"Development of A Web-Based System For Early Detection of Maternal Risk In Pregnant Women Using The Mamá Score Algorithm"

This This study presents the development and pilot implementation of a web-based clinical decision support system designed for the early detection of maternal risk using the MAMÁ Score algorithm. The main objective of this research is to automate the calculation of the MAMÁ Score, enabling continuous monitoring and generating early alerts, which facilitates real-time clinical decision-making and reduces the risk of human errors. The key contribution of this study lies in the integration of the digital system with clinical processes, improving both efficiency and accuracy in obstetric risk assessment.The methodology adopted involved the development of a web-based platform using PHP and Laravel under a Model-View-Controller (MVC) architecture, which allowed the creation of a modular and scalable system. The system was integrated into the clinical workflow of a primary healthcare center in Quito, Ecuador, facilitating the structured registration of key clinical variables such as gestational age, blood pressure, and proteinuria. Using this data, the system automatically calculates the MAMÁ Score, generating real-time risk stratification, color-coded (green, yellow, red).One of the system's main innovations is its continuous monitoring capability through automated alerts, which allows for the early identification of potential complications and improves the continuity of prenatal follow-up. The results showed a 17.8% reduction in the consolidated risk score, with high clinical stability in 98.6% of the cases. These findings suggest that the automation of prenatal monitoring can significantly improve maternal outcomes, especially in resource-constrained settings.