Journal of Doctoral Nursing Practice最新文献

Background: Artificial intelligence (AI) offers promising solutions for nurse practitioner education, especially in addressing challenges related to evaluating Objective Structured Clinical Examinations (OSCEs), such as examiner bias and delayed feedback. AI tools employing natural language processing and generative AI have the potential to enhance the accuracy and efficiency of clinical assessments. Objective: This product evaluation was conducted to determine whether AI-generated OSCE assessments align with faculty evaluations. Methods: A descriptive correlational design was used to assess product acceptability, feasibility, and agreement between AI and faculty assessments. A convenience sample of 13 nurse practitioner students was randomly divided into either a traditional evaluation group or an AI-assisted group. The AI-generated transcripts were scored using the same rubric used by faculty, and agreement was measured with Spearman's correlation, Cohen's Kappa, and interrater reliability percent agreement (IRR%). Results: Spearman's correlations ranged from negligible to moderate, with the highest in the physical/mental health exams category (r = .54, p < .05). However, Cohen's Kappa (.14-.41) and IRR% (31%-54%) showed weak agreement. Conclusions: These results suggest that AI feedback was inconsistent with faculty assessments, possibly due to technical issues and limitations in the rubric. Despite these limitations, this product evaluation demonstrated that the AI tool was easy to use and that faculty believed it could improve feedback quality. Implications for Nursing: These findings underscore both the promise and the current limitations of AI-supported clinical assessment. With thoughtful attention to these shortcomings, the ease of integration and capacity for enhanced learning offered by AI tools can help advance clinical competence and foster excellence in nurse practitioner and doctoral nursing education.

Background: The lack of palliative care education and communication among frontline nurses impacts care for patients with advanced chronic diseases. Literature suggested that additional educational approaches positively influence nurses' knowledge and communication. Objective: The aim of this study was to augment their understanding of palliative care and enhance their self-efficacy in palliative care communication by an innovative educational intervention tailored for acute care nurses. Methods: A total of 13 participants completed multimodal education, including a 50-minute recorded lecture, a virtual reality-based clinical vignette, and a laminated infographic badge. Four outcomes were measured pre- and postimplementation. Results: Engagement in palliative care discussions was increased by approximately 24.4%. More than 60% of participants expressed extreme presence in a virtual reality environment, and 92% of them would use virtual reality again in the future. Conclusion: Multimodal palliative education integrated with cutting-edge technology successfully helped acute care nurses engage in more palliative care discussion. Virtual reality was well accepted as an educational method. Expanding this educational approach may increase utilization of palliative care services, reduce health care costs, and prevent human suffering from unnecessary medical services near the end of life. Implications for Nursing: Innovative approaches to palliative care education such as virtual reality can be used as a successful educational modality for registered nurses.

Background: Adverse symptoms associated with menopause can resemble burnout symptoms. Resilience and menopausal symptom-related burnout have not been addressed in middle-aged climacteric women. Objectives: The aim of the study is to investigate the association between resilience and burnout in middle-aged climacteric women. Methods: Two hundred middle-aged women aged 44-55 years were recruited through an online survey panel developed by a survey company. Data were collected using Korean versions of the Copenhagen Burnout Inventory's Personal Burnout Instrument and Brief Resilience Scale. Results: Resilience, living alone, and the absence of menopausal symptoms were significantly associated with reduced burnout. In contrast, the perception of oneself as unhealthy and a body mass index range representing underweight were significantly associated with increased burnout. Findings using mean analysis showed that the more severe the menopausal symptoms are, the higher the burnout score is. Conclusions: Resilience, menopausal symptoms, living alone, the perception of unhealthiness, and a very low body weight should be considered important factors when addressing burnout among middle-aged climacteric women in community and clinical practice. Implications for Nursing: The relationships among burnout, menopausal symptoms, and resilience should be considered in clinical practice.

Background: The rapid integration of artificial intelligence (AI) into health care and education is reshaping the landscape of nursing practice. For Doctor of Nursing Practice (DNP) educators, this evolution demands a reimagining of how faculty teach, lead, conduct research, and prepare future nurse leaders. Objective: This manuscript examines the transformative role of AI in DNP education, emphasizing strategies to support faculty development and successful integration. Methods: Drawing on current literature and expert insights, the manuscript outlines institutional strategies to support faculty in adopting AI. Key barriers addressed include ethical concerns, data security, and resistance to change. Results: AI enhances teaching strategies, research capacity, leadership development, and professional growth. Recommendations are offered for embedding AI literacy in DNP curricula and strengthening faculty competencies in ethical and effective AI use. Conclusions: Faculty teaching in DNP programs must be prepared to lead AI integration. Faculty development and institutional support are critical to ensure alignment with nursing's professional and ethical standards. Implications for Nursing: DNP educators are uniquely positioned to champion AI adoption, ensuring innovations in nursing remain grounded in the core values of integrity, equity, and humanism. Building AI literacy among faculty is essential for sustaining excellence in education and advancing the profession in the digital age.

Background: The rapid integration of artificial intelligence (AI) into health care systems offers new opportunities for advanced practice registered nurses (APRNs). However, this innovation also introduces a variety of concerns regarding ethical and responsible use and implications for patient care, privacy, data integrity, clinical systems, and professional practice. Ethical implications associated with the integration of AI in health care require critical examination by APRNs. Objective: This article examines ethical, legal, and social implications of integrating AI in advanced practice nursing. Grounded in the American Nurses Association Code of Ethics and the American Association of Colleges of Nursing Essentials, it provides practical strategies for APRNs to engage with AI technologies in ways that uphold core values such as patient autonomy, equity, trust, and human connection. Methods: A narrative review of the literature was conducted, supplemented by an analysis of current AI tools and illustrative case studies, to identify ethical considerations and educational strategies for implementing AI in advanced practice nursing. Real-world examples, including clinical decision support systems, predictive analytics, and AI-assisted documentation, were examined to highlight the potential benefits and risks of AI integration. Results: AI tools can enhance diagnostic accuracy, streamline documentation, and improve patient care. However, ethical risks include biased algorithms, privacy breaches, and depersonalized care. Case examples from inpatient emergency care, outpatient primary care, and academic settings illustrate how APRNs can mitigate these risks through transparent communication, interdisciplinary collaboration, and targeted technology training. Key themes that emerged from the literature highlight the potential of AI to enhance diagnostic accuracy, streamline clinical documentation, and improve patient education. However, significant ethical concerns were also identified, including algorithmic bias, data privacy risks, and the potential for depersonalized care. Conclusions: APRNs will play a critical role in ensuring that AI is used ethically, responsibly, and effectively. By actively engaging in implementation efforts, they help balance technological innovation with preserving human interaction and clinical judgment. Implications for Nursing: APRNs must take the lead in driving the ethical integration of artificial intelligence by shaping policies, advancing education, and implementing clinical strategies that protect patient safety, promote equity, and uphold professional values. To lead effectively in this rapidly changing landscape, they must actively develop AI competencies, engage in interdisciplinary collaboration, and advocate for responsible, patient-centered innovation in practice.

Background: Artificial intelligence (AI) has rapidly emerged as both an opportunity and a challenge for nursing education. As AI tools become increasingly prevalent in clinical practice, Doctor of Nursing Practice (DNP) programs must reconsider traditional pedagogical strategies. Embedding competencies in informatics and technology into curricula is essential to align with the American Association of Colleges of Nursing Essentials and evolving health care practice. Objective: This article aims to present a practical instructional design framework that guides the integration of AI competencies into DNP curricula, preparing graduates to lead innovation while maintaining the unique identity and impact of the DNP-prepared nurse. Methods: A combined instructional design approach synthesizing Bloom's Revised Taxonomy (BRT) and the Substitution, Augmentation, Modification, and Redefinition (SAMR) model was developed. Bloom's taxonomy provides a structured method for creating learning outcomes across cognitive complexity levels and knowledge domains. The SAMR model complements this taxonomy by scaffolding technology integration from basic enhancement to transformative applications. Results: Synthesizing BRT and SAMR highlighted a flexible instructional design blueprint that aligns cognitive progression with levels of AI engagement. This pairing supports educators to intentionally embed AI-related competencies into coursework, ensuring students advance in both critical thinking and technological fluency. Recent literature reinforces the need for structured AI competencies in DNP education, highlighting the value of frameworks that integrate cognitive and technological dimensions. Conclusions: Deliberate use of BRT and SAMR together offers a practical strategy for embedding AI into nursing curricula. This approach equips faculty with a tool aimed at intentional design that balances cognitive learning outcomes with meaningful AI integration. Implications for Nursing: Adopting this framework allows nursing educators to enhance students' critical thinking, promote technological fluency, and prepare DNP-prepared nurses to effectively leverage AI in health care settings. Through structured instructional design, programs can ensure graduates are ready to navigate and lead innovation in a technology-driven health care climate.

Background: Depression affects nearly 20% of the American population and incurs an economic burden of more than $300 billion in annual costs. According to the World Health Organization, more than 300 million people are affected worldwide. The U.S. Preventive Services Task Force recommends all patients over 18 years be screened for depression at least annually. Objective: At a rural primary care clinic, only 1.2% of patients were being screened for depression with no formal protocol for performing depression screenings. The purpose of the project was to implement an evidence-based depression screening protocol to improve depression screening, diagnosis, and treatment. Methods: The knowledge-to-action model served as the framework, while a retrospective chart review was used to aggregate data. Descriptive statistics were used for analysis. Interventions: A validated screening instrument, self-administration of the instrument, chart reminders, workflow redesign, and a treatment-decision algorithm were all used. Results: The overall screening rate improved from 1.2% to 35%. Depressive disorders were identified in 23% of the patient population. Among those screened, treatment consisted of a combination of pharmacotherapy (57%), mental health counseling (8%), both pharmacotherapy and counseling (22%), and a watchful waiting approach (13%). Strengths include the ease of administration of the screening instrument and office manager support. Limitations were lack of staff buy-in, simultaneous implementation of other projects, and the lack of technology. Conclusions/Implications for Nursing: Implementation of an evidence-based workflow redesign, including self-administration of a depression screening tool, can lead to increased screening, diagnosis, and treatment of depression.

Background: Bowel cleansing is essential to the completion of colonoscopy. Inadequate bowel preparations were occurring below the local site's goals of 95%. Inadequate bowel preparations run the risk of bowel perforation and missed detection of abnormal tissue. Objective: The aim of this quality improvement (QI) initiative was to improve adequate bowel preparation to 95%. Methods: Through the utilization of technology, a QI initiative was instituted at the local level to improve adequate bowel preparation rates. Results: Results demonstrated an average of 91% adequate bowel preparations in the 6-month postintervention period compared to 89% adequate bowel preparations in the preintervention period. Conclusions: Evidence-based interventions to improve bowel preparation quality should be implemented for all individuals undergoing colorectal cancer screening with colonoscopy to improve patient outcomes. Implications for Nursing: Evidence supports the use of both nurse-led education and technology-based interventions for the reinforcement of education prior to beginning bowel preparation for colonoscopy.

Background: Hepatitis B (HepB) and human papillomavirus (HPV) are both vaccine-preventable sexually transmitted infections. However, according to the National Immunization Surveys, 13-17-year-olds are more likely to be protected against HepB than HPV. Objective: The purpose of this article is to report on a quality improvement project aimed at identifying traditional college-age students who lack protection against HPV and Hepatitis B, and to address their need for vaccination education. Methods: A quality improvement project implemented two strategies via new electronic health record (EHR) system at a university health clinic in 2023. Results: In the fall of 2023, more students had documented protection against HepB (72%) compared with HPV (55%). Students who had their first dose of HPV vaccination >14 years (17%) were more likely to have an incomplete HPV series compared with those who had their first HPV vaccination ≤14 years. Conclusions: A college health EHR system with patient portal operability for vaccine upload and screening supports identification of students who lack protection against vaccine-preventable infections, particularly those who begin the HPV series after age 14 years. Implications for Nursing: To close the gap between HepB and HPV protection, nurses must advocate to include review of vaccination status in sexual health risk screening processes and provide education and vaccinations on campus.