Journal of Nursing Regulation最新文献

{"title":"Policy Perspective: The burden of education debt for today’s nursing workforce","authors":"Thomas Harrington BA","doi":"10.1016/j.jnr.2025.08.011","DOIUrl":"10.1016/j.jnr.2025.08.011","url":null,"abstract":"","PeriodicalId":46153,"journal":{"name":"Journal of Nursing Regulation","volume":"16 3","pages":"Pages 261-264"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271107","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":"A scoping review of virtual nursing models in inpatient, noncritical care settings","authors":"Tajudaullah Bhaloo PhD, MHA ,&nbsp;Caitlin McVey MBA, RN, CPHQ, CLSSBB ,&nbsp;Jessica Peterson PhD, RN ,&nbsp;Marjory Williams PhD, RN","doi":"10.1016/j.jnr.2025.08.005","DOIUrl":"10.1016/j.jnr.2025.08.005","url":null,"abstract":"<div><h3>Background</h3><div>The success of virtual nursing models in intensive care units has prompted its expansion to other acute care settings.</div></div><div><h3>Purpose</h3><div>The aim was to summarize peer-reviewed literature that described or evaluated virtual nursing models in noncritical care settings and describe policy and practice implications and future research needed to create actionable evidence.</div></div><div><h3>Methods</h3><div>Using the Arksey and O'Malley methodological framework, the PubMed, CINAHL, and Embase databases were searched for relevant published literature. The research team screened titles and abstracts; agreement from at least two members was required for article inclusion and data extraction.</div></div><div><h3>Results</h3><div>Of the 588 articles retrieved, 35 were included. Most virtual nursing care models had a specific, focused role for virtual nurses (e.g., admissions and discharges) rather than a co-caring model in which the virtual nurse had a more expanded role. Patient and nurse satisfaction were the most common outcome measures, followed by hospital throughput and efficiency measures. Factors associated with successful implementation included incorporating bedside nurse input during model development, in-person team building, and ongoing bi-directional communication between bedside and virtual nurses.</div></div><div><h3>Conclusion</h3><div>Model variability substantiates the need for more specific operational guidelines that define scopes and standards of practice with detailed role descriptions. Long-term implications of dividing nursing responsibilities remain unclear but may include the need to maintain competencies among bedside nurses and the development of virtual care competencies. Future research needs a unifying framework and should utilize longitudinal and multisite studies that test models in different environments.</div></div>","PeriodicalId":46153,"journal":{"name":"Journal of Nursing Regulation","volume":"16 3","pages":"Pages 171-182"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271123","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":"Regulating at the AI frontier: The collision of policy, regulation, and nursing practice","authors":"Benjamin J. Galatzan PhD, RN ,&nbsp;Elizabeth A. Johnson PhD, MS-CRM, RN ,&nbsp;Meghan Reading Turchioe PhD, MPH, RN, FAHA ,&nbsp;Christina Baker PhD, RN, NCSN, NI-BC ,&nbsp;Ann Wieben PhD, RN","doi":"10.1016/j.jnr.2025.08.012","DOIUrl":"10.1016/j.jnr.2025.08.012","url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI) is rapidly transforming clinical decision-making and healthcare delivery, yet state-level legislation addressing AI integration in patient care often lacks alignment with nursing-specific regulatory frameworks.</div></div><div><h3>Purpose</h3><div>This policy analysis examines enacted and proposed AI-related legislation across the United States, with specific attention on implications for nursing practice, licensure, and professional accountability.</div></div><div><h3>Methods/Results</h3><div>Using a structured review and thematic categorization of state legislation, eight major policy domains were identified, such as “AI in Clinical Decision-Making,” “Nursing Scope-of-Practice or Autonomy Protections,” and “AI Governance via Task Forces or Commissions.” While development of state-level AI policy is gaining momentum, few legislative efforts explicitly define nursing roles, responsibilities, or protections within AI-integrated environments. Furthermore, the majority of proposals addressing nursing autonomy are pending rather than enacted.</div></div><div><h3>Conclusion</h3><div>These findings highlight a growing regulatory gap that may expose nurses to increased liability, ambiguous role expectations, and reduced clinical authority in AI-augmented care settings. Key AI policy and regulatory priorities for the nursing profession focus on competencies in nursing education, updated licensure frameworks, and structured approaches for boards of nursing to assess disciplinary concerns arising from AI use. Foundational insights for nursing organizations seeking to proactively engage with AI policy development and ensure safe, ethical nursing practice in the digital era are provided.</div></div>","PeriodicalId":46153,"journal":{"name":"Journal of Nursing Regulation","volume":"16 3","pages":"Pages 207-215"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271213","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":"Prevalence of artificial intelligence use and instruction in nursing education: A national study of prelicensure nursing programs in the United States","authors":"Brendan Martin PhD ,&nbsp;Michaela Reid BS","doi":"10.1016/j.jnr.2025.08.003","DOIUrl":"10.1016/j.jnr.2025.08.003","url":null,"abstract":"<div><h3>Background</h3><div>There is ample evidence that the integration of artificial intelligence (AI) tools into nursing practice is becoming more commonplace, but there are fewer national resources indicating to what degree prelicensure nursing programs employ these technologies and incorporate related topics into their curriculum.</div></div><div><h3>Purpose</h3><div>The current survey study sought to determine the prevalence of registered nurse (RN) and licensed practical nurse (LPN) education programs’ use of generative AI technologies, and the extent to which they embed AI and other digital health topics into their instructional content.</div></div><div><h3>Methods</h3><div>A national survey was conducted of all RN and LPN program administrators nationwide for which we had email contact information (<em>N</em> = 2744).</div></div><div><h3>Results</h3><div>Prelicensure RN programs (<em>n</em> = 122, 24 %) were more likely to use generative AI technology than LPN programs (<em>n</em> = 27, 12 %, <em>p</em> &lt; 0.001), but more than three-quarters of both types of programs reported they do not use such tools or are not sure. In addition to the low usage of generative AI technology, few programs reported teaching advancements in AI and/or other digital health–related topics to their students (RN <em>n</em> = 87, 17 %; LPN <em>n</em> = 25, 11 %).</div></div><div><h3>Conclusion</h3><div>Nursing education programs that limit integration of AI into their curriculum risk potentially limiting students’ learning on evidence-based practice and may miss opportunities to promote critical reflection. The results of our study underscore the need to support nursing faculty to ensure prelicensure instructional content prepares nursing students for advancements in clinical practice.</div></div>","PeriodicalId":46153,"journal":{"name":"Journal of Nursing Regulation","volume":"16 3","pages":"Pages 216-222"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271214","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":"Technology and the nursing needs of tomorrow: Innovation and regulation","authors":"Carol Anne Timmings (Interim Editor in Chief)","doi":"10.1016/j.jnr.2025.08.017","DOIUrl":"10.1016/j.jnr.2025.08.017","url":null,"abstract":"","PeriodicalId":46153,"journal":{"name":"Journal of Nursing Regulation","volume":"16 3","pages":"Pages 137-138"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271211","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":"Transforming healthcare education with immersive virtual reality simulation: An interprofessional case study","authors":"Dawn Terzulli DNP, RN, PCCN, CHSE ,&nbsp;Kristen Poston DNP, APRN, FNP-C, CHSE ,&nbsp;Marc Lapointe PharmD, BCPS ,&nbsp;Brandi Townsend BSN, RN ,&nbsp;Tese Stephens PhD, MSN, RN, CNE","doi":"10.1016/j.jnr.2025.08.009","DOIUrl":"10.1016/j.jnr.2025.08.009","url":null,"abstract":"<div><h3>Background</h3><div>Educators are challenged with finding innovative interventions to meet the evolving curricular and technological demands of the complex and rapidly changing healthcare landscape. A growing body of literature supports immersive virtual reality simulation (IVRS) as an effective instructional methodology.</div></div><div><h3>Purpose</h3><div>The purpose of this case study is to explore best practices for IVRS integration with an interprofessional team.</div></div><div><h3>Methods</h3><div>An interprofessional IVRS pilot was implemented with college of nursing and college of pharmacy students at a large, state-supported academic health sciences center in South Carolina.</div></div><div><h3>Results</h3><div>Twenty nursing and 20 pharmacy students participated in the pilot. Learners and faculty reported immediately seeing value in the learning experience. The data suggested a positive trend: following the interprofessional IVRS session, all respondents indicated agreement or strong agreement with the statement “I feel confident providing care in an interprofessional, team-based environment,” reflecting increased confidence in their ability to function effectively within an interprofessional team.</div></div><div><h3>Conclusions</h3><div>Faculty and administrators should use a targeted approach to IVRS implementation, beginning with a thorough evaluation of potential curricular needs followed by designing experiences that align with established regulatory guidelines. If possible, interprofessional IVRS training experiences that are developed and guided by simulation experts are recommended. The flexibility and efficiency that IVRS technology brings to historically labor-intensive IP simulation training is a revolutionary development.</div></div>","PeriodicalId":46153,"journal":{"name":"Journal of Nursing Regulation","volume":"16 3","pages":"Pages 255-260"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271106","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":"Advancing nursing regulation in the digital era: Harnessing AI to bridge workforce gaps and strengthen practice competency and safety","authors":"Elizabeth H. Zhong PhD ,&nbsp;Nancy Spector PhD, RN, FAAN ,&nbsp;Charlie O'Hara PhD ,&nbsp;Nicole Livanos JD ,&nbsp;Jose Delfin Castillo III PhD, MSNA, CRNA, APRN, FAANA","doi":"10.1016/j.jnr.2025.08.015","DOIUrl":"10.1016/j.jnr.2025.08.015","url":null,"abstract":"<div><div>Projections point to persistent and potentially worsening nursing workforce shortages in the United States, with the resulting inadequate staffing posing a risk to patient care. Artificial intelligence (AI) offers a disruptive opportunity, not only to relieve staffing pressures but also to fundamentally reshape nursing roles and the healthcare infrastructure. This review aims to advance insight into AI's transformative potential in nursing by examining current and prospective applications of AI across five key domains: workforce planning, education, practice, regulatory frameworks, and the AI-human ecosystem. It highlights key innovations alongside emerging opportunities and risks tied to AI adoption, and it explores ethical concerns, gaps in regulatory guardrails, and implementation challenges that could hinder the responsible and effective integration of AI into nursing practice. Rather than offering definitive answers, the present review aims to encourage ongoing inquiry into the multifaceted role of AI in nursing, fostering solutions that are not only technologically advanced but also ethically sound and human-centered.</div></div>","PeriodicalId":46153,"journal":{"name":"Journal of Nursing Regulation","volume":"16 3","pages":"Pages 150-164"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271523","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 the ethical dimensions of AI integration in nursing practice: A systematic review","authors":"Khaldoon Aied Alnawafleh RN, MSN, PhD ,&nbsp;Wesam Taher Almagharbeh RN, MSN, PhD ,&nbsp;Hazem AbdulKareem Alfanash RN, MSN, PhD ,&nbsp;Amal Ali Alasmari RN, MSN, PhD (Assistant Professor) ,&nbsp;Amal Ali Alharbi RN, MSN, PhD ,&nbsp;Mashael Hasan Alamrani RN, MSN, PhD ,&nbsp;Sameer A. Alkubati RN, MSN, PhD ,&nbsp;Malik A. Altayar PhD ,&nbsp;Khulud Ahmad Rezq RN, MSN, PhD","doi":"10.1016/j.jnr.2025.08.001","DOIUrl":"10.1016/j.jnr.2025.08.001","url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI) is being increasingly integrated into nursing practice, offering tools such as clinical decision support systems, predictive analytics, and robotic aids. While these technologies promise greater efficiency and precision, they also raise complex ethical challenges specific to the relational, advocacy-driven nature of nursing.</div></div><div><h3>Purpose</h3><div>To systematically review and synthesize the ethical implications of AI integration in nursing practice, focusing on five key domains: patient autonomy, privacy, accountability, equity and algorithmic bias, and nurse–patient relationships.</div></div><div><h3>Methods</h3><div>Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guidelines, a systematic review was conducted using PubMed, CINAHL, IEEE Xplore, and Google Scholar. Studies published between 2018 and 2025 that addressed AI ethics within nursing were included. Data were extracted from included studies and analyzed through thematic synthesis.</div></div><div><h3>Results</h3><div>Thirty-three articles met the inclusion criteria. Patient autonomy (67%), privacy (61%), and accountability (49%) were the most frequently discussed ethical concerns. AI’s opacity often hindered informed consent and shared decision-making. Privacy risks included secondary data use and insufficient data governance. Accountability remained diffuse in cases of AI error, with nurses caught between professional duty and opaque algorithmic suggestions. Equity and algorithmic bias issues emerged in 42% of studies, especially when AI was trained on nondiverse datasets. Finally, nurse–patient relationships were strained in settings where AI mediated or replaced human contact, particularly in elder care.</div></div><div><h3>Conclusion</h3><div>Ethical integration of AI in nursing requires nurse-centered system design, transparent governance protocols, and ethical education. Future efforts must emphasize equitable data practices, clarify liability, and preserve the relational foundation of nursing.</div></div>","PeriodicalId":46153,"journal":{"name":"Journal of Nursing Regulation","volume":"16 3","pages":"Pages 228-237"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271102","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":"Enhancing nursing practice through simulation: Addressing barriers and advancing the integration of artificial intelligence in healthcare","authors":"Mohamed Benfatah PhD ,&nbsp;Ilham Elazizi MSN ,&nbsp;Hajar Belhaj PhD ,&nbsp;Abderrahmane Lamiri PhD","doi":"10.1016/j.jnr.2025.08.004","DOIUrl":"10.1016/j.jnr.2025.08.004","url":null,"abstract":"<div><h3>Background</h3><div>The integration of Artificial Intelligence (AI) in nursing practice represents a significant advancement in healthcare, offering promising improvements in clinical decision-making, workflow efficiency, and patient care management. However, its widespread implementation faces obstacles, such as inadequate training, resistance to technological change, and regulatory uncertainties.</div></div><div><h3>Purpose</h3><div>This study assesses nurses' receptiveness to AI in critical care settings, to identify the main barriers hindering its adoption, and to evaluate the effectiveness of AI-based simulation training in enhancing nurses’ competencies and promoting acceptance of AI technologies in clinical practice.</div></div><div><h3>Methods</h3><div>A quasi-experimental mixed-methods design was employed. Nurses participated in simulated clinical scenarios using AI tools, including IBM Watsonx and Qventus. Data collection methods included direct clinical observation, competency assessments, satisfaction surveys, and qualitative interviews to gain comprehensive insight into user experience and outcomes.</div></div><div><h3>Results</h3><div>The study revealed a significant increase in nurses’ confidence in using AI—from 35.9 % before training to 81.3 % after training (<em>p</em> &lt; 0.001)—along with a notable reduction in clinical response time (from 21.4 s to 13.0 s).</div></div><div><h3>Conclusion</h3><div>Simulation-based training involving AI tools effectively improves nurses’ clinical competencies and confidence, contributing to enhanced patient safety and operational efficiency. To support successful AI integration in nursing practice, healthcare institutions must address training gaps and regulatory barriers. Future initiatives should focus on implementing structured educational programs and developing clear policies to facilitate the ethical and efficient adoption of AI technologies in clinical settings.</div></div>","PeriodicalId":46153,"journal":{"name":"Journal of Nursing Regulation","volume":"16 3","pages":"Pages 242-248"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271104","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":"Regulatory Reflection: AI, cybersecurity, and the new tools of the trade","authors":"Adrian Guerrero CPM","doi":"10.1016/j.jnr.2025.08.014","DOIUrl":"10.1016/j.jnr.2025.08.014","url":null,"abstract":"<div><div>This article explores the transformative role of artificial intelligence (AI) and cybersecurity in the evolving landscape of nursing regulation. As regulatory agencies manage increasingly complex responsibilities, AI offers opportunities to enhance decision-making, streamline processes, and deliver more consistent and efficient public protection. From automating licensure workflows to enabling predictive analytics, AI is emerging as a powerful advisory tool for boards of nursing while maintaining the necessity of human oversight. Simultaneously, cybersecurity has become a critical priority, requiring regulators to adopt proactive strategies to safeguard sensitive data, strengthen governance, and ensure ethical implementation of emerging technologies. Highlighting initiatives at the Kansas State Board of Nursing, the article underscores the need for cultural adaptation, workforce training, and cross-jurisdictional collaboration. It calls on regulators to embrace digital literacy, demand algorithmic transparency, and prepare for a future where AI-driven insights and robust cybersecurity frameworks become integral to effective, ethical, and innovative regulation.</div></div>","PeriodicalId":46153,"journal":{"name":"Journal of Nursing Regulation","volume":"16 3","pages":"Pages 139-140"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271109","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}