Assigment .Apa seven . All instructions attached.
19 days ago 35
LeadershipinQI-Week2Outline.docx
LeadershipinQualityImprovement.docx
LeadershipinQI-Week2Outline.docx
2
Leadership in Quality Improvement: IT Projects to Improve Daily Weight Compliance in Heart Failure Patients
Vianka Concepcion
Florida National University
Nursing Leadership and Management
Dr. Nora Hernandez-Pupo
September 12, 2026
1
Leadership in Quality Improvement: IT Projects to Improve Daily Weight Compliance in Heart Failure Patients
Scenario & Project Instructions
My unit of practice is Heart Failure/Cardiac Step-Down, and I am the new nurse manager and my goal is to improve adherence to daily weight checks among our HF population, which is one of the earliest, lowest cost markers of fluid overload and/or impending decompensation (De Lathauwer et al., 2025). This outline aims to tackle issues uncovered by a root cause analysis of current compliance gaps, including inconsistent technology adoption and low data literacy among staff, with three IT projects, as well as a discussion of appropriate approaches to data and AI and a plan for the implementation and sustainability of the team.
IT Projects (3 Total)
IT Project 1: Remote Patient Monitoring (RPM) Platform for Daily Weights. A scalable system that automatically syncs patient weight info to the EHR, mitigating manual recording and enhancing timely clinical notifications for problematic weight patterns (De Lathauwer et al., 2025)..
IT Project 2: Automated EHR Weight-Compliance Dashboard. In real-time, a dashboard would alert nursing staff to missing any daily weights by showing the number of nursing observations that did not result in a recorded weight (the same automation and large-scale data feedback that supports nursing-led quality improvement efforts, as described in Goodfellow & Bird, 2022).
IT Project 3: Patient-Facing Mobile Reminder Application. Reminding and logging tool to improve adherence for patients becoming discharged, basically bringing RPM monitoring into the transition from hospital to home, and has been found to lower hospitalization and death risk (De Lathauwer et al., 2025).
Data Analysis & AI
There are two general types of machine learning techniques. In supervised learning, models are trained on labeled data, such as a historical dataset of patients that were compliant with treatment and those that were not to predict an outcome such as the risk of a patient being readmitted (An et al., 2023). Unsupervised learning is a method used to identify patterns in unlabeled data, which may not be known beforehand and can be applied to cluster patients into groups based on risk factors (An et al., 2023). Both methods involve the use of interdisciplinary teams and significant resources as a reliable model requires well-structured, clean data, clinical knowledge of the domain to interpret the results, and statistical knowledge to validate its performance (An et al., 2023).
Health Data Literacy
Nurse leaders are expected to demonstrate high levels of health data literacy, as they influence the effectiveness that frontline staff are able to achieve when implementing digital systems; managers with greater informatics skills have been associated with increased digital health system documentation and increased documentation consistency (Hariyati et al., 2024). Even the best systems can be designed to be adopted without this literacy (Hariyati et al., 2024).
Data Sources & Data Types
This project will utilize clinical data from the EHR, RPM reading from devices and nursing documentation/audit logs. The ability to use automated data analytics for quality improvement is increasingly being seen as the primary method as it enables a larger sample size, and provides quick and easy feedback for staff, which are not possible through manual chart audits (Goodfellow & Bird, 2022). This requires key resources such as time to train staff, data visualization tools, and support with integration of EHRs (Goodfellow & Bird, 2022).
Project Team Members
Frontline nursing staff: Primary users of the RPM and dashboard tools, nursing staff on the front lines: provide ongoing feedback on workflow fit.
Data analyst (ETL): Extracts, transforms and loads weight and compliance data from EHR and RPM platform for reporting and analysis (Goodfellow & Bird, 2022).
Statistician: constructs and tests predictive models, e.g., supervised classification in the risk of re-hospitalization, and assesses the significance of the outcome data (An et al., 2023).
Nurse informaticist/IT liaison: creates a bridge between the clinical workflow demands and technical requirements and to support the development of digital literacy skills among staff (Hariyati et al., 2024).
Unit nurse manager (project lead): coordinates the team and is aligned with the priorities of the clinical team within the unit.
Follow-Up & Sustainability
Follow-up will be incorporated in all IT projects from the beginning instead of added on to the end, as the retention of IT projects in healthcare QI is highly correlated with its integration, maintaining competency in the QI method within the team, and ongoing alignment to clinical needs (Lee & Chang, 2026). Future plans involve 3, 6, and 12-month compliance audits, periodic staff retraining related to system changes (Lee & Chang, 2026); and the unit's QI team to review the dashboard on a regular basis.
Conclusion
This Heart Failure/Cardiac step-down unit needs more than one change to improve daily weight compliance. The three recommended IT projects solve the problem from admission to discharge and beyond: Remote patient monitoring, Automated compliance dashboard, Reminder app to patient. They must be coupled with improved health data literacy amongst staff, strategic deployment of machine learning and a team that includes front-line nurses, a data analyst and a statistician alongside nursing leadership. This initiative is best placed to achieve long-lasting and impactful patient outcomes if it is approachable from the outset and takes a holistic and integrated approach to building follow-up and sustainability planning.
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), Article 4178. https://doi.org/10.3390/s23094178
De Lathauwer, I. L. J., Nieuwenhuys, W. W., Hafkamp, F., Regis, M., Brouwers, R. W. M., Funk, M., & Kemps, H. M. C. (2025). Remote patient monitoring in heart failure: A comprehensive meta-analysis of effective programme components for hospitalization and mortality reduction. European Journal of Heart Failure, 27(9), 1670–1685. https://doi.org/10.1002/ejhf.3568
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
Hariyati, R. T. S., Handiyani, H., Wildani, A. A., Afriani, T., Nuraini, T., & Amiruddin, M. H. (2024). Disparate digital literacy levels of nursing manager and staff, specifically in nursing informatics competencies and their causes: A cross-sectional study. Journal of Healthcare Leadership, 16, 415–425. https://doi.org/10.2147/JHL.S470456
Lee, J.-Y., & Chang, J.-T. (2026). Predicting the sustainability of quality improvement initiatives in healthcare: A data-driven study. The Joint Commission Journal on Quality and Patient Safety, 52(8), 358–364. https://doi.org/10.1016/j.jcjq.2026.04.006
LeadershipinQualityImprovement.docx
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.
Attempt feedback
Changes for Final Submission
The outline contains four pages of content, exceeding the three-page maximum. Although most required sections appear within the first three content pages, the conclusion extends the outline to a fourth page and therefore does not meet the stated length requirement.
Project 3 focuses on a post-discharge patient reminder application, while the assignment centers on inpatient daily-weight compliance on a cardiac step-down unit. Revise this project or explain more directly how it improves unit operations. De Lathauwer et al. (2025) supports comprehensive remote heart-failure monitoring, but it does not establish that a reminder application alone reduces hospitalization and mortality. Likewise, Hariyati et al. (2024) identifies differences in digital literacy but does not directly support the claim that stronger informatics skills increase documentation consistency. Ensure each conclusion remains within the findings of the cited study.
The Data Sources and Data Types section needs further development. It names the EHR, RPM devices, and audit logs but does not clearly classify structured, unstructured, categorical, continuous, and time-series data or explain how each will be analyzed. Add resources such as secure storage, data dictionaries, cybersecurity oversight, device integration, software, training time, and ongoing technical support. The team would also benefit from including quality-improvement, biomedical engineering, privacy, cardiology, and patient representatives.
Correct the contradictory sentence, “Even the best systems can be designed to be adopted without this literacy,” which should indicate that successful adoption may not occur without adequate literacy. Also revise the double period after Project 1, “RPM reading” to “RPM readings,” “patients becoming discharged” to “patients preparing for discharge,” and “if it is approachable from the outset” to “if it is approached systematically from the outset.” Ensure APA Level 1 headings are centered and bold in the final paper.
Show less
Rubric
Rubric Result
87.5/ 100
· Learning Activity Question
50% of total result
37.5
Satisfactory
The learning activity questions are not answered comprehensively. All the following elements are included: a. The problem or issue you are presented with no supportive evidence and somewhat clearly explained, and the outline follows some of the established formats for outlines. There is a vague introduction to the problem being presented.
Criterion Feedback
Project 3 focuses on a post-discharge patient reminder application, while the assignment centers on inpatient daily-weight compliance on a cardiac step-down unit. Revise this project or explain more directly how it improves unit operations. De Lathauwer et al. (2025) supports comprehensive remote heart-failure monitoring, but it does not establish that a reminder application alone reduces hospitalization and mortality. Likewise, Hariyati et al. (2024) identifies differences in digital literacy but does not directly support the claim that stronger informatics skills increase documentation consistency. Ensure each conclusion remains within the findings of the cited study.
The Data Sources and Data Types section needs further development. It names the EHR, RPM devices, and audit logs but does not clearly classify structured, unstructured, categorical, continuous, and time-series data or explain how each will be analyzed. Add resources such as secure storage, data dictionaries, cybersecurity oversight, device integration, software, training time, and ongoing technical support. The team would also benefit from including quality-improvement, biomedical engineering, privacy, cardiology, and patient representatives.
· Reference Page REFERENCES have DOI Numbers and are searchable via the DOI number
25% of total result
25
Excellent
Utilizes at least 5 sources and all sources are current within 5 years. Cited following APA format. All REFERENCES have DOI Numbers
· Grammar, Spelling, APA, Page Requirements
25% of total result
25
Excellent
Minimal spelling and/or grammar mistakes. APA format is maintained. Page requirement is maintained. Similarity index and AI index 20% or lower.
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LeadershipinQI-Week2Outline.docx
2
Leadership in Quality Improvement: IT Projects to Improve Daily Weight Compliance in Heart Failure Patients
Vianka Concepcion
Florida National University
Nursing Leadership and Management
Dr. Nora Hernandez-Pupo
September 12, 2026
1
Leadership in Quality Improvement: IT Projects to Improve Daily Weight Compliance in Heart Failure Patients
Scenario & Project Instructions
My unit of practice is Heart Failure/Cardiac Step-Down, and I am the new nurse manager and my goal is to improve adherence to daily weight checks among our HF population, which is one of the earliest, lowest cost markers of fluid overload and/or impending decompensation (De Lathauwer et al., 2025). This outline aims to tackle issues uncovered by a root cause analysis of current compliance gaps, including inconsistent technology adoption and low data literacy among staff, with three IT projects, as well as a discussion of appropriate approaches to data and AI and a plan for the implementation and sustainability of the team.
IT Projects (3 Total)
IT Project 1: Remote Patient Monitoring (RPM) Platform for Daily Weights. A scalable system that automatically syncs patient weight info to the EHR, mitigating manual recording and enhancing timely clinical notifications for problematic weight patterns (De Lathauwer et al., 2025)..
IT Project 2: Automated EHR Weight-Compliance Dashboard. In real-time, a dashboard would alert nursing staff to missing any daily weights by showing the number of nursing observations that did not result in a recorded weight (the same automation and large-scale data feedback that supports nursing-led quality improvement efforts, as described in Goodfellow & Bird, 2022).
IT Project 3: Patient-Facing Mobile Reminder Application. Reminding and logging tool to improve adherence for patients becoming discharged, basically bringing RPM monitoring into the transition from hospital to home, and has been found to lower hospitalization and death risk (De Lathauwer et al., 2025).
Data Analysis & AI
There are two general types of machine learning techniques. In supervised learning, models are trained on labeled data, such as a historical dataset of patients that were compliant with treatment and those that were not to predict an outcome such as the risk of a patient being readmitted (An et al., 2023). Unsupervised learning is a method used to identify patterns in unlabeled data, which may not be known beforehand and can be applied to cluster patients into groups based on risk factors (An et al., 2023). Both methods involve the use of interdisciplinary teams and significant resources as a reliable model requires well-structured, clean data, clinical knowledge of the domain to interpret the results, and statistical knowledge to validate its performance (An et al., 2023).
Health Data Literacy
Nurse leaders are expected to demonstrate high levels of health data literacy, as they influence the effectiveness that frontline staff are able to achieve when implementing digital systems; managers with greater informatics skills have been associated with increased digital health system documentation and increased documentation consistency (Hariyati et al., 2024). Even the best systems can be designed to be adopted without this literacy (Hariyati et al., 2024).
Data Sources & Data Types
This project will utilize clinical data from the EHR, RPM reading from devices and nursing documentation/audit logs. The ability to use automated data analytics for quality improvement is increasingly being seen as the primary method as it enables a larger sample size, and provides quick and easy feedback for staff, which are not possible through manual chart audits (Goodfellow & Bird, 2022). This requires key resources such as time to train staff, data visualization tools, and support with integration of EHRs (Goodfellow & Bird, 2022).
Project Team Members
Frontline nursing staff: Primary users of the RPM and dashboard tools, nursing staff on the front lines: provide ongoing feedback on workflow fit.
Data analyst (ETL): Extracts, transforms and loads weight and compliance data from EHR and RPM platform for reporting and analysis (Goodfellow & Bird, 2022).
Statistician: constructs and tests predictive models, e.g., supervised classification in the risk of re-hospitalization, and assesses the significance of the outcome data (An et al., 2023).
Nurse informaticist/IT liaison: creates a bridge between the clinical workflow demands and technical requirements and to support the development of digital literacy skills among staff (Hariyati et al., 2024).
Unit nurse manager (project lead): coordinates the team and is aligned with the priorities of the clinical team within the unit.
Follow-Up & Sustainability
Follow-up will be incorporated in all IT projects from the beginning instead of added on to the end, as the retention of IT projects in healthcare QI is highly correlated with its integration, maintaining competency in the QI method within the team, and ongoing alignment to clinical needs (Lee & Chang, 2026). Future plans involve 3, 6, and 12-month compliance audits, periodic staff retraining related to system changes (Lee & Chang, 2026); and the unit's QI team to review the dashboard on a regular basis.
Conclusion
This Heart Failure/Cardiac step-down unit needs more than one change to improve daily weight compliance. The three recommended IT projects solve the problem from admission to discharge and beyond: Remote patient monitoring, Automated compliance dashboard, Reminder app to patient. They must be coupled with improved health data literacy amongst staff, strategic deployment of machine learning and a team that includes front-line nurses, a data analyst and a statistician alongside nursing leadership. This initiative is best placed to achieve long-lasting and impactful patient outcomes if it is approachable from the outset and takes a holistic and integrated approach to building follow-up and sustainability planning.
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), Article 4178. https://doi.org/10.3390/s23094178
De Lathauwer, I. L. J., Nieuwenhuys, W. W., Hafkamp, F., Regis, M., Brouwers, R. W. M., Funk, M., & Kemps, H. M. C. (2025). Remote patient monitoring in heart failure: A comprehensive meta-analysis of effective programme components for hospitalization and mortality reduction. European Journal of Heart Failure, 27(9), 1670–1685. https://doi.org/10.1002/ejhf.3568
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
Hariyati, R. T. S., Handiyani, H., Wildani, A. A., Afriani, T., Nuraini, T., & Amiruddin, M. H. (2024). Disparate digital literacy levels of nursing manager and staff, specifically in nursing informatics competencies and their causes: A cross-sectional study. Journal of Healthcare Leadership, 16, 415–425. https://doi.org/10.2147/JHL.S470456
Lee, J.-Y., & Chang, J.-T. (2026). Predicting the sustainability of quality improvement initiatives in healthcare: A data-driven study. The Joint Commission Journal on Quality and Patient Safety, 52(8), 358–364. https://doi.org/10.1016/j.jcjq.2026.04.006
LeadershipinQualityImprovement.docx
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.
Attempt feedback
Changes for Final Submission
The outline contains four pages of content, exceeding the three-page maximum. Although most required sections appear within the first three content pages, the conclusion extends the outline to a fourth page and therefore does not meet the stated length requirement.
Project 3 focuses on a post-discharge patient reminder application, while the assignment centers on inpatient daily-weight compliance on a cardiac step-down unit. Revise this project or explain more directly how it improves unit operations. De Lathauwer et al. (2025) supports comprehensive remote heart-failure monitoring, but it does not establish that a reminder application alone reduces hospitalization and mortality. Likewise, Hariyati et al. (2024) identifies differences in digital literacy but does not directly support the claim that stronger informatics skills increase documentation consistency. Ensure each conclusion remains within the findings of the cited study.
The Data Sources and Data Types section needs further development. It names the EHR, RPM devices, and audit logs but does not clearly classify structured, unstructured, categorical, continuous, and time-series data or explain how each will be analyzed. Add resources such as secure storage, data dictionaries, cybersecurity oversight, device integration, software, training time, and ongoing technical support. The team would also benefit from including quality-improvement, biomedical engineering, privacy, cardiology, and patient representatives.
Correct the contradictory sentence, “Even the best systems can be designed to be adopted without this literacy,” which should indicate that successful adoption may not occur without adequate literacy. Also revise the double period after Project 1, “RPM reading” to “RPM readings,” “patients becoming discharged” to “patients preparing for discharge,” and “if it is approachable from the outset” to “if it is approached systematically from the outset.” Ensure APA Level 1 headings are centered and bold in the final paper.
Show less
Rubric
Rubric Result
87.5/ 100
· Learning Activity Question
50% of total result
37.5
Satisfactory
The learning activity questions are not answered comprehensively. All the following elements are included: a. The problem or issue you are presented with no supportive evidence and somewhat clearly explained, and the outline follows some of the established formats for outlines. There is a vague introduction to the problem being presented.
Criterion Feedback
Project 3 focuses on a post-discharge patient reminder application, while the assignment centers on inpatient daily-weight compliance on a cardiac step-down unit. Revise this project or explain more directly how it improves unit operations. De Lathauwer et al. (2025) supports comprehensive remote heart-failure monitoring, but it does not establish that a reminder application alone reduces hospitalization and mortality. Likewise, Hariyati et al. (2024) identifies differences in digital literacy but does not directly support the claim that stronger informatics skills increase documentation consistency. Ensure each conclusion remains within the findings of the cited study.
The Data Sources and Data Types section needs further development. It names the EHR, RPM devices, and audit logs but does not clearly classify structured, unstructured, categorical, continuous, and time-series data or explain how each will be analyzed. Add resources such as secure storage, data dictionaries, cybersecurity oversight, device integration, software, training time, and ongoing technical support. The team would also benefit from including quality-improvement, biomedical engineering, privacy, cardiology, and patient representatives.
· Reference Page REFERENCES have DOI Numbers and are searchable via the DOI number
25% of total result
25
Excellent
Utilizes at least 5 sources and all sources are current within 5 years. Cited following APA format. All REFERENCES have DOI Numbers
· Grammar, Spelling, APA, Page Requirements
25% of total result
25
Excellent
Minimal spelling and/or grammar mistakes. APA format is maintained. Page requirement is maintained. Similarity index and AI index 20% or lower.