https://orcid.org/0009-0006-0925-0542
Figures
Abstract
Telemedicine expanded rapidly during the COVID-19 pandemic, yet its diffusion has remained uneven across health systems. Whether implementation challenges differ systematically across economic contexts, and what implications these differences hold for global digital health equity, has not been comprehensively characterized. This study presents an umbrella review (PROSPERO CRD42024615998) of systematic reviews and meta-analyses indexed in PubMed and Scopus through December 2025 that reported barriers and/or facilitators to telemedicine implementation. Methodological quality was appraised using R-AMSTAR. Reported items were extracted verbatim, embedded with the Universal Sentence Encoder, and grouped into thematic clusters using HDBSCAN density-based clustering, with parameters optimized by SLSQP for cluster cohesion (median intra-cluster similarity ≥ 0.5). Analyses were stratified by World Bank country income group to characterize context-specific implementation profiles, identify evidence gaps, and detect orphaned barriers, defined as challenges lacking any corresponding documented facilitator. A total of 161 systematic reviews were included, yielding 1,333 barriers and 504 facilitators (corresponding to a barrier-to-facilitator ratio of 2.6:1). Across all settings, the most frequently reported barriers were high costs (n = 106), technical issues (n = 94), and training and knowledge (n = 91). Thematic profiles differed markedly by income group: high-income countries reported predominantly second-generation challenges centered on workflow integration, interoperability, and user experience, whereas lower-middle-income countries reported foundational barriers centered on infrastructures and costs. Evidence produced from low-income countries was entirely absent. Several high-frequency barriers lacked corresponding facilitator clusters, indicating challenges for which the published literature provides no documented solutions. The global telemedicine evidence base is itself inequitably distributed and risks reinforcing the disparities it is intended to reduce. High-income settings require implementation research focused on human and organizational factors, whereas lower-income settings require foundational research and investment in basic infrastructures before workflow-level optimization becomes meaningful. Without context-specific, equity-oriented strategies, telemedicine risks widening rather than narrowing the global digital health divide.
Citation: Capodici A, Filippeschi A, Noci F, Michelucci A, El Motarajji S, Benedetto V, et al. (2026) Mapping global inequities in telemedicine implementation: An umbrella review of barriers and facilitators. PLoS One 21(7): e0351885. https://doi.org/10.1371/journal.pone.0351885
Editor: Yagnik Dave, Marwadi University, INDIA
Received: December 16, 2025; Accepted: June 2, 2026; Published: July 13, 2026
Copyright: © 2026 Capodici et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All analyses were implemented in Python, and the full code, as well as all data, is available for replication at: https://github.com/AlessandroFilippeschi/Umbrella_review_Barriers_Facilitators.
Funding: This manuscript is part of two projects, which are the following: Tuscany Health Ecosystem (Funded by the European Union, PNRR); Proximity Care (Funded by Fondazione Cassa di Risparmio di Lucca). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
Few healthcare innovations have scaled as rapidly, or as unevenly, as telemedicine during the COVID-19 pandemic. Adoption rates that would otherwise have taken decades unfolded in weeks, with virtual consultations more than doubling in some healthcare systems within months [1]. Yet four years later, a paradox has emerged: despite unprecedented investment and proven technical feasibility, the global diffusion of telemedicine remains uneven [2]. It is plausible that the digital divide extends beyond platform usability; preliminary observations suggest that while high-income settings refine clinical workflows, lower-resource regions may struggle with foundational deficits like limited broadband [3] and unreliable electricity [4]. Understanding the true extent of these potential asymmetries is a fundamental step in addressing broader challenges to global health equity and Universal Health Coverage.
The promise of telemedicine as a “great equalizer” in healthcare access [5] confronts an uncomfortable reality: digital health interventions may be widening the very disparities they aim to address. Notably, it is estimated that 37% of all people globally remain offline [6], predominantly in regions with the greatest healthcare needs. When telemedicine strategies designed for resource-rich settings are exported without adaptation, they predictably fail. Despite this, the global health community still lacks a systematic, comparative understanding of which implementation challenges arise in which contexts, and why these challenges differ across health systems.
This evidence gap carries tangible consequences: misaligned investments waste scarce resources, failed deployments erode institutional and patient trust, and missed opportunities translate into avoidable health losses [7]. Implementation science has, in recent years, produced a substantial body of systematic reviews examining barriers to and facilitators of telemedicine adoption across diverse clinical specialties and care settings. However, these reviews remain scattered across disease areas, geographical regions, and organizational contexts, with limited cross-referencing or thematic integration. As a result, their findings have only limited utility for strategic decision-making at the level of national digital health policy or international health governance, where comparative and equity-sensitive evidence is most needed.
Previous attempts to synthesize narrative evidence have relied on manual categorization of barriers and facilitators, an approach that limits reproducibility, and struggles to handle the volume of available evidence [8–10]. More critically, these syntheses have failed to systematically examine how implementation challenges differ across economic contexts, treating telemedicine implementation as if the barriers faced by a tertiary hospital in a high-income country are comparable to those in a primary clinic in a low-income one. This oversight reflects a broader epistemic bias in global health, where evidence generation and policy design remain concentrated in high-income contexts.
Against this backdrop, the present umbrella review is guided by a single overarching question: how are reported barriers and facilitators to telemedicine implementation distributed across global economic contexts, and what does this distribution reveal about equity in the digital health evidence base?
To address this question, this study pursues four interrelated objectives. First, it synthesizes the existing body of systematic reviews and meta-analyses on telemedicine implementation by identifying and aggregating reported barriers and facilitators across heterogeneous clinical, geographical, and organizational settings. Second, it classifies these barriers and facilitators into coherent thematic clusters. Third, it compares the frequency, type, and thematic composition of barriers and facilitators across World Bank income groups to characterize context-specific implementation profiles. Finally, it identifies structural evidence gaps, including under-represented regions, “orphaned” barriers lacking documented facilitators, and asymmetries in the barrier-to-facilitator ratio, that may perpetuate global digital-health inequities and that should inform future implementation research and policy. Taken together, these objectives are intended to provide policymakers, funders, and researchers with an evidence-based, equity-sensitive framework for context-appropriate telemedicine investment.
As nations commit billions to digital health infrastructure and the World Health Organization develops its Global Strategy on Digital Health [11], understanding these implementation disparities is essential to ensuring that telemedicine fulfills its promise of expanding, rather than restricting, global healthcare access. By mapping these disparities through a reproducible computational approach, we aim to inform global health governance with evidence that is both scientifically rigorous and equity-sensitive.
2. Methods
A literature search was conducted on November 11, 2024, and on December 14, 2025 using PubMed and Scopus to identify systematic reviews and meta-analyses addressing telemedicine and related digital health interventions. This review was registered on PROSPERO (Record ID: CRD42024615998). The PRISMA Checklist can be found in the supplemental material.
Ethics Approval: Not applicable, as this umbrella review is a synthesis of previously published studies and did not involve direct contact with human participants.
The search strings combined a wide array of telemedicine-related terms, including
- On PubMed
(telemedicine OR telehealth OR telecare OR mobile health OR untact health OR mhealth OR m-health OR e-health OR ehealth OR digital health OR virtual care OR virtual consultation OR telenursing OR teleconsultation OR e-consultation OR econsult* OR e-medicine OR emedicine OR telemonitoring OR telepractice) AND (adoption OR implement* OR uptake OR accept* OR integrat* OR diffus* OR utiliz* OR utilis* OR deploy* OR embrac*)
AND (barrier* OR obstacle* OR challenge* OR hindrance* OR limitation* OR difficult* OR resist* OR constraint* OR deterrent* OR impediment* OR reluctan* OR inhibit* OR shortcoming* OR drawback* OR bottleneck* OR weakness* OR “stumbling block” OR hurdle* OR burden* OR deficiency OR flaw* OR opposition OR friction)
- On Scopus
(TITLE-ABS-KEY (telemedicine OR telehealth OR telecare OR “mobile health” OR “untact health” OR mhealth OR “m-health” OR ehealth OR “e-health” OR “digital health” OR “virtual care” OR “virtual consultation” OR telenursing OR teleconsultation OR econsult* OR “e-consultation” OR emedicine OR “e-medicine” OR telemonitoring OR telepractice)) AND (TITLE-ABS-KEY (adoption OR implement* OR uptake OR accept* OR integrat* OR diffus* OR utiliz* OR utilis* OR deploy* OR embrac*)) AND (TITLE-ABS-KEY (barrier* OR obstacle* OR challenge* OR hindrance* OR limitation* OR difficult* OR resist* OR constraint* OR deterrent* OR impediment* OR reluctan* OR inhibit* OR shortcoming* OR drawback* OR bottleneck* OR weakness* OR “stumbling block” OR hurdle* OR burden* OR deficiency OR flaw* OR opposition OR friction)) AND (LIMIT-TO (DOCTYPE, “re”))
The retrieved studies were screened blindly first by title by four authors: AC, FN, VB, AV. The remainder was screened first by abstract and then by full-text by two authors: AC, FN. In instances of disagreement, discussion was attempted first, and if a consensus was not reached a third, independent, tie-breaker was consulted (AG). Methodological quality was assessed independently and blindly by four raters (AC, FN, AM, SEM) using the R-AMSTAR tool [12].
2.1. Eligibility Criteria
Eligible studies had no language restriction, had to involve human subjects, and assess telemedicine interventions by exploring barriers and/or facilitators to telemedicine implementation.
Only systematic reviews and meta-analyses were considered eligible for the Umbrella Review. Studies were included if they reported on barriers or facilitators to telemedicine adoption, with the main outcome being the identification and/or quantification of these factors. Eligible reviews could present findings either through effect measures such as relative risks, odds ratios, and risk differences or via narrative synthesis.
2.2. Data extraction
Data extraction was performed by four independent reviewers (AC, FN, AM, SEM) using a predesigned Excel spreadsheet. Extracted data encompassed: author, publication year, journal, DOI, study design, country of origin (operationalized as the country of the first author’s first listed affiliation) and its income level as defined by the World Bank [13], type of intervention, barriers and facilitators identified, alongside the quantitative and qualitative methods employed. Demographic and population-specific data were harmonized to include sample size, population type (e.g., disease focus), ethnicity, race, and age. Finally, the setting of the study was analyzed.
Barriers and facilitators were recorded verbatim from the original reviews when reported in a concise format. In cases where items were presented in a dialogic or descriptive manner, they were initially transcribed as stated and subsequently standardized through consensus by two independent reviewers (AC, FN) to ensure consistency in terminology and categorization.
2.3. Data analysis
The analysis employed a data-driven pipeline to identify thematic clusters of barriers and facilitators. The process began with text preprocessing, where entries were normalized by converting them to lowercase, removing punctuation, and standardizing domain-specific terms through a combination of exact and fuzzy matching [14]. After this, the text was lemmatized [15], and stripped of stopwords. Following this preparation, the cleaned text was converted into numerical vectors using the Universal Sentence Encoder to capture the semantic meaning of each phrase. A density-based algorithm, HDBSCAN [16,17] was then used to group the embedded text into semantically coherent clusters. This method excels at finding natural groupings and identifying outliers (noise), which ensures high intra-cluster similarity (ICS). An optimization procedure, implemented using the SLSQP algorithm [18], fine-tuned the clustering parameters to maximize the number of included items while ensuring a high median ICS (≥0.5), and a second clustering phase was run on the “noise” data to recover additional themes. Finally, to identify thematic differences across socioeconomic strata, the entire analysis was repeated on data subsets stratified by country income level. All analyses were implemented in Python, and the full code, as well as all data, is available for replication at: https://github.com/AlessandroFilippeschi/Umbrella_review_Barriers_Facilitators. Further details can be found in Supporting Information S1 File.
3. Results
The systematic search identified 14,027 articles, which after duplicate removal and all screening phases yielded 161 systematic reviews [19–179] meeting inclusion criteria (Fig 1, Table 1).
The geographical distribution showed stark disparities: high-income countries contributed most evidence, while lower-middle-income countries had limited representation and low-income settings produced no eligible studies.
Cluster analysis identified 1,333 barriers (ICS: 0.82) and 504 facilitators (ICS: 0.76), yielding a 2.6:1 barrier-to-facilitator ratio (Table 2). Subsequent analyses used non-reclustered data for optimal cluster consistency. Complete reclustered data and detailed cluster compositions are available in the public repository (https://github.com/AlessandroFilippeschi/Umbrella_review_Barriers_Facilitators).
By income level, high-income countries contributed 721 barriers and 355 facilitators; upper-middle-income countries 210 barriers and 27 facilitators; lower-middle-income countries 103 barriers with no facilitators. Low-income countries showed no clusters.
3.1. Thematic structure of implementation barriers
Clustering identified distinct barrier themes (Table 3, Supporting Information S2 File). The most frequent were “High Costs” (n = 106, ICS: 0.92), “Technical Issues” (n = 94, ICS: 0.99), and “Training & Knowledge” (n = 91, ICS: 0.99).
Barrier patterns varied substantially by income level (Fig 2 and 3), with high-income countries showing diverse, specific challenges while lower-income settings displayed fewer, overlapping barrier clusters.
Table 4 presents the top 10 barriers stratified by income level, while Supporting Information S3 File provides the list of all barrier items classified in each cluster, including all the remaining not presented in the table.
High-income countries showed distinct clusters led by “Connectivity & Access” (n = 82, ICS: 0.73), “Financial & Personal Costs” (n = 64, ICS: 0.94), and “Training & Knowledge” (n = 59, ICS: 1.00) (Fig 3). Upper-middle-income countries exhibited clusters led by “Technical Issues” (n = 32, ICS: 0.98), “Platform Costs” (n = 32, ICS: 0.92), and “Connectivity & Speed” (n = 24, ICS: 0.88). Lower-middle-income countries displayed only three barrier clusters: “Training & Compliance” (n = 72, ICS: 0.17), “Infrastructure & Costs” (n = 17, ICS: 0.89), and “Connectivity & Access” (n = 14, ICS: 0.86). No barrier clusters were identified for low-income countries.
3.2. Thematic structure of implementation facilitators
The analysis identified facilitator clusters (Table 3, Supporting Information S2 File), led by “Connectivity & Access” (n = 25, ICS: 0.64) and “Training & Knowledge” (n = 22, ICS: 1.00).
The variation in distribution of facilitators across income levels was high as illustrated in Fig 3. Table 5 shows the top facilitators stratified per income level, while Supporting Information S4 File lists all facilitator items classified in each cluster, including all the remaining not presented in the table.
3.3. Implementation Gaps: Barriers Without Facilitators
Numerous high-frequency implementation challenges lack corresponding, well-documented solutions. To systematically identify these evidence gaps, Table 6 maps each barrier against the availability of associated facilitator. This analysis reveals critical “orphaned barriers”, categorized as high-frequency obstacles without documented enablers that represent urgent priorities for implementation research (bolded in the table).
Even where facilitators are present, substantial imbalances persist. High Costs presents a concerning 7.5:1 deficit, with 106 barrier instances but only 14 facilitator instances, suggesting that financial constraints remain poorly addressed by existing support structures. The Legal and Regulatory framework presents a similarly wide imbalance with an approximate 5:1 barrier-to-facilitator ratio (63 vs. 13 instances), indicating limited progress in contexts lacking enabling legislation. Language and Cultural barriers (n = 27) show a smaller yet persistent shortfall with a 1.5:1 ratio, with 18 facilitators reflecting partial but still insufficient adaptation strategies considering the high prevalence of problems related to this issue. S1 –S4 Images are presented as Supporting Information illustrating barriers and facilitators per income level.
4. Discussion
The COVID-19 pandemic acted as a global stress test for digital health systems, accelerating the adoption of telemedicine to a degree that few healthcare innovations have ever experienced. Four years later, this umbrella review of 161 systematic reviews allows a structured assessment of what this acceleration has produced in terms of accumulated implementation knowledge, and for whom such knowledge is available. Rather than a homogeneous global evidence base, the findings reveal a stratified landscape, in which the geography of telemedicine research closely mirrors global income distribution.
Three findings stand out and frame the analysis that follows. First, barriers (n = 1,333) outnumber facilitators (n = 504) by a ratio of 2.6 to 1, a global asymmetry that holds across income strata. Second, the thematic structure of barriers is not universal but income-contingent, with high-income countries reporting fragmented, “second-generation” challenges and lower-middle-income settings reporting miscellaneous and infrastructural ones. Third, several high-frequency barrier domains, particularly psychosocial, organizational and workload-related, exist as “orphaned” clusters, lacking any corresponding facilitator in the literature. Each of these findings is examined in detail in the following sections, before their combined implications for practice, research and policy are considered.
4.1. The dominant barrier landscape: cost, connectivity, training and trust
The four most frequent barrier clusters globally (High Costs (n = 106), Technical Issues (n = 94), Training & Knowledge (n = 91) and Connectivity & Access (n = 89)) together account for roughly one-third of all reported barriers. Their persistence across different health systems suggests that they are a recurrent core of friction in the deployment of telemedicine. Notably, the cost cluster shows the largest barrier-to-facilitator imbalance in the dataset (7.5:1), implying that financial obstacles are extensively diagnosed but rarely matched by validated economic models, reimbursement frameworks or sustainable funding pathways. This is consistent with prior critiques that the business case for telemedicine remains under-specified outside narrow specialty applications [180].
The Privacy & Security and Legal & Regulatory clusters (n = 81 and n = 63 respectively) further refine this picture. Legal-regulatory barriers display a 5:1 deficit relative to facilitators, reinforcing that the gap is not in identifying regulatory friction but in producing transferable governance solutions. In practical terms, telemedicine deployments continue to operate in jurisdictions where licensure portability, cross-border data flows and reimbursement codes lag behind clinical practice [181]. The clustering pattern thus suggests that, even in mature digital ecosystems, telemedicine is currently being scaled on top of legal and financial scaffolding that was not designed for it.
4.2. Orphaned barriers: the human and organizational frontier
The systematic identification of “orphaned” barriers, defined as as high-frequency challenges without any documented facilitator counterpart, is the most actionable finding of this review. Psychosocial Factors (n = 46), Psychosocial Support (n = 27), Time & Workload (n = 26), Organizational Issues (n = 26), Privacy Concerns (n = 22), Clinical Limitations (n = 19) and Data Management (n = 18) collectively define a frontier where the field has produced extensive diagnostics but virtually no therapeutics. This negative skew is unlikely to be coincidental.
Several mutually reinforcing mechanisms plausibly contribute. Publication and funding incentives have historically favored the documentation of problems, which are easier to operationalize as research outcomes than solutions, whose effectiveness depends on context-specific co-design and longer follow-up [182]. Implementation studies grounded in established frameworks (e.g., CFIR, RE-AIM) tend to surface organizational and workload barriers but stop short of evaluating organizational interventions to mitigate them. Finally, psychosocial and clinician-burden dimensions sit at the intersection of clinical, behavioral and managerial sciences, and may fall through the disciplinary cracks of conventional telemedicine evaluation. Whatever the mix of causes, the practical effect is the same: managers, clinicians and patients confronted with these barriers cannot, today, draw on a comparable evidence base of facilitators.
4.3. The geographic gradient and the digital ladder
Beyond the global picture, the income-stratified analysis reveals a structural gradient in how barriers are organized. High-income countries display diverse, semantically distinct clusters consistent with mature but fragmented digital ecosystems, where Connectivity & Access (n = 82), Financial & Personal Costs (n = 64) and Training & Knowledge (n = 59) coexist with more granular concerns around usability, personal motivation and integration. Upper-middle-income countries occupy an intermediate profile, dominated by Technical Issues (n = 32), Platform Costs (n = 32) and Connectivity & Speed (n = 24). Lower-middle-income countries exhibit only three clusters, in which training, infrastructure, costs and connectivity collapse into compounded thematic blocks.
This gradient empirically substantiates the “digital ladder” hypothesis [183], which argues that infrastructural and foundational prerequisites (electricity, broadband, digital literacy) must be addressed before higher-order enablers such as workflow integration or personalization can deliver measurable benefit. The collapse of conceptual distinctions in lower-resource settings is therefore not analytical noise but a substantive signal: when several barriers are simultaneously binding, they cease to behave as separable problems and instead form a single systemic constraint. This reframing has direct consequences for how telemedicine programs should be designed, sequenced and evaluated in different contexts, and helps explain why models optimized for broadband-rich, highly regulated systems frequently fail to transfer [184]. Because country attribution in this synthesis follows the first author’s first affiliation, this absence is most precisely characterized as an evidence-leadership void: systematic reviews touching on low-income settings exist but are not authored from within them, with consequences for whose questions, framings, and priorities shape the synthesized evidence base.
4.4. Asymmetry in the facilitator landscape
The facilitator analysis amplifies the same pattern. High-income countries account for the great majority of documented facilitators (n = 355), upper-middle-income countries report a far smaller and less consistent set (n = 27, ICS often below 0.5), and lower-middle- and low-income countries report none at all. This is not simply a quantitative gap but a qualitative one: it means that, in the contexts where telemedicine is most needed and where implementation conditions differ most sharply from those of high-income settings, there is currently no empirically derived menu of “what works” to inform local program design [185–187]. The risk, well documented in implementation science, is that absence of evidence is interpreted as absence of effective practice, reinforcing a deficit-based narrative that frames lower-income settings as recipients of failed interventions rather than as sites of frugal innovation and reciprocal learning [184].
4.5. Methodological contribution
A secondary contribution of this work is methodological. The HDBSCAN-based clustering approach allows large volumes of qualitative findings, drawn from heterogeneous reviews, to be aggregated into reproducible thematic structures. The high internal consistency scores observed for the major clusters (ICS ≥ 0.90 in several cases) support the reliability of the resulting taxonomy, while the open release of data and code enables independent reanalysis, parameter sensitivity testing and extension to other domains of digital health. This addresses a long-standing limitation of narrative syntheses in implementation science, in which classification schemes have been largely investigator-dependent [8–10].
4.6. Implications for practice
The findings translate into practice recommendations that are not uniform but conditional on system maturity. In high-income settings, where foundational infrastructure is largely in place, implementation efforts should prioritize the “orphaned” domains identified above: integrating telemedicine into clinician workload calculations, formalizing organizational change management, and developing structured psychosocial support pathways for both patients and providers; furthermore, reimbursement reform, parity legislation and licensure portability remain central given the persistent 5:1 legal-regulatory imbalance [181].
In upper-middle-income settings, where technical issues and platform costs dominate, practice priorities lie in interoperability standards, vendor consolidation and procurement frameworks that prevent the proliferation of fragmented, non-communicating platforms. In lower-middle-income settings, the collapsed cluster structure indicates that telemedicine programs should not be launched in isolation but as part of broader digital health system strengthening, with explicit attention to electricity reliability, last-mile connectivity, frontline worker training and supportive supervision. In low-income settings, where the evidence base is essentially empty, the immediate priority is not large-scale deployment but feasibility studies, formative implementation research and the documentation of context-specific facilitators that can later guide investment [188].
4.7. Implications for research
Three research priorities follow directly from the findings. First, the field needs a deliberate shift from barrier documentation to facilitator validation, with funders and journals explicitly rewarding studies that test, rather than describe, implementation strategies. Second, the orphaned-barrier domains should be treated as a coordinated research agenda, including organizational interventions for workload, change-management trials, and structured psychosocial support models, particularly for clinicians, whose burden is poorly captured in current telemedicine outcome sets. Third, locally led implementation research in lower-middle- and low-income countries must be scaled, ideally through partnerships designed as genuine co-production rather than as extractive data collection. The methodology presented here can serve as a living framework, periodically re-run as new reviews accumulate, to monitor whether these gaps are closing.
4.8. Implications for policy and global health governance
At the policy level, the results offer concrete guidance for the implementation of the WHO Global Strategy on Digital Health [11] and adjacent regional frameworks. International donors and multilateral agencies (WHO, World Bank, regional development banks) should explicitly earmark research funding for low-income and lower-middle-income settings, and condition large-scale deployment grants on the documentation of locally relevant facilitators rather than barrier inventories alone. Regional bodies such as the African Union’s Africa CDC, the Pan American Health Organization and ASEAN have a particular role in harmonizing digital health regulation across jurisdictions of similar maturity, reducing the duplication of legal-regulatory friction documented in the present cluster analysis.
National governments face context-specific choices. In high-income jurisdictions, the policy frontier is integration: aligning reimbursement, workforce regulation and data governance to remove second-generation frictions. In middle-income jurisdictions, it is consolidation: rationalizing platform ecosystems and codifying interoperability. In lower-resource jurisdictions, it is sequencing: investing in digital and energy infrastructure as health-system foundations rather than as adjuncts to vertical telemedicine programs.
4.9. Limitations
This umbrella review has several inherent limitations. As a synthesis of systematic reviews, it inherits any biases present in the included reviews, including publication bias and the over-representation of high-income settings. The HDBSCAN algorithm requires parameter choices that influence cluster granularity; we mitigate this through public release of code and data, but alternative parameterizations may yield finer or coarser thematic structures. Some clusters in lower-resource settings exhibited low internal consistency (ICS < 0.5), reflecting either genuine conceptual heterogeneity or insufficient data for stable pattern recognition. Finally, the World Bank income classification, while standard, is a coarse proxy that may obscure substantial within-country variation in telemedicine readiness, particularly in large federal states. Relatedly, because country attribution relied on the first author’s first affiliation, the evidence absences we report for low-income settings should be interpreted as gaps in research produced from those countries rather than gaps in research about them.
4.10. Conclusion
This umbrella review shows that the global telemedicine evidence base does not merely describe digital health inequities but reproduces them: in the volume of evidence available, in the type of barriers that can be analytically distinguished, and in the near-total absence of documented facilitators outside high- and upper-middle-income settings. The 2.6:1 barrier-to-facilitator ratio, the orphaned barrier domains, and the empty evidence space for low-income countries are facets of a single asymmetry. Addressing it requires a coordinated shift, in funding, in methods, and in governance, from documenting why telemedicine fails to systematically generating, in every relevant context, the conditions under which it can succeed. Whether telemedicine narrows or widens the global digital health divide over the coming decade will depend less on the maturity of the technology than on the equity of the evidence base built around it.
Supporting information
S2 File. Complete data, all incomes, for barriers and facilitators clusters.
https://doi.org/10.1371/journal.pone.0351885.s002
(XLSX)
S3 File. Complete data, divided by income, for barriers’ clusters.
https://doi.org/10.1371/journal.pone.0351885.s003
(XLSX)
S4 File. Complete data, divided by income, for facilitators’ clusters.
https://doi.org/10.1371/journal.pone.0351885.s004
(XLSX)
S1 Image. Barriers and Facilitators Word-Clouds for High Income derived Instances.
https://doi.org/10.1371/journal.pone.0351885.s005
(PNG)
S2 Image. Barriers and Facilitators Word-Clouds for Upper-Middle Income derived Instances.
https://doi.org/10.1371/journal.pone.0351885.s006
(PNG)
S3 Image. Barriers and Facilitators Word-Clouds for Lower-Middle Income derived Instances.
https://doi.org/10.1371/journal.pone.0351885.s007
(PNG)
S4 Image. Barriers and Facilitators Word-Clouds for Low Income derived Instances.
https://doi.org/10.1371/journal.pone.0351885.s008
(PNG)
S1 Checklist. PRISMA Checklist for the review.
https://doi.org/10.1371/journal.pone.0351885.s009
(DOCX)
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