Cin-Computers Informatics Nursing最新文献

Smartwatch wearables are a promising health information technology to monitor older adults with complex chronic care needs. Pilot and feasibility studies have assessed smartwatch use with community-dwelling older adults, but less is known about their use in nursing homes. The purpose of this study was to test the feasibility and acceptability of smartwatch technology in a real-world nursing home setting to generate initial evidence about potential use. Using a qualitative descriptive approach, we conducted a pilot feasibility and acceptability study of smartwatch technology: Phase 1, pretrial semistructured interviews and focus groups with nursing home leaders, staff, and residents/families; Phase 2, a 7-day smartwatch trial deployment with residents; and Phase 3, posttrial semistructured interviews and focus groups. Themes related to feasibility findings included a part of the workflow and making the technology work . Themes related to acceptability findings included it's everywhere anyway , how will you protect me , knowing how you really are , more information = more control , and knowing how they are doing . These findings have important implications for the design of technology-supported interventions incorporating these devices within the unique context of residential nursing homes to best meet the needs of older adult residents, families, and staff caretakers.

This study aimed to compare Generations X, Y, and Z in terms of anxiety and readiness levels regarding artificial intelligence and investigate the relationship between anxiety and readiness levels regarding artificial intelligence in midwives across generations. This study is cross-sectional and comparative with a study sample of 218 midwives working in a province in the east of Turkey. Data were collected with the "Personal Information Form," "Artificial Intelligence Anxiety Scale," and "Medical Artificial Intelligence Readiness Scale." The evaluation of the data was carried out using the IBM SPSS Statistics version 22.0 (IBM Inc., Armonk, NY, USA) package program. Descriptive statistics, a one-way analysis of variance test, Pearson correlation, and regression analysis were used to analyze the data. The total mean score of midwives from the Artificial Intelligence Anxiety Scale was 47.07 ± 12.10 in Generation X, 43.91 ± 12.63 in Generation Y, and 36.16 ± 12.61 in Generation Z ( P < .05), and the difference between the groups was significant. Generation X had a higher level of artificial intelligence anxiety than Generation Y, and Generation Y had higher levels of artificial intelligence than Generation Z. The total mean score of midwives from the Medical Artificial Intelligence Readiness Scale was 67.43 ± 14.28 in Generation X, 66.78 ± 17.83 in Generation Y, and 74.73 ± 16.15 in Generation Z ( P < .05), and the difference between the groups was significant. Generation Z is more ready for medical artificial intelligence than Generation X, and Generation X is more ready for medical artificial intelligence than Generation Y. In addition, in the regression analysis, there was a weakly negative and significant relationship between the mean scores of Artificial Intelligence Anxiety Scale and Medical Artificial Intelligence Readiness Scale in Generation Z midwives, and as artificial intelligence anxiety levels increased, medical artificial intelligence readiness levels decreased. The artificial intelligence anxiety levels of midwives differed by generation, being highest in Generation X and lowest in Generation Z, and the level of medical artificial intelligence readiness was highest in Generation Z and lowest in Generation Y. As artificial intelligence anxiety increased in Generation Z midwives, the level of medical artificial intelligence readiness decreased.

Empathy is essential in nursing practice and can be taught throughout nursing curriculum using a variety of methods including clinical experiences, in-person simulation, virtual reality, and didactic lecture. Empathy can also change over time, often decreasing the longer nurses practice. A cohort of upper-level nursing students viewed a short immersive virtual reality simulation as part of routine curriculum and completed the Toronto Empathy Questionnaire before viewing (time 1), 2 weeks later (time 2), and, for a small cohort, several months later (time 3). The sample included 110 undergraduate nursing students. There were no improvements in Toronto Empathy Questionnaire scores from time 1 to time 2. There was no improvement from time 1 to time 3 for the cohort who completed the Toronto Empathy Questionnaire three times. There were no significant differences in Toronto Empathy Questionnaire scores between cohorts for any measurement times. Total mean empathy scores were comparatively high in this study and did not decline over time. Although this virtual reality simulation scenario appears to have protected against decline in empathy, it may have been insufficient to foster an increase in empathy scores. Empathic training needs to be immersed throughout their nursing education in both didactic and clinical settings.

Immigrants face barriers to accessing healthcare owing to language and cultural differences. Considering the eHealth literacy of immigrant mother is important, particularly as many rely on online resources for information on childcare. This observational cross-sectional study aimed to identify the factors influencing eHealth literacy among immigrant mothers with young children in South Korea. We hypothesized that factors influencing eHealth literacy include perceived ease of seeking, credibility, positive experiences, and subjective norms for online health information, as conceptualized by the Technology Acceptance Model, including cultural adaptation. The analysis results revealed that perceived ease of seeking ( β = .45), positive experiences ( β = .14), and subjective norms ( β = .15) significantly affected eHealth literacy. Additionally, integrated cultural adaptation ( β = .23) and the child's medical history ( β = .11) significantly influenced eHealth literacy. To enhance eHealth literacy related to parenting for immigrant mothers, educating them on search strategies for online health information and fostering positive user experiences are crucial. Furthermore, these interventions should adopt a family-focused approach, with integrated cultural adaptation proving more beneficial for effective settlement support of immigrant mothers.

This study proposes an evaluation of the efficacy of machine learning algorithms in classifying chronic pain based on Italian nursing notes, contributing to the integration of artificial intelligence tools in healthcare within an Italian linguistic context. The research aimed to validate the nursing diagnosis of chronic pain and explore the potential of artificial intelligence (AI) in enhancing clinical decision-making in Italian healthcare settings. Three machine learning algorithms-XGBoost, gradient boosting, and BERT-were optimized through a grid search approach to identify the most suitable hyperparameters for each model. Therefore, the performance of the algorithms was evaluated and compared using Cohen's κ coefficient. This statistical measure assesses the level of agreement between the predicted classifications and the actual data labels. Results demonstrated XGBoost's superior performance, whereas BERT showed potential in handling complex Italian language structures despite data volume and domain specificity limitations. The study highlights the importance of algorithm selection in clinical applications and the potential of machine learning in healthcare, specifically addressing the challenges of Italian medical language processing. This work contributes to the growing field of artificial intelligence in nursing, offering insights into the challenges and opportunities of implementing machine learning in Italian clinical practice. Future research could explore integrating multimodal data, combining text analysis with physiological signals and imaging data, to create more comprehensive and accurate chronic pain classification models tailored to the Italian healthcare system.

Electronic incident reporting is a key quality and a safety process for healthcare organizations that assists in evaluating performance and informing quality improvement initiatives. Although it is mandatory for high-severity incident reports to be investigated, the majority, classified as low severity, are seldom examined due to the large volume of reports, constraints of human cognitive capacity to process such large amounts of data, and the limited resources available in healthcare organizations. The purpose of this study was to investigate low-severity incident reports for suitability of future machine learning to identify actionable interventions for harm prevention. This qualitative descriptive study used a yearlong dataset of low incident severity rating reports to model the incident reporting documentation workflow and explored findings with five nursing and healthcare quality and safety experts. Incident severity reports were reported to have multiple conflicting issues including information duplication, subjective data, too many selection options, and absence of contextual information resulting in a lack of usefulness of information for machine learning. Next steps include analysis of a dataset for machine learning suitability. Recommendations include end-user involvement in system redesign to ensure hospital incident reports are comprised of meaningful data.

Through virtual reality technology, users experience challenging situations in a virtual world without physical experiences. This study aims to develop educational content using virtual reality to help patients undergoing magnetic resonance imaging and evaluate its usability. This pilot study developed virtual reality educational content using the ADDIE (analysis, design, development, implementation, and evaluation) model. An educational needs assessment targeted 20 experts and patients at a tertiary hospital. The content developed included pre-magnetic resonance imaging nursing, the magnetic resonance imaging process, and post-magnetic resonance imaging nursing. In pre-magnetic resonance imaging nursing, patients completed consent forms and received preparation instructions. The magnetic resonance imaging process included the environment, vision, and noise experienced during the examination. Post-magnetic resonance imaging nursing included precautions. An additional 12 experts and patients subsequently participated in virtual reality implementation and evaluation. Virtual reality evaluation included survey and semistructured face-to-face individual interviews. It scored 96.5 points out of 100 in usability, with little difference between experts' and patients' evaluations. In the qualitative evaluation, virtual reality educational content was revealed to be a useful approach, and the final virtual reality educational content was completed by reflecting the improvements suggested by participants. The findings offer tangible benefits for both healthcare professionals and patients by addressing the challenges associated with magnetic resonance imaging procedures through innovative educational interventions using virtual reality technology. Virtual reality educational content can be used as a practical training method in clinical settings.

The purpose of this study was to describe the development of a health literacy-focused heart failure self-care intervention (H2Lit Web-based app) and assess its usability using an online testing platform. We used an iterative approach, wherein participants evaluated more refined versions of H2Lit over four rounds of testing. Healthy participants were recruited for the earlier rounds of testing, and participants with heart failure were recruited for the final round. A total of 44 participants (10 participants with heart failure) were enrolled in the study. The participants had a mean age of 47.6 years, 57% were female, 70% identified as White, 70% were college-educated, and 34% had low health literacy. Using the System Usability Scale (score range of 0 to 100), the participants gave H2Lit a mean usability score of 74.1 in round 1, 54.3 in round 2, 85.3 in round 3, and 82.5 in round 4. H2Lit's usability score did not significantly differ between participants with adequate health literacy and those with low health literacy after controlling for age, sex, education level, and computer use duration. Further research is needed to determine the effect of the H2Lit intervention on heart failure self-care and heart failure-related outcomes.