7 DISCUSSION

REQUIRED SOURCES:

· American Nurses Association. (2022).  Nursing informatics: Scope and standards of practice Links to an external site.  (3rd ed.).

· “Standard 5: Implementation” (pp. 85–87)

· “Standard 5a: Coordination of Activities” (pp. 87–88)

· “Standard 6: Evaluation” (pp. 89–90)

· “Standard 9: Communication” (pp. 94–96)

· “Standard 11: Leadership” (pp. 97–98)

· “Standard 16: Resource Utilization” (pp. 103–105)

· Billingsley, L., Calderon, A., & Agosta, L. (2024).  Transforming health care: Exploring artificial intelligence integration, data governance, and ethical considerations in nursingLinks to an external site. .  Journal of Radiology Nursing,  43(2), 107–111. https://doi.org/10.1016/j.jradnu.2024.04.002

· Calik, A., Ozkul, D., & Kapucu, S. (2024).  Smart glasses use experience of nursing graduate students: Qualitative studyLinks to an external site. .  BMC Nursing,  23(1), 1–10. https://doi.org/10.1186/s12912-024-01852-w

· Dordunoo, D., Limoges, J., Chiu, P., Puddester, R., Carlsson, L., & Pike, A. (2023).  Genomics-informed nursing strategies and health equity: A scoping review protocolLinks to an external site. .  PLoS ONE,  18(12), e0295914. https://doi.org/10.1371/journal.pone.0295914

· Krupp, A., & Lopez, K. D. (2023).  Leveraging implementation science with using decision support technology to drive meaningful change for nurses and nursing leadershipLinks to an external site. .  Nurse Leader,  21(6), 636–640. https://doi.org/10.1016/j.mnl.2023.08.003

· Madi, M., Nielsen, S., Schweitzer, M., Meyer, G., Langensiepen, S., Stephan, A., Siebert, M., & Körner, D. (2024).  Acceptance of a robotic system for nursing care: A cross-sectional survey with professional nurses, care recipients and relativesLinks to an external site. .  BMC Nursing,  23(1). https://doi.org/10.1186/s12912-024-01849-5

INSTRUCTIONS:

· Review the Learning Resources associated with the topics: AI, Machine Learning, Genomics, Precision Health, and Robotics.

· Consider the role of these technologies in your healthcare organization or nursing practice.

· Analyze the differences of these technologies as they may impact healthcare delivery and nursing practice.

· Reflect on the potential use of each of these topics and your personal experiences with their implementation into practice.

Post a response to your blog for the following:  

· Choose one of the five topics: AI, Machine Learning, Genomics, Precision Health, or Robotics, how has this technology been applied in your practice or in the lives of your patients

· How do you think this innovation will impact the future of your practice?

Read a selection of your colleagues’ blog posts and respond to at least two of your colleagues on two different days by expanding upon their responses or sharing additional or alternative perspectives. 

POST 1:

Artificial Intelligence in Psychiatric Mental Health Nursing: Transforming Patient Care and Clinical Practice

Among the five innovative technologies, artificial intelligence (AI), machine learning, genomics, precision health, and robotics, I chose to focus on artificial intelligence because it is the innovation I encounter most frequently in my daily practice as a Psychiatric Mental Health Nurse Practitioner (PMHNP) in an inpatient psychiatric hospital. While each of these technologies has the potential to improve healthcare delivery, AI has had the most direct and noticeable impact on my clinical workflow and the care my patients receive. I believe AI is uniquely positioned to strengthen psychiatric nursing practice while allowing providers to spend more meaningful time with patients. Its growing influence on patient outcomes and healthcare delivery is why I selected AI over the other emerging technologies.

Artificial intelligence (AI) has increasingly become part of psychiatric nursing practice by supporting clinical decision-making, improving documentation, and enhancing patient safety (Milasan & Scott-Purdy, 2025). As a Psychiatric Mental Health Nurse Practitioner (PMHNP) working in an inpatient psychiatric hospital, I have seen AI integrated into the electronic health record (EHR) through clinical decision support systems that alert providers to potential drug interactions, allergies, duplicate medications, and other patient safety concerns (Choudhury & Asan, 2020). AI-assisted suicide risk screening tools and predictive analytics are also beginning to identify patients who may be at greater risk for self-harm, aggression, seclusion, restraint, or psychiatric readmission, allowing the interdisciplinary team to intervene earlier. In the lives of our patients, AI has also been applied through mental health mobile applications, virtual cognitive behavioral therapy (CBT) programs, medication reminder systems, symptom-tracking platforms, and chatbots that provide coping strategies and crisis resources between appointments. These technologies help patients remain engaged in treatment, monitor their symptoms, and access support outside the hospital while complementing the therapeutic relationship with mental health providers (Haleem et al., 2021).

I believe AI will significantly influence the future of my practice as a PMHNP by making psychiatric care more personalized, proactive, and efficient. AI has the potential to analyze large amounts of patient data, including previous psychiatric history, medication responses, laboratory findings, and social determinants of health to support individualized treatment planning and improve clinical outcomes (Fahim et al., 2025). It may also reduce administrative burdens such as documentation and chart review, giving providers more time for direct patient care and therapeutic communication. Additionally, AI can strengthen telepsychiatry services and improve access to mental health care for individuals living in rural and underserved communities (Perez et al., 2025) Despite these benefits, AI should never replace the clinical judgment, ethical reasoning, and therapeutic presence of a PMHNP. Instead, it should serve as a decision-support tool that enhances evidence-based practice while allowing clinicians to continue delivering compassionate, patient-centered psychiatric care.

Reference

 Choudhury, A., & Asan, O. (2020). Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR medical informatics, 8(7), e18599.  https://doi.org/10.2196/18599 Links to an external site.

Fahim, Y. A., Hasani, I. W., Kabba, S., & Ragab, W. M. (2025). Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives. European journal of medical research, 30(1), 848.  https://doi.org/10.1186/s40001-025-03196-w Links to an external site.

Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2021). Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors international, 2, 100117. https://doi.org/10.1016/j.sintl.2021.100117

Milasan, L. H., & Scott-Purdy, D. (2025). The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review. International journal of mental health nursing, 34(1), e70003. https://doi.org/10.1111/inm.70003

Perez, K., Wisniewski, D., Ari, A., Lee, K., Lieneck, C., & Ramamonjiarivelo, Z. (2025). Investigation into Application of AI and Telemedicine in Rural Communities: A Systematic Literature Review. Healthcare (Basel, Switzerland), 13(3), 324. https://doi.org/10.3390/healthcare13030324

POST 2:

Of the five topics, I chose Artificial Intelligence (AI), which is increasingly woven into everyday nursing practice and whose influence is especially visible in clinical decisionmaking, documentation, and patient monitoring (El Arab et al., 2025). In my clinical environment, AIenhanced tools support early identification of patient deterioration, streamline documentation, and reduce cognitive burden (El Arab et al., 2025). These applications reflect the broader trends described by Billingsley et al. (2024), who emphasize that AI is transforming nursing workflows while raising important considerations around data governance and ethical use.

In practice, AIdriven clinical decision support systems help nurses interpret complex data more efficiently (Madi et al., 2024). For example, predictive algorithms embedded in the electronic health record generate alerts for sepsis risk, abnormal lab patterns, or potential medication interactions (Madi et al., 2024). These tools enhance situational awareness and allow nurses to intervene earlier, improving patient outcomes. AIsupported documentation assistants also reduce charting time by suggesting structured language or capturing clinical details from nursepatient interactions (Madi et al., 2024). This shift allows more time for direct patient care, aligning with the profession’s emphasis on relational practice. Patients benefit from AI through more personalized and timely care (El Arab et al., 2025). Earlywarning systems, for instance, can detect subtle physiological changes before they become clinically obvious, prompting rapid assessment and intervention (Madi et al., 2024). Automated reminders and patientfacing digital tools also support adherence to treatment plans and improve health literacy. These innovations contribute to a more proactive and individualized model of care, consistent with the movement toward precision health (El Arab et al., 2025).

In the coming times, AI will continue to reshape nursing practice. As Krupp and Lopez (2023) note, successful integration of decisionsupport technologies requires thoughtful implementation strategies that prioritize usability and nurse engagement. Nurses will increasingly need competencies in data literacy, algorithmic reasoning, and ethical evaluation of AI systems (El Arab et al., 2025). Additionally, emerging technologies such as augmentedreality smart glasses, explored by Calik et al. (2024), may soon enhance realtime access to clinical information and support complex procedures. Ultimately, AI will not replace the nurse’s clinical judgment but will augment it, enabling more efficient workflows and more precise, patientcentered care (Nashwan et al., 2024).

References

Billingsley, L., Calderon, A., & Agosta, L. (2024).  Transforming health care: Exploring artificial intelligence integration, data governance, and ethical considerations in nursing Links to an external site. . Journal of Radiology Nursing, 43(2), 107–111.  https://doi.org/10.1016/j.jradnu.2024.04.002 Links to an external site.

Calik, A., Ozkul, D., & Kapucu, S. (2024).  Smart glasses use experience of nursing graduate students: Qualitative study Links to an external site. . BMC Nursing, 23(1), 1–10.  https://doi.org/10.1186/s12912-024-01852-w Links to an external site.

Dordunoo, D., Limoges, J., Chiu, P., Puddester, R., Carlsson, L., & Pike, A. (2023).  Genomics-informed nursing strategies and health equity: A scoping review protocol Links to an external site. . PLoS ONE, 18(12), e0295914.  https://doi.org/10.1371/journal.pone.0295914 Links to an external site.

El Arab, R. A., Al Moosa, O. A., Sagbakken, M., Ghannam, A., Abuadas, F. H., Somerville, J., & Al Mutair, A. (2025). Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions. Frontiers in public health, 13, 1619378.  https://doi.org/10.3389/fpubh.2025.1619378 Links to an external site.

Krupp, A., & Lopez, K. D. (2023).  Leveraging implementation science with using decision support technology to drive meaningful change for nurses and nursing leadership Links to an external site. . Nurse Leader, 21(6), 636–640.  https://doi.org/10.1016/j.mnl.2023.08.003 Links to an external site.

Madi, M., Nielsen, S., Schweitzer, M., Meyer, G., Langensiepen, S., Stephan, A., Siebert, M., & Körner, D. (2024). Acceptance of a robotic system for nursing care: A cross-sectional survey with professional nurses, care recipients and relatives. BMC Nursing, 23(1).  https://doi.org/10.1186/s12912-024-01849-5 Links to an external site.

Nashwan, A. J., Abujaber, A., & Ahmed, S. K. (2024). Charting the Future: The Role of AI in Transforming Nursing Documentation. Cureus, 16(3), e57304.  https://doi.org/10.7759/cureus.57304 Links to an external site.

RUBRIC:

NURS_8210_Week7_Blog_Rubric

NURS_8210_Week7_Blog_Rubric
Criteria	Ratings	Pts
This criterion is linked to a Learning OutcomeMain Posting: Idea and Content (40 points)	40 to >29.0 ptsExcellent• Thoroughly responds to the blog prompt/s. • Post provides comprehensive insight, understanding, or reflection about the topic through a focused analysis of the topic supported by personal experiences and/or examples. • Personal opinions are expressed and are clearly related to the topic, activity or process identified in blog prompts. • The post reflects in-depth engagement with the topic. • Detailed reply to faculty. 29 to >19.0 ptsGood• Responds to all of the blog prompt/s. • Post provides insight, understanding, or reflection about the topic through a reasonably focused analysis of the topic supported by personal experiences and/or examples. • Personal opinions are expressed and are but not fully developed to align with blog prompts. • The post reflects moderate engagement with the topic. • Appropriate reply to faculty. 19 to >9.0 ptsFair• Partially responds to the blog prompt/s. • Posts are typically short and may contain some irrelevant material. • The post is mostly description or summary without connections or analysis between ideas. • The post reflects minimal engagement with the topic. • Brief response to faculty with minimal effort. 9 to >0 ptsPoor• Does not respond to the blog prompt/s or entries lack insight, depth or are superficial. • The entries are short and are frequently irrelevant to the events. • They do not express opinion clearly and show little understanding. • The post does not reflect engagement with the topic. • Fails to respond to faculty inquiries.	40 pts
This criterion is linked to a Learning OutcomeFirst Response: (30 points) Post to classmate’s main blog post shows evidence of insight, understanding, or reflective thought about the topic.	30 to >21.0 ptsExcellent• Presents a focused and cohesive viewpoint in addressing this response. • Response includes focused questions or examples related to classmate’s post. • Response stimulates dialogue and commentary. • Posts on separate day. 21 to >12.0 ptsGood• Presents a specific viewpoint that is focused and cohesive. • Response includes at least one focused question or example related to classmate’s post. • There is some attempt to stimulate dialogue and commentary. • Posts on separate day. 12 to >4.0 ptsFair• Presents a specific viewpoint but lacks supporting examples or focused questions related to classmate’s post. • The posting is brief and reflects minimal effort to connect with classmate. • Posts on separate day. 4 to >0 ptsPoor• Response lacks a specific viewpoint and supporting examples or focused questions related to classmate’s post. • The post does not stimulate dialogue or connect with the classmate. • Posts on same day.	30 pts
This criterion is linked to a Learning OutcomeSecond Response: (30 points) Post to second classmate’s blog post shows evidence of insight, understanding, or reflective thought about the topic.	30 to >21.0 ptsExcellent• Presents a focused and cohesive viewpoint in addressing this response. • Response includes focused questions or examples related to classmate’s post. • Response stimulates dialogue and commentary. • Posts on separate day. 21 to >12.0 ptsGood• Presents a specific viewpoint that is focused and cohesive. • Response includes at least one focused question or example related to classmate’s post. • There is some attempt to stimulate dialogue and commentary. • Posts on separate day. 12 to >4.0 ptsFair• Presents a specific viewpoint but lacks supporting examples or focused questions related to classmate’s post. • The posting is brief and reflects minimal effort to connect with classmate. • Posts on separate day. 4 to >0 ptsPoor• Response lacks a specific viewpoint and supporting examples or focused questions related to classmate’s post. • The does not stimulate dialogue or connect with the classmate. • Posts on same day.	30 pts

Total Points: 100