alturath Nursing Journal

Background: Burnout is a worldwide occupational hazard, especially in resource-stretched health systems. In Iraqi intensive care units (ICUs), the increasing workloads, resource scarcity and institutional support systems make the environment a perfect storm for burnout; however, few quantitative studies have linked burnout with patient safety events in the Iraqi ICU literature. Objective: The present study aimed to investigate associations between nurse burnout, workload, patient safety events and quality of care outcomes in intensive care units (ICUs) of tertiary hospitals in Iraq. Methods: We conducted a cross-sectional, multi-site study, involving 294 ICU nurses from eight tertiary hospitals in the governorates of Baghdad, Basra and Salah al-Din. The Maslach Burnout Inventory - Human Services Survey (MBI-HSS) was used to measure burnout. Nurses were divided into low (n = 86), moderate (n = 112) and high (n = 96) burnout categories. Data on patient safety and quality were extracted from hospital surveillance systems and patient care records for a concurrent six-month period. Predictors of high burnout were determined by binary logistic regression. Results: High-burnout nurses managed significantly more patients per shift (9.8 ± 2.1 vs. 5.4 ± 1.2; p < 0.001) and worked longer weekly hours (58.7 ± 6.3 vs. 44.2 ± 5.1; p < 0.001). Medication errors (6.7 ± 1.8 vs. 1.8 ± 0.7 per 100 orders), patient falls (4.3 ± 1.1 vs. 0.9 ± 0.4 per 100 bed-days), and 30-day mortality indices (13.4 ± 2.8% vs. 5.1 ± 1.6%) were substantially higher in the high-burnout group (all p < 0.001). Logistic regression identified patient load ≥ 8 per shift (OR = 4.62; 95% CI: 2.91–7.34) and weekly hours ≥ 56 (OR = 3.87; 95% CI: 2.43–6.16) as the strongest risk factors for high burnout. Conclusion: Nurse burnout is strongly and negatively related to patient safety and quality of care in Iraqi ICUs. Evidence-based workload management policies, institutional psychological support strategies and mentoring programs by peers should be developed.

Clinical decision support systems (CDSSs) augmented by artificial intelligence (AI) are revolutionising modern health care, but little is known about their use in Iraqi tertiary hospitals. Nurses are the primary decision-makers in clinical practice, and poor decision support can pose a patient safety risk. Objective: The aim of this study was to compare the effectiveness of AI-supported CDSS with traditional (non-AI) methods in improving nursing practice and certain patient outcomes in tertiary hospitals in three Iraqi governorates. Methods: A prospective, comparative, cross-sectional design was used, involving 294 registered nurses grouped into three categories: Group I (non-CDSS, n = 98), Group II (simple rule-based CDSS, n = 96), and Group III (AI-assisted CDSS, n = 100). Eight-month data (February-September 2023) was gathered via structured observation checklists, validated measures of nursing practice, hospital records and patient satisfaction surveys. Data were analysed using one-way ANOVA with Bonferroni post hoc tests (p < 0.05). Results: AI-CDSS implementation produced marked improvements in clinical decision accuracy (84.7 ± 6.1% vs. 61.4 ± 8.2% in controls; p < 0.001), substantial reductions in medication error rates (2.1 ± 0.9 vs. 6.8 ± 1.9 per 100 orders; p < 0.001), shorter hospital stays (6.4 ± 1.7 vs. 9.8 ± 2.4 days; p < 0.001), and higher patient satisfaction scores (86.5 ± 6.9 vs. 63.2 ± 9.8; p < 0.001). The main challenges for implementation were the lack of digital infrastructure (74.5% for Group I) and AI-literacy training for nurses (81.6%). Conclusion: AI-powered CDSS resulted in improved nursing practice and patient outcomes in Iraqi hospitals. Continued infrastructure development and AI literacy training for nursing staff are vital for future use.

Background: The rapid uptake of generative artificial intelligence (GenAI) tools, particularly ChatGPT, has outpaced institutional guidance in many low- and middle-income health systems. Evidence on Iraqi nurses' knowledge, attitudes, and clinical use of these tools is limited. Aim: To assess Iraqi nurses' knowledge, attitudes, and self-reported clinical use of ChatGPT, and to identify factors associated with its use. Methods: A multicenter cross-sectional survey was conducted between January and March 2026 in three teaching hospitals in central Iraq, in line with the STROBE statement. The protocol was prospectively registered (ATU-RIR-2025-19, registered 12 December 2025). A 50-item self-administered questionnaire covering demographics, knowledge of GenAI (15 items, multiple-choice), attitudes (20 Likert items), and use practices (15 items) was developed and pilot-validated (Cronbach's α: knowledge 0.81, attitudes 0.84). A stratified sample of 412 registered nurses across medical–surgical, intensive care, and emergency wards was approached; 387 returned complete responses (response rate 93.9%). Descriptive statistics, t-tests/ANOVA for group differences, and a multivariable logistic regression model (with cluster-robust standard errors at the hospital level) were used to identify predictors of ChatGPT use. Results: Mean knowledge score was 64.2 ± 14.8 of 100, with the lowest scores on data privacy and patient confidentiality (38.4% correct). Attitudes were cautiously positive (mean 3.43 ± 0.71 on a 5-point scale): 64.1% agreed GenAI could enhance work efficiency, but 51.4% worried it might erode clinical judgment. In total, 38.0% (147/387) reported any clinical use of ChatGPT in the prior six months, most commonly for literature search (28.2%), drug information (22.0%), and patient-education materials (18.1%). The strongest concerns were information accuracy (78.0%), patient privacy (72.1%), and professional liability (67.7%). In the adjusted model, age <30 years (aOR 2.12, 95% CI 1.34–3.36), high English proficiency (aOR 2.84, 1.78–4.52), and prior digital health training (aOR 2.41, 1.55–3.74) were independent predictors of clinical use. Conclusion: Iraqi nurses are using ChatGPT in clinical contexts despite moderate knowledge and unresolved concerns about accuracy and privacy. Hospitals should issue institutional GenAI policies, integrate AI literacy into nursing curricula, and establish patient-data safeguards before deployment broadens further.