Supporting health insurance expansion: do electronic health records have valid insurance verification and enrollment data?

Abstract

Objective: To validate electronic health record (EHR) insurance information for low-income pediatric patients at Oregon community health centers (CHCs), compared to reimbursement data and Medicaid coverage data.

Materials and methods: Subjects Children visiting any of 96 CHCs (N = 69 189) from 2011 to 2012. Analysis The authors measured correspondence (whether or not the visit was covered by Medicaid) between EHR coverage data and (i) reimbursement data and (ii) coverage data from Medicaid.

Results: Compared to reimbursement data and Medicaid coverage data, EHR coverage data had high agreement (87% and 95%, respectively), sensitivity (0.97 and 0.96), positive predictive value (0.88 and 0.98), but lower kappa statistics (0.32 and 0.49), specificity (0.27 and 0.60), and negative predictive value (0.66 and 0.45). These varied among clinics.

Discussion/conclusions: EHR coverage data for children had a high overall correspondence with Medicaid data and reimbursement data, suggesting that in some systems EHR data could be utilized to promote insurance stability in their patients. Future work should attempt to replicate these analyses in other settings.

Keywords: Medicaid expansion; children; community health centers; electronic health records; health insurance; health insurance claims.

© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Figures

Figure 1:

Distribution of Clinic-specific Correspondence Statistics in the Comparison of OCHIN EHR Coverage Data vs Reimbursement Data Note: Kernel Density Estimates of the distribution of clinic agreement, sensitivity, specificity, and kappa statistics. The kernel density estimator is a nonparametric method to estimate the probability distribution of the four statistical measures.