Management and Treatment of Concussions via Tele-Concussion in a Pediatric Setting: Methodological Approach and Descriptive Analysis
Notice bibliographique
Résumé
BACKGROUND: Approximately 2 million children in the United States sustain a concussion annually, resulting in an economic impact as high as US $20 billion. Patients who receive treatment at concussion specialty clinics, versus primary care, experience faster recovery, thereby reducing patient burden and subsequent medical-related costs. Accessibility to specialty clinics is typically limited by the availability of in-office visits. This is particularly relevant in light of the severe acute respiratory syndrome coronavirus 2 pandemic and subsequent guidance to eliminate all non-medically necessary in-clinic visits. Telehealth has been used to effectively deliver in-clinic care across several disciplines including psychiatry, psychology, and neuropsychology. However, a model of telehealth delivered concussion assessment, treatment, and management has not been established. OBJECTIVE: The purposes of this paper are to describe a pediatric concussion specialty clinic's experiences in delivering telehealth concussion services and to provide preliminary descriptive data on a sample of pediatric telehealth patients with concussions. METHODS: The specialty pediatric concussion clinic described here began providing telehealth services in 2019 and is part of the largest and fastest-growing telehealth hospital network in the United States. The clinical care process will be described, including accessing the telehealth platform, assessment during the initial appointment, injury management including communication with relevant patient stakeholders (eg, parent or guardians, athletic trainers), dissemination of rehabilitation exercises, and nature of follow-up visits. Descriptive data will include patient demographics, the radius of care, the time between the date of injury and initial visit, the average number of follow-up visits, and days until medically cleared for return-to-learn and return-to-play. RESULTS: The analytic sample included 18 patients with concussions who were seen for all of their visits via telehealth between August 2019 and April 2020. The mean age of the sample was 14.5 (SD 2.5) years. The radius of care was a median of 17 (IQR 11.0-31.0) miles from the clinic with a median time between injury and the first visit of 21 (IQR 6.0-41.5) days. The mean number of visits was 2.2 (SD 0.8) with a median days between visits of 5.4 (IQR 3.0-9.3) to manage and treat the concussion. Of the 18 patients, 55.6% (n=10) were medically cleared for return-to-learn or -play in a median of 15.5 (IQR 11.0-29.0) days. CONCLUSIONS: Limited access to health care is a well-understood barrier for receiving quality care. Subsequently, there are increasing demands for flexibility in delivering concussion services remotely and in-clinic. This is the first paper to provide a clinically relevant framework for the assessment, management, and treatment of acute concussion via telehealth in a pediatric population.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 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 | 0,000 |
| 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.
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