ArticleDiabetes care2026
A Novel Electronic Medical Record Search Method to Identify Patients With Ketosis-Prone Diabetes: Implications for Discovery of Atypical Diabetes.
Article in Diabetes care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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14 authors.
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Abstract
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.
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