Electronic Health Records contain valuable data but, due to the inherent private nature of the data, this data is difficult to share. Electronic Health Records usually contain patient notes in free-text, making it an interesting application for Natural Language Processing. Synthetic data generation is a proposed application of Natural Language Processing where language models trained on the data from an Electronic Health Record could serve as a replacement for the original dataset while avoiding privacy issues, thus allowing more data to be shared for research. This thesis explores generating synthetic training text to be used for downstream tasks, evaluating the utility of the generated data. Based on the research question, “Will a NER-model trained on a synthetic dataset perform as well as a NER-model trained on a pseudonymized corpus?”, we formulate three hypotheses. The goal of the thesis is to evaluate whether the Named Entity Recognition (NER) model trained on synthetic data, can perform as well as another NER-model trained on pseudonymized data, thus implicating the synthetic dataset utility and indicating whether the dataset can adequately replace the pseudonymized dataset. The thesis is conducted with an experiment where the generated data is evaluated based on the n-gram overlap and performance in an Named Entity Recognition downstream task. Two NER-models are trained, one on the synthetic dataset and one on the pseudonymized data. These models are then evaluated on a manually annotated dataset, used as a gold standard. The generated synthetic dataset, Stockholm EPR Gastro Synthetic Corpus, while showing some promise in being linguistically coherent with minor spelling and grammatical errors, does not adequately capture the variance of the pseudonymized data. The synthetic dataset has a higher 8-gram overlap with the pseudonymized counterpart when compared to similar studies, indicating a higher risk of leaking data. The NER-model trained on the synthetic dataset performs worse in almost every metric when compared to the NER-model trained on the pseudonymized dataset. The experiment suggests that the synthetic training data cannot adequately replace the pseudonymized data.