A Systems Theory Classification of EMR Hazards: Preliminary Results
Bibliographic record
Abstract
Jurisdictions in Canada, the US, the EU and Australia are struggling with regulation of ever evolving software in medicine. Recently this discussion has had a focus on electronic medical records (EMRs). There is a mountain of evidence that EMRs have actualized potential to lead to the injury of patients through the information they offer to facilitate care. We are undertaking a systematic review of relevant literature in the field to uncover some of the latent hazards. We hypothesize that this exploration, using a variation on Leveson's system theoretic accidents models and processes (STAMP) model as a classification tool, will provide two benefits. First, the model will be sufficient to capture the complexity of the domain and its hazards, thus providing a holistic perspective on the problem. Second, the classification process will provide insight as to what steps might be taken to mitigate the risk that medical errors associated with these software tools will arise in health care systems which employ them. In this continuation of our study we still have not been able to produce evidence which contradicts either hypothesis.
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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 | 0.041 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".