Ethics in Telehealth: Comparison between Guidelines and Practice-based Experience -the Case for Learning Health Systems
Bibliographic record
Abstract
OBJECTIVES: To understand ethical issues within the tele-health domain, specifically how well established macro level telehealth guidelines map with micro level practitioner perspectives. METHODS: We developed four overarching issues to use as a starting point for developing an ethical framework for telehealth. We then reviewed telemedicine ethics guidelines elaborated by the American Medical Association (AMA), the World Medical Association (WMA), and the telehealth component of the Health Professions council of South Africa (HPCSA). We then compared these guidelines with practitioner perspectives to identify the similarities and differences between them. Finally, we generated suggestions to bridge the gap between ethics guidelines and the micro level use of telehealth. RESULTS: Clear differences emerged between the ethics guidelines and the practitioner perspectives. The main reason for the differences were the different contexts where telehealth was used, for example, variability in international practice and variations in the complexity of patient-provider interactions. Overall, published guidelines largely focus on macro level issues related to technology and maintaining data security in patient-provider interactions while practitioner concern is focused on applying the guidelines to specific micro level contexts. CONCLUSIONS: Ethics guidelines on telehealth have a macro level focus in contrast to the micro level needs of practitioners. Work is needed to close this gap. We recommend that both telehealth practitioners and ethics guideline developers better understand healthcare systems and adopt a learning health system approach that draws upon different contexts of clinical practice, innovative models of care delivery, emergent data and evidence-based outcomes. This would help develop a clearer set of priorities and guidelines for the ethical conduct of telehealth.
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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 | 0.126 | 0.210 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".