Can ICTs Contribute to the Efficiency and Provide Equitable Access to the Health Care System in Sub-Saharan Africa? The Mali Experience
Why this work is in the frame
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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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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it