Trends in Teledermatology Utilization in the United States
Bibliographic record
Abstract
Background Teledermatology is an effective health care delivery model that has seen tremendous growth over the last decade. This growth can be attributed to a variety of factors, including but not limited to an increased access to dermatologic care for those with socioeconomic or geographic barriers, a reduction in health care costs for both the patient and the physician, and the delivery of high-quality dermatologic care. However, the associated barriers include practice reimbursements, interstate licensing, and liability. Despite these apparent barriers, the emergence of COVID-19 afforded teledermatology a surge of demand and loosened regulations, allowing dermatologists to see higher volumes of teledermatology patients. In this paper, we analyzed the American Academy of Dermatology’s DataDerm registry teledermatology utilization and patient demographic trends throughout the COVID-19 pandemic. Objective The aim of this paper was to characterize national-level teledermatology demographic data in the setting of the COVID-19 pandemic. Methods National-level data were curated for all practices enrolled in the American Academy of Dermatology’s DataDerm registry from April 1, 2020, through June 30, 2021. Encounter utilization rates were collected for visit type (ie, teledermatology versus in person), sex, race, age, insurance provider, and location (ie, in state versus out of state). The aggregate total data, as opposed to individual encounter data, were collected. Results The proportion of women who utilized services via teledermatology (65,023/98,642, 65.9%) was greater than that of those who utilized in-person services (29,40,122/50,48,450, 58.2%). Non-White patients made up a higher percentage of teledermatology utilizers (8920/62,324, 15%) when compared with in-person utilizers (3,94,580/35,08,150, 11.7%). Younger patients (aged <40) contributed more to teledermatology service utilization (62,695/75,319, 83.2%) when compared with in-person services (13,29,218/33,01,175, 40.3%). Medicare was a larger payor contributor for in-person services (8232/1,53,279, 25.2%) than for teledermatology services (10,89,777/43,30,882, 5.4%). Utilization by out-of-state patients was proportionally higher for teledermatology services (19,422/1,33,416, 14.6%) compared with in-person services (5,80,358/1,38,31,400, 4.2%). Conclusions Teledermatology services may reach and benefit certain populations (female, younger patients, those with non-White racial backgrounds, and out-of-state patients) more so than others. These baseline demographics may also serve to highlight populations for potential future teledermatology outreach efforts. Conflict of Interest None declared.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".