149 Virtual Care: A Quality Improvement Project on the Experience of Paediatricians during the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Primary Subject area Practice/Office Management Background Prior to the COVID-19 pandemic, in-person visits were the standard of care for paediatricians at our centre. With the pandemic onset, virtual care (VC) was adopted at an unprecedented scale and pace. Studies have reported positive patient VC experience; however, few have explored physician experience. This quality improvement (QI) initiative sought to qualify the VC experience of local paediatricians during the pandemic, with the intention of implementing VC clinical practice changes at the department level. Objectives To determine key factors that have supported and challenged the adoption of, and that will support integration of, VC in the future. Design/Methods The Donabedian model for healthcare QI was used to evaluate VC experience through an online survey with a focus on structure, process, and outcome measures. All physicians affiliated with the Department of Paediatrics (generalists and subspecialists in medicine and surgery) were invited to participate via email. Three reminder emails were sent at 2-week intervals. Descriptive statistics were reported. Results The response rate was 32.3% (63 of 195 physicians). The majority of respondents were subspecialists (84.1%), and at academic centres (87.5%) (Table 1). Pre-pandemic, only 30.1% used VC and saw <10% of patients virtually. During March-May 2020, 93.8% transitioned to VC, with > 50% seeing over 75% of patients virtually. By summer 2020, VC use declined, but remained higher than pre-pandemic (53.6% seeing < 25% of patients). OTN and telephone were platforms most used (32.8% and 28.6%, respectively). Most conducted visits from their work location (55.2%) versus home (44.8%). VC experience was considered positive by most physicians (73.6%), and only 18.8% found VC difficult to use despite technical difficulties reported by 41.5% (Figure 1). Physicians with ≤ 5 years in practice were most likely to find VC convenient (93.8%). Challenges with VC included lack of physical exam, diagnostic uncertainty, lower patient volumes, and poor patient VC etiquette. Regardless of practice location, specialty, years in practice, and prior experience, 96% would continue VC to 25% of patients, ideally for patients who live far away (26.4%) and for follow-ups of patients with established diagnoses (21.4%). Conclusion A rapid transition to VC during the COVID-19 pandemic was associated with challenges but also positive experiences. Willingness to continue VC was high. VC experience could be improved with greater patient education and focus on select patient populations. Future research is needed to improve practice efficiency and to inform regulatory guidelines for VC at a local level.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".