Abstract
According to the World Health Organization, rare diseases currently represent a global public health priority. Although it has a low prevalence in the general population, this type of condition collectively affects up to 10% of the entire world population. Therefore, these pathologies are numerous and of a diverse nature, and some factors imply significant challenges for public health, such as the lack of structured and standardized knowledge about rare diseases in health units, the need for communication between multidisciplinary teams to understand phenomena and definition of accurate diagnoses, and the scarcity of experience on specific treatments. In addition, the often chronic and degenerative nature of these diseases generates a significant social and economic impact. This paper aims to present an initiative to develop a network of specialized reference centers for rare diseases in Brazil, covering all country regions. We propose collecting, mapping, analyzing data, and supporting effective communication between such centers to share clinical knowledge, evolution, and patient needs, through well-defined and standardized processes. We used validated structures to ensure data privacy and protection from participating health facilities to create this digital system. We also applied systems lifecycle methodologies, data modeling techniques, and quality management. Currently, the retrospective stage of the project is in its final phase, and some preliminary results can be verified. We developed an intuitive web portal for consulting the information collected, offering filters for personalized queries on rare diseases in Brazil to support evidence-based public decision-making.
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Funding
This study was funded by the National Council for Scientific and Technological Development – CNPq and the Ministry of Health of Brazil – MoH.
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Yamada, D.B. et al. (2022). National Network for Rare Diseases in Brazil: The Computational Infrastructure and Preliminary Results. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_4
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DOI: https://doi.org/10.1007/978-3-031-08757-8_4
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