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mHealth for Clinical Decision-Making in Sub-Saharan Africa: A Scoping Review

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ABSTRACT

Background: In a bid to deliver quality health services in resource-poor settings, mobile health (mHealth) is increasingly being adopted. The role of mHealth in facilitating evidence-based clinical decision-making through data collection, decision algorithms, and evidence-based guidelines, for example, is established in resource-rich settings. However, the extent to which mobile clinical decision support systems (mCDSS) have been adopted specifically in resource-poor settings such as Africa and the lessons learned about their use in such settings are yet to be established.

Objective: The aim of this study was to synthesize evidence on the use of mHealth for point-of-care decision support and improved quality of care by health care workers in Africa.

Methods: A scoping review of 4 peer-reviewed and 1 grey literature databases was conducted. No date limits were applied, but only articles in English language were selected. Using pre-established criteria, 2 reviewers screened articles and extracted data. Articles were analyzed using Microsoft Excel and MAXQDA.

Results: We retained 22 articles representing 11 different studies in 7 sub-Saharan African countries. Interventions were mainly in the domain of maternal health and ranged from simple text messaging (short message service, SMS) to complex multicomponent interventions. Although health workers are generally supportive of mCDSS and perceive them as useful, concerns about increased workload and altered workflow hinder sustainability. Facilitators and barriers to use of mCDSS include technical and infrastructural support, ownership, health system challenges, and training.

Conclusions: The use of mCDSS in sub-Saharan Africa is an indication of progress in mHealth, although their effect on quality of service delivery is yet to be fully explored. Lessons learned are useful for informing future research, policy, and practice for technologically supported health care delivery, especially in resource-poor settings.

No MeSH data available.


Stepwise flow of study selection.
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figure1: Stepwise flow of study selection.

Mentions: Supported by multiple discussion meetings, two authors (IOOA and BJAA) applied the inclusion-exclusion criteria on the title and abstract sections of each article, after which 36 articles were selected for full text reading. Where primary reviewers disagreed on inclusion, a third reviewer (MZ) arbitrated. Alongside 3 articles identified from snowballing the reference section of secondary studies and 1 article identified from the weekly alerts, the 36 articles were further assessed for inclusion through full-text reading. Only 22 articles were eventually retained for synthesis and analysis. The stepwise flow is presented in Figure 1.


mHealth for Clinical Decision-Making in Sub-Saharan Africa: A Scoping Review
Stepwise flow of study selection.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5383806&req=5

figure1: Stepwise flow of study selection.
Mentions: Supported by multiple discussion meetings, two authors (IOOA and BJAA) applied the inclusion-exclusion criteria on the title and abstract sections of each article, after which 36 articles were selected for full text reading. Where primary reviewers disagreed on inclusion, a third reviewer (MZ) arbitrated. Alongside 3 articles identified from snowballing the reference section of secondary studies and 1 article identified from the weekly alerts, the 36 articles were further assessed for inclusion through full-text reading. Only 22 articles were eventually retained for synthesis and analysis. The stepwise flow is presented in Figure 1.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: In a bid to deliver quality health services in resource-poor settings, mobile health (mHealth) is increasingly being adopted. The role of mHealth in facilitating evidence-based clinical decision-making through data collection, decision algorithms, and evidence-based guidelines, for example, is established in resource-rich settings. However, the extent to which mobile clinical decision support systems (mCDSS) have been adopted specifically in resource-poor settings such as Africa and the lessons learned about their use in such settings are yet to be established.

Objective: The aim of this study was to synthesize evidence on the use of mHealth for point-of-care decision support and improved quality of care by health care workers in Africa.

Methods: A scoping review of 4 peer-reviewed and 1 grey literature databases was conducted. No date limits were applied, but only articles in English language were selected. Using pre-established criteria, 2 reviewers screened articles and extracted data. Articles were analyzed using Microsoft Excel and MAXQDA.

Results: We retained 22 articles representing 11 different studies in 7 sub-Saharan African countries. Interventions were mainly in the domain of maternal health and ranged from simple text messaging (short message service, SMS) to complex multicomponent interventions. Although health workers are generally supportive of mCDSS and perceive them as useful, concerns about increased workload and altered workflow hinder sustainability. Facilitators and barriers to use of mCDSS include technical and infrastructural support, ownership, health system challenges, and training.

Conclusions: The use of mCDSS in sub-Saharan Africa is an indication of progress in mHealth, although their effect on quality of service delivery is yet to be fully explored. Lessons learned are useful for informing future research, policy, and practice for technologically supported health care delivery, especially in resource-poor settings.

No MeSH data available.