LAUSR

INTRODUCTION As coronavirus disease 2019 (COVID-19) hit the US, there was widespread and urgent implementation of telemedicine programs nationwide without much focus on the impact on patient populations with known existing healthcare disparities. To better understand which populations cannot access telemedicine during the coronavirus disease 2019 pandemic, this study aims to demographically describe and identify the most important demographic predictors of telemedicine visit completion in an urban health system. METHODS Patient de-identified demographics and telemedicine visit data (N = 362,764) between March 1, 2020 and October 31, 2020 were combined with Internal Revenue Service 2018 individual income tax data by postal code. Descriptive statistics and mixed effects logistic regression were used to determine impactful patient predictors of telemedicine completion, while adjusting for clustering at the clinical site level. RESULTS Many patient-specific demographics were found to be significant. Descriptive statistics showed older patients had lower rates of completion (p < 0.001). Also, Hispanic patients had statistically significant lower rates (p < 0.001). Overall, minorities (racial, ethnic, and language) had decreased odds ratios of successful telemedicine completion compared to the reference. DISCUSSION While telemedicine use continues to be critical during the coronavirus disease 2019 pandemic, entire populations struggle with access-possibly widening existing disparities. These results contribute large datasets with significant findings to the limited research on telemedicine access and can help guide us in improving telemedicine disparities across our health systems and on a wider scale.