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

Bahareh Ahmadzadeh¹, Christopher Patey², Paul Norman³

¹PhD Candidate in Clinical Epidemiology, Center for Rural Health Studies, Faculty of Medicine, Memorial University of Newfoundland, St John's, NL, Canada.

The Journal of Emergency Medicine

|July 15, 2025

Summary

Artificial intelligence (AI) shows promise for optimizing emergency department (ED) operations and reducing wait times. However, current research lacks real-world ED implementation studies and expert input, indicating AI integration is still in its early stages.

Keywords:

artificial intelligence emergency department living systematic review wait time

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Area of Science:

Health Informatics
Artificial Intelligence in Healthcare
Operations Research

Background:

Emergency departments (EDs) face global challenges with overcrowding and long wait times, impacting patient outcomes and staff satisfaction.
Artificial intelligence (AI) presents a potential technological solution to optimize ED operations and mitigate delays.

Purpose of the Study:

To establish a strategic framework for evaluating AI modeling in the context of emergency department flow.
To assess the current state of AI-based strategies for improving ED patient flow.

Main Methods:

A comprehensive literature search was conducted across Embase, MEDLINE, CINAHL, and Scopus for English-language studies.
Study quality was assessed using the ROBINS-I tool, and risk of bias/applicability were examined with PROBAST.
Searches were performed for studies published from January 1, 1946, to August 17, 2023, with plans for ongoing updates.

Main Results:

Out of 17,569 screened studies, 16 quantitative observational studies met the inclusion criteria.
The most effective algorithms identified included regression-based methods, single-model machine learning, neural networks/deep learning, natural language processing, and ensemble methods.
While simulations suggested wait-time reductions (7–43.2 minutes), no studies reported on AI's impact in a real-world ED setting.

Conclusions:

The integration of AI into emergency departments is currently in its nascent stages.
A significant gap exists due to the absence of real-world ED implementation studies and limited involvement of ED experts in current research.
Further research and reviews are crucial to understand AI's practical impact and guide its effective, collaborative adoption in ED workflows.