数字健康公平审计在预防有害信息流行病学中的作用。

IF 2.3 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2025-05-30 DOI:10.2196/75495
Massimiliano Biondi, Fabio Filippetti, Giorgio Brandi, Elsa Ravaglia, Sofia Filippetti, Pamela Barbadoro
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引用次数: 0

摘要

背景:健康差距持续存在,并受到数字化转型的影响。尽管数字工具提供了机会,但它们也可能加剧现有的不平等现象,COVID-19大流行和相关信息大流行加剧了这一问题。卫生公平审计(HEA)工具,如联合王国开发的工具,提供了评估公平的框架,但需要根据数字环境进行调整。数字健康决定因素(DDoH)日益被认为是影响数字时代健康结果的关键因素。目的:这篇社论提出了一种扩展HEA原则的方法,以创建一个具体的框架,即数字卫生公平审计(DHEA),旨在系统地评估和解决数字卫生技术设计、实施和评估中的卫生不公平问题,重点是DDoH。方法:我们提出了一个基于现有HEA原则的周期性DHEA模型,并将其与数字健康公平框架相结合。脱氢表雄酮周期包括六个阶段:(1)确定审计范围并动员团队(包括社区成员);(2)发展数字卫生公平概况并确定不公平现象(评估个人、人际、社区和社会层面的DDoH);(3)确定高影响力的行动,以解决DDoH和不平等问题;(4)优先采取行动,以最大限度地影响公平;(5)实施和支持变革;(6)评估进度和影响,并进行细化。这种方法强调多层次干预和利益相关者参与。结果:主要结果是DHEA框架的清晰表达:一个结构化的6阶段周期模型,用于指导组织分析和主动缓解与健康相关的数字差异。该框架明确整合了多个层面(个人、人际、社区、社会)的DDoH评估,并促进制定有针对性的干预措施,以确保数字解决方案促进公平。结论:脱氢表雄酮模型提供了一个综合的方法来考虑社会、流行病学、健康和技术变量,旨在通过有意识地使用新技术来减少卫生不平等。它强调,数字技术可以是不平等的原因或解决方案;DHEAs被提议作为促进公平的工具。它的系统采用,以及协作方法(共同设计)和信任建立,可以帮助确保卫生数字化的好处得到公平分配,同时加强对机构的信任。应对大数据和人工智能时代的信息流行病学等新挑战,需要持续关注。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Role of Digital Health Equity Audits in Preventing Harmful Infodemiology.

Background: Health disparities persist and are influenced by digital transformation. Although digital tools offer opportunities, they can also exacerbate existing inequalities, a problem amplified by the COVID-19 pandemic and the related infodemic. Health equity audit (HEA) tools, such as those developed in the United Kingdom, provide a framework to assess equity but require adaptation for the digital context. Digital determinants of health (DDoH) are increasingly recognized as crucial factors influencing health outcomes in the digital era.

Objective: This editorial proposes an approach to extend HEA principles to create a specific framework, the digital health equity audit (DHEA), designed to systematically assess and address health inequities within the design, implementation, and evaluation of digital health technologies, with a focus on DDoH.

Methods: We propose a cyclical DHEA model based on existing HEA principles, integrating them with digital health equity frameworks. The DHEA cycle comprises six phases: (1) scoping the audit and mobilizing the team (including community members); (2) developing the digital health equity profile and identifying inequities (assessing DDoH at individual, interpersonal, community, and societal levels); (3) identifying high-impact actions to address DDoH and inequities; (4) prioritizing actions for maximum equity impact; (5) implementing and supporting change; and (6) evaluating progress and impact, and refining. This method emphasizes multilevel interventions and stakeholder engagement.

Results: The main result is the articulation of the DHEA framework: a structured, 6-phase cyclical model to guide organizations in the analysis and proactive mitigation of digital health-related disparities. The framework explicitly integrates the assessment of DDoH across multiple levels (individual, interpersonal, community, societal) and promotes the development of targeted interventions to ensure digital solutions promote equity.

Conclusions: The DHEA model offers an integrated approach to consider social, epidemiological, health, and technological variables, aiming to reduce health inequities through the conscious use of new technologies. It is emphasized that digital technologies can be the cause or the solution to inequalities; DHEAs are proposed as a tool to foster equity. Its systematic adoption, along with a collaborative approach (co-design) and trust building, can help ensure that the benefits of health digitization are equitably distributed while strengthening trust in institutions. Continued attention is needed to manage emerging challenges such as infodemiology in the era of big data and artificial intelligence.

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