1
CEM2 Task 1: Clinical Practice Experience E-Portfolio
WGU D029 - CEM2 Task 1 E-Portfolio: Clinical
Practice Experience Analysis | Passed on First
Attempt |Latest Update with Complete Solution
MSN Core E-Portfolio
Phase 1
1a. CPE schedule table of tasks and timelines.
Phase One Tasks Estimated Time Anticipated Completion
Date
1a. CPE Schedule Table 0.5 hr 1/20/2024
1b. Annotated Bibliography 4.0 hr 1/20/2024
1c. Narrative Essay 1.0 hr 1/20/2024
1d. Technology Summary 1.5 hr 1/20/2024
1e. GoReact Video 0.5 hr 2/9/2024
1e. Peer Responses 0.5 hr 2/9/2024
1f. Reflection Summary 1.0 hr 2/9/2024
Phase Two Tasks Estimated Time Anticipated Completion
Date
2a. Summary Median Income 0.5 hr 1/21/2024
2b. Summary Eligibility 0.5 hr 1/21/2024
2c. Summary Choice 0.5 hr 1/21/2024
2d. Pivot BBxRural 0.5 hr 1/21/2024
2d. Pivot AirxPop 0.5 hr 1/21/2024
Phase Three Tasks Estimated Time Anticipated Completion
Date
3a. Bar Chart 0.5 hr 1/22/2024
3a. Pie Chart 0.5 hr 1/22/2024
3a. Scatter Chart 0.5hr 1/22/2024
3a. Column Chart 0.5 hr 1/23/2024
3a. Line Chart 0.5 hr 1/23/2024
3a. Treemap Chart 0.5 hr 1/23/2024
3b. GoReact Video 0.5 hr 2/10/2024
3b. Peer Responses 0.5 hr 2/10/2024
3c. Reflection Summary 1.0 hr 2/10/2024
1b. Annotated bibliography of five relevant sources, published within the last five years on
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CEM2 Task 1: Clinical Practice Experience E-Portfolio
current or emerging technologies that have the potential to enhance nursing or healthcare.
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare:
transforming the practice of medicine. Future healthcare journal, 8(2), e188–e194.
https://doi.org/10.7861/fhj.2021-0095
This article discusses how clinical use of Artificial Intelligence (AI) could help bridge the
significant shortages in healthcare that are currently apparent and are predicted to worsen over
the next decade. This article recognizes that although significant research has already gone into
2
, 3
CEM2 Task 1: Clinical Practice Experience E-Portfolio
AI use in the healthcare field, there are several levels of AI use, each level predicted to take 5-10
years to become efficient and accurate. Currently AI in the healthcare field is in the trial and
entry level phase with focus on improving productivity by taking assessment data to prepopulate
H&P and progress notes. In the long-term phase, it is predicted that AI will not only focus on
efficiency and productivity but also on patient safety by allowing a “digital twin” of a patient to
be created to predict how they would tolerate recommended treatment interventions. The article
acknowledges that for this high level of AI to be successfully implemented, the levels before it
must have gone through significant testing and trials before being considered “completed”. This
article used resources from 50 different peer reviewed articles or reputable sites like Dept. of
Health and Social care with more than 50% being less than five years old.
Morgan, A. A., Abdi, J., Syed, M. A. Q., Kohen, G. E., Barlow, P., & Vizcaychipi, M. P. (2022).
Robots in Healthcare: a Scoping Review. Current robotics reports, 3(4), 271–280.
https://doi.org/10.1007/s43154-022-00095-4
This article discusses the way robots are currently being used in the healthcare system
and what the future of healthcare robotics could look like. The majority of healthcare robotics
has been focused on the da Vinci Surgical System that has been implemented in hospitals and
surgical centers around the world. With the cascading affect from staffing shortages, focus is
now being placed on other areas robots can be used to promote efficiency and productivity.
This article examined data from over 900 resources and found that since the COVID-19
pandemic, focus has been put on how robots can assist with mundane repetitive tasks such as
transporting meds from the pharmacy to the inpatient units, retrieving equipment and supplies
from other areas in the hospital, and bringing items such as meals and linen to inpatient rooms
to help free up staff to focus on tasks that cannot be delegated to robots. The article
acknowledges that the challenges are not so much programing the robots to carry out these
tasks but being able to carry out these tasks in a challenging environment and will rely on
detailed engineering and strong infrastructure and networks.
Grosman-Rimon, L., Li, D. H. Y., Collins, B. E., & Wegier, P. (2023). Can we improve
healthcare with centralized management systems, supported by information technology,
predictive analytics, and real-time data?: A review. Medicine, 102(45), e35769.
https://doi.org/10.1097/MD.0000000000035769
3
CEM2 Task 1: Clinical Practice Experience E-Portfolio
WGU D029 - CEM2 Task 1 E-Portfolio: Clinical
Practice Experience Analysis | Passed on First
Attempt |Latest Update with Complete Solution
MSN Core E-Portfolio
Phase 1
1a. CPE schedule table of tasks and timelines.
Phase One Tasks Estimated Time Anticipated Completion
Date
1a. CPE Schedule Table 0.5 hr 1/20/2024
1b. Annotated Bibliography 4.0 hr 1/20/2024
1c. Narrative Essay 1.0 hr 1/20/2024
1d. Technology Summary 1.5 hr 1/20/2024
1e. GoReact Video 0.5 hr 2/9/2024
1e. Peer Responses 0.5 hr 2/9/2024
1f. Reflection Summary 1.0 hr 2/9/2024
Phase Two Tasks Estimated Time Anticipated Completion
Date
2a. Summary Median Income 0.5 hr 1/21/2024
2b. Summary Eligibility 0.5 hr 1/21/2024
2c. Summary Choice 0.5 hr 1/21/2024
2d. Pivot BBxRural 0.5 hr 1/21/2024
2d. Pivot AirxPop 0.5 hr 1/21/2024
Phase Three Tasks Estimated Time Anticipated Completion
Date
3a. Bar Chart 0.5 hr 1/22/2024
3a. Pie Chart 0.5 hr 1/22/2024
3a. Scatter Chart 0.5hr 1/22/2024
3a. Column Chart 0.5 hr 1/23/2024
3a. Line Chart 0.5 hr 1/23/2024
3a. Treemap Chart 0.5 hr 1/23/2024
3b. GoReact Video 0.5 hr 2/10/2024
3b. Peer Responses 0.5 hr 2/10/2024
3c. Reflection Summary 1.0 hr 2/10/2024
1b. Annotated bibliography of five relevant sources, published within the last five years on
1
, 2
CEM2 Task 1: Clinical Practice Experience E-Portfolio
current or emerging technologies that have the potential to enhance nursing or healthcare.
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare:
transforming the practice of medicine. Future healthcare journal, 8(2), e188–e194.
https://doi.org/10.7861/fhj.2021-0095
This article discusses how clinical use of Artificial Intelligence (AI) could help bridge the
significant shortages in healthcare that are currently apparent and are predicted to worsen over
the next decade. This article recognizes that although significant research has already gone into
2
, 3
CEM2 Task 1: Clinical Practice Experience E-Portfolio
AI use in the healthcare field, there are several levels of AI use, each level predicted to take 5-10
years to become efficient and accurate. Currently AI in the healthcare field is in the trial and
entry level phase with focus on improving productivity by taking assessment data to prepopulate
H&P and progress notes. In the long-term phase, it is predicted that AI will not only focus on
efficiency and productivity but also on patient safety by allowing a “digital twin” of a patient to
be created to predict how they would tolerate recommended treatment interventions. The article
acknowledges that for this high level of AI to be successfully implemented, the levels before it
must have gone through significant testing and trials before being considered “completed”. This
article used resources from 50 different peer reviewed articles or reputable sites like Dept. of
Health and Social care with more than 50% being less than five years old.
Morgan, A. A., Abdi, J., Syed, M. A. Q., Kohen, G. E., Barlow, P., & Vizcaychipi, M. P. (2022).
Robots in Healthcare: a Scoping Review. Current robotics reports, 3(4), 271–280.
https://doi.org/10.1007/s43154-022-00095-4
This article discusses the way robots are currently being used in the healthcare system
and what the future of healthcare robotics could look like. The majority of healthcare robotics
has been focused on the da Vinci Surgical System that has been implemented in hospitals and
surgical centers around the world. With the cascading affect from staffing shortages, focus is
now being placed on other areas robots can be used to promote efficiency and productivity.
This article examined data from over 900 resources and found that since the COVID-19
pandemic, focus has been put on how robots can assist with mundane repetitive tasks such as
transporting meds from the pharmacy to the inpatient units, retrieving equipment and supplies
from other areas in the hospital, and bringing items such as meals and linen to inpatient rooms
to help free up staff to focus on tasks that cannot be delegated to robots. The article
acknowledges that the challenges are not so much programing the robots to carry out these
tasks but being able to carry out these tasks in a challenging environment and will rely on
detailed engineering and strong infrastructure and networks.
Grosman-Rimon, L., Li, D. H. Y., Collins, B. E., & Wegier, P. (2023). Can we improve
healthcare with centralized management systems, supported by information technology,
predictive analytics, and real-time data?: A review. Medicine, 102(45), e35769.
https://doi.org/10.1097/MD.0000000000035769
3
