Health decision-makers currently face the challenge of accumulating health data in time to inform evidence-based interventions to improve health outcomes. The Brazilian healthcare system is in need of daily primary care data reported in real-time to support evidence-based policy decisions. This study aims to detail the development of a solution for geospatial monitoring in public health called AUTOMAP. Its main objective is to facilitate epidemiological surveillance and promote that rapidly available data improve the provision of health services. AUTOMAP is an application that articulates concepts inherent to epidemiological surveillance, geographic information systems, and free access technologies to design a monitoring tool of health conditions. The system architecture consists of three modules: user, application, and database. They work together to collect information regarding health conditions, its processing, and dynamic viewing. AUTOMAP design uses the statistical language R, which employs literate programming through a Shiny application package to transform statistical results of health conditions into interactive maps in real-time. AUTOMAP is a web application that has two interfaces: one for loading data and another for generating dynamic epidemiological maps. AUTOMAP allows a variety of clinical solutions, such as risk calculators, spatial evaluation of events of interest, decision models, simulations, and epidemiological patient monitoring. The software is open-source with easy accessibility, allowing anyone to make adjustments and handle a myriad of health conditions, thus being applicable globally. AUTOMAP is a tool that will facilitate and advance data collection for evidence generation and expedite evidence-based health system improvements.

Main Topic

Geospatial analysis