Cin-Computers Informatics Nursing最新文献

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.

Exploratory data analysis involves observing data in graphical formats before making any assumptions. If interesting relationships or patterns among variables are identified, hypotheses are developed for further testing. This study aimed to identify significant differences in the levels of exhaustion, resilience, sleep quality, and sleep hygiene according to the personal characteristics of middle-aged women transitioning into menopause or postmenopause through exploratory data analysis. A total of 200 women aged 44 to 55 years were recruited online in August 2023. Data were collected using valid instruments and analyzed through data visualization, pattern identification in the visualized data, and hypothesis establishment based on the visualized patterns. Hypotheses were tested through the independent-samples t test, analysis of variance, and the Kruskal-Wallis test. A total of 11 patterns and corresponding hypotheses were identified. According to the statistically supported pattern-based hypotheses, middle-aged women who were in their perimenopausal period perceived themselves as unhealthy, had professional occupations, and had the highest level of exhaustion and the lowest levels of resilience, sleep quality, and sleep hygiene. This study demonstrated that data visualization is an efficient way to explore relationships or patterns between data. Data visualization should be considered an informatics solution that can provide insight in the field of healthcare.

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.

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.

Over the past several years, hospitals have utilized agency staffing to combat staffing shortages. Increased use of agency staffing presented an opportunity for implementation of an education project related to the potential variance in practice of permanent staffing, specifically with the use of infusion interoperability in the inpatient setting at the University of Pittsburgh Medical Center St Margaret hospital. Discussion around variables causing agency nurse setbacks with utilizing infusion interoperability while trying to meet the required standard laid the groundwork for this project. Improving agency workflows allowed for process improvement including enhanced quality, documentation, and adherence. Early data analysis revealed variance in adherence between agency and permanent staffing prompting further analysis. Investigational methods included assessment of agency nurse infusion interoperability usage through interviews and observations, review of adherence reports, review of education and onboarding, and interviewing of nurse leaders. Findings suggested lack of experience, inability to troubleshoot, and underutilized resources contributed to lower adherence with agency compared with permanent staff. These findings lead the informaticists to make changes to the curriculum for new hire onboarding, increase rounding and interactions with agency staff, and increase access to resources. These interventions resulted in increased adherence scores and verbalized satisfaction by the agency nurses.

This study aimed to use wearable technology to predict the sleep quality of family caregivers of people with dementia among underrepresented groups. Caregivers of people with dementia often experience high levels of stress and poor sleep, and those from underrepresented communities face additional burdens, such as language barriers and cultural adaptation challenges. Participants, consisting of 29 dementia caregivers from underrepresented populations, wore smartwatches that tracked various physiological and behavioral markers, including stress level, heart rate, steps taken, sleep duration and stages, and overall daily wellness. The study spanned 529 days and analyzed data using 70 features. Three machine learning algorithms-random forest, k nearest neighbor, and XGBoost classifiers-were developed for this purpose. The random forest classifier was shown to be the most effective, boasting an area under the curve of 0.86, an F1 score of 0.87, and a precision of 0.84. Key findings revealed that factors such as wake-up stress, wake-up heart rate, sedentary seconds, total distance traveled, and sleep duration significantly correlated with the caregivers' sleep quality. This research highlights the potential of wearable technology in assessing and predicting sleep quality, offering a pathway to creating targeted support measures for dementia caregivers from underserved groups. The study suggests that such technology can be instrumental in enhancing the well-being of these caregivers across diverse populations.

Dashboards display hospital quality and patient safety measures aimed to improve patient outcomes. Although literature establishes dashboards aid quality and performance improvement initiatives, research is limited from the frontline nurse manager's perspective. This study characterizes factors influencing hospital nurse managers' use of dashboards for unit-level quality and performance improvement with suggestions for dashboard design. Using a descriptive qualitative design, semistructured interviews were conducted with 11 hospital nurse managers from a health system in the Midwestern United States. Thematic analysis was used to describe four perceived factors influencing dashboard use: external, data, technology features, and personal. External factors included regulatory standards, professional standards of care, organizational expectations, and organizational resources. Data factors included dashboard data quality and usefulness. Technology features included preference for simple, interactive, and customizable visual displays. Personal factors included inherent nurse manager qualities and knowledge. Guidelines for dashboard design involve display of required relevant quality measures that are accurate, timely, useful, and usable. Future research should involve hospital nurse managers in user-centered design to ensure dashboards are favorable for use. Further, opportunities exist for nurse manager informatics training and education on dashboard use in preparation for their role and responsibilities in unit-level quality and performance improvement.

Due to visiting restrictions at intensive care units during the COVID-19 pandemic, a digital video technology was developed and implemented. This study evaluated the use of digital visits at four intensive care units after COVID-19. Nurses' use of the technology and managerial perspectives on implementation were examined in an explanatory sequential mixed-methods study. Data were explored by inferential statistics (quantitative data) and content analysis (qualitative data). Results revealed that 52.9% of nurses had not used digital visits. Users indicated that the technology supported the patient-relative-nurse relationship, but needs reimplementation, aligning it with the post-COVID-19 setting.