Artificial Intelligence Solutions to Improve Emergency Department Wait Times: Living Systematic Review

Abstract

Background

Overcrowding and long wait times in emergency departments (EDs) remain global challenges that negatively affect patient outcomes and staff satisfaction. As an emerging technology, artificial intelligence (AI) offers the potential to optimize ED operations and reduce wait times.

Objective

Establish a strategy to evaluate AI modeling as it relates to utilizing AI based strategies for ED flow.

Methods

We searched Embase, MEDLINE, CINAHL, and Scopus for English-language studies published from January 1, 1946, to August 17, 2023, and we will update our search to ensure currency. The ROBINS-I tool assessed study quality, while PROBAST examined the risk of bias and applicability.

Results

Out of 17,569 screened studies, 65 full-text articles were evaluated for eligibility, with 16 quantitative observational studies meeting inclusion criteria. The best-performing algorithms included regression-based methods (n = 2), traditional single-model machine learning (n = 8), neural networks/deep learning (n = 3), natural language processing (n = 1), and ensemble methods (n = 2). None of the studies examined AI’s impact in a real ED setting, though four simulations reported wait-time reductions ranging from 7 to 43.2 minutes.

Conclusions

AI integration in ED is still in its infancy. Our review found no real-world ED implementation studies, and most of the existing research lacked involvement from ED experts. This gap highlights the lack of insight into AI’s practical impact. Future reviews and research must clarify these dimensions, guiding AI's effective, collaborative adoption in ED workflows.