Jake Luo|Ling Tong|Bradley H. Crotty|Melek Somai|Bradley Taylor|Kristen Osinski|Ben George
Telemedicine Adoption during the COVID-19 Pandemic: Gaps and Inequalities
University of Wisconsin–Milwaukee|Froedtert Hospital|Medical College of Wisconsin|University of Wisconsin–Milwaukee|Froedtert Hospital|Medical College of Wisconsin|Froedtert Hospital|Medical College of Wisconsin|Froedtert Hospital|Medical College of Wisconsin|Froedtert Hospital|Medical College of Wisconsin|Froedtert Hospital|Medical College of Wisconsin
telemedicine industry experiencing fast growth recent years outbreak coronavirus disease 2019 covid19 accelerated deployment utilization telemedicine services analysis socioeconomic characteristics telemedicine users understand potential socioeconomic gaps disparities critical improving adoption telemedicine services among patientsthis study aims measure correlation socioeconomic determinants use telemedicine services milwaukee metropolitan areaelectronic health record review patients using telemedicine services compared using telemedicine services within academiccommunity health system patient demographics eg age gender race ethnicity insurance status socioeconomic determinants obtained blocklevel census data milwaukee area telemedicine users compared patients using regression analysis telemedicine adoption rates calculated across regional zip codes analyze geographic patterns telemedicine adoptiona total 104139 patients used telemedicine services study period patients used video visits younger median age 4812 likely white odds ratio 134 95 confidence interval ci 131137 private insurance 143 ci 141146 patients used telephone visits older median age 5758 likely black 131 ci 128135 public insurance 130 ci 127132 general latino asian populations less likely use telemedicine women used telemedicine services general men multiple regression analysis social determinant factors across 126 zip codes college education coefficient 141 p 001 strong correlation video telemedicine adoption rateadoption telemedicine services significantly impacted social determinant factors health income education level race insurance type study reveals potential inequities disparities telemedicine adoption