Improving same-day discharge after catheter ablation procedures: the Hawthorne effect or an epiphenomenon of the COVID-19 pandemic?
Bibliographic record
Abstract
Abstract: Same-day discharge after catheter ablation procedures may improve patient flow and reduce hospital costs. Despite this, same-day discharge is underutilized in multiple jurisdictions globally. Strategies to improve same-day discharge have not been reported. Altering physician behavior is necessary to facilitate change. In this quality improvement study, we evaluated the utility of a monthly email on increasing same-day discharge after a supraventricular tachycardia (SVT) ablation procedure. To assess for a Hawthorne effect we evaluated changes in the rate of same-day discharge for ablation procedures for an unrelated arrhythmia—atrial fibrillation (AF). Additional analyses were performed in an attempt to account for the impact of the coronavirus disease 2019 (COVID-19) pandemic which coincidentally occurred during our study period. We noted an increase in same-day discharge for SVT ablation procedures during the intervention period. Incidentally, we noted an increase in same-day discharge for the alternative arrhythmia mechanism (AF ablation). While we speculate that the increase in the use of same-day discharge for AF ablation was due to the Hawthorne effect, analyses accounting for the rate of increase in same-day discharge by different epochs during the time of the COVID-19 pandemic suggest that the COVID-19 pandemic may have had an important influence on the use of same-day discharge after catheter ablation procedures. Our work demonstrates one approach to improve same-day discharge after catheter ablation procedures.
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".