Demographic data associated with digital inequity reported in patient-to-provider teledermatology studies in the United States from 2011 to 2021: a scoping review
ABSTRACT
Background:
Patient-to-provider teledermatology relies on a patient’s access to technology to ensure a successful visit. However, access to broadband internet and technology varies between populations in the United States – leading to the digital divide. While teledermatology has been recognized as a model to improve access, little is known about how often demographic data associated with digital inequity is captured in studies.
Objective:
To characterize how often demographic data associated with digital inequity is reported in patient-to-provider teledermatology studies.
Methods:
A scoping literature review search was conducted using search term teledermatology for the following databases: PubMed, Embase, and Cochrane Database of Systematic Reviews. All studies published between December 31, 2011 and December 31, 2021 that evaluated patient-to-provider teledermatology were eligible.
Results:
1412 publications describing teledermatology were identified, of which 46 met the inclusion criteria. Race or ethnicity was the most frequently reported demographic characteristic (61%). However, only 41% of studies were representative of race or ethnicity, defined as including > 20% non-white participants. Studies rarely reported participants greater than 65 years old (30%), preferred language (20%), income (13%), highest level of education (11%), or access to a device (9%). Studies conducted after the onset of the COVID-19 pandemic were significantly more likely to report preferred language and appeared more likely to report other demographic data associated with digital inequity without reaching statistical significance.
Conclusions:
Demographic data associated with digital inequity is rarely reported in patient-to-provider teledermatology studies to-date. These studies frequently lack adequate representation of racial and ethnic minorities. Demographic data associated with digital inequity should be reported in teledermatology studies to advance inclusive research methodologies and ensure generalizable conclusions.
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