Examining the Patterns of Virtual Visit Uptake Across Clinical Disciplines Using Hospital Administrative Data During the COVID-19 Pandemic: A Descriptive Study. (Preprint)
Bibliographic record
Abstract
BACKGROUND The COVID-19 pandemic resulted in a dramatic and rapid shift away from physical visits, resulting in an instantaneous and unplanned adoption of virtual (phone and video) visits. OBJECTIVE Administrative data at a large urban Canadian university-affiliated hospital network was analyzed to understand how clinical disciplines adopted phone and video visits early in the pandemic and how their use of virtual visits grew, sustained or was abandoned through the pandemic. METHODS Virtual visit adoption by clinical discipline was compared during both the early pandemic (Apr-May 2020) and peak reopening time periods (Oct-Nov 2020) in an attempt to categorize clinical disciplines by their adoption of virtual visits, and thus understand how best to provide change management support. RESULTS At our largest academic site, for which we had full data, 50.8% of ambulatory visits were provided by phone or video during the pandemic (94.5% phone, 5.5% video). There was considerable variability across services in terms of how they adopted virtual visits in the early pandemic and peak reopening. Phone was the dominant modality, but video had high usage (up to 95% of virtual visits) in select disciplines. CONCLUSIONS We identified 4 patterns that provide opportunities for dedicated support in some disciplines: early sustained adoption, non-adoption, late growth, and late abandonment. The phone was the dominant modality (>90% of virtual visits) but video had high use in some disciplines necessitating targeted support. Additional high-quality research examining phone vs. video visits across disciplines and contexts is critical; until then both should be supported.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".