Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19

Abstract

Objective: Through the coronavirus disease 2019 (COVID-19) pandemic, telemedicine became a necessary entry point into the process of diagnosis, triage, and treatment. Racial and ethnic disparities in healthcare have been well documented in COVID-19 with respect to risk of infection and in-hospital outcomes once admitted, and here we assess disparities in those who access healthcare via telemedicine for COVID-19.

Materials and methods: Electronic health record data of patients at New York University Langone Health between March 19th and April 30, 2020 were used to conduct descriptive and multilevel regression analyses with respect to visit type (telemedicine or in-person), suspected COVID diagnosis, and COVID test results.

Results: Controlling for individual and community-level attributes, Black patients had 0.6 times the adjusted odds (95% CI: 0.58-0.63) of accessing care through telemedicine compared to white patients, though they are increasingly accessing telemedicine for urgent care, driven by a younger and female population. COVID diagnoses were significantly more likely for Black versus white telemedicine patients.

Discussion: There are disparities for Black patients accessing telemedicine, however increased uptake by young, female Black patients. Mean income and decreased mean household size of a zip code were also significantly related to telemedicine use.

Conclusion: Telemedicine access disparities reflect those in in-person healthcare access. Roots of disparate use are complex and reflect individual, community, and structural factors, including their intersection-many of which are due to systemic racism. Evidence regarding disparities that manifest through telemedicine can be used to inform tool design and systemic efforts to promote digital health equity.

Keywords: COVID-19; digital health; disparities; racism; telemedicine.

Figure 1.

Overview of places where disparities may manifest through healthcare access, provider diagnoses, and testing process. Sample sizes for each visit type and healthcare process by year are provided. Each analysis and the corresponding Table where results are reported are also indicated. Red font/tables listed on right side used for analyses on data from all patients; blue font/tables on left side represent analyses on telemedicine patients only. Bold font used to indicate regression analyses. Triangle sizes are for illustration only.

Figure 2.

New York City map illustrating the change from March 19–April 30 2019 to the same time period in 2020 of number of telemedicine patients shaded by the patients’ home zip codes.