Value propositions of mHealth projects

Gorski, Irena; Bram, Joshua T.; Sutermaster, Staci; Eckman, Molly; Mehta, Khanjan

doi:10.1080/03091902.2016.1213907

Cited by 32 publications

(25 citation statements)

References 8 publications

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“…In Gorski et al [17] the authors address this issue by analysing the downstream value proposition and attempting to design from the customers’ perspective to address their specific problems and/or create appreciable value. The authors suggest that a customer-centric view, where direct tangible benefits of interventions are identified and communicated effectively, can drive customer engagement and advance projects toward self-sustaining business models.…”

Section: Overview Of Articles In This Special Issuementioning

confidence: 99%

E-health in low to middle income countries

Clifford

2016

Journal of Medical Engineering & Technology

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Section: Overview Of Articles In This Special Issuementioning

confidence: 99%

E-health in low to middle income countries

Clifford

2016

Journal of Medical Engineering & Technology

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“…Deglise et al published a systematic review on short messaging service (SMS) interventions for disease prevention and demonstrated that these mHealth projects were concentrated mostly in South Africa, Kenya and India [ 15 ]. Also, Gorski et al (2016) found from their meta-analysis that globally, most mHealth projects are located in sub-Saharan Africa [ 16 ]. According to Njoroge et al (2017), the growth of mHealth in sub-Saharan Africa is particularly outstanding in Kenya [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Mapping mHealth (mobile health) and mobile penetrations in sub-Saharan Africa for strategic regional collaboration in mHealth scale-up: an application of exploratory spatial data analysis

2017

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BackgroundMobile health (mHealth), a term used for healthcare delivery via mobile devices, has gained attention as an innovative technology for better access to healthcare and support for performance of health workers in the global health context. Despite large expansion of mHealth across sub-Saharan Africa, regional collaboration for scale-up has not made progress since last decade.MethodsAs a groundwork for strategic planning for regional collaboration, the study attempted to identify spatial patterns of mHealth implementation in sub-Saharan Africa using an exploratory spatial data analysis. In order to obtain comprehensive data on the total number of mHelath programs implemented between 2006 and 2016 in each of the 48 sub-Saharan Africa countries, we performed a systematic data collection from various sources, including: the WHO eHealth Database, the World Bank Projects & Operations Database, and the USAID mHealth Database. Additional spatial analysis was performed for mobile cellular subscriptions per 100 people to suggest strategic regional collaboration for improving mobile penetration rates along with the mHealth initiative. Global Moran’s I and Local Indicator of Spatial Association (LISA) were calculated for mHealth programs and mobile subscriptions per 100 population to investigate spatial autocorrelation, which indicates the presence of local clustering and spatial disparities.ResultsFrom our systematic data collection, the total number of mHealth programs implemented in sub-Saharan Africa between 2006 and 2016 was 487 (same programs implemented in multiple countries were counted separately). Of these, the eastern region with 17 countries and the western region with 16 countries had 287 and 145 mHealth programs, respectively. Despite low levels of global autocorrelation, LISA enabled us to detect meaningful local clusters. Overall, the eastern part of sub-Saharan Africa shows high-high association for mHealth programs. As for mobile subscription rates per 100 population, the northern area shows extensive low-low association.ConclusionsThis study aimed to shed some light on the potential for strategic regional collaboration for scale-up of mHealth and mobile penetration. Firstly, countries in the eastern area with much experience can take the lead role in pursuing regional collaboration for mHealth programs in sub-Saharan Africa. Secondly, collective effort in improving mobile penetration rates for the northern area is recommended.Electronic supplementary materialThe online version of this article (doi:10.1186/s12992-017-0286-9) contains supplementary material, which is available to authorized users.

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“…Third, compared to other regions, sub-Saharan Africa has been known to be the place with burgeoning supply and demand for m-health. 12 The growth of m-health in sub-Saharan Africa over the past decade proved that several countries in this region such as Kenya and South Africa are ready to lead the m-health initiative globally. 9 …”

Section: Discussionmentioning

confidence: 99%

“…Also, a dummy variable for sub-Saharan African country versus others was included as the m-health is growing most rapidly in sub-Saharan Africa, but at the same time, resources are more limited in this region. 12 Last, a country's gross national income (GNI) per capita was included as a covariate to control for the effect of size of a country's economy on m-health policy readiness.…”

Section: Methodsmentioning

confidence: 99%

“…Sub-Saharan Africa was chosen as focus of the study because there has been an explosion in m-health investment in this region to address public health problems in terms of financial resources, workforce, and infrastructure. 12 , 13 To benchmark the status of m-health policy uptake in the region, OECD countries are compared to sub-Saharan Africa as they are known to be relatively wealthy with larger amount of resources and policy support.…”

Section: Introductionmentioning

confidence: 99%

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m-Health Policy Readiness and Enabling Factors: Comparisons of Sub-Saharan Africa and Organization for Economic Cooperation and Development Countries

Lee

Begley

Morgan

et al. 2018

Telemedicine and e-Health

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Background: As an innovative solution to poor access to care in low- and middle-income countries (LMICs), m-health has gained wide attention in the past decade.Introduction: Despite enthusiasm from the global health community, LMICs have not demonstrated high uptake of m-health promoting policies or public investment.Materials and Methods: To benchmark the current status, this study compared m-health policy readiness scores between sub-Saharan Africa and high-income Organization for Economic Cooperation and Development (OECD) countries using an independent two-sample t test. In addition, the enabling factors associated with m-health policy readiness were investigated using an ordinal logistic regression model. The study was based on the m-health policy readiness scores of 112 countries obtained from the World Health Organization Third Global Survey on e-Health.Results: The mean m-health policy readiness score for sub-Saharan Africa was statistically significantly lower than that for OECD countries (p = 0.02). The enabling factors significantly associated with m-health policy readiness included information and communication technology development index (odds ratio [OR] 1.57; 95% confidence interval [CI] 1.12–2.2), e-health education for health professionals (OR 4.43; 95% CI 1.60–12.27), and the location in sub-Saharan Africa (OR 3.47; 95% CI 1.06–11.34).Discussion: The findings of our study suggest dual policy goals for m-health in sub-Saharan Africa. First, enhance technological and educational support for m-health. Second, pursue global collaboration for building m-health capacity led by sub-Saharan African countries with hands-on experience and knowledge.Conclusion: Globally, countries should take a systematic and collaborative approach in pursuing m-health policy with the focus on technological and educational support.

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Value propositions of mHealth projects

Cited by 32 publications

References 8 publications

E-health in low to middle income countries

E-health in low to middle income countries

Mapping mHealth (mobile health) and mobile penetrations in sub-Saharan Africa for strategic regional collaboration in mHealth scale-up: an application of exploratory spatial data analysis

m-Health Policy Readiness and Enabling Factors: Comparisons of Sub-Saharan Africa and Organization for Economic Cooperation and Development Countries

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