EHealth Investment Appraisal in Africa: A Scoping Review
Bibliographic record
Abstract
eHealth is an opportunity cost, competing for limited available funds with other health priorities such as clinics, vaccinations, medicines and even salaries. As such, it should be appraised for probable impact prior to allocation of funds. This is especially pertinent as recognition grows for the role of eHealth in attaining Universal Health Coverage. Despite optimism about eHealth's potential role, in Africa there remain insufficient data and skills for adequate economic appraisals to select optimal investments from numerous competing initiatives. The aim of this review is to identify eHealth investment appraisal approaches and tools that have been used in African countries, describe their characteristics and make recommendations regarding African eHealth investment appraisal in the face of limited data and expertise. Methods: Literature on eHealth investment appraisals conducted in African countries and published between January 1, 2010 and June 30, 2020 was reviewed. Selected papers' investment appraisal characteristics were assessed using the Joanna Briggs Institute checklist for economic evaluations and a newly developed Five-Case Model for Digital Health (FCM-DH) checklist for investment appraisal. 5 papers met inclusion criteria. Their assessments revealed important appraisal gaps. In particular, none of the papers addressed risk exposure, affordability, adjustment for optimism bias, clear delivery milestones, practical plans for implementation, change management or procurement, and only 1 paper described plans for building partnerships. Discussion: Using this insight, an extended 5-Case Model is proposed as the foundation of an African eHealth investment appraisal framework. This, combined with building local eHealth appraisal capabilities, may promote optimal eHealth investment decisions, strengthen implementations and improve the number and quality of related publications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".