Patterns of telehealth utilization during the COVID-19 pandemic and preferences for post-pandemic telehealth use: A national survey of oncology clinicians.
Notice bibliographique
Résumé
1580 Background: Rarely used in routine practice pre-pandemic, telehealth utilization for cancer care rose significantly during the COVID-19 pandemic. Increased familiarity with telehealth has led to calls to continue its use after the pandemic ends. Yet national patterns of oncology telehealth utilization by visit type, preferences for telehealth use post-pandemic and barriers to telehealth for patients with cancer have not been described. Methods: 9,336 survey invitations were emailed to US-based ASCO members who have agreed to receive communications. Survey distribution was equally divided over five US regions, and practice type (e.g., academic, community) was reflective of ASCO membership proportions. The survey was open and data collected from January 4-28, 2021. Non-respondents received two reminder emails at week intervals. Analysis is descriptive. Results: 200 respondents completed the survey (2%). Respondents were 72% medical oncologists, 66% urban, 64% academic-affiliated, and from 42 states. 99% currently offered telehealth. 63% used telehealth for <=30% of all patient visits in the last 30 days; 18% used telehealth for more than half of visits. Telehealth utilization varied by visit type (table). 64% reported that the care delivered in telehealth visits was similar quality to in-person visits (29% worse). Assuming no regulatory or financial barriers to telehealth use after the pandemic, 92% would like to use telehealth for at least some visit types; only 8% prefer not to use telehealth. 20% would like to use telehealth for all visits types, and 64%, 54%, 33% and 17% would like to use telehealth for survivorship, symptom management, evaluation of patients receiving treatment and new patient visits, respectively (multiple selections allowed). Major barriers to telehealth were lack of patient access to technology (reported by 81%), limited patient technological proficiency (80%), language barriers (45%), uncertainty about future reimbursement (41%) and lack of administrative resources to support clinicians (33%). 68% agreed that the barriers increase cancer care disparities. Conclusions: Telehealth utilization was widespread during the COVID pandemic and varied by visit type. Most respondents plan to use telehealth in the future, but report barriers to continued use that worsen disparities.[Table: see text]
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,000 | 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 tête enseignante, 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 ».