BACKGROUND:
The efficient management of large public health systems depends on data interoperability to optimize resources and decision-making. In the Brazilian Public Health System (SUS), the lack of communication between systems is a major challenge. Salvador faces system fragmentation, resulting in reactive management, suboptimal resource allocation, and discontinuity of care. The lack of a unified view of the patient's journey limits responses to critical events and perpetuates inequities.

METHODS:
This project aims to implement a Center for Digital Health Intelligence (CISD), based on the creation of an interoperable Municipal Health Data Network (RMDS). The strategy focuses on connecting Surveillance, Healthcare, and Regulation sectors using Big Data and AI technologies. The approach utilizes participatory co-design to ensure tool relevance and is executed in phases (structuring, development, implementation) under multi-sectoral governance, aligned with the National Health Data Network (RNDS).

RESULTS:
The project development phase established a portfolio of solutions, including predictive alerts and integrated dashboards. The proposed architecture creates a digital ecosystem capable of overcoming current fragmentation. Supported by successful benchmarks in data integration, the model is designed to provide a comprehensive view of health data, facilitating the transition from reactive to proactive management.

CONCLUSIONS:
The implementation of the CISD is expected to qualify health management and care in Salvador by optimizing resources and reducing emergency response times. The project will consolidate a digital health governance model, strengthening the analytical capacity of the Health Secretariat and addressing health inequities through better data usage.