Analysis of Real-World Data extracted from electronic medical records in the Webiomed platform

State programs in the field of healthcare informatization implemented in 2011–2021 resulted in over 91% of state and municipal medical organizations implementing various medical information systems. This made it possible to start the transition to electronic medical records (EMR). The extraction of real-world data (RWD) from the accumulated EMRs and subsequent analysis of these data opens new and promising opportunities for the development of domestic healthcare.

Aim: to analyze the RWD extracted from anonymized EMRs.

Materials and methods: we used Webiomed's predictive analytics platform database, which at the time of the study had accumulated anonymized EMRs of over 29 million patients, including 229 million different medical documents. Data providers for the platform were 856 medical organizations from 28 regions of the Russian Federation. The functionality of the Webiomed platform allows processing unstructured medical documents, extracting data from them suitable for analysis using artificial intelligence technologies.

Results. This paper presents: analysis of medical organizations as data providers for Webiomed platform, analysis of EMC structure and composition, analysis of patient population. The analysis was performed at the moment of data upload on 16.10.2023. More than 4 billion 558 million structured attributes were extracted from the accumulated anonymized EMRs and systematized by different types. The platform contains 147,886,190 cases of diseases classified according to the ICD-10. 8,393,403 patients (28.71% of the total) have medical information in the EMR. The proportion of “empty” EMCs (which did not contain any medical record) was 71.29%. However, EMRs of 3,448,797 patients had more than 10 medical records. The structure of EMRs is dominated by protocols of medical examinations, protocols of laboratory tests, electronic prescriptions, and instrumental examinations. 4,456,263 patients have a depth of data collection of more than 3 years.

Conclusion. The results show that the extraction and processing of RWD from anonymized EMRs do allow the creation of large sets of structured data. Currently, to the best of our knowledge, the Webiomed platform contains the largest database of RWD extracted from EMRs in Russia. The material presented in this paper is the first analysis of EMRs and extracted features performed and published in Russia. Ensuring the quality of work with RWD at all stages, from the development of EMR structure and data input to the formation of digital twins, is the most important condition for their application to solve various tasks in the healthcare system and pharmaceutical industry.

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