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利用公共数据集确定眼科远程医疗的重点领域。

Using Public Datasets to Identify Priority Areas for Ocular Telehealth.

机构信息

Division of Ophthalmology, Department of Surgery, University of Vermont Larner College of Medicine, Burlington, Vermont, USA.

Division of Ophthalmology, Department of Surgery, University of Vermont Medical Center, Burlington, Vermont, USA.

出版信息

Telemed J E Health. 2021 Nov;27(11):1293-1298. doi: 10.1089/tmj.2020.0433. Epub 2021 Feb 18.

Abstract

Telemedicine can expand access to ocular services, but barriers include restrictive policies and poor reimbursement. A tool to identify priority regions for interventions is needed. Eye care provider (ECP) density, self-reported visual disability, and demographics were calculated using census data and professional registries. The relationship between visual disability and ECP density was explored in fractional regression models. These data were compared with state telemedicine policy favorability. For each additional ECP per 100,000 population, there was 0.0111% less disability in the county (95% confidence interval -0.0150% to -0.00719%) in an adjusted model. Of 3,142 counties, 1,078 (34%) were in the worst population-weighted quartile for ECP density and visual disability. Low ECP density is associated with higher visual disability, suggesting an opportunity for ocular telehealth. Counties with favorable policy climates should be prioritized for telemedicine implementation. Public datasets can be used to survey wide geographic areas to identify areas worthy of detailed needs assessments.

摘要

远程医疗可以扩大眼科服务的可及性,但存在政策限制和报销不足等障碍。需要一种工具来确定干预的优先区域。利用人口普查数据和专业登记处计算了眼科保健提供者(ECP)密度、自我报告的视力障碍和人口统计数据。在分数回归模型中探讨了视力障碍与 ECP 密度之间的关系。将这些数据与州远程医疗政策的有利程度进行了比较。在调整后的模型中,每增加每 10 万人 1 名 ECP,该县的残疾率就会降低 0.0111%(95%置信区间-0.0150%至-0.00719%)。在 3142 个县中,有 1078 个(34%)县的 ECP 密度和视力障碍在人口加权四分位中处于最差水平。ECP 密度低与视力障碍程度高相关,表明有机会开展眼科远程医疗。政策环境有利的县应优先考虑远程医疗的实施。公共数据集可用于调查广泛的地理区域,以确定需要详细需求评估的区域。

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