Rapid Transition to Virtual Care during the COVID-19 Epidemic: Experience of a Supportive Care Clinic at a Tertiary Care Cancer Center
Bibliographic record
Abstract
Background: COVID-19 pandemic necessitated rapid adoption of telemedicine at our supportive care center (SCC) to ensure continuity of care while maintaining social distancing. Objective: To document the process of transition from in-person to virtual care. Design: The charts of 1744 consecutive patients in our SCC located in the United States were retrospectively reviewed during the four weeks before transition (February 14–March 12), four weeks after transition (March 20–April 16), and transition week (March 13–March 19). Patient demographics, vital aspects of a supportive care visit such as assessments (Edmonton Symptom Assessment Scale-Financial and Spiritual [ESAS-FS], Cut-down, Annoyed, Guilty, Eye-opener Screen-Adapted to Include Drugs [CAGE-AID], and Memorial Delirium Assessment Scale [MDAS]), interdisciplinary team involvement, and visit type were recorded. Results: In total 763 patients were seen before transition, 168 during the transition week, and 813 after transitioning to virtual care. Patient characteristics, ESAS-FS, CAGE-AID, and nurse assessment did not significantly differ among the three groups. The after-transition group had a small reduction in counseling intervention compared with before (20.2% vs. 26.2%; p = 0.0068). MDAS completion was higher after transition (99.6% vs. 98%; p = 0.007). In-person visits decreased from 100% before to 12.7% after transition ( p < 0.0001) and virtual visits increased to 49.3% (video) and 38% (telephone). In-person visits decreased to 49% in the week one, 3% in week two, and <2% in week four after transition ( p < 0.0001). Conclusions: Our supportive care team transitioned from in-person care to virtual visits within weeks while maintaining a high patient volume, continuity of care, and adherence to social distancing. Our transition can serve as a model for other centers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".