The need for a telemedicine strategy for Botswana? A scoping review and situational assessment
Bibliographic record
Abstract
BACKGROUND: Health, healthcare, and healthcare system problems within the developing world are well recognised. eHealth, the use of Information and Communications Technologies (ICT) for health, is frequently suggested as one means by which to ameliorate such problems. However, to identify and implement the most appropriate ehealth solutions requires development of a thoughtful and broadly evidence-informed strategy. Most published strategies focus on health informatics solutions, neglecting the potential for other aspects of ehealth (telehealth, telemedicine, elearning, and ecommerce). This study examined the setting in Botswana to determine the need for a telemedicine-specific strategy. METHODS: A situational assessment of ehealth activities in Botswana was performed through a scoping review of the scientific and grey literature using specified search terms to July 2018; an interview with an official from the major mhealth stakeholder; and benchtop review of policies and other relevant Government documents including the country's current draft eHealth Strategy. RESULTS: Thirty-nine papers were reviewed. Various ehealth technologies have been applied within Botswana. These include Skype for educational activities, instant messaging (WhatsApp for telepathology; SMS for transmission of laboratory test results, patient appointment reminders, and invoicing and bill payment), and robotics for dermatopathology. In addition health informatics technologies have been used for surveillance, monitoring, and access to information by healthcare workers. The number of distinct health information systems has been reduced from 37 to 12, and 9 discrete EMRs remain active within the public health institutions. Many infrastructural issues were identified. A critical assessment of the current draft ehealth strategy document for Botswana showed limitations. Many telemedicine services have been introduced over the years (addressing cervical cancer screening, teledermatology, teleradiology, oral medicine and eye screening), but only one project was confirmed to be active and being scaled up with the intervention of the Government. CONCLUSIONS: Botswana's draft 'ehealth' strategy will not, in and of itself, nurture innovative growth in the application of telemedicine initiatives, which currently are fragmented and stalled. This lack of focus is preventing telemedicine's recognised potential from being leveraged. A specific Telemedicine Strategy, aligned with and supportive of the pre-existing ehealth strategy, would provide the necessary focus, stimulus, and guidance.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".