CID-SP EMERGENCIES Project: Public Health Data Intelligence Center - HCFMUSP / São Paulo State Department of Health.

• Grant number: 25/11502-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2025
• End date: June 30, 2026
• Field of knowledge: Health Sciences - Medicine - Medical Clinics
• Principal Investigator: Ludhmila Abrahão Hajjar
• Grantee: Leonardo Cadorin de Araújo
• Host Institution: Hospital das Clínicas da Faculdade de Medicina da USP (HCFMUSP). Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil
• Associated research grant: 24/01114-4 - CID-SP EMERGENCIES: Public Health Data Intelligence Center - HCFMUSP/STATE HEALTH DEPARTMENT OF SÃO PAULO., AP.CCD

Abstract

To develop a state-of-the-art reference model for the Central Regulation of Health Service Offers (CROSS), based on Big Data analysis and Artificial Intelligence, as well as the use of Telemedicine and the Internet of Things (IoT), aimed at increasing the efficiency in addressing the demand for the regulation of urgent and emergency care in the state of São Paulo. The model should reduce the time to care, possess the capability to prioritize the treatment of patients at higher risk of complications (predictive analysis), promote the allocation of the patient to the closest and most capable facility in terms of human and structural resources to provide efficient care, and simultaneously be able to structure the care model for critically ill patients according to pre-established protocols for the best patient outcome.The focus will be on obtaining, in real time, the following elements: the installed hospital capacity and information on all equipment dedicated to SUS (Brazilian Unified Health System); patient flows, both internally and between health regions; geolocation of all health services within the state of São Paulo, including philanthropic and contracted entities, accessible both to managers and patients; access regulation, conducted regionally but executed centrally, with clear protocols for referrals and priority definitions; queue management with information transparency for the population and the application and auditing of treatment protocols. The integration of Artificial Intelligence and the use of Big Data in the real-time assessment of public health resources and in resource allocation can bring unquestionable benefits to critically ill patients.