The Utilization of an Electronic Consultation Service During the Coronavirus Disease 2019 Pandemic
Bibliographic record
Abstract
Objective: The coronavirus disease 2019 (COVID-19) pandemic forced many clinicians to rapidly adopt changes in their practice. In this study, we compared patterns of utilization of Ontario eConsult before and after the onset of the COVID-19 pandemic, to assess COVID 19's impact on how eConsult is used. Materials and Methods: We conducted a longitudinal analysis of registration and utilization data for Ontario eConsult. All primary care providers (PCPs) and specialists who joined the service between March 2019 and November 2020, and all eConsult cases closed during the same period were included. The data were divided into two timeframes for comparison: prepandemic (March 2019–February 2020) and pandemic (March 2020–November 2020). Results: In total, 5,925 PCPs joined during the study period, more than doubling total enrollment to 11,397. The average monthly number of eConsults increased from 2,405 (standard deviation [SD] = 260) prepandemic to 3,906 (SD = 420) pandemic. Case volume jumped to 24.3% in the first month of the pandemic, and increased by 71% during the COVID-19 pandemic timeframe. The median response time was similar in both timeframes (prepandemic: 1.0 days; pandemic: 0.9 days). The proportion of cases resulting in new/additional information (prepandemic: 55%, pandemic: 57%) or avoidance of a contemplated referral (prepandemic: 52%, pandemic: 51%) remained consistent between timeframes. Conclusions: Registration to and usage of eConsult increased during the pandemic. Metrics of the service's impact, including response time, percentage of cases resulting in new or additional information, and avoidance of originally contemplated referrals were all consistent between the prepandemic and COVID-19 pandemic timeframes, suggesting scalability.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".