Electronic medical records as a source of real-world clinical data

Abstract

Currently, information technologies are being actively introduced in the healthcare of the Russian Federation. The share of state and municipal medical organizations that have implemented various medical information systems increased from 3.9 % in 2007 to 91 % in 2021. One of the key tasks of informatization is the introduction of electronic medical records (EMRs), which accumulate large amounts of Real-World Data (RWD). Despite the importance of EHR as a source of RWD, they have a number of shortcomings, such as the decentralized nature of database management systems, unstructured information storage, etc. The article describes the sequential processes for collecting high-quality RWD based on EHR, including the use of artificial intelligence technologies, for the purposes of scientific research, the creation of decision support systems, statistical analysis, etc. The basis of the proposed methodology is the centralized collection of information from EMR in the so-called data lakes, where as much as possible of raw data on the patient is accumulated and subsequent extraction of data from unstructured records through natural language processing (NLP) models. The proposed technology, subject to continuous improvement, will provide a correct and comprehensive solution for the skilful understanding of any text from any medical record.

Keywords

• real-world clinical data
• electronic medical record
• big data
• artificial intelligence
• machine learning
• decision support system