Bibliographic record
Abstract
Background Telehealth has historically been used to increase access to care for marginalized populations living in rural and underserved communities and those who require frequent medical care. Video visits have been used to address distance barriers for routine and specialty care, and remote patient monitoring has been used to help those with chronic medical conditions. However, following the COVID-19 pandemic, telehealth has become standard and is less likely to be used by the populations who could benefit most from its use. Objective This review aimed to evaluate whether telemedicine use is lower among patients without insurance, racial/ethnic minority individuals, and non–English-speaking patients. Methods From reviews of the literature and US data, comparisons of telehealth use between different populations were conducted. Utilization rates were compared between racial/ethnic groups (Black, Asian, and White; Hispanic and non-Hispanic) and among different telehealth use cases: on-demand, direct-to-consumer care; scheduled ambulatory video visits; and remote patient monitoring applications. Results Among telehealth users in the United States, the highest share of visits that used video services occurred among young adults aged 18-24 years (72.5%), those earning at least US $100,000 (68.8%), those with private insurance (65.9%), and White individuals (61.9%). Video telehealth rates were lowest among those without a high school diploma (38.1%); adults aged ≥65 years (43.5%); and Hispanic (50.7%), Asian (51.3%), and Black individuals (53.6%). Conclusions Telehealth use increased dramatically during the COVID-19 pandemic, but research suggests that access to telehealth was not equitable across different population subgroups. Following the pandemic, the use of telehealth has gone from a tool that was used to primarily address barriers in access among minority populations to a model of care that serves those who are better insured, English-speaking, and White. Interventions to address inequities involve payment policies, ambulatory operations, and investments in making telehealth more accessible by underserved populations.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".