Approach for Electronic Medical Record Data Analysis

Abstract

As the healthcare environment is being digitalized and changed rapidly, research using medical big data is increasing. One of the most applicable data is electronic medical records which can provide a large amount of clinically practical meaning. Electronic medical data include patient’s demographic information, laboratory test results, imaging and biosignal data. In this article, we provide support for a wide variety of researchers in their efforts to use electronic medical record data accurately and usefully in their work. From the basic concept of the research using electronic medical records to challenging aspects like data integration between multiple institutions are described. Also, examples of each type of data are covered; structured such as numeric data and unstructured such as images, biosignals and narrative text. Using these kinds of electronic medical records, analyses are processed by data cleansing, transforming, and reducing in order. Many kinds of variables such as the exposure and outcome of interest, covariate and the research design can be chosen during the preprocessing. As many machine-learning-based studies as well as epidemiologic-based studies have been conducted using electronic medical records, various research frameworks have been proposed. However, data quality management and data standardization for multicenter data analysis are still remaining as challenging tasks.