Name
Chamberlain University
NR-716: Analytic Methods
Prof. Name
Date
The dataset revealed notable changes in the percentage of patients with uncontrolled diabetes (HbA1c > 7) before and after the intervention. Initially, 9 out of 10 patients (90%) fell within the uncontrolled range. However, after the implementation of the evidence-based intervention, this percentage decreased substantially to 50% (5 out of 10 patients). This reduction highlights a meaningful improvement in glycemic control among participants following the intervention.
The mean HbA1c values demonstrated measurable progress in patient outcomes. Pre-intervention, the average HbA1c level was 7.96, whereas post-intervention, the mean declined to 7.5. This shift indicates a clinically relevant improvement in glucose management that may reduce risks of long-term complications.
The median HbA1c values provided additional insight into central tendency. Before the intervention, the median value was 7.65, but afterward, it declined to 7.0. This reduction further supports the overall effectiveness of the intervention in lowering blood sugar levels across most participants.
Standard deviation (SD) helps measure the degree of variability in HbA1c results. The SD in the pre-intervention phase was 1.33, compared to 1.36 in the post-intervention phase. Although the average HbA1c decreased, the slight increase in SD suggests some variability remained within the patient population. This finding points to differences in individual response levels despite the overall improvement.
The range, which measures the spread between the highest and lowest HbA1c values, also provided useful insights. Pre-intervention, the range was 5.0 (11.8 – 6.8), while post-intervention, it decreased slightly to 4.9 (11.3 – 6.4). The consistent reduction in range suggests that improvements were relatively stable, though extreme values (outliers) continued to affect the distribution.
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 |
The analysis shows that HbA1c levels improved after the intervention. The mean HbA1c value decreased from 7.96 to 7.5, indicating that patients achieved better glycemic control overall. However, the presence of outliers, particularly patient #10, influenced the overall distribution by keeping the mean higher than it might have been without their data.
Given the small sample size of 10 patients, these results should be interpreted cautiously. Small datasets are highly sensitive to outliers and may not accurately represent broader patient populations. Therefore, future research should include a larger and more diverse cohort of patients to strengthen generalizability. Moreover, consideration must be given to patient adherence factors, including lifestyle behaviors such as diet, exercise, and consistent monitoring of blood glucose. To maximize the effectiveness of such interventions, structured follow-ups, counseling sessions, and multidisciplinary support are recommended.
Patient #10 presented a clear outlier, with HbA1c values of 11.8 pre-intervention and 11.3 post-intervention. This patient’s results skewed the dataset upward, particularly affecting the mean, and obscured the overall success of the intervention. While most patients demonstrated improved glycemic control, this individual showed limited progress.
Outliers such as this highlight the importance of individualized care planning. Patient #10 may have faced additional barriers to improvement, including socioeconomic factors, limited access to healthy food, reduced social support, age-related challenges, or difficulties maintaining diet and exercise routines. Such barriers emphasize the need for healthcare professionals to tailor interventions, provide targeted education, and adopt personalized approaches to care.
From a clinical perspective, the recognition of outliers serves as an opportunity for deeper investigation rather than simple exclusion from analysis. As noted by Muñoz-López et al. (2020), evaluating outliers provides insight into adherence patterns, barriers to care, and areas for additional support. Ultimately, clinicians must balance population-level improvements with the recognition of individual patient variability. This ensures that interventions are both evidence-based and patient-centered, improving outcomes across diverse groups.
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.
