Seven years of telemedicine in Médecins Sans Frontières demonstrate that offering direct specialist expertise in the frontline brings clinical and educational value
Bibliographic record
Abstract
BACKGROUND: Médecins Sans Frontières (MSF), a medical humanitarian organization, began using store-and-forward telemedicine in 2010. The aim of the present study was to describe the experience of developing a telemedicine service in low-resource settings. METHODS: We studied the MSF telemedicine service during the period from 1st July 2010 until 30th June 2017. There were three consecutive phases in the development of the service, which we compared. We also examined the results of a quality assurance program which began in 2013. RESULTS: During the study period, a total of 5646 telemedicine cases were submitted. The workload increased steadily, and the median referral rate rose from 2 to 18 cases per week. The number of hospitals submitting cases and the number of cases per hospital also increased, as did the case complexity. Despite the increased workload, the allocation time reduced from 0.9 to 0.2 hours, and the median time to answer a case decreased from 20 to 5 hours. The quality assurance scores were stable. User feedback was generally positive and more than 90% of referrers who provided a progress report about their case stated that it had been sent to an appropriate specialist, that the response was sufficiently quick and that the teleconsultation provided an educational benefit. Referrers noted a positive impact of the system on patient outcome in 39% of cases. CONCLUSIONS: The quality of the telemedicine service was maintained despite rising caseloads. The study showed that offering direct specialist expertise in low-resource settings improved the management of patients and provided additional educational value to the field physicians, thus bringing further benefits to other patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".