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This study used text network analysis and topic modeling to examine the knowledge structure of family-centered care in neonatal ICU nurses. Text was extracted from abstracts of 110 peer-reviewed articles published between 1995 and 2023 and analyzed by identifying keywords, topics, and changes in research topics over time. Analysis of keywords revealed significant terms including “infant,” “family,” “experience,” “interventions,” and “parent participation,” highlighting family's central roles in family-centered care in neonatal ICU discourse. The research topics identified included “family-centered partnerships,” “barriers to implementing family-centered care,” “infant-mother attachment intervention,” “family participation intervention,” and “parenthood.” Over time, research on family-centered care in neonatal ICUs nurses has steadily increased, with notable increases in “family-centered partnerships” and “barriers to implementing family-centered care.” The findings underscore the evolving landscape of family-centered care in neonatal ICUs, emphasizing the critical role of collaborative care models in enhancing neonatal and familial outcomes. These insights provide a foundation for developing family-centered care programs that empower both nurses and families, supporting the holistic care of vulnerable infants. This study's results offer comprehensive insights into understanding family-centered care in the neonatal ICUs and could serve as a foundation for future studies to develop family-centered care programs for neonatal ICU nurses and families. Based on this study, it is recommended that nursing education programs integrate family-centered care training into their curricula, with an emphasis on communication, cultural competence, and family partnerships. No abstract available The aim of this study was to determine the effect of real-time videos with smart glasses on the performance of cardiopulmonary resuscitation performed by nursing students. In this randomized controlled pilot study, the students were randomly assigned to the smart glass group (n = 12) or control group (n = 8). Each student's cardiopulmonary resuscitation performance was evaluated by determining sequential steps in the American Heart Association algorithm they applied and the accuracy and time of each step. A higher number of participants correctly checked response breathing, requested a defibrillator, activated the emergency response team, and provided appropriate chest compressions and breaths in the smart glass group than the control group. There were significant differences between groups. Furthermore, more participants significantly corrected chest compression rate and depth and hand location, used a defibrillator, and sustained cardiopulmonary resuscitation until the emergency response team arrived in the smart glass group than in the control group. Additionally, a significantly shorter time was observed in the smart glass group than in the control group in all variables except time to activate the emergency response team (P Nursing students often experience anxiety, stress, and fear during a clinical rotation in a mental health setting due to stressors and biases toward the setting as well as lack experience in caring for patients with mental health conditions. One in four people worldwide suffers from a mental disorder; therefore, it is critical that nurses feel confident interacting with these patients to provide equitable care. Undergraduate training is a critical period for changing students' attitudes toward this population. This study's goal was twofold. First, we offered students’ exposure to common behaviors and symptoms displayed by a patient with mental illness through an engaging and immersive virtual reality simulation experience before taking care of patients in a clinical setting. Second, we aimed to determine if a virtual reality simulation will change students' attitude and stigma, favorably, toward patients with mental health conditions. We used a mixed-method comparative analysis to collect information and identify themes on undergraduate students’ attitudes and stigma toward patients with mental health conditions. Our findings demonstrate that virtual reality simulations enhance awareness and sensitivity to the situations of others (empathy) while improving their communication skills. The use of virtual reality in a baccalaureate curriculum deepens the understanding of health equity in behavioral health for nursing students. For nurse leaders to excel in leadership roles in the clinical world of informatics, a comprehensive understanding of nursing informatics as translated within the broader scope of health informatics including clinical informatics and business intelligence is necessary. The translation of nursing informatics in the comprehensive scope of health informatics is not consistently taught in graduate nursing leadership curricula. Collaboratively, from an interprofessional education stance, a graduate nurse informatics course was re-visioned using translational pedagogy: the idea of teaching related concepts by translating each and vice versa. Specifically, we translated nursing informatics amid health informatics concepts including business intelligence. Leadership students in the re-visioned course experienced the ability to visualize, conceptualize, and understand how work in information systems impacts broader aspects of clinical and business decision-making. Looking at nursing informatics through the lens of health informatics will develop students' ability to visualize, conceptualize, and understand how work in information systems has an impact on the broader aspects of clinical decision-making and support. Further, this paradigm shift will enhance students' ability to utilize information systems in leadership decision-making as future knowledge workers. Patient education and self-management are essential for patients with liver cirrhosis. Based on Fisher and Fisher's Information-Motivation-Behavior Skills model, a Cirrhosis Care App was developed to support the education and self-management of these patients. To evaluate the effectiveness of the application, a randomized controlled trial was conducted with patients having liver cirrhosis who were being followed up in the outpatient area of a medical center in Taiwan. The experimental group used the app for 1 month, whereas a control group continued to receive conventional patient education. A pretest and posttest questionnaire was used to evaluate the app's effectiveness in improving the knowledge and practice of self-care. In addition, a questionnaire was developed based on the Technology Acceptance Model to understand satisfaction with the app. Results showed that following the implementation of the Cirrhosis Care App, patients' self-care knowledge and ability to promote self-care practice improved. User satisfaction with the app was measured and reflected in its frequency of use. This study confirmed that the Cirrhosis Care App, based on the Information-Motivation-Behavior Skills model, can improve patient knowledge and self-care practice and be actively promoted to benefit patients with cirrhosis. The purpose of this scoping review was to identify and summarize how technology can promote patient participation in the rehabilitation process in hip replacement. We conducted a scoping review following the steps outlined by the Joanna Briggs Institute. The PRISMA Checklist (Preferred Reporting Items for Systematic reviews and Meta-Analyses) was utilized to systematically organize the gathered information. A thorough search of articles was performed on PubMed, Scopus, and CINAHL databases for all publications up to December 2022. Twenty articles were included in this study. Various technologies, such as mobile applications, Web sites, and platforms, offer interactive approaches to facilitate total hip replacement rehabilitation. The analyzed studies were based on the rehabilitation of total hip arthroplasty, which in most of them was developed in mobile applications and Web sites. The studies identified reflect trends in the application of digital health technologies to promote patient engagement in the rehabilitation process and provide risk monitoring and patient education. Technological developments and nursing shortages have become global trends. To solve the problem of shortage of healthcare professionals, technology may be used as a backup. Nurses constitute the largest working group in the healthcare system. Therefore, nurses are very important to the success of implementing digitization in hospitals. This cross-sectional study used the characteristics and adoption roles of innovation diffusion theory to understand technology use within the organization. Data were collected through structured questionnaires and open-ended questions from March 21 to May 31, 2022, in two hospitals in Taiwan. In total, 159 nurses agreed to participate in the study. The results of this study revealed that observability, simplicity, advantage, trialability, and compatibility positively improved the acceptance of digital nursing technology. In the distribution of users' innovative roles, early adopters had a significant impact on innovation characteristics and technology acceptance. Nurses in acute and critical care units perceived a greater comparative advantage and trial availability of digital nursing technology use than did those in general wards and outpatient clinics. In addition, based on user opinions and suggestions, the development of smart healthcare and the use of digital technology are expected to improve the quality of nursing care. Nurses faced numerous challenges during the pandemic, particularly with the increased burden of electronic documentation. Surges in patient volume and visits led to rapid changes in nursing documentation, prompting diverse responses from regulatory and healthcare organizations. Nurses expressed safety concerns and struggled with changes, calling for national standards and regulatory support. Policy relaxations, such as the 1135 Waiver, sparked debate on the future of nursing care plan documentation. Using mixed-methods exploratory design, the study identified modifications of nursing documentation during crises, commonalities in documentation burden reduction for applicability beyond pandemics, and consensus on the definition of “surge.” Documentation patterns were assessed from February to November 2022, involving 175 North American nurse leaders and informaticists. Data analysis included descriptive statistics, thematic analysis, and Pearson correlation coefficient. Significant differences were found between rural and urban settings (P = .02), with urban areas showing higher odds of changes to care plans (odds ratio, 4.889; 95% confidence interval, 1.27-18.78). Key findings highlighted the persistence of postcrisis documentation changes and varied definitions of surge criteria based on organizational leadership, policy, and mandates. The study yielded insights for modifying documentation, offering policy recommendations, and emphasizing ongoing collaboration and evidence-based approaches for future nursing practices. This study shows the development of a software for calculating the number of nursing team members required for providing care during the coronavirus disease 2019 pandemic. Study about the development of a technology based on the literature about data and indicators. The indicators were systematized in the following dimensions: institutional, professional, and occupational structure, all with a focus on coronavirus disease 2019. The software was created to be used on the Web, client-server, in browsers such as Internet Chrome, Explorer, and/or Mozilla Firefox, accessing via an Internet address and also allowing access by Windows, Android, and Linux operating systems, with MySQL database used for data storage. The data and indicators related to the institutional structure for coronavirus disease 2019 were systematized with 10 dimensions and indicators, and the professional and occupational structure, with 14 dimensions and indicators. The construction of computer requirements followed the precepts of software engineering, with theoretical support from the area. In the evaluation of the software, data simulation revealed points that had to be adjusted to ensure security, data confidentiality, and easy handling. The software provides to calculate the size and quality of the team, nursing sizing required due to the needs generated by the coronavirus disease 2019 pandemic. Family-centered care is an approach to promote the health and well-being of children with chronic diseases and their families. This study aims to explore the knowledge components, structures, and research trends related to family-centered care for children with chronic conditions. We conducted the keyword network analysis in three stages using the keywords provided by the authors of each study: (1) search and screening of relevant studies, (2) keyword extraction and refinement, and (3) data analysis and visualization. The core keywords were child, adolescence, parent, and disabled. Four cohesive subgroups were identified through degree centrality. Research trends in the three phases of a recent decade have been changed. With the systematic understanding of the context of the knowledge structure, the future research and effective strategy establishment are suggested based on family-centered care for children with chronic disease. The objective of this scoping review was to survey the literature on the use of AI/ML applications in analyzing inpatient EHR data to identify bundles of care (groupings of interventions). If evidence suggested AI/ML models could determine bundles, the review aimed to explore whether implementing these interventions as bundles reduced practice pattern variance and positively impacted patient care outcomes for inpatients with T2DM. Six databases were searched for articles published from January 1, 2000, to January 1, 2024. Nine studies met criteria and were summarized by aims, outcome measures, clinical or practice implications, AI/ML model types, study variables, and AI/ML model outcomes. A variety of AI/ML models were used. Multiple data sources were leveraged to train the models, resulting in varying impacts on practice patterns and outcomes. Studies included aims across 4 thematic areas to address: therapeutic patterns of care, analysis of treatment pathways and their constraints, dashboard development for clinical decision support, and medication optimization and prescription pattern mining. Multiple disparate data sources (i.e., prescription payment data) were leveraged outside of those traditionally available within EHR databases. Notably missing was the use of holistic multidisciplinary data (i.e., nursing and ancillary) to train AI/ML models. AI/ML can assist in identifying the appropriateness of specific interventions to manage diabetic care and support adherence to efficacious treatment pathways if the appropriate data are incorporated into AI/ML design. Additional data sources beyond the EHR are needed to provide more complete data to develop AI/ML models that effectively discern meaningful clinical patterns. Further study is needed to better address nursing care using AI/ML to support effective inpatient diabetes management. No abstract available As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths. The last-minute cancellation of surgeries profoundly affects patients and their families. This research aimed to forecast these cancellations using EMR data and meteorological conditions at the time of the appointment, using a machine learning approach. We retrospectively gathered medical data from 13 440 pediatric patients slated for surgery from 2018 to 2021. Following data preprocessing, we utilized random forests, logistic regression, linear support vector machines, gradient boosting trees, and extreme gradient boosting trees to predict these abrupt cancellations. The efficacy of these models was assessed through performance metrics. The analysis revealed that key factors influencing last-minute cancellations included the impact of the coronavirus disease 2019 pandemic, average wind speed, average rainfall, preanesthetic assessments, and patient age. The extreme gradient boosting algorithm outperformed other models in predicting cancellations, boasting an area under the curve value of 0.923 and an accuracy of 0.841. This algorithm yielded superior sensitivity (0.840), precision (0.837), and F1 score (0.838) relative to the other models. These insights underscore the potential of machine learning, informed by EMRs and meteorological data, in forecasting last-minute surgical cancellations. The extreme gradient boosting algorithm holds promise for clinical deployment to curtail healthcare expenses and avert adverse patient-family experiences. New nurses must acquire accurate knowledge of medication administration, as it directly affects patient safety. This study aimed to develop a microlearning-based self-directed learning chatbot on medication administration for novice nurses. Furthermore, the study had the objective of evaluating the chatbot feasibility. The chatbot covered two main topics: medication administration processes and drug-specific management, along with 21 subtopics. Fifty-eight newly hired nurses on standby were asked to use the chatbot over a 2-week period. Moreover, we evaluated the chatbot's feasibility through a survey that gauged changes in their confidence in medication administration knowledge, intrinsic learning motivation, satisfaction with the chatbot's learning content, and usability. After using the chatbot, participants' confidence in medication administration knowledge significantly improved in all topics (P There is an increasing need for highly accessible health management platforms for comprehensive symptoms of Parkinson disease. Mobile apps encompassing nonmotor symptoms have been rarely developed since these symptoms are often subjective and difficult to reflect what individuals actually experience. The study developed an app for comprehensive symptom management and evaluated its usability and feasibility. A single-group repeated measurement experimental design was used. Twenty-two participants used the app for 6 weeks. Monitoring of nonmotor symptoms, games to address motor symptoms, and medication management were incorporated in the app. Quantitative outcomes were self-assessed through an online questionnaire, and one-on-one telephone interviews were conducted to understand the user's point of view. The successful experience of self-monitoring had improved participants' self-efficacy (Z = −3.634, P 
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