Using Medical Emergency Teams to detect preventable adverse events
Bibliographic record
Abstract
INTRODUCTION: Medical Emergency Teams (METs), also known as Rapid Response Teams, are recommended as a patient safety measure. A potential benefit of implementing an MET is the capacity to systematically assess preventable adverse events, which are defined as poor outcomes caused by errors or system design flaws. We describe how we used MET calls to systematically identify preventable adverse events in an academic tertiary care hospital, and describe our surveillance results. METHODS: For four weeks we collected standard information on consecutive MET calls. Within a week of the MET call, a multi-disciplinary team reviewed the information and rated the cause of the outcome using a previously developed rating scale. We classified the type and severity of the preventable adverse event. RESULTS: We captured information on all 65 MET calls occurring during the study period. Of these, 16 (24%, 95% confidence interval [CI] 16%-36%) were felt to be preventable adverse events. The most common cause of the preventable adverse events was error in providing appropriate therapy despite an accurate diagnosis. One service accounted for a disproportionate number of preventable adverse events (n = 5, [31%, 95% CI 14%-56%]). CONCLUSIONS: Our method of reviewing MET calls was easy to implement and yielded important results. Hospitals maintaining an MET can use our method as a preventable adverse event detection system at little additional cost.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".