# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 8 Conclusions  
This study introduces a scientifically grounded definition of cyclical treatment structures and explores their implications for EMRs and CDSS. A comprehensive review of existing studies identified a critical gap in how treatment cycles are represented in EMRs,which often fail to capture the iterative nature of medical interventions. We proposed a structured, cycle-based approach to EMR design to address this issue, outlining its necessity in improving data organization and supporting more precise clinical decision-making.  
Beyond the conceptual foundation, we examined the practical implementation of this approach, detailing how cyclical treatment structures can be integrated within EMRs to enhance the accuracy and relevance of stored medical data. We empirically demonstrated its advantages by analyzing structured treatment cycles, showing that this method preserves crucial variations in patient responses that would otherwise be overlooked. The findings indicate that incorporating treatment cycles accounts for a substantial portion of the variance in both post-treatment glucose levels and treatment success rates, reinforcing the value of this structured approach in medical decision-making.  
By aligning EMR data structures more closely with real-world clinical workflows, this study provides a framework that meets physicians' needs for structured, context-aware data analysis. The proposed approach ensures that patient records reflect the actual progression of treatment, aiding physicians in evaluating past interventions and optimizing future treatment strategies. This is particularly critical in complex and long-term conditions where treatment plans evolve.  
Moreover, this research contributes to the ongoing evolution of medical data management by proposing a structured methodology that balances data granularity with usability. While workflow integration and user adoption must be addressed, this study establishes a foundation for future research into AI-driven analytics, predictive modeling, and personalized medicine.  
In conclusion, the current study provides a theoretical and empirical basis for the integration of cyclical treatment structures into EMRs, offering a scientifically validated solution to a pressing challenge in CDSS design. As medical data systems evolve continuously, adopting this structured approach will improve diagnostic accuracy, refine treatment plans, and ultimately enhance patient care.  
Conceptualization, A.A.; Methodology, A.A. and F.J.G.-P.; Formal analysis, A.A. and F.J.G.-P.; Investigation, A.A.; Data curation, A.A. and F.J.G.-P.; Validation, A.A. and F.J.G.-P.; Visualization, A.A.; Writing--original draft, A.A.; Writing--review and editing, F.J.G.-P.; Supervision and project administration, F.J.G.-P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data supporting the findings of this study are publicly available on Zenodo repository at [[https://zenodo.org/records/14968776](https://zenodo.org/records/14968776)]([https://zenodo.org/records/14968776](https://zenodo.org/records/14968776)) (accessed on 4 March 2025).  
The authors declare no conflicts of interest.