A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions - Kuang-Ming Kuo; Chao Sheng Chang (BMC Medical Informatics and Decision Making)
Abstract Background The rapid advancement of Artificial Intelligence (AI) has led to its widespread application across various domains, showing encouraging outcomes. Many studies have utilized AI to forecast emergency department (ED) disposition, aiming to forecast patient outcomes earlier and to allocate resources better; however, a dearth of comprehensive review literature exists to assess the objective performance standards of these predictive models using quantitative evaluations. This study aims to conduct a meta-analysis to assess the diagnostic accuracy of AI in predicting ED disposition, encompassing admission, critical care, and mortality. Methods Multiple databases, including Scopus, Springer, ScienceDirect, PubMed, Wiley, Sage, and Google Scholar, were searched until December 31, 2023, to gather relevant literature. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. Pooled estimates of sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated to evaluate AI’s predictive performance. Sub-group analyses were performed to explore covariates affecting AI predictive model performance. Results The study included 88 articles possessed with 117 AI models, among which 39, 45, and 33 models predicted admission, critical care, and mortality, respectively. The reported statistics for sensitivity, specificity, and AUROC represent pooled summary measures derived from the component studies included in this meta-analysis. AI’s summary sensitivity, specificity, and AUROC for predicting admission were 0.81 (95% Confidence Interval [CI] 0.74–0.86), 0.87 (95% CI 0.81–0.91), and 0.87 (95% CI 0.84–0.93), respectively. For critical care, the values were 0.86 (95% CI 0.79–0.91), 0.89 (95% CI 0.83–0.93), and 0.93 (95% CI 0.89–0.95), respectively, and for mortality, they were 0.85 (95% CI 0.80–0.89), 0.94 (95% CI 0.90–0.96), and 0.93 (95% CI 0.89–0.96), respectively. Emergent sample characteristics and AI techniques showed evidence of significant covariates influencing the heterogeneity of AI predictive models for ED disposition. Conclusions The meta-analysis indicates promising performance of AI in predicting ED disposition, with certain potential for improvement, especially in sensitivity. Future research could explore advanced AI techniques such as ensemble learning and cross-validation with hyper-parameter tuning to enhance predictive model efficacy. Trial registration This systematic review was not registered with PROSPERO or any other similar registry because the review was completed prior to the opportunity for registration, and PROSPERO currently does not accept registrations for reviews that are already completed. We are committed to transparency and have adhered to best practices in systematic review methodology throughout this study.