ArticleRisk management and healthcare policy2025
The Role of Artificial Intelligence in Managing Central Line-Associated Bloodstream Infection (CLABSI) for Patient Safety and Quality of Care.
Article in Risk management and healthcare policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Central Line-Associated Bloodstream Infections (CLABSI) pose significant challenges in healthcare systems globally, contributing to increased morbidity, mortality, and healthcare costs. As healthcare organizations strive to improve patient safety and quality of care, Artificial Intelligence (AI) presents considerable promise in the prevention, detection, and management of CLABSI. This paper proposes a conceptual framework that integrates AI within healthcare systems, aligning technological innovations with human workflows, system design, and risk management strategies. By taking a systems approach, the framework supports the implementation of AI tools in ways that are compatible with the complexity of healthcare delivery. The paper explores the potential and significance of AI in enhancing healthcare through the prevention, early detection, and management of patient safety concerns, including CLABSI. It highlights how AI applications can predict infection risks, support timely interventions, and operate in tandem with standard infection control protocols to reduce the incidence of CLABSI. This integrated approach aims to promote safer, more efficient, and patient-centered care.
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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.