Discordance Between Electronic Health Records and Self-Reported Data: Evidence from Traumatic Brain Injury and...

Background/Objectives: Discordance between electronic health records (EHR) and self-reported survey data may reflect incomplete clinical documentation on the provider side, as well as sociodemographic differences among survey participants. Cancer conditions are frequently reported with the least discordance. Traumatic brain injury (TBI) may be particularly prone to discordance. The aim of this study was to nationally investigate discordance between EHR and self-reported data for TBI and colorectal cancer. Methods: This cross-sectional study used data from the national All of Us Research Program, including participants with both linked EHR and self-reported survey data. Participants in each condition were stratified into four groups: EHR+/Survey+ (concordant positive), EHR-/Survey- (concordant negative), EHR+/Survey- (discordant), and EHR-/Survey+ (discordant). EHR-documented and survey-reported conditions were compared using a 2 × 2 classification framework to assess concordance. Agreement metrics, including sensitivity, specificity, predictive values, overall concordance/discordance, directional discordance, and Cohen's kappa, were calculated. Logistic regression models were used to examine the association between the outcomes and sociodemographic factors. Machine learning models additionally investigated the predictive performance of these factors. Results: For TBI, concordance between EHR and survey was fair (κ = 0.33), with sensitivity of 60.9% and specificity, 92.9%. In regression models, increasing age was associated with higher odds of both discordant groups (EHR+/Survey- and EHR-/Survey+); lower educational levels and non-White participants had higher odds of discordance specifically in EHR+/Survey- group. Medicaid insurance had higher odds in the EHR-/Survey+ group. In contrast, colorectal cancer showed stronger concordance (κ = 0.66; sensitivity 74.5%; specificity 98.6%) and fewer sociodemographic associations in regression models. The association between race and Medicaid coverage showed a similar pattern to TBI. Machine learning results were also consistent with logistic regression models. Conclusions: Concordance between EHR and self-reported data was fair for TBI. Older age, lower education, non-White race, and Medicaid insurance were associated with greater discordance. These sociodemographic patterns were less pronounced in colorectal cancer, except for race and Medicaid insurance. Policies are needed to improve concordance between EHR and self-reported data, particularly across certain sociodemographic groups.