# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 6 Empirical Evaluation of the Cycle Approach's Utilization in EMRs
### Results
#### 6.4.2 Information Loss Analysis  
To examine the impact of the cyclical approach, we analyzed the post-treatment glucose levels across different treatment cycles and compared them to a non-cyclical perspective. The left panel of Figure 5 presents a box plot illustrating the post-treatment glucose levels for each treatment cycle. In contrast, the right panel displays a histogram accompanied by a KDE representing the overall post-treatment glucose distribution without consideration of cycle distinctions. As depicted in the left panel of Figure 5, the post-treatment glucose levels fluctuate significantly across treatment cycles. The box plot shows that Cycle 3 has substantially lower post-treatment glucose levels than Cycles 1 and 2, indicating a potential trend where the glucose levels decrease as the treatment progresses. This suggests that successive cycles contribute to more effective glucose regulation, an insight that would be overlooked without cycle-specific analysis. Additionally, the variation in glucose levels, as indicated by the interquartile ranges and whiskers, differs significantly among cycles, underscoring the importance of evaluating treatment phases separately. The right panel of Figure 5, which aggregates all data without accounting for cycles, provides a significantly less detailed view. The histogram shows the overall distribution of the glucose levels but does not reveal whether specific cycles are responsible for higher or lower values. The red dashed line indicates the mean glucose level. Still, without distinguishing between treatment cycles, assessing whether this average is influenced by specific cycles performing better or worse is impossible. A quantitative analysis further supports these findings. By incorporating treatment cycles, we can observe distinct differences between the cycles. These variations are lost without this distinction, as shown in the overall distribution, leading to a less precise understanding of glucose trends. Specifically, our analysis shows that 14.96% of the variance in the post-treatment glucose levels can be explained by the treatment cycles. This means that, by ignoring the cyclical nature of treatment, we lose about 15% of the relevant information, potentially overlooking significant patterns in the patient response. A similar trend is observed in the treatment success rates. The grouping of the data by cycle explains 37.31% of the variance, meaning that over one-third of the variation in treatment outcomes is directly related to the treatment cycle structure. Without tracking the treatment cycles separately, the mentioned information will be lost, leading to an incomplete or potentially misleading interpretation of the treatment efficacy. Figure 5: Post-treatment glucose evaluation using different approaches. These percentages originate from our ANOVA test, which quantifies how much of the total variance in the dataset is explained by differences between treatment cycles. Specifically, the ANOVA partitions the variance into two components:  
**Variance between treatment cycles--**how much the mean values differ across cycles. **Variance within cycles--**how much individual values vary within each treatment cycle. The proportion of the variance explained by the treatment cycles, known as the eta-squared (\(\eta^{2}\)), is calculated as the ratio of the between-group variance to the total variance [65]. This metric quantifies the extent to which treatment cycles contribute to variations in the post-treatment glucose levels and treatment success rates. The obtained values suggest that incorporating treatment cycles is essential in accurately assessing glucose regulation and treatment efficacy, as it accounts for a significant portion of the observed variance. These findings reinforce the importance of a cyclical EMR design, demonstrating that a non-cyclical system fails to capture crucial variations in patient outcomes.