NR 716 Week 5 Discussion: Analyzing Descriptive Statistics

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

The percentage of patients with uncontrolled diabetes (HbA1c > 7) was assessed both before and after the intervention. In the pre-implementation phase, 9 of the 10 patients (90%) were in the uncontrolled range. After implementation, only 5 out of 10 patients (50%) remained uncontrolled. This indicates that the intervention contributed to a notable improvement in glycemic regulation, cutting the proportion of uncontrolled cases nearly in half.

b. Mean HbA1c Values

The average HbA1c values demonstrated the overall effectiveness of the intervention. Before the intervention, the mean HbA1c was 7.96, whereas afterward, the mean reduced to 7.50. This decline illustrates improved blood glucose control across the group, supporting the evidence-based intervention’s positive effect.

c. Median HbA1c Values

Examining the median provides another measure of central tendency. The pre-implementation median HbA1c was 7.65, while the post-implementation median was 7.0. This consistent reduction highlights that most patients experienced lower HbA1c levels following the intervention, reinforcing the mean results.

d. Standard Deviation of HbA1c Levels

The standard deviation (SD) reflects variability in the data. Pre-intervention, the SD was 1.33, while post-intervention, it increased slightly to 1.36. This suggests that although the group average improved, individual variations in HbA1c values persisted, indicating that not all patients responded equally to the intervention.

e. Range of HbA1c Values

The range measures the spread between the highest and lowest values. Initially, the range was 5.0 (11.8 – 6.8). Post-implementation, it slightly narrowed to 4.9 (11.3 – 6.4). This indicates that while overall glucose control improved, some patients continued to have extreme values influencing the data 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?

The analysis of descriptive statistics demonstrates that the intervention led to a measurable improvement in glycemic control. Specifically, the mean HbA1c decreased from 7.96 to 7.50, indicating overall better glucose regulation. However, the results also revealed the influence of outliers, such as patient #10, who consistently presented with elevated HbA1c levels (11.8 before and 11.3 after intervention). This individual’s persistently high values skewed the group mean upward, masking the full extent of improvement for most patients.

Another important consideration is the small sample size of only 10 patients, which limits generalizability. Larger studies with diverse populations are necessary to confirm the intervention’s effectiveness. Additionally, differences in patient adherence to diet, exercise, and medication regimens must be taken into account, as inconsistent adherence may confound outcomes. Future projects should expand the participant pool and include ongoing monitoring to evaluate long-term effectiveness and compliance with lifestyle modifications.

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 clearly represents an outlier, as their HbA1c levels remained elevated despite the intervention. This significantly impacts the interpretation of the results by artificially raising the mean, making the intervention seem less effective overall. Outliers reduce the accuracy of general conclusions and emphasize the importance of analyzing both group averages and individual patient responses.

For this patient, multiple factors could explain the lack of improvement, such as socioeconomic barriers, poor social support, advanced disease progression, or challenges in adhering to treatment plans. This highlights the need for individualized approaches, where advanced practice nurses assess patient-specific challenges and implement tailored education, counseling, and support strategies.

By identifying outliers, clinicians can better understand the limitations of group-level statistics and recognize the importance of personalized care. As noted by Muñoz-López et al. (2020), descriptive statistical tools are valuable not only for identifying trends but also for detecting anomalies that require targeted clinical attention.

References

Chakrabarty, D. (2021). Measuremental data: Seven measures of central tendency. International Journal of Electronics, 8(1).

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.

NR 716 Week 5 Discussion: Analyzing Descriptive Statistics

NR 716 Week 5 Discussion: Analyzing Descriptive Statistics.