Architecting a co-designed clinical decision support system for diabetes management

Abstract

Diabetes mellitus, particularly Type 2 diabetes (T2D), imposes a substantial and inequitable burden on New Zealand, with domestic communities disproportionately affected. Early prediction of diabetes complications using machine learning (ML) has shown strong potential, yet most models remain disconnected from real-world clinical workflows and health information systems. This study outlines a co-design and design science research (DSR) programme to develop a machine learning–enabled clinical decision support (CDS) framework for predictive diabetes management in the Waikato region. Working with Health New Zealand (HNZ) Waikato, clinicians, and health informaticians, the research proceeds through three stages: situation awareness of current diabetes management processes, co-design of a data-driven CDS architecture, and evaluation via predictive performance metrics and process simulation. Expected contributions include an empirically grounded CDS framework tailored to local clinical and cultural contexts, improved predictive capabilities for T2D complications and resource planning, and a scalable pathway toward AI-enabled diabetes services that support health advancement and health equity.