Virtual care use prior to emergency department admissions during a stable COVID-19 period in Ontario, Canada
Bibliographic record
Abstract
Abstract Background The increased use of telemedicine 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 telemedicine use prior to ED admissions and characterized the patients with prior telemedicine 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 seems to be able to appropriately triage patients to the ED and may even prove beneficial for diverting patients away from the ED when an ED admission is not appropriate. The COVID-19 pandemic has led to the emergence of standard use of telemedicine in health care across the globe(1,2). In Ontario, Canada the proportion of ambulatory visits completed virtually has been maintained at slightly above 50% from 2020 to 2021 (3). Despite its widespread adoption, it is still unclear when virtual visits are clinically appropriate and how such wide use of telemedicine impacts patient outcomes and healthcare utilization metrics. Before the pandemic, there had been concerns that telemedicine may lead to an increased use of outpatient services with patients having both a virtual and an in-person visit for the same clinical issue(4,5). For example, pre-pandemic data (2007-2016) from Manitoba showed that telemedicine users had on average 1.3 times more ambulatory visits than non-users.(6) In addition, studies have produced mixed evidence with regard to the effect of telemedicine on urgent services such as emergency department (ED) admissions and hospitalizations (7). Many of the studies reported in the literature are based on data from site-specific programs and therefore have limited generalizability. Finally, policymakers and some physicians have become concerned that the high rates of telemedicine during COVID-19 have led to an increase in emergency department admissions because of poor access to in-person outpatient care (8). This concern is exacerbated when one considers rural and lower socioeconomic status patients who already had poor access to care before the pandemic(9). Combined with reports of lower uptake of telemedicine among these patients(10,11), it is not clear how the transition of care from in-person to virtual impacts ED use. The high adoption of telemedicine during the pandemic, in the context of a publicly funded healthcare system allowing us access to most visits across the entire population, offers a unique opportunity to examine the frequency of telemedicine use prior to ED admissions. Therefore, the goal of this study was to characterize the frequency and modality (in-person vs virtual) of outpatient care prior to ED admissions. We examined whether there was an overall increase in outpatient visits prior to ED admissions during a period of the pandemic when access to telemedicine was available compared to a seasonality matched period before the pandemic where access to telemedicine was quite limited. We also aimed to characterize the patients who had a telemedicine visit prior to an ED admission vs. those who had an in-person visit and the physicians who saw patients with virtual only visits prior to their ED admission compared to those who saw patients virtually or in-person prior to their ED admission.
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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 | 0.000 | 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 | 0.000 |
| 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".