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Nowadays, having an electronic health record properly adopted by medical bodies is no longer a challenge. In fact, the critical issue for health practitioners is related to the exchange of health data between different institutes. While some existing standards provide interoperability for e-health systems, they still not offer a coherent solution that can be integrated and used easily. In this author, the paper present OpenEHR, a consistent health standard based on the dual-level scheme, which separates the reference model from the archetypes, allowing a flexible modeling of clinical concepts. However, getting into OpenEHR implementation can be very complex. The purpose of this article is to simplify the integration of OpenEHR, by introducing a stepwise methodology of the migration from legacy SQL-based EHR to an interoperable OpenEHR based NoSQL oriented document model. Successful consolidation was achieved through the deployment of metadata and mapping rules in Java environment project, which allowed a practical automation of the interoperability integration process.

The novel coronavirus disease 2019 (COVID-19) is disrupting all aspects of our lives as the global spread of the virus continues. In this difficult period, various research projects are taking place to study and analyse the dynamics of the pandemic. In the present work, we firstly present a deep overview of the main forecasting models to predict the new cases of COVID-19. In this context, we focus on univariate time series models in order to analyze the dynamic change of this pandemic through time. We secondly shed light on multivariate time series forecasting models using weather and daily tests data, to study the impact of exogenous features on the progression of COVID-19. In the final stage of this paper, we present our proposed approach based on LSTM and GRU ensemble learning model and evaluate the results using the MAE, RMSE and MAPE for the prediction of new cases. The results of our experiments using the Canadian dataset show that the ensemble model performs well in comparison to other models. In addition, this research provides us with a new outcome regarding the dynamic correlation between temperature, humidity and daily test data and its impact on the new contaminated cases.

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