## 4 Discussion
While the patient cohort generator demonstrates strong performance across various datasets and correlation scenarios, several limitations should be acknowledged. First, the tool currently operates within a static, cross-sectional framework and does not support longitudinal data simulation, limiting its applicability in modeling time-dependent outcomes such as disease progression or treatment response. Second, despite using robust techniques like Cholesky decomposition and expert-informed correlation inputs, the generator relies on predefined parameters and cannot autonomously infer correlations from raw datasets when such data are partially available. Moreover, the reliance on user-provided estimates or literature-derived correlations introduces a potential for bias if the source population does not match the target cohort. The model also currently assumes that the defined constraints and logical rules are exhaustive, which may not capture rare or complex clinical scenarios. Lastly, while implemented in a widely accessible Excel/VBA environment, this platform imposes scalability limitations and may restrict integration with more advanced machine learning pipelines. On the other hand, the underlying logic and conceptual framework are programming-language agnostic, making the tool readily transferable to other environments such as Python or R. The core value lies in the algorithmic structure and methodological rigor, not the implementation platform. Finally, as the method described in the article generates data based on existing statistical information, it is difficult in principle to generate data for completely new diseases or extremely rare patient groups for which no prior research or clinical data exists. Another important limitation of the current model is its inability to directly reproduce complex non-linear relationships between variables due to the reliance on Cholesky decomposition, which assumes linear dependence. For instance, the cardiovascular risk profile in females undergoes a notable shift around menopause, where associations with variables such as lipid levels or blood pressure may change direction or escalate non-linearly. While the model does not currently handle such dynamics natively, two practical workarounds can be employed. First, the population can be stratified into subgroups where the variable relationships are approximately linear (e.g., premenopausal vs. postmenopausal), and synthetic cohorts can be generated for each subgroup separately before merging them. Second, users can define non-linear relationships using the model's linking feature, which allows a parameter to be calculated from one or more others using custom equations. While this approach permits complex transformations (e.g., conditional expressions, non-linear scaling), it introduces dependency between variables and may reduce flexibility in certain analyses. Future developments will focus on expanding the generator's capabilities to support longitudinal synthetic data generation, enabling the simulation of disease trajectories, treatment pathways, and repeated measures over time. Incorporating multilevel data structures, such as hierarchical models to simulate clusters of patients within hospitals or regions, would further enhance realism for health systems modeling. Another key area of advancement will be the integration of semi-supervised learning to refine correlation matrices based on partial real-world data, reducing dependence on user-supplied inputs. A more scalable implementation in Python or R is also planned to support larger datasets, parallel processing, and integration with AI workflows. Additionally, a user-friendly graphical interface and automated diagnostic tools could facilitate broader adoption among non-technical users. A fundamental limitation of our method is its reliance on existing statistical summaries, expert knowledge, or clinical literature. Consequently, the model faces inherent challenges in generating synthetic cohorts for completely new or extremely rare conditions lacking any prior data.