ArticleCureus2025
Nurses' Perception of Artificial Intelligence-Driven Monitoring Systems for Enhancing Compliance With Infection Prevention and Control Measures in Al-Ahsa, Saudi Arabia.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in nursing: a systematic review of attitudes, literacy, readiness, and adoption intentions among nursing students and practicing nurses.Frontiers in digital health · 2025Pooled it
- Attitudes, Perceptions, and Factors Influencing the Adoption of AI in Health Care Among Medical Staff: Nationwide Cross-Sectional Survey Study.Journal of medical Internet research · 2025Article
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
Background Healthcare-associated infections (HCAIs) represent a major risk to patient safety, increasing morbidity, mortality, and costs. Effective infection prevention and control (IPC) compliance is crucial, but nurse adherence remains inconsistent, necessitating innovative solutions such as artificial intelligence (AI)-driven monitoring. However, the success of such technologies heavily relies on the perceptions and acceptance of frontline healthcare workers, particularly nurses. This study aimed to determine the nurses' perception of AI-driven monitoring in improving IPC compliance in selected hospitals. Methodology A cross-sectional study was conducted among nurses working at a public hospital in Al-Ahsa, Saudi Arabia. Computer-generated numbers randomly selected 246 nurses. A structured, self-administered questionnaire was used to gather data on demographics, knowledge, perceptions, and perceived barriers to AI-driven monitoring in IPC practices. Descriptive statistics were utilized for continuous variables, while inferential statistics, such as chi-square, were used for categorical variables to analyse the results. Results Out of 246 nurses, 183 (74.4%) had average knowledge about AI applications in IPC practices. The overall mean knowledge score regarding AI-based IPC measures was 17.00 ± 3.97 out of 20, which showed that most nurses had moderate knowledge, but some domains scored well. Regarding perception about AI-driven monitoring IPC practices, many nurses had a positive attitude. However, insufficient training, financial limitations, and limited organizational support are perceived as the most critical barriers. There was a significant association found between the level of knowledge and age, highest educational qualification, job role, and AI technology-based IPC training (p < 0.05). Nurses expressed willingness to adopt AI systems if adequate training and support were ensured. Conclusion AI-driven monitoring may enhance IPC compliance among nurses if barriers are addressed, helping to reduce HCAIs and improve patient safety. Its success depends on addressing key barriers such as training, infrastructure, and stakeholder support. These findings can guide policymakers and healthcare leaders in effectively adopting AI-based IPC solutions.
Indexed as
Identifiers
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.