Virtual Care During the COVID-19 Pandemic for Patients With Hematologic Malignancies: A Single-Institution Experience
Bibliographic record
Abstract
PURPOSE: The use of virtual care rapidly increased during the COVID-19 pandemic and has persisted as a routine method of care delivery. Much of the literature on virtual care in oncology has focused on solid tumors, and little is known about its application in malignant hematology. METHODS: We performed a retrospective review of patients with hematologic malignancies at Princess Margaret Cancer Centre from October 2019 to March 2021 to determine the use of virtual care during this period, cost-savings associated with virtual visits, and patient satisfaction. Patient satisfaction was assessed using the Your Voice Matters survey, a provincially administered survey to evaluate patient experience. RESULTS: Overall, 12.1% (1,122/9,295) of patients had a virtual visit during the study period (0% from October 2019 to February 2020, 36% from March to August 2020, and 30% from September 2020 to March 2021), of which 36% were in the lymphoma clinic and 46% were in the myeloma clinic. The mean two-way opportunity cost for an in-person visit was $168.00 CAD per person with public transit, and $120.40 CAD per person driving. Responses to the Your Voice Matters survey indicated that patients with a virtual visit reported that physical symptoms were discussed appropriately (mean 4.73/5), and were more likely to ask for a follow-up virtual visit compared with patients with in-person visits (mean 4.50/5 v 3.02/5, respectively; P < .01). CONCLUSION: These findings suggest that virtual care may be a feasible and well-received tool for delivering care to a substantial proportion of patients with hematologic malignancies, while enabling substantial cost-savings to patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".