Applications of Artificial Intelligence in Emergency Departments to Improve Wait Times: Protocol for an Integrative Living Review

Bahareh Ahmadzadeh; Christopher Patey; Oliver Hurley; John Knight; Paul Norman; Alison Farrell; Stephen Czarnuch; Shabnam Asghari

doi:10.2196/52612

Applications of Artificial Intelligence in Emergency Departments to Improve Wait Times: Protocol for an Integrative Living Review

JMIR Res Protoc. 2024 Apr 12:13:e52612. doi: 10.2196/52612.

Authors

Bahareh Ahmadzadeh¹, Christopher Patey^{2

3}, Oliver Hurley¹, John Knight^{4

5}, Paul Norman², Alison Farrell⁶, Stephen Czarnuch^{7

8}, Shabnam Asghari¹

Affiliations

¹ Centre for Rural Health Studies, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada.
² Eastern Health, Carbonear Institute for Rural Reach and Innovation by the Sea, Carbonear General Hospital, Carbonear, NL, Canada.
³ Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada.
⁴ Data and Information Services, Digital Health, NL Health Services, St. John's, NL, Canada.
⁵ Division of Community Health and Humanities, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada.
⁶ Health Sciences Library, Memorial University of Newfoundland, St. John's, NL, Canada.
⁷ Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada.
⁸ Discipline of Emergency Medicine, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada.

PMID: 38607662
PMCID: PMC11053385
DOI: 10.2196/52612

Abstract

Background: Long wait times in the emergency department (ED) are a major issue for health care 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 have covered a wide range of AI implementation issues. However, the lack of an iterative update strategy limits the use 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: This study aims 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 supporting the application of AI in reducing ED wait times.

Methods: We plan to conduct a living systematic review (LSR). 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 using Whittemore and Knafl's framework will be performed to compile and summarize 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 literature search was completed by September 22, 2023, and identified 17,569 articles. The title and abstract screening were completed by December 9, 2023. In total, 70 papers were eligible. The full-text screening is in progress.

Conclusions: The review will summarize AI applications that improve ED wait time. The LSR enables researchers to maintain high methodological rigor while enhancing the timeliness, applicability, and value of the review.

International registered report identifier (irrid): DERR1-10.2196/52612.

Keywords: AI; ED; LSR; artificial intelligence; emergency department; living systematic review; wait time.

©Bahareh Ahmadzadeh, Christopher Patey, Oliver Hurley, John Knight, Paul Norman, Alison Farrell, Stephen Czarnuch, Shabnam Asghari. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 12.04.2024.