Patterns of Telehealth Use Across the Cancer Care Continuum and Assessment of Patient and Geographic Factors Associated With Key Healthcare Outcomes: Retrospective Study
Notice bibliographique
Résumé
Background: Although the use of telehealth has declined since the pandemic, it remains a popular mode of care delivery across the cancer care continuum. Understanding telehealth in the context of cancer care is essential, as its benefits and challenges may differ among diverse population groups and geographic areas. Objective: This study aimed to examine patterns of telehealth utilization across the cancer care continuum and to identify factors associated with the receipt of telehealth in a large patient population. This study also aimed to assess the telehealth's impact on key health care delivery outcomes. Methods: We used an annualized retrospective cohort design using patient data from the Johns Hopkins Health System (JHHS), a large regional academic health center in Maryland. The study analyzed electronic health record (EHR) data covering the period from January 1, 2019, to December 31, 2023. Chronic conditions were defined through the Johns Hopkins Adjusted Clinical Groups (ACG) System, which identifies comorbidities based on the International Classification of Diseases, Tenth Revision, Clinical Modification, codes in the electronic health record. In addition, we used publicly available geospatial data (eg, internet connectivity, rural-urban commuting area) to assess telehealth receipt associations. Statistical modeling, including generalized estimating equations, was used to evaluate variations in telehealth utilization and outcomes. Results: A total of 124,974 adult patients receiving cancer-related care at Johns Hopkins Health System were identified during the study period. Telehealth users were significantly older (52.2% aged ≥65 years, 19,942 patients) compared to nonusers (48.7%, 42,209 patients). In addition, these users were more likely to be male (45.4%, 17,365 patients vs 40.2%, 34,839 patients) and to identify as White (70.8%, 27,071 patients vs 64.7%, 56,122 patients). Telehealth users also had a higher prevalence of comorbidities, with 61.5% (23,503 patients) reporting 3 or more chronic conditions compared to 38.0% (33,000 patients) among nonusers. A positive correlation was noted between rural-urban commuting area codes and telehealth service utilization (ρ=0.36; P<0.05), indicating higher usage in more rural areas. Conversely, average maximum download and upload speeds showed an inverse relationship with telehealth utilization (ρ=-0.22; P<0.05; and ρ=-0.34; P<0.05, respectively). Adjusted analyses indicated that concurrent telehealth use was associated with reduced odds of emergency department visits (0.916, 95% CI 0.884-0.948) and hospitalizations (0.830, 95% CI 0.799-0.863), acknowledging the potential influence of residual confounding. Conclusions: Telehealth has emerged as a crucial mode of care delivery for patients with complex conditions such as cancer. Understanding usage patterns and factors influencing telehealth across the cancer care continuum, including geographic barriers, is vital to optimizing its implementation and ensuring health care systems meet the diverse needs of patients with cancer in a value-based care environment.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,000 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».