A Bayesian approach to correct for misclassification error in EHR data

Music City Center 

Health equity in pediatric care is an important mission for medical institutions in the US. Increasing research is done to identify inequities in health care outcomes using Electronic Health Records (EHR). However, EHR on pediatric patients often have inaccurate records of patient race (doi:10.1001/jamanetworkopen.2024.31073). Ignoring the misattribution in racial designations in EHR, studies run the risk of bias inferences. Further, accuracy of racial designations is important to clinical care improvement efforts and health outcomes. We propose an empirical Bayesian model to correct for misclassification in racial designation or in EHR. The model uses a survey sample (n=1,594) to estimate the misclassification error between the recorded race in EHR and the self-identified race. The sample is used to derive an empirical prior distribution for the misclassification error, which can be used in future studies using EHR data to derive posterior distributions corrected for race misclassification error. The race corrected posterior distribution is used to derive inferences. The proposed approach is applied to a pediatric study using EHR data from CS Mott Children's Hospital.

Measurement error

Missing data

Sensitive analysis 

Section on Bayesian Statistical Science