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Defining a staged-based process for economic and financial evaluations of mHealth programs

Overview of attention for article published in Cost Effectiveness and Resource Allocation, April 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#17 of 236)
  • High Attention Score compared to outputs of the same age (84th percentile)

Mentioned by

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17 tweeters

Citations

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9 Dimensions

Readers on

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65 Mendeley
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Title
Defining a staged-based process for economic and financial evaluations of mHealth programs
Published in
Cost Effectiveness and Resource Allocation, April 2017
DOI 10.1186/s12962-017-0067-6
Pubmed ID
Authors

Amnesty E. LeFevre, Samuel D. Shillcutt, Sean Broomhead, Alain B. Labrique, Tom Jones

Abstract

Mobile and wireless technology for health (mHealth) has the potential to improve health outcomes by addressing critical health systems constraints that impede coverage, utilization, and effectiveness of health services. To date, few mHealth programs have been implemented at scale and there remains a paucity of evidence on their effectiveness and value for money. This paper aims to improve understanding among mHealth program managers and key stakeholders of how to select methods for economic evaluation (comparative analysis for determining value for money) and financial evaluation (determination of the cost of implementing an intervention, estimation of costs for sustaining or expanding an intervention, and assessment of its affordability). We outline a 6 stage-based process for selecting and integrating economic and financial evaluation methods into the monitoring and evaluation of mHealth solutions including (1) defining the program strategy and linkages with key outcomes, (2) assessment of effectiveness, (3) full economic evaluation or partial evaluation, (4) sub-group analyses, (5) estimating resource requirements for expansion, (6) affordability assessment and identification of models for financial sustainability. While application of these stages optimally occurs linearly, finite resources, limited technical expertise, and the timing of evaluation initiation may impede this. We recommend that analysts prioritize economic and financial evaluation methods based on programmatic linkages with health outcomes; alignment with an mHealth solution's broader stage of maturity and stage of evaluation; overarching monitoring and evaluation activities; stakeholder evidence needs; time point of initiation; and available resources for evaluations.

Twitter Demographics

The data shown below were collected from the profiles of 17 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 2%
Unknown 64 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 23%
Researcher 11 17%
Student > Ph. D. Student 8 12%
Student > Bachelor 6 9%
Student > Postgraduate 4 6%
Other 10 15%
Unknown 11 17%
Readers by discipline Count As %
Medicine and Dentistry 20 31%
Nursing and Health Professions 8 12%
Engineering 7 11%
Business, Management and Accounting 3 5%
Economics, Econometrics and Finance 3 5%
Other 8 12%
Unknown 16 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 26 March 2018.
All research outputs
#1,408,216
of 13,791,430 outputs
Outputs from Cost Effectiveness and Resource Allocation
#17
of 236 outputs
Outputs of similar age
#41,719
of 265,349 outputs
Outputs of similar age from Cost Effectiveness and Resource Allocation
#1
of 1 outputs
Altmetric has tracked 13,791,430 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done particularly well, scoring higher than 92% of its peers.
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 265,349 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them