidence Interval [CI]: 0.44-0.67), (2) Specificity: 0.92 (95% CI: 0.86-0.96), and (3) AUROC: 0.81 (95% CI: 0.71-0.88). Subgroup analysis identified nationality, missing value-handling strategies, and specific disease samples as potential contributors to between-study heterogeneity. Future research should focus on improving missing value processing and using specific disease samples to enhance model reliability.
Indexed as
Identifiers
40522351What OpenQuestion holds
Registered trials
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.