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Disparities in telemedicine during COVID‐19

BACKGROUND: Oncology rapidly shifted to telemedicine in response to the COVID‐19 pandemic. Telemedicine can increase access to healthcare, but recent research has shown disparities exist with telemedicine use during the pandemic. This study evaluated health disparities associated with telemedicine u...

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Detalles Bibliográficos
Autores principales: Qian, Alexander S., Schiaffino, Melody K., Nalawade, Vinit, Aziz, Lara, Pacheco, Fernanda V., Nguyen, Bao, Vu, Peter, Patel, Sandip P., Martinez, Maria Elena, Murphy, James D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855911/
https://www.ncbi.nlm.nih.gov/pubmed/34989148
http://dx.doi.org/10.1002/cam4.4518
Descripción
Sumario:BACKGROUND: Oncology rapidly shifted to telemedicine in response to the COVID‐19 pandemic. Telemedicine can increase access to healthcare, but recent research has shown disparities exist with telemedicine use during the pandemic. This study evaluated health disparities associated with telemedicine uptake during the COVID‐19 pandemic among cancer patients in a tertiary care academic medical center. METHODS: This retrospective cohort study evaluated telemedicine use among adult cancer patients who received outpatient medical oncology care within a tertiary care academic healthcare system between January and September 2020. We used multivariable mixed‐effects logistic regression models to determine how telemedicine use varied by patient race/ethnicity, primary language, insurance status, and income level. We assessed geospatial links between zip‐code level COVID‐19 infection rates and telemedicine use. RESULTS: Among 29,421 patient encounters over the study period, 8,541 (29%) were delivered via telemedicine. Several groups of patients were less likely to use telemedicine, including Hispanic (adjusted odds ratio [aOR] 0.86, p = 0.03), Asian (aOR 0.79, p = 0.002), Spanish‐speaking (aOR 0.71, p = 0.0006), low‐income (aOR 0.67, p < 0.0001), and those with Medicaid (aOR 0.66, p < 0.0001). Lower rates of telemedicine use were found in zip codes with higher rates of COVID‐19 infection. Each 10% increase in COVID‐19 infection rates was associated with an 8.3% decrease in telemedicine use (p = 0.002). CONCLUSIONS: This study demonstrates racial/ethnic, language, and income‐level disparities with telemedicine use, which ultimately led patients with the highest risk of COVID‐19 infection to use telemedicine the least. Additional research to better understand actionable barriers will help improve telemedicine access among our underserved populations.