## 4 Discussion
Generative Adversarial Networks (GANs), which consist of two neural networks competing to produce increasingly realistic data, have been effective in generating synthetic datasets for specific medical conditions like leukemia [21], but their narrow scope limits generalizability. Variational Autoencoders (VAEs), another common method, work by encoding data into a latent space and then decoding it to generate new data. While useful for creating smooth and coherent synthetic samples, VAEs can struggle with high-fidelity generation, especially for complex tabular healthcare data. Moreover, many existing models are evaluated using limited validation strategies and are not tested against real-world datasets [22]. Our patient cohort generator addresses these gaps by allowing users to define specific parameters and generate realistic patient data under different scenarios of data sparsity. Validation through R2 and SSE metrics showed near-perfect matching when accurate correlations were used (e.g., R2 > 99% in the normal and low-dimensional cohorts), reinforcing the tool's precision in modeling both simple and complex datasets. It is validated using correlation analysis, ensuring both accuracy and applicability for clinical research, while maintaining patient confidentiality. One of the key practical advantages of our tool is its flexibility. It supports user-defined levels of data sparsity and accommodates correlation matrices informed by expert judgment or the literature, empowering researchers and health policymakers to simulate realistic scenarios even in the absence of real patient data. This makes the generator especially valuable in early-phase feasibility studies, educational simulations, or health policy modeling where control over variable relationships is essential. Moreover, the generator produces interpretable, tabular outputs that mirror conventional epidemiological datasets, making it easier for clinical researchers to validate and work with the simulated data without the need of advanced machine learning expertise. The accuracy and consistency of expert-derived correlations are critical to the integrity of the simulated data. While we previously recommended that experts be selected from the same clinical and geographic context as the modeled population, we have taken additional measures to ensure quality. Specifically, all participating clinicians were provided with a structured guide (Appendix D) that explained the concept of correlation, offered visual aids, included numeric translation rules, and referenced real-world medical examples to help calibrate their inputs. To evaluate internal consistency, we conducted comparative validation using inputs from each expert individually and in combination (Appendix F, Table A4). Results showed that using the average estimates from multiple experts provided greater stability across simulations. In future applications, the use of structured elicitation methodologies, such as the Delphi method or formal consensus-building techniques, may further enhance the reliability of expert-informed models. Although the model is hosted within Microsoft Excel, all core calculations are executed via an integrated VBA engine rather than Excel formula functions. This allows for fast in-memory processing, with Excel serving only as a user interface for input configuration and output display. In performance testing using the Framingham dataset, the tool consistently generated a 10,000-patient cohort in under 10 s on a standard commercial laptop (Excel 365 64-bit, Intel Core i7-1365U CPU, 32 GB RAM, no GPU). Even when scaled to 100,000 patients, the tool completed processing in approximately 3 min. Given that synthetic cohort generation is typically a one-time task per modeling or simulation application, the computational burden is minimal and well within acceptable operational limits. While future updates may explore multithreading to further enhance performance during iterative steps, current performance levels do not necessitate such optimization.