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.