Aim: This study aimed to explore the relationships between burnout, emotional intelligence (EI), and work engagement (WE) among hospital nurses. Specifically, it examined the mediating role of burnout in the relationship between EI and WE. Background: Nurses are frequently exposed to emotionally and physically demanding environments, which may lead to sustained occupational stress. Prolonged exposure to such conditions can contribute to burnout, adversely affecting both personal well-being and professional performance. EI is increasingly recognised as a protective factor that may alleviate burnout and enhance WE. Methods: A quantitative, cross-sectional correlational design was employed. A quota sampling technique was used to select 336 nurses working in public healthcare facilities in Ha’il, Saudi Arabia. Data were collected using standardised self-report instruments: the 14-item Shirom–Melamed Burnout Questionnaire (SMBM), the short-form Genos Emotional Intelligence Inventory (Genos EI), and the 9-item Utrecht Work Engagement Scale (UWES-9). Structural equation modelling examined associations and the mediating role of burnout between EI and WE. Results: EI was positively associated with WE and negatively with burnout. Burnout, in turn, was negatively associated with WE. Mediation analysis confirmed that burnout partially mediated the effect of EI on WE, indicating that EI nurses were less likely to experience burnout and more likely to remain engaged in their roles. Discussion: The results emphasise the role of EI in reducing burnout and enhancing WE among nurses. Burnout partially mediates this relationship, suggesting that EI influences WE both directly and indirectly. Conclusions and Implications for Nursing: Integrating EI training into professional development and implementing measures to reduce burnout may improve WE and retention. Policy efforts should ensure supportive work environments and adequate staffing to sustain nurse well-being.

Background: Patients with heart failure (HF) are at high risk of readmission, contributing to substantial healthcare costs. This study investigated machine learning (ML) approaches to predict HF readmissions. Methods: A systematic review was conducted using several medical databases, adhering to the PRISMA guidelines, to identify studies employing ML to predict HF readmissions. Three reviewers independently screened the articles and extracted data. Results: Twenty-two studies from six countries were included in this study. Some studies examined 30-day readmissions, whereas others assessed 90-day, 180-day, or 1- to 3-year readmissions. Fourteen studies used supervised learning algorithms, with area under the curve (AUC) values ranging from 0.70 to 0.99, and unsupervised algorithms had AUCs of 0.69 to 0.72. The average age of the patients was 73 years, with approximately equal numbers of males and females. Conclusions: ML can predict HF-related hospitalization across various time frames. Supervised ML approaches and the incorporation of clinical knowledge may enhance model performance. Collaboration between providers and data scientists is needed to improve patient outcomes and reduce costs by using more accurate predictive models.