Cin-Computers Informatics Nursing最新文献_第10页

Nursing in the Age of Artificial Intelligence. 人工智能时代的护理。

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2025-01-01 DOI: 10.1097/CIN.0000000000001204

Janet H Davis

引用次数: 0

The Impact of a Digital Cancer Survivorship Patient Engagement Toolkit on Older Cancer Survivors' Health Outcomes. 数字癌症幸存者患者参与工具包对老年癌症幸存者健康结果的影响。

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2025-01-01 DOI: 10.1097/CIN.0000000000001254

引用次数: 0

Perceptions of Cognitive Load and Workload in Nurse Handoffs: A Comparative Study Across Differing Patient-Nurse Ratios and Acuity Levels. 对护士交接工作中认知负荷和工作量的看法：不同病人-护士比例和严重程度的比较研究。

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2025-01-01 DOI: 10.1097/CIN.0000000000001216

Benjamin J Galatzan, Liang Shan, Elizabeth Johnson, Patricia A Patrician

{"title":"Perceptions of Cognitive Load and Workload in Nurse Handoffs: A Comparative Study Across Differing Patient-Nurse Ratios and Acuity Levels.","authors":"Benjamin J Galatzan, Liang Shan, Elizabeth Johnson, Patricia A Patrician","doi":"10.1097/CIN.0000000000001216","DOIUrl":"10.1097/CIN.0000000000001216","url":null,"abstract":"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.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548740","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}

引用次数: 0

Best Practices in Supporting Inpatient Communication With Technology During Visitor Restrictions: An Integrative Review. 在访客限制期间利用科技支持住院病人交流的最佳实践：综合评述。

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2025-01-01 DOI: 10.1097/CIN.0000000000001200

Stephanie Brown, Jamie Guillergan, Eric Beedle, Andre Gnie, Sterling Wilmer, Kristy Wormack, Nadine Rosenblum

{"title":"Best Practices in Supporting Inpatient Communication With Technology During Visitor Restrictions: An Integrative Review.","authors":"Stephanie Brown, Jamie Guillergan, Eric Beedle, Andre Gnie, Sterling Wilmer, Kristy Wormack, Nadine Rosenblum","doi":"10.1097/CIN.0000000000001200","DOIUrl":"10.1097/CIN.0000000000001200","url":null,"abstract":"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.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479873","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}

引用次数: 0

A Systematic Review of Features Forecasting Patient Arrival Numbers. 对预测患者到达人数特征的系统性回顾。

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2025-01-01 DOI: 10.1097/CIN.0000000000001197

Markus Förstel, Oliver Haas, Stefan Förstel, Andreas Maier, Eva Rothgang

{"title":"A Systematic Review of Features Forecasting Patient Arrival Numbers.","authors":"Markus Förstel, Oliver Haas, Stefan Förstel, Andreas Maier, Eva Rothgang","doi":"10.1097/CIN.0000000000001197","DOIUrl":"10.1097/CIN.0000000000001197","url":null,"abstract":"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.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11709000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479894","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}

引用次数: 0

Analysis of YouTube Videos on Endotracheal Tube Aspiration Training in Terms of Content, Reliability, and Quality. YouTube气管插管吸吸训练视频内容、可靠性和质量分析

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2025-01-01 DOI: 10.1097/CIN.0000000000001217

Yasemin Kalkan Ugurlu, Hanife Durgun, Dilek Kucuk Alemdar

{"title":"Analysis of YouTube Videos on Endotracheal Tube Aspiration Training in Terms of Content, Reliability, and Quality.","authors":"Yasemin Kalkan Ugurlu, Hanife Durgun, Dilek Kucuk Alemdar","doi":"10.1097/CIN.0000000000001217","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001217","url":null,"abstract":"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 < .005). Useful videos had higher reliability and quality scores than misleading videos (P < .05). In addition, the reliability and quality scores of videos uploaded by official institutions were significantly higher than those of videos uploaded by individual users (P < .05). This study found that the majority of endotracheal tube aspiration training videos reviewed in the study were published by individual users, and a significant proportion of these videos had low levels of reliability and quality.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":"43 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142923691","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}

引用次数: 0

Exploring Objective Simulation Competency Assessment Experience E-Learning Module Analytics: A Mixed-Methods Study to Improve Nursing Faculty Feedback. 探索客观模拟能力评估体验电子学习模块分析：改进护理教师反馈的混合方法研究。

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2024-12-01 DOI: 10.1097/CIN.0000000000001164

Suhasini Kotcherlakota, Elizabeth Mollard, Kevin Kupzyk, Jennifer Cera

{"title":"Exploring Objective Simulation Competency Assessment Experience E-Learning Module Analytics: A Mixed-Methods Study to Improve Nursing Faculty Feedback.","authors":"Suhasini Kotcherlakota, Elizabeth Mollard, Kevin Kupzyk, Jennifer Cera","doi":"10.1097/CIN.0000000000001164","DOIUrl":"10.1097/CIN.0000000000001164","url":null,"abstract":"Abnormal uterine bleeding is a common clinical concern for adolescent women. This research study aims to improve the clinical reasoning skills of advanced practice nursing students instructed in blended Objective Simulation Competency Assessment clinical experiences by enhancing feedback loops given to students during simulated experiences. A sequential explanatory mixed-methods study design was conducted with two cohorts of first-year women's health nurse practitioner graduate nursing students enrolled in the Women's Health Program at a large Midwestern university. Data were collected across 2 years from two separate cohorts, and analyses included data from 15 participants. The Abnormal Uterine Bleeding module designed with decision pathways was a worthy effort, and faculty value using data analytics from the e-learning module to evaluate student learning. This study describes how nursing faculty created abnormal uterine bleeding content in an online module format that can aid the diagnostic reasoning process and enable feedback to students.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":"862-871"},"PeriodicalIF":1.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447489","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}

引用次数: 0

A Machine Learning-Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults. 基于机器学习的中国社区老年人跌倒风险概率预测模型。

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2024-12-01 DOI: 10.1097/CIN.0000000000001202

Zhou Zhou, Danhui Wang, Jun Sun, Min Zhu, Liping Teng

引用次数: 0

Effects of Immersive Straight Catheterization Virtual Reality Simulation on Skills, Confidence, and Flow State in Nursing Students. 沉浸式直管导管术虚拟现实模拟对护理专业学生技能、信心和流动状态的影响。

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2024-12-01 DOI: 10.1097/CIN.0000000000001141

Hyeongyeong Yoon

{"title":"Effects of Immersive Straight Catheterization Virtual Reality Simulation on Skills, Confidence, and Flow State in Nursing Students.","authors":"Hyeongyeong Yoon","doi":"10.1097/CIN.0000000000001141","DOIUrl":"10.1097/CIN.0000000000001141","url":null,"abstract":"Core nursing procedures are essential for nursing students to master because of their high frequency in nursing practice. However, the experience of performing procedures in actual hospital settings decreased during the coronavirus disease 2019 pandemic, necessitating the development of various contents to supplement procedural training. This study investigated the effects of a straight catheterization program utilizing an immersive virtual reality simulation on nursing students' procedural performance, self-confidence, and immersion. The study employed a nonequivalent control group pretest-posttest design with 29 participants in the experimental group and 25 in the control group. The experimental group received training through a computer-based immersive virtual reality program installed in a virtual reality hospital, with three weekly sessions over 3 weeks. The control group underwent straight catheterization using manikin models. The research findings validated that virtual reality-based straight catheterization education significantly improved students' procedural skills, self-confidence, and flow state. Therefore, limited practical training can be effectively supplemented by immersive virtual reality programs.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":"872-878"},"PeriodicalIF":1.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141238801","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}

引用次数: 0

Nursing Variables Predicting Readmissions in Patients with a High Risk: A Scoping Review. 预测高危患者再入院的护理变量：一项范围综述。

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2024-12-01 DOI: 10.1097/CIN.0000000000001241

引用次数: 0