Kuo Kuang-Ming, Wu Wen-Shiann, Chang Chao Sheng
Department of Business Management, National United University, No.1, Lienda, Miaoli, 360301, Taiwan.
Department of Cardiology, Chi Mei Medical Center, Tainan City, Taiwan.
J Med Syst. 2025 Jun 16;49(1):81. doi: 10.1007/s10916-025-02210-2.
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.
急诊科（ED）复诊是急诊护理质量的关键指标。人们已经提出了各种策略来减少急诊科复诊，包括使用人工智能（AI）模型进行预测。然而，AI模型的性能差异很大，其真正的预测能力仍不明确。为了填补这些空白，本研究的主要目的是通过荟萃分析评估AI在预测急诊科复诊方面的性能。具体而言，本研究旨在：（1）定量评估AI在急诊科复诊预测中的预测性能；（2）确定导致研究间异质性的协变量。于2024年12月31日在多个电子数据库中进行了系统检索，包括Scopus、SpringerLink、ScienceDirect、PubMed、Wiley、Sage和谷歌学术，以识别符合以下标准的相关研究：（1）使用机器学习、深度学习或人工智能技术预测患者返回急诊科复诊；（2）英文撰写；（3）经过同行评审。使用合并敏感性、特异性和受试者操作特征曲线下面积（AUROC）评估诊断准确性，同时进行亚组分析以探索导致异质性的因素。这项荟萃分析包括20篇文章，包含27个AI模型。急诊科复诊预测的汇总估计如下：（1）敏感性：0.56（95%置信区间[CI]：0.44 - 0.67）；（2）特异性：0.92（95%CI：0.86 - 0.96）；（3）AUROC：0.81（95%CI：0.71 - 0.88）。亚组分析确定国籍、缺失值处理策略和特定疾病样本是研究间异质性的潜在因素。未来的研究应专注于改进缺失值处理并使用特定疾病样本以提高模型的可靠性。
