Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
Antibiotic de-escalation (ADE) is important to help optimize antibiotic use and balance the positive and negative effects of antimicrobial therapy. ADE should be performed promptly, and infections should be treated with the shortest course of antimicrobials as clinically feasible to avoid unnecessary use of broad-spectrum antimicrobials. Several tools have been developed to increase efficient ADE, including rapid diagnostic tests (ex. multiplex PCR), MRSA nasal PCR/culture, and biomarkers. Multiplex PCR and MRSA nasal PCR/culture have been associated with reductions in inappropriate antibiotic use. Procalcitonin, a biomarker, has been associated with shorter antimicrobial durations in some studies; however, widespread use may be limited by lack of specificity for bacterial infections, cost, and lack of set cut-off points. Additional biomarkers such as IL-6, HMGB1, presepsin, sTREM-1, CD64, PSP, proadrenomedullin, and pentraxin-3 are currently being studied. As technology improves, additional tools may be leveraged to better optimize ADE even better, such as antimicrobial spectrum scoring tools and artificial intelligence (AI). Spectrum scores, which quantify antibiotic activity using specific numeric values, could be incorporated into electronic health records to identify patients on unnecessarily broad antibiotics. AI modeling has the potential to predict personal antibiograms or provide the probability that an empiric regimen may cover a particular infection, among other potential applications. This review will discuss the literature associated with ADE in the ICU, selected tools to help guide ADE, and perspectives on how to implement ADE into clinical practice....
Background: Eye tracking technology, when used in nursing, helps to reduce medication errors by analyzing eye movements. In education, it provides insights into student learning, cognitive load, and instructional design, allowing for more personalized learning. Despite challenges such as the need for technical expertise, privacy concerns, and cost, eye tracking offers real-time feedback that enhances both teaching and learning effectiveness. Objectives: To explore the current evidence on the application of eye tracking technology in training nursing students for drug administration. Methods: Eligible studies included peer-reviewed empirical papers, both qualitative and quantitative, and reports published in English. Excluded were studies involving Non-Eye Glass Tracking, nonnursing students, or non-English articles. Searches were conducted in nine databases. The risk of bias was assessed using the JBI SUMARI tool, and the results were synthesized narratively, presented with the PRISMA-P flow diagram. Results: From 739 studies, 10 focusing on medication training were identified. Eye tracking helped to reveal differences in visual focus between novice and expert nurses, with certain interventions shown to improve attention and concentration. Conclusions: Eye tracking has strong potential in nursing education, especially for improving attention and enhancing situational awareness in medication administration. However, limitations such as small sample sizes, technical barriers, and a lack of long-term data remain. Future research should address these gaps with larger, more diverse samples and extended follow-ups....
Background/Objectives: Enhancing delirium nursing performance in trauma intensive care units (TICUs), where the prevalence of delirium is high, causes early detection of delirium and improves the quality of nursing care. TICU nurses experience various stress levels while caring for patients with delirium, which negatively affects their performance. Self-efficacy improves delirium nursing performance based on their capacity. Person-centred care identifies the holistic needs of patients in TICUs, which stimulates their recovery. This study aimed to examine the relationship of delirium-related stress, selfefficacy, and person-centred care with delirium nursing performance and identify factors influencing delirium nursing performance among nurses in TICUs. Methods: This crosssectional descriptive survey study was conducted on 170 TICU nurses from eight hospitals in Korea from 22 July to 30 September 2024. Data was collected using self-reported questionnaires after informed consent was provided. Data were analysed using multiple regression analysis. Results: Delirium nursing performance showed significant positive correlations with person-centred care (r = 0.51, p < 0.001) and self-efficacy (r = 0.41, p < 0.001). Regression analysis revealed person-centred care (β = 0.46, p < 0.001) and self-efficacy (β = 0.24, p = 0.004) as significant predictors of delirium nursing performance in TICUs, accounting for 28.6% of the variance. Conclusions: Interventions focused on person-centred care may help improve delirium nursing performance and practice holistic care....
Background/Objectives: Nursing diagnosis is a complex process that requires clinical judgment, time, and resources and whose implementation is hindered by factors such as workload, lack of time, and resistance to computerized systems. This study aimed to compare the quality and efficiency of care plans generated by nursing professionals versus those produced by an artificial intelligence (AI) model, using the NANDA, NOC, and NIC taxonomies as criteria. Methods: An observational study was carried out with three simulated clinical cases. Thirty experts, fifty-four nursing professionals, and the ChatGPT model (GPT-4) were included. The experts established the referral plans using the Delphi technique. Responses were evaluated with a validated rubric (EADE-2) and analyzed using nonparametric tests. Professionals’ perceptions on the use of computer systems were also collected. Results: ChatGPT scored significantly higher on several dimensions (p < 0.001) and resolved all three cases in 35 s, compared to an average of 30 min for practitioners. Professionals expressed dissatisfaction with current diagnostic documentation systems. Conclusions: AI demonstrates high potential in optimizing the diagnostic process in nursing, although for its implementation human supervision, ethical aspects and improvements in current systems must be considered to achieve effective integration....
The COVID-19 pandemic has led to a general increase in the workload in Intensive Care Units (ICUs). The objective here was to analyze the nursing workload in a Cardiology ICU of a tertiary and teaching inner hospital in Brazil before and during the COVID-19 pandemic. A retrospective and ecological study was conducted. Nursing Activities Score mean by month (NAS-mm) data were collected from the unit’s opening in October 2014 until May 2023. The data were divided into pre-pandemic and pandemic periods, with the pandemic further divided into three phases/years. A workload decrease was observed during the pandemic and varied across different pandemic years. In the pre-pandemic period, the mean was 53.80 points (95%CI: 52.99; 54.60; n = 65), whereas during the pandemic, it was 52.02 points (95%CI: 50.88; 53.17; n = 39). The first year had the lowest mean workload at 50.94 points, followed by the second year with 48.37 points, while the third year had the highest with 55.82 points, exceeding the pre-pandemic period’s workload. Amid the COVID-19 pandemic scenario, a decrease in nursing workload was observed in the unit, only returning to reference values in the third pandemic year, possibly associated with patient and administrative profile changes....
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