Social Health Inequalities and eHealth: A Literature Review With Qualitative Synthesis of Theoretical and Empirical Studies

Abstract

BackgroundeHealth is developing rapidly and brings with it a promise to reduce social health inequalities (SHIs). Yet, it appears that it also has the potential to increase them.ObjectivesThe general objective of this review was to set out how to ensure that eHealth contributes to reducing SHIs rather than exacerbating them. This review has three objectives: (1) identifying characteristics of people at risk of experiencing social inequality in health; (2) determining the possibilities of developing eHealth tools that avoid increasing SHI; and (3) modeling the process of using an eHealth tool by people vulnerable to SHI.MethodsFollowing the EPPI approach (Evidence for Policy and Practice of Information of the Institute of Education at the University of London), two databases were searched for the terms SHIs and eHealth and their derivatives in titles and abstracts. Qualitative, quantitative, and mixed articles were included and evaluated. The software NVivo (QSR International) was employed to extract the data and allow for a metasynthesis of the data.ResultsOf the 73 articles retained, 10 were theoretical, 7 were from reviews, and 56 were based on empirical studies. Of the latter, 40 used a quantitative approach, 8 used a qualitative approach, 4 used mixed methods approach, and only 4 were based on participatory research-action approach. The digital divide in eHealth is a serious barrier and contributes greatly to SHI. Ethnicity and low income are the most commonly used characteristics to identify people at risk of SHI. The most promising actions for reducing SHI via eHealth are to aim for universal access to the tool of eHealth, become aware of users’ literacy level, create eHealth tools that respect the cultural attributes of future users, and encourage the participation of people at risk of SHI.ConclusionseHealth has the potential to widen the gulf between those at risk of SHI and the rest of the population. The widespread expansion of eHealth technologies calls for rigorous consideration of interventions, which are not likely to exacerbate SHI.

eHealth versus equity: Using a feminist poststructural framework to explore the influence of perinatal eHealth resources on health equity

“…To gather relevant literature, electronic databases, including PubMed and CINAHL, were used to employ a series of searches with keywords (e.g., “eHealth”; “eHealth resources OR interventions”; “perinatal”; “nursing”; “health education”; “user centered design”; “discourse”; “power”; “feminist poststructuralism”; “equity”). Since there have been significant advancements in technology and eHealth initiatives, the search was limited to the past 10 years, gathering literature published, in English, between 2007–2017 (Latulippe et al., ). Guided by a framework developed for the practical application of FPS in research, the literature was critically evaluated as it related to key principles of FPS, as described above, including power as relational; binary opposites; discourse analysis; language and meaning; beliefs, values and practices and subjectivity and agency (Aston, ).…”

“…At the foundational level, there is a clear separation between technology users and nonusers, whereby users have the advantage to access these additional eHealth resources with relative ease through their own personal devices. Nonusers have typically been found from an older population or those with low income (Latulippe, Hamel, & Giroux, ). With the shift of focus towards eHealth as an essential component to the future healthcare delivery, without structures in place to mitigate this separation, nonusers are likely to fall through the cracks and receive care of a lesser quality.…”

Using patient data for patients’ benefit

“…To many the digital world, social media campaigns, and hashtags are meaningless, intimidating, and a barrier to realistic engagement. 8 When patients neither know nor understand, they are unlikely to consent to use of their data. However, if patient benefit is explained, many will be happy to cooperate and expectations of risk, benefit, and harm will be better managed.…”

“…In the UK, this vision is reflected in several strategic publications by government, research councils, and charities. [5][6][7][8][9][10][11][12] If efforts in many health systems to improve interoperability and to link data across sources and sectors are successful, 10 there is unprecedented scope to build "learning health systems. 13 These integrate and facilitate data capture and use across research, evidence based guidelines, and clinical care, ultimately increasing the value of routine data for individual patients.…”

“…[4][5][6][7][8][9][10][11][12][13][14][15] Finally, the optimism in data driven care, whether in precision medicine or clinical decision support tools, must be evidence based-and that evidence should be patient centred.…”