NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology

You are currently viewing NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology

Implementing the New Educational Technology 

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology

Different technological advancements have been introduced in nursing education to improve the learning outcomes of students so that they become highly skilled healthcare professionals and deliver the best healthcare services to the people. John Hopkins University is one of the most reputed institutes in the United States which offers multiple programs in nursing education. It uses various technologies like telehealth services in the health department to achieve efficiencies in learning and healthcare outcomes (Hwang et al., 2022). The use of artificial intelligence has been implemented to improve the learning of students. Artificial intelligence is a form of machine-based learning that develops the framework of critical thinking, enhances the understanding level of palliative care, and promotes cognitive thinking as well (Buchanan et al., 2021). 

The main features of AI involve machine learning, processing of language, and facial recognition which evaluate the already present data and after evaluation makes an update in it through the prediction of possible outcomes. It also suggests the best strategies to overcome future health problems that can be encountered in the future and implements quality improvement plans by promoting the clinical decision support system (Chang et al., 2022). The use of artificial intelligence has revolutionized nursing education and clinical practices by creating a visual environment through the help of human-like robots and different cyborgs. These types of visual scenarios help in the prevention of medication errors in the real clinical environment and promote patient safety (Abuzaid et al., 2022). 

Outline for Implementation of Plan 

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology

Different steps will be carried out at John Hopkins University to implement artificial intelligence in nursing education and un-certainty issues will be minimized about decision making as well as consumption of extra time during the processing. 

  • The first step is getting the healthcare and education executives to sign off by developing the best understanding of opportunities for the implementation of AI in nursing education. It will involve using different cases where AI has provided great benefits in education and ultimately in healthcare as well (Ronquillo et al., 2021). 
  • After taking approval from executives, the maturity level of the institute is assessed regarding artificial intelligence. It will involve the assessment of the organization’s workflow and administration approach for the care of patients and determines whether the organization is ready to implement artificial intelligence or not. 
  • After the identification of the readiness of the company for AI, the gaps are identified for broadening the expertise of the organization in the context of data engineering. The team will be developed comprising experts in AI, consultants, business analysts, healthcare staff, and leaders for the implementation of the technology. 
  • The team will design the interventions for the implementation of AI and then propose a solution for improving the current infrastructure of the organization. These interventions are divided into further sub-categories for achieving better results in the project.
  • A competent model is designed on which the whole workflow will be based and variables will be adjusted according to the learning preferences of students. 
  • The high quality and reliable data are collected which is a critical part of the evaluation of learning systems. The data will be collected, cleaned, and then analyzed. The storage options are also added as a fast and reliable solution to meet the organization’s objectives. 
  • The next stage is the translation of the model in which artificial intelligence develops a structural framework of healthcare concepts. The cyborgs and three-dimensional models are developed having physiology and processes similar to the humans and healthcare needs and treatment are translated on these models which will guide the students about the administration of safe care to the patients. 
  • After the translation of the model, it is verified and checked. The verification process is performed by using different virtual animations and dummies. Then validation is carried out by cross-checking and assuring that the same results will be obtained in the real-time settings as the virtual settings. This type of verification is done through different computational tools. 
  • Experiments are performed and integration of AI is done at different pilot stages by the development of different models to minimize the risks of errors in the real environment.
  • Then the results are compared after critical analysis and the best strategies are implemented to minimize the identified gaps and the models providing the maximum outcomes are selected and implemented on a large scale. 
  • The performance of the implemented project is continuously monitored and recommendations for continuous improvement are suggested. The KPIs are measured and an evaluation is made that the organization has met the objectives that include increased understanding of patient care, treatment, and satisfaction, developing the best strategies for lowering the readmission rates in hospitals and lowering the healthcare costs, etc (Team, 2019). 

By following all these steps, AI can be successfully integrated into John Hopkins University and positive outcomes can be observed in students learning as well as patients care. 

Requirement for Resources 

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology

Different types of resources are required to implement artificial intelligence in the learning environment for nursing students. These resources include different human as well as capital resources, different budget operations, technical support, etc. Different experts will be required which include healthcare staff, nursing educators, IT experts and data engineers, the human resources manager, etc (Hofstee, 2022). 

Healthcare professionals will guide patient care and introduce technology based on community health needs. The nursing educators will help in the implementation of technology based on the student’s learning needs and their level of understanding (Kiester & Turp, 2022). The human resources manager will determine the availability of funds and resources for the execution of the project. The data engineers will develop the best models of AI that will be utilized in the academics of nursing students. 

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology

The HR manager will determine the budget projections for implementing artificial intelligence into the institute. The budget designed for the implementation of AI will include the costs for the availability of high computing capacity in the form of the GPU (Graphics Processing Unit), high capacity for storage, the presence of infrastructure for networking, and security tools for securing data (Weber et al., 2022).

Technical support is important for the implementation of AI which includes highly skilled staff that can interpret the data efficiently and provides the technical solutions to improve the infrastructure of the organization (Von Gerich et al., 2021). Technical staff will help in the implementation plan by encountering technical difficulties and providing the best solutions to overcome these barriers. 

End User Training Requirements 

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology

Currently, the typical learning and training sessions are used which are devoid of practical demonstration of using technological tools for teaching students about patient care. Healthcare professionals and educators provide conventional teaching which is unable to develop the ability of critical thinking in students. The learning needs of students are compromised due to a lack of cognitive thinking (Lavin et al., 2022). End-user training assures that the target students are in sync with the organization’s objective and develop the skills of competent healthcare professionals. There are different expectations associated with the use of end-user training that is conceptual learning can be promoted by end-user training to improve the professional skills of nursing students, achieve the organization’s objectives, and enhance the accuracy level in technical operations (Ronquillo et al., 2021). 

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology 

There are different modes of end-user training that can be used which include virtual training, self-instructional and face-to-face sessions. These training sessions will train the students in patient care and community needs assessments. The integration of technology like artificial intelligence has become a fundamental need of nursing students to administer safe healthcare plans to patients in the profession (Hurst, 2021). 

The end users need technical help in getting the expertise of practical work in the lab. The course relevant to Artificial intelligence and data science will be included in the training sessions that will help the students to get insight into the technological skills for enhancing patient care (de Hond et al., 2022). Concept-based learning will be developed that assist the students in the management of healthcare plans and introducing the best interventions for patient care.

Evaluation of Effectiveness 

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology

The evaluation of outcomes can be performed to determine the effectiveness of artificial intelligence in learning. The evaluation metrics include the development of skills and higher learning outcomes in students, increased patient care, efficiencies in organization management, and reduction of healthcare costs. By using AI-based models and robots, nursing students will get the competent skills of practitioners and they will be able to perform healthcare procedures such as surgeries, acupuncture in muscles, CPR (Cardio Pulmonary Resuscitation), and giving first aid to patients (Shang, 2021). Ultimately, patient satisfaction will also be increased besides the cognitive development of students. Cost-effective treatment is another criterion for evaluation that will show that AI has been integrated successfully. The lower rate of readmissions and the least medication errors will also provide an evaluation of AI (Seibert et al., 2021). The improvement of organizational infrastructure is another evaluation criterion that will assess the success rate of technological changes in the organization. SMART goals will evaluate the effectiveness of the technology implementation plan. 

S (specific): The development of specific skills of competent practitioners such as surgery, CPR and acupuncture, etc will demonstrate that students have acquired enough skills from the AI-based models including virtual reality and augmented reality.

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology 

M (measurable): The evaluation will be performed by tracking learning outcomes and the efficient learning outcomes will show that technology has been implemented successfully. 

A (achievable): The use of 3D models and simulation-based cyborgs will mimic human physiology and develop the best understanding of human anatomy and care plans which will show that the goals are achievable by developing high management skills for human care and management plans. 

R (realistic): The AI will be implemented in different forms within the real settings of John Hopkins University to improve the healthcare understanding of students and patient safety. 

T (timebound): The AI-relevant technologies will be introduced and outcomes will be evaluated after six months of implementation. Specific tests and practical labs will be conducted and their learning outcomes will demonstrate that implemented technology has provided improved outcomes. 


NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology

Learning based on artificial intelligence can provide positive outcomes in the context of students learning and their professional skills. AI can be integrated by carefully designing the implementation plan through the collaboration of multiple experts. Financial, technical, and human resources will be used for successful implementation which will provide improved outcomes in learning and patient care.

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology 

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology


Abuzaid, M. M., Elshami, W., & Fadden, S. M. (2022). Integration of artificial intelligence into nursing practice. Health and Technology.

Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2021). Predicted influences of artificial intelligence on nursing education: Scoping review. JMIR Nursing, 4(1), e23933.

Chang, C., Jen, H., & Su, W. (2022). Trends in artificial intelligence in nursing: Impacts on nursing management. Journal of Nursing Management.

de Hond, A. A. H., Leeuwenberg, A. M., Hooft, L., Kant, I. M. J., Nijman, S. W. J., van Os, H. J. A., Aardoom, J. J., Debray, T. P. A., Schuit, E., van Smeden, M., Reitsma, J. B., Steyerberg, E. W., Chavannes, N. H., & Moons, K. G. M. (2022). Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. Npj Digital Medicine, 5(1), 1–13.

Hofstee, E. (2022, October 7). What are the infrastructure requirements for AI? Leaseweb Blog.

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology

Hurst, A. (2021, February 16). Improving understanding of machine learning for end-users. Information Age.

Hwang, G.-J., Tang, K.-Y., & Tu, Y.-F. (2022). How artificial intelligence (AI) supports nursing education: profiling the roles, applications, and trends of AI in nursing education research (1993–2020). Interactive Learning Environments, 1–20.

Kiester, L., & Turp, C. (2022). Artificial intelligence behind the scenes: PubMed’s Best Match algorithm. Journal of the Medical Library Association, 110(1).

Lavin, A., Gilligan-Lee, C. M., Visnjic, A., Ganju, S., Newman, D., Ganguly, S., Lange, D., Baydin, A. G., Sharma, A., Gibson, A., Zheng, S., Xing, E. P., Mattmann, C., Parr, J., & Gal, Y. (2022). Technology readiness levels for machine learning systems. Nature Communications, 13(1), 6039.

Ronquillo, C. E., Peltonen, L., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Cato, K., Hardiker, N., Junger, A., Michalowski, M., Nyrup, R., Rahimi, S., Reed, D. N., Salakoski, T., Salanterä, S., Walton, N., Weber, P., Wiegand, T., & Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative. Journal of Advanced Nursing, 77(9), 3707–3717.

NURS FPX 6109 Assessment 4 Vila Health: Implementing New Educational Technology

Seibert, K., Domhoff, D., Bruch, D., Schulte-Althoff, M., Fürstenau, 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.

Shang, Z. (2021). A concept analysis on the use of artificial intelligence in nursing. Cureus.

Team, M. (2019, September 4). So you want to implement ai in healthcare: 6 steps to success. Datafloq.

Von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., Michalowski, M., Mitchell, J., Nibber, R., Olalia, M. A., Pruinelli, L., Ronquillo, C. E., Topaz, M., & Peltonen, L.-M. (2021). Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies, 104153.

Weber, M., Engert, M., Schaffer, N., Weking, J., & Krcmar, H. (2022). Organizational capabilities for AI Implementation—coping with inscrutability and data dependency in AI. Information Systems Frontiers.