This work aims to describe how EHRs have been used to meet the needs of healthcare providers and researchers in a 1,300-beds tertiary Hospital during COVID-19 pandemic. For this purpose, essential clinical concepts were identified and standardized with LOINC and SNOMED CT. After that, these concepts were implemented in EHR systems and based on them, data tools, such as clinical alerts, dynamic patient lists and a clinical follow-up dashboard, were developed for healthcare support. In addition, these data were incorporated into standardized repositories and COVID-19 databases to improve clinical research on this new disease. In conclusion, standardized EHRs allowed implementation of useful multi- purpose data resources in a major Hospital in the course of the pandemic.
BackgroundThe propagation of COVID-19 in Spain prompted the declaration of the state of alarm on March 14, 2020. On 2 December 2020, the infection had been confirmed in 1,665,775 patients and caused 45,784 deaths. This unprecedented health crisis challenged the ingenuity of all professionals involved. Decision support systems in clinical care and health services management were identified as crucial in the fight against the pandemic.MethodsThis study applies Deep Learning techniques for mortality prediction of COVID-19 patients. Two datasets with clinical information (medication, laboratory tests, vital signs etc.) were used. They are comprised of 2,307 and 3,870 COVID-19 infected patients admitted to two Spanish hospital chains. Firstly, we built a sequence of temporal events gathering all the clinical information for each patient. Next, we used the temporal sequences to train a Recurrent Neural Network (RNN) model with an attention mechanism exploring interpretability. We conducted extensive experiments and trained the RNNs in different settings, performing hyperparameter search and cross-validation. We ensembled resulting RNNs to reduce variability and enhance sensitivity.ResultsWe assessed the performance of our models using global metrics, by averaging the performance across all the days in the sequences. We also measured day-by-day metrics starting from the day of hospital admission and the outcome day and evaluated the daily predictions. Regarding sensitivity, when compared to more traditional models, our best two RNN ensemble models outperform a Support Vector Classifier in 6 and 16 percentage points, and Random Forest in 23 and 18 points. For the day-by-day predictions from the outcome date, the models also achieved better results than baselines showing its ability towards early predictions.ConclusionsWe have shown the feasibility of our approach to predict the clinical outcome of patients infected with SARS-CoV-2. The result is a time series model that can support decision-making in healthcare systems and aims at interpretability. The system is robust enough to deal with real world data and it is able to overcome the problems derived from the sparsity and heterogeneity of the data. In addition, the approach was validated using two datasets showing substantial differences. This not only validates the robustness of the proposal but also meets the requirements of a real scenario where the interoperability between hospitals’ datasets is difficult to achieve.
Reuse of EHRs requires data extraction and transformation processes are based on homogeneous and formalized operations in order to make them understandable, reproducible and auditable. This work aims to define a common framework of data operations for obtaining EHR-derived datasets for secondary use. Thus, 21 operations were identified from different data-driven projects of a 1,300-beds tertiary Hospital. Then, ISO 13606 standard was used to formalize them. This work is the starting point to homogenize ETL processes for the reuse of EHRs, applicable to any condition and organization. In future studies, defined data operations will be implemented and validated in projects of different purposes.
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