Hampton Jazmin, Mugambi Purity, Caggiano Emily, Eugene Reynalde, Valente Alycia, Taylor Melissa, Carreiro Stephanie
Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.
Washington University of Health and Science, San Pedro, Belize, Central America.
J Psychiatr Brain Sci. 2024;9(1). doi: 10.20900/jpbs.20240002. Epub 2024 Mar 26.
Digital health interventions are exploding in today's medical practice and have tremendous potential to support the treatment of substance use disorders (SUD). Developers and healthcare providers alike must be cognizant of the potential for digital interventions to exacerbate existing inequities in SUD treatment, particularly as they relate to Social Determinants of Health (SDoH). To explore this evolving area of study, this manuscript will review the existing concepts of the digital divide and digital inequities, and the role SDoH play as drivers of digital inequities. We will then explore how the data used and modeling strategies can create bias in digital health tools for SUD. Finally, we will discuss potential solutions and future directions to bridge these gaps including smartphone ownership, Wi-Fi access, digital literacy, and mitigation of historical, algorithmic, and measurement bias. Thoughtful design of digital interventions is quintessential to reduce the risk of bias, decrease the digital divide, and create equitable health outcomes for individuals with SUD.
数字健康干预措施在当今医疗实践中迅速兴起,在支持物质使用障碍(SUD)治疗方面具有巨大潜力。开发者和医疗服务提供者都必须认识到数字干预措施可能加剧SUD治疗中现有的不平等现象,特别是与健康的社会决定因素(SDoH)相关的不平等。为了探索这一不断发展的研究领域,本文将回顾数字鸿沟和数字不平等的现有概念,以及SDoH作为数字不平等驱动因素所起的作用。然后,我们将探讨所使用的数据和建模策略如何在SUD数字健康工具中产生偏差。最后,我们将讨论弥合这些差距的潜在解决方案和未来方向,包括智能手机拥有情况、Wi-Fi接入、数字素养,以及减轻历史、算法和测量偏差。精心设计数字干预措施对于降低偏差风险、减少数字鸿沟以及为患有SUD的个体创造公平的健康结果至关重要。