Co-design and implementation of a clinical web application for pediatric type 1 diabetes data analysis and management

Type 1 diabetes is a chronic metabolic disease in which the immune system destroys insulin-producing pancreatic beta-cells, resulting in lifelong reliance on insulin therapy and continuous glucose monitoring. As the condition primarily affects children, its management is particularly challenging due to developmental variability and behavioral complexity. To decide the most appropriate therapy, pediatric diabetologists must review large volumes of heterogeneous data often within limited consultation time and this makes it difficult to deliver truly personalized, data-driven care. To assist clinicians in handling the complexity and volume of patient data, clinical web applications have become increasingly common, offering tools for data aggregation and visualization. However, existing platforms often fall short in usability, real-time data integration, and contextual flexibility. The TWIN project was conceived to address these challenges by providing a clinician-centered solution for pediatric diabetes care, implementing a clinical decision support system that leverages digital twin technology. This thesis presents the design and implementation of the TWIN web interface, co-designed with pediatric diabetologists from Bambino Gesù Hospital. Built with Flutter, the application offers a responsive, cross-platform interface optimized for clinical use. The resulting system features a modular dashboard for structured data analysis, including glucose metrics, an interactive visualization of aggregated glucose trends with adjustable thresholds, historical comparisons, automated pattern detection, and detailed daily views. All functionalities aim to enhance interpretability and support real-time, informed decision-making in pediatric diabetes care.