DEVELOPMENT OF METHODS OF ANALYSIS OF STRUCTURED MEDICAL RECORDS FOR THE MEDICAL INFORMATION EXCHANGE SYSTEM

Abstract

In the past, medical records were often unstructured and difficult to access. This made it difficult to effectively track and manage patient data. However, with the advent of EHR systems, medical records have become more structured and easier to access. This has made it possible to better analyze patient data and improve patient care. The development of EHR systems has made it possible to better analyze patient data and improve patient care. However, there are still some challenges with these systems. One challenge is that EHR systems are often soloed. This means that data from different EHR systems is not always compatible. There are various methods of analysing structured medical records. One common method is to use data mining techniques. This involves using algorithms to extract information from the data. This information can then be used to improve patient care. Structured medical data can be a powerful tool for researchers studying the effectiveness of medical treatments. However, analysing this data can be challenging, as it is often stored in complex databases. In this article, we review some common methods for analysing structured medical data, including regression analysis, machine learning, and natural language processing. We also discuss some of the challenges associated with each of these methods and offer some tips for overcoming these challenges. Structured medical data is data that is organized in a specific way. This type of data can be found in medical records, clinical trials, and other research studies. There are many methods of analysis that can be used to examine this type of data. Some common methods include descriptive statistics, inferential statistics, and machine learning. We have proposed a FVA-model for working with structured medical data, on the basis of which a database model for the Element medical information exchange system has been built.