Application of Business Intelligence for the automation in the Epidemiological Surveillance: XBI a successful case study from Municipal Health Department of Ribeirão Preto, Brazil (2020-2024)

• DOI: https://doi.org/10.22533/at.ed.1594312425037

• Palavras-chave: -

• Keywords: Epidemiological Surveillance (ES); Business Intelligence (BI); Geographic Information System (GIS)

• Abstract:

  The emergence of the Covid-19 pandemic prompted the Epidemiological Surveillance (ES) unit within the Municipal Health Department (SMS) of Ribeirão Preto to address the necessity for enhanced data management strategies. In August 2020, the XBI methodology was created to analyze the epidemiological landscape of Covid-19 with more speediness, subsequently extended to encompass all other ES priorities. XBI aims to engender SMS in automating the generation of paginated reports detailing epidemiological indicators and developing interactive dashboards improve the ES singularity and internal analyzes and broadcasting of epidemiological trends. Implementation of this methodology necessitates access to the Minimum Data Set (MDS) with the notifications specific to Ribeirão Preto within national surveillance systems (SINAN, SIVEP, SIM, SINASC, ESUS, etc.). Moreover, XBI effectively functions as an instrument capable of automating the generation of reports that incorporate epidemiological indicators, quality metrics, and the identification of inconsistencies within data sets. This involves establishing connections with the MDS, processing data with relational and non-relation databases, and pivot-table analyses. For a suitable reproducibility, can be used MS Excel, Power BI and ArcGIS or other software with support scripting languages (e.g., VBA, R, Python, or M) and facilitate the creation of dynamic dashboards, preferably alongside secure publishing services. The implementation of the XBI methodology has enabled the automated generation of epidemiological reports covering all ES priorities. Since 2021, these dashboards have been integrated with Geographic Information Systems (GIS). Furthermore, in 2024, the establishment of a Public Epidemiologic Observatory was accomplished, featuring interactive dashboards for monitoring various health indicators including live births, mortality rates, incidences of infectious diseases, and sentinel respiratory virus surveillance. In conclusion, our findings suggest the feasibility and reproducibility of this model, particularly for Brazilian municipalities seeking to transition from surveillance models reliant on pre-existing DATASUS tabulators to the XBI framework.