D029 Task 1 E-Portfolio: Clinical Practice Experience Analysis

D029 Task 1 E-Portfolio: Clinical Practice Experience Analysis

Student Name

Western Governors University

D029 Informatics for Transforming Nursing Care

Prof. Name:

Date

MSN Core E-Portfolio Phase 1

What is the schedule for the CPE tasks and timelines in Phase One?

The Clinical Practice Experience (CPE) Phase One includes several tasks scheduled with estimated completion times and dates. These tasks begin with creating a CPE schedule table, annotated bibliography, narrative essay, and technology summary, all anticipated to be completed by January 20, 2024. Subsequent activities such as creating a GoReact video, peer responses, and a reflection summary are scheduled for February 9, 2024.

Task	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

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What are the Phase Two tasks and their timelines?

Phase Two focuses on data summaries and pivot tables related to median income, eligibility, choice, broadband availability, and air pollution by population. These tasks are expected to be completed by January 21, 2024, each requiring approximately half an hour.

Task	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 Table: Broadband by Rural Eligibility	0.5 hr	1/21/2024
2d. Pivot Table: Air Pollution by Population	0.5 hr	1/21/2024

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What does Phase Three involve?

Phase Three comprises creating various charts—bar, pie, scatter, column, line, and treemap—to visualize data, with deadlines primarily set for January 22 and 23, 2024. Additionally, it includes GoReact video submissions, peer responses, and a reflection summary, all due by February 10, 2024.

Task	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	0.5 hr	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

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Annotated Bibliography on Emerging Technologies in Healthcare

What are some current technologies enhancing nursing and healthcare?

The annotated bibliography includes five peer-reviewed sources published within the last five years, focusing on innovations that improve healthcare delivery and nursing practice.

Artificial Intelligence (AI) in Healthcare
Bajwa et al. (2021) explore the transformative potential of AI to address healthcare workforce shortages by automating documentation and eventually creating “digital twins” for patient care simulations. AI is currently in an early trial phase aimed at increasing productivity, with full-scale patient safety applications predicted in the next 5-10 years.

Robotics in Healthcare
Morgan et al. (2022) review the role of robots, particularly post-COVID-19, in performing repetitive tasks like medication transport and supply delivery. These functions aim to alleviate staff shortages and improve operational efficiency, though challenges remain in adapting robots to complex hospital environments.

Centralized Management Systems
Grosman-Rimon et al. (2023) analyze how command centers, supported by predictive analytics and real-time data, can streamline hospital operations. These systems reduce delays in patient transfers and discharges, coordinate bed availability, and communicate capacity issues across hospital networks, improving overall patient flow.

Wearable Health Devices
Lu et al. (2020) examine wearable technologies used for vital sign monitoring, chronic disease management, illness prediction, and rehabilitation. Although these devices offer increased patient independence and timely data transmission, concerns about privacy, regulation, and equitable access persist.

Telemedicine
Haleem et al. (2021) highlight the rapid expansion of telemedicine during the COVID-19 pandemic, emphasizing improved patient access, particularly for vulnerable populations. However, limitations include the lack of comprehensive physical assessments and reimbursement challenges by insurers.

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Narrative Essay: Interview with a Nurse Informaticist

Who was interviewed and what were their insights?

Lisa Porter, MSN, RN, a clinical informatics leader at Mass General Brigham, shared her extensive experience managing healthcare technology, including overseeing an EHR system transition. She praised the smooth implementation supported by institutional and vendor collaboration but noted that COVID-19 diverted staff attention, delaying full utilization of the new system’s capabilities.

Lisa also highlighted technologies that positively impact care: patient portals increase patient engagement but can lead to confusion when patients receive test results before provider interpretation. Telemedicine enhances healthcare accessibility, especially for seniors and those with transportation challenges. Looking forward, Lisa is optimistic about AI reducing clinicians’ documentation burden and aiding patients with low health literacy by summarizing visits and care plans in understandable language.

She emphasized the importance of including end-users, including patients, in the technology implementation process to ensure practical feedback and acceptance.

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Technology Summary: Five Recommended Innovations to Enhance Nursing and Healthcare Outcomes

What are the five technologies recommended for healthcare transformation?

Technology	Description	Potential Impact
Artificial Intelligence (AI)	AI can automate documentation by analyzing provider-patient interactions and prepopulating clinical notes, freeing clinicians to focus on critical patient care.	Addresses clinician workload and improves efficiency.
Service Robots	Robots perform routine tasks like delivering medications, transporting supplies, and providing companionship to patients, particularly in long-term care.	Reduces nursing staff’s repetitive workload and enhances patient morale.
Centralized Command Centers	These centers use real-time data to manage patient flow, notify environmental services, and coordinate with nearby hospitals during capacity surges.	Improves bed availability, reduces ED boarding time, and facilitates timely care.
Wearable Medical Devices	Devices monitor vital signs and chronic conditions remotely, allowing healthcare providers to adjust treatments promptly based on real-time data.	Enhances patient-centered care and early intervention.
Telemedicine Services	Provides rural and resource-limited hospitals access to specialists for remote consultation on complex cases.	Speeds diagnosis and treatment, reduces provider stress, and improves patient outcomes.

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GoReact Video Reflection and Peer Responses

What were the key points discussed in the video reflection?

In the reflection video, the focus was on emerging technologies with the most interest placed on AI’s role in clinical documentation to reduce clinician workload. The use of service robots was discussed as a method to alleviate nursing burdens, especially with routine tasks. Centralized command centers were noted for their capacity to enhance patient flow and reduce treatment delays.

Wearable devices were recognized for enabling remote monitoring and timely care adjustments. Telemedicine’s benefits for rural hospitals in accessing specialty care were also highlighted. The reflection underscored the importance of appropriate technology implementation involving end-users for successful adoption.

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Phase Two and Three Data Analysis Tasks

How were data analysis tasks completed and what insights were gained?

In Phase Two, summary tabs and pivot tables related to median income, eligibility, broadband access, and pollution were completed, providing foundational data organization skills.

Phase Three involved creating various charts—bar, pie, scatter, column, line, and treemap—to visualize data effectively. Initial challenges with data visualization transformed into appreciation for how graphical representations clarify community health trends.

The user reflected on the higher patient-to-primary care provider ratio in their county, aligning with common patient complaints about access barriers. The experience strengthened Excel skills and prepared the user for a career shift into clinical informatics.

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Reflection Summary on Data Visualization and Career Impact

Although initial perceptions of the data visualization tasks were tedious, the user eventually valued the opportunity to enhance Excel skills and translate data into meaningful visuals. The use of local statistics helped personalize learning and understand healthcare access issues in their community.

This phase reinforced the user’s interest in moving from management to clinical informatics, recognizing the relevance of data analytics in healthcare transformation.

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References

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

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

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

Lu, L., Zhang, J., Xie, Y., Gao, F., Xu, S., Wu, X., & Ye, Z. (2020). Wearable health devices in health care: Narrative systematic review. JMIR mHealth and uHealth, 8(11), e18907. https://doi.org/10.2196/18907

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