please responds to these people with at least 2 paragraphs supporting or disagreeing with their work and provide references:
KK
Feb 24 5:31pm
Reply from Kuljinder Kaur
In an era of significant innovations in healthcare technology, the effective integration of AI tools into patient assessments, data analytics, and clinical reasoning is crucial. Decision-making and diverse healthcare services are significantly shaped by artificial intelligence. AI enhances advanced nursing practice competencies by facilitating interprofessional collaboration through information sharing. Access to real-time data by pharmacists, physicians, nurses, and others promotes coordinated decision-making (Rony et al., 2025). AI integration can also enhance competencies by streamlining documentation, promoting care planning, and improving workflow efficiency. They can also support decision-making during patient assessments. When AI tools are integrated into clinical decision support systems ( CDSS), nurses’ clinical reasoning improves significantly, leading to accurate diagnosis. I believe that AI tools could help achieve APNs’ competencies when appropriately employed for guidance, but not as a substitute for clinical reasoning.
Associated potential benefits of integrating AI into practice include enhanced clinical decision support. This is crucial for reducing medical errors, such as incorrect diagnoses and incorrect medication dosages, resulting in better outcomes(Rony et al., 2025). Additionally, nurses can have more time for patient engagement when workflow and documentation are streamlined by these tools. On the other hand, AI tool integration, especially in addressing the healthcare needs of underserved populations, could exacerbate healthcare disparities. Some individuals with low digital literacy may find it challenging to use AI-enabled tools, necessitating training in their use and interpretation (Atalla et al., 2025).
AI integration can also raise significant ethical concerns about patient information access and storage(Rony et al., 2025). In the event of unauthorized access to AI tools’ data storage, patients’ data and confidentiality would have been violated, potentially leading to accountability issues. Additionally, poorly configured AI algorithms and inadequate data training can lead to bias, especially in recommendations. In reality, AI tools should help and augment clinical reasoning for practitioners, but not replace human judgment.
AI may improve patient outcomes by alerting staff to missed elements in diagnosing a condition that could lead to complications (El Arab et al., 2025). Implementing AI- enabled tools in telehealth programs could help manage underserved populations remotely, thereby improving access to care. In the long run, it helps address access barriers such as transportation constraints. Additionally, readmissions and admissions are reduced due to increased follow-up.
Reflections on AI content and course context significantly strengthen my understanding of APNs’ competencies, which align with clinical decision-making. I also give confidence in evaluating AI-supported clinical recommendations. It is also key in preparing me to lead interprofessional stakeholders to support technology-driven environments.
References
Atalla, A. D. G., El-Gawad Mousa, M. A., Hashish, E. A. A., Elseesy, N. A. M., Abd El kader Mohamed, A. I., & Sobhi Mohamed, S. M. (2025). Embracing artificial intelligence in nursing: Exploring the relationship between artificial intelligence-related attitudes, creative self-efficacy, and clinical reasoning competency among nurses. BMC Nursing, 24(1), 661.
El Arab, R. A., Al Moosa, O. A., Abuadas, F. H., & Somerville, J. (2025). The role of AI in nursing education and practice: Umbrella review. Journal of Medical Internet Research, 27, e69881.
Rony, M. K. K., Das, A., Khalil, M. I., Peu, U. R., Mondal, B., Alam, M. S., … & Akter, F. (2025). The role of artificial intelligence in nursing care: An umbrella review. Nursing Inquiry, 32(2), e70023.
AS
Feb 24 5:06pm
Reply from Abasi Semakula
Artificial Intelligence and Advanced Practice Nursing Competencies
Advanced practice nursing competencies, including comprehensive patient assessment, evidence-based practice (EBP), interprofessional collaboration, leadership, and ethical decision-making, form the foundation of nurse practitioner (NP) practice. As artificial intelligence (AI) becomes increasingly integrated into healthcare, it has demonstrated measurable potential to enhance clinical reasoning, improve patient outcomes, and promote health equity when implemented with ethical oversight and professional judgment.
AI strengthens advanced patient assessment through predictive analytics and clinical decision support systems (CDSS). A scoping review examining AI-driven CDSS found evidence that machine learning-based decision support improves clinical decision-making processes, care delivery efficiency, and patient-related outcomes across multiple clinical domains. For adult-gerontology NPs managing complex patients with multimorbidity, AI-supported risk stratification enhances anticipatory judgment and supports proactive intervention. Rather than replacing provider expertise, AI serves as a decision-support adjunct that refines assessment precision and promotes preventative care.
AI-driven diagnostic tools also demonstrate improved clinical accuracy. In a multi-center diagnostic study, Shen et al. (2022) reported that AI-assisted breast cancer screening reduced false-positive findings while maintaining sensitivity. Similarly, Rajpurkar et al. (2022) demonstrated that deep learning models improved diagnostic interpretation accuracy in medical imaging. These findings indicate that validated AI systems can enhance evidence-based decision-making, improve patient safety, and reduce unnecessary interventions.
AI further supports EBP by synthesizing large datasets into actionable insights at the point of care. However, ethical challenges remain central to implementation. Cross (2024) warns that algorithmic bias in medical AI may perpetuate disparities if datasets lack adequate representation of diverse populations. Nurse practitioners must critically evaluate AI recommendations, advocate for equitable algorithm design, and safeguard patient autonomy. Transparency, privacy protection, and informed consent remain essential components of ethical AI integration.
When responsibly implemented, AI contributes to improved outcomes and reduced disparities. Evidence indicates that AI-enhanced clinical decision support improves workflow efficiency and patient-centered care measures. In underserved and rural communities, AI-driven telehealth and remote monitoring technologies may expand access to timely care. However, equitable implementation requires intentional oversight to prevent widening existing gaps in healthcare delivery.
Reflecting on this course and the evolving role of technology in healthcare has reinforced the importance of developing competency in health informatics, ethical leadership, and systems-based practice. Understanding AI applications strengthens my ability to critically evaluate emerging technologies, integrate evidence into clinical decision-making, and advocate for equitable innovation. As I continue to pursue advanced practice in a technology-driven healthcare environment, refining these competencies will support my career goal of delivering accessible, patient-centered, and evidence-based care while ensuring that technological advancement enhances, not replaces the human foundation of nursing practice.
References:
Cross, J. L. (2024). Bias in medical artificial intelligence: Implications for clinical decision-making and health equity. PLOS Digital Health, 3(1), e0000651.
Rajpurkar, P., et al. (2022). Deep learning for medical image interpretation: Validation and clinical implications. JAMA Network Open, 5(7), e2221234.
Shen, Y., Shamout, F. E., Wu, N., et al. (2022). Artificial intelligence system reduces false-positive findings in breast cancer screening: A multi-center diagnostic accuracy study. The Lancet Digital Health, 4(9), e651e661.
Susanto, A. P., Lyell, D., Widyantoro, B., Berkovsky, S., & Magrabi, F. (2023). Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: A scoping review. Journal of the American Medical Informatics Association, 30(11), 20502063.
DP
Feb 24 11:04am
Reply from Destiny Peters
Main Post:
Artificial intelligence (AI) has the potential to strengthen the performance of Advanced practice nurses (APRNs) and their clinical judgment; however, it should not be a replacement. As APRNs, it is critical that competencies such as patient assessment, education, clinical decision-making, and evidence-based practice (EBP) are mastered to provide safe and ethical care to patients. AI tools can help identify inpatient abnormal trends, flag medication interactions, and support early recognition of patient deterioration, helping APRNs make more informed decision as the point of care. AI can also aid efficiently in documentation, specifically regarding coding, aiding in accuracy, quality reporting, and reimbursement integrity (CMS 2024). However, APRNs must be accountable, and critically analyze AI recommendations to ensure that challenges of over-reliance on AI, workflow disruption, and algorithmic biass does not hinder patient care (Olawade et al., 2024). Challenges also include whether AI is ethical regarding its integration within the healthcare system. As nurses, it is important to uphold the commitment to equity and patient advocacy, and AI comes with challenges that include issues with data privacy, transparency, informed consent, bias, and accountability must all be considered to protect the patient. According to WHO, AI systems are historically biased that highlight disparities amongst patients in marginalized populations (2021). The content in this course such as the role of AI can refine competencies and support future career goals in advanced nursing practice regarding technology in the healthcare environment.
References
Centers for Medicare and Medicaid Services (CMS). (2024). ICD-10-CM official guidelines for coding and reporting. Links to an external site..
Olawade, D. B., David-Olawade, A. C., Wada, O. Z., Asaolu, A. J., Adereni, T., & Ling, J. (2024). Artificial intelligence in healthcare delivery: Prospects and pitfalls. Journal of Medicine, Surgery, and Public Health, 3, 100108.
World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. WHO. .
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