Resumo
Objetivo: Analisar as aplicações de Business Intelligence (BI) na vigilância em saúde pública, com ênfase no enfrentamento das Doenças Tropicais Negligenciadas (DTNs) e na doença de Chagas. Métodos: Revisão integrativa conduzida segundo a recomendação PRISMA 2020 em três camadas de busca nas bases PubMed, BVS/LILACS e Periódicos CAPES, com descritores controlados e recorte temporal de 2016 a 2026. Resultados: Constatou-se a ausência de estudos relacionando diretamente ferramentas de BI ao monitoramento das Doenças Tropicais Negligenciadas (n = 0), revelando uma importante lacuna de formalização científica em agravos associados à vulnerabilidade social. Contudo, foram selecionados 14 artigos no âmbito da vigilância epidemiológica e da saúde coletiva, cujas experiências com Power BI, geoprocessamento (GIS), painéis em tempo real e relatórios automatizados fornecem modelos tecnológicos transponíveis para a vigilância das doenças negligenciadas e, em especial, da doença de Chagas em territórios endêmicos. Considerações finais: O estudo se destaca ao evidenciar a invisibilidade e o descompasso na incorporação de tecnologias analíticas no campo das Doenças Tropicais Negligenciadas, demonstrando que a modernização da vigilância dessas enfermidades no Sistema Único de Saúde (SUS) é epidemiologicamente necessária e viável, embora condicionada ao enfrentamento de barreiras de interoperabilidade, infraestrutura e governança de dados.
Referências
ADESHINA YT. Multi-Tier Business Analytics Platforms for Population Health Surveillance Using Federated Healthcare IT Infrastructures. International Journal of Research Publication and Reviews, 2025; 6(5): 1-10.
ADENYI AO, et al. Leveraging big data and analytics for enhanced public health decision-making: A global review. GSC Advanced Research and Reviews, 2024; 18(2): 1-12.
ARISTIZÁBAL-TORRES D, et al. An interactive web-based dashboard to track COVID-19 in Colombia: Case study: five main cities. Revista de Salud Pública, 2020; 22(2): 214-219.
BOOTH A, et al. Systematic Approaches to a Successful Literature Review. 2nd ed. SAGE Publications: London, 2016; p288.
BRANDA F, et al. Assessing the Burden of Neglected Tropical Diseases in Low-Income Communities: Challenges and Solutions. Viruses, 2024; 17(1): 29.
BUCHBINDER M, et al. Advancing a Data Justice Framework for Public Health Surveillance. AJOB Empirical Bioethics, 2022; 13(4): 238-248.
BURALLI RJ, et al. The Brazilian System for Monitoring Workers and General Population Exposed to Asbestos: Development, Challenges, and Opportunities for Workers' Health Surveillance. International Journal of Environmental Research and Public Health, 2023; 20(5): 4295.
CHAN HW, LO NA. A Study on Human Rights Impact with the Advancement of Artificial Intelligence. Journal of Posthumanism, 2025; 5(2): 1-15.
CHEN J, et al. Global trends and burdens of neglected tropical diseases and malaria from 1990 to 2021: a systematic analysis of the Global Burden of Disease Study 2021. BMC Public Health, 2025; 25: 22477.
GARCIA EG, et al. O processo de monitoramento, controle e avaliação do Programa Mais Médicos no Estado da Bahia. Revista Baiana de Saúde Pública, 2022; 46(1): 203-215.
GEORGE NS, et al. Addressing neglected tropical diseases in Africa: a health equity perspective. Global Health Research and Policy, 2023; 8: 314.
KATAPALLY TR, IBRAHIM SM. Digital Health Dashboards for Decision-Making to Enable Rapid Responses During Public Health Crises: Replicable and Scalable Methodology. JMIR Research Protocols, 2023; 12: e46810.
KOHARA MELCHIOR LA, et al. Business Intelligence no apoio à gestão estratégica em saúde: um relato de experiência. Revista de Epidemiologia e Controle de Infecção, 2022; 12(4): 180-183.
KOSCHORKE M, et al. Mental health, stigma, and neglected tropical diseases: a review and systematic mapping of the evidence. PLOS Global Public Health, 2022; 2(10): e0000808.
LEE A, et al. COVID-19 data driven planning: The SouthEast Texas approach. Journal of Emergency Management, 2022; 20(7): 39-56.
LV JJ, et al. Global, regional, national epidemiology and trends of neglected tropical diseases in youths and young adults aged 15–39 years from 1990 to 2019: findings from the Global Burden of Disease Study 2019. BMC Public Health, 2024; 24: 19190.
MAGALHÃES AR, et al. Neglected tropical diseases risk correlates with poverty and early ecosystem destruction. Infectious Diseases of Poverty, 2023; 12: 1084.
MARTINS-MELO FR, et al. Burden of Chagas disease in Brazil, 1990-2016: findings from the Global Burden of Disease Study 2016. International Journal for Parasitology, 2019; 49(3-4): 301-310.
MOSCOU K, et al. Governance and pharmacovigilance in Brazil: a scoping review. Journal of Pharmaceutical Policy and Practice, 2016; 9(3): 1-15.
NAIK N, et al. Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Frontiers in Surgery, 2022; 9: 862322.
PAGE MJ, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 2021; 372: n71.
PAGSUYOIN S, et al. Coupling wastewater‐based epidemiology with data‐driven machine learning for managing public health risks. Risk Analysis, 2025; 45: 70075.
PATEL S, et al. Neglected Tropical Diseases: A Systematic Review. Journal of Pharmacy & Bioallied Sciences, 2025; 17: 940.
RADANLIEV P. Privacy, ethics, transparency, and accountability in AI systems for wearable devices. Frontiers in Digital Health, 2025; 7: 1431246.
RAFAEL AF, et al. Overview of Chagas disease surveillance in an endemic region in Southeastern Brazil. Revista do Instituto de Medicina Tropical de São Paulo, 2023; 65: e51.
RØIKJER J, et al. Declining Incidence of Major Lower-Extremity Amputations in the Northern Danish Region Between 2016 and 2021: The Impact of Diabetes and Preventive Vascular Procedures. Annals of Vascular Surgery, 2024; 109: 407-413.
SHALLA C, et al. Bringing the Neglected out of Neglected Tropical Diseases: A Systematic Literature Review. Rehabilitation Science, 2025; 10(3): 11-20.
SOUZA LCP, et al. iCardio: Applying Business Intelligence to Assess Regional Disparity in Cardiovascular Care with Real-World Data. Arquivos Brasileiros de Cardiologia, 2026; 123(4): e20250765.
SZEWRAŃSKI S, et al. Socio-Environmental Vulnerability Mapping for Environmental and Flood Resilience Assessment: The Case of Ageing and Poverty in the City of Wrocław, Poland. Integrated Environmental Assessment and Management, 2018; 14(5): 592-597.
TIFFIN N, et al. How to use relevant data for maximal benefit with minimal risk: digital health data governance to protect vulnerable populations in low-income and middle-income countries. BMJ Global Health, 2019; 4(2): e001395.
TORRES HA, et al. Implementation of Universal Hepatitis C Virus Screening in a Tertiary Cancer Center. Journal of the National Comprehensive Cancer Network, 2024; 22(2D): e237332.
TREVISAN G, et al. Macroepidemiological aspects of porcine reproductive and respiratory syndrome virus detection by major United States veterinary diagnostic laboratories over time, age group, and specimen. PLoS ONE, 2019; 14(10): e0223544.
WILLGERT K, et al. Influence of SARS-CoV-2 surveillance outputs produced by the UK health security agency (UKHSA) outbreak surveillance team on decision-making by local stakeholders. BMC Public Health, 2023; 23: 926.
XAVIER F, et al. Análise de redes sociais como estratégia de apoio à vigilância em saúde durante a Covid-19. Estudos Avançados, 2020; 34(99): 261-282.
ZHAO Y, et al. Ethics, Integrity, and Retributions of Digital Detection Surveillance Systems for Infectious Diseases: Systematic Literature Review. Journal of Medical Internet Research, 2021; 23(10): e32328.
