ReviewJournal of internal medicine2025
The future of healthcare-associated infection surveillance: Automated surveillance and using the potential of artificial intelligence.
Review in Journal of internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Semi-Automated Retrospective Surveillance of Surgical Site Infections (sAReS-SSI) - using hospital routine data for SSI tracking.Antimicrobial resistance and infection control · 2026Article
- The Best of Both Worlds: How Combining a Large Language Model and a Rule-Based Algorithm Makes Catheter-Associated Urinary Tract Infection Surveillance More Efficient.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026Article
- Beyond the Reported Numbers:Antibiotics (Basel, Switzerland) · 2026Article
- Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.Infection · 2026Review
- First results of a national deployment of a fully automated central-line-associated bloodstream infection (CLABSI) surveillance system, Switzerland, 2022 to 2023.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2026Article
- Mapping antimicrobial resistance and healthcare-associated infections indicators for actionable benchmarking: a cross-network overview in a data-driven era.Antimicrobial resistance and infection control · 2025Review
- Comparative Evaluation and Performance of Large Language Models in Clinical Infection Control Scenarios: A Benchmark Study.Healthcare (Basel, Switzerland) · 2025Article
- The future of healthcare-associated infection surveillance: Automated surveillance and using the potential of artificial intelligence.Journal of internal medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Healthcare-associated infections (HAIs) are common adverse events, and surveillance is considered a core component of effective HAI reduction programmes. Recently, efforts have focused on automating the traditional manual surveillance process by utilizing data from electronic health record (EHR) systems. Using EHR data for automated surveillance, algorithms have been developed to identify patients with (ventilator-associated) pneumonia and (catheter-related) bloodstream, surgical site, (catheter-associated) urinary tract and Clostridioides difficile infections (sensitivity 54.2%-100%, specificity 63.5%-100%). Mostly methods based on natural language processing have been applied to extract information from unstructured clinical information. Further developments in artificial intelligence (AI), such as large language models, are expected to support and improve different aspects within the surveillance process; for example, more precise identification of patients with HAI. However, AI-based methods have been applied less frequently in automated surveillance and more frequently for (early) prediction, particularly for sepsis. Despite heterogeneity in settings, populations, definitions and model designs, AI-based models have shown promising results, with moderate to very good performance (accuracy 61%-99%) and predicted sepsis within 0-40 h before onset. AI-based prediction models detecting patients at risk of developing different HAIs should be explored further. The continuous evolution of AI and automation will transform HAI surveillance and prediction, offering more objective and timely infection rates and predictions. The implementation of (AI-supported) automated surveillance and prediction systems for HAI in daily practice remains scarce. Successful development and implementation of these systems demand meeting requirements related to technical capabilities, governance, practical and regulatory considerations and quality monitoring.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.