JMIR nursing最新文献

{"title":"Natural Language Processing in Nursing Practice for Clinical Reasoning and Workflow: Scoping Review.","authors":"Keisuke Iwase, Taiki Teshima, Miyae Yamakawa, Yasushi Takeya","doi":"10.2196/94778","DOIUrl":"10.2196/94778","url":null,"abstract":"<p><strong>Background: </strong>Recent advancements in digital health technologies have led to the accumulation of large volumes of unstructured text data, particularly in electronic health records. Given that nursing documentation frequently includes detailed descriptions of daily living activities and psychosocial contexts, existing medical natural language processing (NLP) tools may not be directly applicable without adaptation. Moreover, the integration of standardized nursing terminologies into NLP systems remains limited. Although NLP has the potential to support risk detection, information management, and care planning, its practical applications in nursing have not yet been systematically mapped.</p><p><strong>Objective: </strong>This scoping review aimed to map the practical applications of NLP in nursing practice, with a specific focus on real-world clinical use, particularly for assessment, documentation, and interventions, and to identify the study characteristics, application domains, outcomes, and challenges.</p><p><strong>Methods: </strong>Following the Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a systematic search was conducted across PubMed, CINAHL, the Cochrane Library, Scopus, Embase, and Ichushi-Web in October 2023 and June 2025. The population-concept-context framework (population: nurses; concept: NLP applications; context: clinical settings) was used to guide study selection. Two independent reviewers screened 2889 unique records and reviewed the full texts of 72 studies. Data were descriptively synthesized and thematically classified.</p><p><strong>Results: </strong>Six studies met the inclusion criteria, indicating that empirically evaluated applications of NLP in real-world nursing practice remain limited. NLP applications were categorized into three domains: (1) text summarization and generation, (2) information extraction and classification, and (3) real-time detection and screening. NLP was applied across multiple stages of nursing practice, including handovers, assessment, care planning, continuity of care, and patient safety management. Notably, NLP was used as a form of augmented intelligence to complement, rather than replace, nurses' clinical judgment and to support clinical decision-making. These findings also highlight a substantial gap between rapid technological advances and the limited implementation of NLP in real-world nursing practice.</p><p><strong>Conclusions: </strong>This review highlights the substantial gap between rapid technological advances and the limited implementation of NLP in real-world nursing practice. NLP may help reduce manual review workload, support early risk identification, and facilitate individualized care planning in selected nursing contexts. However, direct empirical evidence regarding its effects on nursing workflows, nurses' clinical decision-making, and patie","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e94778"},"PeriodicalIF":5.0,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13674688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208325","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":"Integration of Virtual and Augmented Reality Into Obstetric Nursing and Midwifery Education: Systematic Review.","authors":"Mariana Luisa Firmiano, Ana Paula Assunção Moreira, Flaviana Vely Mendonça Vieira, Carla Sa-Couto, Alexandrina Cardoso","doi":"10.2196/90252","DOIUrl":"10.2196/90252","url":null,"abstract":"<p><strong>Background: </strong>Technologies such as virtual reality (VR) and augmented reality (AR) have been increasingly incorporated into nursing education to support the development of cognitive, psychomotor, and behavioral competencies. In midwifery, immersive environments offer opportunities to simulate high-risk or low-frequency clinical scenarios, strengthening students' confidence and preparedness for professional practice. However, there is still significant variability in how VR and AR are pedagogically implemented, with limited understanding of their theoretical grounding, instructional design, and educational outcomes.</p><p><strong>Objective: </strong>This systematic review aimed to synthesize and critically evaluate the characteristics of educational programs using VR and AR to develop competencies among nursing and midwifery students and professionals.</p><p><strong>Methods: </strong>This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 and Synthesis Without Meta-Analysis (SWiM) guidelines and was registered in PROSPERO (International Prospective Register of Systematic Reviews). A comprehensive search was conducted in August 2024 across PubMed, Web of Science, Scopus, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases. Eligible studies were experimental, quasi-experimental, or observational, and involved undergraduate or postgraduate students in nursing, obstetric nursing, or midwifery, as well as certified midwives or nurse-midwives. Two independent reviewers conducted screening, data extraction, and quality appraisal using Joanna Briggs Institute (JBI) tools and the Medical Education Research Study Quality Instrument (MERSQI) scale. Data were synthesized narratively according to predefined thematic categories.</p><p><strong>Results: </strong>Four studies published between 2021 and 2024 met the inclusion criteria, encompassing 634 participants (360 midwifery students and 274 midwives or nurse-midwives). Two randomized controlled trials and 2 quasi-experimental studies were included. Interventions used VR in 3 studies and AR in one, simulating scenarios such as normal and complicated childbirth, neonatal resuscitation, and emergency obstetric procedures. Most interventions focused on developing technical competencies, while one also addressed teamwork and communication. Only 2 studies explicitly reported theoretical or pedagogical frameworks, and 2 mentioned alignment with the INACSL Healthcare Simulation Standards of Best Practice. Structured briefing or debriefing was rarely described. All studies assessed learning outcomes (Kirkpatrick level 2), and 3 included participants' satisfaction (level 1). Methodological quality, measured by MERSQI, ranged from 10.0 to 14.5, with a mean score of 12.9 (SD 2.0), indicating moderate to high quality.</p><p><strong>Conclusions: </strong>Evidence suggests that VR and AR can enhance learning in obstetric nursing ","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e90252"},"PeriodicalIF":5.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119624","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":"AI-Based Structured Information Extraction From Synthetic Nursing Handover Transcripts: Comparative Evaluation of Large Language Models.","authors":"Aaron Conway, Adriana Hada, Jessica Schluter, Hui Grace Xu, Dan Lowden, Tim Miller, Ken Donald, Andrew Teodorczuk","doi":"10.2196/106133","DOIUrl":"https://doi.org/10.2196/106133","url":null,"abstract":"<p><strong>Background: </strong>Clinical handover is the process during which responsibility and accountability for care are transferred between clinicians. AI has the potential to improve the reliability and completeness of clinical handover by helping clinicians detect predefined content areas that have been communicated, identify explicit information gaps, and prompt clarification before responsibility is transferred.</p><p><strong>Objective: </strong>This study evaluated the performance of several large language models and prompt optimization strategies for structured information extraction of synthetic nursing handover transcripts.</p><p><strong>Methods: </strong>Two registered nurses independently annotated a dataset of 203 synthetic handover transcripts to produce consensus labels for information extraction tasks. Tasks included (1) labeling spans of text into SBAR (Situation, Background, Assessment, Recommendation) categories, (2) content detection to determine if specific pieces of information were communicated, and (3) labeling spans of text that communicated information using uncertain terms that included a subtask for identifying unknown facts. Baseline and Genetic-Pareto (GEPA)-optimized prompts were compared for the GPT-5.2, GPT-5-nano, and MedGemma 27B large language models. Additionally, the LangExtract framework was evaluated for span-extraction tasks.</p><p><strong>Results: </strong>The GPT-5.2-optimized model achieved a micro<i>-F</i><sub>1</sub>-score of 0.85 (95% CI 0.83-0.88) for content detection, an absolute improvement of +0.08 compared with the matched baseline. GPT-5-nano also performed better after optimization for content detection (micro<i>-F</i><sub>1</sub>-score 0.81, 95% CI 0.78-0.84), suggesting that this structured task was not limited to the highest-capacity model. For SBAR span extraction, GPT-5.2 with prompt optimization achieved a micro<i>-F</i><sub>1</sub>-score of 0.76 (95% CI 0.72-0.79), improving by +0.24 compared with baseline and exceeding LangExtract; GPT-5-nano also improved to a micro<i>-F</i><sub>1</sub>-score of 0.69 (95% CI 0.66-0.72). Broad uncertainty-span extraction remained comparatively weak despite prompt optimization (micro<i>-F</i><sub>1</sub>-score 0.41, 95% CI 0.33-0.48; absolute improvement +0.06). In contrast, explicit unknown-fact extraction was more accurate with GPT-5.2 (micro<i>-F</i><sub>1</sub>-score 0.84, 95% CI 0.63-1.00), GPT-5-nano (micro<i>-F</i><sub>1</sub>-score, 0.84 95% CI 0.63-1.00), and MedGemma 27B (micro<i>-F</i><sub>1</sub>-score 0.80, 95% CI 0.63-1.00). Genetic-Pareto-optimized prompts outperformed the LangExtract approach across each span-extraction task.</p><p><strong>Conclusions: </strong>Prompt optimization improved matched-model point estimates, with the highest performance observed for predefined content detection and SBAR span extraction. Broad uncertainty extraction remained less accurate than the narrower unknown-fact task. These technical results do not ","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e106133"},"PeriodicalIF":5.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13638071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119635","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":"Virtual Reality-Enabled Physical AI Training for Supportive Nursing Robotics: Nurse-in-the-Loop, Site-Specific Conceptual Framework.","authors":"Seungman Kim","doi":"10.2196/96848","DOIUrl":"https://doi.org/10.2196/96848","url":null,"abstract":"<p><strong>Unlabelled: </strong>Interest in physical AI and robotics in health care is increasing, but the nursing literature shows that the evidence base remains early, nurse-centered applications are underdeveloped, and real-world experiential evidence is limited. Nurses are more likely to accept robots that reduce physically demanding and repetitive work while preserving the interpersonal and judgment-intensive core of nursing practice. This conceptual paper proposes a nursing-centered framework in which virtual reality functions not merely as a simulator but as a scaffolded training infrastructure for supportive physical AI systems, enabling nurse augmentation, site-specific adaptation through digital twins, and staged simulation-to-real transfer. The framework was developed through a conceptually integrative and implementation-aware synthesis drawing on nursing robotics, AI in nursing, immersive simulation, digital twins, human-in-the-loop learning, and physical AI development literature. It is organized around 5 linked elements and supported by a 4-layer technical architecture that outlines functional requirements and implementation pathways. Four key propositions ground the framework: (1) nursing robot training should focus on competency formation, not on decontextualized data accumulation; (2) training should proceed through progressive fidelity and staged autonomy; (3) digital twins should function as operational bridges for local ward adaptation; and (4) simulation-to-real transfer should be governed by explicit nursing-relevant validation criteria and retained human accountability. The proposed nurse-in-the-loop, site-specific virtual reality framework offers a nursing-centered complement to general-purpose physical AI pipelines by making workflow fit, role boundaries, local adaptation, and governed transfer explicit design requirements.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e96848"},"PeriodicalIF":5.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119644","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":"The Double-Edged Sword Effect of AI Application Among Clinical Nurses Based on the Job Demands-Resources Model: Qualitative Study.","authors":"Xiaoyan Zhang, Jiaxin Fang, Sihan Chen, Jiayin Luo","doi":"10.2196/100916","DOIUrl":"https://doi.org/10.2196/100916","url":null,"abstract":"<p><strong>Background: </strong>AI is rapidly transforming clinical nursing, promising administrative relief and decision support. However, the frontline reality presents a double-edged sword effect, where technological empowerment is frequently offset by novel occupational burdens and technostress.</p><p><strong>Objective: </strong>This study aims to theoretically deconstruct the bidirectional impacts of AI application among clinical nurses and identify buffering conditions, using the Job Demands-Resources (JD-R) theoretical framework.</p><p><strong>Methods: </strong>A descriptive qualitative study was conducted across multiple general hospitals in mainland China. Using maximum variation and purposive sampling, semistructured in-depth interviews were conducted with registered nurses who actively use clinical AI systems. Data were analyzed using directed qualitative content analysis guided by predefined JD-R constructs.</p><p><strong>Results: </strong>The analysis revealed 3 overarching domains comprising 9 main themes and 24 subthemes. On the gain path (job resources), AI empowered nurses through a workflow efficiency leap, clinical cognitive empowerment, and professional capital appreciation. Conversely, along the drain path (job demands), hidden costs were exposed, conceptualized as cognitive impediment, relational attrition, and digital involution driven by performance inflation and competitive perfectionism. The interplay between these pathways was perceived to be buffered by contextual mechanisms, specifically nurses' proactive coping strategies, professional boundary demarcation, and the provision of a cohesive organizational support architecture.</p><p><strong>Conclusions: </strong>The impact of AI integration in nursing is not technologically deterministic. While AI provides valuable cognitive and operational support, it concurrently generates novel digital demands. To prevent AI from devolving into an occupational hazard, health care administrators must establish multidimensional life cycle AI governance, cultivate comprehensive AI literacy, and safeguard the irreplaceable humanistic core of clinical care.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e100916"},"PeriodicalIF":5.0,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13632912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119675","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":"Predictors of Treatment Adherence in Outpatients With Chronic Obstructive Pulmonary Disease: Cross-Sectional Study.","authors":"Hien Nguyen, Trieu Nguyen, Hoai Nguyen, Giang Luong, Doanh Vu, Thong Tran","doi":"10.2196/83132","DOIUrl":"10.2196/83132","url":null,"abstract":"<p><strong>Background: </strong>Gaps in knowledge, inhalation skills, and adherence continue to hinder effective chronic obstructive pulmonary disease (COPD) management.</p><p><strong>Objective: </strong>The study was conducted to describe the status of COPD-related knowledge, practical skills for inhalation and breathing exercises, and treatment adherence among patients with COPD and to identify the relationships between these variables.</p><p><strong>Methods: </strong>A cross-sectional study was conducted among 420 outpatients with COPD. Data were collected in 2 rounds, including interviews to assess patients' knowledge and treatment adherence and checklist-based observations of patients' practical skills. Multivariable logistic regression analysis was used to evaluate the associations between knowledge and practical skills and the status of treatment adherence.</p><p><strong>Results: </strong>A total of 420 outpatients with COPD were included; 331 (78.8%) participants had inadequate COPD-related knowledge, 268 (63.8%) demonstrated adequate practical skills, and 155 (36.9%) achieved overall treatment adherence. In multivariable logistic regression, higher knowledge scores were associated with better treatment adherence (adjusted odds ratio [aOR] 1.20, 95% CI 1.06-1.31; <i>P</i>=.001). Participants who correctly performed inhaler-use techniques were more likely to be adherent than those with incorrect technique (aOR 3.10, 95% CI 2.10-4.70; <i>P</i>=.001). Likewise, adequate breathing-exercise skills were associated with higher adherence (aOR 1.70, 95% CI 1.08-2.71; <i>P</i>=.02).</p><p><strong>Conclusions: </strong>Higher COPD-related knowledge and better practical skills in inhaler use and breathing exercises were associated with better treatment adherence among outpatients with COPD. Because of the cross-sectional design, these findings indicate associations rather than causal relationships. These observed associations may help inform future intervention studies evaluating patient education and skill-based training, although their effectiveness should be confirmed in longitudinal or interventional studies.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e83132"},"PeriodicalIF":5.0,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13614212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066477","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":"Effects of a Digital Decision Support System on Diabetes Management in a Real-World Home Care Setting: Prospective Quasi-Experimental Feasibility and Implementation Study.","authors":"Katharina Maria Lichtenegger, Lara Scholle-Winkler, Julia Kopanz, Angela Libiseller, Klaus Donsa, Felix Aberer, Thomas Rudolf Pieber, Julia Katharina Mader","doi":"10.2196/89687","DOIUrl":"10.2196/89687","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Older adults with diabetes are vulnerable, facing multimorbidity and challenges in reaching glycemic targets. When insulin therapy is required, limited knowledge of geriatric therapeutic goals often leads to regimens that increase the risk of hypoglycemia. Simplified approaches such as once-daily basal insulin are recommended, yet titration protocols and unclear regimens remain barriers in home care nursing. Digital decision support systems (DDSSs) such as GlucoTab address this gap by offering evidence-based titration guidance integrated into nursing workflows. DDSSs also strengthen nurse autonomy, which leads to improved care processes and better outcomes in home care settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study assessed the effects of a nurse-guided intervention including a DDSS for diabetes management in older adults with type 2 diabetes receiving home care nursing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The prospective quasi-experimental feasibility and implementation study was conducted using GlucoTab for diabetes management as part of a multicomponent intervention at 2 home care nursing sites. Nine adults with type 2 diabetes receiving insulin therapy were enrolled (n=5 women; mean age 77, SD 10 years; mean BMI 27.7, SD 4.7 kg/m&lt;sup&gt;2&lt;/sup&gt;; mean baseline hemoglobin A&lt;sub&gt;1c&lt;/sub&gt; 59.5, SD 12.8 mmol/mol). After receiving training, nurses implemented the DDSS, which integrates evidence-based diabetes management recommendations for home care and nursing workflows. Diabetes-related hospital admissions and glycemic outcomes were assessed before, during, and after implementation of the DDSS intervention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Before implementation, 5 diabetes-related emergency admissions led to 4 hospitalizations, compared with none during implementation and 4 after implementation, mostly due to hyperglycemia. The median hospital stay was 9 (IQR 8-15) days (before and after implementation). Diabetes management shifted from predominantly premixed insulin therapy at baseline to basal insulin with or without bolus insulin during implementation. Diabetes-related home care visits decreased from 1.81 to 1.01 per day (before vs during implementation), and general practitioner contacts for insulin adjustments declined (19 before implementation vs 16 after implementation). Mean morning blood glucose decreased from 212 (SD 72) mg/dL to 162 (SD 42) mg/dL, and the proportion of measurements within the target range (70-180 mg/dL) increased from 39.0% to 70.8% before vs during implementation. No incidence of hypoglycemia requiring third-party help was observed during implementation. Adherence of nurses to DDSS recommendations exceeded 94%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study suggests that integrating a DDSS in a multicomponent intervention into home care nursing yielded longitudinal changes in diabetes outcomes. Emergency visits and hospitalizations were lower during implementation, no severe hypoglycemi","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e89687"},"PeriodicalIF":5.0,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13608231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042517","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":"Knowledge Acquisition, Case Discussion, and Engagement in Health Professional Students Using Interactive Virtual Patient Cases Versus Written Case Studies: Randomized Controlled Trial.","authors":"Jeffrey J Borckardt, Debra Henninger-Borckardt, Kelly Barth, Dusti Annan-Coultas, Lisa Langdale, Kimberly Kascak, Christopher Pelic, Michael A de Arellano, Kathleen Brady","doi":"10.2196/98700","DOIUrl":"10.2196/98700","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Effective pain and opioid management education remains a persistent challenge in health professions training. Static case studies and lecture-based instruction are widely used but may be insufficient to develop the clinical reasoning and communication skills required in practice. Interactive virtual patient simulations, including those powered by large language models, offer scalable alternatives, but head-to-head comparisons of interactive and static case formats using identical content among interprofessional learner populations remain limited.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This randomized controlled trial examined whether learner knowledge, quiz performance, and engagement differed when educational case studies were delivered through an interactive, AI-enabled virtual patient format versus a static written format. Because the formats differ in interactivity as well as in their use of AI, the trial was designed to test the effect of an \"interactive virtual patient format\" rather than to isolate any AI-specific effect.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Health professional students from medicine, nursing, dentistry, pharmacy, and allied health programs at a US academic health sciences university viewed a 12-minute video on pain management and were then randomized 1:1 to complete either 3 static written case studies or 3 interactive virtual patient case studies using the Figment Learning Labs Virtual Clinic. Case content was identical across conditions; only the delivery format was manipulated. The virtual patient system used an AI-enabled conversational interface (GPT-4o) delivering facilitator-authored case material, including a viewable medical record. Knowledge was assessed preintervention and postintervention using an 8-item test. Case discussion quiz accuracy and learner engagement (17 items) were also measured. Of 74 enrolled participants, 58 (78%) completed all study components and were included in this completer-based analysis. Analyses included a 2×2 mixed ANOVA and 2-tailed independent-sample &lt;i&gt;t&lt;/i&gt; tests (α=.05).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A significant time (pre-post)×group (interactive vs written) interaction was observed (&lt;i&gt;F&lt;/i&gt;&lt;sub&gt;1,56&lt;/sub&gt;=4.39; &lt;i&gt;P&lt;/i&gt;=.04; partial η²=0.07), indicating greater knowledge gains in the interactive condition. Participants in the virtual patient group demonstrated higher case discussion quiz accuracy (mean 0.79, SD 0.15) than those in the written case group (mean 0.64, SD 0.11; &lt;i&gt;t&lt;/i&gt;&lt;sub&gt;56&lt;/sub&gt;=4.48; &lt;i&gt;P&lt;/i&gt;&lt;.001). Agreement scores were significantly higher in the interactive group on 13 of 17 engagement items (&lt;i&gt;P&lt;/i&gt;&lt;.05), including items addressing realism, enjoyment, and perceived clinical relevance; because these were uncorrected item-level comparisons, they should be read as supportive rather than definitive.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;In this preliminary trial, exposure to interactive virtual patient cases was associated with higher ","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e98700"},"PeriodicalIF":5.0,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149032114","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":"The Role of Digital Health Literacy and Cultural Intelligence in Cultural Competence of Private Hospital Nurses: Cross-Sectional Study.","authors":"Emad Shdaifat, Amira Alshowkan, Bilal Alsaaideh","doi":"10.2196/97334","DOIUrl":"10.2196/97334","url":null,"abstract":"<p><strong>Background: </strong>As patient populations become increasingly diverse, nurses must develop cultural competence to provide effective and equitable care.</p><p><strong>Objective: </strong>This study aims to examine the predictors of cultural competence among nurses, specifically investigating the roles of digital health literacy (DHL) and cultural intelligence, while also assessing how demographic factors, competence levels, and DHL relate to culturally competent behaviors.</p><p><strong>Methods: </strong>A cross-sectional study was conducted with 296 nurses at a private hospital in Saudi Arabia. Data were collected using validated instruments to assess DHL, cultural intelligence, and cultural competence. Descriptive statistics, independent <i>t</i> tests, Pearson correlations, and hierarchical multiple linear regression analyses were performed using SPSS (IBM Corp) to evaluate the associations and predictors of cultural competence.</p><p><strong>Results: </strong>In a sample of 296 nurses, cultural intelligence was observed at a moderate level, present in 35.8% (n=106) of participants. Conversely, more than half of the participants (n=158, 53.4%) exhibited low DHL. The majority of the nurses (n=195, 65.9%) demonstrated moderate cultural competence. Education was the only demographic factor significantly associated with cultural competence, with nurses holding a Bachelor of Science degree scoring higher than those with a diploma (<i>P</i>=.003). Furthermore, cultural intelligence and DHL each showed a strong positive correlation with cultural competence (<i>P</i><.001). Hierarchical regression analyses indicated that both cultural intelligence (β=.427; <i>P</i><.001) and DHL (β=.315; <i>P</i><.001) were significant predictors of cultural competence, jointly explaining 47.7% of the variance (adjusted <i>R</i>²=0.460; <i>P</i><.001). Nationality also emerged as a significant predictor (β=.105; <i>P</i>=.03), whereas other demographic variables were not statistically significant. Cultural intelligence accounted for the largest proportion of explained variance (Δ<i>R</i>²=0.375), followed by DHL (Δ<i>R</i>²=0.051).</p><p><strong>Conclusions: </strong>Cultural competence among nurses is predominantly influenced by cultural intelligence and DHL, rather than by most demographic variables. Consequently, enhancing DHL and cultural intelligence may strengthen culturally competent practice. It is recommended that targeted educational and professional development interventions be implemented to elevate competence and support high-quality patient care across diverse health care settings.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e97334"},"PeriodicalIF":5.0,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13596763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026858","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":"Recommendations From a Health Care Provider's Perspective for Better Implementation of Telemedicine and Telenursing: Descriptive Qualitative Approach.","authors":"Rr Tutik Sri Hariyati, Min-Huei Hsu, Hanny Handiyani, Muhammad Hanif Amiruddin, Tuti Afriani, Dara Ayu Wardhani, Satria Dwi Pranoto, Andi Amalia Wildani, Arieny Rizafni, Citra Hafilah Shabrina","doi":"10.2196/90610","DOIUrl":"10.2196/90610","url":null,"abstract":"<p><strong>Background: </strong>Digital technology-based health care services like telemedicine and telenursing are expanding quickly globally. Although the use of telemedicine and telenursing has shown promise, there are a number of obstacles to overcome.</p><p><strong>Objective: </strong>This study aimed to determine the experiences of stakeholders with using telenursing and telemedicine during and after the COVID-19 pandemic.</p><p><strong>Methods: </strong>This study combined descriptive qualitative analysis using a focus group discussion (FGD) and validation using a review of the literature. The literature search used a convenience approach with the keywords benefits, advantages, disadvantages, obstacles, and constraints of telenursing or telemedicine to find relevant journal articles. Two FGDs were conducted during the COVID-19 period, namely 2020-2022, with the first FGD held in 2022. The second FGD was conducted in 2023-2024 to determine the implementation of telenursing and telemedicine during the recovery period, with data collection in 2024. The FGDs were conducted via the online Zoom platform, and a total of 23 participants were selected based on purposive sampling. People involved in the development and implementation of telemedicine or telenursing were included. Data analysis was conducted in 2 stages: the first using an inductive method, which was followed by triangulation using a deductive method. The results were verified and validated based on the reviewed literature.</p><p><strong>Results: </strong>We identified three themes: (1) health care providers have experience using telemedicine/telenursing; (2) telemedicine and telenursing have many benefits; and (3) telemedicine and telenursing have 5M (method, man, money, material, and machine) factors that must be anticipated. Key statements were identified and validated, then matched with the literature review.</p><p><strong>Conclusions: </strong>The benefits and barriers of telemedicine and telenursing were identified. For telemedicine to run smoothly, preparation of 5M elements is necessary. Implications for health care services necessitate the creation of operational guidelines for the implementation of telemedicine and telenursing.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e90610"},"PeriodicalIF":5.0,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13557319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148922047","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}