The development of ethical guidelines for telemedicine in South Africa
Bibliographic record
Abstract
Telemedicine has the potential to assist in the provision of healthcare in South Africa (SA). This means of healthcare service provision involves patients, doctors and machines working together, with few constraints imposed by geography, or national or institutional boundaries. Although the practice is largely beneficial, certain legal and ethical challenges arise from the use of electronic healthcare services. Certain ethical challenges are identified as: the changing nature of the traditional doctor-patient relationship; standards of care; quality of care; privacy; confidentiality; data protection; accountability; liability; consent; record-keeping; data storage; and authentication. While various legal, regulatory and governance measures offer potential solutions and remedies for protection, ethical direction may be achieved through statutory bodies set up to promote and foster ethical compliance with normative healthcare standards. Recently, the Health Professions Council of SA (HPCSA) made an attempt to address the ethical issues by publishing a set of telemedicine guidelines. Despite this, issues around the practice of telemedicine remain unresolved. This article seeks to inform the development of a new ethical framework by addressing three distinct and relevant ethical issues: the fiduciary nature of healthcare and the changing nature of the doctor-patient relationship; privacy, confidentiality and the sensitivity of health data; and informed consent. It does so by proposing a broader and more nuanced solution to these ethical obstacles by identifying conceptual and operational difficulties within the existing HPCSA telemedicine guidelines, and advancing suggestions for reform. This speaks to a more highly integrated perspective that is culturally and contextually aware, and which affirms the need to strike a balance between individual rights protection and transformative, ethical, healthcare innovation.
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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 | 0.103 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".