Synthetic patient data offer a promising solution to the privacy and accessibility constraints that hinder data-driven innovation in healthcare, particularly for clinical trial research. This paper presents a systematic study of synthetic data gener- ation and evaluation using a real-world clinical cohort from the Fondazione Italiana Linfomi. We define a standardized, model- agnostic workflow to benchmark diverse generative paradigms across the critical dimensions of fidelity, privacy and utility. Our findings reveal that no single model excels across all metrics, emphasizing the need to evaluate models on a case-by-case basis. By proposing a structured, literature-informed evaluation suite, this work facilitates context-aware model selection to support reproducible clinical research in the context of clinical trials.