# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 4 Two Different Approaches Within EMRs for the Patient Treatment Process
### Conventional Approach  
Healthcare professionals increasingly leverage EMRs to streamline clinical workflows, with advancements such as smart forms enhancing functionality [51]. One of the primary advantages of EMRs is their ability to support clinical decision-making by providing software solutions tailored to individual patient needs [52]. Given the central role of electronic data forms in EMRs, optimizing their structure and functionality across multiple dimensions is crucial.  
A key consideration is ensuring that EMR data entry processes align with real-world medical workflows while maintaining transparency in medical reports and, ultimately, in clinical decision-making. Physicians document relevant medical information within designated data forms when patients visit a hospital or medical center. As the treatment progresses, they continuously input updates into the data fields embedded within the EMR system. After treatment, the outcomes are recorded to facilitate a comprehensive evaluation.  
For medical teams to assess both the initial diagnosis and the effectiveness of the chosen treatment path, a structured and accessible data framework is essential. Regardless of whether a treatment is successful, the recorded medical data and outcomes remain valuable for the individual patient and similar future cases. The critical limitation of the current EMR approach is the absence of a well-defined treatment cycle structure, leading to fragmented data entry where each data point is recorded in isolation, without considering its relationships with other relevant forms. This scattered approach impairs the ability to track and evaluate treatment progression effectively. The prevalent data entry workflow among physicians, which illustrates these challenges, is depicted in Figure 2.  
In the conventional EMR data entry approach, inconsistencies arise due to the lack of a structured treatment cycle framework. A crucial issue is that certain data forms may be filled out multiple times during a patient's treatment course, while others (equally relevant to the case) may only be completed once. This irregularity creates ambiguity for  
Figure 2: Ambiguity in current EMR systems for assigning data entries to treatment cycles.  
physicians when tracking and analyzing a patient's treatment route. Physicians struggle to determine whether a set of records represents a single continuous treatment plan or multiple fragmented encounters without a standardized cycle-based structure.  
A well-defined treatment cycle should include a consistent set of core data forms for every patient referral, ensuring that each treatment iteration is comprehensively documented. However, in the current system, these essential forms are not systematically present across each patient's visit. This inconsistency is exemplified in Figure 2, where the number of completed data forms does not align with the number of patient referrals. In the given case, a patient underwent three separate treatment attempts due to unsuccessful outcomes in prior visits. Despite this, some key data forms were only utilized once or twice rather than being completed systematically for each referral.  
This fundamental flaw (the absence of a structured and clearly defined treatment cycle) leads to critical gaps in medical documentation, making it challenging to evaluate treatment progress, identify patterns, and ensure data completeness for clinical decision support. To address this issue, we propose a novel EMR model that systematically integrates treatment cycles, ensuring that each cycle is adequately defined, structured, and consistently documented across all patient encounters.