Telehealth Availability for Cancer Care During the COVID-19 Pandemic: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Telehealth was an important strategy for maintaining continuity of cancer care during the coronavirus pandemic and has continued to play a role in outpatient care; however, it is unknown whether services are equally available across cancer hospitals. OBJECTIVE: This study aimed to assess telehealth availability at cancer hospitals for new and established patients with common cancers to contextualize the impact of access barriers to technology on overall access to health care. METHODS: We conducted a national cross-sectional secret shopper study from June to November 2020 to assess telehealth availability at cancer hospitals for new and established patients with colorectal, breast, and skin (melanoma) cancer. We examined facility-level factors to determine predictors of telehealth availability. RESULTS: Of the 312 investigated facilities, 97.1% (n=303) provided telehealth services for at least 1 cancer site. Telehealth was less available to new compared to established patients (n=226, 72% vs n=301, 97.1%). The surveyed cancer hospitals more commonly offered telehealth visits for breast cancer care (n=266, 85%) and provided lower access to telehealth for skin (melanoma) cancer care (n=231, 74%). Most hospitals (n=163, 52%) offered telehealth for all 3 cancer types. Telehealth availability was weakly correlated across cancer types within a given facility for new (r=0.16, 95% CI 0.09-0.23) and established (r=0.14, 95% CI 0.08-0.21) patients. Telehealth was more commonly available for new patients at National Cancer Institute-designated facilities, medical school-affiliated facilities, and major teaching sites, with high total admissions and below-average timeliness of care. Telehealth availability for established patients was highest at Academic Comprehensive Cancer Programs, nongovernment and nonprofit facilities, medical school-affiliated facilities, Accountable Care Organizations, and facilities with a high number of total admissions. CONCLUSIONS: Despite an increase in telehealth services for patients with cancer during the COVID-19 pandemic, we identified differences in access across cancer hospitals, which may relate to measures of clinical volume, affiliation, and infrastructure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".