Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality

Electronic medical records (EMRs) are fundamental to clinical decision support systems (CDSS). Conventional EMR structures still fail to capture the cyclical nature of treatment plans, leading to fragmented data representation and reduced decision accuracy. This study addresses this gap by proposing a cycle-based EMR framework that systematically integrates treatment cycles, enabling structured, sequential data organization. Treatment cycles categorize patient data into iterative phases, reflecting disease progression and repeated interventions, ensuring data continuity and analytical precision. A dataset inspired by MIMIC-III was developed to empirically evaluate this approach, incorporating treatment cycle fields to enhance data continuity and analytical precision. The results indicate that cycle-based structuring preserves critical variations in patient responses, improves treatment outcome predictions, and strengthens CDSS recommendations. While this approach offers substantial benefits, challenges such as workflow adaptation, usability, and interoperability must be addressed to facilitate seamless integration into clinical practice. Despite these challenges, this study establishes a scientifically validated foundation for cycle-based EMRs, aligning data structures with real-world clinical workflows. By rectifying data organization, this approach elevates diagnostic accuracy, optimizes treatment planning, and enhances patient outcomes, contributing to the future of precision medicine.