Patient Factors Associated with Interest in Teledermatology: Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND: Teledermatology is a conduit for patients communicating with dermatologists on the internet, which bypasses in-person visits. It holds promise to address access needs for dermatologic care; however, the interest in using teledermatology is unknown in underserved populations with potential barriers to the use of health care technology. OBJECTIVE: This study aimed to characterize the association between demographic characteristics with interest in exchanging digital images or videos of skin lesions with health care providers electronically. METHODS: We examined data from the Health Information National Trends Survey (HINTS) 4 cycle 4 (2014) of the National Cancer Institute. HINTS is a cross-sectional, nationally representative household survey conducted annually, which collects information on demographics, perceptions and use of health information, and provides information on how cancer risks are perceived. HINTS 4 cycle 4 had a sample of 3677 participants. We examined the outcome to the question, "how interested are you in exchanging digital images or videos (eg, photos of skin lesions) with a health care provider electronically?" We dichotomized the outcome by a high level of interest (responding with "very") and those who did not have a high level of interest (responding with "somewhat," "a little," or "not at all") in exchanging images or videos. We used a multivariable logistic regression model developed through backwards selection, with all final covariates associated with varying levels of teledermatology use at P<.05. Sensitivity analysis was performed by changing the outcome dichotomy to model those who were "not at all" interested. Two-sided tests were performed with P<.05 considered significant. RESULTS: Among 3447 respondents, 888 (weighted prevalence=26.2%) were "very" interested in participating in teledermatology. A higher interest in using teledermatology was associated with a younger age, higher educational attainment, higher household income, internet usage, type of mobile device ownership, history of electronic medical information exchange with a clinician within the past 12 months, and high level of trust in web-based information on cancer (for all, P<.01), but not with the female gender, race or ethnicity, health insurance status, or having a regular medical provider. CONCLUSIONS: Modifiable access barriers to teledermatology adoption include trust, experience with teledermatology, and use of health apps. Teledermatology program implementation should address these specific factors within the digital divide to promote equitable access to care across diverse patient 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".