Patient Factors Associated With Teledermatology Visit Type and Submission of Photographs During the COVID-19 Pandemic: Cross-sectional Analysis
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic necessitated the widespread adoption of teledermatology, and this continues to account for a significant proportion of dermatology visits after clinics have reopened for in-person care. Delivery of high-quality teledermatology care requires adequate visualization of the patient's skin, with photographs being preferred over live video for remote skin examination. It remains unknown which patients face the greatest barriers to participating in a teledermatology visit with photographs. OBJECTIVE: The aim of this study was to identify patient characteristics associated with type of telemedicine visit and the factors associated with participating in teledermatology visits with digital photographs versus those without photographs. METHODS: We performed a cross-sectional analysis of the University of Pennsylvania Health System electronic health record data for adult patients who participated in at least 1 teledermatology appointment between March 1, 2020, and June 30, 2020. The primary outcomes were participation in a live-interactive video visit versus a telephone visit and participation in any teledermatology visit with photographs versus one without photographs. Multivariable logistic regression was performed to evaluate the associations between patient characteristics and the primary outcomes. RESULTS: In total, 5717 unique patients completed at least 1 teledermatology visit during the study period; 68.25% (n=3902) of patients participated in a video visit, and 31.75% (n=1815) participated in a telephone visit. A minority of patients (n=1815, 31.75%) submitted photographs for their video or telephone appointment. Patients who submitted photographs for their teledermatology visit were more likely to be White, have commercial insurance, and live in areas with higher income, better education, and greater access to a computer and high-speed internet (P<.001 for all). In adjusted analysis, older age (age group >75 years: odds ratio [OR] 0.60, 95% CI 0.44-0.82), male sex (OR 0.85, 95% CI 0.75-0.97), Black race (OR 0.79, 95% CI 0.65-0.96), and Medicaid insurance (OR 0.81, 95% CI 0.66-0.99) were each associated with lower odds of a patient submitting photographs for their video or telephone visit. Older age (age group >75 years: OR 0.37, 95% CI 0.27-0.50) and Black race (OR 0.82, 95% CI 0.68-0.98) were also associated with lower odds of a patient participating in a video visit versus telephone visit. CONCLUSIONS: Patients who were older, male, or Black, or who had Medicaid insurance were less likely to participate in teledermatology visits with photographs and may be particularly vulnerable to disparities in teledermatology care. Further research is necessary to identify the barriers to patients providing photographs for remote dermatology visits and to develop targeted interventions to facilitate equitable participation in teledermatology care.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».