Changes in Short-term, Long-term, and Preventive Care Delivery in US Office-Based and Telemedicine Visits During the COVID-19 Pandemic
Bibliographic record
Abstract
Importance: While the COVID-19 pandemic has been associated with some substitution of telemedicine for office-based care in the US, to our knowledge, little is known regarding the pandemic's association with the clinical content of ambulatory care. Objective: To characterize changes in the clinical content of ambulatory care among office-based vs telemedicine encounters in the US before vs during the COVID-19 pandemic. Design Settings and Participants: This analysis of serial cross-sectional data from the IQVIA National Disease and Therapeutic Index was a 2-stage, stratified nationally representative audit of outpatient care in the US from January 1, 2018, through December 31, 2020. The National Disease and Therapeutic Index generates approximately 33 617 quarterly visits that are projected to 306.7 million national visits based on the survey design. Main Outcomes and Measures: (1) Prevalence of common diagnoses and (2) mix of long-term, short-term, and preventive care. Results: The mean (SD) number of projected quarterly, in-person, office-based visits was 282.1 (1.4) million in 2018 and 284.7 (10.3) in 2019 before declining to 250.8 million in quarter 1 of 2020 and 147.8 million in quarter 2 of 2020 and then increasing moderately to 181.5 million in quarter 3 of 2020 and 180.2 million in quarter 4 of 2020. The mean (SD) number of telemedicine visits was 2.8 (0.4) million in 2018 and 3.0 (0.1) million in 2019 before increasing to 8.6 million in quarter 1 of 2020 and 72.2 million in quarter 2 of 2020 and then declining notably to 43.8 million in quarter 3 of 2020 and 44.2 million in quarter 4 of 2020. Office-based care during the second through fourth quarters of 2020 involved 58.0% long-term, 23.0% short-term, and 25.6% preventive care. In contrast to office-based care, 4 of the top 10 diagnoses that were treated by telemedicine during 2020 were for psychiatric or behavioral conditions: depression, attention deficit/hyperactivity, anxiety, and bipolar disorders. Throughout this period, approximately half of office-based visits and nearly two-thirds of telemedicine visits were for established rather than new patients. Conclusions and Relevance: This cross-sectional study's findings suggest that while telemedicine rapidly increased early during course of the COVID-19 pandemic, its use declined modestly since then. In contrast to office-based care, telemedicine was more commonly used for established patients and substantially greater delivery of psychiatric or behavioral treatments rather than preventive care.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".