NR 716 Week 5 Discussion: Analyzing Descriptive Statistics
Student Name
Chamberlain University
NR-716: Analytic Methods
Prof. Name
Date
1. Perform the following calculations
a. Percentage of Patients with Uncontrolled Diabetes
Based on the dataset provided, the percentage of patients with uncontrolled diabetes (HbA1c > 7) was determined both pre- and post-implementation of the intervention. Prior to implementation, 9 out of 10 patients (90%) had uncontrolled diabetes. Following the intervention, only 5 out of 10 patients (50%) remained in the uncontrolled category. This indicates a significant improvement in glycemic control after the evidence-based change.
b. Mean HbA1c Values
The mean HbA1c levels were calculated for both groups of patients. The pre-implementation mean HbA1c level was 7.96, while the post-implementation mean reduced to 7.5. This reduction demonstrates that the intervention had a positive effect on average glucose management.
c. Median HbA1c Values
The median values provide another measure of central tendency. In the pre-implementation phase, the median HbA1c was 7.65, while in the post-implementation phase, it decreased to 7.0. This decline again supports the effectiveness of the intervention.
d. Standard Deviation of HbA1c Levels
The standard deviation (SD) was used to measure the spread of HbA1c values. The pre-implementation SD was 1.33, and the post-implementation SD was 1.36. Although the values are relatively close, the slight increase post-implementation suggests that while average levels improved, individual variations remained.
e. Range of HbA1c Values
The range shows the difference between the highest and lowest HbA1c values. Pre-intervention, the range was 5.0 (11.8 – 6.8), while post-intervention, it slightly decreased to 4.9 (11.3 – 6.4). This indicates relatively consistent control, with outliers still influencing the distribution.
Table 1
Descriptive Statistics of HbA1c Levels Pre- and Post-Implementation
| Measure | Pre-Implementation | Post-Implementation |
|---|---|---|
| % of Patients with HbA1c > 7 | 90% | 50% |
| Mean HbA1c | 7.96 | 7.50 |
| Median HbA1c | 7.65 | 7.00 |
| Standard Deviation (SD) | 1.33 | 1.36 |
| Range | 5.0 | 4.9 |
2. Based on your analysis of the descriptive statistics, what determinations related to the mean HbA1c levels following implementation of the evidence-based intervention can be made?
From the descriptive statistics, it is clear that HbA1c levels improved after implementation of the intervention. The mean HbA1c decreased from 7.96 to 7.5, indicating better glycemic control. However, the presence of outliers, particularly patient #10, impacted the distribution of values. This patient consistently had higher HbA1c levels (11.8 pre-intervention and 11.3 post-intervention).
These findings highlight the importance of including a larger sample size in future studies. With only 10 patients, the results may not represent the entire patient population. Furthermore, assumptions regarding patient adherence to diet, exercise, and blood glucose monitoring may not hold true for all individuals. For stronger conclusions, the project should involve a more diverse and larger patient population while incorporating follow-up reviews to ensure compliance with lifestyle changes.
3. As you reflect upon HbA1c levels, you observe that patient #10 HbA1c levels are an outlier. What does this do to your understanding of the data?
Patient #10’s results clearly represent an outlier, as this individual did not show significant improvement despite the intervention. This affects the overall interpretation of the dataset by pulling the mean higher than it might otherwise be. Outliers such as this can mask the true effectiveness of an intervention for the majority of participants.
For patient #10, additional barriers may explain the lack of improvement, including financial constraints, limited social support, advanced age, or challenges in following diet and exercise recommendations. Such cases highlight the importance of tailoring evidence-based interventions to individual patient needs. Advanced practice nurses must recognize potential barriers and incorporate personalized education and support, particularly for patients who require more intensive management.
Using descriptive statistics in this way allows clinicians to identify anomalies, guide further assessment, and strengthen interventions for improving patient outcomes (Munoz-Lopez et al., 2020). This reinforces the idea that data interpretation must go beyond averages and consider the context of individual patient results.
References
Chakrabarty, D. (2021). Measuremental data: Seven measures of central tendency. International Journal of Electronics, 8(1).
NR 716 Week 5 Discussion: Analyzing Descriptive Statistics
Muñoz-López, D. B., Reyes, V. P., Garay-Sevilla, E. M., & Preciado-Puga, M. D. (2020). Validation of an instrument to measure adherence to type 2 diabetes management. International Journal of Clinical Pharmacy, 43(3), 595–603.