Access to Primary Care Telemedicine and Visit Characterization in a Pediatric, Low-Income, Primarily Latino Population: Retrospective Study
Notice bibliographique
Résumé
BACKGROUND: Since the COVID-19 pandemic, telemedicine has been widely integrated into primary care pediatrics. While initial studies showed some concern for disparities in telemedicine use, telemedicine uptake for pediatric patients in a low-income, primarily Latino community over a sustained period has yet to be described. OBJECTIVE: We aimed to assess the relationship between demographics, patient portal activation, and telemedicine visits, as well as characterize diagnoses addressed in telemedicine, in a low-income, primarily Latino population over time. METHODS: A multidisciplinary team conducted outreach for telemedicine and patient portal activation with the adoption of a new electronic health record. Data were collected on all in-person and telemedicine visits from February 2020 through April 2021 for 4 community-based pediatric practices. The outcomes included patient portal activation, telemedicine use, and reason for telemedicine visits. Bivariate tests and multivariate regression analyses were conducted to assess the independent effects of demographics on the likelihood of portal activation and having a telemedicine visit. Telemedicine diagnoses were categorized, and subanalyses were conducted to explore variations by age and month. RESULTS: There were 12,377 unique patients and 7127 telemedicine visits. Latino patients made up 83.4% (n=8959) of the population. Nearly all patients (n=10,830, 87.5%) had an activated portal, and 33.8% (n=4169) had at least 1 telemedicine visit. Portal activation decreased with age >2 years (2-4 years: adjusted odds ratio [aOR] 0.62, 95% CI 0.51-0.76; 5-11 years: aOR 0.28, 95% CI 0.23-0.32; 12-14 years: aOR 0.29, 95% CI 0.23-0.35; and 15-17 years: aOR 0.46, 95% CI 0.36-0.58). Spanish-speaking (aOR 0.52, 95% CI 0.45-0.59) and non-Latino patients (aOR 0.64, 95% CI 0.54-0.76) had decreased odds of activation and having a telemedicine visit (aOR 0.81, 95% CI 0.74-0.89 and aOR 0.71, 95% CI 0.62-0.81, respectively). The top 5 diagnostic categories for telemedicine were infectious disease (n=1749, 26.1%), dermatology (n=1287, 19.5%), gastrointestinal (n=771, 11.7%), well and follow-up care (n=459, 7%), and other specialty-related care (n=415, 6.3%). Infectious disease showed the most variation over time. Age-based patterns included a decrease in the proportion of infectious disease diagnoses by increasing age group and a higher proportion of well and follow-up care in older ages. Additional telemedicine diagnoses included common infant concerns for patients younger than 2 years of age; pulmonary, asthma, and allergy concerns for toddler or school-age children; behavioral health concerns for younger adolescents; and genitourinary and gynecologic concerns for older adolescents. CONCLUSIONS: The high engagement across demographics suggests feasibility and interest in telemedicine in this low-income, primarily Latino population, which may be attributable to the strength of outreach. Language-based disparities were still present. Telemedicine was used for a wide range of diagnoses. As telemedicine remains a vital component of pediatric health care, targeted interventions may enhance engagement to serve diverse pediatric patient populations.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».