1 research outputs found
The impact of electronic health records (EHR) data continuity on prediction model fairness and racial-ethnic disparities
Electronic health records (EHR) data have considerable variability in data
completeness across sites and patients. Lack of "EHR data-continuity" or "EHR
data-discontinuity", defined as "having medical information recorded outside
the reach of an EHR system" can lead to a substantial amount of information
bias. The objective of this study was to comprehensively evaluate (1) how EHR
data-discontinuity introduces data bias, (2) case finding algorithms affect
downstream prediction models, and (3) how algorithmic fairness is associated
with racial-ethnic disparities. We leveraged our EHRs linked with Medicaid and
Medicare claims data in the OneFlorida+ network and used a validated measure
(i.e., Mean Proportions of Encounters Captured [MPEC]) to estimate patients'
EHR data continuity. We developed a machine learning model for predicting type
2 diabetes (T2D) diagnosis as the use case for this work. We found that using
cohorts selected by different levels of EHR data-continuity affects utilities
in disease prediction tasks. The prediction models trained on high continuity
data will have a worse fit on low continuity data. We also found variations in
racial and ethnic disparities in model performances and model fairness in
models developed using different degrees of data continuity. Our results
suggest that careful evaluation of data continuity is critical to improving the
validity of real-world evidence generated by EHR data and health equity