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.