Realistic synthetic electronic health records (EHRs) can be leveraged to acceler- ate methodological developments for research purposes while mitigating privacy concerns associated with data sharing. However, the training of Generative Ad- versarial Networks remains challenging, often resulting in issues like mode col- lapse. While diffusion models have demonstrated progress in generating qual- ity synthetic samples for tabular EHRs given ample denoising steps, their perfor- mance wanes when confronted with missing modalities in heterogeneous tabular EHRs data. For example, some EHRs contain solely static measurements, and some contain only contain temporal measurements, or a blend of both data types. To bridge this gap, we introduce FLEXGEN-EHR– a versatile diffusion model tai- lored for heterogeneous tabular EHRs, equipped with the capability of handling missing modalities in an integrative learning framework. We define an optimal transport module to align and accentuate the common feature space of hetero- geneity of EHRs. We empirically show that our model consistently outperforms existing state-of-the-art synthetic EHR generation methods both in fidelity by up to 3.10% and utility by up to 7.16%. Additionally, we show that our method can be successfully used in privacy-sensitive settings, where the original patient-level data cannot be shared.