# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 6 Empirical Evaluation of the Cycle Approach's Utilization in EMRs
### Cycling vs. Non-Cycling Approaches  
In this regard, we conducted a detailed analysis of treatment outcomes by comparing metrics across treatment cycles, conducting statistical tests, and visualizing the results. The studies mentioned were performed within the Python environment, and the provided codes are available at the Zenodo repository ([[https://zenodo.org/records/15046756](https://zenodo.org/records/15046756)]([https://zenodo.org/records/15046756](https://zenodo.org/records/15046756)), accessed on 18 March 2025).  
The cyclical and non-cyclical analyses were conducted using the same dataset, including identical patient records, variables, and outcome definitions. The only difference between the two approaches lies in the additional fields that we introduced to support treatment cycle identification (e.g., treatment_cycle, cycle_tag). These enhancements enabled data tagging that facilitated cycle-based analysis, without altering the underlying data.  
**Cyclical analysis:** Rather than treating all patient records as part of a uniform dataset, entries were grouped based on their corresponding treatment cycles (e.g., Cycle 1, Cycle 2, Cycle 3), as defined by the treatment_cycle field. This cyclical approach enabled an evaluation of variations across individual cycles by calculating summary statistics (mean, standard deviation) and visualizing cycle-specific distributions. Such categorization allowsfor the detection of meaningful patterns and clinical insights that could be overlooked in aggregate-level analyses.  
Table 2 shows that the data can be broken down by treatment cycle by exploiting the cyclical approach. For each cycle (for example, Cycle 1, Cycle 2, etc.), we calculated multiple metrics, such as the mean, standard deviation, median, minimum, maximum, and count for post-treatment glucose levels, alongside similar statistics for the treatment success rate. Each row corresponds to a treatment cycle, and each column shows a specific metric. For instance, regarding the cell in row [2; 1] of this table, it represents the value of the first metric (typically the post-treatment glucose mean) for the treatment cycle listed in the second row (which here corresponds to, e.g., Cycle 2). This cell gives a quick snapshot of this cycle's average post-treatment glucose value, providing insights into how the treatment outcomes may vary from one cycle to another.  
**Non-cyclical analysis:** For comparison, an overall analysis was performed by treating the dataset as a single group, disregarding cycle information. This approach provides an aggregated view of the treatment outcomes, helping to highlight the potential loss of granularity when treatment cycles are not considered.  
Table 3 demonstrates that the conventional approach aggregates all data without considering the treatment cycles. It computes the same metric types (mean, standard deviation, median, etc.) for the entire dataset. Essentially, it gives a single, overall view of the post-treatment glucose levels and treatment success rates for the whole patient population. This approach is practical when we require an aggregate metric. Still, as our further analysis shows, it hides any differences or variability across treatment cycles.  
In summary, the cycle-based table reveals nuances between individual treatment cycles (highlighted by values such as cell [2; 1] for Cycle 2's average post-treatment glucose level). In contrast, the overall table provides a single, unified benchmark. This two-pronged approach illustrates that significant differences between information stemming from treatment cycles might be lost if one only looks at the global (non-cyclical) summary.