JMIR nursing最新文献

{"title":"Digital Leadership Scale for Clinical Nurses: Development and Validation of an Instrument.","authors":"Ji-Hyeon Lee, Sun-Young Jung","doi":"10.2196/82101","DOIUrl":"10.2196/82101","url":null,"abstract":"<p><strong>Background: </strong>The rapid advancement of digital technologies, combined with the evolving complexity of health care environments, has introduced a new paradigm in nursing practice. Clinical nurses are now required not only to deliver safe and effective patient care but also to demonstrate competencies in digital literacy and innovation. Among these emerging competencies, digital leadership has become a critical attribute-enabling nurses to lead digital transformation, ensure patient safety, enhance care quality, and support system-level change within health care organizations. Despite its increasing relevance, there is a notable absence of validated measurement tools tailored to assess digital leadership in clinical practice.</p><p><strong>Objective: </strong>This study aimed to develop and psychometrically validate a Digital Leadership Scale for Clinical Nurses (DLS-CN) to systematically evaluate the digital leadership capabilities of nurses working in clinical settings.</p><p><strong>Methods: </strong>The scale development process followed a rigorous multistep procedure. Initial items were derived from previous qualitative research involving a literature review and in-depth interviews, complemented by an additional literature review conducted in this study. The content validity of 38 preliminary items was evaluated by 9 experts over 2 rounds. A pilot test was conducted with 30 nurses, followed by cognitive interviews with 5 nurses to refine item clarity and relevance. The final set of items was administered to 446 clinical nurses across various health care institutions. Data were randomly split for exploratory factor analysis and confirmatory factor analysis. Additional analyses were conducted to evaluate item discrimination, convergent validity, and internal consistency using IBM SPSS 25.0 and AMOS 23.0.</p><p><strong>Results: </strong>The finalized DLS-CN consists of 29 items grouped under four domains: (1) ability to use digital technology, (2) digital safety management, (3) digital collaboration mindset, and (4) organizational influence. These 4 factors explained 56.9% of the total variance. The scale showed strong internal consistency (Cronbach α=0.95). Convergent validity was demonstrated through strong positive correlations with the Nursing Informatics Competency Scale (Pearson correlation coefficient r=0.82; P<.001) and the Self-Leadership Scale (Pearson correlation coefficient r=0.83; P<.001).</p><p><strong>Conclusions: </strong>The DLS-CN is a valid and reliable instrument for measuring digital leadership among clinical nurses. It offers a practical tool for educators, administrators, and researchers to assess and enhance digital leadership capabilities-ultimately supporting the digital transformation of health care systems.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e82101"},"PeriodicalIF":5.0,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13286069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148304219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Social Media Listening to Understand the Pressure Injury Experience: A Qualitative Descriptive Study.","authors":"Jessica Zihui Song, Isabel Tsang, Parthena Mouratidis, Manahil Saeed, Alma A Ornelas L, Alaul Islam, Sharon Gabison","doi":"10.2196/76682","DOIUrl":"10.2196/76682","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Pressure injuries (PIs) are a common complication in people with reduced mobility or sensation and can be burdensome for individuals with PIs and their caregivers. Valuable insights and real-world challenges faced by individuals living with PIs can be captured through candid accounts posted on social media. Social media listening (SML) is a tool that can enhance the understanding of those with lived experience by offering firsthand accounts that are irreproducible from controlled studies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to capture the candid experiences of individuals with PIs and caregivers through social media.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A noninterventional qualitative descriptive analysis was conducted using SML. Social media posts made on X (formerly Twitter), Reddit, and YouTube between January and December 2022 were compiled using SML tools X Pro (formerly TweetDeck) and Awario, and using Boolean search terms. Posts were manually screened for relevance, and duplicates were removed. Relevant posts were hand-coded by two independent reviewers. Inductive content analysis was used to analyze the posts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The search yielded 666 relevant posts from 498 unique social media users. Most posts were made in the United States (170/666, 25.5%), the United Kingdom (150/666, 22.5%), and Canada (62/666, 9.3%). Social media users provided detailed descriptions of the PIs, including the setting in which the PI occurred, the cause of the PI, and how the PI was managed. The majority of PIs (197/666, 29.6%) were reported to have occurred in the hospital setting due to a perceived lack of care from care providers, and local wound care was often cited (99/666, 14.9%) as a PI management strategy. Three key themes were developed regarding living with or caring for someone with a PI: (1) challenges experienced when living with or caring for a PI, (2) needs related to PI prevention and management, and (3) emotions experienced when living with or caring for a PI. Social media users frequently discussed challenges associated with living with a PI, including negative personal impacts and poor perceived treatment quality. Users also described a critical need for health care, education, and social support. Finally, users often expressed anger and/or sadness related to living with or caring for a PI.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;SML captured candid insights into the experiences, challenges, and needs of individuals living with PIs and their caregivers globally that may not be gleaned from controlled studies. Individuals with lived experience and their caregivers often struggled with negative personal impacts regarding their physical health and daily functioning related to PIs, further highlighting the urgent need to address barriers to appropriate PI care. Clinicians and policymakers should consider practices and policies that optimize the delivery of person-centered PI care in orde","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e76682"},"PeriodicalIF":5.0,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13271712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148267209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of Artificial Intelligence-Based Nursing Interventions for Chronic Illnesses Care: An Umbrella Review.","authors":"Jee Young Joo, Megan F Liu, Young Woo Cho, Hyungbin Cho","doi":"10.2196/97905","DOIUrl":"10.2196/97905","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI)-based nursing interventions are increasingly being employed to manage chronic illnesses; however, their definitive impact on clinical outcomes remains inconclusive, necessitating a comprehensive evidence synthesis.</p><p><strong>Objective: </strong>This umbrella review aimed to synthesize the evidence regarding the effectiveness of AI-based nursing interventions for chronic illness care and their subsequent impact on healthcare outcomes in clinical settings.</p><p><strong>Methods: </strong>We conducted an umbrella review and prospectively registered the protocol. A systematic search of five electronic databases (PubMed, CINAHL, Cochrane Library, Scopus, and Web of Science) was performed to identify systematic reviews and meta-analyses published in English between 2021 and 2025. The methodological quality of included studies was evaluated using the Joanna Briggs Institute Critical Appraisal Checklist.</p><p><strong>Results: </strong>Eight high-quality systematic reviews were included, with machine learning identified as the predominant technology. Three primary outcome domains emerged: predictive, psychosocial, and hospital utilization. Due to measurement heterogeneity, the results were synthesized narratively. Our findings demonstrated that AI-based nursing interventions are effective in predicting adverse clinical events, unplanned hospital utilization, and healthcare costs. However, evidence regarding psychosocial outcomes remains insufficient.</p><p><strong>Conclusions: </strong>This review provides systematic evidence supporting the utility of AI in chronic illness management, particularly for improving predictive and utilization outcomes. These findings offer actionable insights for nursing leaders to integrate AI into clinical practice and education. Future research should prioritize rigorous empirical designs to further strengthen the evidence base for AI-driven nursing care.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148254921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association Between Electronic Health Record-Based Nursing Workload and Turnover: Retrospective Cohort Study.","authors":"Linlin Xia, Daphne Lew, Lindsay Tessmer, Elise Eiden, Sunny Lou, Thomas Kannampallil","doi":"10.2196/89645","DOIUrl":"10.2196/89645","url":null,"abstract":"<p><strong>Background: </strong>Nurse turnover remains a major challenge for health systems, yet objective, scalable measures of workload that predict turnover are limited. Electronic health record (EHR) audit logs offer a potential data source to quantify nursing work patterns.</p><p><strong>Objective: </strong>This study aimed to evaluate the association between EHR-derived measures of nursing workload and turnover among inpatient nurses.</p><p><strong>Methods: </strong>We analyzed work-related activities of staff nurses from medical and surgical inpatient units at a large academic medical center from January 1, 2022, to December 31, 2022. Data included deidentified demographics (age, sex, years since licensure, and service group), shift characteristics (number of shifts worked, proportion of night shifts, timing, and location), and measures of work activities derived from EHR audit logs. Audit logs were used to develop measures related to nurse workload, including (1) nurse activities (information review, medication administration, alert management, navigation, documentation, and communication), (2) patient load (based on the number of unique patient charts accessed), and (3) cognitive load (based on the number of patient switches). For nurses who left, we excluded the 6 weeks immediately preceding termination (washout period) and measured workload during the preceding 6-week period; for those who remained, a random 6-week working period was selected. Associations between workload measures and turnover were assessed using mixed-effects logistic regression, adjusting for demographics and shift-related characteristics.</p><p><strong>Results: </strong>Among 432 nurses (n=363, 84% female; median age 27, IQR 23-36 years), contributing 6812 shifts and approximately 13 million audit log actions, 84 (19%) left the institution in 2022. A higher proportion of medication administration actions was associated with higher odds of turnover (odds ratio [OR] 2.20, 95% CI 1.36-3.54), whereas greater alert engagement (OR 0.48, 95% CI 0.32-0.72) and more years since licensure (OR 0.57, 95% CI 0.38-0.84) were associated with lower odds of turnover.</p><p><strong>Conclusions: </strong>EHR-derived workload measures, particularly greater medication administration burden and lower alert engagement, were independently associated with the risk of nurse turnover. Patterns of EHR use may help identify nurses at higher risk of leaving an institution and can potentially inform targeted workforce retention strategies.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e89645"},"PeriodicalIF":5.0,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13263017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148240770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Digital Literacy Among Nursing Faculty Under the Artificial Intelligence-Enhanced Technological Pedagogical Content Knowledge Framework: Cross-Sectional Survey Analysis.","authors":"Junyu Zhao, Xiaomin Hua, Rong Hu, Chu Chen, Wanxin Wu, Yaming Zhang, Yuhe Zhang, Aijing Wu, Yu Zhang","doi":"10.2196/88016","DOIUrl":"10.2196/88016","url":null,"abstract":"<p><strong>Background: </strong>Digital literacy has become a core competence for nursing faculty to adapt to the digital transformation of education, improve teaching quality, and cultivate innovative nursing talents.</p><p><strong>Objective: </strong>This study aimed to investigate the digital literacy status and influencing factors among nursing faculty in colleges and universities in Fujian province, China.</p><p><strong>Methods: </strong>A cross-sectional survey analysis was conducted among 339 participants from July 2023 to June 2024 by selecting nursing faculty from 3 medical universities and 5 colleges in Fujian province. A general information questionnaire and a teacher digital literacy questionnaire (designed based on the artificial intelligence-enhanced technological pedagogical content knowledge theoretical model) were used to conduct the survey. The questionnaires included the following dimensions: an intelligent integration ethics layer (digital social responsibility), attention to the moral boundaries of applying technology; an awareness layer (digital awareness), the willingness to actively adapt to technological changes; a knowledge layer (digital technology knowledge and skills), mastery of the integration point of intelligent tools and subject teaching; an ability layer (digital application), the ability to design intelligent teaching plans; and a thinking layer (professional development; innovative thinking for the critical integration of technology). Statistical analysis included descriptive statistics, a 1-way ANOVA, Pearson correlation analysis, and multiple linear regression (using SPSS 28.0 software).</p><p><strong>Results: </strong>The overall digital literacy of the nursing faculty was at a medium level (mean score 101.92, SD 16.47), with the highest score in digital awareness (mean 20.21, SD 9.43) and the lowest score in digital technology knowledge and skills (mean 9.68, SD 2.92). For the dimension of digital technology knowledge and skills, both age (β=-.142; P=.009) and years of teaching (β=-.147; P=.006) were significant negative predictors. Regarding digital application, age was found to be a significant negative predictor (β=-0.124; P=.02) but teaching experience was a positive predictor (β=0.123, P=.02). Similarly, age also had a significant negative impact on the professional development dimension (β=-.153; P=.005) and the overall digital literacy level (β=-.136; P=.01).</p><p><strong>Conclusions: </strong>The digital literacy of nursing faculty is at a medium level, with especially low scores in technical knowledge. Age and teaching experience are key influencing factors. Recommendations to promote balanced development of awareness and skills include providing targeted training for senior faculty, integrating courses with clinical practice, optimizing the allocation of digital resources, promoting interdisciplinary cooperation, and strengthening ethical and safety training.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e88016"},"PeriodicalIF":5.0,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13256485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148229657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Informatics Competency and Technology Self-Efficacy Levels Among Undergraduate Nursing Students: Cross-Sectional Study.","authors":"Nader Alnomasy, Habib Alrashedi, Sharifah Alsayed, Petelyne Pangket, Razan Alsayed","doi":"10.2196/85283","DOIUrl":"10.2196/85283","url":null,"abstract":"<p><strong>Background: </strong>The Saudi Arabian health care sector is transforming under Vision 2030 with the goal of digitizing its services. This necessitates a digitally prepared nursing workforce; however, there is evidence suggesting that nursing students have limited informatics competency and that these skills are minimally covered in their training programs.</p><p><strong>Objective: </strong>This study measured the baseline informatics competency and technology self-efficacy of undergraduate nursing students at the University of Hail, Saudi Arabia.</p><p><strong>Methods: </strong>Using a descriptive cross-sectional design, data were collected from 243 undergraduate nursing students at the University of Hail through an online survey. The survey measured demographics, informatics competency (Canadian Nurse Informatics Competency Assessment Scale), and digital technology self-efficacy. Data analysis used descriptive statistics, 2-tailed t tests, ANOVA, and hierarchical regression analysis.</p><p><strong>Results: </strong>Students reported a moderate level of informatics competency, with a mean Canadian Nurse Informatics Competency Assessment Scale item score of 2.57 (SD 0.84) on a scale from 1 to 4. They also showed moderate to high self-efficacy for digital technology, with a mean item score of 2.7 (SD 0.56) out of 4. Informatics competency scores were substantially higher in students with prior informatics training and frequent electronic health record exposure. In the underpowered exploratory hierarchical regression model, self-efficacy for digital technology showed a small positive association with informatics competency that approached statistical significance; however, the overall model was not statistically significant and explained only a limited proportion of the variance.</p><p><strong>Conclusions: </strong>This single-site study of undergraduate nursing students at the University of Hail found moderate self-reported informatics competency despite higher digital technology self-efficacy, indicating a need for strengthened informatics education to support the Saudi Vision 2030. While underpowered, exploratory analyses and self-report data mean that these institution-specific findings should be interpreted cautiously and used primarily to guide future multisite and mixed methods research.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e85283"},"PeriodicalIF":5.0,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13245837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148207071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voice-Based Structured Nursing Documentation Using Automatic Speech Recognition and Large Language Models: Development and Evaluation Study.","authors":"Meng-Han Su, Wei-Chun Wang, Yi-Min Hsu, Shih-Yen Hou, Su-Jung Chuang, Shih-Sheng Chang","doi":"10.2196/88567","DOIUrl":"10.2196/88567","url":null,"abstract":"<p><strong>Background: </strong>For clinical nurses, manually entering information into hospital information systems (HISs) remains time-consuming and prone to omissions. Although speech recognition can reduce the need for manual entry, its use in clinical settings has historically been limited by code-switching, medical terminology, and noisy ward environments. Recent advances in customized automatic speech recognition (ASR) and large language models (LLMs) now make speech-based, structured documentation aligned with nursing frameworks such as DART (data, action, response, and teaching) increasingly feasible.</p><p><strong>Objective: </strong>This study developed and evaluated an integrated ASR and LLM system that transforms spoken nursing input into structured DART notes and evaluated its accuracy, usability, and clinical feasibility within HIS workflows.</p><p><strong>Methods: </strong>A code-switching nursing speech corpus from emergency and ward settings was used to fine-tune the Whisper large-v2 model with parameter-efficient adaptation. The LLM generated schema-constrained DART records from ASR transcripts, which were verified by nurses before being uploaded to the corresponding HIS fields. Evaluation included mixed error rate for ASR accuracy, F1-scores, and agreement statistics for DART classification, hallucination assessments based on factual correctness, and analysis of nurse feedback on system use.</p><p><strong>Results: </strong>The fine-tuned ASR model reduced the mixed error rate from 44.79% to 6.67%. DART generation achieved a macroaveraged F1-score of 0.82 (95% CI 0.80-0.84) and met the noninferiority margin relative to human transcripts (δ=-0.04). The hallucination rate was 2.51%. During deployment, the monthly volume of valid nursing notes generated through voluntary use of the ASR system increased from 32,724 to 65,417, where each note represented a single documentation entry generated per patient care episode. Among 120 participating nurses, 91 (75.8%) reported reduced workload and improved completeness.</p><p><strong>Conclusions: </strong>The integrated ASR and LLM system was feasible and showed strong performance, with good acceptance among clinical nurses. It reduced the manual documentation burden, improved record completeness, and demonstrated the value of an ASR- and LLM-supported workflow for nursing documentation.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e88567"},"PeriodicalIF":5.0,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13240795/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facilitating Digital Transformation in Nursing Through Nursing Development Units: Scoping Review.","authors":"Annabell Gangnus, Pascal Müller, Katrin Gayen, Uta Kirchner-Heklau, Sebastian Hofstetter, Patrick Jahn","doi":"10.2196/89051","DOIUrl":"10.2196/89051","url":null,"abstract":"<p><strong>Background: </strong>Nursing Development Units (NDUs) are structured clinical environments designed to enhance professional development, collaboration, and organizational learning. While NDUs have been widely studied for their impact on nursing practice, their role in supporting digital transformation in health care has been less explicitly examined.</p><p><strong>Objective: </strong>This scoping review aimed to map the characteristics, structures, and processes of NDUs and identify how these elements may relate to digital readiness and the integration of digital tools in nursing practice.</p><p><strong>Methods: </strong>We conducted a scoping review of 40 publications describing NDUs. Data were coded iteratively to identify patterns at the individual, interpersonal, and organizational levels. Core elements and subcategories of NDUs were mapped, highlighting potential connections to capacities required for digital transformation.</p><p><strong>Results: </strong>NDUs operate across three levels: (1) individual-fostering professional role development, leadership, and resilience; (2) interpersonal-promoting participatory cultures, collaborative practice, and shared unit visions; and (3) organizational-providing structured frameworks for practice philosophy, role development, resource and change management, and embedded evaluation systems. While most primary publications did not explicitly address digital competencies, the structures, reflective practices, and collaborative processes described represent foundational capacities for engaging with digital tools, innovation, and organizational change.</p><p><strong>Conclusions: </strong>NDUs are characterized by multilevel structures and processes that enhance professional and organizational capacities. Although explicit evidence on digital transformation is limited, these capacities align with key prerequisites for the adoption and effective use of digital technologies in nursing practice. Further research is needed to examine how NDUs directly support digital innovation in clinical settings.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e89051"},"PeriodicalIF":5.0,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13235844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148165977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Readiness Assessment for AI in Nursing Care Projects: Multimethods Study.","authors":"Kathrin Seibert, Dominik Domhoff, Janissa Altona, Sebastian Jäger, Felix Bießmann, Alessia Nowak, Rahel Gubser, Matthias Schulte-Althoff, Daniel Fürstenau, Jörg Pohle, Lea Bergmann, David Walter, Kathi Beier, Dagmar Borchers, Karin Wolf-Ostermann","doi":"10.2196/84148","DOIUrl":"10.2196/84148","url":null,"abstract":"<p><strong>Background: </strong>Integrating artificial intelligence (AI) systems into nursing care often encounters obstacles stemming from unmet requirements and insufficient engagement with well-documented sociotechnical pitfalls. Readiness models offer a systematic way to evaluate project preparedness and to build the capabilities needed for successful artificial intelligence in nursing care (AINC) research, development, and implementation. As of yet, an evidence-based AI readiness assessment prioritizing AINC projects and accounting for their diversity in care settings is missing.</p><p><strong>Objective: </strong>This study aimed to develop a comprehensive artificial intelligence nursing care readiness assessment (AINCRA) to support planning, execution, and evaluation of AINC projects.</p><p><strong>Methods: </strong>In a sequential exploratory multimethods bottom-up approach to maturity model development, key AI readiness dimensions and attributes were identified to develop a pilot readiness assessment. The pilot version was grounded on insights from an expert workshop (n=21) and expert interviews (n=14), an online survey (n=53), a rapid review (n=292), and a nominal group consensus process. A systematic literature review (n=7) further triangulated AI readiness attributes. Finally, a think-aloud interview study and focus group discussions involving experts (n=18) from nursing practice, nursing science, and AI research and development who had conducted AINC projects prior to data collection validated the attributes.</p><p><strong>Results: </strong>The resulting AINCRA encompasses 5 core dimensions: regulatory, processual, technical, social, ethical, and community building requirements and aspects. Including 69 attributes and capabilities of AI nursing care readiness, the core dimensions reflect key areas of action where AINC project stakeholders can influence project outcomes. Clinical partners can assess their organization's maturity level in relation to the implementation of AI. An assessment of each dimension and its attributes across 5 maturity levels allows reflecting on and proactively shaping individual project approaches. Overall, experts regarded AINCRA as a useful instrument for the development, management, and evaluation of AINC projects while emphasizing that established principles of good practice in project and data management should not be neglected when using AINCRA as a project management tool.</p><p><strong>Conclusions: </strong>AINCRA enables practitioners from AI research and development, clinical partners, and nursing and health scientists to plan, evaluate, and enhance AI projects across their lifecycle, thereby supporting effective AI integration in nursing care. While AINCRA was developed within the European and German legal framework for AI in health care settings, respective attributes can be adapted to international requirements.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e84148"},"PeriodicalIF":5.0,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13229396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148152398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation-Based Training for Nursing Students to Improve Patient Safety: Systematic Review.","authors":"Azucena González-Sanz, José Carlos López-García, Elena Sutil-Rodríguez, Raúl Juárez-Vela, José Ángel Santos-Sánchez, Noelia Navas-Echazarreta, Antonio Martínez-Sabater, Consuelo Sancho-Sanchez","doi":"10.2196/87898","DOIUrl":"10.2196/87898","url":null,"abstract":"<p><strong>Background: </strong>Patient safety is a fundamental pillar of health care quality. Simulation-based training provides a controlled environment for nursing students to develop safety competencies and error-recognition skills before clinical practice.</p><p><strong>Objective: </strong>This systematic review aimed to describe and characterize the simulation-based education features and modalities used to address patient safety outcomes in undergraduate nursing students, identifying the strategies that contribute to improvements in safety-related competencies.</p><p><strong>Methods: </strong>A systematic review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines across PubMed, Web of Science, Scopus, CINAHL, Cochrane, and Lilacs (2019-2024). Inclusion criteria focused on original studies involving undergraduate nursing students and simulation interventions measuring patient safety outcomes. Studies in languages other than English, Spanish, or Portuguese were excluded. Two reviewers independently performed study selection and data extraction. Methodological quality was assessed using Joanna Briggs Institute tools, applying a 60% quality threshold for inclusion. Results were synthesized through a narrative approach.</p><p><strong>Results: </strong>A total of 20 studies from 12 countries were included. The methodological quality was high (n=14) and moderate (n=6). Findings revealed that high-fidelity simulation and virtual reality are the primary strategies used. Simulation proved effective in enhancing both technical skills (medication administration accuracy) and nontechnical skills (communication via SBAR [Situation, Background, Assessment, Recommendation] and ISBAR [Identification, Situation, Background, Assessment, Recommendation] tools, teamwork, and adverse event reporting). Key strategies contributing to safety included repetitive practice and interprofessional simulation, which significantly improved error detection and clinical judgment.</p><p><strong>Conclusions: </strong>Simulation is an essential pedagogical strategy for preparing nursing students to deliver safe care. Practical implications include the need to integrate structured simulation into nursing curricula to bridge the theory-practice gap. Future research should prioritize longitudinal designs to assess the retention of these safety skills in clinical settings and develop standardized metrics for measuring patient safety outcomes.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e87898"},"PeriodicalIF":5.0,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13205464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148038219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}