NURS FPX 6025 Assessment 6 Practicum and MSN Reflection

Name

Capella university

NURS-FPX 6025 MSN Practicum

Prof. Name

Date

Practicum and MSN Reflection

During the capstone project, I implemented the Population, Intervention, Comparison, Outcome, and Timeframe (PICOT) framework to guide the integration of GE monitoring devices into clinical workflows for staff nurses. This initiative significantly bolstered my practical knowledge and confidence in leveraging advanced health technologies. The experience enhanced my ability to make informed clinical decisions, promote data-driven care, and improve nurse engagement. This reflection offers an overview of my journey through the MSN program, emphasizing core achievements, obstacles faced, practicum completion, and future career aspirations. The program has deepened my clinical competence and honed my ability to incorporate GE monitoring systems to drive evidence-based nursing care.

Throughout my MSN journey, I developed the ability to lead technology-driven interventions in healthcare settings. One of the significant innovations explored was the application of GE monitoring systems integrated with Electronic Health Records (EHRs). These devices allow the automated transmission of patient vitals, reducing the likelihood of Medication Errors (MEs) and ensuring seamless data accuracy (Krittanawong et al., 2020). My newly acquired competencies enable me to translate patient data into actionable care plans that are both personalized and population-focused.

Additionally, I utilized the PICOT model to assess real-time data analytics through GE monitoring systems, leading to more accurate and timely clinical decision-making. The integration of this data into the EHR offered a significant advancement in information processing, minimizing manual input errors and enhancing treatment accuracy (Stucky et al., 2020). This capability has equipped me to support and train staff nurses, ensuring optimal utilization of monitoring technologies.

PICOT Application Outcome	Impact
Real-time data integration	Enabled accurate, timely decision-making for staff nurses
Reduced manual data errors	Improved patient safety and trust in clinical information
Enhanced staff training programs	Promoted effective device usage and empowered nursing staff
Streamlined clinical documentation	Boosted efficiency and reduced administrative burdens

Achievements and Obstacles During the Practicum

As part of my practicum, I successfully implemented strategies based on the PICOT model that improved the operational use of GE monitoring devices among staff nurses. These achievements included the creation of tailored training sessions, the deployment of education tools, and collaboration with interdisciplinary professionals. By facilitating technology adoption, I was able to monitor patient health trends effectively and improve clinical accuracy and workflow efficiency.

Despite these accomplishments, I faced several challenges. Time constraints and limited financial resources within traditional healthcare environments hindered the implementation process. There was also a lack of seamless communication among the interdisciplinary team members—including nurse informaticists, health technologists, and medical staff—which occasionally disrupted collaborative efforts (Wranik et al., 2019). Nonetheless, these challenges provided valuable lessons in conflict resolution, communication strategies, and prioritization of tasks to align with available resources.

Category	Achievements	Obstacles
Technological Implementation	GE device integration into daily nursing workflows	Limited funding and time for project execution
Education & Training	Conducted staff development sessions on device usage	Resistance to change and lack of initial staff engagement
Interdisciplinary Collaboration	Engaged with informaticists and IT professionals to enhance coordination	Communication gaps among team members hindered continuity in planning
Outcome Monitoring	Modified monitoring protocols based on feedback for improved patient outcomes	Required ongoing adjustment to address patient diversity and evolving needs

During this experience, I fulfilled the required 20 practicum hours. These hours were spent applying evidence-based practices, engaging staff in hands-on learning, and evaluating device-related outcomes. The exposure allowed me to bridge the gap between theoretical knowledge and clinical application, strengthening my readiness for advanced nursing roles.

Future Career Options

Earning an MSN degree has created multiple pathways for professional advancement. The integration of informatics into nursing has become a cornerstone of modern care delivery. As someone well-versed in health technologies, particularly GE monitoring systems and Clinical Decision Support Systems (CDSS), I am positioned to take on leadership roles in technology implementation and data-driven care processes. My capacity to interpret health data and guide resource-efficient care protocols aligns with current healthcare trends emphasizing quality and accountability (Wilson et al., 2020).

Furthermore, I am interested in roles such as healthcare data coordinator or systems analyst, where I can assist with designing, implementing, and evaluating systems to ensure patient data integrity and compliance with ethical standards. My background also supports potential contributions as a nurse educator, helping future nurses and clinical teams master the use of GE monitoring tools. In addition, I see opportunities to contribute to remote care models through device-based telemonitoring programs—a strategy that supports continuity of care beyond traditional settings (Haleem et al., 2021).

Career Pathway	Description
Nurse Informaticist	Manage clinical data, support EHR and CDSS usage, promote interprofessional care
Nurse Educator	Train healthcare professionals in using GE monitoring devices
Healthcare Data Analyst	Collect and interpret patient information for policy and program development
Telemonitoring Coordinator	Oversee remote patient monitoring using GE systems
Medical Systems Analyst	Evaluate technology usage and ensure adherence to legal and ethical standards

Conclusion

In summary, the MSN program and practicum have equipped me with comprehensive skills in applying GE monitoring technologies to enhance patient outcomes. Through the PICOT framework, I developed targeted interventions that demonstrated the value of integrating informatics into routine nursing care. The obstacles encountered further refined my leadership abilities, while the practical experiences strengthened my capability to support, train, and collaborate with healthcare professionals. With a solid foundation in evidence-based practice and informatics, I am confident in advancing my nursing career and influencing healthcare transformation.

References

Amir, H., & Sudarman, S. (2020). Reflective Case Discussion (RCD) for nurses: A systematic review. STRADA Jurnal Ilmiah Kesehatan, 9(2), 332–337. https://doi.org/10.30994/sjik.v9i2.306

Backonja, U., Langford, L. H., & Mook, P. J. (2021). How to support the nursing informatics leadership pipeline. CIN: Computers, Informatics, Nursing, Publish Ahead of Print(1), 8–20. https://doi.org/10.1097/cin.0000000000000827

Balak, N., Broekman, M. L. D., & Mathiesen, T. (2020). Ethics in contemporary health care management and medical education. Journal of Evaluation in Clinical Practice, 26(3), 699–706. https://doi.org/10.1111/jep.13352

NURS FPX 6025 Assessment 6 Practicum and MSN Reflection

Berryman, J. (2021). Use of EBP as a problem‐solving approach to improve patient satisfaction while overcoming the COVID pandemic barriers. Worldviews on Evidence-Based Nursing, 18(6), 389–391. https://doi.org/10.1111/wvn.12541

Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2021). Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors International, 2(2), 100117. https://doi.org/10.1016/j.sintl.2021.100117

Jamil, F., Ahmad, S., Iqbal, N., & Kim, D.-H. (2020). Towards a remote monitoring of patient vital signs based on IoT-based blockchain integrity management platforms in smart hospitals. Sensors, 20(8), 2195. https://doi.org/10.3390/s20082195

Kelly, J. T., Campbell, K. L., Gong, E., & Scuffham, P. (2020). The internet of things: Impact and implications for healthcare delivery. Journal of Medical Internet Research, 22(11), e20135. https://doi.org/10.2196/20135

Krittanawong, C., Rogers, A. J., Johnson, K. W., Wang, Z., Turakhia, M. P., Halperin, J. L., & Narayan, S. M. (2020). Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management. Nature Reviews Cardiology, 18(2), 75–91. https://doi.org/10.1038/s41569-020-00445-9

Pandey, H., & Prabha, S. (2020). Smart health monitoring system using IoT and machine learning techniques. 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), 1–4. https://doi.org/10.1109/icbsii49132.2020.9167660

Papa, A., Mital, M., Pisano, P., & Del Giudice, M. (2020). E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation. Technological Forecasting and Social Change, 153, 119226. https://doi.org/10.1016/j.techfore.2018.02.018

Stucky, C. H., De Jong, M. J., & Rodriguez, J. A. (2020). A five‐step evidence‐based practice primer for perioperative RNs. AORN Journal, 112(5), 506–515. https://doi.org/10.1002/aorn.13220

Wilson, M. L., Elias, B. L., & Moss, J. A. (2020). Education in nursing informatics. In Health Informatics (pp. 23–43). https://doi.org/10.1007/978-3-030-53813-2_3

NURS FPX 6025 Assessment 6 Practicum and MSN Reflection

Wranik, W. D., Price, S., Haydt, S. M., Edwards, J., Hatfield, K., Weir, J., & Doria, N. (2019). Implications of interprofessional primary care team characteristics for health services and patient health outcomes: A systematic review with narrative synthesis. Health Policy, 123(6), 550–563. https://doi.org/10.1016/j.healthpol.2019.03.015