Digital exclusion in healthcare services: a scoping review

Abstract

Background

With the widespread adoption of digital technologies in healthcare, digital exclusion—the phenomenon in which individuals are excluded from digital technologies due to a lack of access, skills, motivation, and other factors—has emerged as a significant contributor to health inequities. However, conceptual ambiguity, inconsistent measurement approaches, and an unclear impact on health outcomes within healthcare settings limit the development of effective strategies to address digital exclusion. This scoping review aims to clarify the concept and dimensions of digital exclusion in healthcare, examine its measurement methods, and understand its effects on health.

Methods

A scoping review was conducted following the PRISMA-ScR guidelines for reporting. A systematic search was performed in PubMed, CINAHL, Emerald, Web of Science Core Collection, PsycINFO, Ovid, Embase, IEEE Xplore, and Google Scholar for English-language literature published up to December 1, 2024. Two independent reviewers performed screening and data extraction, and findings were synthesized narratively using thematic analysis.

Result

The initial search retrieved 3,392 studies. A total of 39 studies were finally included. The concept of digital exclusion can be categorized into three levels focusing on access, user capabilities, and meaningful outcomes. Thematic analysis identified five core dimensions: affordability barriers, technical resource barriers, digital competence, psychological barriers, and usage and support barriers. Nineteen assessment tools were identified, including 16 individual-level and 3 population-level instruments. However, most lacked validation and were not designed specifically for healthcare contexts. Evidence indicates that digital exclusion is associated with various adverse health outcomes, including restricted access to medical resources, psychosocial problems, cognitive and functional decline, and increased disease risk and poorer prognosis.

Conclusion

Digital exclusion in healthcare is a multidimensional, complex phenomenon with significant negative impacts on health. Future research should deepen conceptual understanding within healthcare contexts, develop validated clinical assessment tools, and investigate mechanisms through which digital exclusion affects a broad range of health outcomes, thereby promoting equitable access to digital health services.

Introduction

Over the past decade, the global healthcare sector has witnessed profound changes fueled by the rapid evolution of digital technologies. In our interconnected world, digital health technologies such as telemedicine, electronic health records (EHRs), mobile health applications (mHealth), and wearable monitoring devices are increasingly integral to the delivery of healthcare services [1–4]. While digital tools offer significant promise for enhancing healthcare accessibility, efficiency, and quality, their implementation also risks exacerbating existing inequalities and creating new forms of exclusion for marginalized populations [5].

A portion of the population faces barriers in accessing digital technologies due to multiple factors, such as inadequate Internet infrastructure, limited digital skills, and a lack of confidence in their usage, limiting their ability to benefit fully from participation in a digital society. This phenomenon is referred to as digital exclusion [6–9]. The digital exclusion phenomenon is closely related to the digital divide, which highlights the gap between those who have access to digital technologies and those who have limited or no access to digital technologies [10]. This divide is often described as existing across three levels. The first level refers to the gap in access to computers and the internet. The second level encompasses inequalities in digital skills, digital competences, and digital usage patterns, as well as differences in psychological acceptance of digital technologies. The third level focuses on the consequences and outcomes of digital technology use, particularly the extent to which individuals are able to benefit from it [11]. Therefore, digital exclusion is not merely about lacking devices or connectivity, it encompasses deeper systemic challenges related to skills, trust, benefits, and contributes to a new form of social inequality that reflects and reinforces existing disparities [12].

Digital exclusion in healthcare illustrates a paradoxical phenomenon known as the digital inverse care law, whereby those who are most in need of healthcare services are least likely to benefit from digital innovations designed to improve access and quality of care [13]. At the heart of this contradiction lies an inverse relationship between healthcare need and resource. It is well-established in the literature that individuals of higher socioeconomic status (SES) typically experience better health outcomes, attributable to their superior living conditions, education, lifestyles, and healthcare access [14–16]. These high-SES groups generally possess a greater capacity to afford digital devices and internet access and, with higher levels of digital skills and health literacy, are positioned to access and benefit from digital technologies more effectively [17–19]. In contrast, populations with lower SES, who often have the greatest health needs, are often among the least likely to access digital health services, owing to barriers such as device scarcity, inadequate digital skills, or a lack of trust in the technology [13, 20]. This disparity is particularly evident when observing the challenges faced by a particular-population group. For instance, research conducted in Finland showed that older patients are less likely to use online services for viewing test results, requesting repeat prescriptions and scheduling appointments [21]. Likewise, studies within the United States have highlighted that certain groups, such as ethnic minorities, non-native English speakers, and rural residents, have more limited access to video consultations and health information [22, 23]. The detrimental impact of digital exclusion in the healthcare sector extends far beyond the traditional digital divide. Further research indicates that digital exclusion directly contributes to the decline in essential health behaviors. A systematic review that synthesized evidence from 47 studies, primarily conducted in North America and Europe, concluded that those who do not engage with digital patient portals generally exhibit poorer outcomes in blood pressure control, glycaemic management, medication adherence, and timely medical visits [24]. This indicates that digital exclusion in healthcare is not merely a matter of technological accessibility; rather, it actually makes existing health inequalities worse by restricting access to health information, prolonging delays in seeking timely medical care, and reducing adherence to prescribed treatments.

Therefore, it is imperative to investigate the characteristics of digital exclusion as it manifests within healthcare sectors. By employing context-appropriate instruments, the identification of high-risk populations facing digital exclusion in healthcare becomes feasible. This, in turn, facilitates the development of inclusive intervention strategies, with the aim of ensuring digital healthcare genuinely serves the cause of health equity. Yet the term digital exclusion itself remains conceptually ambiguous [25, 26]. Existing literature often lacks a critical and analytical perspective on digital exclusion, or treats it as a taken-for-granted concept, using it without theoretical grounding. Moreover, there is no consensus on its precise definition, dimensions, or how it interacts with broader systemic inequities [27–30]. This lack of clarity creates challenges for systematically measuring and addressing digital exclusion, particularly in the context of healthcare. Despite the growing reliance on digital health technologies, research exploring the health consequences of digital exclusion remains limited [18]. While some studies acknowledge the disparities in digital access and usage, there is insufficient exploration of how digital exclusion impacts health outcomes. Therefore, this study aims to address these gaps by:

1. Clarifying the concept of digital exclusion and its theoretical dimensions in the context of healthcare.

2. Exploring methods to measure digital exclusion.

3. Understanding associations between digital exclusion and health outcomes.

Methods

This study employs a scoping review methodology, which is designed to systematically map the key concepts, evidence types, and research gaps within a broad topic area [31]. The concept of digital exclusion in healthcare is characterized by conceptual ambiguity and heterogeneous measurement approaches, making it unsuitable for a systematic review, which typically addresses narrowly-defined questions of intervention effectiveness [32]. Therefore, a scoping review offers an ideal methodology to clarify the core concept of digital exclusion and its dimensions, map its various evaluation methods, and characterize the evidence linking it to health outcomes.

This scoping review of the literature was conducted following the methodological framework established by Arksey and O’Malley, and later refined by Levac, Colquhoun, and O’Brien [31, 33]. The review adhered to the six key stages outlined in their framework: (1) identifying research questions; (2) searching for relevant studies; (3) defining a relevant study selection; (4) charting the data; (5) collating, summarizing, and reporting results. Our results were presented in accordance with the PRISMA extension for scoping reviews (PRISMA-ScR) [34]. The methods of this review have been registered in the Open Science Framework (OSF) Registries (Registration DOI: 10.17605/OSF.IO/MW2GN)

Identifying research questions

This scoping review was designed to answer the following questions:(1) How is digital exclusion defined and conceptualized in the context of healthcare services? What are its main dimensions? (2) What are the methods used to identify or measure digital exclusion? (3) Does the existing literature identify a relationship between digital exclusion and health outcomes?

Searching for relevant studies

To identify relevant publications addressing these research questions, we conducted searches in electronic databases and examined reference lists for both peer-reviewed articles and grey literature. The databases searched included PubMed, CINAHL, Emerald, Web of Science Core Collection, PsycINFO, Ovid, Embase, IEEE Xplore, and Google Scholar. The search date was set to December 1, 2024. The search strategy for these databases involved two sets of search components and search terms, which were applied to the titles (and abstracts) of publications. Table 1 lists search components and search terms of this scoping review. Search terms were linked using the Boolean operator OR, and the two search components were linked using the Boolean operator AND. For Google Scholar, the first 500 search results were reviewed. The reference lists of the included articles were manually searched to identify additional relevant studies.

Table 1.

Search components and search terms

Search components	Search terms
Digital Exclusion	Digital exclusion, digitally excluded, digital excl*, digital incl*, digital inclusion, digital divide, digital gap, digital inequality
Healthcare	Digital health*, digital health technology, eHealth, mHealth, telemedicine, health*, health outcomes, nursing, nursing care, primary care nursing, health services, health services for the aged, delivery of healthcare, personal health services, health inequities, healthcare disparities, health status disparities, minority health, rural health services, home care services, home health nursing, family nursing, community health nursing, community health services, rehabilitation nursing, outpatients, ambulatory care, inpatients, cancer survivors, chronic disease, long-term care, mental health services, psychiatric nursing, public health nursing

Defining a relevant study selection

Articles were included if they met any of the following criteria: (1) the article defined digital exclusion and/or identified existing assessment tools; (2) the article identified health outcomes affected by digital exclusion or explored the relationship between digital exclusion and health outcomes. Exclusion criteria: (1) the publication was unrelated to the subject of digital technologies; (2) conference abstracts, comments, book chapters and book reviews; (3) the publication was not written in English; (4) the full text of the article was not accessible. Two reviewers independently reviewed the articles, and a third reviewer resolved any discrepancies and provided clarifications to the primary reviewers.

Charting the data

The data derived from the selected studies were entered into an Excel spreadsheet using a narrative approach. The data extraction process included general publication characteristics such as the authors, year of the study, geographical location, demographic samples, study designs, and research objectives. Additionally, the extraction focused on the concept of digital exclusion, as well as the tools or survey items used for its assessment. Dimensions, measurement methodologies, and psychometric characteristics were also extracted. The main results of the studies related to health outcomes were recorded. To ensure consistency between the data extraction methods and research objectives, initial data were drawn from the first five studies, and the data extraction form was updated during the extraction process.

Collating, summarizing, and reporting results

The analytical process was organized into three distinct steps [31]. First, we conducted a comprehensive numerical analysis of the results to examine the distribution and general characteristics of the studies included in this review. Second, we conducted an inductive thematic analysis guided by the framework of Braun and Clarke [35] to identify key themes within the included literature. The process began with two reviewers (Z.X. and Y.W.) familiarizing themselves with the articles, focusing on how digital exclusion was defined, measured, and described within healthcare contexts. They systematically coded relevant sections line by line to generate initial codes. Initial codes were collated into broader categories, which were then developed into candidate themes. The two reviewers (Z.X. and Y.W.) collaboratively refined the themes by checking them against both the coded extracts for validity and the entire dataset for coherence. This iterative process involved merging or splitting themes as necessary until the final framework accurately represented the data. Reviewers (Z.X. and Y.W.) collaboratively defined and named each theme through discussion and revision. Any discrepancies in coding or theme generation were resolved through consensus. Finally, we synthesized the themes into a coherent narrative to present our findings, which formed the basis for a discussion on their implications for future research and practice.

Results

Literature search and study characteristics

The electronic database search yielded 3,392 items. Following the removal of 1,660 duplicates, 1,732 papers remained for screening. After title and abstract screening, the full texts of 203 articles were reviewed. The final sample included 39 publications. The screening process is depicted in the PRISMA-ScR flow diagram in Fig. 1. The years of publication ranged from 2003 to 2024. Studies on digital exclusion have shown a clear upward trend. Early publications were sparse, with single articles appearing in 2012 [36], 2013 [37], 2016 [38], and 2020 [9], and two in both 2014 [39, 40] and 2021 [7, 41]. A gradual increase began in 2019, with four articles published [42–45]. A more pronounced rise occurred in 2022 (n = 5) [46–50], and the majority of articles were published in 2023 (n = 9) [6, 29, 51–57] and 2024 (n = 13) [8, 27, 28, 58–67].

Fig. 1.

PRISMA-ScR flow diagram

The research was published and conducted in a variety of countries, led by the United Kingdom (n = 21). Other countries with multiple publications include Australia (n = 2), Belgium (n = 2), China (n = 2), Finland (n = 2), Israel (n = 2), and the USA (n = 2). Single studies originated from Poland, Saudi Arabia, Slovenia, and South Korea (n = 1 each). There were also two studies (n = 2) that involved research across multiple countries, including China, UK, USA, Mexico, and Europe.

Included articles used a variety of study designs. Cross-sectional studies were most prevalent (n = 14), followed by observational qualitative studies (n = 11), mixed-methods studies (n = 3), longitudinal cohort studies (n = 3), reviews (n = 2), and retrospective case series study (n = 1). Reports (n = 4) and government document(n = 1) also contributed to this literature. In addition, 66.7% (26/39) of the studies were focused on healthcare contexts. Table 2 describes the characteristics of the included articles.

Table 2.

Article characteristics

Year	Author	Country	Study design	Objective	Study population (sample size)	Focused on healthcare contexts
2013	T. O. Crnic [37]	Slovenia	Cross-sectional study	To identify who remains digitally offline in Slovenia and how this status relates to sociodemographic factors, class, and cultural capital.	General population (n = 820)	No
2014	Martinez, et al. [40]	USA	Cross-sectional study	To examine how internet access and usage frequency relate to demographics, socioeconomic status, disease severity, comorbidities, and satisfaction with healthcare.	COPD patients (n = 914)	Yes
2016	Robotham, et al. [38]	UK	Cross-sectional study	To understand the context of digital exclusion for people who experience mental illness.	People with a primary diagnosis of psychosis or depression (n = 241)	Yes
2022	F. Li [47]	USA	Cross-sectional study	To explore the pathways through which digital exclusion could exacerbate the impacts of the pandemic and to examine the relationship between digital access and COVID-19 outcomes in U.S. counties.	General population (n = not applicable)	No
2023	Anrijs, et al. [29]	Belgium	Cross-sectional study	To investigate the proportion of digitally excluded versus digitally included individuals within and between groups of varying socioeconomic resources.	General population aged over 18 (n = 674)	No
2023	Goldman, et al. [51]	Israel	Cross-sectional study	To explore the implications of digital use on the wellbeing of older people during the pandemic	Adults aged 70 years and older who currently live in Israel (n = 30)	Yes
2023	Hider, et al. [52]	UK	Cross-sectional study	To examine digital access, health and digital literacy, and the impact on confidence and satisfaction with remote consultations in people with inflammatory rheumatic diseases.	People with inflammatory rheumatic diseases (n = 693)	Yes
2023	Liu, et al. [53]	China	Cross-sectional study	To assess the relationships between cognitive impairment risk and digital exclusion.	General population (n = 10 325)	No
2023	Scherrenberg, et al. [55]	Belgium	Cross-sectional study	To design a questionnaire for evaluating patients’ digital readiness.	Patients visiting the cardiology department (n = 315)	Yes
2023	Ueno, et al. [56]	UK	Cross-sectional study	To investigate internet non-use in the UK, and to identify main factors associated with internet non-use.	General population (n = 56 724)	No
2024	E. Yang, M. J. Kim and K. H. Lee [67]	South Korea	Cross-sectional study	To explore the relationship between internet use among elderly individuals with disabilities and difficulties in accessing health resources during the COVID-19 pandemic.	Older adults aged 55 and above with disabilities (n = 4 871)	Yes
2024	Mee, et al. [59]	UK	Cross-sectional study	This paper describes the development of a composite indicator and online tool to quantify digital exclusion at a local level.	General population (n = not applicable)	No
2024	Shorthose, et al. [62]	UK	Cross-sectional study	It aimed to determine whether frailty is a significant risk factor for digital exclusion in accessing video consultations, and whether a support network mitigates this risk.	Patients in primary care, hospital at home, and secondary care services (n = 255)	Yes
2024	Wang, et al. [63]	China, UK, USA, Mexico and Europe	Cross-sectional study	To investigate the relationship between digital exclusion and cognitive impairment.	Individuals aged 60 and above (n = 62 413)	No
2019	T. Śmiałowski and L. Ochnio [68]	Poland	Longitudinal cohort study	To assess the impact of economic conditions on the extent and disparities in digital exclusion among Polish households.	General population (n = not reported)	No
2022	Metherell, et al. [49]	UK	Longitudinal cohort study	To test whether mental health models differ for those without access to a computer or good internet connection compared with those with this digital access.	10–15-year-olds (n = 1 387)	Yes
2022	X. Lu, Y. Yao and Y. Jin [48]	China, UK, USA, Mexico and Europe	Longitudinal cohort study	To investigate the association between digital exclusion and functional dependency among older adults from high-income countries and low- and middle-income countries.	Older adults(age > 60) (n = 108 621)	No
2023	P. Pierce, M. Whitten and S. Hillman [54]	UK	Mixed-method study	The study aimed to quantify mycare usage among pregnant women, to assess sociodemographic disparities, to explore vulnerable women’s views, to identify barriers and utility, and to gather healthcare professionals’ feedback on the app’s implementation and improvements.	Vulnerable pregnant women(n = 636)	Yes
2024	Wanless, et al. [64]	UK	Mixed-method study	To evaluate the acceptability and potential causes of digital exclusion related to the musculoskeletal self-management app.	Patients with musculoskeletal conditions (n = 256) and clinicians working in primary care(n = 16).	Yes
2024	Wilson-Menzfeld, et al. [28]	UK	Mixed-method study	To identify the scale and key characteristics of digitally excluded groups and to understand key factors contributing to digital exclusion.	General population (n = 9 181)	No
2012	F. Baum, L. Newman and K. Biedrzycki [36]	Australia	Qualitative study	To explore how people’s existing resources influence their access to and use of digital technologies and the potential impact of exclusion on the social determinants of health.	Individuals with lower socio-economic status (n = 55)	No
2019	Greer, et al. [43]	UK	Qualitative study	To explore the issues of digital exclusion among mental health service users and to identify potential facilitators to overcome these challenges.	Mental health service users (n = 20)	Yes
2021	Vera San Juan, et al. [41]	UK	Qualitative study	To explore user experiences with the transition to remote mental health care and to identify factors that facilitate or hinder remote engagement.	Telemental health users (n = 44)	Yes
2022	Kaihlanen, et al. [46]	Finland	Qualitative study	To examine the challenges faced by vulnerable groups in using digital health services during the COVID-19 pandemic.	Older adults, migrants, mental health service users, high users of health services, and the unemployed (n = 74)	Yes
2022	R.Middle and L. Welch [50]	UK	Qualitative study	To understand experiences of digital exclusion and the impact on health in people with severe mental illness.	People with severe mental illness (n = 9)	Yes
2023	Virtanen, et al. [57]	Finland	Qualitative study	To explore the patterns of acceptance and use of digital health services among frequent attenders.	Adults visiting in outpatient care (n = 30)	Yes
2024	A. Oliver, E. Chandler and J. A. Gillard [61]	UK	Qualitative study	To examine the impact of facilitating digital inclusion in mental health access.	Digital Inclusion Scheme users (n = 12)	Yes
2024	D. Y. Wazqar [65]	Saudi Arabia	Qualitative study	The study aimed to explore the challenges faced by cancer patients and their family caregivers in Saudi Arabia when using digital health technology platforms during the COVID-19 pandemic.	Participants from a public hospital providing cancer care services (n = 21)	Yes
2024	M. Haimi, U. Goren and Z. Grossman [58]	Israel	Qualitative study	To investigate the challenges and barriers encountered by elderly individuals in Israel when using telemedicine services.	65 years and older (n = 14)	Yes
2024	R. Zhu, X. Yu, and R. Krever [27]	China	Qualitative study	The study explored how many patients faced digital exclusion in healthcare, who needed others’ help for access to care they needed, and how this impacted their self-esteem.	Elderly patients of a rural hospital(n = 44)	Yes
2024	Woodward, et al. [66]	UK	Qualitative study	To explore the barriers and facilitators to self-managing multiple long-term conditions.	Adults diagnosed with two or more long-term conditions (n = 28)	Yes
2024	Mendall, et al. [60]	UK	Retrospective case series study	To examine associations between the DERI/IMD, clinical parameters, and app use.	Patients with retinal pathologies（n = 89）	Yes
2019	Borg, et al. [42]	Australia	Review	To identify barriers and facilitators to digital inclusion and to evaluate interventions aimed at improving digital inclusion.	Not applicable	Yes
2020	Honeyman, et al. [9]	UK	Review	To provide a scoping review of existing literature to understand the relationship between digital technology use and health inequalities and to identify potential ways to mitigate risks or reduce inequalities.	Not applicable	Yes
2021	Good Things Foundation [7]	UK	Report	It highlights the critical link between digital exclusion and health inequalities, analyzing their impact and presenting collaborative insights for addressing the issue.	Not applicable	Yes
2022	NHS Digital [44]	UK	Report	To guide local health and care organisations in understanding and addressing digital exclusion to ensure the delivery of inclusive digital health and social care services.	Not applicable	Yes
2023	House of Lords [6]	UK	Report	To critically assess the extent and impact of digital exclusion in the UK, to evaluate the government’s current insufficient response, and to recommend a refreshed, coordinated strategy with specific actions for government, regulators, industry, and civil society to address the issue comprehensively.	Not applicable	No
2024	Good Things Foundation [8]	UK	Report	To guide health policy makers, providers, and commissioners on understanding the link between digital exclusion and health inequalities and how to identify and mitigate the associated risks.	Not applicable	Yes
2014	Cabinet Office [39]	UK	Government document	To set out the UK government’s strategy with cross-sector partners to increase digital inclusion by addressing barriers and achieving targets, ensuring everyone who can be is digitally capable.	Not applicable	No

Definitions of digital exclusion

Table 3 shows that 14 studies provided explicit definitions of digital exclusion [6–9, 38, 47–49, 52, 53, 60, 61, 63, 64], and four other studies explored key dimensions of digital exclusion, despite not providing a specific definition [39, 44, 59, 62]. These definitions, revealing nuanced differences, can be broadly categorized into three groups. The first group centers on access and highlights that digital exclusion is the lack of physical access to the Internet or digital devices [47, 63, 64]. The second group focuses on user capabilities, shifting the focus from the technology itself to the individual. From this perspective, digital exclusion is caused by user-specific characteristics that restrict the effective use of technology. These individual factors, such as insufficient digital skills or capability to use the technology, a lack of motivation, or low confidence [48, 49, 53, 60, 61], may restrict how people use these technologies, leading to infrequent or otherwise limited engagement even when access to devices and connectivity are not an issue [38, 52]. The third category centers on meaningful outcomes, focusing on the ability to translate digital use into tangible value. This perspective suggests that digital exclusion also occurs when individuals fail to derive meaningful benefits from their digital use. This requires users to use digital tools to gain real benefits (e.g., accessing services, improving wellbeing, and participating in society [6, 9]), while also handling risks like data privacy threats or false information [7, 8]. Thus, skills and access alone do not prevent digital exclusion if concrete benefits do not follow [9].

Table 3.

Definitions of digital exclusion

Categories	Definition	References	Examples
Access	Lack of or inability to connect to the internet, digital devices, or related infrastructure.	F. Li [47], Wang, et al. [63], Wanless, et al. [64], Hider, et al. [52], Lu, et al. [48], Liu, et al. [53], Mendall, et al. [60], Metherell, et al. [49], Oliver, et al. [61], Robotham, et al. [38], House of Lords [6], Good Things Foundation [7, 8].	“The share of households with no internet access and the share of individuals who have no computer at home” [47].
User Capabilities	Insufficiency in an individual’s capabilities, skills, confidence, or motivation for using digital technologies	Hider, et al. [52], Lu, et al. [48], Liu, et al. [53], Mendall, et al. [60], Metherell, et al. [49], Oliver, et al. [61], Robotham, et al. [38], House of Lords [6], Good Things Foundation [7, 8].	“Experiencing connectivity and accessibility barriers, as well as lacking digital skills and motivation” [49].
Meaningful Outcomes	Inability to derive meaningful benefits from the use of digital technology, or to effectively manage associated risks.	House of Lords [6], Good Things Foundation [7, 8], Honeyman, et al. [9].	“The situation when people and groups in society are unable to exploit the benefits that using digital technologies might make available to them” [9].

Definitions of digital exclusion

Digital exclusion in healthcare has been associated with different issues, which are often interrelated. The five core dimensions identified—affordability barriers, technical resource barriers, digital competence, psychological barriers, and usage and support barriers—interact dynamically to create a cycle of disadvantage (Table 4).

Table 4.

Dimensions of digital exclusion

Dimensions	Categories	Description
Affordability barriers	Financial burden	The struggle to afford suitable devices and specific digital health services, and recurring costs for internet access [36, 57].
Technical resource barriers	Digital devices	Lack of internet-enabled devices, e.g., computer, smartphone, tablet, laptop [47, 62]. Insufficient device performance, e.g., the phone’s functionality is insufficient to support the use of digital services for health self-management [57].
	Internet connectivity	Lack of or suboptimal access to the internet, e.g., broadband coverage, speed and signal [8, 9].
	Suitable places	Lack of private and confidential space to receive digital health services like remote care or online health consultation [8, 41].
Digital competence	Digital skill	Basic computer skills, e.g., proficiency in operating smartphones/computers and common applications [42, 46]. Information navigation, e.g., the ability to efficiently process and manage online information and content [6, 28]. Digital problem-solving, e.g., taking part in a video health consultation [62], using digital applications for rehabilitation exercises [64].
	Digital health literacy	Lack of the capacity to obtain, process, and understand basic health information from electronic sources to make appropriate health decisions, e.g., not knowing where to find trustworthy information and help [8], inadequate data security skills to protect sensitive health information [46].
Psychological barriers	Motivation	Low perceived usefulness, e.g., not seeing digital services as relevant or offering additional value compared to traditional methods [58], or believing that health needs are too complex for digital solutions [57]. Low perceived ease of use, e.g., finding digital healthcare difficult to use [54]. Lack of interest, e.g., an ingrained preference for face-to-face services [46], a lack of willingness to use digital technologies [38, 54].
	Confidence	Lacking confidence in using digital health services, which often leads to a fear of making mistakes with negative consequences, e.g., non-renewal of prescriptions or delays in treatment [27, 46].
	Negative impression	Previous negative experiences and a distrust in the quality, privacy, and security of digital health services often lead to skepticism about their effectiveness compared to traditional face-to-face care [61, 65].
	Mental health condition	Individuals with mental health disorders, e.g., psychosis, memory difficulties, can impair the ability to use internet-enabled technology effectively [43].
Usage and support barriers	Usage barriers	Poor design and usability, e.g., the digital services and products are not designed to be user-friendly [44]. Lack of clarity, e.g., health information online is provided in a difficult-to-understand manner, often using overly complex language or specialized terminology [46].
	Support barriers	Lack of support from family members and healthcare professionals, e.g., healthcare providers failing to provide adequate assistance with technology, offer clear instruction on digital platforms, or introduce available digital service options [43, 50]. Inadequate support for vulnerable populations, e.g., visual impairment that hinders reading digital information [64], failing to meet the needs of individuals relying on assistive technology [39].

Digital exclusion has often been linked with intertwined technical resource and affordability barriers. While affordability is recognized as a significant constraint in several studies (8/39), the literature more frequently (21/39) points to resource barriers as the main challenge faced by users. Financial constraints directly determine access to devices and connectivity, but it is the quality and usability of these technical resources that shape the technical experience of users [42, 54]. A poor technical experience can foster frustration, anxiety, and self-doubt, which erodes users’ confidence, breeds distrust, and ultimately leads to avoidance and psychological resistance toward digital tools [52, 57, 58].

Psychological barriers (16/39) have also been frequently associated with digital exclusion, often in close relationship with digital competence (23/39). On the one hand, low digital competence is linked to psychological distress. For those unfamiliar with digital technology, each interaction carries a greater cognitive burden and a heightened likelihood of making mistakes [46]. This experience often generates performance anxiety and a fear of negative consequences, such as mismanaging sensitive health information [8, 65]. On the other hand, pre-existing psychological barriers may act as obstacles to skill acquisition. Users who distrust digital platforms, fear for their privacy, or perceive no benefit from online services lack the intrinsic motivation essential for learning [46, 57, 61]. This cycle is particularly potent in healthcare, a high-stakes environment where the required digital health literacy extends beyond basic operations to the critical evaluation of complex medical information, raising the barrier to meaningful participation even higher [9].

In contrast to the factors above, usability and support (13/39) function as external modulators that can either intensify or mitigate these barriers. When digital health services are poorly designed with unintuitive interfaces or clinical terminology, or when assistance from family and healthcare providers is lacking, these factors can amplify users’ existing difficulties with digital health tools, reinforce users’ feelings of incompetence and anxiety, and leave individuals isolated in their struggle [42, 44, 54]. Conversely, user-friendly interfaces, clear instructions, timely technical support, and patient guidance can buffer the anxiety stemming from low skills and a lack of confidence, encouraging users’ engagement with digital health services [7, 8].

Digital exclusion measurement tools discovered

The review of the existing literature identified 19 tools used to assess digital exclusion. These tools can be classified into two categories: individual-level assessments (n = 16, Table 5) and population-level assessments (n = 3, Table 6).

Table 5.

Individual-level tools

Author	Country	Name	Target group	Purpose	Adminis-tration	Types of Assessment Tool	Numbers of Items	Scoring	Reliability and validity	Research areas	Afford-ability barriers	Technical resource barriers	Digital compe-tence	Psycho-logical barriers	Usage & support barriers
T. Śmiałowski and L. Ochnio, 2019 [68]	Poland	Digital Exclusion Index	General population	To assess the level of digital exclusion of Polish households	Secondary data analysis	Index (Formula-based)	40	Classify individuals into four groups: digitally excluded, at risk of digital exclusion, partially using ICT solutions, and fully using ICT solutions.	NA	•Possession of ICT •Use of ICT •Digital competences		√	√
Anrijs, et al., 2023 [29]	Belgium	Digital difficulties scale (DDS)	General population	To understand who encounters digital difficulties and may be disadvantaged in digitizing societies	Self-reported survey	Likert scale	12 (16 items from original version)	Responses on the 4-point Likert scale were dichotomized (0 = yes, 1 = no). These dichotomous items were summed to create a count variable ranging from 0 to 12. Respondents were classified as digitally excluded if they indicated an inability to use internet services for 6 or more essential needs.	Construct validity: three-factor solution explaining 73.03% of variance. CFI = 0.97, TLI = 0.96, RMSEA = 0.064, SRMR = 0.048. All factor loadings ≥ 0.62 Reliability: Cronbach’s alpha = 0.94 ICC = 0.84	•Specific Digital Difficulties •General Digital Difficulties •Worries about Future Digital Difficulties			√	√	√
Scherrenberg, et al., 2023 [55]	Belgium	Digital health readiness questionnaire (DHRQ)	Patients visiting the cardiology department	To assess the digital readiness of patients in a routine clinical setting	Self-reported survey	Likert scale	20	5-point Likert scale. Scores ranged from 15 to 75, with lower scores indicating a higher risk of digital exclusion.	Construct validity: SRMR = 0.065, RMSEA = 0.098, TLI = 0.895, CFI = 0.912. Reliability: Cronbach α = 0.732~0.937	•Digital usage •Digital skills •Digital literacy •Digital health literacy •Digital learnability		√	√	√	√
Liu, et al., 2023 [53]	China	Not mentioned	Chinese individuals aged 45 and older	To evaluate the relationship between digital exclusion and cognitive impairment	Self-reported survey	Questions	2	Not using WeChat or mobile payments indicates digital exclusion.	Not mentioned	•Use of mobile payments •Use of social media
Goldman, et al., 2023 [51]	Israel	Not mentioned	Older adults	To explore how digital use affects the wellbeing of older people.	Self-reported survey/phone interview	Questions	5	Not scored	Not mentioned	•Frequency of use of digital communication tools
Hider, et al., 2023 [52]	UK	Not mentioned	Patient with inflammatory rheumatic diseases	To examine how digital access impacts confidence and satisfaction with remote consultations for people with inflammatory rheumatic diseases.	Self-reported survey	Questions	2	Not scored	Not mentioned	•Access to the internet •Frequency of internet use		√
T. O. Crnic, 2013 [37]	Slovenia	Not mentioned	General population	To identify the key social and cultural barriers hindering digital access and contributing to digital exclusion	Self-reported survey	Questions	6	Not scored	Not mentioned	•Motivational access •Material access •Skills access	√	√	√	√
Shorthose, et al., 2024 [62]	UK	Not mentioned	Patients in primary care, hospital at home, and secondary care services.	To determine if frailty is a significant risk factor for digital exclusion in accessing video consultations	Self-reported survey/phone interview	Questions	4	Not scored	Not mentioned	•Access to technology •Skills in using technology •Confidence using technology •Motivation using technology, and frequency of internet usage		√	√	√
Robotham, et al., 2016 [38]	UK	Digital inclusion survey	Patient with psychosis or unipolar depression	To understand the context of digital exclusion for people who experience mental illness.	Self-reported survey	Questions	5	Not scored	Not mentioned	•Internet access •Familiarity •Confidence •Daily use •Motivation to use internet-enabled technology	√	√	√	√
Wilson-Menzfeld, et al., 2024 [28]	UK	Not mentioned	General population	To identify the scale and characteristics of digitally excluded individuals in one borough in North East England	Self-reported survey	Questions	13	Respondents were classified as “digitally excluded” if they met at least one of the following indicators: 1.Selected “no access” to all digital tools, 2.Indicated “never use,” “choose not to use,” or “may use digital tools annually” as their frequency of digital tool use, 3.Reported having “no confidence at all” in using digital tools, or 4. Rated their digital skills as “very poor.”	Cronbach’s alpha = 0.887	•Access to digital tools •Use of digital tools and apps •Self-assessment of digital skills •Confidence in using digital tools		√	√	√
R.Middle and L. Welch, 2022 [50]	UK	Digital inclusion scale	Patients with severe mental illness	To understand experiences of digital exclusion and the impact on health in people with mental illness	Self-reported survey	Rating scale	1	A higher score indicates a greater level of digital capability; a score of 7 represents the minimum capability required for effective internet utilization	NA	•Self-perceived digital capability for health			√
Metherell, et al., 2022 [49]	UK	Not mentioned	Adolescents	To investigate the relationship between the inability to use computers and mental well-being	Self-reported survey	Single-item screen	1	Students without access to a computer or without access to a good internet connection are considered digitally excluded	NA	•Without access to a computer •Without access to good internet connection		√
Martinez, et al., 2014 [40]	USA	Not mentioned	COPD patients	To investigate how COPD patients access and use the internet for health-related information and management	Self-reported survey	Single-item screen	1	Respondents who reported no internet access for obtaining information about their condition or treatment were digitally excluded.	NA	•Frequency of internet use for health information
E. Yang, M. J. Kim and K. H. Lee, 2024 [67]	South Korea	Not mentioned	People with disabilities	To explore how internet use affected access to health resources for older adults with disabilities during COVID-19	Self-reported survey	Single-item screen	1	Those answering ‘no’ to ‘Do you use the Internet?’ were classified as digitally excluded	NA	•General internet use		√
X. Lu, Y. Yao and Y. Jin, 2022 [48]	China, UK, USA, Mexico and Europe	Not mentioned	Older adults	To investigate the association between digital exclusion and functional dependency among older adults	Self-reported survey	Single-item screen	1	Due to varying databases, digital exclusion measures differed: CHARLS defined it as no internet use in the past month; ELSA, as never using the internet; HRS, as not regularly using the internet; MHAS, as lacking home internet access; and SHARE, as using the internet less than once a week.	NA	•Internet use (measured variably by cohort: recency, frequency, or access method)		√
Wang, et al., 2024 [63]	China, UK, USA, Mexico and Europe	Not mentioned	Older adults	To explore the association between digital exclusion and cognitive impairment among older adults	Self-reported survey	Single-item screen	1	Due to varying databases, digital exclusion measures differed: CHARLS defined it as no internet use in the past month; ELSA, as never using the internet; HRS, as not regularly using the internet; MHAS, as lacking home internet access; and SHARE, as using the internet less than once a week.	NA	•Internet use (measured variably by cohort: recency, frequency, or access method)		√

Table 6.

Population-level tools

Author, Year	Country	Name	Target group	Purpose	Administration	Data sources	Types of assessment tool	Numbers of items or indicators	Scoring	Indicators included in measurement tools	Afford-ability barriers	Technical resource barriers	Digital compet-ence	Psycho-logical barriers	Usage & support barriers
Mee, et al., 2024 [59]	UK	The Lincolnshire Digital Health Toolkit	General population in Lincolnshire	To quantify relative local differences in digital exclusion and identify individuals and local geographic areas of greatest need.	Online toolkit utilizing secondary data	•National Readership Survey (NRS) •Department for Work and Pensions (DWP) •Office for National Statistics (ONS) •Ofcom •Experian Mosaic	Indicators	7	The toolkit visualizes digital exclusion on an interactive map using color gradients, in quintiles. A lower Digital Exclusion Index rank (1–420), shown by a deeper red, signifies higher exclusion	•DER: Percentage in social grades D/E •GPC: Pension credit recipients per 1000 (age 65+) •INT: Average download speed (Mb/sec) •LAC: Percentage with severely limited daily activity •NQR: Percentage with no qualifications (age 16+) •UNP: Percentage unemployed & claiming benefits (age 16+) •EXP: email, smartphone, instant messaging, social networking competence (Experian Mosaic indicators)	√	√	√
Mendall, et al., 2024 [60]	UK	Digital Exclusion Risk Index (DERI)	General population in Great Britain	To visualise and understand the risk of digital exclusion at a localised geographic level.	Online toolkit utilizing secondary data	•Ofcom •Department for Work and Pensions (DWP) •Ministry of Housing, Communities and Local Government (MHCLG) •Office for National Statistics (ONS)	Indicators	9	Selected LSOAs are visualized on the map to illustrate their risk of digital exclusion, ranging from 0 (low risk, blue) to 10 (high risk, red)	•Proportion of population aged 65+ •Proportion of population aged 75+ •Proportion of homes unable to receive at least 10MBit/s •Proportion of premises unable to receive at least 30MBit/s •Average download speed •Unemployment rate •Index of Multiple Deprivation score •Percentage of 16+ population with no qualifications •Numbers of Guaranteed Pension Credit claimants per 1000 residents aged 65+	√	√
F. Li, 2022 [47]	USA	NA	General population	To investigate the relationship between digital exclusion and COVID-19 outcomes at the county level.	Secondary data analysis	American Community Survey	Indicators	3	Not scored	•the share of households with no internet access •the share of individuals who have no computer at home •the share of individuals who have broadband internet subscription.		√

Individual-level tools

Individual-level tools are designed to evaluate the degree of digital exclusion experienced by individuals. They assess barriers such as material and financial access to technology; the necessary digital skills, literacies, and support structures for effective use; and psychological factors that shape an individual’s willingness to engage. A detailed review of these tools reveals varying emphasis on these key dimensions [28, 29, 37, 38, 40, 48–53, 55, 62, 63, 67, 68]. Technical resource barriers were the most frequently assessed dimension, appearing in 11 out of 16 of the tools [28, 37, 38, 48, 49, 52, 55, 62, 63, 67, 68]. Digital competence was also commonly addressed, being included in half of the tools (8/16) [28, 29, 37, 38, 50, 55, 62, 68]. A notable number of tools incorporated psychological barriers (6/16) [28, 29, 37, 38, 55, 62]. In contrast, usage and support barriers were addressed far less frequently (3/16) [29, 38, 55]. The least assessed dimension was affordability, appearing in only a small fraction of the reviewed tools [37, 38]. The comprehensiveness of these tools also varied. None of the tools assessed all five dimensions. Three tools (3/16) assessed four dimensions [37, 38, 55], three tools (3/16) assessed three dimensions [28, 29, 62], one tool (1/16) assessed two dimensions [68] and six tools (6/16) focused on a single dimension [48–50, 52, 63, 67]. Three of the reviewed tools (3/16) did not measure any barriers of the five dimensions of digital exclusion [40, 51, 53]. Instead, they assessed outcomes of digital exclusion, rather than the barriers themselves.

The target populations for these individual-level tools fall into three broad categories. The first is the general population (n = 5). The second category includes vulnerable groups, including adolescents (n = 1), individuals aged 45 and older (n = 1), older adults (n = 3), and people with disabilities (n = 1). The third category comprises specific patient populations, including patients with COPD (n = 1), patients with inflammatory rheumatic diseases (n = 1), patients with mental illness (n = 2), patients visiting cardiology department (n = 1), and patients in primary care, hospital at home, and secondary care services (n = 1). Regarding administration methods, the predominant approach is the self-reported survey (n = 13). A small number of studies employed a combination of self-reported surveys and telephone interviews (n = 2). One study utilized pre-existing datasets (n = 1) to derive an individual-level measure. The types of assessment tools used are diverse:

1. Formulas (n = 1): The digital exclusion index developed by Śmiałowski [45] at the Warsaw University of Life Sciences in Poland used pre-existing datasets and a formula-based approach to calculate a digital exclusion index, categorizing individuals into four digitally excluded groups.

2. Likert scales (n = 2): Two tools, the Digital Difficulties Scale (DDS) and the Digital Health Readiness Questionnaire (DHRQ). DDS [29], developed at Ghent University in Belgium, is a 12-item scale that uses a 4-point Likert scale to assess digital difficulties in the general population. DHRQ [55], developed at Hasselt University in Belgium, is a 20-item scale using a 5-point Likert scale to assess the digital readiness of cardiology patients. These are the only two tools that reported rigorous psychometric testing, including both reliability and validity analyses.

3. Rating scales (n = 1): The Digital Inclusion Scale [50] is a single-item rating scale developed by the Government Digital Service (GDS) in the UK to assess digital capability among patients with severe mental illness.

4. Questions (n = 7): These tools used sets of researcher-developed questions, ranging in items from 2 to 13, to assess the degree of digital exclusion. Of these, only the tool developed by Wilson-Menzfeld et al. [28] reported a reliability analysis.

5. Single-Item Screens (n = 5): Five studies [40, 48, 49, 63, 67] used a single question to classify individuals as digitally excluded or not. However, none of these studies reported on the sensitivity or specificity of these single-item measures.

Population-level tools

Population-level tools are designed to evaluate the risk of digital exclusion among general populations in specific geographic areas from a macro perspective [47, 59, 60]. The evaluation indicators of population-level tools primarily emphasize macro-level socioeconomic factors, such as unemployment rates, poverty levels, broadband speeds. To capture these multifaceted influences, population-level tools rely heavily on pre-existing datasets, such as national census data (e.g., Office for National Statistics, American Community Survey), socio-economic surveys (e.g., Department for Work and Pensions, National Readership Survey, Office of Communications, Ministry of Housing, Communities and Local Government) and commercial data (e.g., Experian). The indicators derived from these sources predominantly focus on the key dimensions of technical resource barriers (3/3), digital competence (1/3), and affordability barriers (2/3). In terms of result presentation, these tools often employ interactive visualization methods. For example, the Lincolnshire Digital Health Toolkit [59] and the Digital Exclusion Risk Index (DERI) [60] utilize interactive maps to display population-level variations in digital exclusion. This visualization approach enables users to identify geographic areas with higher risk levels of digital exclusion.

Impacts of digital exclusion on health outcomes

Of the 14 studies examining health outcomes, most utilized cross-sectional (n = 6), qualitative (n = 5), or mixed-method (n = 1) designs. These studies found associations between digital exclusion and adverse health outcomes, but their observational nature precludes the establishment of causality. However, two longitudinal studies offer greater methodological robustness. They provide predictive evidence that digital exclusion precedes specific forms of health deterioration, linking it to worsened adolescent mental health [49] and increased functional dependence in older adults [48].

Despite these limitations, the results consistently highlight patterns of association across four main dimensions within three tiers (Table 7). The first domain, system-level barriers, is characterized by limited access to healthcare resources and services. Five studies (5/14) highlighted the difficulties faced by digitally excluded individuals in obtaining health information and using mHealth services, including those related to antenatal care and mental health support [54, 61, 67]. The second domain refers to individual-level functional impacts, which manifest across two interconnected dimensions, which are psychological-social consequences and cognitive-executive decline. Four studies (4/14) documented how digital exclusion contributes to social isolation, negative self-perception, declining mental health, and a lower sense of wellbeing [27, 49–51]. Four other studies [48, 53, 63, 66] reported cognitive and functional declines (4/14), including increased risk of cognitive impairment, difficulties with daily routines, and challenges in the self-management of long-term conditions. Finally, the third domain refers to health deterioration and disease burden, reported in two studies (2/14). These findings suggest that digital exclusion is associated with a higher prevalence of chronic conditions [40], as well as higher infection and mortality rates and lower vaccination uptake [47].

Table 7.

Key health outcomes linked to digital exclusion

Domain	Consequences	Description	Examples
I- System-level barriers	Limited access to healthcare resources and services	Digitally excluded individuals face barriers in obtaining information about essential healthcare services and resources via digital platforms.	Difficulties acquiring COVID-19 information and protective equipment [67] Inability to access online mental health services [61] Limited utilization of digital health platforms [27, 65] Lower engagement with pregnancy health apps [54]
II- Individual-level functional impacts	Psychological and social consequences	Individuals experiencing digital exclusion often suffer from social isolation, negative self-perception, and mental health issues	Social isolation and shame, loss of self-esteem [27, 50] Worsened adolescent mental health [49] Reduced wellbeing [51]
	Cognitive decline and functional dependency	Digitally excluded individuals are at risk of cognitive impairment and face challenges in daily activities.	Increased risk of cognitive impairment [53, 63] Increased functional dependence among older adults [48] Greater challenges to self-management [66]
III- Health deterioration	Higher disease risk and worse health outcomes	Digital exclusion is associated with a higher prevalence of chronic conditions and poorer disease management.	Higher prevalence of hypertension, diabetes, arthritis and heart disease [40] Higher COVID-19 infection/mortality and lower vaccination rates [47]

Discussion

Concept and dimensions of digital exclusion in healthcare services

A key theoretical implication of our findings is that understanding digital exclusion in healthcare requires a conceptual shift, moving away from general-purpose definitions and toward a more elaborated, context-specific framework. While existing concepts have evolved from focusing on access to including user skills and beneficial outcomes [9, 49, 63], they often address these dimensions in isolation and fail to account for the unique dynamics of the healthcare context.

The nature of digital exclusion is shaped by individual- and population-level factors, which can significantly amplify barriers. It imposes a dual burden on users, demanding not only foundational digital capacity—including material access and technical skills—but also the specific digital health literacy required to interpret clinical information and evaluate its credibility [8, 9]. Simultaneously, the high-stakes nature of healthcare intensifies psychological barriers, such as the fear of making costly clinical errors and heightened concerns over the privacy of sensitive personal data [46, 61, 65]. Consequently, when a lack of support compounds these practical and psychological obstacles, an individual’s failure to engage with digital health services is more accurately reframed as a symptom of structural exclusion, not a personal failure. [46].

Therefore, exploring digital exclusion in healthcare services must consider the complexity of influencing factors. We consequently propose to operationalize this concept as: Digital exclusion in healthcare services is a multidimensional phenomenon wherein systemic and individual factors across five dynamically interacting dimensions—affordability barriers, technical resource barriers, digital competence, psychological barriers, and usage & support barriers—interact to create overlapping barriers that hinder effective use of and transformative health benefits from digital health technologies, thereby exacerbating health disparities. This integrated definition is intended to provide a conceptual foundation that can inform future studies and intervention strategies.

Measurement of digital exclusion

Our findings reveal a significant gap between the multidimensional nature of digital exclusion and the way it is currently measured. Most prevailing assessment tools focus on single dimensions, such as access to devices or connectivity or digital skills, in isolation [50, 51, 67]. Furthermore, these instruments are seldom designed for the complexities of healthcare, meaning they might underestimate an individual’s risk of exclusion when faced with tasks like navigating patient portals or interpreting clinical data. This mismatch between a multidimensional problem and narrowly focused tools limits our ability to accurately identify those most in need.

Bridging this gap necessitates a screening strategy that integrates population-level and individual-level approaches. The former identifies settings where structural barriers converge [59, 69], while the latter identifies the specific categories of individuals who are struggling [55, 62]. This two-tiered approach allows for more efficient and targeted resource allocation, focusing on the assessments of both individuals and the communities where structural barriers are most prevalent. Such a strategy also enables more effective policy development, clarifying whether interventions should target infrastructure, digital skills training, or other dimensions of digital exclusion.

To ensure that even vulnerable individuals in low-risk digital exclusion areas are not neglected, this strategy should be implemented through opportunistic screening embedded within existing public service pathways. Brief screening tools can be integrated into routine clinical pathways, particularly in settings like primary care and specialist services such as oncology, where patients frequently encounter complex digital health tasks, such as managing multiple appointments, contacting clinicians, filling prescriptions, and handling insurance [70]. This can be complemented by screening at specific community settings, such as social service centres, homeless shelters or elderly care home that already serve individuals facing socioeconomic disadvantages [71]. By embedding screening into these settings, support can be directed according to individual needs, creating a more proactive and equitable approach of identification.

Digital exclusion and health impacts

Our study also reviewed the links between digital exclusion and adverse health outcomes. To fully grasp its public health significance, digital exclusion must be understood not as a root cause of inequity, but as a compounding factor that exacerbates pre-existing social and economic disadvantages.

The risk of digital exclusion is fundamentally driven by upstream social determinants of health [29]. Socioeconomic factors such as lower income and educational attainment create direct barriers to material access and digital competence [29, 72]. Poor pre-existing health—both physical and mental—can result in lack of adequate cognitive capacity and motivation required to engage with new technologies [66, 73].

Therefore, interventions that merely provide devices or skills training are inherently limited, as they only address the symptoms. Achieving meaningful equity requires a dual approach: addressing immediate digital needs while also tackling the root problems of socioeconomic inequality and inadequate health support. Digital inclusion, in this light, is inseparable from the broader goal of social inclusion.

Limitations

This scoping review was conducted under certain limitations. First, only peer-reviewed articles published in English were included, which may exclude certain countries, particularly developing countries where other languages are predominantly used. Second, given the scarcity of research on digital exclusion specifically within healthcare, we included studies from other areas. We considered this inclusive approach appropriate because digital exclusion in healthcare is a subset of a broader phenomenon, and insights from other areas may offer valuable conceptual insights.

Conclusion

This scoping review synthesises current knowledge on the concepts, assessment methods, and health impacts of digital exclusion in healthcare. The findings highlight a lack of conceptual consistency, with digital exclusion variably defined across studies and often insufficiently tailored to healthcare contexts. The concept of digital exclusion encompasses access to technology and infrastructure as well as user-specific factors like skills and confidence, alongside the ability to convert technology use into beneficial outcomes. The findings highlight five core dimensions of digital exclusion: affordability barriers, technical resource barriers, digital competence, psychological barriers, and usage and support barriers. These dimensions interact to form a complex picture of digital exclusion, which requires integrated measurement approaches to support effective clinical decision-making and targeted interventions.

Author contributions

Z.X. conducted the search strategy and collected the articles for the scoping review; Z.X. and Y.W. screened the articles for inclusion in the review; Z.X. and Y.W. charting, analysed, and interpreted the data; N.Z. prepared all figures and tables and was responsible for manuscript revisions; All authors contributed to writing the manuscript, and Z.X. was the main contributor in writing the manuscript; All authors reviewed and approved the final manuscript.

Funding

This study was funded by Zhejiang Medical Science and Technology Project (2025HY0449).

Data availability

All data generated or analyzed during this study are included in this published article.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.