Telemedicine use in 2020 during the COVID-19 pandemic among community dwelling U.S. Medicare beneficiaries
Bibliographic record
Abstract
Objective: CMS reimbursement regulations for telemedicine changed after the onset of the COVID-19 pandemic. This study aimed to assess telemedicine utilization patterns offered by health care providers and used by Medicare beneficiaries during the COVID-19 pandemic during 2020.Methods: This study used the Fall 2020 Medicare Current Beneficiary Survey (MCBS) supplemental COVID-19 survey to identify Medicare beneficiaries (≥ 65 years) with a regular place for medical care that offered telemedicine during 2020. Major outcomes: prevalence for whether telemedicine was offered before and during the pandemic, telemedicine use, and digital access to telemedicine. Logistic regression identified the demographic factors associated with telemedicine use.Results: The study sample included 4,380 eligible individual Medicare beneficiaries ≥ 65 years. Of those, 42.9% made telemedicine visits during the pandemic. Approximately 60% of the telemedicine visits were conducted via telephone. Telemedicine was offered to 18% of the respondents before the pandemic vs. 64% during year 2020 of the pandemic. Among telemedicine users, 57.2%, 28.3%, and 14.5% used voice calls, video calls, and both voice and video calls for health care appointments, respectively. Overall telemedicine use varied by sex, race, and region. Individuals 65-74 years, female, living in a metropolitan area, with higher incomes were more likely to make video visits. Experience using telecommunications via the internet influenced telemedicine use significantly.Conclusions: Telemedicine offered to older Medicare beneficiaries increased dramatically after the onset of the COVID-19 pandemic. Yet, less than half used telemedicine and differences in utilization existed by demographic characteristics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".