Factors Associated With Portal and Telehealth Uptake and Use in a Minoritized, Low-Income Community: Mixed Methods Study
Bibliographic record
Abstract
Background: Despite evidence that use of patient portals and telehealth is associated with many health benefits, disparities exist in awareness, adoption, and use. Understanding factors and strategies specific to underserved populations is key to achieving digital equity and better health. Objective: This study assesses portal and telehealth experiences among residents of a minoritized and lower-resource area of Dallas, Texas. Methods: Using an explanatory sequential design, we conducted surveys and semistructured interviews with English- and Spanish-speaking adults in 15 ZIP Codes surrounding a community-based clinic. We recruited participants via a patient portal, flyers, emails distributed by clinic and community partners, and in person. Surveys were offered online and on paper. We used Fisher exact tests to identify factors associated with telehealth and/or portal use. We also recruited a subsample of survey participants to expound on survey findings in semistructured interviews. Our thematic analysis assessed convergence in survey and interview findings. Results: Among 182 survey respondents, most were older (n=109, 66%; age ≥60 years), African American or Black (n=120, 65%), and female (n=142, 79%); a little more than half (n=97, 54%) had completed ≥1 telehealth appointment, and a majority (n=131, 72%) had used a patient portal at least once. Compared with those who used the portal and/or telehealth, those reporting no use of portal or telehealth were more likely to have a high school education or less (P<.001) or be Spanish speakers (P<.011). A majority, regardless of portal or telehealth use, agreed with health promotion activity survey statements like "Using the Internet for health-related activities makes me feel actively involved with my health care" (n=103, 59%) and "I find the Internet useful for monitoring my health" (n=100, 58%). In interviews with 20 individuals, most of whom were older, Black, female, and had digital technology experience, seven factors were key to increased engagement in portals and telehealth: (1) improving patient autonomy, (2) integrating digital health technology into daily life, (3) receiving recommendations from trusted individuals, (4) appreciating the value of digital health technologies, (5) enlisting the support of care partners or peers, (6) managing severe or chronic illness, and (7) accessing test results rapidly. Conclusions: This study builds on previous work by confirming and contributing insights about factors key to technology uptake and use among underserved populations. Interventions using digital health technologies should focus on these factors to promote digital and health equity and achieve better health outcomes. Future research should explore which clinical settings and contexts are most conducive to increasing digital technology uptake and use, and implementation should leverage partnerships with community groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".