GESTAR: An Integrated Telehealth Platform for Obstetric Risk Stratification, e-Learning, and Health Information Management

  • Rodrigo Lima UFPE
  • Tarcísio Lima Ferreira UFAL
  • Marcelo Costa Oliveira UFAL
  • Davy de Medeiros Baia UFAL
  • Pedro Pimentel UFF
  • Baldoino Fonseca UFAL
  • Marcio Ribeiro UFAL

Abstract


Maternal mortality remains a major challenge in Brazil’s Northeast Region due to fragmented care pathways, socioeconomic constraints, and limited access to specialists. This paper presents GESTAR, a telehealth platform deployed in Alagoas. The system integrates: (i) obstetric risk stratification (SESAU/AL protocol NT15/2025; 73 factors), automatically generating the risk classification and a referral package ready to be routed through the state regulation workflow; (ii) telementoring and continuing education for healthcare professionals; and (iii) clinical decision support through automated, guidelinebased care pathway recommendations. Built on a microservices architecture (Python/FastAPI, Next.js), the platform implements AES-256 encryption and automated report generation.

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Published
2026-06-01
LIMA, Rodrigo; FERREIRA, Tarcísio Lima; OLIVEIRA, Marcelo Costa; BAIA, Davy de Medeiros; PIMENTEL, Pedro; FONSECA, Baldoino; RIBEIRO, Marcio. GESTAR: An Integrated Telehealth Platform for Obstetric Risk Stratification, e-Learning, and Health Information Management. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 26. , 2026, Ouro Preto/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2026 . p. 1343-1348. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2026.20920.