The Implementation of a Virtual Emergency Department: Multimethods Study Guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) Framework (Preprint)
Bibliographic record
Abstract
BACKGROUND While the COVID-19 pandemic dramatically increased virtual care uptake across many health settings, it remains significantly underused in urgent care. OBJECTIVE This study evaluated the implementation of a pilot virtual emergency department (VED) at an Ontario hospital that connected patients to emergency physicians through a web-based portal. We sought to (1) assess the acceptability of the VED model, (2) evaluate whether the VED was implemented as intended, and (3) explore the impact on quality of care, access to care, and continuity of care. METHODS This evaluation used a multimethods approach informed by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. Data included semistructured interviews with patients and physicians as well as postvisit surveys from patients. Interviews were transcribed and analyzed using thematic analysis. Data from the surveys were described using summary statistics. RESULTS From December 2020 to December 2021, the VED had a mean of 153 (SD 25) visits per month. Among them, 67% (n=677) were female, and 75% (n=758) had a family physician. Patients reported that the VED provided high-quality, timely access to care and praised the convenience, shorter appointments, and benefit of the calm, safe space afforded through virtual appointments. In instances where patients were directed to come into the emergency department (ED), physicians were able to provide a “warm handoff” to improve efficiency. This helped manage patient expectations, and the direct advice of the ED physician reassured them that the visit was warranted. There was broad initial uptake of VED shifts among ED physicians with 60% (n=22) completing shifts in the first 2 months and 42% (n=15) completing 1 or more shifts per month over the course of the pilot. There were no difficulties finding sufficient ED physicians for shifts. Most physicians enjoyed working in the VED, saw value for patients, and were motivated by patient satisfaction. However, some physicians were hesitant as they felt their expertise and skills as ED physicians were underused. The VED was implemented using an iterative staged approach with increased service capabilities over time, including access to ultrasounds, virtual follow-ups after a recent ED visit, and access to blood work, urine tests, and x-rays (at the hospital or a local community laboratory). Physicians recognized the value in supporting patients by advising on the need for an in-person visit, booking a diagnostic test, or referring them to a specialist. CONCLUSIONS The VED had the support of physicians and facilitated care for low-acuity presentations with immediate benefits for patients. It has the potential to benefit the health care system by seeing patients through the web and guiding patients to in-person care only when necessary. Long-term sustainability requires a focus on understanding digital equity and enhanced access to rapid testing or investigations.
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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 | 0.055 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".