Aim To describe the experiences of Italian nurses who have been urgently and compulsorily allocated to a newly established COVID‐19 sub‐intensive care unit. Background In the context of the COVID‐19 pandemic, no studies have documented the experience of nurses urgently reallocated to a newly created unit. Method A qualitative descriptive study. Twenty‐four nurses working in a sub‐intensive care unit created for COVID‐19 patients participated in four focus groups. Audio‐recorded interviews were verbatim‐transcribed; then, a thematic analysis was performed. Results The experience of nurses was summarized along three lines: (a) ‘becoming a frontline nurse’, (b) ‘living a double‐faced professional experience’ and (c) ‘advancing in nursing practice’. Conclusions Nurses who experienced being mandatorily recruited and urgently reallocated to a COVID‐19 unit lived through a mix of negative feelings in the early stages, a double‐faced situation during the episode and, at the end, the perception of global growth as a person, as a team and as a professional. Implication for nursing management Nurse managers could play a key role in identifying and preparing nurses in advance to mitigate their concerns and their sense of unpreparedness. The value attributed to nursing care should be promoted both during and after the current COVID‐19 pandemic.

Background: Artificial intelligence (AI) has been increasingly employed in healthcare across diverse domains, including medical imaging, personalized diagnostics, therapeutic interventions, and predictive analytics using electronic health records. Its integration is particularly impactful in critical care, where AI has demonstrated the potential to enhance patient outcomes. This systematic review critically evaluates the current applications of AI within the domain of critical care nursing. Methods: This systematic review is registered with PROSPERO (CRD42024545955) and was conducted in accordance with PRISMA guidelines. Comprehensive searches were performed across MEDLINE/PubMed, SCOPUS, CINAHL, and Web of Science. Results: The initial review identified 1364 articles, of which 24 studies met the inclusion criteria. These studies employed diverse AI techniques, including classical models (e.g., logistic regression), machine learning approaches (e.g., support vector machines, random forests), deep learning architectures (e.g., neural networks), and generative AI tools (e.g., ChatGPT). The analyzed health outcomes encompassed postoperative complications, ICU admissions and discharges, triage assessments, pressure injuries, sepsis, delirium, and predictions of adverse events or critical vital signs. Most studies relied on structured data from electronic medical records, such as vital signs and laboratory results, supplemented by unstructured data, including nursing notes and patient histories; two studies also integrated audio data. Conclusion: AI demonstrates significant potential in nursing, facilitating the use of clinical practice data for research and decision-making. The choice of AI techniques varies based on the specific objectives and requirements of the model. However, the heterogeneity of the studies included in this review limits the ability to draw definitive conclusions about the effectiveness of AI applications in critical care nursing. Future research should focus on more robust, interventional studies to assess the impact of AI on nursing-sensitive outcomes. Additionally, exploring a broader range of health outcomes and AI applications in critical care will be crucial for advancing AI integration in nursing practices.

Nitrogen balance (NB) is considered a good marker of adequate protein intake and it has been suggested to be a good predictor of patients’ health outcomes. However, in literature, there is a lack of large randomized trials examining NB-guided protein intake in patients in intensive care units (ICUs). A randomized controlled trial enrolling patients admitted to ICU was done to compare changes in NB. Participants were randomized to a standard or protein-fortified diet (protein intake of 1.8 g/kg/day according to the guidelines of the Society of Critical Care Medicine and the American Society for Parenteral and Enteral Nutrition). The primary endpoint was represented by the NB on Day 1, 3, and study exit. Forty patients were enrolled in the study (19 in the protein-fortified group). The longitudinal analysis showed that, on Day 3, patients randomized to the protein-fortified diet were more likely (p < 0.001) to present better NB (at 3 days, patients in the protein-fortified diet were estimated to have a nitrate value of 5.22 g more than patients in the standard diet, 95% CI 3.86–6.58). The protein-fortified diet was found to be significantly and directly associated with changes in NB in critically ill patients admitted to ICU.