Prospective validation of an AI algorithm to identify adult-onset type 1 diabetes misclassification: protocol for a non-interventional multicentre study

Introduction Adult-onset type 1 diabetes (T1D) is often misclassified as type 2 diabetes (T2D), resulting in delayed treatment, missed opportunities for referrals to specialists and increased risk of complications including diabetic ketoacidosis. An electronic medical record (EMR)-based algorithm—originally trained on a large national EMR dataset to identify likely misclassified adult-onset T1D cases—was tested and retrained on a health information exchange (HIE) dataset from HealthShare Exchange (HSX).

Promising results were achieved on historical data, particularly when using the retrained algorithm. However, its prospective validation is essential to more reliably assess its clinical utility and real-world precision in flagging high-risk patients for clinician review.

Methods and analysis This is a prospective, multicentre, non-interventional cohort study in two HSX-member healthcare organisations (HCOs) in southeastern Pennsylvania. At the onset of the study, all adult T2D patients are scored by the algorithm analysing HIE data on relevant predictors found in the 24-month lookback period.