Designs, facilitators, barriers, and lessons learned during the implementation of emergency department led virtual urgent care programs in Ontario, Canada
Bibliographic record
Abstract
Introduction: Virtual patient care has seen incredible growth since the beginning of the COVID-19 pandemic. To provide greater access to safe and timely urgent care, in the fall of 2020, the Ministry of Health introduced a pilot program of 14 virtual urgent care (VUC) initiatives across the province of Ontario. The objective of this paper was to describe the overall design, facilitators, barriers, and lessons learned during the implementation of seven emergency department (ED) led VUC pilot programs in Ontario, Canada. Methods: We assembled an expert panel of 13 emergency medicine physicians and researchers with experience leading and implementing local VUC programs. Each VUC program lead was asked to describe their local pilot program, share common facilitators and barriers to adoption of VUC services, and summarize lessons learned for future VUC design and development. Results: Models of care interventions varied across VUC pilot programs related to triage, staffing, technology, and physician remuneration. Common facilitators included local champions to guide program delivery, provincial funding support, and multi-modal marketing and promotions. Common barriers included behaviour change strategies to support adoption of a new service, access to high-quality information technology to support new workflow models that consider privacy, risk, and legal perspectives, and standardized data collection which underpin overall objective impact assessments. Conclusions: These pilot programs were rapidly implemented to support safe access to care and ED diversion of patients with low acuity issues during the COVID-19 pandemic. The heterogeneity of program implementation respects local autonomy yet may present challenges for sustainability efforts and future funding considerations.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 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".