Self-Reported Patient and Provider Satisfaction With Neurology Telemedicine Visits After Rapid Telemedicine Implementation in an Urban Academic Center: Cross-Sectional Survey
Notice bibliographique
Résumé
BACKGROUND: Many clinics and health systems implemented telemedicine appointment services out of necessity due to the COVID-19 pandemic. OBJECTIVE: Our objective was to evaluate patient and general provider satisfaction with neurology telemedicine implementation at an urban academic medical center. METHODS: Patients who had completed 1 or more teleneurology visits from April 1 to December 31, 2020, were asked to complete a survey regarding their demographic information and satisfaction with teleneurology visits. Providers of all specialties within the same hospital system were given a different survey to gather their experiences of providing telemedicine care. RESULTS: Of the estimated 1500 patients who had completed a teleneurology visit within the given timeframe, 117 (7.8%) consented to complete the survey. Of these 117 respondents, most appointments were regarding epilepsy (n=59, 50.4%), followed by multiple sclerosis (n=33, 28.2%) and neuroimmunology (n=7, 6%). Overall, 74.4% (n=87) of patients rated their experience as 8 out of 10 or higher, with 10 being the highest satisfaction. Furthermore, 75.2% (n=88) of patients reported missing an appointment in the previous year due to transportation issues and thought telemedicine was more convenient instead. A significant relationship between racial or ethnic group and comfort sharing private information was found (P<.001), with 52% (26/50) of Black patients reporting that an office visit is better, compared to 25% (14/52) of non-Black patients. The provider survey gathered 40 responses, with 75% (n=30) of providers agreeing that virtual visits are a valuable tool for patient care and 80% (n=32) reporting few to no technical issues. The majority of provider respondents were physicians on faculty or staff (n=21, 52%), followed by residents or fellows (n=15, 38%) and nurse practitioners or physician assistants (n=4, 10%). Of the specialties represented, 15 (38%) of the providers were in neurology. CONCLUSIONS: Our study found adequate satisfaction among patients and providers regarding telemedicine implementation and its utility for patient care in a diverse urban population. Additionally, while access to technology and technology literacy are barriers to telemedical care, a substantial majority of patients who responded to the survey had access to devices (101/117, 86.3%) and were able to connect with few to no technological difficulties (84/117, 71.8%). One area identified by patients in need of improvement was comfortability in communicating via telemedicine with their providers. Furthermore, while providers agreed that telemedicine is a useful tool for patient care, it limits their ability to perform physical exams. More research and quality studies are needed to further appreciate and support the expansion of telemedical care into underserved and rural populations, especially in the area of subspecialty neurological 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 | 0,002 | 0,005 |
| 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 | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».