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 dataset demonstrated significant improvement in the percentage of patients with uncontrolled diabetes (HbA1c > 7) following the intervention. Initially, 9 out of 10 patients (90%) were classified as having uncontrolled diabetes. After implementing the evidence-based intervention, this number dropped to 5 out of 10 patients (50%). This notable reduction reflects a substantial improvement in glycemic control among participants post-intervention.

b. Mean HbA1c Values

The mean HbA1c levels showed measurable progress after the intervention. Before the implementation, the mean HbA1c was 7.96, whereas after the intervention, it declined to 7.50. This reduction demonstrates a clinically meaningful improvement in glucose regulation, potentially reducing the risk of diabetes-related complications.

c. Median HbA1c Values

The median HbA1c values further supported the observed improvement. The median decreased from 7.65 in the pre-intervention phase to 7.00 post-intervention. This decline in the central value indicates that most patients experienced lower blood glucose levels following the intervention.

d. Standard Deviation of HbA1c Levels

The standard deviation (SD) reflects variability within the data. The SD slightly increased from 1.33 before the intervention to 1.36 afterward. Although the average HbA1c values improved, this marginal increase in SD suggests minor variations in patient responses, indicating that while most benefited, some individuals showed differing levels of improvement.

e. Range of HbA1c Values

The range measures the difference between the highest and lowest HbA1c values. Pre-intervention, the range was 5.0 (11.8 – 6.8), and post-intervention, it decreased slightly to 4.9 (11.3 – 6.4). This marginal reduction implies that the improvements were generally stable, though a few outliers continued to influence the spread of data.

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 indicates that mean HbA1c levels improved significantly following the implementation of the evidence-based intervention. The mean HbA1c decreased from 7.96 to 7.50, reflecting enhanced glycemic control across participants. However, this improvement was slightly tempered by the influence of an outlier (patient #10), whose persistently high HbA1c levels raised the overall mean.

Given the small sample size (n = 10), these findings should be interpreted with caution. Small datasets are highly sensitive to outliers and may not accurately reflect trends in larger populations. Future research should include a larger and more diverse patient sample to enhance generalizability.

Additionally, factors such as patient adherence, lifestyle habits (e.g., diet and exercise), and self-monitoring behaviors must be considered, as they significantly affect intervention outcomes. To ensure sustained glycemic control, healthcare providers should integrate structured follow-ups, educational counseling, and multidisciplinary care approaches that address both medical and behavioral components of diabetes management.

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 represents a clear outlier, exhibiting HbA1c levels of 11.8 pre-intervention and 11.3 post-intervention. This patient’s persistently elevated HbA1c values skewed the dataset upward, especially affecting the mean and potentially obscuring the intervention’s overall effectiveness. While the majority of participants demonstrated improved glycemic control, this patient’s limited progress highlights the variability in treatment response.

Outliers such as Patient #10 underscore the necessity for individualized care planning. Factors contributing to poor outcomes may include limited access to healthcare, socioeconomic barriers, inadequate dietary management, age-related challenges, or inconsistent adherence to lifestyle modifications. Recognizing and addressing these issues through personalized interventions can help improve individual outcomes.

Clinically, outliers should not be disregarded; rather, they offer valuable insights into patient-specific barriers and behavioral patterns. As emphasized by Muñoz-López et al. (2020), analyzing outlier data supports the identification of adherence challenges and helps refine patient-centered strategies. Ultimately, effective diabetes management requires a balance between population-based strategies and individualized care, ensuring both collective improvement and personalized health support.

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.