discussion reply

Reply to two of your classmates. Your reply to posts should:

  • Ask a clarifying question about the video discussed.
  • Share another video relevant to the discussion that was created within the last two years.
  • Doctoral education requires you to address and approach your classmates and professors in a professional and respectful manner. It is necessary to refer to them by the names they use and be consistent throughout the course.

Discussion 1:

This video focuses on new healthcare technology advances from information learned during the COVID-19 pandemic. Artificial intelligence (AI) and machine learning (ML) can help diagnose, monitor, and track patient health with individualized care plans. Data points collected from ML can also forecast, trend, and predict future health events. Predictive analytics are used to increase the accuracy of remote monitoring. AI is used for medical image analysis for consistency, accuracy, and time efficiency. ML can quickly assemble differing drug combinations for treatment to identify the best therapy. Healthcare systems are using the cloud to store massive amounts of information. The Internet of Medical Things (IoMT) connects patients and physicians by providing real-time condition data. IoMT also assists with medical record linking, digital health products such as apps for heart monitors, health alerts, fitness, health, in-home patient monitoring with wearables, smart clothing and glucose trackers, heart rate, and calorie consumption. Clinics are using telemedicine, video, and smart devices to attend to patients who may typically have a difficult time getting to the office. Smart implant technology, such as 3D printing, is making prosthetics lighter and more accessible and customizable. Additionally, Virtual reality is used to help medical students develop skills and therapy patients perform therapy at home (ISI Technology, n.d.).

Healthcare Improvement in Multiple Settings
Three forms of SI in patient care include clinical, operational, and behavioral analytics (McGrow, 2019). Clinical AI includes clinical pathway and disease progression predictions, health risk prediction and scoring, and virtual assistants that help with workflow improvement. Operational AI improves management care processes by predicting and tracking operational issues, safety metrics, supply chain monitoring, identity fraud, and equipment maintenance. Behavioral AI focuses on behavioral patterns related to patient engagement, well-being, health, and readmissions (McGrow, 2019). A review by Seibert et al. (2021) found AI applications used in healthcare settings that support patient care coordination, communication, nursing rosters and schedules, fall detection and classification, and pressure ulcer prediction and classification.

Patient Perspective
Although there are many avenues where AI could benefit healthcare, respondents in a study by Khullar et al. (2022) mostly had positive views but also voiced concerns about misdiagnosis, decreased personal time with clinicians, increased costs, and privacy breaches (Khullar et al., 2022). Also, although many AI capabilities, such as wearables, can be highly beneficial to alerting health professionals to concerns, some populations find the technology challenging and confusing and prefer more personal encounters with clinical personnel. As technology advances, I worry that people who prefer not to use AI will be left behind or forgotten.


ISI Technology. (n.d.). Healthcare technology trends in 2023 [Video]. YouTube.

Khullar, D., Casalino, L. P., Qian, Y., Lu, Y., Krumholz, H. M., & Aneja, S. (2022). Perspectives of patients about artificial intelligence in health care. JAMA Network Open, 5(5), e2210309.

McGrow, K. (2019). Artificial intelligence. Nursing, 49(9), 4649.

Seibert, K., Domhoff, D., Bruch, D., Schulte-Althoff, M., Frstenau, D., Biessmann, F., & Wolf-Ostermann, K. (2021). Application scenarios for artificial intelligence in nursing care: Rapid review. Journal of Medical Internet Research, 23(11), e26522.

Discussion 2:

What relevant information did you find out in the video you chose?

According to an interview on the American Nurses Association (ANA) (2023) YouTube channel, nursing students are not ready for the workforce when they graduate and dont feel comfortable functioning independently within the clinical environment. Registered nurses (RN) leave the profession because of their work environment and perceived inadequacy or diminished comfort. Because of this, academia needs to do a better job of educating nursing students. The Ohio State University (OSU) has integrated extended reality (XR), artificial intelligence (AI), and machine learning (ML) into its curriculum. An example is that students can see multiple patients and practice prioritizing care and intervening when patients are deteriorating with the assistance of virtual reality (VR). OSU has plans to develop toolkits for other schools to use as they are getting started with this new and relevant technology, as well as offering micro-credentialing for educators to increase their knowledge of application and learning strategies. Rapid-changing medical environments have brought equally rapid changes in technology, especially during the pandemic, which brought virtual reality to the front of nursing education (Bodur, Turhan, Kucukkaya, & Goktas, 2024).

How do you feel that AI, robots, and implantable devices may improve nursing care in multiple settings? (Provide one reference)

I feel that AI, specifically used in VR and ML in nursing education will improve learning on multiple levels, which will translate to the clinical environment and better patient outcomes. The better prepared the students are as new graduates, the better it is for the healthcare profession. A current, technology-integrated curriculum will result in higher quality care given to patients, and increased confidence and skills for nurses. In a recent study, most students indicated that VR was immersive and realistic and recommended incorporating it in learning clinical skills (Choi, J., & Thompson, C. E. (2023).

Bodur et al., (2024) identified a gap in understanding how student nurses perceptions of new technology relate to their development of learning skills. A study will 309 nursing students was conducted using a blend of survey scales and a tree-based machine-learning model to measure and analyze students views, attitudes, and self-directed learning levels. Results indicated positive reviews of VR technology and high-level self-directed learning skills. Males scored higher than females in reviews. The results support the use of VR in nursing curriculums and highlight the importance of customizing teaching strategies based on insights from machine learning analyses. Taking this approach has the potential to improve both the overall educational experience of students and their quality of nursing education.

What do you think about the patient’s perspective in response to these recent changes?

Advanced technologies are vital to meet the changing needs of emerging healthcare and understanding is particularly important in medical education, where integrating technology can impact teaching strategies and prepare new generations of nursing students for future healthcare challenges (Bodur, Turhan, Kucukkaya, & Goktas, 2024). Using VR to potentially shape and improve the complexities of patient care and improve students’ self-directed learning abilities is necessary for current healthcare trends (Bodur, Turhan, Kucukkaya, & Goktas, 2024). I think enhancing nursing student learning with the latest technology and VR software would be fully supported by patients and the community. Students should learn in a realistic simulated environment to improve skills, critical thinking, and prioritization in a controlled environment before being expected to work independently with real patients in their first role as staff nurses.


American Nurses Association (ANA). (2023, Mar 23). Disrupting Nursing Education With XR, AI and ML. [Video]. YouTube.

Bodur, G., Turhan, Z., Kucukkaya, A., & Goktas, P. (2024). Assessing the virtual reality perspectives and self-directed learning skills of nursing students: A machine learning-enhanced approach. Nurse Education in Practice, 75, N.PAG.

Choi, J., & Thompson, C. E. (2023). Faculty Driven Virtual Reality (VR) Scenarios and Students Perception of Immersive VR in Nursing Education: A Pilot Study. AMIA … Annual Symposium Proceedings. AMIA Symposium, 2022, 377384.