From triage to treatment
Bibliographic record
Abstract
Background: The COVID-19 pandemic has had a significant impact on healthcare systems worldwide, leading to changes in presentation types, service utilization, and admission rates to emergency departments (ED). This study examines changes in ED visit patterns and triage misclassifications during the pandemic in Alberta, Canada. Methods: We conducted a retrospective population-level time-series analysis of all patients who presented to 12 EDs in the Edmonton Alberta Zone between March 3, 2019, and March 3, 2022. Routinely collected electronic health record data were analyzed and with primary categories of reporting including Canadian Triage Acuity Scale (CTAS), age, Canadian Emergency Department Information System (CEDIS) presenting complaint, admission status, triage misclassifications, and time markers for patient care. Results: 1.24 million cases from 12 hospitals were included. When we compare the patterns of presentation to the pre-pandemic period, we found a relative increase of 12.5% in EMS volumes, a 43.2% relative reduction in the proportion of patients presenting to tertiary EDs, 17.2% relative reduction in the number of patients under the age of 18, and a global increase in acuity with the highest relative increase 19.7% coming from patients in the highest acuity level: CTAS 1. Complaint distributions during these periods demonstrated that mental health, substance use, and environmental complaints experienced 15.5%, 22.4%, and 26.7% relative increases in volume, respectively; pediatric specific complaints experienced a 56.5% relative reduction. By the end of the study period, patients spent an average of 59 minutes longer in the ED compared to the pre-pandemic period. The proportion of patients triaged using Epic increased from 7.8% of all patients triaged in the pre-pandemic period to over 66.1% during the pandemic, and there was a 22.9% and 24.2% relative reduction in high-risk triage misclassifications (22.9%) and pain related triage misclassifications (24.2%) by the end of the period compared to the before the pandemic. Conclusion: Our study adds to the pandemic-related emergency care knowledge base by describing ED visit trends, changes in presenting complaint categories and time markers for patient care over a big-data pre and post pandemic dataset. Nursing-specific ED quality indicators that have not been previously described over a three-year duration between March 3rd 2019 and March 3rd 2022. are also presented. Our study findings have significant implications for healthcare professionals and policymakers in understanding both the impact of the pandemic on ED care delivery as well as future pandemic and post-pandemic ED operations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".