Using Implementation Science to Understand Teledermatology Implementation Early in the COVID-19 Pandemic: Cross-sectional Study
Notice bibliographique
Résumé
Background: Implementation science has been recognized for its potential to improve the integration of evidence-based practices into routine dermatologic care. The COVID-19 pandemic has resulted in rapid teledermatology implementation worldwide. Although several studies have highlighted patient and care provider satisfaction with teledermatology during the COVID-19 pandemic, less is known about the implementation process. Objective: Our goal was to use validated tools from implementation science to develop a deeper understanding of the implementation of teledermatology during the COVID-19 pandemic. Our primary aims were to describe (1) the acceptability and feasibility of the implementation of teledermatology and (2) organizational readiness for the implementation of teledermatology during the COVID-19 pandemic. We also sought to offer an example of how implementation science can be used in dermatologic research. Methods: An anonymous, web-based survey was distributed to Association of Professors of Dermatology members. It focused on (1) the acceptability, feasibility, and appropriateness of teledermatology and (2) organizational readiness for implementing teledermatology. It incorporated subscales from the Organizational Readiness to Change Assessment-a validated measure of organizational characteristics that predict implementation success. Results: Of the 518 dermatologists emailed, 35 (7%) responded, and all implemented or scaled up teledermatology during the pandemic. Of the 11 care providers with the highest level of organizational readiness, 11 (100%) said that they plan to continue using teledermatology after the pandemic. Most respondents agreed or strongly agreed that they had sufficient training (24/35, 69%), financial resources (20/35, 57%), and facilities (20/35, 57%). However, of the 35 respondents, only 15 (43%) agreed or strongly agreed that they had adequate staffing support. Most respondents considered the most acceptable teledermatology modality to be synchronous audio and video visits with supplemental stored digital photos (23/35, 66%) and considered the least acceptable modality to be telephone visits without stored digital photos (6/35, 17%). Overall, most respondents thought that the implementation of synchronous audio and video with stored digital photos (31/35, 89%) and telephone visits with stored digital photos (31/35, 89%) were the most feasible. When asked about types of visits that were acceptable for synchronous video/audio visits (with stored digital photos), 18 of the 31 respondents (58%) said "new patients," 27 (87%) said "existing patients," 19 (61%) said "medication monitoring," 3 (10%) said "total body skin exams," and 22 (71%) said "lesions of concern." Conclusions: This study serves as an introduction to how implementation science research methods can be used to understand the implementation of novel technologies in dermatology. Our work builds upon prior studies by further characterizing the acceptability and feasibility of different teledermatology modalities. Our study may suggest initial insights on how dermatology practices and health care systems can support dermatologists in successfully incorporating teledermatology after the pandemic.
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 | 0,056 | 0,099 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,006 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».