# Bayesian Profiling Multiple Imputation for Missing Electronic Health Records

## TL;DR

This paper introduces a Bayesian latent profiling multiple imputation method to improve the quality of missing data in electronic health records, specifically focusing on longitudinal A1c measurements in diabetic patients.

## Contribution

It presents a novel individualized Bayesian approach for imputing missing longitudinal health data, capturing patient heterogeneity and providing clinically relevant insights.

## Key findings

- Effective imputation of missing A1c data in EHRs.
- Identified patient profiles linked to health outcomes.
- Method is computationally efficient and adaptable.

## Abstract

Electronic health records (EHRs) are increasingly used for clinical and comparative effectiveness research, but suffer from missing data. Motivated by health services research on diabetes care, we seek to increase the quality of EHRs by focusing on missing values of longitudinal glycosylated hemoglobin (A1c), a key risk factor for diabetes complications and adverse events. Under the framework of multiple imputation (MI), we propose an individualized Bayesian latent profiling approach to capture A1c measurement trajectories subject to missingness. The proposed method is applied to EHRs of adult patients with diabetes in a large academic Midwestern health system between 2003 and 2013 and had Medicare A and B coverage. We combine MI inferences to evaluate the association of A1c levels with the incidence of acute adverse health events and examine patient heterogeneity across identified patient profiles. We investigate different missingness mechanisms and perform imputation diagnostics. Our approach is computationally efficient and fits flexible models that provide useful clinical insights.