Assigment .apa seven . all instructions attached.

NursingLeadershipandManagement.docx

Nursing Leadership and Management-DBX-DL01 Nora Hernandez-Pupo

Leadership in Quality Improvement

Data to support patient care comes from a variety of sources that contain differing data types. 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.

The scope of your IT project will determine the level of data access required and the associated data storage needs. Data used in multisite projects will require IRB oversight and often require the execution of a DUA if transferring data outside of the institution or receiving data from another institution.

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 that 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.

There are multiple approaches to analyzing data. 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 robust, machine learning approaches require interdisciplinary teams and large resource dedication to complete.

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.

Let us consider the following for the quality improvement project:

You are a new manager on your Heart Failure/Cardiac step-down unit and have high hopes for your floor.

Identify several 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.

As you do your RCA analysis you realize that compliance to many of the issues causing experiences on your floor is due to the poor health data literacy within your nursing staff. Why is it important for nurse leaders to develop health data literacy?

As you begin to form your team for your IT projects you question yourself as to who will comprise the team.

Who are the various team members to consider adding to the team? Identify their roles and contributions to the project.

Important Note on the Use of AI Tools

Students may choose to use AI platforms to assist with grammar checks or writing guidance, such as Grammarly. While these tools can be helpful for improving clarity and mechanics, it is essential that all submitted work remains your own. AI-generated text should be treated as a guide, not a substitute for your original writing. Copying and pasting directly from an AI platform, even if done innocently, constitutes a violation of academic honesty.

If you would like additional support in strengthening your writing, please take advantage of the Writing Studio and the Library resources, where you can receive individualized assistance in developing your ideas, refining your grammar, and ensuring your work reflects your own academic voice. These services are designed to help you grow as a writer and succeed with integrity.

Please review the rubric prior to submitting- remember this assignment is 18% of your grade.

REFERENCES Must have DOI Numbers for me to look them up- If I am unable to verify the references points will be deducted.

Great resource to assist you: Reference List: Author/Authors - Purdue OWL® - Purdue University

This paper should be minimally 7-9 pages, but NO MORE than 9 pages, not counting references and cover page.

Submission

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Rubric Details

Maximum Score

100 points

Excellent

The paper demonstrates that the author understands and has applied concepts learned in the course. Concepts are integrated into the writer’s own insights. The writer provides concluding remarks that show analysis and synthesis of ideas.

25

Satisfactory

The paper demonstrates that the author, mostly, understands and has applied concepts learned in the course. Some conclusions, however, are not supported in the body of the paper.

20

Unsatisfactory

The paper demonstrates that the author, to a certain extent, understands and has applied concepts learned in the course

15

Unacceptable

The paper does not demonstrate that the author has understood, and applied concepts learned in the course.

10

Excellent

The topic is focused narrowly enough for the scope of this assignment. A thesis statement provides direction for the paper, either by a statement of a position or hypothesis. The topic is consistently well thought out, thorough offers insight into the topic, and includes cited evidence to support the topic.

25

Satisfactory

The topic is focused but lacks direction. The paper is about a specific topic, but the writer has not established a position. The topic is somewhat well thought out, offers limited insight into the topic, but does not include cited evidence to support the topic.

20

Unsatisfactory

The topic is too broad for the scope of this assignment.

15

Unacceptable

The topic is unclear or unrelated to the discussion topic with little or no supporting evidence.

10

Excellent

In-depth discussion and elaboration in all sections of the paper. Ties together information from all sources. Paper flows from one issue to the next with no headings. The author’s writing demonstrates an understanding of the relationship among material obtained from all sources Mostly, it ties together information from all sources. There is an introduction and a conclusion in the submission.

25

Satisfactory

In-depth discussion and elaboration in most sections of the paper. Mostly, it ties together information from all sources. Paper flows with only some disjointedness. The author’s writing demonstrates an understanding of the relationship among material obtained from all sources. There is an introduction and a conclusion in the submission.

20

Unsatisfactory

The writer has omitted content. Quotations from others outweigh the writer’s own ideas excessively. Sometimes ties together information from all sources. The paper does not flow. Disjointedness is apparent. The author’s writing does not demonstrate an understanding of the relationship between material obtained from all sources. There is an introduction and/or conclusion in the submission, but not both.

15

Unacceptable

Cursory discussion in all the sections of the paper or brief discussion in only a few sections It does not tie together information. Paper does not flow and appears to be created from disparate issues. Headings are necessary to link concepts. Writing does not demonstrate an understanding of any relationship. There is NO introduction or conclusion in the submission.

10

Excellent

5 current sources are used and are peer-review journal articles or scholarly books. Sources include both general background sources and specialized sources. Special-interest sources and popular literature and acknowledged as such if they are cited. All websites utilized are authoritative. All REFERENCES have DOI Numbers for me to look them up

7

Satisfactory

Used 4 current sources, which are peer-review journal articles or scholarly books. All websites utilized are authoritative. 4 REFERENCES have DOI Numbers for me to look them up

5.6

Unsatisfactory

Used 2 current sources which are peer-reviewed journal articles or scholarly books. All websites utilized are credible. 2-3 REFERENCES have DOI Numbers for me to look them up

4.2

Unacceptable

Fewer than 2 current sources are used which are peer-reviewed journal articles or scholarly books. Not all websites utilized are credible, and/or sources are not current. Less than 2 REFERENCES have DOI Numbers for me to look them up

2.8

Excellent

Fewer than 5 incomplete citations and/or quotations, and APA format errors. The paper is developed using APA-approved headings throughout the paper.

6

Satisfactory

More than 5 but fewer than 10 incomplete citations and/or quotations, and APA format errors. The paper is developed using and introduction APA approved heading- but no headings within the rest of the paper.

4.8

Unsatisfactory

More than 10 incomplete citations and/or quotations, or APA format errors. The paper is not developed at the graduate level APA format.

3.6

Unacceptable

The citation style is inconsistent or incorrect. It does not cite sources. The paper submitted is NOT in APA Format.

2.4

Excellent

Fewer than 5 grammatical, spelling, capitalization, or punctuation errors The required word count has been met.

12

Satisfactory

More than 5 but fewer than 10 grammatical, spelling, capitalization & punctuation errors The required word count is 25 words below the minimum required count.

9.6

Unsatisfactory

More than 10 grammatical, spelling, capitalization & punctuation errors The required word count is 50 words below the minimum required count.

7.2

Unacceptable

An unacceptable number of spelling and/or grammar mistakes. The required word count is more than 50 words below the minimum required count.

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Week2_QI_Outline1.docx

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Leadership in Quality Improvement: Increasing Daily Weight Compliance on a Heart Failure Step-Down Unit

Mirlenys Diaz

Florida National University

Nursing Leadership and Management

Dr. Nora Hernandez-Pupo

July 11, 2026

Leadership in Quality Improvement: Increasing Daily Weight Compliance on a Heart Failure Step-Down Unit

Low compliance with daily weight monitoring was one of the quality gaps identified during a root cause analysis as part of the process I have now embarked upon as a new nurse manager for the Heart Failure/Cardiac step-down unit, and the health data literacy of staff members was also identified as an issue. Inconsistency in the measurement of daily weights is a major early warning sign for fluid retention, fluid overload and impending decompensation in patients with heart failure, thus impacting negatively on readmission rates and patient safety on the unit. The outline below specifies three IT projects, the data to be used to support these projects, and a sustainability plan that will be used to guide the entire quality improvement proposal.

IT Projects

1. According to the ITEC-CHF model, which incorporated nurse follow-up and remote weight monitoring and increased patient adherence, EHR-integrated, Bluetooth-enabled digital scales that automatically send daily weights (Ding et al., 2020) were incorporated. Capture is automated, eliminating manual charting that is the root cause of many entries being missed or made late, leaving early signs of fluid retention on the unit obscured. This project is the most important of the three, since it is a program to address data capture directly instead of fixing data gaps after they are made.

1. IT Project 2: An EHR-embedded clinical decision support dashboard to warn the charge nurse for same-shift follow-up when patients do not receive their daily weight, rather than audit patients at the end of the shift. Real-time alerting reduces the time between missed measurement and corrective action, enabling staff to take action before a documentation gap turns into a missed clinical warning sign.

1. After discharge, a patient-facing mobile reminder app that automatically uploads home weights to the database, and extends compliance monitoring.Compliance monitoring after discharge, with a patient-facing mobile reminder app that auto uploads home weights. This project addresses the transition-of-care time period when patients are highest risk for readmission and less closely monitored by the care team.

Data Analysis and AI

1. While supervised machine learning algorithms can be trained on labelled data, for example, historical weight trends associated with known readmission, unsupervised learning can uncover hidden patterns in data that are not labelled, for example, clustering patients by weight-fluctuation behaviour (An et al., 2023). These strategies enable the unit to shift from a reactive weight monitoring to an earlier detection of patients who are becoming fluid overloaded.

1. AI projects rely on multi-disciplinary teams with clinical, data science, and IT skills, as these models rely on big data and statistical expertise (An et al., 2023). It is important for nurse leaders to account for this expertise when contemplating budgeting for the development and maintenance of predictive tools, and to think beyond the initial implementation and plan for ongoing collaboration with data science partners.

Health Data Literacy

1. Without nurse leaders' literacy of dashboards, even the best-designed data tools are ineffective in changing frontline practice (Burgess & Honey, 2022). Health Data literacy is the key, since it's the process that turns compliance data into bedside intervention and not merely a report.

1. A common challenge to nursing informatics adoption that is linked to nurse leaders' lack of confidence with digital health tools is the inability to model and teach digital health tools (Burgess & Honey, 2022). The success of all three IT projects will be dependent on having this competency built in myself and my charge nurses.

Data Sources and Data Types

1. Structured clinical data will be derived from daily weight, vitals, labs, and other EHR fields; unstructured data will be from free-text nursing notes on missed weights (Seinen et al., 2025). The data from both sources must be captured at the same time or the dashboard and predictive models will not accurately reflect what is occurring on the floor.

1. While structured data can be used to automate the tracking and modelling, unstructured notes provide context; it is necessary to read them manually (Seinen et al., 2025). Requirements are connected scales, IT infrastructure support to connect record to scale, a data analyst to receive and review a log of data collected from scales, staff time to become familiar with the new process without interfering with patient care, and time to train staff on the new process.

Project Team Members

1. They also use the tools on a daily basis and see things that a technical team might not, so if the scales and dashboard are going to be used as intended, frontline nursing staff will play a key role in making sure that happens from the very beginning and throughout the process; getting their input from the beginning will help ensure buy-in and sustained adoption.

1. A data analyst performs the extract, transform and load (ETL) process, which involves converting raw data from devices into a usable EHR dataset (Goodfellow & Bird, 2022), ensuring that the compliance dashboard contains accurate and up-to-date data.

1. Before using a predictive model, a statistician models the relationship between compliance and readmission, and validates any predictive tool to ensure the unit will not act on a less reliable or poorly calibrated model (Gagnon et al., 2024).

1. A decision-maker, like the nurse manager, helps increase the chances of findings being implemented in practice (Gagnon et al., 2024) by securing the resources, mapping the project to the unit's priorities and ensuring that the team is held accountable for deadlines.

Follow-Up and Sustainability

1. Implementation should be driven by reviewing and revision from the beginning of the project, not after the project is completed, with key performance indicators being clearly defined before the project begins and reviewed with the project team during implementation (Goodfellow & Bird, 2022).

1. I will maintain the projects on an ongoing basis, reviewing compliance trends every quarter, adjusting compliance alert levels, and retraining staff to ensure compliance as necessary to make sustainability a leadership responsibility and not a one-time project implementation issue, and maintaining communication back to staff on the projects' impact.

References

An, Q., Rahman, S., Zhou, J., & Kang, J. J. (2023). A comprehensive review on machine learning in healthcare industry: Classification, restrictions, opportunities and challenges. Sensors, 23(9), 4178. https://doi.org/10.3390/s23094178

Burgess, J.-M., & Honey, M. (2022). Nurse leaders enabling nurses to adopt digital health: Results of an integrative literature review. Nursing Praxis in Aotearoa New Zealand, 38(3). https://doi.org/10.36951/001c.40333

Ding, H., Jayasena, R., Chen, S. H., Maiorana, A., Dowling, A., Layland, J., Good, N., Karunanithi, M., & Edwards, I. (2020). The effects of telemonitoring on patient compliance with self-management recommendations and outcomes of the innovative telemonitoring enhanced care program for chronic heart failure: Randomized controlled trial. Journal of Medical Internet Research, 22(7), e17559. https://doi.org/10.2196/17559

Gagnon, J., Breton, M., & Gaboury, I. (2024). Decision-maker roles in healthcare quality improvement projects: A scoping review. BMJ Open Quality, 13(1), e002522. https://doi.org/10.1136/bmjoq-2023-002522

Goodfellow, D., & Bird, J. (2022). Using data analytics to enhance quality improvement projects. Nursing Management, 29(4), 32–40. https://doi.org/10.7748/nm.2022.e2042

Seinen, T. M., Kors, J. A., van Mulligen, E. M., & Rijnbeek, P. R. (2025). Using structured codes and free-text notes to measure information complementarity in electronic health records: Feasibility and validation study. Journal of Medical Internet Research, 27, e66910. https://doi.org/10.2196/66910