Virtual care use prior to emergency department admissions during a stable COVID-19 period in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The increased use of telemedicine to provide virtual outpatient visits during the pandemic has led to concerns about potential increased emergency department (ED) admissions and outpatient service use prior to such admissions. We examined the frequency of virtual visits use prior to ED admissions and characterized the patients with prior virtual visit use and the physicians who provided these outpatient visits. METHODS: We conducted a retrospective, population-based, cross-sectional analysis using linked health administrative data in Ontario, Canada to identify patients who had an ED admission between July 1 and September 30, 2021 and patients with an ED admissions during the same period in 2019. We grouped patients based on their use of outpatient services in the 7 days prior to admission and reported their sociodemographic characteristics and healthcare utilization. RESULTS: There were 1,080,334 ED admissions in 2021 vs. 1,113,230 in 2019. In 2021, 74% of these admissions had no prior outpatient visits (virtual or in-person) within 7 days of admission, compared to 75% in 2019. Only 3% of ED admissions had both virtual and in-person visits in the 7 days prior to ED admission. Patients with prior virtual care use were more likely to be hospitalized than those without any outpatient care (13% vs 7.7.%). INTERPRETATION: The net amount of ED admissions and outpatient care prior to admission remained the same over a period of the COVID-19 pandemic when cases were relatively stable. Virtual care seemed to be able to appropriately triage patients to the ED and virtual visits replaced in-person visits ahead of ED admissions, as opposed to being additive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".