A machine learning algorithm for the prediction of complications incorporated in electronic medical records improves type 2 diabetes care

Aims: Early identification of patients with type 2 diabetes (T2D) at high risk for complications may help reduce clinical inertia and improve care quality. This study assessed the clinical impact of integrating a machine learning-based prediction tool into electronic medical records (EMRs) in Italian diabetes clinics. Methods: A validated algorithm estimating the 5-year risk of six major diabetes complications was embedded in the EMRs of 38 centers. A pre-post comparison over 12 months was conducted between patients whose risk score was generated (test group) and those eligible but not assessed (control group). Results: Among 138,558 eligible patients, 20,314 (14.7 %) had at least one score generated. Compared to controls, test group patients showed significantly greater improvements in HbA1c ≤7.0 % (+9.0 % vs. +4.5 %), LDL-C <70 mg/dL (+27.9 % vs. +20.7 %), and BMI <25 kg/m2 (+16.5 % vs. +11.0 %), with larger reductions in HbA1c >8.0 % (–18.4 % vs. –10.1 %). They also more frequently initiated antihypertensive, lipid-lowering, and cardio-renal protective therapies. Conclusions: Embedding an AI-based prediction tool in routine clinical practice improved several quality indicators and therapeutic decisions. Its real-world application shows promise in overcoming clinical inertia and promoting personalized diabetes management.