Ethical, legal and administrative implications of the use of video and audio recording in an emergency department in Ontario, Canada
Bibliographic record
Abstract
While video and audio recording (VAR) of patients is well described for clinical research, its application to quality improvement in the emergency department has thus far been limited and hindered by potential obstacles. We believe this technology holds promise to incite marked systems improvement but only if deployed in a thoughtful and principled manner. Experts in clinical, regulatory, legal, quality improvement, patient safety and ethical domains collaborated to articulate the salient considerations and challenges to implementation of a VAR programme. We describe this implementation using the lens of legislation and other principles specific to our current context. The landscape of ethical, legal and regulatory barriers and a case example of how a VAR programme has been implemented in an emergency department in Ontario, Canada are outlined. The potential to harness VAR data to drive quality and to improve safety is remarkable. Articulating the most contentious issues and illustrating how they can be addressed may guide others hoping to implement similar VAR programmes.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".