Department of Health Informatics and Administration, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States.
Clinical and Translational Science Institute of Southeastern Wisconsin, Froedtert and Medical College of Wisconsin Health Network, Milwaukee, Wisconsin, United States.
Appl Clin Inform. 2021 Aug;12(4):836-844. doi: 10.1055/s-0041-1733848. Epub 2021 Sep 8.
The telemedicine industry has been experiencing fast growth in recent years. The outbreak of coronavirus disease 2019 (COVID-19) further accelerated the deployment and utilization of telemedicine services. An analysis of the socioeconomic characteristics of telemedicine users to understand potential socioeconomic gaps and disparities is critical for improving the adoption of telemedicine services among patients.
This study aims to measure the correlation of socioeconomic determinants with the use of telemedicine services in Milwaukee metropolitan area.
Electronic health record review of patients using telemedicine services compared with those not using telemedicine services within an academic-community health system: patient demographics (e.g., age, gender, race, and ethnicity), insurance status, and socioeconomic determinants obtained through block-level census data in Milwaukee area. The telemedicine users were compared with all other patients using regression analysis. The telemedicine adoption rates were calculated across regional ZIP codes to analyze the geographic patterns of telemedicine adoption.
A total of 104,139 patients used telemedicine services during the study period. Patients who used video visits were younger (median age 48.12), more likely to be White (odds ratio [OR] 1.34; 95% confidence interval [CI], 1.31-1.37), and have private insurance (OR 1.43; CI, 1.41-1.46); patients who used telephone visits were older (median age 57.58), more likely to be Black (OR 1.31; CI 1.28-1.35), and have public insurance (OR 1.30; CI 1.27-1.32). In general, Latino and Asian populations were less likely to use telemedicine; women used more telemedicine services in general than men. In the multiple regression analysis of social determinant factors across 126 ZIP codes, college education (coefficient 1.41, = 0.01) had a strong correlation to video telemedicine adoption rate.
Adoption of telemedicine services was significantly impacted by the social determinant factors of health, such as income, education level, race, and insurance type. The study reveals the potential inequities and disparities in telemedicine adoption.
近年来，远程医疗行业发展迅速。2019 年冠状病毒病（COVID-19）的爆发进一步加速了远程医疗服务的部署和利用。分析远程医疗用户的社会经济特征对于了解潜在的社会经济差距和差异，从而提高患者对远程医疗服务的采用率至关重要。
本研究旨在衡量密尔沃基大都市区社会经济决定因素与远程医疗服务使用之间的相关性。
对在学术社区卫生系统内使用远程医疗服务的患者与未使用远程医疗服务的患者进行电子病历回顾：患者人口统计学特征（如年龄、性别、种族和族裔）、保险状况以及通过密尔沃基地区的街区级人口普查数据获得的社会经济决定因素。使用回归分析比较远程医疗使用者与所有其他患者。根据区域邮政编码计算远程医疗采用率，以分析远程医疗采用的地理模式。
在研究期间，共有 104139 名患者使用了远程医疗服务。使用视频就诊的患者更年轻（中位数年龄 48.12 岁），更有可能是白人（优势比[OR]1.34；95%置信区间[CI]，1.31-1.37），并且拥有私人保险（OR 1.43；CI，1.41-1.46）；使用电话就诊的患者年龄较大（中位数年龄 57.58 岁），更有可能是黑人（OR 1.31；CI 1.28-1.35），并且拥有公共保险（OR 1.30；CI 1.27-1.32）。一般来说，拉丁裔和亚裔人群使用远程医疗服务的可能性较低；女性总体上比男性使用更多的远程医疗服务。在对 126 个邮政编码的社会决定因素进行多元回归分析时，大学教育（系数 1.41， = 0.01）与视频远程医疗采用率呈正相关。
远程医疗服务的采用受到健康的社会决定因素（如收入、教育水平、种族和保险类型）的显著影响。该研究揭示了远程医疗采用方面的潜在不平等和差异。
