LO32: Are EMS offload delay patients at increased risk of adverse outcomes?
Bibliographic record
Abstract
Introduction: ED and hospital overcrowding cause offload delays that remove EMS crews from service and compromise care delivery to patients. Prolonged ED boarding times are associated with increased hospital LOS and patient mortality, but the impact of offload delays has not been studied. Our objective was to determine whether offload delays are associated with adverse system and patient outcomes. Methods: From July 2013 to June 2016, administrative data was collated from four Calgary adult EDs. All CTAS 2 and 3 EMS arrivals were studied. Those assigned an ED care space within 15 minutes were considered controls while those with delays of ≥60-minutes were considered ‘delayed’. Multivariable logistic regression was used to determine propensity scores, which were used to match delayed patients to nearest neighbor controls. Matching variables for propensity modeling included age, sex, CTAS level, ED site, arrival day and time, living situation (homecare/facility vs. independent), complaint category (medical, cardiovascular, mental health/neuro, GI, trauma/MS, other) and previous ED use (visits within 1 year). The primary outcome was 7-day mortality. Secondary outcomes included hospital LOS and 30-day mortality. Results: A total of 111,743 patients were studied: 70711 controls and 41032 delayed (median time to stretcher of 8 vs. 109 minutes). There was significant baseline covariate imbalance: Delayed patients were more likely to be female, older, have lower CTAS acuity, arrive on weekdays and evenings, to have general medical complaints, and to arrive at the slowest offload site. In the unmatched analysis, delayed patients had lower 7-day mortality (2.1% vs. 2.6%), similar 30-day mortality (3.5% vs. 3.6%), and longer hospital LOS (10.3 vs. 9.8 days). In the propensity-matched analysis (41016 patients per group), covariate balance was substantially improved and outcomes differed slightly. Seven and 30-day mortality were essentially unchanged, but between group differences for hospital LOS disappeared (10.3 vs. 10.2 days). Conclusion: Propensity analysis suggests that EMS patients exposed to offload delays have similar 30-day mortality and slightly lower 7-day mortality than patients who receive timely ED access. While offload delays lead to substandard hallway care, patient dissatisfaction, and remove EMS crews from service, the levels of offload delay studied here were not associated with higher mortality or prolonged hospital LOS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".