Quantifying the Impact of COVID-19 on Telemedicine Utilization: Retrospective Observational Study
Bibliographic record
Abstract
BACKGROUND: While telemedicine has been expanding over the past decade, the COVID-19-related restrictions regarding in-person care have led to unprecedented levels of telemedicine utilization. To the authors' knowledge, no studies to date have quantitatively analyzed both national and regional trends in telemedicine utilization during the pandemic, both of which have key implications for informing health policy. OBJECTIVE: This study aimed to investigate how trends in telemedicine utilization changed across the course of the COVID-19 pandemic. METHODS: Using data from doxy.me, the largest free telemedicine platform, and the NIH (National Institutes of Health) Clinical Center, the largest clinical research hospital in the United States, we assessed changes in total telemedicine minutes, new provider registrations, monthly sessions, and average session length from March to November 2020. We also conducted a state-level analysis of how telemedicine expansion differed by region. RESULTS: National telemedicine utilization peaked in April 2020 at 291 million minutes and stabilized at 200 to 220 million monthly minutes from May to November 2020. Surges were strongest in New England and weakest in the South and West. Greater telemedicine expansion during the COVID-19 pandemic was geographically associated with fewer COVID-19 cases per capita. The nature of telemedicine visits also changed, as the average monthly visits per provider doubled and the average visit length decreased by 60%. CONCLUSIONS: The COVID-19 pandemic led to an abrupt and subsequently sustained uptick in telemedicine utilization. Regional and institute-level differences in telemedicine utilization should be further investigated to inform policy and procedures for sustaining meaningful telemedicine use in clinical practice.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".