Cin-Computers Informatics Nursing最新文献

{"title":"Use of Virtual Reality Glasses During Healthcare: An Integrative Review.","authors":"Vitória Talya Dos Santos Sousa, Maria Rayssa do Nascimento Nogueira, Hévila Ferreira Gomes Medeiros Braga, Ana Caroline Rocha de Melo Leite, Emanuella Silva Joventino Melo, Patrícia Freire de Vasconcelos","doi":"10.1097/CIN.0000000000001242","DOIUrl":"10.1097/CIN.0000000000001242","url":null,"abstract":"<p><p>Integrating technology into healthcare services has direct implications for the efficacy and performance of client care. In view of this, the aim was to identify the possibilities of using virtual reality glasses in healthcare. An integrative literature review was conducted in October 2024, searching in MEDLINE, LILACS, BDENF, Scopus, Web of Science, EMBASE, and Science Direct. Original articles were included without restriction on publication period or language, whereas duplicates and those not addressing the guiding question were excluded. The level of evidence was evaluated following Melnyk and Fineout-Overholt's method. Data were synthesized in tables, figures, and in narrative form. The 47 studies in the final sample were published between 2007 and 2024, with most conducted in Turkey and predominantly clinical trials. Various models of glasses were used, with VRBox being the most cited, and video interventions were prominent. Main focuses of use included areas such as rehabilitation, invasive procedures, preoperative care, obstetrics, examinations, dentistry, and wound care. The use of virtual reality glasses has proven effective for distraction, pain reduction, and anxiety management across various health domains. Experimental studies indicate a high level of scientific evidence, which is essential for evidence-based practices; however, more objective investigations are still needed.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining the Impact of Interface Design and Nurse Characteristics on Satisfaction With Machine Learning Decision Support Explanations.","authors":"Ann Wieben, Linsey Steege, Roger Brown, Andrea Gilmore-Bykovskyi","doi":"10.1097/CIN.0000000000001323","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001323","url":null,"abstract":"<p><p>Machine learning has the potential to drive the next generation of clinical decision support systems by identifying patterns in health data to enhance efficiency and safety. Explanatory information is intended to help clinicians understand the outputs of these complex systems. No studies have evaluated associations between display design strategies or nurse characteristics and nurse satisfaction with machine learning explanatory information. This gap leaves much unknown about designing explanatory displays that meet nurses' information needs, supporting effective use and adoption in practice settings. To address this, we aimed to describe nurses' satisfaction with explanatory information displays for machine learning clinical decision support, examine associations between the format and complexity of explanatory information and nurse satisfaction, and investigate the influence of nurse characteristics, such as numeracy and graphical literacy, on satisfaction. Our findings indicate that local feature-based explanatory information may not satisfy nurses' information needs, and that nurse age, artificial intelligence training level, and numeracy influence preferences. We found no significant effects of the format or complexity of explanatory displays on satisfaction. These insights into the usability of machine learning clinical decision support for nurses can inform the design of more effective displays.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility and Acceptability of Smartwatches for Use by Nursing Home Residents.","authors":"Alisha Harvey Johnson, Knoo Lee, Blaine Reeder, Lori Popejoy, Amy Vogelsmeier","doi":"10.1097/CIN.0000000000001245","DOIUrl":"10.1097/CIN.0000000000001245","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Relationship Between Anxiety and Readiness Levels Regarding Artificial Intelligence in Midwives: An Intergenerational Comparative Study.","authors":"Ayşe Nur Yilmaz, Sümeyye Altiparmak, Remziye Sökmen","doi":"10.1097/CIN.0000000000001269","DOIUrl":"10.1097/CIN.0000000000001269","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Immersive Virtual Reality Simulation Scenario to Improve Empathy in Nursing Students.","authors":"Rosemary Collier, Rosa Darling, Karen Browne","doi":"10.1097/CIN.0000000000001259","DOIUrl":"10.1097/CIN.0000000000001259","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consumer Access and Utilization of Patient Portals for Electronic Health Records: A Cross-Sectional Study in Saudi Arabia.","authors":"Raniah N Aldekhyyel, Norah Alshafi, Lina Almohsen, Tharaa Alhowaish, Lina Alabbad, Raseel Alwahibi, Dena Alsuhaibani, Reem Aldekhyyel, Sripriya Rajamani","doi":"10.1097/CIN.0000000000001244","DOIUrl":"10.1097/CIN.0000000000001244","url":null,"abstract":"","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Immersive Virtual Reality Simulation Scenario to Improve Empathy in Nursing Students.","authors":"","doi":"10.1097/CIN.0000000000001321","DOIUrl":"10.1097/CIN.0000000000001321","url":null,"abstract":"","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":"43 5","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors Influencing eHealth Literacy Related to Parenting Among Asian Immigrant Mothers in South Korea.","authors":"Hyunmi Son, Young-Sil Sohn, Jung-Hee Jeon","doi":"10.1097/CIN.0000000000001253","DOIUrl":"10.1097/CIN.0000000000001253","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12129383/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Chronic Pain Nursing Diagnosis Through Machine Learning: A Performance Evaluation.","authors":"Davide Macrì, Nicola Ramacciati, Carmela Comito, Elisabetta Metlichin, Gian Domenico Giusti, Agostino Forestiero","doi":"10.1097/CIN.0000000000001277","DOIUrl":"10.1097/CIN.0000000000001277","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Suitability of Low-Severity Rating Hospital Incident Reports for Machine Learning.","authors":"Rebecca Miriam Jedwab, Leonard Hoon, Caroline Luu, Bernice Redley","doi":"10.1097/CIN.0000000000001249","DOIUrl":"10.1097/CIN.0000000000001249","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}