Artificial Intelligence Generated Content (AIGC) has achieved considerable success in image generation and chatbots . It is natural to apply related techniques, such as Generative Adversarial Networks (GANs) and the Diffusion model , to synthesize trial data for AI training. Many GAN-based approaches have been applied to the EHR generation, including CONAN , CorGAN , EHR-M-GAN , EMR-WGAN , HGAN , MedGan , MedWGAN , SynTEG . However, these GAN-based methods have limitations when generating sparse and high-dimensional data like EHR data. To address this issue, Theodorou et al. proposes a Hierarchical Autoregressive Language Model for generating longitudinal high-dimensional EHR, capturing the hierarchical distribution of EHR records and their temporal relationships without the need for variable selection or aggregation. Another consideration is that GANs are challenging to train and prone to mode collapse. EHRDiff introduces diffusion models for realistic EHR synthesis, achieving better quality of synthetic EHR data for the first time.