A Novel Electronic Medical Record Search Method to Identify Patients With Ketosis-Prone Diabetes: Implications for Discovery of Atypical Diabetes

We developed Python-based Expeditious Program for Parsing Electronic Records (PEPPER) as a novel electronic medical record (EMR) search tool. We tested its utility and efficiency to automate the first step of identifying patients with A−β+ ketosis-prone diabetes (KPD). Electronic charts of 1,660 youth with type 2 diabetes (T2D) were analyzed by PEPPER to identify those with diabetic ketoacidosis (DKA) within 6 months of diagnosis. The efficiency and accuracy of PEPPER were compared with manual review. Further review confirmed A−β+ KPD per the Rare and Atypical Diabetes Network criteria. PEPPER identified 110 youth with T2D and DKA, of whom 21 met full A−β+ KPD criteria. PEPPER significantly reduced chart review time for this initial critical step compared with manual searching (mean SD 13.4 ± 3.9 s vs. 26.6 ± 9.4 s per chart; P < 0.001), and was 100% accurate. PEPPER streamlines EMR review, significantly reducing manual effort without sacrificing accuracy.