Patterns of telehealth utilization during the COVID-19 pandemic and preferences for post-pandemic telehealth use: A national survey of oncology clinicians.
Bibliographic record
Abstract
1580 Background: Rarely used in routine practice pre-pandemic, telehealth utilization for cancer care rose significantly during the COVID-19 pandemic. Increased familiarity with telehealth has led to calls to continue its use after the pandemic ends. Yet national patterns of oncology telehealth utilization by visit type, preferences for telehealth use post-pandemic and barriers to telehealth for patients with cancer have not been described. Methods: 9,336 survey invitations were emailed to US-based ASCO members who have agreed to receive communications. Survey distribution was equally divided over five US regions, and practice type (e.g., academic, community) was reflective of ASCO membership proportions. The survey was open and data collected from January 4-28, 2021. Non-respondents received two reminder emails at week intervals. Analysis is descriptive. Results: 200 respondents completed the survey (2%). Respondents were 72% medical oncologists, 66% urban, 64% academic-affiliated, and from 42 states. 99% currently offered telehealth. 63% used telehealth for <=30% of all patient visits in the last 30 days; 18% used telehealth for more than half of visits. Telehealth utilization varied by visit type (table). 64% reported that the care delivered in telehealth visits was similar quality to in-person visits (29% worse). Assuming no regulatory or financial barriers to telehealth use after the pandemic, 92% would like to use telehealth for at least some visit types; only 8% prefer not to use telehealth. 20% would like to use telehealth for all visits types, and 64%, 54%, 33% and 17% would like to use telehealth for survivorship, symptom management, evaluation of patients receiving treatment and new patient visits, respectively (multiple selections allowed). Major barriers to telehealth were lack of patient access to technology (reported by 81%), limited patient technological proficiency (80%), language barriers (45%), uncertainty about future reimbursement (41%) and lack of administrative resources to support clinicians (33%). 68% agreed that the barriers increase cancer care disparities. Conclusions: Telehealth utilization was widespread during the COVID pandemic and varied by visit type. Most respondents plan to use telehealth in the future, but report barriers to continued use that worsen disparities.[Table: see text]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".