PDF(Pubmed)
Abstract：
UNASSIGNED: 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 across 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 are captured in studies.
UNASSIGNED: Given the expansion of teledermatology over the past decade, we sought to determine how often demographic data associated with digital inequity are reported in patient-to-provider teledermatology studies.
UNASSIGNED: A scoping literature review search was conducted using the search term teledermatology in the following databases: PubMed, Embase, and the Cochrane Database of Systematic Reviews. All studies published between December 31, 2011, and December 31, 2021, that evaluated patient-to-provider teledermatology were eligible.
UNASSIGNED: In total, 1412 publications describing teledermatology were identified, of which 46 met the inclusion criteria. Race or ethnicity was the most frequently reported demographic characteristic (28/46, 61%). However, only 41% (19/46) of studies were representative of race or ethnicity, defined as including >20% nonwhite participants. Studies rarely reported the number of participants greater than 65 years of age (14/46, 30%), preferred language (9/46, 20%), income (6/46, 13%), highest level of education (5/46, 11%), or access to a device (4/46, 9%). Studies conducted after the onset of the COVID-19 pandemic were significantly more likely to report preferred language (9/25, 36% vs 0%; P=.002) and appeared more likely to report other demographic data associated with digital inequity, without reaching statistical significance (P>.05).
UNASSIGNED: Demographic data associated with digital inequity are rarely reported in patient-to-provider teledermatology studies to date. These studies frequently lack adequate representation of racial and ethnic minorities. With increased calls for equitable representation in dermatology studies, future teledermatology studies can improve the reporting of race and ethnicity and consider demographic data associated with digital inequity as an important criterion in research design.
UNASSIGNED: Given the expansion of teledermatology over the past decade, we sought to determine how often demographic data associated with digital inequity are reported in patient-to-provider teledermatology studies.
UNASSIGNED: A scoping literature review search was conducted using the search term teledermatology in the following databases: PubMed, Embase, and the Cochrane Database of Systematic Reviews. All studies published between December 31, 2011, and December 31, 2021, that evaluated patient-to-provider teledermatology were eligible.
UNASSIGNED: In total, 1412 publications describing teledermatology were identified, of which 46 met the inclusion criteria. Race or ethnicity was the most frequently reported demographic characteristic (28/46, 61%). However, only 41% (19/46) of studies were representative of race or ethnicity, defined as including >20% nonwhite participants. Studies rarely reported the number of participants greater than 65 years of age (14/46, 30%), preferred language (9/46, 20%), income (6/46, 13%), highest level of education (5/46, 11%), or access to a device (4/46, 9%). Studies conducted after the onset of the COVID-19 pandemic were significantly more likely to report preferred language (9/25, 36% vs 0%; P=.002) and appeared more likely to report other demographic data associated with digital inequity, without reaching statistical significance (P>.05).
UNASSIGNED: Demographic data associated with digital inequity are rarely reported in patient-to-provider teledermatology studies to date. These studies frequently lack adequate representation of racial and ethnic minorities. With increased calls for equitable representation in dermatology studies, future teledermatology studies can improve the reporting of race and ethnicity and consider demographic data associated with digital inequity as an important criterion in research design.
摘要：
UNASSIGNED：患者对提供者的远程皮肤病学依赖于患者对技术的访问，以确保成功的访问。然而,在美国，宽带互联网和技术的接入因人口而异，这导致了数字鸿沟。虽然远程皮肤病学被认为是改善访问的典范，关于在研究中捕获与数字不平等相关的人口统计数据的频率知之甚少。
未经评估：在过去十年中，由于皮肤学的扩展，我们试图确定在患者-提供者远程皮肤病学研究中报告与数字不平等相关的人口统计学数据的频率.
UNASSIGNED：使用以下数据库中的搜索词teledermatology进行了范围界定文献综述搜索：PubMed，Embase,和Cochrane系统评价数据库。2011年12月31日至2021年12月31日发表的所有评估患者对提供者远程皮肤病学的研究均符合条件。
未经批准：总共，确定了1412种描述皮肤病学的出版物，其中46人符合纳入标准。种族或民族是最常报告的人口统计学特征（28/46，61%）。然而,只有41%(19/46)的研究代表种族或民族，定义为包括>20%的非白人参与者。研究很少报道超过65岁的参与者人数（14/46，30％），首选语言(9/46，20%)，收入（6/46，13%），最高教育水平(5/46，11%)，或访问设备(4/46，9%)。在COVID-19大流行爆发后进行的研究更有可能报告首选语言(9/25，36%vs0%；P=0.002)，并且似乎更有可能报告与数字不平等相关的其他人口统计数据，无统计学意义(P>0.05)。
UNASSIGNED：与数字不平等相关的人口统计学数据很少在患者对提供者远程皮肤病学研究中报告。这些研究往往缺乏种族和族裔少数群体的充分代表性。随着要求皮肤病学研究公平代表性的呼声越来越高，未来的远程皮肤病学研究可以改善种族和种族的报告，并将与数字不平等相关的人口统计数据视为研究设计的重要标准。
未经评估：在过去十年中，由于皮肤学的扩展，我们试图确定在患者-提供者远程皮肤病学研究中报告与数字不平等相关的人口统计学数据的频率.
UNASSIGNED：使用以下数据库中的搜索词teledermatology进行了范围界定文献综述搜索：PubMed，Embase,和Cochrane系统评价数据库。2011年12月31日至2021年12月31日发表的所有评估患者对提供者远程皮肤病学的研究均符合条件。
未经批准：总共，确定了1412种描述皮肤病学的出版物，其中46人符合纳入标准。种族或民族是最常报告的人口统计学特征（28/46，61%）。然而,只有41%(19/46)的研究代表种族或民族，定义为包括>20%的非白人参与者。研究很少报道超过65岁的参与者人数（14/46，30％），首选语言(9/46，20%)，收入（6/46，13%），最高教育水平(5/46，11%)，或访问设备(4/46，9%)。在COVID-19大流行爆发后进行的研究更有可能报告首选语言(9/25，36%vs0%；P=0.002)，并且似乎更有可能报告与数字不平等相关的其他人口统计数据，无统计学意义(P>0.05)。
UNASSIGNED：与数字不平等相关的人口统计学数据很少在患者对提供者远程皮肤病学研究中报告。这些研究往往缺乏种族和族裔少数群体的充分代表性。随着要求皮肤病学研究公平代表性的呼声越来越高，未来的远程皮肤病学研究可以改善种族和种族的报告，并将与数字不平等相关的人口统计数据视为研究设计的重要标准。
