Bayesian Profiling Multiple Imputation for Missing Electronic Health Records
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Electronic health records (EHRs) are increasingly used for clinical and comparative effectiveness research but suffer from usability deficiencies. Motivated by health services research on diabetes care, we seek to increase the quality of EHRs by focusing on missing longitudinal glycosylated hemoglobin (A1c) values. Under the framework of multiple imputation (MI) we propose an individualized Bayesian latent profiling approach to capturing A1c measurement trajectories related to missingness. We combine MI inferences to evaluate the effect of A1c control on adverse health event incidence. We examine different missingness mechanisms and perform model diagnostics and sensitivity analysis. The proposed method is applied to EHRs of adult patients with diabetes who were medically homed in a large academic Midwestern health system between 2003 and 2013. Our approach fits flexible models with computational efficiency and provides useful insights into the clinical setting.
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