Telehealth as a Bright Spot of the COVID-19 Pandemic: Recommendations From the Virtual Frontlines ("Frontweb")
Bibliographic record
Abstract
The coronavirus disease (COVID-19) pandemic has accelerated the telehealth tipping point in the practice of family medicine and primary care in the United States, making telehealth not just a novel approach to care but also a necessary one for public health safety. Social distancing requirements and stay-at-home orders have shifted patient care from face-to-face consultations in primary care offices to virtual care from clinicians' homes or offices, moving to a new frontline, which we call the "frontweb." Our telehealth workgroup employed the Clinical Transformation in Technology implementation framework to accelerate telehealth expansion and to develop a consensus document for clinician recommendations in providing remote virtual care during the pandemic. In a few weeks, telehealth went from under 5% of patient visits to almost 93%, while maintaining high levels of patient satisfaction. In this paper, we share clinician recommendations and guidance gleaned from this transition to the frontweb and offer a systematic approach for ensuring "webside" success.
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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 | 0.044 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".