Current status of digital health interventions in the health system in Burkina Faso
Bibliographic record
Abstract
Abstract Background Digital health is being used as an accelerator to improve the traditional healthcare system. It can help countries achieve their sustainable development goals. Burkina Faso aims to harmonize its digital health interventions to guide its digital health strategy for the coming years. The current assessment is an upstream work to guide the development of this strategic plan. Methods This was a quantitative, descriptive study conducted between September 2022 and April 2023. A two-part survey was carried out, a self-administered questionnaire with healthcare information managers in facilities, and a direct interview with software developers. This was complemented by a qualitative review of the country's strategic documents on digital transformation. Results Burkina Faso has a fairly extensive body of governance texts relating to digital transformation. The study identified a total of 35 digital health interventions. Analysis showed that 89% of funding came from technical and financial partners and the private sector. The use of open-source technologies for development is well established (77%), but there is a lack of integration of data from different platforms. Furthermore, the classification of interventions shows an unbalanced distribution between the different elements according to the domain: the health system, the classification of digital health interventions (DHI) and the subsystems of the National Health Information System (NHIS). Most digital health interventions are in the pilot phase (66%), with isolated electronic patient record initiatives not yet completed. In the public sector, this record is of the electronic register type or an isolated specialty record in a hospital. In the private sector, some tools are implemented depending on the needs expressed by the structure. The difficulty remains in the use of interoperability norms and standards in tool design. Very little use is made of the data generated by the implemented tools. Conclusion This study provides an overview of the digital health environment in Burkina Faso and raises major challenges in terms of intervention strategies. The results will be the starting point for drawing up the digital health strategic plan; if the shortcomings are taken into account, it will provide a framework for future digital health initiatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 source (direct Gemma or distilled Codex), 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".