# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 6 Empirical Evaluation of the Cycle Approach's Utilization in EMRs  
To investigate the efficacy of a cyclical approach within EMRs for data management and analysis, we constructed a synthetic dataset comprising 750 rows inspired by real-world clinical data and tailored to emphasize iterative treatment cycles and data tagging. This dataset, titled \"CycleDiabetes750\", is available at [[https://bit.ly/MIMIC-Cycle750](https://bit.ly/MIMIC-Cycle750)]([https://bit.ly/MIMIC-Cycle750](https://bit.ly/MIMIC-Cycle750)) (accessed on 4 March 2025), is rooted in the structure of the Medical Information Mart for Intensive Care III (MIMIC-III) database, a widely recognized public dataset sourced from Beth Israel Deaconess Medical Center and accessible via PhysioNet [57]. MIMIC-III was selected due to its comprehensive coverage of clinical variables pertinent to diabetes care, including patient demographics (e.g., subject_id, age, gender), laboratory measurements (e.g., glucose), medication administrations (e.g., treatment_type), diagnoses (e.g., diagnosis), and admission details (e.g., admission_type). These data fields align closely with the study's requirements, providing a robust framework for the simulation of diabetes treatment scenarios.  
To specifically address the cyclical nature of treatment plans and enable the detailed analysis of iterative interventions, we augmented the MIMIC-III-inspired dataset with custom fields not natively present in the original database. These included \"treatment_cycle\", \"cycle_tag\", \"cycle_outcome\", \"treatment_start_date\", \"treatment_end_date\", and \"total_cycles\". The \"treatment_cycle\" field distinguishes successive treatment phases (Cycle 1, 2, 3), while \"cycle_tag\" categorizes patient glucose states (Initial_VeryHigh, Followup_Moderate) to reflect the clinical severity and progression. The \"cycle_outcome\" field (Improved, Stable) evaluates the treatment efficacy related to each cycle, and the temporal fields (the treatment_start_date, the treatment_end_date) track intervention durations. Finally, \"total_cycles\" indicates the number of cycles per patient, facilitating longitudinal analysis. These modifications transform the static, single-event focus of traditional EMR datasets like MIMIC-III into a dynamic, cycle-based framework, enabling us to explore features and analytical capabilities unique to this design.  
To ensure that incorporating treatment cycle fields does not introduce bias, the dataset's construction followed rigorous validation protocols, aligning with established guidelines for synthetic dataset development in medical research [58,59]. The statistical distribution of patient characteristics was preserved to reflect real-world clinical settings, minimizing artificial distortions. Cycle assignments were structured based on predefined glucose thresholds rather than arbitrary labeling, preventing systematic misclassification. Additionally, confounding variables such as patient demographics and admission types were analyzed to confirm that cycle-based grouping did not disproportionately affect treatment cycle categorization [60]. Lastly, potential observer bias was mitigated by maintaining transparency in variable definitions and ensuring that treatment cycle classifications were based on objective glucose level thresholds rather than subjective clinical interpretations. These precautions collectively enhanced the study's internal validity, ensuring that the findings were robust and not driven by dataset modifications.  
The primary objective of this empirical analysis is to demonstrate enhanced analytical precision and multifaceted insights afforded by the cyclical approach in EMRs, which are inaccessible without such a design. The dataset comprises patient records with variables such as treatment dates, treatment cycles, post-treatment metrics, and success rates. To assess the effect of treatment cycles, we compared the results obtained from analyses considering cycle tagging with those obtained from studies that ignored cyclic patterns.