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Electronic health records (EHRs) are increasingly used for clinical andcomparative effectiveness research, but suffer from missing data. Motivated byhealth services research on diabetes care, we seek to increase the quality ofEHRs by focusing on missing values of longitudinal glycosylated hemoglobin(A1c), a key risk factor for diabetes complications and adverse events. Underthe framework of multiple imputation (MI), we propose an individualizedBayesian latent profiling approach to capture A1c measurement trajectoriessubject to missingness. The proposed method is applied to EHRs of adultpatients with diabetes in a large academic Midwestern health system between2003 and 2013 and had Medicare A and B coverage. We combine MI inferences toevaluate the association of A1c levels with the incidence of acute adversehealth events and examine patient heterogeneity across identified patientprofiles. We investigate different missingness mechanisms and performimputation diagnostics. Our approach is computationally efficient and fitsflexible models that provide useful clinical insights.

Bayesian Profiling Multiple Imputation for Missing Electronic Health Records