The Role of Information Management-Based Blood Glucose Management Pathways in Improving the Diagnostic Rate of Newly Diagnosed Diabetes Patients

The global prevalence of diabetes mellitus (DM) continues to rise, with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) being the most common subtypes. T1DM is characterised by the autoimmune destruction of pancreatic β-cells leading to absolute insulin deficiency, whereas T2DM is associated with insulin resistance and relative insulin insufficiency, often linked to lifestyle factors. Both subtypes are frequently misdiagnosed or underdiagnosed due to insufficient screening awareness, outdated diagnostic processes, and poor patient compliance, leading to delayed interventions and increased complication risks. This review examines information-management-based blood glucose control pathways, focusing on their role in improving the diagnostic rates of newly diagnosed T1DM and T2DM. It specifically examines the applications of key technologies: electronic health records (EHRs) for integrating multi-source data (e.g., autoantibodies for T1DM, metabolic indicators for T2DM), mobile health (mHealth) applications for real-time monitoring and targeted screening reminders, artificial intelligence (AI) for developing subtype-specific risk prediction models, Internet of Things (IoT) devices for capturing subtype-specific glycemic patterns, and blockchain for secure data sharing. Furthermore, the review describes how these technologies enhance early detection by optimising screening workflows, improving patient adherence, and facilitating accurate subtype differentiation. Despite demonstrated potential, challenges include data security, technological accessibility, and system interoperability. Future research should prioritise personalised pathways for each subtype, integrate multi-omics data, refine AI algorithms for subtype-specific diagnosis, and strengthen policy support to develop a precise, efficient early screening system for DM.