# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 2 Related Works
### Literature Review on System Design Solutions for Medical Data Arrangement and Labeling in EMRs  
System design in medical settings, such as CDSS and EMRs, plays a vital role in managing the flow and quality of data that drives medical decision-making [30]. A crucial aspect of this process involves data tagging (or labeling), where raw data are organized and annotated to enhance their usability across systems. Proper data arrangement and labeling in EMRs are fundamental in enabling adequate clinical decision support [31]. While numerous studies explore system design and data flows, we attempt to collect different perspectives to articulate and compare them within the medical domain.  
[PERSON] and [PERSON] [32] analyzed data flows within EMR systems, emphasizing the difficulties posed by unstructured data. They highlighted that the absence of robust tagging mechanisms makes retrieving specific patient information inefficient and prone to errors. On the other hand, [PERSON] et al. [33] delved into the issue from a system integration perspective, emphasizing standardized data tags to streamline interoperability between EMRs and CDSS. These studies highlight the importance of tagging in two different aspects. [PERSON] and [PERSON] investigated probable internal data flaws, while [PERSON] et al. were concerned with external data compatibility during communication between two medical platforms.  
[PERSON] et al. [34] explored tagging automation, introducing AI-powered tools that classify and annotate data during entry. Their study demonstrated that automated tagging significantly reduced the manual workload and enhanced the data consistency. Meanwhile, [PERSON] et al. [35] investigated tagging from a user interface perspective, showing that user-friendly tagging systems improved the data entry accuracy and reduced clinician frustration. These findings underscore the interplay between technological advancements and user-centric design in optimizing tagging practices.  
Structured tagging, such as labeling treatment cycles, enables the systematic organization of critical patient data, which is significant in generating actionable insights in clinical workflows [36]. In this respect, cyclical approaches (like the Plan-Do-Check-Act model) have successfully improved the timeliness and quality of EMR documentation by embedding iterative feedback loops into data entry processes, thereby aligning tags with evolving clinical phases [37]. Advanced frameworks, such as patient similarity-based EMR systems, leverage tagged diagnostic and demographic data to dynamically update treatment recommendations, illustrating how structured tagging supports adaptive decision-making [38]. Similarly, efforts to convert unstructured EMR text into standardized formats (like using BART-based models for entity extraction in lung cancer records) highlight the role of semantic tagging in harmonizing data for analytics [36].  
However, challenges persist in psychiatric EMRs, where unstructured narrative data dominate. In such cases, hybrid tagging strategies will be needed to balance clinical nuance with computational usability [39]. These insights underscore that cyclical tagging and structured arrangement enhance data interoperability and drive predictive accuracy and clinical safety in CDSS applications [38; 40].