Introduction: Artificial intelligence (AI) is transforming healthcare, with benefits such as personalized treatment and efficient diagnosis; however, it also comes with challenges such as data protection and ethical concerns. Aim: This study examined nurse leaders’ perceptions of AI's impact on their professional roles and their level of preparedness for AI integration. Methods: This descriptive correlational study was conducted at various hospitals in Hail City, Saudi Arabia. The Shinners Artificial Intelligence Perception tool was used to evaluate 155 nurse leaders’ perceptions of AI and their readiness for its adoption. Results: The SHAIP tool, previously validated in international studies, was further adapted and validated for this context using confirmatory factor analysis and reliability testing, ensuring robust measurement of nurse leaders’ perceptions and preparedness regarding AI. Nurse leaders showed a positive perception of AI's impact on their roles (mean score = 3.72), but lower perceived preparedness (mean score = 3.32). Education level (where holders of a Bachelor of Science in Nursing showed lower perceptions, with a coefficient of −0.66, p = .006) and department significantly influenced their perceptions of AI. No significant predictors of preparedness for AI were identified. Further, there was no correlation between the perceived AI impact and preparedness. Conclusion: Although nurse leaders recognize AI's potential in healthcare, there is a gap between their perceptions and preparedness. Tailored education and training programs are required to enhance the integration of AI into nursing leadership and practice.

Background: Assessing the current landscape of nurses’ knowledge and attitudes is a critical first step in facilitating a smooth and effective transition toward artificial intelligence (AI)–enhanced critical care. Objective: This study aimed to assess the levels of and factors affecting the knowledge of and general attitudes toward AI in critical care among nurses. Methods: A cross-sectional correlational design was used with 203 critical care nurses in Hail, Saudi Arabia, using the Nurses’ AI Knowledge Questionnaire and the 20-item General Attitudes Toward Artificial Intelligence Scale from May 2025 to July 2025. Data were analyzed using 2-tailed t tests, ANOVA, Pearson correlation, and multivariable linear regression. Statistical significance was set at P <.05. Results: Critical care nurses demonstrated moderate knowledge of (mean score 4.93, SD 1.78) and positive attitudes toward AI (mean score 64.39, SD 8.26). A moderate positive correlation was found between knowledge of and attitudes toward AI ( r =0.45; P <.001). In multivariable analyses, older age was associated with lower knowledge (≥40 years: β=−1.29, 95% CI −2.12 to −0.45; P =.003) and less positive attitudes (β=−8.97, 95% CI −12.49 to −5.44; P <.001). Female nurses reported lower knowledge (β=−0.69, 95% CI −1.20 to −0.19; P =.007) and less positive attitudes (β=−2.65, 95% CI −4.78 to −0.52; P =.02) than male nurses. Greater experience (>5 years) was positively associated with knowledge (β=1.20, 95% CI 0.65‐1.75; P <.001) and attitudes (β=8.08, 95% CI 5.76‐10.41; P <.001). Conclusions: Critical care nurses in Hail demonstrated moderate knowledge of and positive attitudes toward AI, which varied based on their demographic and professional characteristics. These findings highlight the need to strengthen AI literacy and provide targeted support to groups with lower scores, which may enhance readiness for AI integration in critical care settings.

Background: Heart failure (HF) is the most common condition for rehospitalization among people aged ≥65 years in the United States, with 35,197,725 hospitalizations between 2014 and 2017. Hospitalized patients with HF have the highest 30-day readmission rate (25%). Overall, HF management, despite its progress, remains a challenge. Although several studies have evaluated interventions designed to reduce HF-related hospital readmissions, research comparing their effectiveness remains insufficient. Purpose: This systematic review and meta-analysis focused on studies that investigated the effectiveness of nonpharmacological interventions (NPIs) on reducing rehospitalization among patients with HF. Methods: This review conformed to the preferred reporting items for systematic reviews and meta-analyses guidelines, used four databases: Cumulative index to Nursing and Allied Health Literature, PubMed, Cochrane, and Web of Science. Studies were included in the review according to the following criteria: (a) included only randomized control trials (RCTs), (b) included participants with HF who were over 18 years of age, (c) peer-reviewed, (d) written in English, and (e) rehospitalizations occurring within 30-day, 90-day, and 1 year of discharge from the initial hospitalization. Results: Fourteen studies were included, with a total of 2,035 participants. Meta-analysis showed that rehospitalization was different between the intervention and usual care groups. The odds ratio was 0.54 (95% confidence interval [0.36, 0.82, p < 0.01]). Conclusions/Implications for Practice: NPIs designed to increase HF knowledge and self-management may effectively reduce rehospitalization among HF patients. NPIs can be delivered at the patient's home through visits, phone calls, or digital platforms and technologies.

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.

Objective: To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score. Methods: Over a two-day period (total elapsed time approximately 28 hours), we conducted a datathon that challenged interdisciplinary teams to investigate potential sources of bias in the Global Open Source Severity of Illness Score. Teams were invited to develop hypotheses, to use tools of their choosing to identify potential sources of bias, and to provide a final report. Results: Five teams participated, three of which included both informaticians and clinicians. Most (4/5) used Python for analyses, the remaining team used R. Common analysis themes included relationship of the GOSSIS-1 prediction score with demographics and care related variables; relationships between demographics and outcomes; calibration and factors related to the context of care; and the impact of missingness. Representativeness of the population, differences in calibration and model performance among groups, and differences in performance across hospital settings were identified as possible sources of bias. Discussion: Datathons are a promising approach for challenging developers and users to explore questions relating to unrecognized biases in medical machine learning algorithms. Author summary: Disadvantaged groups are at risk of being adversely impacted by biased medical machine learning models. To avoid these undesirable outcomes, developers and users must understand the challenges involved in identifying potential biases. We conducted a datathon aimed at challenging a diverse group of participants to explore an open-source patient severity model for potential biases. Five groups of clinicians and informaticians used tools of their choosing to evaluate possible sources of biases, applying a range of analytic techniques and exploring multiple features. By engaging diverse participants with hands-on data experience with meaningful data, datathons have the potential to raise awareness of potential biases and promote best practices in developing fair and equitable medical machine learning models.

Background: Nursing informatics is essential for digital health transformation; however, the technology acceptance of undergraduate nursing students in Saudi Arabia remains underexplored. Objective: This study examined factors influencing nursing students’ intention to use informatics technologies using the technology acceptance model. Methods: A cross-sectional survey was conducted with 132 undergraduate nursing students. Data were analyzed using descriptive, correlational, and hierarchical regression analyses. Results: Perceived usefulness (mean 3.68, SD 1.22) and perceived ease of use (mean 3.64, SD 1.32) were the strongest predictors of acceptance, together explaining 87% of the variance ( R ²=0.87; β =0.323 for usefulness, P <.001; β =0.195 for ease of use, P =.032). Only 25.8% (n=34) of the students often used electronic health records, while 31.8% (n=42) had no electronic health record experience, indicating a clear gap in practical informatics exposure. Conclusions: Nursing students’ acceptance of informatics is primarily driven by its perceived usefulness and perceived ease of use. These findings highlight the urgent need to integrate practical, user-centered informatics training and clinical simulation into undergraduate nursing curricula to better prepare students for technology-based practice.