Design, Integration, and Evaluation of a Demand Forecasting Service in the Context of Primary Healthcare - Pereira, Luis F. Alves; Ferreira, Luann Bento; Nascimento, Dimas Cassimiro; Vanderlei, Igor Medeiros; Silva, Daliton; Santos, Veruska Borges; Gomes, Mágno Sillas; Melo, Ines Alessandra (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2025: Anais do XXI Simpósio Brasileiro de Sistemas de InformaçãoDO - 10.5753/sbsi.2025.246552, 2025)
Context: Efficient management of Primary Healthcare (PHC) within Brazil’s Unified Health System is essential for ensuring quality healthcare services, particularly in addressing challenges related to resource allocation and workforce planning. Ensuring an equitable and sustainable distribution of healthcare resources is crucial to meeting the diverse needs of Brazil’s population. Problem: A key challenge in PHC is accurately forecasting demand, which is vital for optimizing resource allocation and preventing facility overcrowding. In the absence of precise projections, healthcare services may experience overburdened facilities and prolonged wait times, ultimately compromising the quality of care. Proposed Solution: This study presents a demand forecasting service tailored to Brazil’s PHC. By leveraging regression techniques, the system facilitates data-driven decision-making in resource allocation and workforce planning. Predictive analytics enable public health managers to anticipate demand more effectively, ensuring a better-prepared healthcare system capable of delivering effective services. Information Systems (IS) Theory: This research is grounded in the Theory of Socio-Technical Systems, as the proposed solution integrates diverse datasets and predictive models into an information system that actively interacts with existing humans workflows in PHC. By influencing decision-making and resource allocation, the system fosters a seamless integration between technological automation and human expertise, enhancing the adaptability and efficiency of healthcare services. Method: Following a prescriptive approach, the study evaluates the forecasting service through a detailed case study. A quantitative analysis assesses its accuracy and practical applicability, ensuring its effectiveness in supporting decision-making in PHC. Summary of Results: Experimental results demonstrate that the proposed service accurately predicts PHC demand by leveraging real-world data, which can lead to more timely and efficient resource allocation. Contributions and Impact on IS: This research contributes to the Information Systems field by offering a scalable and adaptable tool for Brazil’s public healthcare context, enabling efficient healthcare planning and enhancing resource management.