BT01 The effectiveness of telemedicine in dermatology services: a systematic review
Notice bibliographique
Résumé
Abstract Teledermatology (TD), the remote diagnosis and management of skin conditions using digital platforms, has rapidly evolved since its initial adoption in 1995. The COVID-19 pandemic accelerated the use of TD, particularly in regions with limited access to dermatologists, offering a viable alternative to face-to-face consultations. As TD continues to evolve, understanding its clinical utility, patient outcomes and economic impact becomes increasingly important. This systematic review evaluates the effectiveness of TD across three key domains: clinical outcomes, patient satisfaction and cost-effectiveness. It aims to provide a comprehensive assessment of the utility of TD as an alternative to traditional in-person dermatology services, with a focus on diagnostic accuracy, patient experiences and financial implications. A systematic search was conducted across MEDLINE, Embase and Web of Science for studies published between 2010 and July 2024. Studies comparing TD with face-to-face consultations in terms of diagnostic accuracy, patient satisfaction and cost-effectiveness were included. The quality of the included studies was assessed using the Newcastle–Ottawa Scale. Of 2768 articles identified, 23 studies met the inclusion criteria. Clinical outcomes indicated moderate agreement between TD and face-to-face consultations, with a mean kappa coefficient of 0.57, reflecting diagnostic concordance. High-resolution imaging was found to significantly improve diagnostic accuracy, particularly in asynchronous TD services. Notably, the mean kappa value for asynchronous TD was higher than that for synchronous TD (0.71 vs. 0.56), highlighting the importance of image quality. Additionally, TD demonstrated substantial cost savings, averaging USD 81.31 per patient, with savings ranging from 6.27% to 45.3%, depending on the healthcare system. TD also showed significant operational efficiencies, reducing overhead costs and improving appointment scheduling, especially in rural and underserved areas. Patient satisfaction varied widely, with 26.6% of patients willing to replace face-to-face consultations with TD. Satisfaction was notably influenced by the quality of the technical infrastructure and the availability of support during consultations. Older patients and those with lower digital literacy reported more difficulties, which reduced their willingness to adopt TD. TD offers moderate diagnostic accuracy, significant cost savings and varying degrees of patient acceptance. High-resolution imaging, clinician training and robust technical infrastructure are critical for optimizing diagnostic performance and patient satisfaction with TD. While TD can be an effective tool, particularly for minor or nonurgent dermatological conditions, it should complement, rather than replace, in-person consultations for more complex cases. Further research is needed to refine the role of TD in dermatology, exploring ways to integrate it effectively into healthcare systems and ensure its equitable accessibility.
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 | 0,012 | 0,066 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,010 |
| Bibliométrie | 0,010 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».