Implementation of Chest Compression Feedback Technology to Improve the Quality of Cardiopulmonary Resuscitation in the Emergency Department: A Quality Initiative Test-of-change Study
Bibliographic record
Abstract
Background Cardiopulmonary resuscitation (CPR) metrics including compression rate and depth are associated with improved outcomes and the need for high-quality CPR is emphasized in both the American Heart Association (AHA) and Heart and Stroke Foundation of Canada (HSFC) guidelines. While these metrics can be utilized to assess the quality of CPR, they are infrequently measured in an objective fashion in the emergency department. Objectives As part of an Emergency Department (ED) Quality Improvement (QI) project, we sought to determine the impact of real-time audio-visual (AV) feedback during CPR amongst ED healthcare providers. Methods Participants performed two minutes of uninterrupted CPR without AV feedback, followed by two minutes of CPR with AV feedback after a two-minute rest period in a simulated CPR setting. CPR metrics were captured by the defibrillator and uploaded to review software for analysis of each event. Results The use of real-time AV feedback resulted in a significant improvement in the number of participants meeting AHA/HSFC recommended depth (38%, p = 0.0003) and rate (35%, p = 0.0002). Importantly, 'compressions in target', where participants met both rate and depth simultaneously, improved with AV feedback (19 vs 61%, p < 0.0001). Conclusions We found a significant improvement in compliance with CPR depth and rate targets as well as 'compressions in target' with the use of real-time AV feedback during simulation training. Future research is needed to ascertain whether these results would be replicated in other settings. Our findings do provide a robust argument for the implementation of real-time AV CPR feedback in Hamilton Emergency Departments.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".