NURS FPX 6414 Video Presentation and Spreadsheet Proposal to Administration Paper Example

NURS FPX 6414 Assessment 2 Video Presentation and Spreadsheet: Proposal to Administration

NURS FPX 6414 Video Presentation and Spreadsheet Proposal to Administration Assignment Brief

Course: NURS-FPX6414 Advancing Health Care Through Data Mining

Assignment Title: NURS FPX 6414 Assessment 2 Video Presentation and Spreadsheet: Proposal to Administration

Assignment Overview

This assignment focuses on developing strategies for service line development through the monitoring of outcomes and subsequent analysis for improvement. It involves creating a spreadsheet illustrating data trending over time related to a specific line of service and recording a professional video presentation to communicate the analysis and recommendations to the administration.

Understanding Assignment Objectives

The primary objective of this assignment is to demonstrate proficiency in data management techniques, data representation methods, and communication of technical standards related to nursing practice. Students will analyze data measures, evaluate data trending, and articulate strategies for querying and generating reports from health information system databases.

The Student’s Role

As a student, your role is to assume the position of a healthcare professional responsible for monitoring and analyzing outcomes related to a specific service line within a practice setting or organization. You will create a spreadsheet containing relevant data categories, analyze the data trends, and present your findings and recommendations to the administration through a recorded video presentation.

Competencies Measured

This assignment measures several key competencies:

• Apply data management techniques to decision making in nursing practice: Analyze data measures and trends, and evaluate the what, why, and how to measure specific quality outcomes.
• Create various data representation methods for reporting and professional communications: Develop a spreadsheet illustrating trending data for the service line and present conclusions and recommendations in a scholarly video presentation.
• Articulate strategies for querying and generating reports from health information system databases: Describe data collection methods, evaluate data measures, and interpret data related to benchmarks of the outcome.
• Communicate technical standards as they relate to various informatics technologies: Demonstrate proficiency in creating a video presentation and communicating technical aspects of data management and analysis.

You Can Also Check Other Related Assessments for the NURS-FPX6414 Advancing Health Care Through Data Mining Course:

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NURS FPX 6414 Video Presentation and Spreadsheet Proposal to Administration Paper Example

Proposal to Administration

In a recent comprehensive review by Winkley et al. (2020), the concept of Type 2 Diabetes (T2D) self-management was thoroughly examined, underscoring the crucial role of healthcare professionals, nurses, and stakeholders in addressing this pervasive health issue. With T2D prevalence significantly impacting the US population, it becomes imperative to equip patients with the requisite skills for effective health monitoring. This presentation is dedicated to exploring diverse facets of diabetes self-management within healthcare organizations. It encompasses essential practices such as blood sugar testing, adherence to balanced meal plans, and facilitating regular exercise regimens, as highlighted by Agarwal et al. (2019). By elucidating the rationale and methodology behind monitoring T2D outcomes, our aim is to bolster patient care and management strategies comprehensively.

Why and How to Measure for a Specific Quality Outcome

Measuring specific outcomes in Type 2 Diabetes (T2D) is crucial due to the substantial number of individuals affected by this condition in the US, surpassing 500 million (Adam, 2018; Agarwal et al., 2019). For instance, implementing Diabetes Self-Management Education and Support (DSMES) programs aids patients in acquiring essential skills for managing their condition effectively. These programs focus on imparting knowledge and fostering positive behaviors related to self-management. Additionally, the Chronic Disease Management System (CDMS) plays a pivotal role in assisting individuals in maintaining optimal blood sugar levels and minimizing complications (Agarwal et al., 2019). These measures not only enhance patients’ quality of life but also contribute to cost reduction in healthcare facilities. Moreover, outcome measures serve as fundamental benchmarks for establishing patient baselines, aiding in the assessment and improvement of care standards.

Benchmarks Associated with that Outcome

The benchmarks associated with Type 2 diabetes (T2D) are determined based on criteria set by the American Diabetes Association (ADA), aiming for a target hemoglobin A1c level below 7% among affected individuals in the United States (van Smoorenburg et al., 2019). Additionally, there is a significant emphasis on achieving weight reduction goals of up to 15% for patients, as supported by the efficacy of various drugs and medications (Apovian et al., 2018). Furthermore, it’s crucial to note the relatively high patient mortality rate of 5%, largely attributed to inadequate hospital care quality.

Evaluate Data Measures and Data Trending

When evaluating data measures and trends for a specific line of service, several key aspects need consideration. For instance, notable data measures include early patient deaths and shortened life spans, reflecting critical indicators of healthcare outcomes (Wu, 2019). Additionally, concerning Type 2 diabetes readmissions in the US, there’s a significant readmission rate of nearly 25%, underscoring the importance of effective management and follow-up care (Wu, 2019). Moreover, the level of education and awareness within the population plays a crucial role, with lower levels correlating with higher disease risks. Conversely, individuals with higher education levels are less likely to receive a diagnosis, highlighting disparities in healthcare access and awareness (Wu, 2019). Furthermore, it’s essential to recognize that the risk of Type 2 diabetes is disproportionately higher among Hispanic and black Americans compared to other demographic groups, necessitating targeted interventions and support strategies.

Interpretation of the Data related to the Benchmarks

When examining data associated with benchmarks, it becomes evident that Type 2 diabetes (T2D) incidence rates have exhibited concerning trends across Western countries over the past several decades (Winkley et al., 2020). Despite efforts to curb this trend, there remains a persistent increase, with certain demographic groups, such as middle-aged and baby boomers, experiencing a decline in incidence rates. This suggests a shifting landscape of risk, with younger populations facing a heightened susceptibility to the disease.

Moreover, several metrics guide the management of Type 2 diabetes, including blood sugar levels below 140 mg/dL, which indicate normal or acceptable readings (van Smoorenburg, 2019). Readings exceeding this threshold signify elevated risks, particularly when surpassing 200 mg/dL, underscoring a higher likelihood of diabetes onset. These insights emphasize the critical importance of Type 2 diabetes self-management programs in mitigating risks and reducing readmission rates.

Data Spreadsheet: An Overview

The World Health Organization highlights diabetes mellitus as a significant global health challenge, with adult prevalence doubling from 4.7% to 8.5% between the 1980s and 2015 (Agarwal et al., 2019). Alarmingly, Type 2 diabetes ranks as the seventh leading cause of death in the USA, as evidenced by nearly 87,647 death certificates in 2019 (Adam, 2018). It’s imperative to consider these statistics, alongside demographic variations, such as disparities in education and racial backgrounds, which influence diabetes prevalence rates among different segments of the American population.

Conclusion

In conclusion, the comprehensive exploration of Type 2 Diabetes (T2D) management and evaluation underscores the critical need for proactive measures to address this pervasive health issue. The data measures and benchmarks outlined in this proposal offer valuable insights into the prevalence, management, and outcomes associated with T2D, highlighting areas for improvement and intervention. By emphasizing the importance of Diabetes Self-Management Education and Support (DSMES) programs, Chronic Disease Management Systems (CDMS), and adherence to established benchmarks, healthcare providers and administrators can enhance patient care, mitigate risks, and reduce healthcare costs. Furthermore, the interpretation of data trends provides valuable guidance for targeting interventions and resources effectively, ensuring equitable access to care and support for all affected individuals.

References

Adam, L., O’Connor, C., & Garcia, A. C. (2018). Evaluating the impact of diabetes self-management education methods on knowledge, attitudes, and behaviors of adult patients with Type 2 diabetes mellitus. Canadian Journal of Diabetes, 42(5), 470–477.e2.

Agarwal, P., Mukerji, G., Desveaux, L., Ivers, N. M., Bhattacharyya, O., Hensel, J. M., Shaw, J., Bouck, Z., Jamieson, T., Onabajo, N., Cooper, M., Marani, H., Jeffs, L., & Bhatia, R. S. (2019). Mobile app for improved self-management of Type 2 diabetes: Multicenter pragmatic randomized controlled trial. JMIR mHealth and uHealth, 7(1), e10321.

Apovian, C. M., Aronne, L. J., Bessesen, D. H., McDonnell, M. E., Murad, M. H., Pagotto, U., Ryan, D. H., & Still, C. D. (2018). Pharmacological management of obesity: An Endocrine Society Clinical Practice Guideline. The Journal of Clinical Endocrinology & Metabolism, 103(7), 2941–2963.

van Smoorenburg, A. N., Hertroijs, D. F. L., Dekkers, T., Elissen, A. M. J., & Melles, M. (2019). Patient’s perspective on self-management: Type 2 diabetes in daily life. BMC Health Services Research, 19(1), 605.

Winkley, K., Upsher, R., Stahl, D., Pollard, D., Kasera, A., Brennan, A., Heller, S., & Ismail, K. (2020). Psychological interventions to improve self-management of Type 1 and Type 2 diabetes: A systematic review. Health Technology Assessment (Winchester, England), 24(28), 1–232.

Detailed Assessment Instructions for the NURS FPX 6414 Video Presentation and Spreadsheet Proposal to Administration Paper Assignment

Video Presentation and Spreadsheet

Create a spreadsheet that illustrates data trending over time related to a particular line of service that you are monitoring. Then, record a 5-7 minute professional video presentation in which you describe how and why you are monitoring the outcomes for a particular service line in order to improve care.

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Introduction

To develop strategies for service line development, leaders need to know how to monitor outcomes that provide pertinent information in support of strategic development. Leaders then need to be skilled at communicating their analysis of those outcomes to other leaders in the organization in order to create awareness and build support for any proposed strategies arising from the analysis.

Preparation

For this assessment, you will record a video presentation in which you describe how and why you are monitoring the outcomes for a particular service line in order to improve care. You will begin by creating a spreadsheet that contains the pertinent data categories and illustrates data trending over time.

Create a spreadsheet:

• Analyze the what, why, and how to measure for a specific quality outcome related to a service line in a practice setting or organization.
• Chose a specific quality outcome related to a service line in a practice setting or organization.
• Identify the benchmarks associated with that outcome and how often the benchmarks are measured.
• Determine the categories of data you will be measuring in support of the ongoing development of the service line.
• Create a spreadsheet that includes baseline data for each category and additional data that illustrates the trending over time for that category.
• Be ready to explain why you chose these particular data sets and how you intend to use the data to improve outcomes.

Create a video recording:

• Analyze your data and present your conclusions and recommendations to administrators in a recorded video presentation.
• Describe the outcomes you are supporting and the benchmarks related to that outcome.
• Describe your data collection methods and rational.
• Evaluate the data measures and data trending for the specific quality outcome related to the service line.
• Share your interpretation of the data related to the benchmarks of the outcome.
• Support your interpretation with any relevant outside sources.
• Demonstrate to the administrators how you used the data in the spreadsheet to reach your conclusions.

Additional Requirements:

Video Recording:

• Your video recording should be between 3-7 minutes in length. You must appear in professional attire and with a professional demeanor, as if you are presenting to the administration of your organization.
• Kaltura is the preferred tool for creating your video. Any tools other than Kaltura should be cleared with your instructor prior to using.

Competencies Measured

By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and assessment criteria:

• Competency 1: Apply data management techniques to decision making in nursing practice.
• Analyze the what, why, and how to measure for a specific quality outcome related to a service line in a practice setting or organization.
• Competency 2: Create various data representation methods for reporting and professional communications.
• Evaluate data measures and data trending for a specific quality outcome related to a service line in a practice setting or organization.
• Competency 3: Articulate strategies for querying and generating reports from health information system databases.
• Create a data spreadsheet that illustrates trending data for the service line.
• Competency 4: Communicate technical standards as they relate to various informatics technologies.
• Develop a scholarly video presentation of the measures and data for the service line.

Use the resources linked below to help complete this assessment.

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Producing Applications in Practice With Data

• McGonigle, D., & Mastrian, K. (2022).  Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett.  Available in the courseroom via the VitalSource Bookshelf link.
• Chapter 23, “Translational Research: Generating Evidence for Practice.”
• Kurniati, A. P., Rojas, E., Hogg, D., Hall, G., & Johnson, O. A. (2018, November 29).  The assessment of data quality issues for process mining in healthcare using Medical Information Mart for Intensive Care III, a freely available e-health record database .  Health Informatics Journal, 1–16.
• Shameer, K., Perez-Rodriguez, M. M., Bachar, R., Li, L., Johnson, A., Johnson, K. W., Glicksberg, B. S., Smith, M. R., Readhead, B., Scarpa, J., Jebakaran, J., Kovatch, P., Lim, S., Goodman, W., Reich, D., Kasarskis, A., Tatonetti, N. P., & Dudley, J. T. (2018). (2018).  Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining .  BMC Medical Informatics and Decision Making,  18(Suppl. 3), 1–19.
• Vial, G. (2019).  Understanding digital transformation: A review and a research agenda .  The Journal of Strategic Information Systems,  28(2), 118–144.

Evidence-Based Development

• Ansaripour, A., Zendehdel, K., Tadayon, N., Sadeghi, F., Uyl-de Groot, C. A., & Redekop, W. K. (2018).  Use of data-mining to support real-world cost analyses: An example using HER2-positive breast cancer in Iran .  PloS One,  13(10), 1-16.
• Bjarnadottir, R. I., Bockting, W., Yoon, S., & Dowding, D. W. (2019).  Nurse documentation of sexual orientation and gender identity in home healthcare: A text mining study .  CIN: Computers, Informatics, Nursing,  37(4), 213-221.
• Ghorbani, R., & Ghousi, R. (2019).  Predictive data mining approaches in medical diagnosis: A review of some diseases prediction .  International Journal of Data and Network Science,  3(2), 47-70.
• Shi, J., Zheng, M., Yao, L., & Ge, Y. (2018).  Developing a healthcare Dataset Information Resource (DIR) based on semantic web .  BMC Medical Genomics,  11(Suppl. 5), 1-14.

Producing Applications in Practice With Evidence-Based Information

• Ahmed, S., Seddawy, A. I. E., & Nasr, M. (2019).  A proposed framework for detecting and predicting diseases through business intelligence applications .  International Journal of Advanced Networking and Applications,  10(4), 3951-3957.
• Ismail, W. N., Hassan, M. M., Alsalamah, H. A., & Fortino, G. (2018, August 1).  Mining productive-periodic frequent patterns in tele-health systems .  Journal of Network and Computer Applications,  115, 33-47.
• Jones, M. (2019).  What we talk about when we talk about (big) data .  Journal of Strategic Information Systems,  28(1), 3-16.
• Soleimani-Roozbahani, F., Ghatari, A. R., & Radfar, R. (2019).  Knowledge discovery from a more than a decade studies on healthcare big data systems: A scientometrics study .  Journal of Big Data,  6(1), 1-15.
• Yoo, S., Kim, S., Kim, E., Jung, E., Lee, K., & Hwang, H. (2018).  Real-time location system-based asset tracking in the healthcare field: Lessons learned from a feasibility study .  BMC Medical Informatics and Decision Making,  18(80), 1-10.

Scope and Standards of Practice

• American Nurses Association. (2015).  Nursing informatics: Scope and standards of practice  (2nd ed.) . Author.

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