Medical and economic benefits of telehealth in low- and middle-income countries: results of a study in four district hospitals in Mali
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to evaluate the impact of telehealth on 1) the diagnosis, and management in obstetrics and cardiology, 2) health care costs from patients' perspectives, 3) attendance at health centres located in remote areas of Mali. METHODS: The impact of telehealth on health care utilization, quality, and costs was assessed using a five-point Likert-scale based questionnaire consisting of three dimensions. It was completed by health care professionals in four district hospitals. The role of telehealth on attendance at health centres was also assessed based on data collected from the consultations logs before and during the project, between project sites and control sites. Referrals specific to the activities of the research study were also evaluated using a questionnaire to measure the real share of telehealth tools in increasing attendance at project sites. Finally, the cost savings achieved was estimated using the transport and lodging costs incurred if patients were to travel to the capital city for the same tests or care. RESULTS: The telehealth activities contributed to improving medical diagnoses in cardiology and obstetrics (92.6%) and the patients' management system on site (96.2%). The attendance records at health centres increased from 8 to 35% at all project sites during the study period. Patients from project sites saved an average of 12380 XOF (CFA Francs) or 25 USD (American dollar) and a maximum of 35000 XOF or 70 USD compared to patients from neighbouring sites, who must go to the capital city to receive the same care. CONCLUSION: We conclude that in Mali, enhanced training in ultrasound / electrocardiography and the introduction of telehealth have improved the health system in remote areas and resulted in high levels of appropriate diagnosis and patient management in the areas of obstetrics and cardiology. Telehealth can also significantly reduce the cost to the patient.
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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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".