Journal of Doctoral Nursing Practice最新文献

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.

Background: Inappropriate use of observation services for acute hospitalizations can lead to decreased reimbursement for care. Traditional evidence-based criteria are restrictive and do not consistently consider patients' preexisting conditions at the time of hospital arrival, highlighting the need for better utilization management (UM) and decision-making in assigning observation service versus inpatient admission. Objective: Guided by Neuman's Systems Model, the intervention aims to evaluate whether the implementation of an artificial intelligence (AI) tool in a UM registered nurse (RN) department can reduce health system observation service rates by enhancing the identification of patients' comorbidities, as well as improving the assessment of medical necessity and severity of illness for determining inpatient appropriateness in a large academic health system. Methods: Pre- and postimplementation observation versus inpatient volumes at discharge and observation-to-inpatient conversions were compared. Results: Postimplementation observation service discharge rates (12.75% monthly average) were lower compared with preimplementation observation service discharges (16.69% monthly average). UM RNs played a central role in the intervention, using the AI-generated Care Level Score to guide conversations with providers and advocate for appropriate patient placement. Conclusion: The implementations for nursing of an AI tool in the UM review process effectively reduced observation service discharge rates by improving the identification of comorbidities and enhancing the assessment of medical necessity. This approach demonstrated potential for better decision-making in recommending inpatient appropriateness and reducing observation service volume.

Background: Traditional college-age students are a high-risk group for sexually transmitted infections (STI). Objective: The aim of the article is to report on a project to reduce STI prevalence by improving sexual health care for all students accessing a Midwestern university student health clinic. Methods: A quality improvement design was used to implement evidence-based sexual health care by (a) administering a 10-item sexual health risk assessment through the electronic patient portal and (b) formatting the electronic health record (EHR) to display student preferred gender identity and sexual orientation. Results: Of the students accessing the health clinic, 68% completed the sexual health risk assessment. While 38% reported having a new partner within the last 90 days, only 12% had STI testing in the past 12 months. After the implementation, there was a 45% increase in the number of students who completed STI testing. Conclusions: The EHR sexual health risk assessment and chart banner updates were effective strategies to increase STI screening among students who are at high risk. Implications for Nursing: A standardized sexual health risk assessment assures high-quality, individualized care by identifying risk and knowledge deficiencies while recognizing and acknowledging gender identity and sexual orientation. A comprehensive, inclusive approach to college sexual health can decrease STI burden.

Background: Adults with attention-deficit/hyperactivity disorder (ADHD) are at risk for higher rates of substance use, suicide attempts, and accidental injuries than their non-ADHD peers. Treatment can be challenging due to the core features of ADHD which include forgetting to take medication, with approximately 42% of patients not refilling their medications as prescribed. Using a medication reminder app is a strategy shown to improve medication nonadherence. Objective: The aim of this article is to analyze the effects that a medication reminder app will have on medication adherence for adults with ADHD. Methods: Documentation of days between stimulant refills occurred for 3 months prior to the intervention and again for 3 months following the intervention. Results: Mean days between refills preintervention was 46. The mean days between refills postintervention was 34 days. Paired t test compared refill frequency pre- and postintervention indicating statistically significant improvement (p = .02). An effect size of 0.96 shows that the use of the app influenced the number of days between refills in this population. Conclusions: This project demonstrated that using a medication reminder app increases medication adherence. Implications for Nursing: Medication reminder apps can positively impact medication adherence and are easily implemented.

Background: Depression affects approximately 280 million people worldwide. In the United States, about 33% of adults with major depressive disorder abuse substances, and the economic burden is approximately $237 billion. Research recommends universal screening in the adult population. Objectives: The aim of this project was to improve effective depression screening and care in patients with substance use disorder, review the existing literature, audit patients' charts to identify gaps, analyze data, draw conclusions, and make recommendations. Methods: Four Plan-Do-Study-Act quality improvement cycles were implemented over 8 weeks. Two core interventions were used to improve depression screening and increase effective care. Two tests of change (TOC) occurred each cycle, and qualitative and quantitative data were collected and analyzed for the next TOC. Results: Depression screening and effective care increased to 88% from a baseline of 13%. Depression screening improved to 95%, with 90% of positive patients reporting mild to severe depression. Overall, 82% of patients received effective care. Conclusions: Effective depression screening and care improved with team and patient engagement, early intervention, following guidelines, and best practices. Sustaining effective depression management will require utilizing evidence-based interventions to reduce the high depression positivity rate. Implications for Nursing: As mental health gatekeepers, universal depression screening and appropriate referrals for continuity of care are recommended to reduce disease burden.