Harnessing Artificial Intelligence to Optimize Antimicrobial Stewardship Programs: A Scoping Review

Authors

• Philip Tran, Chapman University
• Tien Dinh, Chapman University
• Ivan Portillo, Chapman UniversityFollow
• Emi Minejima, University of Southern CaliforniaFollow
• Amy Y. Kang, Chapman UniversityFollow

Document Type

Conference Proceeding

Publication Date

11-2025

Abstract

Objectives

This study aims to investigate current state of research on the utilization of AI and machine learning technologies in ASP, and how these technologies impact the workload and intervention strategies of ASP pharmacists. Antimicrobial stewardship program (ASP) commonly utilizes a prospective audit with feedback (PAF). This PAF activity can be labor intensive for antimicrobial stewardship teams. Therefore, improving efficiency in PAF activities is crucial. While there have been several publications that investigated the use of artificial intelligence (AI), primarily through statistical and machine learning approaches, to identify interventions that may lead to actionable plans, there has yet to be a comprehensive review of the literature on this topic.

Methods

The search was conducted on July 10, 2024, using databases including Ovid Medline, Embase, IPA, Web of Science, and CINAHL. The search strategy included keywords such as "antimicrobial stewardship,” "antibiotic stewardship,” "machine learning,” and "artificial intelligence,” without language restrictions. The search covered all articles from the start of each database until July 2024. All discovered articles were managed through EndNote 21 and Zotero. Two reviewers independently evaluated articles for inclusion based on criteria through Rayyan software. If necessary, a third final reviewer made a decision if there are any disagreements between the first two reviewers. The data extraction will be performed through REDCap after going through a pilot test of 3 articles. We will extract data using a predeveloped data extraction form. The data extraction will be limited to the predefined outcomes of interest. Extracted data will undergo a verification process by a second team member, and discrepancies will be resolved by a third team member. The quality of the articles will be assessed using the Newcastle-Ottawa Scale (NOS) to help gauge quality and bias.

Results

Our search has populated over 105 articles. Out of those 105 articles, 10 were included at the end of extraction.

Conclusions/Implications

This scoping review aims to provide a comprehensive overview of the current research on the utilization of AI and machine learning technologies in ASPs. By synthesizing available evidence, this review will identify gaps in the literature and assess the potential for AI-driven models to enhance the efficiency of ASPs and reduce the workload of infectious disease pharmacists. The findings of this review are expected to inform future research directions and the feasibility of integrating AI into clinical workflows to optimize antimicrobial stewardship efforts.

Comments

Presented at the APhA 2025 Annual Meeting and Exposition.

Recommended Citation

Article/Presentation: Dinh, T., Kang, A., Minejima, E., Portillo, I. & Tran, P. (2025, October). Current Use of Artificial Intelligence and Machine Learning Technologies in Antimicrobial Stewardship Programs: Scoping Review. Poster Presented at the APhA 2025 Annual Meeting and Exposition.