Implementing a Virtual Emergency Department: Qualitative Study Using the Normalization Process Theory
Bibliographic record
Abstract
BACKGROUND: COVID-19 necessitated the rapid implementation and uptake of virtual health care; however, virtual care's potential role remains unclear in the urgent care setting. In December 2020, the first virtual emergency department (ED) in the Greater Toronto Area was piloted at Sunnybrook Health Sciences Centre by connecting patients to emergency physicians through an online portal. OBJECTIVE: This study aims to understand whether and how ED physicians were able to integrate a virtual ED alongside in-person operations. METHODS: We conducted semistructured interviews with ED physicians guided by the Normalization Process Theory (NPT). The NPT provides a framework to understand how individuals and teams navigate the process of embedding new models of care as part of normal practice. All physicians who had worked within the virtual ED model were invited to participate. Data were analyzed using a combination of inductive and deductive techniques informed by the NPT. RESULTS: A total of 14 physicians were interviewed. Participant experiences were categorized into 1 of 2 groups: 1 group moved to normalize the virtual ED in practice, while the other described barriers to routine adoption. These groups differed in their perception of the patient benefits as well as the perceived role in the virtual ED. The group that normalized the virtual ED model saw value for patients (coherence) and was motivated by patient satisfaction witnessed (reflexive monitoring) at the end of the virtual appointment. By contrast, the other group did not find virtual ED work reflective of the perceived role of urgent care (cognitive participation) and felt their skills as ED physicians were underutilized. The limited ability to examine patients and a sense that patient issues were not fully resolved at the end of the virtual appointment caused frustration among the second group. CONCLUSIONS: As further digital integration within the health care system occurs, it will be essential to support the evolution of staff skill sets to ensure physicians are satisfied with the care they are providing to their patients, while also ensuring the technology and process are efficient.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".