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Exploring the feasibility of eHealth solutions to decrease delays in maternal healthcare in remote communities of Ghana

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2017
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Title
Exploring the feasibility of eHealth solutions to decrease delays in maternal healthcare in remote communities of Ghana
Published in
BMC Medical Informatics and Decision Making, December 2017
DOI 10.1186/s12911-017-0552-z
Pubmed ID
Authors

Pedro Pagalday-Olivares, Bengt Arne Sjöqvist, Jessy Adjordor-van de Beek, Samuel Abudey, Ants R. Silberberg, Ruben Buendia

Abstract

Despite the introduction of the Millennium Development Goal to reduce maternal deaths from 400 to 100 per 100,000 live births, the proportion of maternal deaths is still much higher in most developing countries like Ghana. Various interventions have been implemented in Ghana that focus on increasing skilled maternal care. These are especially needed in rural areas. EHealth has the potential to contribute to reducing the challenges in maternal healthcare (MHC) that poor areas suffer. This paper analyzes the potential of eHealth solutions to improve maternal health in rural Ghana as well as the challenges to their implementation. The work was carried out in cooperation with the local health directorate of Kpando Municipality, one of the administrative areas in the Volta Region. The study is focused on remote peninsulas and islands in Kpando Municipality. Data was gathered through triangulated research methods. Maternal health challenges were identified using the Three Delays Model for MHC. The three delays are delay in seeking care, delay accessing health facilities, and delay receiving adequate care. Challenges to the implementation of eHealth solutions in remote communities were analyzed using the Drury's 5C eHealth model for developing countries. The 5Cs correspond to context, community, capacity, connectivity, and content. The results show that financial dependence of women, a decision-making process based on previous experiences and traditional beliefs, competitiveness between facilities, organizational loopholes, lack of equipment, and geographical situations directly influence MHC outcomes. EHealth solutions, thanks to the high number of health workers with basic IT skills, have high potential to reduce MHC delays. However, poverty, cultural beliefs, organizational issues, connectivity, and lack of human resources were identified as main challenges to the implementation of eHealth solutions. In Ghana's rural areas the three delays proposed in the model affect the outcomes of MHC. These delays are influenced by socio-economic status, access to facilities, and quality of care. EHealth solutions show great potential to reduce the delays. Based on the 5C model, a mHealth solution aiming to improve guidance during pregnancy was outlined.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 253 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 253 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 55 22%
Researcher 27 11%
Student > Bachelor 18 7%
Student > Ph. D. Student 14 6%
Other 12 5%
Other 45 18%
Unknown 82 32%
Readers by discipline Count As %
Medicine and Dentistry 38 15%
Nursing and Health Professions 36 14%
Social Sciences 24 9%
Computer Science 12 5%
Business, Management and Accounting 9 4%
Other 42 17%
Unknown 92 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 December 2017.
All research outputs
#16,253,231
of 24,723,421 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,304
of 2,110 outputs
Outputs of similar age
#263,357
of 448,669 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#22
of 30 outputs
Altmetric has tracked 24,723,421 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,110 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 448,669 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.