Name
Western Governors University
D029 Informatics for Transforming Nursing Care
Prof. Name
Date
The initial phase of the Clinical Practice Experience (CPE) includes several foundational assignments that must be completed within specific deadlines. These tasks consist of creating a CPE schedule table, compiling an annotated bibliography, drafting a narrative essay, and summarizing technology applications. All these assignments are targeted for completion by January 20, 2024. After these initial tasks, participants are expected to submit a GoReact video, engage in peer responses, and write a reflection summary, all due by February 9, 2024.
| Task | Estimated Time | Anticipated Completion Date |
|---|---|---|
| CPE Schedule Table | 0.5 hr | 1/20/2024 |
| Annotated Bibliography | 4.0 hr | 1/20/2024 |
| Narrative Essay | 1.0 hr | 1/20/2024 |
| Technology Summary | 1.5 hr | 1/20/2024 |
| GoReact Video | 0.5 hr | 2/9/2024 |
| Peer Responses | 0.5 hr | 2/9/2024 |
| Reflection Summary | 1.0 hr | 2/9/2024 |
Phase Two focuses on data summarization and analytical skills, including the use of pivot tables to explore different community and environmental metrics. Key topics analyzed include median income, eligibility, choice, broadband availability, and air pollution relative to population density. These exercises are designed to be brief, approximately 30 minutes each, with all tasks planned to be completed by January 21, 2024.
| Task | Estimated Time | Anticipated Completion Date |
|---|---|---|
| Summary Median Income | 0.5 hr | 1/21/2024 |
| Summary Eligibility | 0.5 hr | 1/21/2024 |
| Summary Choice | 0.5 hr | 1/21/2024 |
| Pivot Table: Broadband by Rural Eligibility | 0.5 hr | 1/21/2024 |
| Pivot Table: Air Pollution by Population | 0.5 hr | 1/21/2024 |
The third phase is dedicated to creating various graphical data representations to visually communicate insights derived from the datasets. These visualizations include bar charts, pie charts, scatter plots, column charts, line charts, and treemaps. These charts are scheduled for completion on January 22 and 23, 2024. Additionally, Phase Three incorporates the submission of a GoReact video, peer feedback, and a reflection summary, all due by February 10, 2024.
| Task | Estimated Time | Anticipated Completion Date |
|---|---|---|
| Bar Chart | 0.5 hr | 1/22/2024 |
| Pie Chart | 0.5 hr | 1/22/2024 |
| Scatter Chart | 0.5 hr | 1/22/2024 |
| Column Chart | 0.5 hr | 1/23/2024 |
| Line Chart | 0.5 hr | 1/23/2024 |
| Treemap Chart | 0.5 hr | 1/23/2024 |
| GoReact Video | 0.5 hr | 2/10/2024 |
| Peer Responses | 0.5 hr | 2/10/2024 |
| Reflection Summary | 1.0 hr | 2/10/2024 |
The annotated bibliography highlights five recent peer-reviewed articles published within the past five years, focusing on cutting-edge technologies that are transforming nursing and healthcare delivery. These innovations include Artificial Intelligence (AI), robotics, centralized management systems, wearable health devices, and telemedicine.
Artificial Intelligence (AI) in Healthcare
Bajwa et al. (2021) explore AI’s growing role in mitigating healthcare workforce shortages by automating labor-intensive tasks such as clinical documentation. They highlight novel AI applications, including “digital twins,” which simulate patient conditions to improve care delivery. Full integration of AI is anticipated to enhance patient safety substantially within the next decade.
Robotics in Healthcare
Morgan et al. (2022) discuss how robotics have increasingly been deployed in healthcare settings, especially post-COVID-19, to automate repetitive tasks like medication distribution and supply logistics. These technologies aim to alleviate staff shortages and increase operational efficiency, though their adaptability to complex clinical environments remains a challenge.
Centralized Management Systems
Grosman-Rimon et al. (2023) investigate hospital command centers equipped with predictive analytics and real-time data monitoring. These systems facilitate patient flow management by optimizing bed availability, streamlining discharge processes, and coordinating communication between hospitals, thereby reducing delays and enhancing efficiency.
Wearable Health Devices
Lu et al. (2020) describe the advantages of wearable devices in tracking vital signs and managing chronic illnesses remotely. These devices empower patients with greater autonomy and enable timely clinical intervention. However, issues related to privacy, regulation, and equitable access are ongoing concerns.
Telemedicine
Haleem et al. (2021) emphasize the rapid expansion of telemedicine during the COVID-19 pandemic, which improved access for underserved populations. Nonetheless, limitations persist, such as difficulties in conducting comprehensive physical exams remotely and reimbursement challenges.
Lisa Porter, MSN, RN, a clinical informatics leader at Mass General Brigham, shared her experiences managing healthcare technology projects. She discussed leading a major Electronic Health Record (EHR) system transition, highlighting the importance of collaboration between healthcare institutions and vendors to ensure a smooth rollout. However, COVID-19-related staff redeployments caused delays in fully adopting advanced system functionalities.
Lisa highlighted technologies that have positively impacted care delivery, such as patient portals, which increase patient engagement but sometimes cause confusion when lab results are viewed without clinician interpretation. She also praised telemedicine for improving accessibility, especially for elderly patients and those facing transportation barriers. Looking forward, she expressed optimism about AI’s ability to reduce documentation burdens and assist patients with low health literacy by producing easy-to-understand summaries of care plans and visits.
Importantly, Lisa emphasized the necessity of involving end-users, including patients, throughout technology implementation to ensure feedback-driven improvements and successful adoption.
| Technology | Description | Potential Impact |
|---|---|---|
| Artificial Intelligence (AI) | Automates documentation by analyzing clinical interactions and pre-filling notes. | Reduces clinician workload and enhances care efficiency. |
| Service Robots | Performs routine tasks such as medication delivery, supply transport, and patient companionship. | Eases repetitive workload and improves patient morale. |
| Centralized Command Centers | Utilizes real-time data and predictive analytics to optimize patient flow and hospital capacity. | Enhances bed availability and minimizes emergency delays. |
| Wearable Medical Devices | Enables remote monitoring of vital signs and chronic conditions for timely clinical interventions. | Promotes patient-centered care and early health interventions. |
| Telemedicine Services | Facilitates remote consultations for rural or resource-limited hospitals. | Speeds diagnosis and treatment, reducing provider stress. |
The GoReact video reflection emphasized the transformative role of emerging healthcare technologies, focusing particularly on AI’s potential to streamline clinical documentation and alleviate clinician workload. The discussion highlighted how service robots can reduce nurses’ routine tasks and how centralized command centers improve patient flow and reduce delays in care.
Wearable devices were acknowledged for their role in enabling remote monitoring and timely interventions. Telemedicine’s contribution to connecting rural hospitals with specialists was also underscored. The reflection concluded with a call for active engagement of end-users in the deployment of healthcare technologies to ensure smooth integration and user acceptance.
Phase Two involved creating summary tables and pivot charts to analyze variables like median income, eligibility, broadband access, and air pollution. These exercises strengthened data organization and analytical skills. Phase Three expanded on this by requiring the production of various data visualizations, including bar charts, pie charts, scatter plots, column charts, line charts, and treemaps.
Initially, the user found data visualization challenging but grew to appreciate how graphical displays clarify community health trends. A notable finding was the high patient-to-primary-care-provider ratio in the user’s county, which corroborated local concerns about healthcare access. This experience enhanced the user’s proficiency in Excel and solidified their interest in pursuing a clinical informatics career.
Despite some initial difficulties, the user valued the opportunity to improve data analytics and visualization skills. Applying local health data helped personalize the learning experience and deepened their understanding of healthcare disparities in their community. This phase reaffirmed their desire to transition from healthcare management into clinical informatics, recognizing the critical role of data-driven approaches in advancing healthcare delivery.
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
