Impact of a Cloud-Based Care Coordination Platform on False Activations of the Cardiac Catheterization Laboratory and Unnecessary Team Mobilization: A Retrospective Cohort Study (Preprint)
Bibliographic record
Abstract
BACKGROUND False activation (FA) of cardiac catheterization laboratory (CCL) for ST-segment elevation myocardial infarction (STEMI) is common, adversely affects health care professionals, and results in a financial burden to healthcare systems. OBJECTIVE The current study assessed if FA minimization can be achieved through implementing a dedicated cloud-based care coordination platform. METHODS In September 2021, the McGill University Health Centre (MUHC) implemented a mobile cloud-based STEMI care coordination platform (Stenoa), which allows for systematic case validation with electrocardiogram (ECG) data, followed by prompt case disposition. A retrospective cohort study was conducted on all CCL activations at the MUHC between September 2020 and December 2022. Patients were divided into two groups for analysis: before Stenoa use (Group 0, Sept. 2020 - Sept. 2021) and immediately following Stenoa use (Group 1, Sept. 2021 - Dec. 2022). FA was defined as the activation of the CCL team followed by case cancellation before any procedure was performed. FA rates and the proportion resulting in unnecessary team mobilization (UTM) were compared between groups. The primary outcome of this study was the overall rate of UTM. Data were obtained from the hospital medical records and Stenoa platform data. RESULTS 632 patients were included in this study, with 288 patients in Group 0 and 344 in Group 1. The overall rate of UTM was 8.0% (n=23) in Group 0 compared to 4.1% (n=14) in Group 1 (p=0.04). 27 FAs (9.4%) were reported in Group 0 in comparison to 22 (6.4%) in Group 1 (p=0.16). In Group 0, 23 of 27 (85.2%) FAs resulted in UTM, compared to 14 of 22 FAs (63.6%) in Group 1 (p=0.08). CONCLUSIONS Use of a cloud-based care coordination platform was associated with a significant reduction in unnecessary CCL team mobilization. This suggests that a dedicated platform may be an effective strategy for optimizing care coordination and resource utilization for STEMI patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".