BACKGROUND
Long emergency department (ED) wait times are a major issue for healthcare systems all over the world. The application of artificial intelligence (AI) is a novel strategy to reduce ED wait times when compared to the interventions included in previous research endeavors. To date, comprehensive systematic reviews that include studies involving AI applications in the context of EDs covered a wide range of AI implementation issues. However, the lack of an iterative update strategy limits the utility of these reviews. Since the subject of AI development is cutting-edge and is continuously changing, reviews in this area must be frequently updated to remain relevant.
OBJECTIVE
To provide a summary of the evidence that is currently available regarding how AI can affect ED wait times, discuss the applications of AI in improving wait times, and periodically assess the depth, breadth, and quality of the evidence for supporting the application of AI in reducing ED wait times.
METHODS
We plan to conduct a Living Systematic Reviews (LSRs). Our strategy involves conducting continuous monitoring of evidence, with biannual search updates and annual review updates. Upon completing the initial round of the review, we will refine the search strategy and establish clear schedules for updating the LSR. An interpretive synthesis utilizing Whittemore and Knafl's framework will be performed for compiling and summarising the findings. The review will be carried out using an integrated knowledge translation strategy, and knowledge users will be involved at all stages of the review to guarantee applicability, usability, and clarity of purpose.
RESULTS
The implementation will happen between September 2023 and March 2024, after which the results will be assessed. It is projected that the data analysis and manuscript writing will be finished in the summer of 2024.
CONCLUSIONS
The LSR enables researchers to maintain high methodological rigor while enhancing the timeliness, applicability, and value of the review.