Analysis of YouTube Videos on Endotracheal Tube Aspiration Training in Terms of Content, Reliability, and Quality

This descriptive study aims to investigate the content, quality, and reliability of YouTube videos containing content related to endotracheal tube aspiration. The study was scanned using the keywords “endotracheal aspiration” and “endotracheal tube aspiration,” and 22 videos were included in the study. The contents of the selected videos were measured using the Endotracheal Tube Aspiration Skill Form, their reliability was measured using the DISCERN Survey, and their quality was measured using the Global Quality Scale. Of the 22 videos that met the inclusion criteria, 18 (81.8%) were educational, and four (18.2%) were product promotional videos. When pairwise comparisons were made, the coverage score of open aspiration videos was higher for educational videos than for product promotion videos (P

Perceptions of Cognitive Load and Workload in Nurse Handoffs: A Comparative Study Across Differing Patient-Nurse Ratios and Acuity Levels

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.

Using Virtual Reality in Mental Health Nursing to Improve Behavioral Health Equity

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.

Best Practices in Supporting Inpatient Communication With Technology During Visitor Restrictions: An Integrative Review

Background 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. Methods Our team reviewed the literature to examine best practices in utilizing technology to support communication between nurses, patients, and care partners while visitation is limited. We searched four major databases for recent articles on this topic, conducted a systematic screening and review of 1902 articles, and used the Johns Hopkins Nursing Evidence-Based Practice for Nurses and Healthcare Professionals Model & Guidelines to appraise and translate the results of 23 relevant articles. Results Our evaluation yielded three main findings from the current literature: (1) Virtual contact by any technological means, especially video visitation, improves satisfaction, reduces anxiety, and is well-received by the target populations. (2) Structured video rounding provides effective communication among healthcare workers, patients, and offsite care partners. (3) Institutional preparation, such as a standardized checklist and dedicating staff to roles focused on facilitating communication, can help healthcare workers create environments conducive to therapeutic virtual communication. Discussion In situations that require healthcare facilities to limit visitation between patients and their care partners, the benefits of virtual visitation are evident. There is variance in the types of technologies used to facilitate virtual visits, but across all of them, there are consistent themes demonstrating the benefits of virtual visits and virtual rounding. Healthcare institutions can prepare for future limited-visitation scenarios by reviewing the current evidence and integrating virtual visitation into modern healthcare delivery.

A Systematic Review of Features Forecasting Patient Arrival Numbers

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.

A Machine Learning–Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults

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.

Managing Postembolization Syndrome Through a Machine Learning–Based Clinical Decision Support System: A Randomized Controlled Trial

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.

Re-visioning of a Nursing Informatics Course With Translational Pedagogy

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.

The Development and Impact of a Respiratory Patient Care Mobile Application on Nursing Students

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.

Using a Mobile Application to Promote Patient Education for Patients With Liver Cirrhosis

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.

Using Digital Technology to Promote Patient Participation in the Rehabilitation Process in Hip Replacement: A Scoping Review

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.

The Impact of Undergraduate Informatics Education on Nurses' Acceptance of Information and Communication Technologies: A Cross-sectional Study

This study aimed to examine if exposure to undergraduate nursing informatics educational modalities (ie, lecture, laboratory, and clinical experiences) made a difference in the acceptance of information and communication technologies among nurses in the practice setting. Also, to examine if there was a relationship between selected demographic characteristics and nurses' acceptance of information and communication technologies, a cross-sectional design was used for this study. The Technology Acceptance Model was the theoretical framework for this study. The modified Nursing Acceptance Survey was used to collect data based on the Technology Acceptance Model. The results indicated that exposure to undergraduate informatics education significantly influenced nurses' acceptance of information and communication technologies. The results identified laboratory and clinical as educational modalities influencing nurses' acceptance of information and communication technologies. Demographic characteristics have no statistically significant relationship to nurses' acceptance of information and communication technologies. The results showed that undergraduate informatics education statistically influences nurses' acceptance of information and communication technologies. Findings provide insight into that undergraduate informatics education is important for accepting information and communication technologies among nurses in the practice setting. Also, the findings recognized laboratory and clinical experiences as effective learning modalities for accepting information and communication technologies.

Exploring Nurse Use of Digital Nursing Technology

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.

The Impact of Artificial Intelligence-Assisted Learning on Nursing Students' Ethical Decision-making and Clinical Reasoning in Pediatric Care: A Quasi-Experimental Study

The integration of artificial intelligence such as ChatGPT into educational frameworks marks a pivotal transformation in teaching. This quasi-experimental study, conducted in September 2023, aimed to evaluate the effects of artificial intelligence–assisted learning on nursing students' ethical decision-making and clinical reasoning. A total of 99 nursing students enrolled in a pediatric nursing course were randomly divided into two groups: an experimental group that utilized ChatGPT and a control group that used traditional textbooks. The Mann-Whitney U test was employed to assess differences between the groups in two primary outcomes: (a) ethical standards, focusing on the understanding and applying ethical principles, and (b) nursing processes, emphasizing critical thinking skills and integrating evidence-based knowledge. The control group outperformed the experimental group in ethical standards and demonstrated better clinical reasoning in nursing processes. Reflective essays revealed that the experimental group reported lower reliability but higher time efficiency. Despite artificial intelligence's ability to offer diverse perspectives, the findings highlight that educators must supplement artificial intelligence technology with strategies that enhance critical thinking, careful data selection, and source verification. This study suggests a hybrid educational approach combining artificial intelligence with traditional learning methods to bolster nursing students' decision-making processes and clinical reasoning skills.

A Study to Determine Consensus for Nursing Documentation Reduction in Times of Crisis

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.

COVID-19 Nursing Staff Sizing Technology

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.

Effect of Infection Control Simulation Based on a Negative Pressure Isolation Room Using Mixed Reality

This study aimed to examine the effectiveness of an infection control simulation using mixed reality, comparing simulation fidelity with a high-fidelity mannequin (MN) group and problem-based learning with written cases group. This study used a three-group pretest-posttest quasi-experimental design. Two universities with similar curricula were conveniently selected, and a total of 72 nursing students were recruited. Participants were randomly assigned to three groups of 24 each. In the final analysis, there were 22 participants in the mixed reality groups, 21 in the mannequin groups, and 23 in the problem-based learning with written cases groups. Data were analyzed using descriptive statistics and the χ2, Kruskal-Wallis, and Wilcoxon signed rank tests. The mixed reality groups had a significantly positive effect on clinical reasoning ability and clinical competence than the problem-based learning with written cases groups, whereas the mannequin groups had a significantly positive effect on clinical competence than the problem-based learning with written cases groups. Mixed reality simulation is an appropriate simulation method that enhances learning immersion, satisfaction, and self-confidence in simulation. Additionally, it can substitute for mannequin simulation in terms of clinical reasoning ability and clinical competence. This study suggests that it is important to the various approaches in simulation fidelity to diversely enhance the competency of nursing students in simulation outcomes.

Nomophobia and Phubbing Levels of Nursing Students: A Multicenter Study

Today, with the enhancement in the usage of smartphones, the concepts of nomophobia and phubbing have emerged. Nomophobia refers to the fear of being deprived of smartphones/smart devices. Phubbing is the use of a person's smartphone in situations that are not appropriate for the situation, time, and place. Therefore, the study purposed to evaluate nursing students' nomophobia and phubbing scores in Turkey, Portugal, and the United States. The data were collected with the Personal Information Questionnaire, Nomophobia Scale, and Phubbing Scale from N = 446 nursing students. The mean age of the students was 22.04 ± 4.08 years, and 86.5% were women. It was found that the total nomophobia scores of the nursing students were 80.15 ± 21.96, 72.29 ± 28.09, and 99.65 ± 6.11, respectively in Turkey, Portugal, and the United States. When the countries' Nomophobia Scale total scores, “giving up convenience,” “not being able to communicate,” and “losing connectedness” scores were compared with each other, they were found to be statistically significant (P

Research Trends in Family-Centered Care for Children With Chronic Disease: Keyword Network Analysis

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.

Efficacy of a Telemonitoring System as a Complementary Strategy in the Treatment of Patients With Heart Failure: Randomized Clinical Trial

Episodes of decompensation are the main cause of hospital admissions in patients with heart failure. For this reason, the use of mobile apps emerges as an excellent strategy to improve coverage, real-time monitoring, and timeliness of care. ControlVit is an electronic application for early detection of complications studied within the context of a tertiary university hospital. Patients were randomized to the use of ControlVit versus placebo, during a 6-month follow-up. The primary outcome was the difference in numbers of readmissions and deaths for heart failure between both groups. One hundred forty patients were included (intervention = 71, placebo = 69), with an average age of 66 years old; 71% were men. The main etiology of heart failure was ischemic (60%), whereas the main comorbidities were arterial hypertension (44%), dyslipidemia (42%), hypothyroidism (38%), chronic kidney disease (38%), and diabetes mellitus (27%). The primary outcome occurred more frequently in the control group: readmission due to decompensation for heart failure (control group n = 14 vs intervention group n = 3; P = .0081), and death (control group n = 11 vs intervention group n = 3; P = .024). In heart failure patients, ControlVit is a useful and supplementary tool, which reduces hospital admissions due to episodes of decompensation.