NURS FPX 9030 Assessment 2
NURS FPX 9030 Assessment 2 Data and Data Analysis
Student Name
University
NURS-FPX9030: Doctor of Nursing Practice Across the Lifespan III
Dr.
July, 2026
Introduction
Diabetes mellitus is still one of the most common chronic diseases faced by patients during visits to primary care facilities. A Quarter of patients (42%) with an A1c above 9% have not achieved satisfactory glycaemic control (Chief Nursing Officer, Personal Communication, October 10th, 2020), and only one in 3 have an A1C below 7% (Chief Nursing Officer, Personal Communication, October 10th, 2020) during the past year at the project site. The percentages are much higher than the national benchmark for diabetes management, which shows that around 22% of adult diabetics have poor glycemic control and around 50% of diabetics reach glycemic targets (Adjei et al., 2020; CNO Personal Communication, October 10th, 2020). There was no standardized, protocolized system of follow-up with provisions for providing diabetes education, which led to patients not receiving optimal follow-up and diabetes education regularly. To fill in the gaps, a quality improvement (QI) Project was conducted, focused on the following PICOT question: For nurses providing care to the adult patient population who has diabetes (P), does the ADA diabetes follow-up protocol (I), compared to current practice (C), improve glycemic control (O) after 8 weeks (T)? An American Diabetes Association (ADA) diabetes follow-up protocol was carried out during the eight-week project, and the effect of the protocol on glycemic outcomes, staff clinical competency, compliance with diabetes follow-up and self-management behaviors, and compliance with follow-up was assessed. In this paper, the authors present a project design, approaches for data collection, statistical analyses, and the results of the project itself, along with evidence that indicates that structured, evidence-based protocols for follow-up of ADA can lead to clinically meaningful improvements in glycemic control.
Project Design and Data Collection
The project team employed a pre/post design to obtain pre and post-intervention data for 20 adult RRT participants and 8 nurses who were recruited for the project. The project received institutional review board (IRB) approval before initiation, and all requirements to be compliant with the Health Insurance Portability and Accountability Act (HIPAA) were fulfilled; all participants’ names were changed to coded names to help with the preservation of confidentiality. A standard way to assess data and the effectiveness of structured clinical interventions in quality improvement projects is the pre-post project design. For structured interventions in real-world health care contexts, the pre-post design framework can be adapted to measure the effectiveness of the intervention (Engelsbel et al., 2024). The data collection tools used in a quality improvement project need to be valid and reliable to be able to provide an accurate measurement of clinical outcomes (Lighterness et al., 2024). The content of the data collection tools matched the criteria of being good content through the evaluation of experts, and the application of data collection procedures was consistent, so that the data were collected during the entire duration of a project of 8 weeks.
Data Analysis
The data obtained in the 8-week follow-up period, which involved quantitative measures, were analyzed descriptively to determine the impact of the ADA diabetes follow-up protocol on glycemic control, staff competency, and their self-management behaviors. Paired t-tests were the main inferential statistics used to determine the mean differences in one group at two points in time, as the baseline vs. week 8 HbA1C values from the participants had to be compared. Descriptive statistics (percentages) were calculated for staff competency, checklist data regarding self-management, and adherence to the follow-up. A quality improvement project needs statistical analysis for the team to know if changes in outcomes that are observed indicate valid improvements in the care or whether the changes were a random chance. A quality improvement project is dependent on statistical analysis to determine whether changes in the outcome observed are indicative of a valid improvement in the care that is being provided or just a random chance (Panos & Boeckler, 2023). Moreover, repeated measures (pre and post; same participant group) lead to a high statistical sensitivity and individual variability control (Chicco et al., 2025). All data collected was de-identified to create summary tables for ease of interpretation and analysed using the EMR from the clinic and standardised assessment tools.
Project Results
There was at least a substantial increase in all outcome measures for those who participated in the project as compared with the baseline measures. For instance, after 8 weeks, the average HbA1c was lowered by 1.52 per cent — from 9.95 per cent to 8.22 per cent. There was a relatively low follow-up completion rate of 89.2%, but participants who did complete the process had a high degree of engagement with the structured protocol they had available to them. Of the nursing staff, the average pre-training competence was 59.0%, while the average competence for the nursing staff after training was 85.4%, which is 80% or more. The average score for the self-management engagements reported at week 8 was 7.4/10, with 70% of participants fully compliant with medications, and 65% reporting regular blood glucose monitoring. In summary, the findings suggest positive changes at the level of the participants who terminated the project in all three dimensions of clinical, operational, and behavioural outcomes. Tables 1-6 of Appendix A show the results.
Project Outcomes
The project results showed that the follow-up protocol developed by the American Diabetes Association (ADA) was indeed effective in improving glycaemic control significantly and giving answers to the PICOT question. The mean reduction in HbA1C of 1.52 was above the pre-set success value and demonstrated clinical significance. Only 10% reached the target HbA1C (<7%); however, this was observed within 8 weeks of the intervention, and therefore, sufficient intervention time may be needed to reach the target. The rates of planned visits (67%) and any unintended findings (barriers and problems encountered in transportation) were also factors that affected the glycemic trajectories of the participants. Chronic disease quality improvement projects are undertaken on a regular basis and have shown to result in measurable improvements, most of which involve structured protocols that have been proven to be effective from research studies (Endalamaw et al., 2024). The improvement generated by the outpatient glycemic improvement initiatives must be sustainable, which is best achieved by ongoing monitoring, adaptive intervention, and long-term follow-up of at least one year after reaching the end of the initial plan (Jahed et al., 2025). The project strengths were high gains in staff competency, high follow-up adherence, and high documentation in EHR. Some restrictions concerned the duration of the intervention (only 8 weeks); this may limit the applicability of the results to other adult population groups with diabetes in bigger, more clinical community settings.
Conclusion
In an adult-based nurse-led primary care setting, clinical benefits of the ADA diabetes follow-up protocol were demonstrated within 8 weeks for self-management behavior, improvement in diabetes follow-up, and improved competency of the nurse. Some limitations exist in reaching the full glycemic targets because only a short period of time was in which the participants were followed. The results taken together indicate that promoting and implementing standardized evidence-based diabetes follow-up pathways will result in greater success in improving outcomes of chronic diseases when using an ongoing process of support and implementation in outpatient primary care settings.
References
Adjei, S. K., Adjei, P., & Nkrumah, P. A. (2025). Poor glycemic control and its predictors among type 2 diabetes patients: Insights from a single‐center retrospective study in Ghana. Health Science Reports, 8(3), 8–12. https://doi.org/10.1002/hsr2.70558
Chicco, D., Sichenze, A., & Jurman, G. (2025). A simple guide to the use of Student’s t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics. BioData Mining, 18(1), e56. https://doi.org/10.1186/s13040-025-00465-6
Endalamaw, A., Khatri, R. B., Mengistu, T. S., Erku, D., Wolka, E., Zewdie, A., & Assefa, Y. (2024). A scoping review of continuous quality improvement in the healthcare system: Conceptualization, models and tools, barriers and facilitators, and impact. BioMed Central Health Services Research, 24(1), e487. https://doi.org/10.1186/s12913-024-10828-0
Engelsbel, F., Keet, R., & Nugter, A. (2024). A pre-post study design: Evaluating the effectiveness of a new community-based integrated service model on patient outcomes. International Journal of Mental Health Systems, 18(1), e20. https://doi.org/10.1186/s13033-024-00636-8
Jahed, S. A., Nikoosokhan, A., Moravej, H., Sarkheil, P., Malek, M., Esteghamati, A., Hosseinpanah, F., & Sedaghat, S. (2025). The use of continuous glucose monitoring in outpatient diabetes care: Iranian expert consensus statement. Diabetes Research and Clinical Practice, 230, e112961. https://doi.org/10.1016/j.diabres.2025.112961
Lighterness, A., Adcock, M., Scanlon, L. A., & Price, G. (2024). Data quality–driven improvement in health care: Systematic literature review. Journal of Medical Internet Research, 26, e57615. https://doi.org/10.2196/57615
Panos, G. D., & Boeckler, F. M. (2023). Statistical analysis in clinical and experimental medical research: Simplified guidance for authors and reviewers. Drug Design Development and Therapy, 17, 1959–1961. https://doi.org/10.2147/dddt.s427470
Table 1
Demographic Characteristics and Baseline HbA1c (N = 20)
| Participant ID | Age Group | Sex | Race/Ethnicity | Insurance Type | T2DM Duration (yrs) | Baseline HbA1c (%) |
|---|---|---|---|---|---|---|
| P001 | 45–54 | Female | Hispanic/Latino | Medicaid | 6 | 9.8 |
| P002 | 55–64 | Male | Black/African American | Medicare | 11 | 10.2 |
| P003 | 35–44 | Female | White/Non-Hispanic | Private | 3 | 8.7 |
| P004 | 55–64 | Female | Hispanic/Latino | Medicaid | 9 | 11.1 |
| P005 | 45–54 | Male | Asian | Medicaid | 5 | 9.4 |
| P006 | 65+ | Male | Black/African American | Medicare | 14 | 10.8 |
| P007 | 35–44 | Female | White/Non-Hispanic | Private | 2 | 8.3 |
| P008 | 55–64 | Male | Hispanic/Latino | Medicaid | 8 | 9.9 |
| P009 | 45–54 | Female | Asian | Private | 4 | 8.9 |
| P010 | 65+ | Female | Black/African American | Medicare | 16 | 11.4 |
| P011 | 35–44 | Male | White/Non-Hispanic | Private | 3 | 8.5 |
| P012 | 55–64 | Female | Hispanic/Latino | Medicaid | 10 | 10.6 |
| P013 | 45–54 | Male | Black/African American | Medicaid | 7 | 9.7 |
| P014 | 65+ | Female | Hispanic/Latino | Medicare | 13 | 10.9 |
| P015 | 35–44 | Male | Asian | Private | 2 | 8.2 |
| P016 | 55–64 | Female | White/Non-Hispanic | Private | 9 | 9.3 |
| P017 | 45–54 | Male | Hispanic/Latino | Medicaid | 6 | 10.1 |
| P018 | 65+ | Female | Black/African American | Medicare | 18 | 11.7 |
| P019 | 35–44 | Female | Asian | Private | 1 | 7.8 |
| P020 | 55–64 | Male | White/Non-Hispanic | Private | 11 | 9.6 |
Note. All patient identifiers have been replaced with project codes. Age group, sex, race/ethnicity, and insurance type were self-reported. T2DM duration and baseline HbA1c were extracted from EHR records at Week 1. T2DM = type 2 diabetes mellitus; HbA1c = hemoglobin A1c.
Table 2
HbA1c Outcomes Across Measurement Time Points (N = 20)
| Participant ID | Baseline HbA1c (%) | Week 4 HbA1c (%) | Week 8 HbA1c (%) | Change (Baseline to Wk 8) | Target Met (<7%) |
|---|---|---|---|---|---|
| P001 | 9.8 | 9.1 | 8.4 | −1.4 | No |
| P002 | 10.2 | 9.6 | 8.8 | −1.4 | No |
| P003 | 8.7 | 8.1 | 7.4 | −1.3 | No |
| P004 | 11.1 | 10.3 | 9.2 | −1.9 | No |
| P005 | 9.4 | 8.7 | 7.9 | −1.5 | No |
| P006 | 10.8 | 10.0 | 9.1 | −1.7 | No |
| P007 | 8.3 | 7.6 | 7.0 | −1.3 | No |
| P008 | 9.9 | 9.2 | 8.3 | −1.6 | No |
| P009 | 8.9 | 8.3 | 7.5 | −1.4 | No |
| P010 | 11.4 | 10.7 | 9.6 | −1.8 | No |
| P011 | 8.5 | 7.9 | 7.1 | −1.4 | No |
| P012 | 10.6 | 9.8 | 8.9 | −1.7 | No |
| P013 | 9.7 | 9.0 | 8.2 | −1.5 | No |
| P014 | 10.9 | 10.2 | 9.3 | −1.6 | No |
| P015 | 8.2 | 7.5 | 6.9 | −1.3 | Yes |
| P016 | 9.3 | 8.6 | 7.8 | −1.5 | No |
| P017 | 10.1 | 9.4 | 8.5 | −1.6 | No |
| P018 | 11.7 | 10.9 | 9.8 | −1.9 | No |
| P019 | 7.8 | 7.2 | 6.7 | −1.1 | Yes |
| P020 | 9.6 | 8.9 | 8.0 | −1.6 | No |
Note. HbA1c values (%) were obtained from laboratory results integrated into the clinic EHR at Baseline (Week 1), Week 4, and Week 8. Change score reflects Week 8 HbA1c minus Baseline HbA1c. Target achievement was defined as HbA1c < 7% per ADA Standards of Care. HbA1c = hemoglobin A1c; ADA = American Diabetes Association.
Table 3
Follow-Up Adherence and Visit Completion Data (N = 20)
| Participant ID | Scheduled Visits (n = 6) | Completed Visits (n) | Missed Visits (n) | Telehealth Visits Used | Completion Rate (%) |
|---|---|---|---|---|---|
| P001 | 6 | 6 | 0 | 1 | 100 |
| P002 | 6 | 5 | 1 | 0 | 83 |
| P003 | 6 | 6 | 0 | 2 | 100 |
| P004 | 6 | 4 | 2 | 1 | 67 |
| P005 | 6 | 6 | 0 | 0 | 100 |
| P006 | 6 | 5 | 1 | 2 | 83 |
| P007 | 6 | 6 | 0 | 1 | 100 |
| P008 | 6 | 6 | 0 | 0 | 100 |
| P009 | 6 | 5 | 1 | 1 | 83 |
| P010 | 6 | 4 | 2 | 2 | 67 |
| P011 | 6 | 6 | 0 | 0 | 100 |
| P012 | 6 | 6 | 0 | 1 | 100 |
| P013 | 6 | 5 | 1 | 0 | 83 |
| P014 | 6 | 6 | 0 | 2 | 100 |
| P015 | 6 | 6 | 0 | 0 | 100 |
| P016 | 6 | 5 | 1 | 1 | 83 |
| P017 | 6 | 6 | 0 | 1 | 100 |
| P018 | 6 | 4 | 2 | 2 | 67 |
| P019 | 6 | 6 | 0 | 0 | 100 |
| P020 | 6 | 5 | 1 | 1 | 83 |
Note. Biweekly follow-up visits were scheduled over the 8-week implementation period (6 visits per patient). Telehealth visits were offered to patients with mobility or transportation barriers. Completion rate = (completed visits / 6) x 100.
Table 4
Nursing Staff Competency Assessment Results (N = 8)
| Staff ID | Role | Pre-Training Score (/100) | Post-Training Score (/100) | Score Change | Threshold Met (≥80%) | Checklist Completion (%) |
|---|---|---|---|---|---|---|
| S001 | Nurse Practitioner | 62 | 88 | +26 | Yes | 95 |
| S002 | Nurse Practitioner | 58 | 84 | +26 | Yes | 92 |
| S003 | Nurse Practitioner | 65 | 91 | +26 | Yes | 98 |
| S004 | Medical Assistant | 50 | 78 | +28 | No | 85 |
| S005 | Medical Assistant | 55 | 83 | +28 | Yes | 88 |
| S006 | Care Coordinator | 60 | 86 | +26 | Yes | 94 |
| S007 | Health Educator | 70 | 93 | +23 | Yes | 97 |
| S008 | Medical Assistant | 52 | 80 | +28 | Yes | 89 |
Note. Pre-training and post-training scores were obtained from the validated diabetes management competency assessment instrument administered at Week 1 and Week 8. The pre-defined competency success criterion was a score ≥ 80%. Checklist completion reflects the percentage of randomly audited patient visits with complete fidelity documentation.
Table 5
Self-Management Behavior Checklist — Week 8 (N = 20)
| Participant ID | Blood Glucose Monitoring (Daily) | Medication Adherence (Self-Report) | Diet/Nutrition Log Completed | Physical Activity Goal Met | Engagement Score (/10) |
|---|---|---|---|---|---|
| P001 | Yes | Yes | Yes | Partial | 8 |
| P002 | Partial | Yes | No | No | 5 |
| P003 | Yes | Yes | Yes | Yes | 9 |
| P004 | No | Partial | No | No | 4 |
| P005 | Yes | Yes | Yes | Yes | 10 |
| P006 | Partial | Yes | Yes | Partial | 7 |
| P007 | Yes | Yes | Yes | Yes | 10 |
| P008 | Yes | Yes | Partial | Yes | 8 |
| P009 | Yes | Yes | Yes | Partial | 8 |
| P010 | No | Partial | No | No | 3 |
| P011 | Yes | Yes | Yes | Yes | 9 |
| P012 | Yes | Yes | Yes | Partial | 8 |
| P013 | Partial | Yes | Partial | Yes | 7 |
| P014 | Partial | Yes | Yes | Partial | 7 |
| P015 | Yes | Yes | Yes | Yes | 10 |
| P016 | Yes | Yes | Yes | Yes | 9 |
| P017 | Partial | Partial | Yes | No | 6 |
| P018 | No | Partial | No | No | 3 |
| P019 | Yes | Yes | Yes | Yes | 10 |
| P020 | Yes | Yes | Yes | Partial | 8 |
Note. Self-management behaviors were self-reported by patients at the Week 8 follow-up visit using the standardized self-management checklist. Engagement score was assigned by nursing staff on a 10-point scale based on patient participation, responsiveness, and adherence across the 8 weeks. Partial = behavior was sometimes but not consistently performed.
Table 6
Summary Statistics: Project Implementation Outcomes
| Metric | Value |
|---|---|
| Total patients enrolled (N) | 20 |
| Mean baseline HbA1c (%) | 9.95 |
| Mean Week 8 HbA1c (%) | 8.22 |
| Mean HbA1c reduction | −1.52% |
| Patients achieving HbA1c < 7% at Week 8, n (%) | 2 (10%) |
| Overall follow-up completion rate | 89.2% |
| Staff achieving ≥ 80% competency threshold, n (%) | 7 (87.5%) |
| Mean staff pre-training score | 59.0 |
| Mean staff post-training score | 85.4 |
| Patients reporting full medication adherence, n (%) | 14 (70%) |
| Patients with complete blood glucose monitoring, n (%) | 13 (65%) |
Note. Summary statistics were calculated from EHR data, competency assessments, and patient self-management checklists collected across the 8-week implementation period. HbA1c = hemoglobin A1c; T2DM = type 2 diabetes mellitus.
