BACKGROUND
Cardiac arrest in the hospital (IHCA) is a dramatic phenomenon that requires efficient and urgent response in which nurses are often the first responders.
AIM
The aim of the study was to establish the relationship between nurses' knowledge, attitudes and self-efficacy with respect to adult cardiopulmonary resuscitation (CPR) in acute care at King Fahad Hospital, Saudi Arabia.
STUDY DESIGN
A cross-sectional, descriptive-correlational research design was used. Registered nurses were surveyed using a structured questionnaire assessing CPR-related knowledge, attitudes and self-efficacy.
RESULTS
Total 362 nurses have completed the questionnaire. Nurses demonstrated generally adequate CPR-related knowledge, positive attitudes and good self-efficacy towards CPR. Significant positive correlations were observed between knowledge and attitudes (ρ = 0.696, p < 0.0001), knowledge and self-efficacy (ρ = 0.772, p < 0.0001) and attitudes and self-efficacy (ρ = 0.812, p < 0.0001). Lower knowledge scores were observed in defibrillation-related procedures and medication administration during CPR.
CONCLUSIONS
The findings indicate differences across cognitive (knowledge), affective (attitudes) and self-efficacy domains in nurses' preparedness, which may inform targeted educational strategies to strengthen CPR preparedness among nurses.
RELEVANCE TO CLINICAL PRACTICE
The study supports the need for targeted interventions such as simulation-based training, repeated CPR education and structured debriefing sessions. Institutional policies should emphasise training frequency, policy communication and role clarity to align with international resuscitation standards and improve IHCA outcomes.
BACKGROUND
Artificial intelligence (AI) is reshaping clinical decision support systems (CDSSs). In acute and critical care, nurses provide continuous surveillance, recognise deterioration, coordinate escalation and translate protocols into bedside action. AI-CDSS may be particularly relevant when they support rather than replace clinical judgement.
AIM
To examine whether nurse-used AI-CDSS improve patient-important outcomes in acute and critical care contexts and summarise effects on care processes and nurse-reported outcomes.
STUDY DESIGN
Following PRISMA 2020 and a preregistered protocol, we searched eight databases and major trial registries for English-language studies from 1 January 2010 to 1 January 2026. Searches were conducted on 1 January 2026. We included randomised, quasi-experimental and adjusted cohort studies in which registered nurses or nursing teams were primary users of AI-CDSS generating patient-specific predictions or recommendations. Mortality was pooled using a random effects model; other outcomes were synthesised narratively.
RESULTS
Seven studies involving about 75 000 patients were included. Most evidence came from acute wards, intensive care units, sepsis, deterioration and delirium-prevention contexts, with additional home and palliative care evidence. Three mortality studies were pooled. Nurse-facing AI-CDSS were associated with lower hospital mortality (RR 0.68, 95% CI 0.53-0.87; I2 = 24%), although the prediction interval included possible no effect. Length of stay and protocol adherence generally improved when tools were embedded in nursing workflows. Nurse-reported outcomes were sparse.
CONCLUSION
Nurse-facing AI-CDSS may strengthen acute and critical care nursing by improving surveillance, escalation and protocol delivery for patients at risk of deterioration. Evidence is promising but limited by small study numbers, heterogeneous interventions and sparse nurse-reported outcomes. Critical care implementation should prioritise nurse-centred design, alert burden, equity, safety monitoring and rigorous evaluation before scale-up.
RELEVANCE TO CLINICAL PRACTICE
Nurse-used AI-CDSS show potential to improve patient outcomes and care processes, but evidence remains limited and context dependent.
W. Almagharbeh, S. Alkubati, A. A. Alasmari et al.· Nursing Critical Care· 0 citations
It is indicated that both self-directed learning and AI acceptance are associated with perceived clinical competence, with AI acceptance acting as a mediating factor.
Boshra Karem Mohamed El-Sayed, Ayman Ateq Alamri, M. G. R. Asal et al.· BMC Medical Education· 0 citations