A Meta-Analysis of the Diagnostic Test Accuracy of Artificial Intelligence for Predicting Emergency Department Revisits.

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.