Access to Primary Care Telemedicine and Visit Characterization in a Pediatric, Low-Income, Primarily Latino Population: Retrospective Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".