  Researchers require timely access to real-world longitudinal electronic
health records (EHR) to develop, test, validate, and implement machine learning
solutions that improve the quality and efficiency of healthcare. In contrast,
health systems value deeply patient privacy and data security. De-identified
EHRs do not adequately address the needs of health systems, as de-identified
data are susceptible to re-identification and its volume is also limited.
Synthetic EHRs offer a potential solution. In this paper, we propose EHR
Variational Autoencoder (EVA) for synthesizing sequences of discrete EHR
encounters (e.g., clinical visits) and encounter features (e.g., diagnoses,
medications, procedures). We illustrate that EVA can produce realistic EHR
sequences, account for individual differences among patients, and can be
conditioned on specific disease conditions, thus enabling disease-specific
studies. We design efficient, accurate inference algorithms by combining
stochastic gradient Markov Chain Monte Carlo with amortized variational
inference. We assess the utility of the methods on large real-world EHR
repositories containing over 250, 000 patients. Our experiments, which include
user studies with knowledgeable clinicians, indicate the generated EHR
sequences are realistic. We confirmed the performance of predictive models
trained on the synthetic data are similar with those trained on real EHRs.
Additionally, our findings indicate that augmenting real data with synthetic
EHRs results in the best predictive performance - improving the best baseline
by as much as 8% in top-20 recall.
