Epidemiological data monitoring dashboards as a surveillance and healthcare management strategy

Dados Bibliográficos

ID16828958
AutoresVanessa Coelho de Aquino Benjoino Ferraz (0000-0003-1758-4703, Secretaria Municipal de Saúde de Campo Grande, Brasil), Victor Vohryzek Ferezin (0000-0002-8929-8653, Secretaria Municipal de Saúde de Campo Grande, Brasil), Margarete Knoch (0000-0001-6026-8891, Secretaria Municipal de Saúde de Campo Grande, Brasil), Betina Durovni (0000-0002-5555-8015, Secretaria Municipal de Saúde do Rio de Janeiro, Brasil), Valéria Saraceni (0000-0001-7360-6490, Secretaria Municipal de Saúde do Rio de Janeiro, Brasil), Veruska Lahdo (0009-0004-4922-1215, Secretaria Municipal de Saúde de Campo Grande, Brasil), Mara Lisiane Moraes Dos Santos (0000-0001-6074-0041, Universidade Federal de Mato Grosso do Sul), Alessandro Diogo De-Carli (0000-0002-4560-4524, Universidade Federal de Mato Grosso do Sul)
Ano2024
Volume29
Fascículo11
Data de publicação2024-01-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoCiência & Saúde Coletiva (JOURNAL)
Identificadores do periódicoISSN: 1413-8123 • E-ISSN: 1678-4561
EditoraFapUNIFESP (SciELO) (PUBLISHER)
DOI10.1590/1413-812320242911.04142024en
OpenAlexW4403607911
IdiomaEN
Referências citadas6

This study aimed to analyze the interval between the dates of notification and data entry of suspected dengue cases and discuss the properties of epidemiological data monitoring dashboards. Applied research with quantitative analysis of the time between notification and data entry, using the Cross-Industry Standard Process for Data Mining (CRISP-DM), for the construction of the dashboards. This was developed at the Center for Strategic Health Surveillance Information in Campo Grande. The results revealed a period exceeding seven days in 93.33% of cases. The monitored dashboards included Arboviruses, Respiratory Syndromes, Attendance, and quantitative and qualitative notifications. We observed data integration, as information process are performed in Power BI, consolidating data from two to four health information systems. The contextual study and its temporal relationship are complied with in all dashboards with epidemiological indicators. The study concludes that using interactive epidemiological dashboards for surveillance and healthcare management decision-making is relevant

Data management · Data mining · Data science · Epidemiological surveillance · Health care · Medical emergency · Pathology · Political science · Computer Science · Data-Driven Disease Surveillance · Medical Coding and Health Information · Medicine · Epidemiology