Although transarterial chemoembolization has improved as an interventional method for hepatocellular carcinoma, subsequent postembolization syndrome is a threat to the patients' quality of life. This study aimed to evaluate the effectiveness of a clinical decision support system in postembolization syndrome management across nurses and patient outcomes. This study is a randomized controlled trial. We included 40 RNs and 51 hospitalized patients in the study. For nurses in the experimental group, a clinical decision support system and a handbook were provided for 6 weeks, and for nurses in the control group, only a handbook was provided. Notably, the experimental group exhibited statistically significant improvements in patient-centered caring attitude, pain management barrier identification, and comfort care competence after clinical decision support system implementation. Moreover, patients' symptom interference during the experimental period significantly decreased compared with before the intervention. This study offers insights into the potential of clinical decision support system in refining nursing practices and nurturing patient well-being, presenting prospects for advancing patient-centered care and nursing competence. The clinical decision support system contents, encompassing postembolization syndrome risk prediction and care recommendations, should underscore its role in fostering a patient-centered care attitude and bolster nurses' comfort care competence.

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 < .05). Remote expert assistance with smart glass technology during cardiopulmonary resuscitation is promising. Smart glass led to a significantly better ABC (airway, breathing, circulation) approach, chest compression depth and rate, and hand position. Furthermore, remote expert assistance with smart glass has the potential to improve overall resuscitation performance because it enabled students to initiate resuscitation, use a defibrillator, and defibrillate patients earlier. Nurses may benefit from smart glass technology in real life to provide effective cardiopulmonary resuscitation.

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.

This study aimed to develop a virtual experiential application for respiratory patient care and evaluate its impact on nursing students' knowledge, self-efficacy, clinical practice anxiety, and performance confidence. This application with gamification elements was developed following a structured approach encompassing assessment, design, development, implementation, and evaluation. The experimental group consisted of 21 third-year university students who engaged with the application multiple times a day for 1 week; the control group, comprising 21 students, received traditional prelearning. Data were collected 1 week before and immediately before the clinical practice commencement, from March 7 to 24, 2023, using an online survey. Nursing knowledge, self-efficacy, clinical practice anxiety, and performance confidence were evaluated. Significant improvements were observed in the experimental group's knowledge of respiratory patient care, self-efficacy, clinical practice anxiety, and performance confidence. The application proved to be an effective learning resource and assisted students in implementing the nursing process to enhance patient conditions; it highlighted nursing educators' necessity in developing and evaluating educational content. The developed application was effective in enhancing student nurses' competence and confidence, affecting nursing education and patient care.

Medical errors, often resulting from miscommunication and cognitive lapses during handoffs, account for numerous preventable deaths and patient harm annually. This research examined nurses' perceived workload and cognitive load during handoffs on hospital units with varying patient acuity levels and patient-nurse ratios. Conducted at a southeastern US medical facility, the study analyzed 20 handoff dyads using the National Aeronautics and Space Administration Task Load Index to measure perceived workload and cognitive load. Linear regressions revealed significant associations between patient acuity levels, patient-nurse ratios, and National Aeronautics and Space Administration Task Load Index subscales, specifically mental demand (P = .007) and performance (P = .008). Fisher exact test and Wilcoxon rank sum test showed no significant associations between these factors and nurses' roles (P > .05). The findings highlight the need for targeted interventions to manage workload and cognitive load, emphasizing standardized handoff protocols and technological aids. The study underscores the variability in perceived workload and cognitive load among nurses across different units. Medical-surgical units showed higher cognitive load, indicating the need for improved workload management strategies. Despite limitations, including the single-center design and small sample size, the study provides valuable insights for enhancing handoff communications and reducing medical errors.

In a medical specialty clinic located in a rural community, a nursing team identified an opportunity to decrease the time nursing staff spent preparing charts for patients' upcoming clinical appointments. In collaboration with an informaticist, the nursing project team implemented a quality improvement project with a target goal of decreasing the average time spent preparing charts per patient by 20%, without increasing the number of discrepancies in the chart preparation process. The team used the define, measure, analyze, improve, and control framework to identify two interventions that could decrease time for chart preparation. A standardized chart preparation process was developed, and a condensed nursing view was created within the electronic health record. After the quality improvement project, the average time nurses spent on chart preparation per patient decreased by 18% after the standardized process and 16% after the condensed view was implemented.

Fall is a common adverse event among older adults. This study aimed to identify essential fall factors and develop a machine learning-based prediction model to predict the fall risk category among community-dwelling older adults, leading to earlier intervention and better outcomes. Three prediction models (logistic regression, random forest, and naive Bayes) were constructed and evaluated. A total of 459 people were involved, including 156 participants (34.0%) with high fall risk. Seven independent predictors (frail status, age, smoking, heart attack, cerebrovascular disease, arthritis, and osteoporosis) were selected to develop the models. Among the three machine learning models, the logistic regression model had the best model fit, with the highest area under the curve (0.856) and accuracy (0.797) and sensitivity (0.735) in the test set. The logistic regression model had excellent discrimination, calibration, and clinical decision-making ability, which could aid in accurately identifying the high-risk groups and taking early intervention with the model.

Cancer predominantly affects older adults. An estimated 62% of the 15.5 million American cancer survivors are 65 years or older. Provision of supportive care is critical to this group; however, limited resources are available to them. As older survivors increasingly adopt technology, digital health programs have significant potential to provide them with longitudinal supportive care. Previously, we developed/tested a digital Cancer Survivorship Patient Engagement Toolkit for older adults, Cancer Survivorship Patient Engagement Toolkit Silver. The study examined the preliminary impact of the Cancer Survivorship Patient Engagement Toolkit Silver on older survivors' health outcomes. This was a 2-arm randomized controlled trial with two observations (baseline, 8 weeks) on a sample of 60 older cancer survivors (mean age, 70.1 ± 3.8 years). Outcomes included health-related quality of life, self-efficacy for coping with cancer, symptom burden, health behaviors, and patient-provider communication. Data were analyzed using descriptive statistics, linear mixed models, and content analysis. At 8 weeks, the Cancer Survivorship Patient Engagement Toolkit Silver group showed more improved physical health-related quality of life (P < .001, effect size = 0.64) and symptom burden (P = .053, effect size = -0.41) than the control group. Self-efficacy (effect size = 0.56), mental health-related quality of life (effect size = 0.26), and communication (effect size = 0.40) showed clinically meaningful effect sizes of improvement. Most participants reported benefits on health management (mean, 19.41 ± 2.6 [3-21]). Further research is needed with larger and more diverse older cancer populations.

Adequate nurse staffing is crucial for quality healthcare, necessitating accurate predictions of patient arrival rates. These forecasts can be determined using supervised machine learning methods. Optimization of machine learning methods is largely about minimizing the prediction error. Existing models primarily utilize data such as historical patient visits, seasonal trends, holidays, and calendars. However, it is unclear what other features reduce the prediction error. Our systematic literature review identifies studies that use supervised machine learning to predict patient arrival numbers using nontemporal features, which are features not based on time or dates. We scrutinized 26 284 studies, eventually focusing on 27 relevant ones. These studies highlight three main feature groups: weather data, internet search and usage data, and data on (social) interaction of groups. Internet data and social interaction data appear particularly promising, with some studies reporting reduced errors by up to 33%. Although weather data are frequently used, its utility is less clear. Other potential data sources, including smartphone and social media data, remain largely unexplored. One reason for this might be potential data privacy challenges. In summary, although patient arrival prediction has become more important in recent years, there are still many questions and opportunities for future research on the features used in this area.

To analyze the effectiveness of mobile application-based interventions on medication adherence among pulmonary tuberculosis patients.

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.

During the COVID-19 pandemic, there was a rapid global uptake by healthcare practitioners, including nurses, of digital health to support the healthcare needs of their communities. This increase in the use of technology has impacted nurses, although there is a lack of research that explores nurses' concerns internationally, and this is equally true for New Zealand. We report the qualitative results from two surveys with New Zealand nurses, one in 2020 (n = 220) and the second in 2022 (n = 191), about their concerns of using digital technologies. Similar themes were discovered between the two data sets. Challenges around access were a common theme to both surveys. This included access to systems, connectivity, devices, and the Internet. The 2020 survey also identified inequities as a theme, whereas the 2022 survey noted poor engagement from staff. Changes to the infrastructure of the New Zealand healthcare system have been introduced, and it is hopeful that the issues of access to data and digital technologies across the country will be rectified.

Since the onset of the COVID-19 pandemic, healthcare workers around the world have experimented with technologies to facilitate communication and care for patients and their care partners.

This study aims to explore the challenges and strategies in creating online communities for individuals with diabetes, emphasizing their role in fostering connections among individuals facing similar health conditions. Using a single-case approach, we investigated the design process of a diabetes online community using the classic waterfall model. Participants were recruited from a diabetes local association, and usability was assessed using the System Usability Scale and the think-aloud method. Subsequently, semistructured interviews were conducted to evaluate functionality and user experience. Data collection was conducted from August until December 2023. The development of the community unveiled significant usability challenges, highlighting the need for user feedback and improvement. Ethical considerations, including anonymity, usage conditions, privacy terms, and health information sharing, emerged as critical areas requiring meticulous attention. Furthermore, healthcare professional moderation was deemed essential to ensure a secure environment. Users expressed strong interest in enhanced interaction features and personalized notifications. Although online diabetes communities hold potential for peer support, addressing usability challenges, ethical considerations, and moderation issues is essential. This study emphasizes the ongoing necessity for research to enhance the development of patient communities, ensuring accessibility, mitigating ethical risks, and leveraging nurses as moderators.