Can ICTs Contribute to the Efficiency and Provide Equitable Access to the Health Care System in Sub-Saharan Africa? The Mali Experience
Bibliographic record
Abstract
Summary The aim of this study is to demonstrate from actual projects that ICT can contribute to the balance of health systems in developing countries and to equitable access to human resources and quality health care service. Our study is focused on two essential elements which are: i) Capacity building and support of health professionals, especially those in isolated areas using telemedicine tools; ii) Strengthening of hospital information systems by taking advantage of full potential offered by open-source software. Our research was performed on the activities carried out in Mali and in part through the RAFT (Réseau en Afrique Francophone pour la Télémédecine) Network. We focused mainly on the activities of e-learning, telemedicine, and hospital information systems. These include the use of platforms that work with low Internet connection bandwidth. With regard to information systems, our strategy is mainly focused on the improvement and implementation of opensource tools. Several telemedicine application projects were reviewed including continuing online medical education and the support of isolated health professionals through the usage of innovative tools. This review covers the RAFT project for continuing medical education in French-speaking Africa, the tele-radiology project in Mali, the “EQUI-ResHuS” project for equal access to health over ICT in Mali, The “Pact-e.Santé” project for community health workers in Mali.We also detailed a large-scale experience of an open-source hospital information system implemented in Mali: “Cinz@n”. We report on successful experiences in the field of telemedicine and on the evaluation by the end-users of the Cinz@n project, a pilot hospital information system in Mali. These reflect the potential of healthcare-ICT for Sub-Saharan African countries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".