Active Learning Activity Week 2: Leadership in Quality Improvement Paper Outline Submission

Course: Graduate Nursing Leadership in Quality Improvement
Assessment Type: Week 2 Outline (Preparatory for Week 5 Final Paper)
Semester: Spring 2026

Outline Purpose and Submission Guidelines

Nurse managers who build health data literacy across frontline teams and structure quality improvement projects around supervised machine learning, clear data governance, and defined interdisciplinary roles create the conditions for sustained daily weight compliance in heart failure patients. This brief provides the outline submission requirements for Week 2 of the Leadership in Quality Improvement course and prepares you for the full paper due in Week 5. The outline is the foundation for your final 7–9 page paper, which accounts for 18% of your course grade. Your outline must include at least 5 scholarly references, each with a DOI, and 1–2 clear sentences or bullet points for every required section of the final paper. You will receive feedback on this outline to strengthen your final submission.

What Is Due This Week (Week 2)

Your outline is the only deliverable due this week. The full paper is not yet required. Your outline must include at least 5 scholarly references with DOIs. If the instructor cannot verify your sources, points will be deducted. Provide 1–2 clear sentences or bullet points for every required section of the final paper. Ensure you use your references and cite your points within the outline itself. Maximum length is 3 pages, not counting cover page or reference page. The instructor will stop reading after page 3. Submit in APA 7 format with a cover page and reference page. Use this outline to begin your research, as you will need these sources for your final paper. A helpful resource for formatting your references is the Purdue OWL guide on Reference List: Author/Authors.

Week 5 Final Paper Content

1. Scenario and Project Instructions

You are the new nurse manager on a Heart Failure/Cardiac step-down unit. You have high hopes for your floor. Your task is to identify three IT projects that you as the nurse manager of a nursing unit could develop to support the operations of the nursing floor to promote compliance with daily weights for your HF patients. There are multiple approaches to analyzing data, and AI is the latest advance in machine learning approaches that include supervised, in which data is labeled and the algorithm is guided with statistical considerations, and unsupervised, in which unlabeled data is used to infer meaning. While machine learning approaches are robust, they require interdisciplinary teams and large resource dedication to complete. As you do your RCA analysis, you realize that compliance to many of the issues causing experiences on your floor is due to poor health data literacy within your nursing staff. Why is it important for nurse leaders to develop health data literacy? This question must be answered and supported by scholarly sources. Data to support patient care comes from a variety of sources that contain differing data types, and this must be included in your final submission. In your outline you may summarize your findings. Key activities to use clinical data include identifying the sources of data, understanding the data types and associated methods to work with the data, and identifying the necessary resources to complete your IT project. As you begin to form your team for your IT projects, you question yourself as to who will comprise the team. Identifying and assembling an adequate project team is based on the needs of the project. At a minimum, you will need to include frontline staff who will use the product, a data analyst capable of completing the ETL process on the data, and potentially statisticians to conduct appropriate model building and outcomes analyses. Who are the various team members to consider adding to the team? Identify their roles and contributions to the project. Here you will name and describe their role and function in implementing your projects, so be detailed in your paper. Finally, all projects require review and potential revision over time. Follow-up and review of implemented programs should be included in the initial planning stages and resource allocation decisions at project inception.

2. IT Projects (3 Total)

Identify three IT projects that could improve daily weight compliance for HF patients. Label them clearly:

• IT Project 1: ______

• IT Project 2: ______

• IT Project 3: ______

3. Data Analysis and AI

Briefly explain supervised versus unsupervised machine learning. Explain why AI requires interdisciplinary teams and resources.

4. Health Data Literacy

During your RCA, you discover poor health data literacy among staff. You must answer why health data literacy is essential for nurse leaders. Support with scholarly sources.

5. Data Sources and Data Types

Your final paper must describe different sources of clinical data, different data types, how these data types are used, and what resources are needed to complete your IT projects. For the outline, you may summarize these points.

6. Project Team Members

Identify who should be on your IT project team, including frontline staff, a data analyst for ETL processes, a statistician for model building and outcomes analysis, and any additional roles needed. Explain each member’s role and contribution.

7. Follow Up and Sustainability

All QI projects require ongoing review, revision over time, and planning for follow up from the beginning. Explain how you will monitor and sustain your projects.

Each of these numbered sections makes a great Level 1 APA heading for your paper, and these headings are required at this level.

Sample Answer Excerpts

Data Literacy and Nurse Leadership

Nurse leaders who understand how data flows from bedside documentation to quality dashboards can ask sharper questions about what the numbers actually mean. A qualitative study of 27 nurse leaders across five management levels in the Veterans Health Administration found that participants used data primarily for quality improvement and organizational monitoring, yet faced persistent challenges with data fragmentation, lack of knowledge about available data, and untimely reporting (Wong et al., 2023). These findings suggest that health data literacy is not merely a technical skill but a leadership competency that determines whether quality improvement efforts gain traction or stall. When nurse managers can interpret ETL outputs, distinguish between data types, and question the assumptions behind an algorithm, they become active participants in the data ecosystem rather than passive recipients of reports.

• Health data literacy enables nurse leaders to identify which clinical data sources contain the information needed for daily weight monitoring.

• It supports the selection of appropriate data types for tracking compliance trends over time.

• It prepares leaders to collaborate meaningfully with data analysts and statisticians during project design.

Jedwab et al. (2023) validated a set of health informatics competencies for nursing and midwifery leaders through a modified Delphi study, identifying data literacy as one of four core domains alongside strategic leadership, health informatics, and digital literacy. These competencies map directly onto the nurse manager’s role in a quality improvement project, where the ability to read a data dictionary or interpret a run chart often matters more than writing code. A recent analysis of machine learning applications in nursing found that supervised approaches predominated in the literature, yet barriers to adoption included data quality concerns, model transparency issues, and a lack of machine learning-specific training in nursing curricula (Kosmidis et al., 2025). For the nurse manager designing IT projects to improve daily weight compliance, this evidence points toward a practical conclusion: invest in data literacy education before investing in new technology.

Interdisciplinary Teams and Project Sustainability

Heart failure patients in acute care settings often present with complex fluid balance needs that require consistent daily weight measurement to guide diuretic therapy. A nurse-led home tele-monitoring program that included daily body weight transmission reduced heart failure hospitalizations from 48% before enrollment to 13% after enrollment, with the strongest effects observed when a heart failure nurse coordinated alert management (Benchimol et al., 2023). These results underscore the value of frontline nursing involvement in designing and running monitoring protocols, since nurses understand where documentation workflows break down in real time. The project team that a nurse manager assembles should include a clinical data analyst who can extract, transform, and load weight data from the electronic health record into a usable format. A statistician may be needed to build predictive models that flag patients whose weight trends suggest worsening congestion, particularly when the project moves beyond descriptive dashboards toward machine learning applications. Frontline staff who document daily weights should participate in design sessions because they know which fields are confusing, which alerts get ignored, and which workflow changes actually stick. Successful sustainability planning begins at project inception, with clearly defined outcome measures, a schedule for periodic review, and a process for revising protocols as new evidence emerges.

References

Benchimol, J., et al. (2023). Safety and efficacy of heart failure (HF) nurse-led home tele-monitoring (HTM): A single center report. Archives of Cardiovascular Diseases, 116(12), 534–542. https://doi.org/10.1016/j.acvd.2023.10.255

Jedwab, R. M., et al. (2023). Validation and prioritisation of health informatics competencies for Australian nursing and midwifery leaders: A modified Delphi study. International Journal of Medical Informatics, 170, 104971. https://doi.org/10.1016/j.ijmedinf.2022.104971

Kosmidis, D., Simopoulos, D., Anastasopoulos, K., & Koutsouki, S. (2025). Machine learning in nursing: A cross-disciplinary review. Cureus, 17(7), e87181. https://doi.org/10.7759/cureus.87181

Schmaderer, M. S., Struwe, L., Loecker, C., Lier, L., Lundgren, S. W., Pozehl, B., & Zimmerman, L. (2023). Feasibility, acceptability, and intervention description of a mobile health intervention in patients with heart failure. Journal of Cardiovascular Nursing, 38(5), 481–491. https://doi.org/10.1097/JCN.0000000000000955

Wong, J. J., SoRelle, R. P., Yang, C., Knox, M. K., Hysong, S. J., Dorsey, L. E., O’Mahen, P. N., & Petersen, L. A. (2023). Nurse leader perceptions of data in the Veterans Health Administration: A qualitative evaluation. Computers, Informatics, Nursing, 41(9), 679–686. https://doi.org/10.1097/CIN.0000000000001003

Research, Writing, Citation and Referencing Guide

Why Health Data Literacy Matters in Practice

Health data literacy determines whether a nurse manager can translate quality improvement goals into measurable outcomes. When daily weight compliance drops on a heart failure unit, the manager needs to know whether the problem lies in documentation workflows, staff understanding of why daily weights matter, or the data pipeline itself. Without data literacy, the manager relies on anecdotal explanations and misses structural issues that a careful review of the data would reveal.

• Data sources for HF weight monitoring: Electronic health record flowsheets, bedside vital sign monitors, patient-reported weight logs, and remote tele-monitoring devices.

• Data types encountered: Structured numerical data (weight in kilograms), unstructured clinical notes, time-series data across admission days, and categorical data (alert status, documentation compliance flags).

• Resources needed: Access to the EHR reporting database, a data analyst with SQL or ETL skills, statistical software, and time for frontline staff to participate in design and testing.

FAQ: How Does Machine Learning Support Daily Weight Compliance on a Heart Failure Unit?

Supervised machine learning models trained on labeled weight data can flag patients whose weight gain patterns predict impending fluid overload, enabling nurses to intervene before readmission becomes necessary. The model learns from historical cases where weight trends correlated with documented decompensation events. Unsupervised approaches, by contrast, might cluster patients by weight variability patterns without pre-labeled outcomes, offering exploratory insights rather than actionable alerts. The choice between these approaches depends on data availability, clinical question, and the resources the team can commit to model development and validation.

Next Assignment Preview

Week 3: Discussion — Data Sources and Team Assembly for QI Projects

In Week 3, you will post a discussion that builds on your Week 2 outline by identifying the specific data sources and data types you plan to use in your IT projects. Describe how you will assemble your project team and what each member contributes. Your initial post should be 300–500 words and cite at least two scholarly sources. You will respond to at least two peers with substantive feedback on their data governance and team composition choices. This discussion prepares you for the team roles and data sources sections of the Week 5 final paper.