A bibliometric analysis of the application trends of information technology in antimicrobial stewardship within hospitals
Clinical Snapshot
PICO Framework
| P — Population | Published research literature (2000–2025) on information technology applications in hospital antimicrobial stewardship programs |
| I — Intervention | Bibliometric analysis of IT-driven AMS research, including machine learning, clinical decision support systems, electronic health records, and artificial intelligence |
| C — Comparator | No direct comparator; temporal and network comparisons across publication years, countries, institutions, and keyword clusters |
| O — Outcomes | Publication trends, collaborative networks, keyword co-occurrence clusters, citation bursts, and emerging research themes in IT-enabled AMS |
Bottom Line
This bibliometric analysis maps 25 years of research on information technology applications in hospital antimicrobial stewardship, identifying a growing but still modest corpus of 258 publications dominated by US and European institutions. The field has evolved from foundational AMS strategies toward data-driven approaches incorporating machine learning, clinical decision support systems, and electronic health record integration for resistance prediction and prescribing optimisation. These findings are directionally useful for research prioritisation and health informatics investment planning. However, the study's conclusions must be interpreted cautiously: single-database searching, English-language restriction, and the absence of quality appraisal of included publications substantially limit the comprehensiveness and inferential strength of the analysis. Bibliometric volume is not a proxy for clinical effectiveness — the existence of a growing literature on ML-based AMS does not confirm that these tools improve patient outcomes or reduce AMR rates. For Australian clinicians and health system planners, the identified themes align with national AMS mandates and digital health priorities, but implementation decisions should be grounded in systematic reviews and clinical trial evidence rather than publication trend data alone. This paper is best regarded as a hypothesis-generating landscape study rather than actionable clinical evidence.
Key Findings
P Value: Not reported; inferential statistics not applicable to this study design
Effect Size: The US contributed 131 of 258 publications (50.8%); European nations demonstrated high network centrality (>0.2). No clinical effect sizes are reported — this is a descriptive bibliometric study.
Primary Outcome: Annual publications on IT in hospital AMS showed a fluctuating but overall increasing trend, peaking at 49 articles in 2024, from a baseline of minimal publications in 2000
Nnt Or Sensitivity: Not applicable. Key scientometric metrics: network centrality >0.2 for US and European nodes; keyword burst detection identified ML, CDSS, EHR, and AI as emergent high-frequency terms in recent publication cycles
Confidence Interval: Not reported; not applicable to bibliometric methodology
Clinical Application
The findings are directly applicable to research prioritisation and program planning rather than bedside clinical decision-making. Institutions considering investment in CDSS, EHR-integrated AMS tools, or ML-based resistance prediction models can use this landscape analysis to identify evidence gaps and mature technology areas. Australia has a well-established national AMS framework through the Australian Commission on Safety and Quality in Health Care (ACSQHC) and the National Antimicrobial Prescribing Survey (NAPS). The Antimicrobial Use and Resistance in Australia (AURA) surveillance program aligns with the EHR and data integration themes identified in this study. Australian hospitals accredited under the NSQHS Standards (Standard 3: Preventing and Controlling Healthcare-Associated Infection) are mandated to implement AMS programs. The identified trend toward ML-based AMR prediction and CDSS integration is relevant to Australian digital health investment priorities, including the My Health Record infrastructure and state-based electronic medication management systems. The TGA's regulatory framework for software as a medical device (SaMD) would apply to any CDSS tools identified in this literature. PBS implications are indirect but relevant — optimising antimicrobial prescribing through IT tools has potential to reduce inappropriate broad-spectrum antibiotic use, which has cost and resistance implications for PBS-listed agents. Hospital pharmacists, infectious disease physicians, clinical informaticians, and health system administrators seeking to understand the evidence base and research trajectory for IT-enabled antimicrobial stewardship programs
Abstract
OBJECTIVES: This study uses bibliometric analysis to systematically map research trends, knowledge structure and evolution of information technology (IT) in hospital antimicrobial stewardship (AMS) over the last 20 years. METHODS: A Web of Science Core Collection search (2000-2025) yielded 258 English-language publications on IT applications in hospital AMS. A bibliometric analysis, utilising CiteSpace and Bibliometrix, quantitatively evaluated domain evolution through temporal analysis, network mapping, keyword clustering, burst detection and examination of highly cited publications. RESULTS: The bibliometric analysis reveals a fluctuating yet overall increasing trend in annual publications, peaking at 49 articles in 2024. The US (131 publications) and European nations demonstrate significant research output (centrality >0.2), with major collaborative networks coalescing around institutions including Harvard University and Imperial College London. Keyword analysis identifies 'AMS' as a core theme, closely associated with technological keywords such as 'machine learning (ML)', 'clinical decision support systems (CDSS)', 'electronic health records (EHR)' and 'artificial intelligence (AI)'. Emerging trends suggest a shift in research focus from foundational strategies to data-driven prediction of antimicrobial resistance (AMR) and precision interventions. Highly cited literature emphasises the integration of EHR and ML technologies for optimising prescriptions and predicting resistance patterns. CONCLUSIONS: IT-driven AMS research has shifted from empirical management to data science. Despite EHR integration, and ML and CDSS support, challenges remain in data standardisation, technical deployment and ethics. Future work must emphasise global collaboration, standardisation, design refinement and ethical guidelines, and provide clear algorithm explanations to enhance AMR mitigation.
References
- 1.Ding, Y., Lei, M., Yuan, G., Yu, J., Wu, L., Yang, J., & Zhang, H. (2026). A bibliometric analysis of the application trends of information technology in antimicrobial stewardship within hospitals. European Journal of Hospital Pharmacy: Science and Practice. https://doi.org/10.1136/ejhpharm-2025-004623
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