We have witnessed an explosion of data generation in every field over the last decades. In healthcare, the widespread use of electronic medical records and the advent of wearable monitoring devices, for instance, have contributed to the generation of huge volumes of health data. In public health, it is now easier than before to use computational methods to create datasets with morbidity and mortality data. One challenge is that data collection methods and population characteristics, such as the age pyramid, vary across countries and over time, making it difficult to compare and analyze temporal trends. This is the task of the Global Burden of Disease (), which standardizes indicators of more than 400 health outcomes and risk factors in more than 200 countries and territories, allowing fair comparisons between countries and trends in epidemiological data over time, including in Brazil., Another challenge is that information is often fragmented in distinct datasets. This is the task of the so-called Business Intelligence, which may be used to create tools that transform fragmented, raw data frequently collected from different sources into visual insights, often dynamic, interactive, that allow real-time updates, facilitating whole data interpretation and supporting the decision-making process.
In this issue of Arquivos Brasileiros de Cardiologia, Souza et al. report a study using iCardio, an online platform that leverages Business Intelligence to visualize epidemiological data in interventional cardiology and heart surgery from DATASUS in Brazil (). iCardio, which is part of a project sponsored by the National Council for Scientific and Technological Development (Portuguese: Conselho Nacional de Desenvolvimento Científico e Tecnológico [CNPq]), was developed with data from the Hospital Information System (Portuguese: Sistema de Informações Hospitalares [SIH]) and Information System of Mortality (Portuguese: Sistema de Informação sobre Mortalidade [SIM]). The indicators include the number of high-complexity procedures and hospitalizations, the number of procedures per inhabitants, the geographical location where medical care was provided, the average displacement to the healthcare facility, the hospitalization length of stay, the re-hospitalization rate, the rates of in-hospital mortality and mortality up to 30 days after the procedure or hospital discharge, and costs. Importantly, data may be stratified per sex, age range, race/skin color, location, type of care (urgent/elective), diagnosis, and procedure.,
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