Evaluation of low acuity patients discharged from a virtual emergency department at a major urban academic health sciences centre in Toronto, Canada.
Bibliographic record
Abstract
ObjectiveIn response to the COVID-19 pandemic, Sunnybrook Health Sciences Centre launched the first virtual emergency department (VED) in Toronto, Ontario. The objective of this pilot project was to leverage linked administrative data to describe the healthcare utilization of VED patients compared to matched patients who attended an ED in person. ApproachEvaluation of the VED program was supported by the ICES Applied Health Research Question Program, which is funded by the Ontario Ministry of Health to answer questions directly related to Ontario healthcare policy, planning, or practice. VED visit records from December 2020 to May 2021 were linked with Ontario administrative data. VED patients with low acuity complaints were matched 1:1 with in-person ED comparators according to visit date, presenting complaint, and a propensity score that incorporated age, sex, comorbidities, and other important potential confounders. The primary outcomes were healthcare utilization within 7 days and all-cause mortality within 30 days. ResultsOf the 609 eligible patients discharged from the VED, 600 (98.5%) were successfully matched to a comparator. Mean (SD) age was 43.0 (21.1) and 64.1% were female. In-person ED revisits and hospitalizations were similar for VED and comparator patients at 72 hours (ED: 12.1% vs. 11.3%; Δ 0.8%, 95%: -2.8, 4.5%; hospitalization: 1.2% vs. 1.5%; Δ 0.3%, 95%: -0.7, 1.4%,) and 7 days (ED: 16.1% vs. 14.4%; Δ 1.7, 95%: -2.4, 5.7%; hospitalization: 1.7% vs. 1.8%; Δ 0.2%, 95%: -0.1, 1.4%) following the index visit. The number of patients visiting a primary care provider within 7 days was also similar between groups (36.7% vs. 32.4%; Δ 4.3, 95%: -1.1, 9.8%). No patients died within 30 days. Conclusion/ImplicationsVED patients and their matched comparators had similar healthcare utilization in the 7 days following their index ED visit. Methodology from this study will inform a province-wide evaluation of VED programs across Ontario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".