ArticleDigital health
Mobile consulting (mConsulting) and its potential for providing access to quality healthcare for populations living in low-resource settings of low- and middle-income countries.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Perspectives of health care professionals' on delivering mHealth sexual and reproductive health services in rural settings in low-and-middle-income countries: a qualitative systematic review.BMC health services research · 2022Pooled it
- Formalising remote consulting in primary care services for underserved populations with long-term conditions in sub-Saharan Africa: a process evaluation and stakeholder engagement.BMJ global health · 2026Article
- Digitalisation of medicine in LMICs (sub-Saharan Africa): enablers, barriers, and funding strategies for sustainable health systems.Frontiers in public health · 2026Review
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- Digital Health Technologies for Maternal and Child Health in Africa and Other Low- and Middle-Income Countries: Cross-disciplinary Scoping Review With Stakeholder Consultation.Journal of medical Internet research · 2023Article
- Remote Consulting in Primary Health Care in Low- and Middle-Income Countries: Feasibility Study of an Online Training Program to Support Care Delivery During the COVID-19 Pandemic.JMIR formative research · 2022Article
- An Agile Digital Platform to Support Population Health-A Case Study of a Digital Platform to Support Patients with Delirium Using IoT, NLP, and AI.International journal of environmental research and public health · 2021Article
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- Article
- The Recent Progress and Applications of Digital Technologies in Healthcare: A Review.International journal of telemedicine and applications · 2020Review
- Article
- Delivering High-Quality, Equitable Care in India: An Ethically-Resilient Framework for Healthcare Innovation After COVID-19.Frontiers in public healthArticle
Corrections and comments
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Authors and funding
25 authors.
Funding
Abstract
objectiveThe poorest populations of the world lack access to quality healthcare. We defined the key components of consulting via mobile technology (mConsulting), explored whether mConsulting can fill gaps in access to quality healthcare for poor and spatially marginalised populations (specifically rural and slum populations) of low- and middle-income countries, and considered the implications of its take-up.
methodsWe utilised realist methodology. First, we undertook a scoping review of mobile health literature and searched for examples of mConsulting. Second, we formed our programme theories and identified potential benefits and hazards for deployment of mConsulting for poor and spatially marginalised populations. Finally, we tested our programme theories against existing frameworks and identified published evidence on how and why these benefits/hazards are likely to accrue.
resultsWe identified the components of mConsulting, including their characteristics and range. We discuss the implications of mConsulting for poor and spatially marginalised populations in terms of competent care, user experience, cost, workforce, technology, and the wider health system.
conclusionsFor the many dimensions of mConsulting, how it is structured and deployed will make a difference to the benefits and hazards of its use. There is a lack of evidence of the impact of mConsulting in populations that are poor and spatially marginalised, as most research on mConsulting has been undertaken where quality healthcare exists. We suggest that mConsulting could improve access to quality healthcare for these populations and, with attention to how it is deployed, potential hazards for the populations and wider health system could be mitigated.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.